VILLAGES, VEGETATION, BEDROCK, AND CHIMPANZEES:

HUMAN AND NON-HUMAN SOURCES OF ECOSYSTEM STRUCTURE

IN SOUTHWESTERN MALI

by

Chris S. Duvall

A dissertation submitted in partial fulfillment of

the requirements for the degree of

Doctor of Philosophy

(Geography)

at the

UNIVERSITY OF WISCONSIN-MADISON

2006

VILLAGES, VEGETATION, BEDROCK, AND CHIMPANZEES: HUMAN AND

NON-HUMAN SOURCES OF ECOSYSTEM STRUCTURE IN SOUTHWESTERN MALI

Chris S. Duvall

Under the supervision of Professor Matthew D. Turner

At the University of Wisconsin-Madison

This dissertation shows that both human activities and biophysical processes interact in

complex ways to create an emergent ecosystem structure in southwestern Mali. This dissertation

includes five body chapters. The first chapter is an analysis of settlement history in the research

area, and situates the research in the context of current conservation practice in Mali’s Bafing

Biosphere Reserve. This chapter shows that the indigenous Maninka people practice shifting

settlement, and that frontier-style settlement expansion is not occurring in the area, as

conservationists have assumed. The second chapter is an ethnographic study of Maninka

physical geography terms, and shows that Maninka farmers perceive the landscape as highly

heterogeneous, with few areas suitable for settlement or cultivation. The third chapter examines

floristic patterns across the landscape, and shows that most floristic variation is due to edaphic

features, especially the hydrogeology of a specific type of sandstone bedrock. Human activities

have variable affects on vegetation, depending on various socioeconomic and biophysical

factors. The fourth chapter shows that humans have affected the distribution of the baobab tree

across the research area through activities that create suitable baobab habitat in settlement sites.

The final body chapter shows that anthropogenic baobab groves represent important habitat for

chimpanzees, and that conservation policies that affect settlement practice may reduce baobab

regeneration and thus reduce chimpanzee habitat in the long term.

i Table of contents

Acknowledgements........................................................................................................................ii

Chapter One: Introduction .......................................................................................................... 1

Chapter Two: Settlement geography and biodiversity conservation in Mali’s Bafing

Biosphere Reserve................................................................................................................. 12

Text box and figures for Chapter One .............................................................................. 49

Chapter Three: Folk taxonomy of physical geographic terms used by Maninka farmers in

southwestern Mali................................................................................................................. 60

Figures for Chapter Two................................................................................................... 91

Chapter Four: Human and environmental causes of floristic patterns in southwestern Mali

............................................................................................................................................... 103

Figures, tables, and appendix for Chapter Three ............................................................ 149

Chapter Five: Human settlement and baobab distribution in southwestern Mali............. 183

Figures and tables for Chapter Four................................................................................ 211

Chapter Six: Chimpanzee diet, habitat use, and human settlement in Mali....................... 228

Figures and tables for Chapter Five ................................................................................ 257

Chapter Seven: Conclusion...................................................................................................... 272

Sources cited .............................................................................................................................. 283

ii Acknowledgements

This research was completed with funding from the U.S. Fish and Wildlife Service Great

Ape Conservation Fund, the Wildlife Conservation Society Research Fellowship Program,

Conservation International’s Primate Action Fund, the Zoological Society of Milwaukee County,

and the Association of American Geographers Biogeography Specialty Group.

Many people were crucial in my completing this dissertation. I especially appreciate the

patience and hard work of my research assistants, friends, and hosts in Solo and elsewhere in

southwestern Mali, who have endured my strange ways with constant hospitality and good

humor. My advisor, Matt Turner, has provided several years of good advice, challenging

questions, and generous amounts of time. All members of my dissertation committee—Sara

Hotchkiss, Lisa Naughton, Matt Turner, Tom Vale, and Karl Zimmerer—have been generous

with their time and have provided stimulating and valuable comments on my work.

This dissertation is dedicated to my Grandma Elsie Tschetter, whose interest in

everything about the world around her gave me the ability to complete my formal education as a

student. All of my family, including ancestors I have not met, have contributed to my success as

a student, and I wish to remember particularly my Aunt Aleta Bears and Grandpa Merle Duvall,

who both inspired me and passed away while I was working on this dissertation project. All

manner of support from my Mom, Dad, and brothers (and their families), as well as the

thoughtfulness and generosity of my wife’s parents, Earl and Susan Broidy, have been more

important to me than I can express. Finally, my wife Jen and daughter Hazel have enabled me to

complete this degree through their support and encouragement; my satisfaction in completing the

degree remains far behind the satisfaction I get from spending time with them.

1 Chapter One: Introduction

Human activities have profoundly shaped ecosystems in Africa. In the past century,

humanity’s long history in Africa and the ability of African farmers to sustain livelihoods in

challenging environments were usually represented as causes for the continent’s purported

environmental degradation. It is true that plant diversity in Africa is low relative to tropical Asia

and South America (Richards 1973), but views about the human role in African ecosystems

were, for much of the twentieth century, commingled with colonialist disrespect for African

capabilities that hindered careful analysis of the environmental impacts of indigenous land

management practices. Despite challenging environmental conditions, African farmers were not

only portrayed as unsuccessful, but their purported ignorance of ‘proper’ (i.e. European or North

American) land management was seen as causing an ever-burgeoning degradation that made

African environments so challenging (Bassett & Crummey 2003; Leach & Mearns 1996).

This dissertation is an attempt to carefully assess the long-term impacts of settlement and

farming on an ecosystem in southwestern Mali, and adds to a growing body of work that

challenges the colonialist view—which has persisted in the post-colonial period—that African

land management is inherently destructive in semi-arid environments. However, the main

contribution of this dissertation to human-environment geography is to show that a

methodologically diverse approach is necessary to understand how human-environment

interactions help create the emergent structure of ecosystems. The importance of this

contribution is evident upon examining recent human-environment studies of long-term,

anthropogenic environmental change in semi-arid Africa. These works have shown that the

farming and settlement practices of smallholders often lead to vegetation changes that are not

deforestation—essentially the sole vegetation change recognized by earlier natural resource

2 scientists working in Africa (e.g. Aubréville 1949a; Schnell 1976; Stebbing 1938). Most

prominent have been works attributing habitat enrichment to humans (Amanor 1994; Kreike

2003), especially the work of Fairhead and Leach (1996), who argue that farmers have created

“forest islands” around settlements in “savanna” areas in Guinea, and that these forest patches

were not the remnants of past deforestation as commonly believed by outsiders. Others have

identified different vegetation responses to farming and settlement—such as bush encroachment

(Bassett & Boutrais 2000; Bassett & Koli Bi 2000), loss of species diversity (Devineau 2005;

Lykke 1998; Nyerges 1989; Schreckenberg 1999), and resilience (Chidumayo 2004; McGregor

1994)—but in nearly all cases these activities have not been shown to cause deforestation.

Collectively, these studies represent the rise of an important new awareness in African

geography of the diverse ways in which human societies have affected vegetation, and have

helped chip away at the environmental degradation narrative that is deeply rooted in natural

scientific discourse on Africa.

Yet as studies of African ecosystems that include humans, these have all been limited in

significant ways. In particular, this dissertation asserts that recent studies of the long-term,

environmental impacts of settlement and farming in semi-arid Africa have had three specific,

methodological problems that limit the accuracy and completeness of their analyses. This

dissertation is built on a methodological foundation that reduces these limitations.

First, several of these recent studies are built almost entirely upon qualitative analyses of

environmental characteristics (e.g. Bassett & Boutrais 2000; Bassett & Crummey 2003; Fairhead

& Leach 1996; Kreike 2003). The knowledge of local people, based on long-term observations

of vegetation, is a valuable, and historically undervalued, source of information, and critical

analysis of historic, scientific documents can identify persistent, inaccurate perceptions of

3 ecosystem characteristics held by outsiders. However, such qualitative sources are poorly suited

to recognizing subtle biophysical processes, and to providing precise estimates of human impacts

in spatial or ecological terms, thus limiting both the practical applicability and theoretical

contribution of research findings. Furthermore, local knowledge, like scientific knowledge, is

founded upon culturally specific perceptions and is not inherently more accurate than scientific

knowledge (Agrawal 1995). Quantitative research methods can assess the accuracy of different

perceptions of ecosystem conditions—such as those of scientific and local knowledge—if these

are designed in ways that do not privilege one set of perceptions over another. The specific

methods used in this dissertation—both quantitative and qualitative—are described separately in

each chapter in which a method is used. Broadly, however, quantitative methods are employed

to test the assumptions of scientific viewpoints about specific aspects of the focal ecosystem, and

the findings of quantitative analyses are interpreted using, and are used to interpret, local

knowledge. This ordering—testing scientific knowledge then interpreting local knowledge—

was necessary because the local knowledge was not available when field research began, but its

collection was an aspect of field research.

Indeed, a second limitation of some recent studies of long-term, anthropogenic

environmental change in semi-arid Africa has been an a paucity of local knowledge (Chidumayo

2004; Devineau 2005; Devineau 2001). Quantitative methods have certain advantages, but

qualitative information gathered from local bodies of knowledge is necessary to situate

ecosystem conditions in an appropriate sociocultural context. Socially and culturally determined

beliefs, perceptions, and desires of land managers are primary determinants of the ways in which

humans interact with other ecosystem components (Balick & Cox 1996; Croll & Parkin 1992).

Local knowledge can provide rich detail on human environmental impacts—if it is collected in a

4 manner that assures that it accurately represents the experiences and awareness of the focal

population. An important way of doing this is to empower local people to identify, gather, and

analyze information relevant to specific research questions. Much of this dissertation is based on

data collection in which the residents of Solo village in southwestern Mali—subsistence farmers

representing the Maninka culture—actively participated.

Although I write in a formal, third-person style throughout this dissertation, my personal

history in Solo is important for understanding how and why the qualitative aspect of data

collection and analysis developed as it did. From 1995 to 1997 I visited Solo several times as a

Peace Corps Volunteer helping to develop the Bafing Biosphere Reserve, which was created in

1990, centuries after Solo’s founding. (A complete description of the research area is provided

in Chapter 2.) Although I first visited Solo with a Malian forestry agent who enforced

conservation policies with which Solo’s residents do not always agree, the c.250 people of Solo

warmly received me, which encouraged me to return repeatedly on my own. These visits, along

with experiences elsewhere in southwestern Mali, helped me develop friendships with several of

Solo’s men—mainly hunters with whom I share interests in wildlife, hiking, and natural

history—based in part on my respect for their deep knowledge of the natural environment in the

Bafing area. These relationships continued and improved in my subsequent visits to Solo, in

1999 (for my Master’s research), 2002, and 2003, so that when I began field research for this

dissertation in 2004, I had a decent understanding of how my proposed work fit into the body of

concerns held by people in Solo.

In particular, Solo’s people are concerned about the security of their access to land and

other resources, which is threatened by continued development of the Bafing Biosphere Reserve.

I have also maintained professional relationships within the Malian conservation community.

5 Having a window into the worlds of these conservationists and the people in Solo (and nearby

villages) has provided a curious view of human-environment interactions. The conservationists

have generally seen the land management practices of people in Solo as destructive of

biodiversity resources, while people in Solo see their activities as part of a strategy implemented

over generations to sustain the productivity of diverse ‘domesticated’ and ‘natural’ resources.

These viewpoints have meant that the conservationists have sought to modify or prohibit various

indigenous practices—to attain their goal of stopping purported biodiversity loss—while people

in Solo have sought to maintain indigenous practices while avoiding fines and other negative

consequences of conservation law enforcement—to attain their goal of sustained productivity of

diverse resources. Before beginning this dissertation, I felt that neither group had a complete

understanding of the processes that helped create the biodiversity upon which both were focused,

although I believed that people in Solo certainly had a more complete knowledge than city-based

conservation bureaucrats.

Thus, when I began field research in 2004, I explained to both groups that I sought better

knowledge of the processes that created biodiversity across the Bafing landscape, and

particularly how the activities of people like those in Solo affected these processes. Predictably,

both groups were certain that I would confirm their views, and many people in these groups

willingly helped me collect data I asked for or they thought I should have. Since I lived in Solo,

though, I gave the people there much more opportunity to contribute—I certainly felt that they

had the most to share. Furthermore, since conservationist perspectives have dominated resource

management policy in the Bafing reserve (i.e. Caspary et al. 1998; PREMA 1996; i.e. Warshall

1989), I felt that it was important to gain a different perspective on resource use, that of the

people in Solo. In 2004 I lived in a thatched hut in Solo that had been first offered to me as a

6 visitor in 1995, shared meals and experiences with my host and his family, and spent my time

working and socializing with friends and neighbors. Solo’s people participated in my research in

three main ways. First, many people willingly responded to my questions (which were, to them,

often tedious and naïve) about plants, animals, local history, and other topics, and several people

volunteered additional information I had not specifically asked for, but which they felt was

important for understanding the people-vegetation-wildlife relationships of interest to me.

Second, Solo’s traditional authorities—the chief and several men serving as counselors—helped

me identify and map all settlement sites, permanent water sources, and forest patches by

volunteering information far more specific than I could have asked. Third, and perhaps most

importantly, six men I hired as research assistants undertook the difficult task of searching large

portions of the landscape for chimpanzees twice per week. Their knowledge of the landscape

and of chimpanzee behavior allowed me to amass significant amounts of data on chimpanzees.

Overall, the participation of Solo’s people in this research was crucial for its completion, as well

as the completeness of its content.

The focus on chimpanzees in this dissertation suggests a third limitation of recent human-

environment studies of the impacts of smallholder farming and settlement on semi-arid African

ecosystems. None of these studies have substantially considered impacts on trophic levels other

than vegetation. Of course, the effects of drastic, short-term vegetation changes on wildlife have

received attention because of the rate at which commercial logging is transforming African

rainforest environments (Johns 1982; Johns & Skorupa 1987; Plumptre 2001; Plumptre &

Reynolds 1994; Skorupa 1986). However, the effects of subtle, long-term vegetation changes on

wildlife in Africa are only generally known (Happold 1995), and only a handful of papers have

addressed this issue directly, in rainforest environments (Fimbel 1994b; Wilkie & Finn 1990).

7 Throughout the semi-arid tropics, very little attention has been given the effects of subtle, long-

term vegetation changes on wildlife (cf. Bourlière 1983; Bullock et al. 1995; Cole 1986). If

human-environment geographers accept that many ‘natural’ landscapes are pervasively

humanized (Zimmerer & Young 1998), then greater attention must be paid to identifying and

understanding anthropogenic features of animal, and not just plant, communities (cf. Naughton-

Treves 2002).

The spatial structure of an ecosystem results from interactions between its biotic and

physical components. Humans dominate many ecosystems, but in all ecosystems processes that

operate independently of humans constrain interactions between biotic and physical components

(Vale 1982; Zimmerer & Young 1998). Thus, possible human impacts on ecosystem

characteristics are limited, even if such limits may have little meaning in urban areas. In rural,

agrarian landscapes, though, biophysical limits on the range of possible human environmental

impacts are more important, and their identification is crucial to determining how human

activities contribute to ecosystem structure and function. The difficulty of identifying these

limits is perhaps the most important reason why mixed methods should be used in human-

environment geography. Indeed, by substantially analyzing biophysical as well as sociocultural

components of ecosystems and environmental change, human-environment geographers can also

advance biogeography. Only recently have scholars begun to study biogeographic effects and

processes of anthropogenic vegetation change in Africa using sophisticated sociocultural

evidence to support biogeographic arguments (e.g. Assogbadjo et al. 2006; Maranz & Wiesman

2003; O'Brien & Peters 1998). This dissertation advances both human-environment geography

and biogeography by using mixed methods to address significant questions on how human

activities contribute to ecosystem structure. It is not simply a cultural ecology informed by

8 biogeography, but an analysis of the biogeography of human activities in a landscape where the

ecology and geography of these activities are poorly known.

The body of this dissertation is written as five related, but independent, papers, and not as

a single text divided into chapters. Each paper develops unique arguments and themes, but

several themes—summarized in the following paragraphs—run across several chapters. Each

paper contributes to the overall argument of the dissertation, that settlement and agriculture have

increased the distribution and abundance of chimpanzee habitat in the focal landscape. Figures

and tables associated with each chapter are placed at the end of chapters, but a single

bibliography is provided at the end of the dissertation.

The narrative this dissertation traces is about how Malian society, Maninka culture, and

the biophysical environmental have contributed to the spatial structure of an ecosystem in

southwestern Mali. Its chapters range from a broad analysis of settlement history and

conservation practice to narrowly focused analyses of specific plant and animal components of

the research area, but all chapters are fundamentally about the spatiality of the patterns and

processes observed—where things are and happen across the landscape. The following chapter

provides a detailed description of the research area and of the conservation policy context in

which the research is situated. By providing an analysis of settlement history and practice in

Mali’s Bafing Biosphere Reserve, along with an outline of ongoing conservation policies meant

to address the threat settlement is supposed to represent, this chapter exposes the tension that

exists between local and scientific knowledge of anthropogenic environmental change. This

tension is a primary element in the dissertation’s overall narrative. The main argument of

Chapter 2 is that conservationists have misunderstood the threat to biodiversity posed by

indigenous settlement practices because they have assumed incorrectly that the spatial pattern of

9 settlement indicates a process of frontier-style population expansion. This process has not

occurred for centuries in this landscape, and policies based on the assumption of ongoing

expansion are likely to fail conservation goals in the long term.

Chapter 3 narrows the focus of the narrative by providing an ethnographic analysis of

Maninka physical geographic concepts. This chapter provides an important contrast with

technical, scientific concepts in physical geography, which are more familiar to the reader and

the topic of Chapter 4. Chapter 3 shows that Solo’s residents do not perceive biophysical

diversity across the landscape in the same way as natural resource scientists. Maninka farmers

perceive detailed variation in the abundance and accessibility of resources across the landscape

based not only on tangible characteristics, but also on intangible, socially determined

characteristics. Based on this perceived variation, different parts of the landscape are subject to

different use. Maninka farmers do not consider most portions of the landscape arable or suitable

for settlement. This chapter contributes to the dissertation’s overall narrative by showing that the

conceptual landscape upon which Maninka settlement practices are founded does not include any

type of frontier along which human activities are increasing. Instead, the Maninka conceptual

landscape is a mosaic of areas with different histories of use, and varying potentials for use.

In Chapter 4, the overall narrative moves through a quantitative assessment of the

scientific view that human disturbance is the major cause of floristic variation across the Bafing

landscape. Using floristic analyses of extensive vegetation samples in sites with known

disturbance history, this chapter identifies biophysical and human factors that are significantly

associated with variation in vegetation composition. This chapter underscores the significance of

edaphic features as the primary source of floristic variation. Edaphic features are, of course,

fundamentally important in plant biogeography, but in the West African context their importance

10 is frequently ignored or underemphasized because landscapes are often portrayed as profoundly

humanized. The physical structure of the sandstone that outcrops across the research area creates

highly distinctive plant habitats that are associated with elevated biodiversity. Settlement and

cultivation also affect vegetation composition, but only in sites with relatively deep, arable soil,

and not across the entire landscape. Additionally, in sites with deep, arable soil, the effects of

settlement and cultivation are variable, ranging from species enrichment to species decline.

Chapter 4 shows that human activities have altered the distribution of various economically

important tree species, especially those with edible fruits; Chapter 5 tests this possibility for a

single species, the baobab (Adansonia digitata). This chapter is based on point-pattern analysis

of a census of all baobabs and settlement sites in the research area, and tests two different

scientific views on the reason for apparent spatial correlation in the distribution of settlements

and baobabs across Africa. Chapter 5 shows that human activities lead directly and indirectly to

the creation of baobab groves at settlement sites. The overall narrative is maintained in Chapters

4 and 5 as a study of how the direction and intensity of anthropogenic effects on vegetation

characteristics can be precisely determined, in order to understand how scientific and local

perceptions relate to observed patterns across the landscape. Both chapters build explicitly on

the findings of Chapters 2 and 3.

Finally, in Chapter 6 the overall narrative refocuses on the tension that exists between

local and scientific views of human environmental impacts, and continues the theme explored in

Chapters 4 and 5, on how to precisely determine anthropogenic effects on ecosystem

characteristics. Specifically, Chapter 6 examines chimpanzee distribution and behavior in

relation to human settlement history (Chapter 1) and vegetation characteristics (Chapters 4 and

5) in the research area. The quantitative and qualitative analyses in Chapter 6 show that

11 chimpanzees frequently visit baobab groves at abandoned settlements during the time of year

when baobab fruit composes an important component of their diet. However, through most of

the year, habitats along sandstone outcrops are more important habitat, due to the abundance of

food plants and surface water in these areas. Baobab groves at settlement sites are most

frequently used when food abundance in cliff habitats is lowest. Thus, human activities expand

the distribution and abundance of chimpanzee habitat relative to that which exists due to

biophysical processes operating independently of humans.

The narrative of this dissertation finishes with a short conclusion that returns to the

conservation context described in Chapter 2. The concluding Chapter 7 underscores the

importance of identifying and understanding the biophysical and sociocultural context in which

human-environment interactions occur. A primary argument of this dissertation is that there has

been insufficient attention given to the spatial and temporal contexts of indigenous land

management in the research area in particular, and elsewhere more generally. Better use of

mixed research methods in human-environment geography is necessary to improve knowledge of

the contexts of human-environment interactions. By recognizing how context constrains both

the environmental impacts of human activities and the appropriateness of possible conservation

interventions, human-environment geographers can improve the long-term effectiveness of

biodiversity conservation practice.

12 Chapter Two: Settlement geography and biodiversity conservation in Mali’s Bafing

Biosphere Reserve

Abstract

Studies of rural settlement—defined formally as a distinct land use dedicated to human

shelter—have been more important in cultural than in human-environment geography, although

settlement practice is constrained by the socioeconomic and biophysical processes that dictate

natural resource use. This paper argues that recognizing settlement as a distinct land use

improves our ability to understand and manage human environmental impacts. An analysis of

settlement history and Maninka settlement practice in part of Mali’s Bafing Biosphere Reserve

shows that the Maninka practice a shifting, as opposed to fixed, settlement system that allows

economically and politically marginalized men to improve their access to natural resources

without threatening traditional political authorities. However, conservationists have viewed

Maninka settlements as fixed, rather than shifting, so that settlement pattern has been

misinterpreted to suggest a process of frontier-style settlement expansion, and associated wildlife

habitat loss. This viewpoint is inaccurate, and has led to conservation policies that create

hardship for the marginalized people who benefit most from shifting settlement, and neglect the

geography of biodiversity and settlement in the Bafing area. These policies will likely fail

conservation goals in the long term.

Keywords: settlement; conservation; Mali; Bafing; chimpanzees

13 Introduction

Cultural geographers working have long studied the form and distribution of rural

settlements as a means of understanding perceptions and use of space, demography, historical

processes of landscape change, and spatial aspects of agricultural and economic development. In

the West African context, settlement geography has been studied primarily as a means of

understanding cultural geography and history (e.g. Bernus 1956; de Bruijn & van Dijk 1995;

Gado 1980; Gallais 1975; Queant & de Rouville 1969; Sidikou 1974; Woodford 1974), or

constraints on economic development (Silberfein 1998). Human-environment geographers have

given less attention to settlement, even though settlements are established and abandoned due to

socioeconomic and biophysical processes that affect resource use (Stone 1996). Instead, human-

environment geographers have focused on more directly productive types of rural land use—

such as animal husbandry, agriculture, logging, and conservation—without specifically

examining settlement practices associated with these other land uses. Recently, a handful of

human-environment anthropologists have published valuable studies of settlement ecology

(Amanor 1994; Fairhead & Leach 1996; Stone 1996) that build on important, earlier works in

anthropology (de Schlippe 1956; Green et al. 1978). In geography, however, there have been

few published analyses of rural settlement from a human-environment perspective since about

1970, the major exception being Chisholm’s (1979) third edition of Rural Settlement and Land

Use, originally published in 1962. From a geographical perspective, our knowledge of rural

cultural and political ecology is built largely upon studies of land uses other than settlement.

Settlement—formally defined as the development of human shelter over time through

various social, cultural, spatial, and ecological processes (Chisholm 1979; Christaller 1966

[1933]; Hill 2003; Stone 1996)—is a distinct land use (Morgan 1955). Of course, settlement is

14 not entirely separable from other land uses, especially agriculture, husbandry, logging, and

reserving land for hunting and gathering, or recreation. However, settlement practice includes

many acts that are not part of other types of land use, because shelter is the primary purpose of

settlement, and no other land use. For instance, in settlements, people must identify, construct,

and manage places for resting, cooking, storage, caring for children and the elderly, bathing,

gardening, keeping small animals, and many other activities that are less frequently, if ever,

practiced in places dedicated to other land uses (e.g. Morgan 1955). Settlement is ecologically

the most disruptive land use in agrarian rural landscapes, though it directly affects small areas;

settlement sites are occupied more continuously and are more intensively managed than other

parts of the landscape (Stone 1996). More importantly, settlement constrains the spatial

distribution and intensity of most other land uses (Stone 1996). Farmers seek settlement

locations that minimize the distance between settlements and fields, fallows, water sources,

construction materials, and other resources (Chisholm 1979; Hill 1953; Jarrett 1948; Johnson

1977; Morgan 1955). As a result, resources near settlement sites experience heavier use than

more distant ones (Stone 1996).

Recognizing and studying settlement as a distinct land use requires spatial and temporal

scales of observation that are infrequently used in human-environment geography (Stone 1996).

Cultural ecologists often focus on changes that occur over periods up to about a decade, the

maximum being about the length of time an individual swidden remains productively fertile. In

contrast, the periodicity of settlement establishment and abandonment in non-irrigated, agrarian

landscapes is generally decades to centuries (Hill 2003; Hunter 1967; Udo 1965). Of course,

proxy data have allowed many cultural ecologists to use very long periods of observation, but

such observations generally occur at regional or continental scales that obscure events and

15 processes occurring in specific communities or landscapes. Community-based cultural ecologies

of agriculture often focus on questions at the spatial scale of individual plots or settlements:

What prompts farmers to abandon one plot and move to another? What leads to intensification in

one plot or village but not another? However, settlement occurs at a more expansive scale,

across landscapes measured in tens to hundreds of kilometers that include areas that are suitable

for settlement, but not occupied (Chisholm 1979; Hill 2003; Hunter 1967; Morgan & Woods

1986; Stone 1996). The scale of observation suitable for studies of settlement ecology is more

similar to that often used in studies of pastoralism (e.g. Bassett 1988; Bassett & Koli Bi 2000;

Behnke et al. 1993; Coppolillo 2000; Turner & Hiernaux 2002).

The present paper has two goals. Its primary goal is to describe the settlement practices

of Maninka farmers in southwestern Mali and argue that these subsistence agriculturalists

practice a system of shifting settlement (Stone 1996). This system is an adaptation to a particular

set of socioeconomic and biophysical conditions that are widespread in Africa, where shifting

settlement appears to be widely practiced (de Schlippe 1956; Hill 1953; Hunter 1967; Netting

1993; Richards 1978; Stone 1996). This description of Maninka settlement practice is important

because shifting settlement is ethnographically underdocumented (Stone 1996), although it is

widespread in space and time (Murdock 1967). Cultural geographers of settlement appear to

have also largely overlooked it (cf. Grover 1985; Hill 2003; Kharkwal & Sharma 1990; Sharma

1985) even though Stone (1996) shows clearly how shifting settlement systems may arise based

on models of agricultural, geographic, and demographic change that geographers widely use. In

short, building on Chisholm’s (1979) model of settlement location and Boserup’s (1965) model

of agricultural intensification as modified by Brookfield (1972; 1984), Stone (1996) argues that

shifting settlement systems develop where the following factors co-occur: a) farmers face a

16 shortage of farmland within a reasonable distance of their settlement; b) unused, but relatively

small, patches of farmland exist some distance away from their settlement; and c) the costs of

agricultural intensification in fields near their settlement exceed the costs of abandoning the

settlement and building a new one near an unused patch of farmland. Farmers in shifting

settlement systems probably always practice shifting cultivation (cf. Morgan 1955), but these

two types of land use are distinct and occur over different spatial and temporal scales. Maninka

shifting settlement complements Maninka shifting agriculture and allows farmers to adapt to

socioeconomic changes in a patchy biophysical environment.

However, policy makers have not recognized that the Maninka practice shifting, and not

fixed, settlement, and thus have misinterpreted Maninka settlement pattern as an indication that

frontier-style settlement expansion is occurring (Caspary et al. 1998; PREMA 1996).

Conservationists view the relatively numerous, new settlements in Mali’s Bafing Biosphere

Reserve (BBR) as the disintegration of larger settlements as people move into previously ‘wild’

and unoccupied land. Thus, settlement is seen as an unqualified threat to wildlife habitat. The

second goal of this paper is to challenge this viewpoint by examining the history and practice of

settlement in a portion of the BBR. This examination shows that: a) most settlements are short-

lived; b) virtually all settlements have been established in sites that previously hosted now

abandoned settlements; and c) shifting settlement is a flexible institution that enables Maninka

farmers to adapt to socioeconomic and biophysical change in an environment with sparse, patchy

resources. The paper concludes that understanding the geography and rationality of shifting

settlement will enable conservationists to manage biodiversity resources more effectively while

also reducing the social injustice, and probable long-term failure, of current policies meant to

reduce wildlife habitat loss to human settlement.

17 Geographical Context

Research occurred in an area of 183 km2 around Solo, a settlement of about 200 people

located in the Bafing Biosphere Reserve (BBR) of southwestern Mali (Figure 1, p. 52). The

BBR comprises two national parks and a chimpanzee-specific reserve; a buffer zone surrounding

these areas is awaiting ministerial approval (Duvall et al. 2003). Solo lies on the northern

boundary of one of the national parks, so that about half of its traditional territory lies in the park,

while the remainder lies in the proposed buffer zone (Figure 2, p. 53). The BBR protects an

important population of West African chimpanzees (Pan troglodytes verus) (Duvall et al. 2003),

as well as populations of several threatened or endangered trees (Duvall 2001). Chimpanzees

have been a focal species for conservation activities in the BBR since the mid-1980’s (Maldaque

1985; Moore 1985), and this focus will certainly intensify with increasing international

recognition of the significance of Mali’s population (Kormos & Boesch 2003; Kormos et al.

2003).

The plant and animal species of greatest conservation concern are associated with

sandstone outcrops that rise 200-300 m above surrounding, relatively flat lowlands (Duvall 2001;

Duvall 2000; Jaeger 1959; Lawesson 1995). Topographic complexity in these outcrops helps

create a wide range of microhabitats, and the physical structure of the rock creates very stable

ecological conditions (cf. Larson et al. 2000). Erosion of the sandstone plateaus has formed

narrow ravines, rocky slopes, and plains with relatively infertile sandy and silty soils. The upper

surface of the plateaus consists of bare or shallowly buried bedrock (Jaeger 1950b; Jaeger &

Jarovoy 1952), and similarly xeric ferricrete hardpans are common throughout the area (Dames

& Moore 1992; Michel 1973). The best farmland is located in small, basin-shaped valleys below

the outcrops (PIRT 1983; Samaké et al. 1987). Permanent springs are most common along the

18 sandstone outcrops, where sedimentary layers in the sandstone have been exposed (see Chapter

6). Elsewhere, permanent surface water sources are uncommon, and are primarily deep

depressions in seasonal streambeds. People rely mainly on hand-dug wells for their water needs.

Precipitation is highly seasonal and averages about 1100 mm per year, with high interannual

variation (FAO 1984; Leroux 2001; PREMA 1996).

Woodland vegetation dominates most of this landscape, especially in areas with relatively

deep, fertile soil. Forest patches occur in topographically protected microhabitats with moist soil

conditions along the sandstone outcrops. Locations with shallow or infertile soil host patches of

edaphic bushland or grassland. Based on woody species composition, fifteen vegetation types

have been described for the area, including several types that are associated primarily or

uniquely with abandoned settlement or field sites (see Chapter 4).

Although gazetted originally in 1990, the BBR remains almost non-existent on the

ground. The Malian government, supported by bilateral aid agencies, has developed and adopted

a formal management plan for the area (Caspary et al. 1998; Niaré 2000). The management plan

identifies Maninka settlement practices as one of the main threats to biodiversity in the BBR

because it causes wildlife habitat loss (Caspary 1999; Niaré 2000; PREMA 1996). In particular,

Maninka farmers establish hameaux de culture (‘farming hamlets’) away from their official

villages of residence in order to access patches of arable soil (Cissé 1970; Koenig & Diarra 1998;

Samaké et al. 1987). For clarity, the French terms hameau and village will be used when

referring to a ‘hamlet’ or ‘village’ from the administrative viewpoint of Malian conservationists

and government officials, to distinguish these concepts from the Maninka terms bugu [‘hamlet,

farm’] and dugu [‘village’], described below. The English terms ‘hamlet’ and ‘village’ are used

interchangeably with bugu and dugu. Hameaux are generally small, occupied by just a few

19 nuclear families, although some are as large as a village, hosting many families (PREMA 1996).

Most distressing to conservationists is that many hameaux have been established throughout the

BBR in the past ten years, and that some hameaux, like villages, have been occupied for decades

(Caspary et al. 1998; Niaré 2000; PREMA 1996). Hameaux are not officially recognized

settlements; their residents are counted as part of the population of the villages where they lived

before moving to a hameau. The only legal or administrative status most hameaux have is as

illegal settlements, if located in the BBR, or illegal clearings, since Malian forestry laws

prohibiting clearing vegetation that has not been cleared for ≥10 years (Présidence de la

République du Mali 1995).

Conservationists working in the BBR consider hameaux de culture a spatially uniform

cause of habitat loss that occurs in an essentially chaotic manner through most of the reserve

(Niaré 2000; PREMA 1996). Indigenous land use is believed to happen “without any planning

beforehand”, and the establishment of new hameaux is considered an “uncontrolled swarming”

(1998: 98). No study of settlement practice has been made in the Bafing area; the information

used to support this viewpoint is primarily PREMA’s (1996: 45) list of 83 hameaux associated

with 8 villages in the BBR area, which also shows that many of these have been occupied less

than ten years. Few of these hameaux have been mapped, although PREMA reports that many

are >5 km from their associated villages.

This distance is significant, because the presidential decree that originally established the

BBR (as a game reserve) in 1990 acknowledged the existence of some, but not all, preexisting

“villages” in the protected area, and allowed these “villages” circular “enclaves” 5 km in

diameter, in which farming was allowed (Présidence de la République du Mali 1990). Several

preexisting “villages” were not recognized, and some of the “villages” that were recognized are

20 actually hamlets (i.e. Maninka bugu), apparently because those who drafted the proclamation had

very little information about local geography. The existence of other hamlets in the BBR was

not recognized. In fact, all settlements in the BBR not in the recognized enclaves became illegal

with the reserve’s creation, even though many occupied in 1990 had been occupied for decades.

From the beginning, human presence in the BBR area was erroneously underestimated and

spatially simplified: lawmakers envisioned that existing human settlement could be encapsulated

in a small number of standardized, circular enclaves.

Greater familiarity with the Bafing landscape, which came as a result of various

development projects in the 1990s, caused conservationists to discover that there were many

more settlements in the BBR than previously imagined (Duvall & Niagaté 1997; PREMA 1996).

The increased observation of settlements was interpreted as “[t]he population’s geographic

expansion” (Caspary et al. 1998: 84), even though there was no relevant information reported on

the historical geography of settlement or on demographic change for the area. For instance,

population estimates for the BBR area, based on rapid surveys in many settlements, are paired

with population growth rates for western Mali or all of Mali (stated as 2-4% annually) to suggest

that the population in the BBR is increasing at the same, relatively high rate (Caspary et al. 1998;

PREMA 1996). This method of analysis obviously combines two different scales of

information. More careful demographic analysis has shown that the human population has

declined in the Bafing area since the mid-1980s, and may continue in this direction for the next

several decades (Mission Francaise et al. 1996; Raynaut 1997), even though regional population

centers are growing (Bonavita 2000).

Nonetheless, the ‘expanding population’ interpretation of the observed settlement pattern

led conservationists to recommend revising the enclave strategy to address this perceived

21 problem of habitat loss. Residents would be allowed to farm within traditional “territoires des

villages” (‘village territories’), which were to be delimited in collaboration with residents, but

outside these areas the level of resource protection would increase to make “integral sanctuary

zones” for wildlife, as defined by IUCN (1986), in which no activities (other than tourism and

research) would be allowed (Caspary et al. 1998: 84-86). The likely size of ‘village territories’

was not specified (although they were suggested as replacements for the 5-km enclaves), and the

acceptability of hameaux within ‘village territories’ was not addressed. Although no ‘village

territories’ have been identified, the “integral sanctuary zones” were created in 2002, when the

two national parks, Kouroufing and Wongo, were gazetted (Présidence de la République du Mali

2002a; Présidence de la République du Mali 2002b).

These protected areas have become nationally significant in Mali’s efforts to meet its

biodiversity conservation goals. Under the Convention on Biological Diversity, the Malian

government initiated the “Project for the Long-term Management of Biodiversity in the Bafing

[Biosphere] Reserve” (MEATEU 2000: 83-84). One outcome of this project was the

government’s eviction, in 2004-06, of all hameaux in the BBR’s two national parks, affecting an

unknown number of people. Threats of violence were used to evict settlements: in January 2006,

agents of the national conservation directorate threatened publicly to burn any hameaux

remaining in the BBR. Evictees have received no compensation or assistance. Most have

returned to their home villages, or established new settlements just outside national park

boundaries. Conservationists continue to fear the settlement expansion hameaux are supposed to

represent, and people living in settlements in and near the BBR fear the possibility of more

evictions in the future.

22 Settlement geography in the BBR has not been studied, despite the certainty

conservationists have expressed in representing the relationship between settlement pattern and

process. An examination of settlement history and practice in the BBR shows that there has been

no geographic expansion of settlement for centuries. Settlement pattern represents an ongoing

process of settlement establishment and abandonment observable over decades, not years, arising

from a social institution that enables politically and economically marginal families greater

access to natural resources without threatening more dominant families.

Data collection and analysis

Data on settlement history and practice were collected through documentary sources,

ethnographic interviews and participant observation, and foot surveys of abandoned settlements

in the research area.

Documentary evidence provides some information on settlement history in the research

area. The Scottish explorer Mungo Park passed through Solo during his second visit to what is

now Mali, in 1805 (Park 1954 [1815]). Other European travelers—mainly French military

officers—visited other parts of southwestern Mali in the 1800s, recording observations relevant

to understanding general settlement history in the area (Mage 1868; Mollien 1820 [1967]; Noirot

1885; Tellier 1898). During the 1880s-1890s, the French gained military control over western

Mali, and in 1889 a colonial officer collected names of occupied settlements in part of the

research area and to its northeast, along the Bafing River (Samaké et al. 1986). In 1952, the

French colonial mapping service took aerial photographs of southwestern Mali, and produced

1:200,000 scale topographic maps from these images (Anonymous 1958). While many

settlements are unnamed on this map, the map and photographs record the existence of many

settlements in 1952. Finally, rapid surveys of settlement history around Solo were conducted in

23 the 1980s as part of the resettlement projects associated with construction of the Manantali Dam

(Samaké et al. 1986; Sanogo 1991).

Oral history has retained much more information about past settlement around Solo. Oral

historical interviews were conducted in Solo, in the Maninka language, during May-July 2003

and January-December 2004. These interviews built upon the researcher’s past visits to Solo

since 1995, including visits to now-abandoned hamlets. Abandoned settlements were identified

during individual interviews with c.45 male and female residents of Solo aged c.10-80, and

during three group interviews of Solo’s traditional authorities (chief, land chief, and senior

counselors), all men aged c.40-80 years. With the assistance of interviewees, all occupied and

abandoned settlement sites identified through interviews were visited. The location of each site

was determined using a Garmin GPS-12XL unit, and was recorded as a point corresponding to

the approximate center of the occupied area of each site (as evidenced by the distribution of huts

or remains of hut foundations). For every site, the following information was collected: site

name, estimated dates of establishment and abandonment, causes for establishment and

abandonment, soil texture (via manual analysis: Midwest Geosciences Group 2003), and

preceding and succeeding residences of site occupants. Multiple informants were interviewed to

increase precision by triangulating date estimates and gaining multiple perspectives on other

information (cf. Flowerdew & Martin 1997). Generally, establishment and abandonment dates

were estimated by correlating informant life history markers, changes in site occupation status,

and datable events, such as national elections. In some cases, specific dates of past site

occupation were gathered from the historic documents and aerial photos described above.

The resulting point pattern was analyzed using Ripley’s univariate K function. There are

numerous technical descriptions of Ripley’s K, which is calculated from the number of points in

24 a given distance, h (e.g. Bailey & Gatrell 1995; Diggle 2003; Dixon 2002; Haase 1995). In

application, the K function is usually linearized to stabilize variance and facilitate interpretation,

and in this form is called the L function (Dixon 2002). Additionally, multiple values for h are

used in order to assess spatial pattern at multiple scales. Values of L(h)>0 indicate clustering,

while L(h)<0 indicates regularity in the distribution of points (Dixon 2002). Since the present

application of Ripley’s K is solely to describe the spatial structure of the observed point pattern

and not to assess statistical significance, the assumption of stationarity—which is probably not

held for the distribution of settlements—does not need to be met (Bailey & Gatrell 1995; Diggle

2003). K function analyses were conducted using the SPLANCS package (version 2.01) in the R

statistical software environment (version 2.2.1). For further description of SPLANCS, see

Rowlingson and Diggle (1993), Gatrell et al. (1996), Bivand and Gebhardt (2000), and Diggle

(2003). Edge effects were corrected geometrically (Bailey & Gatrell 1995).

Finally, ethnographic interviews and participant observation were used to identify and

understand Maninka settlement practices. Participant observation provided experiential

knowledge of settlement practices, while interviews clarified observations (Werner & Schoepfle

1987). During the period of research, four settlements were abandoned (three as a result of

eviction from the BBR) and one was established (outside the national park by residents of an

evicted hameau). Informal interviews were conducted while working or socializing with

farmers, and while working with men who were clearing the site of the newly established

settlement. All but two of c.45 interviewees—males and females aged c.10-80—had previously

lived in one or more now-abandoned settlements, and all were subsistence farmers.

Settlement history before 1890

25 Current Maninka settlement practice developed during the settlement history of the

research area, which can be divided into three broad periods: prior to 1890, 1890-1960, and 1960

to the present. Within these broad periods there are several distinct phases, which are described

individually in the following paragraphs.

Early Holocene. During the early Holocene and before, modern humans settled the area

around Solo, as evidenced by stone artifacts and cave shelters (Sanogo 1991). Essentially

nothing is known of these people or their settlement practices.

Pre-Maninka period. At an unknown period, the area was settled by people the Maninka

identify as Jané, Sylla, Sigé, and Somalaka (Samaké et al. 1986). The Maninka entered the

research area at an imprecisely known date following the Manding expansion in Sudanian West

Africa, which peaked in the 1300s (Cissé 1970; Samaké et al. 1986). According to local oral

history, the Maninka militarily defeated the earlier residents, causing the earliest cases of

settlement abandonment for which there is historical evidence (Figures 2 & 3, pp. 53 & 54).

Early Maninka period. The Maninka have never occupied the settlement sites from

which they expelled the Jané, Sylla, Sigé, and Somalaka, some of whom fled, while others were

absorbed into Maninka society as hereditary blacksmiths, leather workers, and griots (Samaké et

al. 1986). Instead, the Maninka established Solo, as well as three other settlements to the east of

Solo, near the Bafing River, outside the research area (Samaké et al. 1986). This broader area,

called the Bafing jamana (‘district’), hosted at least 35 Maninka settlements by the late 1980s

(Samaké et al. 1986), when most of the Bafing was flooded following construction of the

Manantali Dam (Figure 1, p. 52).

The historical record is poor for the early period of Maninka settlement, until about 1890.

Farmers established and abandoned several settlements in the research area other than Solo

26 (Figure 3, p. 54), primarily in order to improve access to farmland (Figure 5, p. 56). In many

cases, however, settlements were established with a primary or secondary goal of improving

their defensive situation during what was a time of political instability and warfare throughout

Sudanian West Africa (Ajayi & Crowder 1985). Although jing (‘stone walls’) were built to

protect Solo and another large settlement occupied during the 1800s (Figure 2, p. 53), a more

common practice of reducing the risk of violence was to establish new settlements in defensive

locations (Mage 1868; Mollien 1820 [1967]; Noirot 1885; Park 1954 [1815]; Sanogo 1991;

Tellier 1898), as elsewhere in nineteenth-century West Africa (Gleave 1966). In the research

area, this practice meant that most settlements established prior to 1890 were built on or just

below sandstone outcrops, which offered refuge. As a result, most settlements from this time

had sandy soil rather than finer-textured soil (Figure 6, p. 57), which is most common farther

away from sandstone outcrops (PIRT 1983).

The research area was not a site of major warfare. Thus, unlike areas along the Bafing

River to the northeast of Solo, which were essentially depopulated at the time of Eugène Mage’s

1868 visit, in the research area no Maninka settlements were abandoned directly as a result of

warfare (Figure 4, p. 55). Oral history indicates that the possibility of war did lead to the

abandonment of Solo’s second site after only c.50 years’ occupation, because its location was

considered poorly defensible.

There were probably other Maninka settlements in the research area before 1890 for

which oral historical evidence does not survive, because hamlet settlement seems to have been

practiced during this period. The best evidence for this comes from Mungo Park’s journal from

his second visit to what is now Mali, in 1805. Park probably visited Solo’s earliest location

(Figure 2, p. 53), “an unwalled village at the bottom of a rocky hill” (1954 [1815]: 318), which

27 was abandoned soon afterward because of increasing concern about the hazard of rockfall from

the adjacent sandstone outcrop. Less than about five miles west of Solo, Park (1954 [1815]: 317-

318) passed through “the village of Gimbia, or Kimbia”, then, after spending a night in Solo,

observed “several villages” along the base of the cliffs immediately east of Solo. There is no

oral historical evidence of these settlements. “Gimbia” was probably a hamlet occupied by

people from Solo, located along the seasonal stream called Guimbaya kò (‘creek at the place

occupied by Guimba’), where two hamlets named Guimbaya have been established and

abandoned in living memory (Figure 3, p. 54). The “villages” to the east were also probably

Solo’s hamlets, since the cliffs on the west side of the Bafing River along Park’s route end about

5 km from Solo’s first location. Additionally, just west of the research area, Park (1954 [1815]:

296) passed through a very small “village” which he described as “belong[ing] to” another,

larger “village”. This is how the village-hamlet relationship is described in the Maninka

language, as explained below. While inconclusive, Park’s journal suggests that hamlet

settlement existed in the research area in 1805, and possibly also just to the west of this area,

which is topographically similar and thus may have been equally protected from warfare. Park’s

journal does not suggest that hamlet settlement existed in the Bafing floodplain or other areas to

the east, without sandstone outcrops.

The expansion of European military authority throughout West Africa after about 1890

quelled warfare between African groups and initiated a period of peace—although the colonial

presence brought other risks, such as forced labor. As described above, shifting settlement

probably existed before 1890 in the research area, especially near sandstone outcrops, but

security and the conditions of colonial rule after 1890 (as described below) helped make shifting

settlement an increasingly important means for farmers to respond to the new socieconomic

28 contexts of French colonialism and Malian independence. Current Maninka settlement practices

are not timeless, but most strongly reflect twentieth-century socioeconomic and biophysical

conditions. In order to understand how processes of socioeconomic and biophysical change

affected settlement history after 1890, it is necessary to first describe Maninka settlement

practice.

Maninka settlement practice

Settlement is a masculine practice for the Maninka. Men decide when and where to

establish new settlements, as well as when to abandon settlements. Women, and to a lesser

extent children, can exert informal influence on these decisions, but adult males have the only

formal roles in settlement practice. The Maninka shifting settlement system allows farmers to

exploit dispersed patches of arable soil in a socioeconomic environment where the costs of living

distant from permanent villages are, for many people, low relative to the costs of remaining

permanently in a village.

The politics of hamlets. The primary reason for the establishment of 95% of occupied or

abandoned settlements in the research area was the need for better access to farmland (Figure 5,

p. 56). Koenig and Diarra (1998) also found this to be the case near Manantali, just north of the

research area. Although a relatively large area of farmland surrounds Solo, it is limited. Solo’s

traditional authorities—the dugutigi (‘village chief’), dugukolo tigi (‘land chief’), and other elder

men acting as counselors (cf. Cissé 1970)—decide how to allot Solo’s farmland to men and their

families. Land rights in Maninka societies are primarily a function of age and historical primacy

(Cissé 1970; Leynaud & Cissé 1978). In Solo, institutions of tenure strongly favor older men

and men belonging to Solo’s founding family, whose oldest male is the chief. In principle, the

chief holds rights to all land within Solo’s traditional territory, and grants usufruct for certain

29 areas to the male heads of Solo’s extended families. The head of each family manages most of

the family’s farmland as a foroba (‘large field’), in which all family members work. The family

head controls all produce from the foroba. Additionally, the family head may allot smaller fields

to adult family members, both men and women, which they manage individually. These fields

are the primary means through which women and young men living in Solo may earn money,

because they retain all produce for their personal use. In all cases, however, the foroba takes

precedence over smaller, individual fields (Leynaud & Cissé 1978). Thus, if a family head has

use rights to less land or less productive land than he would like, he may decide not to allot

individual fields to some or all household members. When such decisions are made, younger

men and women are the first to lose access to farmland they can manage for their personal

benefit. Maninka political and tenure institutions are essentially age-based hierarchies (Leynaud

& Cissé 1978).

The marginality of young men, regardless of their family connections, must be

underscored in order to represent the importance of hamlet settlement in family development.

Establishing or living in a hamlet is a normal aspect of household development in Solo, and

virtually all people in Solo have lived in many hamlets during their lifetimes (Text Box 1, p. 50).

Young men have relatively little influence on decisions about farmland allotment, and thus are

often allotted less or less desirable farmland than they would like within a reasonable distance of

Solo, usually about 3 km. As a result, their agricultural productivity—the source of their

monetary income and their nuclear family’s food security—does not meet its full potential,

hindering household development in numerous ways. While land-hungry farmers do pursue the

option of agricultural intensification to some extent through increased cultivation of secondary

crops, the labor cost of substantial intensification far exceeds that of establishing a new

30 settlement at an unoccupied patch of farmland relatively distant from Solo (cf. Richards 1978;

Stone 1996). Most families in hamlets are led by young, married men.

Maninka shifting settlement centers on the social institution of the bugu (‘hamlet, farm’),

which allows young or politically marginalized men to increase their access to natural resources

and space in a way that does not threaten the traditional political hierarchy. In a rural setting, the

Maninka recognize two settlement types: bugu and dugu (‘village’). Virtually all new

settlements are bugu. Distinguishing villages and hamlets is often easy because most hamlets are

small, geographically marginal, relatively undeveloped, and occupied by a few nuclear families

headed by closely related men. However, some bugu are large, geographically central, well

developed, and occupied by many nuclear families—like most dugu—because the Maninka

definition of these terms is political, not functional. Functionally, hamlets allow men to access

more or better farmland than they can in their home village (Cissé 1970; Koenig & Diarra 1998).

A man who wishes to establish a hamlet must negotiate with his family’s head to decide how he

will continue contributing to the extended family (usually by depositing a portion of his produce

to the family’s granary). As long as sufficient labor remains available for farming around the

village, it is generally in the interest of a family head to allow younger members to establish

hamlets because this increases the family’s overall access to resources—specifically, distant

patches of farmland. Hamlets allow Solo’s residents to efficiently exploit relatively small

patches of farmland dispersed widely across the landscape.

It is also in the interests of Solo’s traditional authorities to grant male residents

permission to establish new hamlets because of their agroecological function. However, hamlets

also pose the risk to Solo’s authorities that they may lose land rights around a hamlet. This can

occur if a bugu grows large and endures over time, until its founding family, other residents, and

31 people in neighboring settlements consider it a dugu. Few hamlets ever attract more than a

handful of nuclear families or last more than a couple decades, so this risk is small. Nonetheless,

hamlets have a clearly secondary status politically: a bugu is considered to “belong” to a dugu.

For instance, Sandiguila, a large settlement occupied for 80 years (until its eviction in 2006) was

Solo ka bugu (‘Solo’s hamlet’), and not a dugu. In daily life, this subordinate status means little

to a hamlet’s residents because its men are free to manage the hamlet as they decide. However,

all major decisions about land use—there is no criteria that makes a decision ‘major’, but an

approximation is that any decision not anticipated within normal agricultural cycles is ‘major’—

belongs to Solo’s traditional authorities. For instance, in 1999 a man from a village south of

Solo, in Kouroufing National Park, sought to establish a hamlet outside the park near Sandiguila.

Solo’s traditional authorities permitted the man to build his hamlet, even though residents of

Sandiguila thought he should not have been permitted to settle where he did. In general, political

tensions between Solo and its hamlets are minimal, and men in both Solo and its hamlets

regularly discuss land-use (and other) decisions as friends and relatives, in order to make the best

possible decisions. Nonetheless, underlying these relationships is a clear political hierarchy that

situates ultimate decision-making authority in Solo (cf. Cissé 1970; Leynaud & Cissé 1978).

In the Bafing area, historical cases in which a village truly split, so that more than one

resulting segment was recognized as a village, arose from intense disputes and social

disintegration within the original village (Samaké et al. 1986). Men choosing to establish new

settlements accept as a cost decreased political authority and decreased access to the resources

available in a village, but gain political autonomy and authority in their hamlets (although this is

ultimately limited). Families or clans that have an unalterable, secondary political status in Solo

have historically occupied long-lived, village-like hamlets because this situation offers them the

32 most authority and autonomy without causing social upheaval. For instance, the Dembélé

family, whose ancestor founded Solo, still dominates in the village, while the Kéïta family,

whose ancestor arrived soon after Solo’s founder, occupied Sandiguila. The historical primacy

of the Dembélé family in Solo means that the Kéïta family will never be politically dominant,

and thus will never have access to the best farmland around Solo. This situation is particularly

galling to the current heads of Solo’s Kéïta family. When Sandiguila was evicted in 2006, its

(male) residents decided to establish a new hamlet, rather than move to Solo, in order to maintain

their autonomy. Overall, the costs of leaving Solo to live in a hamlet are minor—especially in a

small, economically marginal village like Solo—relative to the costs of agricultural

intensification in existing fields, and the potential benefits of increased productivity in a new

settlement.

Site selection and management. While access to farmland is usually the primary criterion

in selecting a settlement site, it is not the only one. Personal history, water availability, distance

to other settlements and main paths, spirit activity, and other criteria are all considered in

choosing a settlement site. Prior to 1890, the defensibility of a settlement site was another

important criterion. Considering a possible settlement site in relation to the distribution of water

sources (including groundwater), other settlements, paths, and various natural resources is a

purely spatial analysis that is undertaken concurrently with the assessment of farmland in a site.

Personal history and the activity of spirits are much more significant in constraining site

selection. First, historical primacy is the main determinant of usufruct for the Maninka, and

families retain settlement and cultivation usufruct for parts of the landscape in which their

ancestors are the earliest remembered Maninka settlers (cf. Hopkins 1972; Leynaud & Cissé

1978; Samaké et al. 1986). Physical features generally delimit such areas, which are often given

33 the name of the first settlement established in them. This name is also given to all subsequent

settlements in a given area. For instance, Santankéni is the toponym for a solon (‘large, basin-

shaped valley’) northeast of Solo that has hosted at least six different settlements called

Santankéni, a name which was given to the first of these settlements by its founder c.1870

because it was the site of a santan tree (‘Daniellia oliveri’) that was considered beautiful (‘ké

ni’). The founder’s descendents retain unchallenged rights to renew settlement in this area even

if no hamlets have been occupied in it for years, and in practice do not need to ask permission of

Solo’s traditional authorities to do this—although in principle this is required. Conversely,

individuals with family history in a particular area are constrained in their use of other parts of

the landscape, because they must seek permission for settlement and cultivation in the other

areas from both Solo’s traditional authorities and the usufruct-holding family. Often, the

usufruct-holding family will grant permission to use only less-desirable patches of unused land,

even if they have no plans for using more desirable patches, because they fear losing clear

usufruct in the future. Men descended from Solo’s founder are its traditional authorities, and

thus control the most and the best farmland near Solo.

Second, the activities of jine (‘spirits’) are extremely important in the success or failure

of a settlement, as well as in farming, hunting, and all other activities (see Chapter 3). Many

spirits, both good and bad, occupy the Maninka landscape. Several steps are taken to determine

the suitability of a site for settlement with respect to spirit activities. To begin with, a man

considering establishing a new settlement often seeks spiritual guidance by consulting a hunter

who understands kènyèndiyon (literally ‘sand children’, referring to marks drawn in sand), an art

of prognostication. Kènyèndiyon allows Maninka hunters, who are extremely knowledgeable

about the spirit world (Cashion 1982; Cissé 1964), to foresee whether an action will benefit a

34 person, that person’s health and material wealth, and the health and material wealth of that

person’s immediate and extended family. Based on the hunter’s findings, a man can decide if he

should establish a new settlement, and—after having selected a potential site—whether a site is

suitable for settlement. In surveying possible settlement sites, those with obvious signs of

malevolent spirit activity—such as the abundance of huyuhiyo (‘Dombeya quinqueseta’) or other

plant species, or unusually warm or cool air—are avoided. Sites exhibiting benevolent signs—

such as trees that have developed small root buttresses—are favored. In sites without obvious,

malevolent spirit activity, a man examines the soil nyama, which is the soil’s inherent

healthfulness and is different from its fertility (see Chapter 3). By digging and refilling a small

hole, and monitoring how this hole changes over several days (changes that are considered

significant appear to relate to bulk density), a man, in consultation with others, can determine

whether humans can safely occupy the site. These steps must be taken before establishing any

settlement, even one that will be built within meters of an abandoned settlement that was a

successful and healthful site for its occupants. Despite these precautions, a substantial number of

settlements have been abandoned because bad spirits in a settlement site have caused many

illnesses or deaths to residents (Figure 4, p. 55).

Taking all these criteria into consideration, there are a limited number of areas across the

landscape that are suitable for settlement, although most of these patches are unoccupied at any

point in time. The limiting factor that reduces the number of possible settlement sites is soil

patchiness (cf. PIRT 1983). Many parts of the landscape are not cultivable (see Chapter 3), and

men can each identify several sites with high fertility, good soil nyama, and an absence of

malevolent spirit activity that have never been farmed because the size of the arable patch is too

small and too distant from other patches for cultivation to be practical. Thus, new settlements

35 tend to be located near abandoned settlements, and relatively distant from occupied settlements

(Figure 7, p. 58).

Once a site has been selected for a new settlement, its actual establishment entails much

physical labor, which may be spread over several years. The first step of establishing a

settlement is to clear an area for housing, men’s work undertaken generally in the early dry

season (January-March), after completing fieldwork and before seasonal surface-water sources

have dried. Clearing vegetation in a settlement site differs from clearing farmland, because in

settlements trees are cut in order to kill them, not simply to remove their crowns, which shade

crops. Thus, trees are felled near the ground and near-surface roots are cut—two acts which are

completely avoided in clearing fields (cf. Nyerges 1989). Housing locations are often selected in

relation to existing trees. Locations with very large trees are avoided because these represent

hazards during windstorms, and may provide habitat for pest birds. Baobab (Adansonia

digitata), shea (Vitellaria paradoxa), and locust bean (Parkia biglobosa) trees are spared, and

each family spares about one medium-sized tree for shade, but all other woody plants are killed

and removed (and not burned in place, as in fields). Depending on site-specific characteristics,

the next step may be to dig a well, a task that men undertake toward the end of the dry season

(May-June), when the water table is low. Most hamlets are located in sites with relatively high

water tables, where it is easier to dig wells. However water may be available—on the surface or

in a well—it is used to make adobe for building huts and granaries. Depending on resources

available near a site, men may build with bricks or, more frequently, using bamboo wattles and

daub. Women finish construction by sealing floors and walls with a hard-drying mixture of mud,

the sap of various lianes, and cow manure, then painting designs on its outside using various

mixtures of ash, soils, and ground siltstone. Construction techniques used in new settlements

36 often differ from those used in larger settlements, because techniques requiring a large number of

workers cannot be used in situations where only one or two are present. Indeed, some

settlements have been abandoned because the number of occupants became too low to sustain

customary labor practices (Figure 4, p. 55). Once a minimal number of huts and granaries are

built—in contrast to older settlements, men and women, and people and domestic animals may

share huts for extended periods of time in new settlements—women prepare the living area by

clearing herbaceous vegetation and sweeping the soil, while men clear vegetation for fields. As

time goes on, additional structures may be built, but, again, settlement establishment often occurs

over several years, and young settlements are often undeveloped, minimalist places of shelter.

Decisions to abandon settlements are usually made relatively easily. Most hamlets are

essentially disposable because people plan to return to their residence in Solo, which they have

maintained while living elsewhere. The mean length of occupation of settlements around Solo is

29.2 years, although the median length, 20 years, is lower because several settlements were or

have been occupied for more than a century (Figure 8, p. 59). Hamlets are generally occupied

for just one or two decades, but even villages and long-lived hamlets are regularly abandoned. In

these cases, however, residents agree to move en masse to a new, nearby site that is, for some

reason, more attractive than the older site. For instance: Solo has occupied three sites, separated

by up to 3 km, since its founding >250 years ago; and from c.1926-2006 the hamlet of

Sandiguila occupied five sites, separated by up to 2 km, near the site of the original Sandiguila,

which was abandoned c.1894 (Figure 2, p. 53). Villages and long-lived hamlets are generally

found in locations with abundant farmland and other resources, and can sustain long periods of

occupation. As a result, these settlements become nodes in the local path network, and are

crucial to maintaining communication between nearby settlements. Nonetheless, over time even

37 once-large settlements in resource-rich locations may dwindle and become liable to

abandonment. In such cases, abandonment is not decided upon lightly, because the

consequences may be significant for nearby settlements, especially in terms of decreased ability

to communicate with more distant villages. While there is no central authority planning

settlement location around Solo, the combined resource needs and use-rights of its occupants

mean that settlements are widely dispersed across the landscape to reduce the rate of resource

depletion in any given area, while certain significant locations with abundant resources are

continuously occupied.

Settlements are abandoned for many reasons, but limited access to farmland is the major

reason why men decide to abandon settlements (Figure 4, p. 55). Hamlets are generally

established on relatively small patches of farmland, access to which may decline as a result of

population growth (due to the arrival of other families) or because there is only enough farmland

to sustain one or two swidden cycles, insufficient time to restore fertility through fallowing.

Whatever the reason, settlements sites that become unsuitable are often abandoned in stages over

several years. First, women, children, and some men return to Solo—or occasionally join an

established hamlet—to establish new fields, while some men stay behind to exploit remaining

fertility in existing fields. Subsequently, these men return to Solo but to begin with sometimes

travel almost daily to the hamlet to manage fields, fallows, fruit trees, and multi-year crops (such

as cassava, Manihot esculenta). Usually after about 4-5 years past occupants rarely visit

abandoned hamlets, because new fields and gardens in Solo fully occupy them—unless

conditions in Solo have again proven unsuitable, in which case these people may have already

established another new hamlet.

38 Over a lifetime, an individual will likely live in several settlements (Text Box 1, p. 50),

generally returning to Solo for several years after abandoning one settlement and moving to the

next, and returning to Solo permanently in old age. The labor and political costs of building and

moving into several new settlements over several decades is less for most individuals than the

costs of working marginal farmland for decades around Solo. Indeed, many of Solo’s current,

traditional authorities lived in hamlets until their influence increased due to the death or illness of

older, male family members (Text Box 1, p. 50). Other men, in less dominant families,

experience similar increases in influence as they become elders, and, at some point, generally

find that the benefits of this influence are greater than the costs of living away from Solo in a

hamlet.

Settlement history after 1890

The cost-benefit relationship of living in Solo versus a series of hamlets has been

maintained through the 1900s in part by economic underdevelopment, which has maintained a

low relative benefit of living in Solo. Combined with the patchiness of arable soil across the

landscape and social differences that limit some men’s access to farmland in specific parts of the

landscape, Solo’s lack of development has reinforced the utility of shifting settlement as a means

of exploiting the area’s sparse socioeconomic and biophysical resources. Recently, however,

political events and somewhat increased economic opportunities have increased the

attractiveness of living in Solo.

Adapting to colonial rule. During 1890-1960, farmers adapted to the socioeconomic

context of colonial rule, even though the Bafing area was marginal to French Soudan’s economy

and bureaucracy. Colonial rule affected settlement in two ways. First, the reduced threat of

violence made farmers feel secure living distant from large settlements or settlements in

39 defensive locations (Samaké et al. 1986). Similar changes in settlement practice have been

widely reported in West Africa (Gleave 1966; Queant & de Rouville 1969; Sidikou 1974;

Woodford 1974). Second, colonial rule brought no benefits to living in large settlements, but

increased the actual or potential cost of doing so, through taxation and forced labor recruitment.

In the colonial period, there were no public services in Solo —such as a medical facility, water

pump, cistern well, or school (although a primary school was founded c.1958)—so living distant

from Solo also carried minimal opportunity cost. People did not completely avoid paying taxes

or supplying forced labor by living in small, remote hamlets instead of larger, more accessible

villages, but in areas that were marginal in colonial geographies, people living in hamlets seem

to have had the benefit of avoiding direct interaction with colonial authorities (cf. Hill 1953). In

the case of Solo, direct interactions often brought costs that could not be passed on to or

dispersed amongst other community members, such as taxation through confiscation of observed

produce, or summons to provide forced labor given to observed individuals. Both of these

factors provided incentive for a more dispersed settlement pattern than had existed before.

Whatever the ultimate causes of increased hamlet settlement before 1960, the proximate

cause remained, in all cases, improved access to farmland. Soil characteristics of settlement sites

established during 1890-1960 suggest that the goal of agricultural production during this period

was primarily grain production (Figure 6, p. 57). Most settlements were established in sites

where silty to loamy soils predominated. The local grain staples, millet, sorghum, and fonio,

thrive in these soils, which most other crops—from peanuts to yams—tolerate even though other

soil types suit them better. Export-oriented peanut production, which was important during the

colonial period elsewhere in western Mali with better access to the Dakar-Bamako railroad

(Hopkins 1972), did not concern farmers around Solo until the 1960s (cf. Kéïta 1972).

40 Settlements were abandoned for various reasons during 1890-1960. The most common

reasons, however, were biophysical changes that made a site unsuitable for shelter, farming, or

both (Figure 4, p. 55). The most important biophysical condition was soil fertility. Settlements

founded to improve access to arable land lost all attractiveness if farmland fertility declined

substantially. However, other biophysical changes—from decreased water availability to

increased pest and malevolent spirit activity—made some sites uninhabitable even if fertile

farmland remained.

Settlement following Malian independence. Following Malian independence in 1960, the

Malian government sought to increase peanut exports by subsidizing production and processing

(Kéïta 1972). This meant that peanuts became a crop with a guaranteed market and price, and

most farmers in Solo focused their efforts on peanut farming. Most importantly, the government

subsidized trucks to travel to all settlements, even hamlets, to purchase peanuts (Kéïta 1972).

Thus, living in Solo lost the benefit it has otherwise always held as a village of having somewhat

better access to agricultural markets via more frequent visits from traveling merchants, and a

greater number of young, male residents who regularly carry produce to commercial markets,

dozens of kilometers away. As a result, beginning in the early 1960s, many new settlements

were established in areas with sandy soil, in which peanuts grow very well (Figure 6, p. 57).

Most of these sandy-soil settlements were abandoned in the early 1980s, when state subsidization

of peanut farming ended (Figure 3, p. 54). Since then, settlements have been established in sites

with a wide range of soil environments, including many with silty soil, because farmers have

renewed their focus on subsistence and grain production.

For the period since 1960, settlements have been abandoned for several reasons (Figure

4, p. 55). Biophysical causes, especially soil fertility decline, remain very important. Settlement

41 abandonment due to water shortage—either because the water table has descended, or surface

water sources have dried—has become more common since 1960, and may be linked to the long-

term drought observed across Sudanian West Africa (cf. Nicholson 1996).

Social causes have become increasingly important in settlement abandonment (Figure 4,

p. 55). Throughout the 1900s some settlements were abandoned because the departure of some

residents, or the abandonment of a nearby settlement, meant that remaining residents felt socially

isolated. Such loss of community has been the most important social cause of settlement

abandonment since 1960 because in the late 1980s over thirty settlements in the impondment

zone for the Bafing Reservoir were relocated north of Manantali Dam (Figure 1, p. 52).

Although little of Solo’s traditional area was flooded, most settlements in the Bafing area

disappeared, representing a loss of community for Solo as a whole. As a result, most of Solo’s

occupants, including nearly all of its hamlets, decided to abandon their settlements and move to

the resettlement zone. Solo and several of its hamlets were never completely abandoned, though,

and since the early 1990s many people have left the resettlement zone to return to the Solo area,

and live closer to family and friends.

Another increasingly important cause of settlement abandonment has been eviction by

political authorities. The Malian nature conservation directorate has carried out all evictions

since 2004, with the intention of reducing wildlife habitat loss in Kouroufing National Park.

Since only part of the research area is in the national park, these evictions have been

accompanied by several cases of settlement establishment outside park boundaries. Since the

mid-1990s, several new hamlets were built on the plateau north of Solo and the park boundary

out of fear of eviction, even though BBR authorities had not at that time publicly presented their

ideas on eviction. In 2004-2005, Solo’s three hamlets in the national park were finally evicted,

42 and most of the affected people built a new hamlet on the plateau, near a small, preexisting

hamlet. All of these hamlets are in an area near the edge of the plateau, near where previous

settlements had been established in easily defensible sites.

Historically, most families who abandoned hamlets did not move directly to another one,

but returned to Solo for some several years or more before moving again to a hamlet. These

returns to Solo renew social contacts and provide new opportunities unavailable in hamlets. Solo

is not disintegrating, but during the early stages of household development most men have

substantial incentive to leave Solo and take advantage of hamlet settlement. In recent years,

living in Solo has offered somewhat more benefit than in the past, which may in the long term

reduce incentives for hamlet settlement. First, there is considerable uncertainty about the new

hamlets north of the national park, since this area is part of the proposed buffer zone. Solo is

legally a village, and thus appears to face little threat of eviction. This fact attracted several

evictees from hamlets that had been in the national park, even though these people could have

probably gained access to more or better farmland by joining the new hamlet on the plateau north

of Solo. Second, the few socioeconomic resources available in Solo are becoming increasingly

attractive because the Bafing area has gained increased attention from regional development and

conservation projects. While there has been little real effect of these efforts in Solo, small

developments—government appointment of a teacher for the community-funded school,

increased opportunities for employment in conservation and research projects, the establishment

of a grain bank—have caused a small number of men to choose to remain in Solo rather than

move to or remain in a hamlet (Figure 4, p. 55). The increased importance of distance to main

paths as a cause for settlement abandonment also indicates the increasing importance to Solo’s

farmers of access to larger-scale socioeconomic networks—provided, for instance, by visits from

43 traveling merchants, medical practitioners who vaccinate children, and political campaigners, all

of whom travel only on main paths. Solo, although economically and geographically marginal in

southwestern Mali, has better access to these large-scale resources than its even more marginal

hamlets, and thus has attracted many men to reside again in Solo, rather than a hamlet.

Nonetheless, settlement continues to be a fluid process, and observation of settlement

pattern at any point in time remains only a snapshot that becomes inaccurate with time (Figure 3,

p. 54). For instance, of the five Solo hameaux PREMA identified in 1996, one was abandoned

within two years and three were evicted in 2004-2006, and one new settlement has been

established. Furthermore, three hamlets had been abandoned in the five years prior to 1996. It is

inaccurate to interpret or represent Maninka settlements as permanent, fixed points in a

distribution to which new points are continuously added over time.

Settlement and conservation in the BBR

Bugu, hameau, hamlet; dugu, village, village: These trios have essentially the same

physical meaning. Indeed, the pattern of Maninka settlement is not difficult to discern:

dispersed, small, young settlements outnumber larger, older ones. However, the functional

meaning of these terms differs. The process of settlement expansion implied by the French terms

hameau and village in conservation policies differs from the process of settlement shifting

experienced by Maninka farmers. Misinterpretation of settlement pattern in the BBR has created

a political environment in which economically and politically marginal men and their families—

marginal locally, regionally, and nationally—can be effectively punished for a human-

environment interaction that does not exist, or at least not in the manner in which it is

represented.

44 Habitat loss to farming, livestock herding, logging, road building, and mining is probably

the greatest medium- to long-term threat to biodiversity resources in the BBR, but the actual

processes of habitat loss are poorly understood (Duvall et al. 2003). While settlement is

inextricably linked to these other land uses, settlement is a distinct land use that is not a direct

cause of habitat loss in the BBR. Current conservation policies that have evicted and prohibit

hamlets are blunt and inefficient means of reducing habitat loss that will likely fail in the long

term for two reasons.

First, these policies seek only to change settlement patterns, and neglect the reasons why

men establish new settlements. The policy of restricting village populations to territoires des

villages (Caspary et al. 1998) will only intensify the socioeconomic and spatial processes that

make living in hamlets more attractive to many men than living in villages. The policy of

prohibiting the establishment of new settlements will increase hardships for economically and

politically marginal men and their families—assuming, unreasonably, that this policy can be

enforced and that these men will not simply establish a new settlement elsewhere. A much more

effective way to decrease the number of hamlets in the BBR and increase the proportion of men

who choose to live in villages would be to increase the range, availability, and quality of

socioeconomic resources in villages—including schools, roads, water pumps, and medical

facilities. On a broader scale, economic policies that decrease the costs of agricultural

intensification—such as subsidizing the cost of chemical fertilizer—will also decrease the rate at

which hamlets are established (Koenig & Diarra 1998). By intensifying competition for

farmland and increasing economic hardships for many people, current, coercive conservation

policies will diminish local support for conservation goals and activities, and preclude

conservation success in the long term.

45 The second reason that current policy toward settlements will fail to meet conservation

goals is that it neglects the geography of biodiversity and settlement in the BBR, and will

intensify human environmental impacts in parts of the landscape where biodiversity is highest.

The boundaries of the BBR’s national parks were drawn with limited knowledge of the

distribution of biodiversity resources. Although these national parks protect areas with relatively

low human population density and the highest local densities of several antelope species

(Caspary et al. 1998; Duvall & Niagaté 1997), they protect negligible amounts of the highly

biodiverse habitats characteristic of sandstone outcrops, in which chimpanzees are most

frequently observed (see Chapter 6). These habitats receive little protection via conservation law

enforcement, but they receive passive protection because they occur in nearly inaccessible

locations that people rarely visit (Duvall 2001). However, in areas with relatively high human

population density these habitats are used more frequently for logging and hunting, which has

contributed to chimpanzee range contraction during the last twenty years (Duvall 2000; Duvall et

al. 2003).

Most people evicted from hamlets in the northern part of Kouroufing National Park have

established new settlements near the edge of the sandstone outcrop just north of the national

park. Assuming that the prohibition of new settlements in the BBR will continue, more men

from nearby villages will decide to establish or join hamlets along this outcrop. The increased

population of this area will increase the environmental impacts of humans on outcrop habitats,

and the vacated parts of the BBR are not equally valuable for biodiversity, or chimpanzee,

conservation. Evicting hamlets may guarantee a low population density in the BBR’s national

parks, but in the long term this may increase threats to the reserve’s nationally and

internationally most important biodiversity resources. Furthermore, decreasing or eliminating

46 possibilities for hamlet settlement will likely encourage men to farm increasingly marginal

farmland around existing settlements on an increasingly permanent basis, and thus lead to

permanent habitat conversion (cf. Koenig & Diarra 1998; Nyerges 1989). While

conservationists have accused Maninka farmers of using land “without any planning beforehand”

(Caspary et al. 1998: 77), current policies on settlement in the BBR seem fairly nearsighted.

More effective conservation policies for the BBR would take advantage of Maninka

settlement practice. Indigenous Maninka residents will need to modify their resource use,

especially hunting, if biodiversity conservation is to succeed, but the dispersed, shifting pattern

of Maninka settlement should be seen as a conservation resource. Dispersed settlements mean

more dispersed human environmental impact, and also spatially more uniform and strategic

surveillance for illegal poaching, logging, and other activities, especially since most large

settlements are located on main paths. Conservationists have recognized the potential of local

residents to contribute to conservation goals in this way by establishing “surveillance

committees” in many villages in the BBR (cf. Caspary et al. 1998). However, such

surveillance—which happens regardless of conservation efforts because people monitor the areas

around their villages—will contribute to conservation only if people in hamlets are empowered

to have an interest in reporting what they see to law enforcement officials. Settlement policies,

enforced by these same officials, that result in hardship remove this interest.

Conclusion

Human-environment geography must recognize rural settlement as a distinct land use.

Human-environment geographers, using the analytical tools of cultural and political ecology,

have contributed significant theoretical and practical knowledge to resource management in

rural, agrarian landscapes through careful examinations of other rural land uses—especially

47 agriculture, pastoralism, and conservation. However, the failure to study settlement as a distinct

land use has limited human-environment geography in two key ways.

First, settlement processes occur at specific spatial and temporal scales (Stone 1996), and

the failure to recognize settlement as a distinct land use has meant that scales of observation used

in human-environment geography have often been inadequate to observe significant aspects of

resource use in rural, agrarian areas. Many human-environment geographers have approached

questions of resource use from the perspectives of cultural ecology and political ecology. Many

cultural ecologies descend methodologically and theoretically from important works such as

Conklin (1961) and Boserup (1965). These works, and many of their descendants, focus on

agricultural practices at the scale of individual plots or settlements, and thus fail to recognize

how agriculture relates to settlement processes that occur over landscapes (areas of tens to

hundreds of square kilometers). On the other hand, political ecologies of resource use, which

explicitly recognize the significance of processes operating at scales broader than communities

(Robbins 2004; Zimmerer & Bassett 2003), have generally jumped over the landscape scale to

focus on national, regional, or international processes. Human-environment geographers have

focused on landscape scales mainly just in the context of pastoralism, because the movement of

people and livestock across landscapes is obviously crucial to this land use and abundantly

obvious over even brief periods of observation. Mobility is also inherently important in

settlement in rural, agrarian landscapes, but this mobility is often not apparent except over

decades. Settlement pattern spatially structures resource use across landscapes (Chisholm 1979;

Stone 1996), and accurate understanding of resource use requires understanding of settlement

processes that unfold over decades and landscapes.

48 Second, by focusing on land uses other than settlement, human-environment geographers

risk simplifying or overlooking settlement processes, and giving the impression that spatial

patterns of settlement accurately substitute for knowledge of these processes. Too frequently,

analysis of rural settlement in human-environment geographies is limited to statements about the

spatial pattern of settlement, reflecting a long tradition in cultural geography that relates the form

and distribution of settlement to various social, economic, cultural, and physical geographic

factors (cf. Hill 2003). However, different biophysical and socioeconomic environments can

create different settlement processes that lead to similar settlement patterns (Stone 1996). For

instance, Ruthenberg (1980: 31) suggests that shifting settlement may develop where shifting

cultivation is practiced and, over time, “[t]he cultivated plots move slowly away from the

previous clearing and the vicinity of the hut. [Thus,] the cost of transportation increases[…].

Beyond a certain distance, it becomes advantageous to build a new hut near the field instead of

carrying the harvest such a long way.” This process of shifting settlement is certainly accurate

for many places and times, possibly including many areas where there is frontier-style settlement

expansion (Netting 1993: 223). However, transportation cost does not lead to shifting settlement

in Mali’s Bafing area. Instead, the patchiness of arable land, and differences between individuals

in their abilities to access good farmland near villages, creates conditions in which shifting

settlement proves beneficial to most families when considered over a decadal timescale. Failure

to recognize settlement as a distinct land use whose observed patterns have distinct and variable

formative processes has limited the ability of human geographers to accurately and precisely

understand land use in agrarian, rural landscapes.

49

Text box and figures for Chapter One

50

Text Box 1. Hamlets and young families. Hamlet settlement is an integral part of household

development for people in Solo, as illustrated by the personal histories of Mbakuru, a 19-year old

woman, and her father Jigiba, a 48-year old man. The names of these people have been changed

to protect their privacy.

In late 1987, Mbakuru, her parents’ eldest, was the first child born in New Solo, where

many of Solo’s residents resettled after construction of the Manantali Dam. Prior to this, Jigiba

and his wife had been living in a hamlet south of Solo, which he had established in 1981, near an

abandoned hamlet, because he “couldn’t get good fields in Solo”. In 1986 Jigiba decided, along

with most of Solo’s men, to move their families to New Solo, in the resettlement zone north of

the dam (Figure 1, p. 52), in order to remain near other resettled villages. Jigiba soon became

frustrated by poor farming conditions at New Solo, and in 1990 moved his family back to Solo,

where his father had become dugutigi (‘chief’). In 1992, Jigiba and two cousins decided to

establish a new hamlet south of Solo, where they thought they would have greater agricultural

productivity, based on Jigiba’s having lived in two nearby hamlets (that his father had joined in

the 1950s and 1960s) as a child and young man. Farming was successful in the new hamlet, but

it was abandoned in 1997 primarily because it was too far from a main footpath: there were few

visitors, and it was too difficult to transport produce to Solo. In 1999, Jigiba began planning a

new hamlet, but in 2000 his father died, giving him increased responsibility and authority within

his extended family. One of his cousins with whom he had established the hamlet in 1992

became dugutigi. Additionally, Jigiba has found intermittent employment in Solo working as a

guide for researchers and visiting Peace Corps Volunteers, and does not wish to lose this

opportunity by moving to another hamlet. He has probably made the transition to fixed

settlement in Solo.

51

After living in several hamlets with her parents and younger siblings, Mbakuru was

married in June 2004, and moved to Kama, her husband’s village, about 20 km west of Solo. In

2005, however, her husband decided to move with her to a hamlet founded by his cousin in 2002,

where they now live (except during the dry season, when they return to Kama). Mbakuru’s first

child, a daughter, was born in the hamlet in late 2005.

52 Figure 1. Western Mali, showing location of Solo and the Bafing Biosphere Reserve.

Abbreviations: BZ=buffer zone for Bafing Biosphere Reserve (BBR); KNP=Kouroufing

National Park, part of the BBR; WNP=Wongo National Park, part of the BBR.

53 Figure 2. Map of the research area. Only settlements named in the text have been shown. The

abandoned settlement sites shown for Solo, Sandiguila, and Santankéni are numbered in the

order of their establishment.

0 5 km

Solo 3

Sandiguilasites

Santankénisites

Guimbayasites

Solo 1

Solo 2

12 3

45

6 12

1 23

4

5 6

Abandoned settlements mentioned in text

Clif °

Primary footpathsSeasonal streamsBafing Reservoir

Occupied settlementsSettlements evicted 2004-06Settlement established 2004

Kouroufing National Park

Study areaWalled settlements, 1800s

54 Figure 3. Distribution of settlement sites in the research area. Only settlements for which there

is oral historical evidence are shown. Four time periods are represented: 1) ‘pre-Maninka’ is the

period up to and including the Maninka occupation of the area, which occurred >250 years ago;

2) ‘early Maninka’ is the period of Maninka occupation of the research area up to 1890; 3) 1890-

1960; and 4) 1960-2006. Black points show settlements established during each time period;

open squares show settlements abandoned during each time period.

early Maninkapre-Maninka

Latit

udin

al d

ista

nce

(met

ers)

Longitudinal distance (meters)

1890-1960 1960-2006

55 Figure 4. Causes of settlement abandonment. Wide, gray bars show primary causes; narrow,

black bars show secondary causes. Total number of settlements abandoned (n) indicated per

time period. Data from oral historical interviews. Time periods as described in Figure 3 (p. 54).

56 Figure 5. Causes of settlement establishment. Wide, gray bars show primary causes; narrow,

black bars show secondary causes. Total number of settlements established (n) indicated per

time period. Data from oral historical interviews. Time periods as described in Figure 3 (p. 54).

Improve farmland access

Outside national park

Reoccupy past residence

Near water source

Good spirits in site

Defensive location

Unknown cause

Number of settlementsCauses for establishment

1960-2006(n=35)

0 4 8 12

33

0 4

earlyManinka

(n=16)

15

pre-Maninka

(n=2)

0 40 4 8 12

39

1890-1960(n=39)

57 Figure 6. Soil texture at settlement sites. Time periods as described in Figure 3 (p. 54).

58 Figure 7. Spatial pattern of settlement sites. Values for Ripley’s L >0 indicate attraction, while

values <0 indicate repulsion. Thus, settlement sites display attraction at distances <2.3 km and

repulsion at distance >2.3 km. Increasingly positive/negative numbers indicate stronger

attraction/repulsion. Observed attraction indicates sequent occupation of preferred habitat

patches by multiple settlements over time, while observed repulsion indicates: a) the patchiness

of preferred habitat, and b) the likelihood that nearby, contemporaneous settlements are

separated by a minimum distance, since the field-to-settlement distance many men consider

reasonable is c.3km.

59 Figure 8. Histogram of temporal lengths of occupation for settlement sites.

60 Chapter Three: Folk taxonomy of physical geographic terms used by Maninka farmers in

southwestern Mali

Abstract

This paper analyzes the nomenclature and taxonomy of physical geographic terms in the

Maninka language as spoken in the Bafing area of southwestern Mali. Its purpose is to

understand the content and structure of this particular body of local knowledge, and to compare

Maninka physical geographic knowledge to that of other cultural groups. The research is based

on participant observation and ethnographic interviews. Main findings include: 1) The Maninka

conceptually bifurcate the biophysical environment based on whether natural resources contained

in observed physical features are openly accessible to all humans. Features that are owned or

otherwise possessed by humans or spirits are not accessible, and are classified according to

physical criteria and the abstract quality of possession. Openly accessible features are classified

based on physical criteria. 2) The main criteria used for classifying openly accessible features

are: hydrology, topography, ground characteristics, vegetation, and microclimate. Physical

features are classified by these criteria alone, or by a sixth criterion representing a synthetic view

of all resources present at a given site. 3) Many classificatory criteria reflect evaluation of

resources based on use-value in the context of Maninka agricultural practice. 4) Maninka natural

resource classification is similar to that reported for related and other cultural groups. However,

culturally specific classifications of locally diverse or highly valued resources are embedded

within the cross-culturally similar, broad framework. This paper concludes that greater attention

should be given to the broad conceptual context of physical geographic terms or concepts

reported in ethnoscientific analyses of local knowledge systems.

61

Keywords

indigenous knowledge; ethnoscience; folk taxonomy; local knowledge; ethnopedology

Introduction

Indigenous farmers and pastoralists successfully manage diverse ecosystems using

sophisticated knowledge of the biophysical environment. Unlike technical scientific knowledge,

local knowledge1 is mediated through everyday language; terms that carry ecologically

significant meanings often carry meanings in other, seemingly unrelated, contexts. Layered

meanings, often overlooked by outsiders, create the context in which local knowledge gains and

retains meaning for its users (Agrawal 2002). Yet outsiders often emphasize limited aspects of

local knowledge systems in order to underscore practical applications these may have in natural

resource management. The practical value of local knowledge is not in question, but

overemphasizing its potential applicability in limited contexts leads to incomplete

characterization of local knowledge of the biophysical environment2 (Agrawal 2002; Scott

1998).

1 Following WinklerPrins (1999), I prefer ‘local knowledge’ to ‘indigenous’, ‘traditional’, or ‘folk knowledge’ because a key attribute of these types of knowledge is their geographically limited extent. ‘Local knowledge’ also implies nothing about the history of knowledge or of those who retain it. I use ‘folk taxonomy’ because it is an established technical term without synonyms. I consider terms of the form ‘ethnoscience’ to denote studies of local knowledge rather than the local knowledge itself. 2 ‘Biophysical environment’ refers to all biological and non-biological, physical features in an area. ‘Natural environment’ or ‘environment’ sometimes carry this meaning, but the former excludes anthropogenic features, and the latter lacks specificity. ‘Physical environment’ refers only to non-biological features. ‘Biophysical environment’ differs from ‘biospiritual environment’, a portion of Maninka geographical knowledge, discussed below.

62 During the last fifty years, many researchers have employed an ethnoscientific approach

to studying local knowledge of the biophysical environment, which entails analysis of particular

aspects of local knowledge systems comparable in referential extent to specified technical

scientific fields. Ethnobotany, ethnozoology, and ethnopedology have received the most

attention (e.g. Balick & Cox 1996; Barrera-Bassols & Zinck 2000; Berlin 1992; Cunningham

2001; Medin & Atran 1999; Winklerprins & Sandor 2003). A few have described local

knowledge of climate (Goloubinoff et al. 1997; Osunade 1994; Ovuka & Lindqvist 2000), while

others have studied local knowledge of habitat variation (Fleck & Harder 2000; Frechione et al.

1989; Osunade 1988; Osunade 1987; Shepard et al. 2001). Very few researchers have analyzed

how knowledge of the biophysical environment as a whole is structured in local knowledge

systems (Barrera-Bassols & Zinck 2003b; Goodenough 1966). This is the referential frame

Blaut (1979) suggested for “ethnogeography”, the study of how cultural groups perceive

variation in the biophysical environment. As an ethnoscience, ethnogeography is

underdeveloped3, even though Blaut’s holistic approach avoids a priori compartmentalization of

indigenous knowledge into categories with limits determined largely by imposed correspondence

with technical scientific fields of study.

Blaut’s concept of ethnogeography is similar to “ethnoecology”, which Barrera-Bassols

and Toledo (2005: 11) define as the “study of how nature is perceived by humans through a

screen of beliefs and knowledge, and how humans, through their symbolic meanings and

representations, use and/or manage landscapes and natural resources”. Ethnoecology emphasizes

the complex layering of local environmental knowledge, from spiritual belief systems that guide

3 Unfortunately, most subsequent uses of “ethnogeography” do not follow Blaut (1979), but refer to descriptions of the distribution of cultural groups.

63 resource use, through bodies of knowledge underpinning resource use, to practices of resource

use (Barrera-Bassols & Toledo 2005; Barrera-Bassols & Zinck 2000). The desire to maintain

local knowledge in context, and ultimately in situ, strongly and explicitly motivates

ethnoecological research.

The ethnoecological approach holds great promise for advancing understanding of local

environmental knowledge systems. However, ethnoecology has continued, albeit subtly, to

compartmentalize local knowledge according to technical scientific criteria. The desire to

identify practical applications of local knowledge has also been a strong and explicit motivation

for ethnoecological research (cf. Barrera-Bassols & Toledo 2005). This goal is arguably at cross

purposes with the goal of maintaining local knowledge in sociocultural context (Agrawal 2002;

Scott 1998), and has led ethnoecologists to privilege certain research questions over others.

Specifically, a central theme in ethnoecology has been the comparison of local knowledge to

specific domains of technical, scientific knowledge (Barrera-Bassols & Toledo 2005), especially

soils (cf. Barrera-Bassols & Zinck 2000; Winklerprins & Sandor 2003). Yet ethnoecologists

have not shown and do not argue that technical, scientific knowledge provides a pan-

environmental standard by which other knowledge systems should be assessed. Comparison of

local knowledge systems across cultures and environments has not been a central theme in

ethnoecology (Barrera-Bassols & Toledo 2005).

Do humans similarly perceive variation in the biophysical environment across

environments and cultures? Many ethnolinguistic groups have highly detailed knowledge of

specific environments, and may have highly detailed classifications of features in those

environments. Folk taxonomies of plants and animals show, in many cases, universal similarities

(Berlin 1992), although biophysical variation between environments and socioeconomic

64 variation between ethnolinguistic groups lead to differences in the detail with which people

distinguish biophysical features (e.g. Birmingham 2003; Voeks 1998). Does local knowledge of

other biophysical features—such as ‘landforms’, ‘water bodies’, or ‘precipitation’—similarly

correlate to environmental and cultural variation? Geographers are well equipped to address

these questions, which have been previously considered mainly by anthropologists. Cultural

anthropologists have explored how humans perceive organisms across cultures and

environments, and have found pan-environmental similarities in folk taxonomies of plants and

animals (Berlin 1992; Brown 1984; Holman 2005). Geographers and others have used the

methods of folk taxonomy to study local soil knowledge (e.g. Williams & Ortiz-Solorio 1981),

but other physical features have not been subject to such analysis. Studies of local soil

knowledge are considered in more detail in the discussion section, below. Evolutionary

anthropologists have proposed that humans prefer habitats that resemble “African savannas”

because this is where humans evolved (Orians & Heerwagen 1992). If this is the case,

comparing local systems of physical geographical knowledge should indicate pan-environmental

similarities in concepts of vegetation structure, if not other biophysical features. Analysis of

local environmental knowledge using folk taxonomy is important for identifying the conceptual

bases of geography—specifically, how culture may or may not constrain individual perceptions

of the biophysical environment (Blaut 1979).

The present paper contributes to our understanding of cross-cultural and -environmental

variation in physical geographic knowledge by analyzing the content and conceptual structure of

Maninka physical geography in southwestern Mali, and comparing this with published

information on other local knowledge systems. The Maninka belong to the wider Manding

culture, which consists of farming societies living throughout West Africa, mainly west of Ghana

65 and Burkina Faso (Figure 1, p. 92). Analysis of the nomenclature and taxonomy of Maninka

physical geographic terms enables comparison of Maninka physical geographic concepts with

those of other Manding groups. Cultural ecological research on several Manding groups is

substantial, including recent works on Maninka (Laris 2002), Kuranko (Fairhead & Leach 1996;

Nyerges 1989; Richards 1985; Richards 1995), Mandinka (Carney 1996; Carney 1991;

Schroeder 1999), and Jula (Bassett & Koli Bi 2000). There are also many older works on these

and other groups, particularly Bamanan farmers in Mali. Nonetheless, it is unclear how widely

these groups share physical geographic knowledge described for specific groups in limited areas.

Manding groups have strong historic, linguistic, and cultural links (Hodge 1971), but occupy a

wide range of biophysical environments, from the Guinean rainforest edge to the Sahel, and from

mangrove-dominated seashores to woodland-dominated uplands (Figure 1, p. 92). The goals of

the present paper are to: a) inventory Maninka knowledge of physical geographic features,

including processes that link features; and b) determine how this knowledge compares with that

of other Manding groups. Understanding how Maninka physical geographic knowledge

compares with that of culturally related groups in different environments provides a basis for

identifying and understanding pan-environmental conceptions of variation in the biophysical

environment.

Research setting

Field research was conducted January-December 2004, in Solo village in the Bafing area

of southwestern Mali (Figure 1, p. 92). Solo was established 400-500 years ago (Samaké et al.

1986). About 250 people live in Solo, subsistence farmers who rely on rainfall to grow millet,

peanuts, and several minor crops. Residents also hunt, fish, collect honey, and gather wild plants

to supplement their diet or supply local markets (cf. Horowitz et al. 1990; Samaké et al. 1987).

66 Farmers follow a complex land management system to maximize crop security under constraint

of the region’s variable precipitation regime (Koenig et al. 1998; Laris 2002; Samaké et al.

1987). Most residents have lived in Solo their entire lives, although many spend rainy seasons in

small farming hamlets dispersed in an area of 183 km2 around Solo, where residents have

traditional usufruct recognized by neighboring villages. This customary tenure is not recognized

in state laws. In practice, Solo’s traditional authorities, principally the dugukolotigi (‘land

chief’), make decisions about allotting land for new fields or hamlets (Cissé 1970; Samaké et al.

1987). Individuals make decisions about the use of particular resources—such as trees for

fuelwood, or wildlife for meat—without needing the approval of traditional authorities, although

the dugutigi (‘village chief’) often must resolve disputes between individuals about use rights.

The Bafing landscape is topographically complex. Sandstone plateaus rise 100-300

meters above undulating plains and valleys that culminate in deep gorges incised into the

plateaus (Michel 1973). Scree slopes form below cliffs along the plateaus. Several sandstone

formations occur in the area, each having distinct color, hardness, and other characteristics;

various fine-grained sedimentary rocks interpose the sandstone layers (Varlet et al. 1977).

Sandy soils dominate (Dames & Moore 1992). Ferricrete crusts occur widely in the landscape.

The edges of these crusts are steep and gravelly, while their upper surfaces may be barren,

exposed hardpans or shallowly covered by silty to clayey soil (Michel 1973). Dolomite

intrusions outcrop to form steep, rounded inselbergs surrounded by silty soil.

Vegetation variation corresponds to hydrological and edaphic variation (Breman &

Kessler 1995; Lawesson 1995). The area is in the broad band of Sudanian woodland that

extends across West Africa (White 1965; White 1983). Different woodland associations occur in

sandy and silty soils (Nasi & Sabatier 1988). Grassland or wooded grassland occurs in sites with

67 shallow soil. Sites with deep soil are favored for agriculture; nearly all have been farmed in the

past two centuries (see Chapter 4). In sites that have been undisturbed for several decades, forest

vegetation forms, dominated by woodland species. Along permanent or seasonal drainages,

gallery forest occurs, dominated by species characteristic of more humid climate zones (Duvall

2001; Lawesson 1995).

Hydrological resources are not diverse (Dames & Moore 1992). The most important

source of water for humans is hand-dug wells. Rainfall is highly seasonal, with nearly all

precipitation falling between July and October (Barth 1986). There are two semi-permanent

creeks, many seasonal drainage channels, and several permanent springs in the 183 km2 research

area. The original course of the Bafing River, the main tributary of the Senegal River, is about

20 km from Solo, outside its traditional territory. Dammed in 1989, the Bafing River now forms

a large reservoir whose shore is about 8 km from Solo, on the edge of its territory. The reservoir

is becoming an important transportation route and valuable fishing site, but for most residents it

remains beyond their normal sphere of experience.

Research methodology

Orthography and language. This paper uses terms in English, French, and Manding.

Maninka (or Malinké), Bamanan (or Bambara), and Mandinka (or Mandingo) are closely related

dialects of the Manding (or Mandé) language, spoken widely in West Africa (Bird 1982).

Differences between dialects are mainly systematic changes in pronunciation, and reflect historic

or geographic separation of Manding populations (Bird 1982; Derive 1990).

Maninka words collected during field research are written in bold italics; spelling follows

Bird’s (1982) linguistic analysis and orthographic conventions. For clarity, only singular

Maninka nouns are provided, even if a plural is given as a gloss or implied by the English

68 context. The plural marker for most Maninka nouns is –lu (Bird 1982). Covert concepts are

given English names and written in brackets, such as [slope]. Previously published terms are

italicized in quotes, and written as in the cited sources. The meanings of English technical terms

are from Thomas and Goudie (2000). Glosses for Maninka words come from field research.

The meanings of previously published words come from the cited sources.

Data collection and analysis. Participant observation offered opportunities to learn the

use of Maninka terms, while ethnographic interviews clarified observations. I lived in Solo

January-December 2004, and participated in about 600 hours of relevant conversation, including

interviews. Conversations, all in Maninka, were conducted while hiking with hunters or working

with farmers.

Interview data came from 35 informants, male and female, aged 13-80. I began research

by seeking names for physical geographic features, particularly landforms, soils, and

atmospheric phenomena. Once I had developed a functional vocabulary of physical features, I

shifted my focus to understanding relationships between features, and the taxonomy created by

these relationships. In interviews, I asked names for physical features my interviewee and I

observed together, then asked how observed features differ from similar ones I knew from

previous experience. I also asked informants to indicate physical features whose names I knew,

but whose forms I did not. When informants showed me features, I sought to determine

precisely what they were indicating, and to collect names for related features. I used specific

question formats, especially triadic and dyadic comparisons (Cotton 1996), to assess conceptual

relatedness.

Data analysis was qualitative. First, listening to conversations revealed grammatical and

semantic classes. Basic grammatical classes—like noun, verb, locative noun, possessive, and

69 descriptive phrase—often suggested broad conceptual categories, while semantic analysis

revealed polysemic terms and covert taxa (Berlin et al. 1968; Berlin et al. 1973; Conklin 1962;

Kay 1971). Second, informants generally responded to comparative questions—such as ‘is X

different from Y?’—with phrases whose meanings in terms of similarity ranking proved

comparable between individuals. To express high to low similarity, informants said: wolu kòrò

be kilin (‘their meaning is one’), wo be kilin (‘it is one’), wolu ka muno (‘they are similar’), wo

te kilin (‘it is not one’), and wolu te muno (‘they are not similar’). Repeated instances of

informants using a single phrase in response to specific comparisons clearly indicated conceptual

relatedness. Finally, after about 400 hours of conversation and interviews, a taxonomic structure

was developed in multiple iterations to represent the relatedness of Maninka concepts (cf. Berlin

1992; Brown 1984; Kay 1971). Discussions with key informants tested whether proposed

taxonomic relationships accurately reflected their knowledge of these concepts. Based on these

discussions, proposed taxonomic structures were changed or retained.

Technical scientific equivalents of Maninka soil and vegetation categories come from a

concurrent study of vegetation characteristics (see Chapter 5). Soil texture was identified using

manual texturing (Midwest Geosciences Group 2003) of samples from 217 sites where

informants provided a Maninka name for the sampled soil unit. Woody vegetation was sampled

using 0.1-ha plots (Phillips & Miller 2002) at 206 sites where informants provided a Maninka

name for either vegetation or land cover. According to tree stem density and crown height

(Lawesson 1995: 24), vegetation was labeled ‘forest’, ‘woodland’, or ‘wooded grassland’. Rock

types were identified from descriptions in Varlet et al. (1977) and Groupement Manantali (1979).

Results

70 Broad concepts. The Maninka biophysical environment is conceptually bifurcated into

[the biospiritual environment] and [the physical environment] (Figure 2, p. 93). This paper

focuses on [the physical environment]. [The biospiritual environment] comprises all beings—

biological or spiritual things that die and are susceptible to illness—and their possessions. The

four categories of being—hadamadèn4 (‘humans’), jine5 (‘spirits’), [animals], and [plants]—are

not clearly separable for several reasons. First, most informants consider humans a type of

animal—specifically, a type of subo (‘mammal’). Many animals, especially large vertebrates,

share with humans the characteristic of having a ja (‘soul’), spiritual power that remains after an

individual’s death. Hunters must propitiate the souls of animals they kill to avoid retribution

(Cashion 1982; Cissé 1964), but the need to respect an animal’s ja diminishes as body size

decreases. Second, subaga (‘sorcerers’) can change forms freely between human and animal.

Some informants also believe sorcerers can also transform between human and plant forms.

Third, jine (‘spirits’), which are generally dangerous, can change forms to look like humans,

animals, or plants. Thus, humans must cautiously interact with other beings because these may

not be what they seem. Most human activities represent acts meant to protect against, or gain the

favor of, spirits (cf. Brun 1907; Lem 1948). Jine also interact with [plants] and [animals] in

manners inconsequential for humans, though prudent humans avoid locations where spirits have

clearly affected other beings. Finally, not all spiritual beings are jine. For instance, when a

4 Literally, ‘a child of Adam’ (Bailleul 1996), indicating derivation from Islamic traditions. 5 An Arabic loan word.

71 human dies, his/her hakili (‘mind, intelligence’) becomes the garajikè of a newborn. Each

human is associated with a garajikè spirit, which assists the human in acquiring things of value.6

Acquisition is an important value that helps distinguish [the biospiritual environment]

and [the physical environment]. In the Maninka subsistence economy, an individual’s ability to

acquire valuable things rests on his/her ability to access natural resources—especially soil

fertility, water, geological materials, microclimate, plants, and animals—and avoid natural

hazards—such as microclimate-induced illness, falling on slopes, and meteorological dangers—

while also avoiding conflict with powerful beings. [The physical environment] is composed of

physical features that indicate the spatiotemporal distribution of natural resources and hazards.

However, some features are owned, occupied, or otherwise possessed by powerful beings,

especially humans and jine. The concept of possession, which connects physical things to

beings, causes individuals to see spiritual and social meaning in physical features, and transforms

these into components of [the biospiritual environment]. Components of [the physical

environment] are considered neutrally available for use to all people, but those of [the

biospiritual environment] are accessible only to people who have socially granted use rights, or

who have the spiritual knowledge and power to overcome or appease jine.

Subjectivity makes it difficult for informants to divide their world into physical and

biospiritual components (Rappaport 1979), but this is clearly practiced when Maninka

individuals discuss natural resources. A hunter can describe a location by saying “the warthog

was past the kèna (‘clearing, field’) on the path to the river” without suggesting anything about

property rights to the field, which is communicated by evoking its social context with a

6 Bailleul (1996) translates garajike as ‘luck’, which is a simplification: Hakara garajike ka nyi means ‘Hakara’s garajike is good’, but more idiomatically ‘Hakara’s luck is good’ or ‘Hakara is lucky’.

72 possessive phrase like nyèmbi ka furu (‘Nyèmbi’s field’). Possessives allow speakers to

emphasize resource ownership and thus imply accessibility for each listener according to

personal identity and history, moving a discussion from physical features to property rights.

Anthropogenic physical features are one type of clue individuals use to locate natural resources,

but these features also carry the abstract, socially determined attribute of possession, which

limits accessibility to resources associated with these features. Similarly, all artifacts are owned,

yet raw materials have no inherent ownership: anyone can use a kuru ge (‘angular block of

sandstone’) unless that kuru ge becomes, for instance, buramakan ka si kuru (‘Buramakan’s

grinding stone’). Ownership can lapse for enduring artifacts like abandoned settlements, so that

they may become simply physical features. However, due to their past association with

humans—including perhaps subaga (‘sorcerers’) or disguised jine (‘spirits’)—these features

carry much symbolic meaning and may not be safe to use.

The physical environment. [The physical environment] consists of all non-living,

physical features of the environment, and comprises three major categories (Figure 3, p. 94).

Ala ka baara (‘the work of Allah’) and mògò ka baara (‘the work of humans’) clearly differ, but

these two, along with [animal-created features], share some subordinate categories. Ala ka

baara and mògò ka baara are unproductive secondary lexemes (cf. Conklin 1962; Kay 1971);

there is no evidence that baara carries a broader meaning equivalent to [the physical

environment]. Ala ka baara includes physical features created by ala (‘Allah’), the omnipotent

spiritual force. Possession of these features is generally impossible, and thus they remain

permanently part of [the physical environment]. Mògò ka baara comprises anthropogenic

physical features, which may be part of either [the physical environment] or [the biospiritual

environment], depending on their ownership status. The noun baara, often translated as ‘manual

73 work or labor’ (Bailleul 1996), also carries the broader meaning ‘action’. Ala ka baara captures

both senses, as when informants remarked, “humans do not dig caves; they are the work of

Allah”, or, after a destructive windstorm, “the work of Allah was too powerful”. In mògò ka

baara, the sense ‘action’ pertains to social interactions, while ‘manual work or labor’ refers to

physical features. This latter sense underscores that anthropogenic physical features, like

[features created by animals], are manipulations of ala ka baara, and not aboriginal creations.

The reference to Allah indicates the historical influence of Islam, not the religiosity of

informants. Few Bafing Maninka are Muslim, but “Allah” designates the omnipotent force in

the Maninka belief system (Brun 1907; Tauxier 1927). All spirits are subordinate to Allah, but

ala ka baara does not include the actions of subordinate spirits. For example, when asked if

instances of garajikè ka baara (‘work of [a] garajikè’)—such as a hunter’s finding game—were

also ala ka baara, informants said universally these are not the same. Allah creates and

maintains the physical environment in which spirits, humans, animals, and plants act. While

Allah affects the actions of these beings, such influence reflects Allah’s will, and is not Allah’s

baara (‘work’).

[Features created by animals] is of limited conceptual extent. It includes the few

enduring features created by animals, such as tun (‘termite mounds’). Abstractly, the animals

creating these features possess them, but such possession means little to humans and accessibility

to resources held in these features is unrestricted.

The components of each of these primary divisions of [the physical environment] are

described in the following sub-sections.

The work of Allah. Ala ka baara directly includes four categories. Dugu (‘earth’)

comprises physical features associated with the ground, including water bodies and certain

74 microclimatic features (specified below). Features composing dugu are above those composing

ju kòrò (‘the deep subsurface’) and below those composing san (‘the sky’). Ju kòrò7 designates

features that are mainly unknowable to humans because of their sub-surface location. San

includes all features considered to originate above the ground surface. I use ‘celestial features’

to designate components of san and ‘terrestrial features’ for components of dugu. Funteno

(‘temperature’) is a usually invisible feature permeating all components of dugu, san, and ju

kòrò.

Ju kòrò is a poorly developed category that some informants divide into dugu ju kòrò

(‘the deep subsurface of the ground’) and ba ju kòrò (‘the riverbed’) (Figure 3, p. 94). However,

for most people ju kòrò means only ‘the deep subsurface of the ground’. Personal history

determines an individual’s perception of ju kòrò. Most informants have direct or indirect

experience with dugu ju kòrò via well digging, but few have substantial experience far from

Solo, where humans can directly access the bottoms of all water bodies. Informants with

experience on the Bafing River, though, consider the riverbed as unknowable as the deep

subsurface of the ground.

Funteno is a polysemous term that also designates the conceptually most salient

temperature state, funteno (‘hotness’), as opposed to nènè (‘cold’) and sumaya (‘coolness’)

(Figure 3, p. 94). Temperature permeates all physical features, and changes predictably due to

interactions of certain celestial and terrestrial features. Tilo (‘the sun’) especially influences

temperature changes, but other features have important effects on microclimate. Funteno (‘hot

7 Ju means ‘base’, ‘foundation’, or ‘below/behind [a thing]’. Ju kòrò is: 1) an adjectival phrase (‘underneath the base [of a thing]’), and, in the sense of focus here, 2) a compound noun taking a postposition (e.g. subo te sòrò ju kòrò la (‘mammals are not found in the deep subsurface’). A postposition is grammatically identical to a preposition, but follows a noun.

75 temperature’) and nènè (‘cold’) can be dangerous, especially if magnified by other features.

Thus, farmers do not clear all trees from fields, although this would allow greater crop plant

density, because nining (‘shade from trees’) is the most important form of sumaya (‘coolness’)

moderating midday heat. Other types of heat indicate poor sites for farming or settlement.

Although solar heating may cause dugu funteno, its intensity is mainly due to ground surface

characteristics: it occurs at night or in shade where nyama (‘soil healthfulness’) is poor.

Similarly, not all shade brings sumaya: coolness persists all day in sites with good soil and low

spirit activity, but bad sites remain hot even if shaded.

San (‘the sky’) is a well-developed category whose subdivisions indicate how observed

features relate to the spatiotemporal distribution of precipitation and changes in air temperature

(Figure 4, p. 95). San comprises funio (‘air’), kuro (‘haze’), kabo (‘clouds’), san (‘weather’),

tilo (‘the sun’), kalo (‘the moon’), lolo (‘stars’), and kèlèbomboli, the locally dominant,

northeasterly storm track. San most frequent means ‘weather’. Several types of weather are

recognized, all associated with rainfall or potential rainfall. The most salient weather is san ji

(‘rain’), often called just san. Weather is perhaps the most discussed feature of the biophysical

environment, due to its obvious importance in farming. Other celestial features differ according

to how they relate to precipitation and microclimatic change. Types of funio (‘air, wind’) and

kabo (‘clouds’) interact with other celestial features to produce weather, while these and kuro

(‘haze’) affect terrestrial microclimate. For instance, munkun (‘fog’), a type of cloud, can cause

dangerously cold air temperature, while kuro (‘haze’) intensifies hot air temperature. Both

situations can cause illness for humans, livestock, and crops, depending on site soil and

topography.

76 Tilo (‘sun’), kalo (‘moon’), and lolo (‘stars’) are monotypic categories that indicate

temporal change. In the past, they may have carried spiritual meaning (cf. de Ganay 1949;

Tauxier 1927; Zahan 1950), but currently they mark time without having strongly expressed

meanings. Sumaya ni tilimo naaningo8 (‘the Milky Way’) also indicates seasonal change, but is

considered a type of kuro (‘haze’).

The most developed category subsumed in ala ka baara is dugu (‘earth’), which shares

some subordinate categories with mògò ka baara. Dugu directly subsumes six categories, in

which physical features are differentiated based on topography, hydrology, ground

characteristics, and vegetation, each of which embody a set of natural resources and hazards.

Additionally, synthetic assessment of all site characteristics represents another criterion by which

features are classified.

Informants classify [vegetation] by structure or composition (Figure 5, p. 96). The covert

category [compositional vegetation] potentially includes many subordinate categories because

these are distinguished according to the most salient species in a site (cf. Sow & Anderson 1996),

and dozens of species are salient and locally abundant (Duvall 2001). In practice, few

compositional vegetation types are recognized, either field vegetation or stands of economically

important wild plants (Figure 4, p. 95). [Structural vegetation] types are either tu (‘vegetation

with high stem density and high stature’) or kèna ge (‘vegetation with low stature’); people can

see long distances in kèna ge, but not in tu. Short grasses dominate kèna ge, which is

characteristic of many, but not all, kèna (‘clearings’), one of several [land-cover] types discussed

below. Different types of tu have high densities of trees, bamboo, or tall grasses.

8 Literally, ‘the boundary between sumaya (‘the wet season’) and tilimo (‘the sunny season’)’.

77 Topography is classified in the covert taxon [land forms], which includes wu (‘cavities’),

[depressions], and [elevations]. Several criteria differentiate topographic features. First, many

features differ according to surface drainage, especially types of [depression] (Figure 6, p. 97).

Many depressions are types of ji jigi silo (‘drainage channel’), distinguished mainly by side-

slope form. Large, bowl-shaped depressions are distinguished based on drainage network: a

solon contains several creeks, a kubo one, and a dinga none.

Second, the effects of topography on microclimate also differentiate features. Several

features have characteristic degrees of shading, such as types of wu (‘cavities’) (Figure 6, p. 97).

Hanhan (‘caves’) contain large, permanently cool areas, and many wòròn (‘pits’) retain

moisture within narrow openings. Degree of shading also distinguishes some types of

[depression]. Deep features like kun sa (‘drainage channel head’) and gouga (‘gorge’) are

frequently shaded, while shallower features like bilan da (‘drainage channel mouth’) and hara

(‘swale’) are not.

Third, slope form differentiates elevated features (Figure 7, p. 98). The concept ‘slope’ is

covert to many older people and most women, but many younger men, who have more exposure

to French through labor migration and radio, label this concept koti, from côte (French: ‘slope’).

Many slope classes are based on how easily they may be climbed: a tinti (‘rise’) is barely

noticeable when walking, but a haya (‘drop’) cannot be climbed or descended. Landscape

position and substrate also differentiate [elevations]. For instance, rice cultivation is possible on

both types of goungou because these are along permanent water bodies; a gongoli (‘hillock’) has

the same shape and soil characteristics, but hillocks are not uniformly arable because they occur

throughout the landscape. Konko (‘hill’) and kuru (‘bedrock outcrop’) are very salient,

78 differentiated by substrate, not slope form: konko are the edges or remnants of ferricrete crusts,

while kuru are dolomite or sandstone outcrops.

Permanency, size, and origin distinguish [water bodies] (Figure 8, p. 99). Within the

categories of permanent and seasonal, many [water bodies] differ according to the duration water

is present during the year or longer periods of time. For example, both gibingibin and ji ja balo

are permanent water pools in deep spots in creeks, but a gibingibin is less likely to dry in

droughts because it has a rock bottom and occurs in a cavity, not a muddy hole like a ji ja balo.

Many water bodies are distinguished by size: a ba (‘river’) is larger than a kò ba (‘creek’), and a

sakanbe (‘spring’) has more abundant flow than a tondi ji (‘seep’). The origin of water bodies

also is important: a kuru bake differs from other flowing water bodies because its water does not

belong to a drainage channel, but comes from drainage through soil overlying exposed bedrock.

Geologic and soil resources are classified in the category dugukolo9 (‘ground’). There

are six components of dugukolo (Figure 9, p. 100). First, nògò (‘organic matter’) is surficial and

decomposed litter that provides habitat for some animals and enhances the inherent fanga

(‘strength, chemical fertility’) of soils. Second, nyama is a gaseous substance that emanates

from the ground surface—especially from soil—that controls the healthfulness of a parcel of

ground. According to an informant, “nògò and chemical fertilizer are the same; nyama is not the

same, but [is] like gaseous pesticide the ground sprays up and makes [some things] sickly even if

they grow”. Nyama can be good or bad, depending on the site and the being exposed to it.

Crops may grow in sites with nyama that is bad for people, but these crops cannot be safely

eaten. Third, sumaya (‘moisture’) consists of both nèma (‘soil moisture’) and kombo (‘dew’),

9 Literally, ‘[the] ground[’s] bone’.

79 which is moisture that has ascended from the ground. Like nyama, sumaya is associated mainly

with soil, but types of rock also have varying moisture characteristics.

The final three components of dugukolo (‘ground’) are more finely differentiated (Figure

9, p. 100). There are three types of bèrè (‘gravel’), distinguished by particle size. Bogo (‘soil’)

is classified based on arability, texture, and color. This category centers on bogo (‘loam’). Bogo

and kènyè (‘sandy loam’) are preferred for farming; less preferred and non-arable soils are

clayey or silty. This division reflects the demands of local staple crops: millet prefers well-

drained soils, while peanuts cannot be easily dug from dry, fine-textured soil. Kuru (‘rock,

stone’) is classified according to which aspect—hardness, form, use value, or landscape

location—is most salient. Five types of rock are identified by hardness, four by form, three by

use value, and three by landscape location (Figure 9, p. 100). Of these taxa, only nari kuru and

kaba kuru, both identified by hardness, correspond to rocks recognized by geologists: dolomite

and sandstones of the Manantali series, respectively. Clusters of features, not any single

characteristic, differentiate types of kuru (cf. Hunn 1976). Many salient features co-occur

because these are inherently related, such as how hardness leads to the typical shape of particular

rock types. Nonetheless, one feature is considered most salient for each type of kuru, even if the

mutual predictability of this and another feature means that the second is, objectively, as

characteristic as the first and explicitly recognized as such. For instance, kuru ge (‘white stone’)

and kuru fing (‘black stone’) are classifications based on form, although, as their names suggest,

their colors are also distinctive. The only stones fitting the size and shape criteria for kuru ge are

made of kaba kuru, light-colored sandstone. Indeed, kaba ge is a synonym for kuru ge.

Similarly, the only rock that forms stones the size and shape of kuru fing is nari kuru, relatively

dark-colored dolomite. San galima kuru (literally ‘thunderstone’) is considered to form where

80 lightning strikes the ground. Archaeologists call these celts, or Neolithic polished-stone axe

heads (Davies 1967). Finally, jaman kuru (‘clear quartz’) is apparently derived from diamant

(French: ‘diamond’). Diamonds are not found locally, but French geologists prospected for them

in the early 1900s (Varlet et al. 1977) and the name may derive from this contact.

Although these separate classifications of topography, hydrology, vegetation,

microclimate, and ground surface features are important, most informants consider them

altogether when classifying or describing parts of the landscape. This synthetic view produces a

separate classification of [land cover].

Land-cover types are either anthropogenic or a type of dan (‘non-anthropogenic land

cover’) (Figure 10, p. 101). Dan is a concept laden with meaning, since jine (‘spirits’) occupy

parts of the landscape with non-anthropogenic land cover (Brun 1907; Cashion 1982). Dan is

the root of danso (‘hunter’) and dansoko (‘hunter’s prowess’), indicating that ‘hunting’

represents mastery of the dan and its spirit occupants; exterminating pest animals in fields is not

considered ‘hunting’, but field management. Dan is subdivided between land cover for which

vegetation, landscape position, or topography are most salient. Many land-cover names come

from the names of dominant soil types or topographic features but are distinguished

grammatically as locative nouns requiring postpositions in all usages. Land-cover types with

such names are not simply soil or topographic classes. For example, both kakakure and kuru ge

to have kènyè soil, which is arable, but neither land-cover type is arable. Soil in a kakakure

shallowly overlies bedrock, and thus has poor soil moisture characteristics; kuru ge to sites are

arable except for the abundance of the grass ngòlò (‘Cenchrus ciliaris’), a weed. Other land-

cover names, such as lemukan (‘arable woodland with sandy soil’), take postpositions only when

used as objects. Land-cover types for which vegetation is most salient are either kèna

81 (‘clearings’) or [not kèna], an unlabeled category. Both categories have multiple subdivisions

relating primarily to ground characteristics. All land-cover types associated with landscape

position are types of mako (‘creekside’), differentiated on ground and vegetation characteristics.

Finally, land cover for which topography is most salient are associated with kuru (‘outcrops’)

and konko (‘hills’). Although many of these cover types have names derived from types of

slope, they are not topographic classifications but require postpositions in all uses. Thus, kuru

sinbe he refers to areas found at a kuru sinbe (‘outcrop toeslope’), which have fertile, deep soil,

tree-dominated vegetation, good soil moisture, and many colluvial boulders. These areas are

valued for agriculture, but not for settlement due to the risk of rock fall.

The work of people. Mògò ka baara comprises enduring physical features created by

humans. Since the act of creating these features imparts possession to their creators, components

of mògò ka baara belong primarily to the biospiritual environment until their possession lapses.

Nonetheless, the context in which a feature is referred determines whether it is perceived as

information about the distribution of natural resources, or as an indication of use rights.

Anthropogenic physical features are profoundly dualistic, being always, to some degree, part of

the biospiritual and physical environments. Many components of mògò ka baara belong to

taxonomic categories that also include features that compose part of ala ka baara (Figure 3, p.

94).

One major division of mògò ka baara is [land cover], shared with ala ka baara (Figure

10, p. 101). Anthropogenic land-cover types are differentiated by use. Use distinguishes

settlement sites from agricultural clearings and fallows. Anthropogenic clearings—both furu

(‘fields’) and gaso (‘unfarmed clearings’)—are part of the broader land-cover category kèna

(‘clearing’). Significantly, some types of anthropogenic clearing—such as millet fields—do not

82 have kèna ge vegetation. Past use characterizes manyang (‘fallows’). Tree density in fallows

varies from grassland to forest, but the criterion of past use lumps fallows into a single category

regardless of between-site differences in soil, vegetation, or other features.

The other major component of mògò ka baara is [artifacts], items or structures whose

endurance allows them to outlast knowledge of their ownership and become simply physical

features. Short-lived artifacts—like baskets, fences, or huts—are inherently part of

hadamadènya (‘humanity’) since their ownership is never in question. As one informant said,

“the belongings of people that [disintegrate] if left [without maintenance] are like [antelope]

horns. They are hard and strong, but once the [antelope] dies the horns are soon gone. When a

man dies his sons may maintain his hut, but when they go [to live elsewhere] his hut will fall

[…]. [However,] some things people can build don’t fall [for so long that] we don’t know who

made them.” There are three classes of artifact—[manufactures], [structures], and [works]—

distinguished by form and use (Figure 11, p. 102).

Features made by animals. The covert category [features created by animals] includes

only kome (‘salt licks’), tun (‘termite mounds’), and wu (‘holes’) (Figures 3, 6, & 11, pp. 94, 97,

& 102). Notably, termite mounds are conceived as a type of digging, and thus are part of the

category tun (‘diggings’) that also includes parts of ala ka baara and mògò ka baara.

Discussion

Manding cultural ecology. Many physical geographic terms used in Bafing Maninka

occur in other Manding dialects, as expected based on their linguistic similarity. Although

published vocabularies are incomplete and published glosses often imprecise, Manding dialects

share terms referring to broad conceptual categories of physical features. For instance,

equivalents to the Bafing Maninka terms san (‘sky’), kaba (‘cloud’), konko (‘hill’), tinti (‘rise’),

83 ba (‘river’), kò ba or kò (‘creek’, ‘stream’), bugu (‘farm’), tumbun (‘ruined settlement’), kolon

(‘well’), and others are reported in many Manding dialects (e.g. Anonymous 1906; Bailleul

1996; Bassett & Koli Bi 2000; Bazin 1965 [1906]; Bernus 1956; Delafosse 1929; Delafosse

1955; Flutre 1957; Gamble 1987; Gregoire 1986; Lem 1948; Spears 1965; Tabor 1993; Travélé

1913).

Manding physical geographic vocabularies are not entirely uniform, however. Some

differences reflect history. Manding peoples expanded throughout West Africa in the 13th-15th

centuries (Hodge 1971), encountering speakers of other African languages. From the 11th

century, Islamic traders spread Arabic across Sahelian Africa, and from the 1400s European

traders spoke several languages along the coast (Oliver 1977). Manding groups have had

varying exposures to these and other influences, and have borrowed words that indicate different

physical geographic knowledge of non-Manding groups. Perhaps the most widespread loan

word is sahelo (‘north’), from Arabic. In the southern portion of the Manding area, where

Islamic traders were less active historically (Dalby 1971), sahelo is less frequently used, in favor

of the Manding words “kènyèka” or “kogodugu” (Bailleul 1981: 286; Delafosse 1929: 549;

Travélé 1913: 86). The Bafing Maninka are adopting côte (French: ‘slope’) for a concept that

appears to have been previously covert (Figure 7, p. 98), while Gambian Mandinka and Guinean

Kuranko have borrowed non-Manding words (from Wolof, Jola, and Kissi languages) for

features that do not appear to have an equivalent Manding name (Carney 1991; Fairhead &

Leach 1996; Gamble 1987). Such loan words may represent past adaptations to previously

unknown natural resources or hazards, or instances when Manding groups came to distinguish

physical features with increased detail following contact with outside groups (cf. Goodenough

1966).

84 The meaning of some widespread terms varies between Manding groups. Some

polysemous terms in Bafing Maninka have fewer meanings in other dialects. Particularly, dugu,

meaning ‘earth’, ‘village site’, and ‘population of a village’ in southwestern Mali (Figs. 3 & 11,

pp. 94 & 102; Cisse 1970; Lem 1948), means only ‘earth’ for Kuranko speakers in southern

Guinea, while “so” means ‘village’ (Fairhead & Leach 1996). Other widespread terms do not

refer to precisely the same physical feature in all areas, indicating the concept behind a term may

be broader than the limited set of physical features it names in a given area. For instance, in

southwestern Mali the term tu (‘forest’) refers to vegetation that ranges from wooded grassland

to forest, but in southern Guinean Kuranko “tu” means only forest; “yèrèn” denotes woodland

and “fòròn”, wooded grassland (Fairhead & Leach 1996: 204). The Kuranko use these terms to

describe vegetation succession. Since equivalent terms are absent in Bafing Maninka, these

groups may not similarly understand succession—which certainly differs qualitatively between

the two areas for ecological reasons.

Some terms have more complex internal taxonomies in particular Manding dialects than

others. For instance, the Gambian Mandinka term “faro” (‘swamp’: Carney 1991), equivalent to

hara (‘seasonally flooded grassland’), is subdivided into at least four types depending on

flooding periodicity (Carney 1991; Gamble 1987). These types of “faro” seem equivalent to

Bafing Maninka [land-cover] types (cf. Carney 1991). The subdivision of “faro” contrasts with

the singularity of hara, and indicates variation in agroecological knowledge between the

Mandinka and Maninka, and, ultimately, differences in the biophysical environment. The

Gambia is a low-lying coastal nation along the Gambia River, where water levels are affected by

precipitation seasons—the sole influence in hilly southwestern Mali—as well as by tides (Carney

1996; Carney 1991; Michel 1973). Rice is the staple of Gambian farmers; the different types of

85 “faro” represent classification of the suitability of flooding zones—which have distinct

hydrological, soil, slope, and vegetation characteristics—for different rice varieties. In

southwestern Mali, rice is a minor crop and most informants recognize only site-specific

variation in agroecological characteristics that makes a hara either suitable or unsuitable for rice.

In contrast, Bafing Maninka distinguish many [land-cover] types associated with elevated

features (Figure 10, p. 101), corresponding to site suitability for the staple crops millet and

peanut. Gambian Mandinka appear to have a less detailed classification of upland areas (Carney

1996; Carney 1991; Gamble 1987; Schroeder 1999; Schroeder & Suryanata 1996). Topography

is also less complex in The Gambia (Michel 1973), suggesting there are fewer elevated features

the Mandinka could recognize independent of use value. Other African ethnolinguistic groups

that occupy upland areas, including the Mende in Sierra Leone (a Manding group), have fairly

well developed topographic classifications (Osunade 1987). The incompleteness of published

vocabularies precludes strong conclusions, but within a widely shared conceptual framework

different Manding groups apparently have distinct taxonomies of locally diverse or highly valued

physical features (cf. Tabor 1993: 32).

This finding supports other research emphasizing the importance of special-purpose

taxonomies in classifications of physical features. Ethnobiological research has shown that

humans classify plants and animals in highly predictable ways. General-purpose (or natural)

classifications of plants and animals are similar across cultures, suggesting a pan-environmental,

general-purpose taxonomy of living things (Berlin 1992; Brown 1984). A pan-environmental

taxonomy may exist because local florae and faunae do not represent continua of variation, but

comprise discrete groups of morphologically distinctive organisms separated by objective

86 discontinuities in the observed range of variation, suggesting a clear classificatory framework

(Hunn 1976; Hunn 1977; Malt 1995).

Differences between physical features—such as landforms, soils, and vegetation—is less

discrete (WinklerPrins 1999). There are certainly objective discontinuities in the physical

environment—sky/earth, elevation/depression, grassland/forest—but within the framework

suggested by these discontinuities there are few obvious breaks in the range of variation

exhibited by particular classes of physical feature. The nature of continuous variability increases

the importance of classificatory criteria in determining taxonomic structure. Since classificatory

criteria for sets of items with continuously variable features are culturally subjective,

classifications of physical features may be dominated by special-purpose (or artificial)

taxonomies (cf. Ellen 1993). Indeed, Williams and Ortiz-Solorio (1981) show that a

Nahuatl/Spanish soil taxonomy from central Mexico differs from that of technical soil science

because the folk taxonomy classifies surface soils (important to Nahuatl farmers) rather than soil

profiles (important to soil scientists). Similarly, Zimmerer (1996; 2001) describes how Andean

farmers in Peru and Bolivia classify “landscape units”. Some of their landscape units correspond

to widely used, general terms—such as ‘valley’ and ‘hill’—while others derive meaning from

use-values in potato farming as practiced in the area.

Soil and land cover. Maninka farmers in southwestern Mali have a more detailed

classification of land-cover types than soil, vegetation, or other features. Soil (and other specific

features) contributes only a portion of the perceived arability of a site, which derives from all the

natural resources and hazards present. While certain soil types are favored for agriculture, many

sites with favorable soil are non-arable due to agroecological constraints posed by topography,

vegetation, or non-soil characteristics of the substrate. Similarly, some sites with less favored

87 soils are arable because the total biophysical environment is suitable for certain crops. Soil

characteristics vary within most land-cover types, but not enough for informants to recognize

soil-based subcategories. The only land-cover types for which a single substrate is diagnostic are

non-arable, but not all non-arable land-cover types are associated with one substrate. The

synthetic view of soil, vegetation, slope, hydrology, and microclimate embodied in Maninka

land-cover categories provides a highly salient and useful indication of agroecological potential

for Maninka farmers. Resource managers should use land-cover terms when communicating

with Maninka farmers about land management (cf. Laris 2002; Osunade 1988; Osunade 1987;

Pulido & Bocco 2003).

The importance of land cover over soil, or any single feature, in classifying site arability

is a widespread, underemphasized aspect of local knowledge systems (cf. Denevan & Padoch

1988; Fleck & Harder 2000; Frechione et al. 1989; McGregor 1994; Osunade 1988; Pulido &

Bocco 2003; Shepard et al. 2001; Verlinden & Dayot 2005). Many works characterized as

describing local soil types actually describe land-cover categories, which often include soil

assessment but are not soil types. For instance, Carney (1991: 40) describes how Gambian

Mandinka farmers recognize “micro-environments” based on hydrology, topography, and soil.

Soil plays a minor role in distinguishing these “micro-environments”, yet reviews of

“ethnopedology” consistently categorize Carney’s paper as describing local soil knowledge (e.g.

Barrera-Bassols & Zinck 2000; WinklerPrins 1999).

Land cover and soil are not interchangeable concepts in local knowledge systems, nor are

land-cover categories simply a portion of local soil knowledge. Nonetheless, the concepts ‘land’

and ‘soil’ are frequently confounded in ethnoscientific publications, suggesting inaccurately that

local people do not differentiate soil from some or all other natural resources in a site. For

88 instance, Barrera-Bassols and Zinck (2000: 19) state, “there is no clear-cut distinction between

soil and land characteristics” in local knowledge systems (cf. Barrera-Bassols & Zinck 2003a:

171). They report that “topography, land use, and drainage” are criteria used to classify soils,

without citing specific studies. However, works in their annotated bibliography that apparently

support these statements actually do not pertain to local knowledge of soils per se, but of land-

cover types (e.g. Carney 1991; Kanté & Defoer 1996; Osunade 1988). In some primary works,

land-cover terms are inaccurately applied to soils found in a given land-cover type: Tabor (1993:

47) translates “fouga” (= huga [‘ferricrete hardpan’]) as a specific soil type, but this is not a soil

term (Fig. 10, Laris 2002). Conversely, some authors refer to soil when actually discussing a

broader set of environmental features, comparable to Maninka [land cover] categories. For

instance, both Osbahr and Allan (2003) and Osunade (1992) repeatedly state that farmers in their

study areas examine “land” characteristics—i.e. a range of biophysical features, and especially

vegetation—in determining the suitability of sites for agriculture, but consistently describe this

as “soil” knowledge. While soil characteristics may be an important aspect of site arability,

farmers clearly know, and use knowledge, about more than soil in selecting arable sites; these

‘land’ characteristics could as accurately be described as ‘vegetation’ knowledge (cf. Fleck &

Harder 2000; Verlinden & Dayot 2005). In contrast, in their 2003 paper, Barrera-Bassols and

Zinck show clearly how local knowledge may be partitioned to distinguish soil and land cover as

separate, though related, aspects of the biophysical environment. They report how Purhépecha

farmers in central Mexico conceive “land” as an integrated whole composed of water, climate,

relief, and soils. The Purhépecha classify “land” according to how its four components interact

at a given site; soil is only one of several variables that determine productive potential (Barrera-

Bassols & Zinck 2003b: 237-240). The failure to clearly distinguish ‘soil’ and ‘land’ has caused

89 misrepresentation of the conceptual extent and distinctness of analogous concepts in local

knowledge systems.

Additionally, past research has misplaced the concept ‘soil’ within folk taxonomies of

physical geographic features. For the Maninka, bogo (‘soil’) is one of several components of

dugukolo (‘the ground’); it is an intermediate-level taxonomic category (Figure 9, p. 100). This

finding contrasts with Williams and Ortiz-Solorio’s (1981) widely accepted position that ‘soil’ is

conceptually equivalent to ‘plant’ or ‘animal’—a “kingdom” in folk taxonomical terms.

Linguistic evidence poorly supports this position. Kingdoms are generally unlabelled (Berlin

1992). Thus, several authors have considered ‘soil’ an exception to this principle since ‘soil’ is a

well-defined, labeled category in all studied languages. More parsimoniously, the fact that ‘soil’

is labeled suggests it is not a kingdom. Indeed, in most folk soil taxonomies (see citations in

Barrera-Bassols & Zinck 2000), ‘soil’ is a primary lexeme that subsumes categories labeled

mainly by other primary lexemes—such as ‘loam’—that in turn subsume categories denoted by

secondary lexemes—such as ‘sandy loam’. In such cases, ‘soil’ fits only the linguistic criteria

for “life form”, not “kingdom” (Berlin 1992).

Researchers who have misclassified ‘soil’ may not have studied broad enough samples of

pertinent local knowledge systems to observe the concept’s full context. Some sources suggest,

as shown here for the Maninka, that ‘soil’ is included in a broader conceptual category that also

includes, at the minimum, ‘stone’ (e.g. Kanté & Defoer 1996; Romig et al. 1995; Ryder 1994;

Sandor & Furbee 1996). For example, the Purhépecha in central Mexico recognize numerous

types of soil using secondary lexemes derived from the primary lexeme “echeri” (‘soil’), and

four types of stone using secondary lexemes based on the primary lexeme “tzacapu” (‘stone’)

(Barrera-Bassols & Zinck 2003b: 239). The authors list both “tzacapu” and “echeri” under the

90 heading “soil terms”, suggesting these are taxonomically equivalent categories subsumed in a

category equivalent to ‘the ground’. Knowledge of soils in relation to agricultural practice, an

important research topic, must not be divorced from broader knowledge of natural resources

farmers use in assessing arability. Soil is but one ground-surface feature farmers assess, and the

ground surface is but one of several broad classes of physical feature that compose

agroecological potential.

This discussion of the taxonomic rank of ‘soil’ in local knowledge systems suggests a

broader question about the conceptual scale of folk taxonomic research. Research on

ethnobiological classification has indicated that humans universally recognize, if covertly, the

concepts ‘plant’ and ‘animal’ (Berlin 1992; Brown 1984). These categories exist as natural

realms in local knowledge systems at the taxonomic rank of “kingdom”, the category that

subsumes all related objects. How are kingdoms related? In the Maninka conceptualization of

the biophysical environment, ‘plant’ and ‘animal’ kingdoms are included in a broader category,

[the biospiritual environment], which includes all beings and thus contrasts with inanimate

features of [the physical environment], even though the two categories are not absolutely

separable (Figure 2, p. 93). This higher taxonomic rank is unnamed, because few researchers

have considered its existence (cf. Rappaport 1979). Inattention to broad taxonomic contexts has

led researchers to misinterpret the taxonomic rank of ‘soil’, for instance, and greater attention

should be given the overarching structures of local knowledge systems in order to clarify the

epistemology of aspects of local knowledge and their scientific analogues. Better knowledge of

how different cultures identify and classify physical geographic features will improve

understanding of the conceptual foundations of physical geography (Atran 1990; Blaut 1979).

91

Figures for Chapter Two

92

93

94

Figure 3. Main categories of the Maninka physical environment. Line formatting indicates taxonomy forthe three subdivisions of [the physical environment]: solid lines= ala ka baara; dotted lines=[features madeby animals]; dashed lines=mògò ka baara. Categories without shading belong uniquely and totally to alaka baara; lightly shaded categories belong uniquely and totally to [features made by animals]; darkly shadedcategories belong uniquely and totally to mògò ka baara; categories outlined in gray subsume categories thatinclude features belonging to more than one of the three primary subdivisions. Commas separate synonymousterms. For categories followed by braces, internal taxonomies are shown in the figures indicated. Intersectionof mògò ka baara and [features made by animals] with the [biospiritual environment] is not represented here;see Fig. 2 and the text for description of these intersections.

ala ka baara

dugu ('earth')

ju kòrò ('deep subsurface')

san, san hutuma ('sky') {Fig. 6}

[features made by animals]

mògò ka baara

[landforms] {Figs. 8, 9}

[water bodies] {Fig. 10}

[vegetation] {Fig. 4}[substrate] {Figs. 11, 12}

[land cover] {Figs. 13}

dugu ju kòrò ('deep subsurface of the ground')ba ju kòrò ('riverbed')

[artifacts] {Figs. 14}

siya ('lair')nyaga ('nest')kome ('salt lick')

funteno ('temperature')funteno ('hot temperature')nènè ('cold temperature') dugu funteno ('heat of the ground')

bin funteno ('humidity over damp grass')kuru funteno ('humidity over damp rock')

sumaya ('coolness') nining ('shade under trees')siniware ('shade from clouds')

95

96

97

98

99

100

101

102

103 Chapter Four: Human and environmental causes of floristic patterns in southwestern Mali

Abstract

This paper presents the results of vegetation studies conducted in southwestern Mali,

which lies in the semi-arid Sudanian bioclimatic zone. The dominant view of the Sudanian zone

is that vegetation distribution and composition has been heavily affected by cultivation, and that

anthropogenic impacts create distinct floristic patterns. Vegetation sampling occurred at 217

sites with disturbance histories determined through interviews of local residents. For each site,

data was collected on edaphic characteristics, topography, hydrography, disturbance, and (for

previously cleared sites) time since abandonment. Floristic analyses (hierarchical cluster

analysis, indicator species analysis, multi-response permutation procedures, and non-metric

multi-dimensional scaling) revealed that: 1) Fifteen vegetation types are recognizable, with

different levels of floristic distinctness. 2) Most vegetation types are reliably indicated by

various species, and are linked to specific environmental or disturbance conditions. 4) Edaphic

features, especially bedrock characteristics, and landscape position explain most floristic

variation. The importance of particular sandstone bedrock conditions in the maintenance of

mesophytic refugia is underscored. 5) Finally, three different vegetation responses to human

disturbance are apparent—vegetation turnover, homogenization, and resilience—depending on

site-specific biophysical and socioeconomic conditions. In particular, vegetation in abandoned

settlements and abandoned fields show distinct response to disturbance, because these two

human activities differ in terms of ecological impact. However, effects of human disturbance are

difficult to identify due to the absence of undisturbed vegetation in arable sites.

Keywords

104 Vegetation; biogeography; ecology; Mali; agriculture; disturbance; NMS; MRPP; ISA

Introduction

In the semi-arid tropics, variation in woody vegetation structure and composition

corresponds almost entirely to variation in edaphic and hydrological features (Bourlière 1983;

Breman & Kessler 1995; Bullock et al. 1995; Cole 1986; Furley et al. 1992; Lawesson 1995;

Lawson 1986; Meave & Kellman 1994; Meave et al. 1991; Schnell 1971; Scholes & Walker

1993; White 1965; White 1983). Woodlands and dry forests dominate semi-arid tropical

regions, particularly in sites with relatively deep, arable soil. However, these regions also host

forests, grasslands, and bushlands in locations predictable from soil and hydrological

characteristics. For instance, in Sudanian West Africa forest patches enriched with mesophytic,

Guinean species occur in sites with elevated soil moisture content, while grasslands and

bushlands host xerophytic Sahelian and Saharan plants in sites with shallow or infertile soil

(White 1965). Adopting a regional scale of observation in the semi-arid tropics underscores the

importance of edaphic and hydrological factors as sources of variation in vegetation composition

and structure.

A less expansive scale of observation that focuses on landscapes (areas of hundreds of

square kilometers) increases the significance of human activities as a source of vegetation

variation. While constraints imposed by edaphic and hydrological variation remain evident in

many landscapes as riparian forest corridors and vegetation patches associated with unusual

substrates (Breman & Kessler 1995; Bullock et al. 1995; Lawesson 1995; Meave & Kellman

1994; Meave et al. 1991), the structure and composition of most vegetation types have been

profoundly modified through human management.

105 Human activities are especially significant as a source of vegetation variation in semi-arid

Africa because of the continent’s extremely long human history. In particular, the composition

of African woodlands reflects millennia of human disturbance via fire, settlement, agriculture,

and edible fruit gathering (O'Brien & Peters 1998; White 1983). Humans have modified the

local, regional, and continental distributions of valuable, indigenous fruit trees, and increased the

absolute and relative abundance of these species around settlements (Boffa 1999; Chevalier

1947; Duvall 2006; Maranz & Wiesman 2003; McGregor 1994; Pullan 1974). More

significantly, vegetation clearing for agriculture and the use of fire in farming, animal husbandry,

and resource management have probably resulted in increases in the abundance and distribution

of species adapted to these disturbances (Bassett & Boutrais 2000; Belsky 1995; Belsky et al.

1993; Breman & Kessler 1995; Laris 2002; Nyerges 1989). Intentional or unintentional

alteration of the distribution and abundance of certain species may create anthropogenic forest

(Amanor 1994; Avenard et al. 1974; Fairhead & Leach 1996; Spichiger & Pamard 1973) and

bushland (Audru 1977; Bassett & Boutrais 2000; Bassett & Koli Bi 2000) patches in some semi-

arid areas. Human activities are clearly significant in vegetation variation as viewed at a

landscape scale.

Indeed, botanists and vegetation scientists working in West Africa have historically seen

woody vegetation characteristics as profoundly indicative of past human activities (e.g.

Aubréville 1949a; Chevalier 1933; Schnell 1976), and have thus overlooked biophysical sources

of vegetation variation while overemphasizing possible human causes (Cole 1986). This

interpretation of vegetation characteristics has been frequently coupled with the view that woody

vegetation in semi-arid West Africa is degraded relative to its supposed, aboriginal condition,

and has been expressed in terms that assign blame for the purported degradation on the supposed

106 destructiveness of indigenous resource management (Bassett & Crummey 2003; Duvall 2003;

Fairhead & Leach 1998; Leach & Mearns 1996; Richards 1985; Turner 1993). Of course, many

researchers have avoided overtly political statements about indigenous practices, but have sought

only ‘natural’ vegetation, ignoring plant communities considered ‘disturbed’ or otherwise not

representative of ‘natural’ biophysical influences. The results of such studies have underscored

the significance of edaphic and hydrological factors in vegetation variation (e.g. Adam 1956;

Lawesson 1995; Nasi & Sabatier 1988; Roberty 1940; White 1965; White 1983). Partially in

response to approaches to vegetation study that represent humans as an exogenous, ‘unnatural’

influence on vegetation, students of cultural ecology have focused on vegetation in fallows,

fields, pastures, and/or settlements. Such studies have shown that indigenous farming, animal

husbandry, and fire management are not inherently destructive of vegetation, and that

anthropogenic vegetation change is not necessarily, or even frequently, the deforestation often

assumed (e.g. Amanor 1994; Bassett & Koli Bi 2000; Chidumayo 2004; Devineau 2005;

Devineau 2001; Fairhead & Leach 1996; Lykke 1998; McGregor 1994; Nyerges 1989).

Nonetheless, few studies in any field of inquiry have simultaneously considered both human and

biophysical causes of vegetation variation in Sudanian West Africa (e.g. Breman & Cissé 1977;

Breman & Kessler 1995), even though these causes interact to create floristic patterns in

populated landscapes (Berkes & Folke 1998; McDonnell & Pickett 1993; Vale 1982). Most

studies of floristic variation in semi-arid West Africa have privileged either human activities or

biophysical variation as explanatory factors, regardless of scale of observation or views about the

destructiveness of human activities.

An emerging challenge in natural resource management is to develop land-use ecologies

that account for vegetation change without resorting to oversimplified or inappropriate

107 explanatory frameworks (Turner 2000). Long-term conservation and management of

biodiversity resources depend on our ability to situate human activities within ecosystem

processes (Baker 1992; Bellemare et al. 2002; Berkes & Folke 1998; Micheli et al. 2001).

Humans have been integral components of African ecosystems for millennia. Through much of

the 1900s, many natural resource scientists recognized the extremely long history of resource

management by African farmers and herders by blaming them for supposed environmental

destruction. Dominant narratives of widespread, anthropogenic deforestation in West Africa

have proven inaccurate or unsupportable (Bassett & Koli Bi 2000; Duvall 2003; Fairhead &

Leach 1996; Ribot 1999). Nonetheless, indigenous agricultural and settlement practices do alter

vegetation structure and composition (Boffa 1999; Lykke 1998; Maranz & Wiesman 2003;

Nyerges 1989; Nyerges & Green 2000; Schreckenberg 1999). Anthropogenic vegetation

changes overlie a pattern of edaphic and hydrological constraints to vegetation development,

which altogether structure semi-arid African ecosystems.

How do anthropogenic vegetation changes interact with floristic patterns that exist

independently of humans? Most studies of the effects of human activities on woody vegetation

in West Africa have been limited to specific portions of landscapes—areas with relatively deep,

arable soil (e.g. Bassett & Koli Bi 2000; Devineau 2005; Fairhead & Leach 1996; Lykke 1998;

Schreckenberg 1999)—where human impacts are greatest and most evident. Indeed, these

studies have sought to identify and describe anthropogenic vegetation characteristics, often in

order to contribute to resource management debates. However, recognizing relationships

between human activities and ecosystem structure requires more thorough vegetation study; the

significance of anthropogenic vegetation change depends upon the full range of vegetation

variation in focal landscapes (Baker 1992; Vale 1982). Focusing on human origins of vegetation

108 characteristics, on the intentionality of anthropogenic changes, or on their perceived

benefit/detriment to preferred uses (or meanings) of focal landscapes risks the conceptual

excision of humans from the biophysical system of study and the privileging of human activities

over non-human factors in explaining observed variation (cf. Craghan 2004). While human

intentions, perceptions, and goals are vitally important in the context of resource management

and conservation, these are less important in understanding the physical processes that spatially

structure ecosystems, even if some of these processes proceed from human activities.

The purpose of the present paper is to examine the relative significance of various

biophysical factors, including past human activities, to floristic variation in a southwestern

Malian landscape. Although this paper identifies vegetation changes attributable to human

activities, its goal is not to identify such changes per se, but instead to identify biophysical

factors, including human activities, that may explain observed variation in the composition of

plant communities. By considering humans as one of several possible sources of vegetation

variation, this paper contributes significantly to identifying and understanding the processes that

create of landscape-scale floristic variation and ecosystem structure in semi-arid Africa.

Research location

Research was conducted in an area of 183 km2 around Solo village, in Mali’s Bafing

Biosphere Reserve (BBR) (Figure 1, p. 150). This area is part of the Manding Plateau, a range of

sandstone plateaus in southwestern Mali, that lies in the Sudanian bioclimatic zone.

Topographic complexity creates a wide range of habitats in this area. Sandstone plateaus

dominated the landscape, rising 200-300 m above surrounding lowlands (IGM 2001). This

upper planation surface erodes to form narrow ravines, rocky slopes, and plains with relatively

infertile sandy and silty soils. Ferricrete hardpans and bare sandstone surfaces, with very dry

109 microclimates, are common (Dames & Moore 1992; Jaeger & Jarovoy 1952; Michel 1973).

Groundwater seeps to the surface permanently or seasonally in some locations where

sedimentary layers in the sandstone have been exposed; if seepage occurs in topographically

sheltered locations (such as ravines), very humid microclimates exist (Duvall 2001). Elsewhere,

permanently moist habitats are uncommon. Soils in most locations are driest from March to

June, when average temperature and evapotranspiration peaks (FAO 1984). Precipitation is

highly seasonal (June-September) and averages about 1200 mm per year, with high interannual

variation (FAO 1984; Leroux 2001).

Since the early 1900s, vegetation in the Manding Plateau has been considered

prototypical for the Sudanian region (Aubréville 1950; Chevalier 1938; Chevalier 1900).

Woodland vegetation dominates flat to sloped areas with good drainage and relatively deep soil,

and exhibits weak floristic variation associated with soil and topography (Lawesson 1995; Nasi

& Sabatier 1988; Projet Inventaire 1990). Three types of forest may be present in patches at sites

with high soil moisture content (Adam 1960; Duvall 2001; Lawesson 1995; Nasi & Sabatier

1988):

• riparian gallery forests, along permanent waterways, with Raphia sudanica and

bamboo (Oxytenanthera abyssinica) characteristic;

• non-riparian gallery forests, in humid microhabitats on bedrock outcrops, with many

Guinean species and often dominated by the tree Gilletiodendron glandulosum,

endemic to the Manding Plateau; and

• non-gallery forests, often on toeslopes with deep soil, with Anogeissus leiocarpus

common.

110 Rupicolous bushland occurs in very rocky sites on plateau tops and has a low density of woody

plants (Raynal & Raynal 1961; White 1983). Another bushland community, with higher stem

density, occupies eroded areas with clayey soil (Nasi & Sabatier 1988). Edaphic grasslands and

wooded grasslands occur where a shallow soil layer covers ferricrete or bedrock, and in

seasonally flooded swales (Lawesson 1995). Finally, past researchers have considered

vegetation in occupied settlements and active fields a single, undifferentiated vegetation type, but

have provided no floristic data (Nasi & Sabatier 1988; Projet Inventaire 1990). Abandoned

settlements and fields have not been recognized as having distinct vegetation.

Evidence of anthropogenic disturbance in many parts of the landscape is not readily

apparent (Duvall 2001). All sites, including those labeled ‘undisturbed’ below, are subject to at

least low intensity or frequency disturbance (cf. Vale 2002). The indigenous Maninka people

settle and cultivate lowland sites with arable soils and good drainage. Rocky areas, plateau tops,

sites with poor soil or drainage, and edaphic grasslands are used only for seasonal livestock

grazing, wild plant and honey collection, and hunting (Duvall 2001; Samaké et al. 1987).

Around settlements, farmers annually burn most grasslands and some woodlands to prevent

destructive fires and to prepare fields (Laris 2002). During field clearing and subsequent

management, farmers preserve individuals of several tree species with edible fruits (Koenig &

Diarra 1998; Samaké et al. 1987), which may dominate vegetation for several decades after

fallowing. Abandoned settlement sites are recognizable as patches of trees with edible fruits—

especially baobab (Adansonia digitata) (see Chapter 5). Although Fairhead and Leach (1996)

describe a teleological process in which Maninka farmers in Guinea purposefully transform

vegetation by planting valuable wild species around settlements, similar practices are not readily

evident in southwestern Mali.

111 Farming is seasonal and rain-fed. Immediately around settlements, farmers maintain

small maize fields that are farmed each year, but most fields, planted with the primary staples

millet, sorghum, fonio, and peanuts, are located farther from settlements and cultivated <10 years

before fallowing >10 years (Samaké et al. 1987). Arable soil is patchily distributed across the

landscape (PIRT 1983), and farmland is limited around Solo. As a result, many farmers must

improve their access to farmland by establishing farming hamlets some distance from Solo, but

within Solo’s area of traditional usufruct (see Chapter 2). Most hamlets are occupied only during

the farming season and only for relatively short time periods—in most cases, <20-30 years—

before abandonment (see Chapter 2). Hamlets are usually occupied by only a small number of

related, nuclear families who return to Solo after a hamlet is abandoned, and often establish other

hamlets after some time in Solo. The practices of hamlet establishment and abandonment

represent a shifting settlement system (cf. Stone 1996). Hamlet farming has probably been

practiced in the research area for at least several hundred years, but has become increasingly

important over the last century (see Chapter 2).

Conservationists working in Mali consider hamlet farming a spatially uniform and

destructive threat to natural habitat in the area (see Chapter 2), but the cultural ecology of hamlet

farming has not been studied. Social changes have led to an increase in hamlet farming

elsewhere in southwestern Mali (Koenig & Diarra 1998), but around Solo hamlet establishment

has declined following the promulgation of policies to forcibly remove unauthorized settlements

from the BBR. In most cases, hamlet establishment is illegal, since national laws prohibit

clearing vegetation that has not been cleared for more than 10 years (Présidence de la

République du Mali 1995). Farmers know this law: Solo’s residents did not agree to allow

112 vegetation sampling in currently occupied settlements or fields, but were supportive of and

assisted in sampling abandoned sites.

Data collection

This research is based primarily on vegetation sampling, complemented by ethnographic

study. Vegetation sampling design had two distinct components: a) a study of chronosequences

of abandoned settlement and abandoned field sites, and b) a study of floristic variation across all

parts of the focal landscape. The results of these studies together allowed identification of

vegetation responses to human disturbance, and the significance of these responses in altering

floristic patterns that exist independently of disturbance caused by settlement or cultivation.

Sampling site selection and characterization. From January to December, 2004,

vegetation sampling occurred at 217 sites. Sampling sites were selected based on substrate

texture, bedrock geology, slope, hydrology, and past human use. These five factors are

collectively called ‘environmental factors’. Additionally, ‘site types’ were informally defined as

locations with similar environmental factors. These informal site types were used to facilitate

the selection of sample sites. At the beginning of research, when vegetation sampling began, as

wide a variety of site types were sought in order to estimate their range and variety in the

research area. After several weeks, when few new site types were being found, research effort

focused on sampling: a) an approximately equal number of sites per type; b) widely dispersed

examples of each site type; and c) approximately equal numbers of sites either with evidence of

past human settlement or cultivation, or without evidence of such past use. For some site types,

few sites were sampled because type-defining characteristics were rare. Grasslands were not

sampled. The location of each site was determined using a Garmin GPS-12XL unit, and

represented as a point corresponding to the approximate center of the area sampled.

113 For each site, the following environmental factors were recorded:

• Substrate texture: For most sites ‘substrate texture’ was recorded as a soil texture

category (e.g. sandy loam, silty clay loam), determined manually (Midwest

Geosciences Group 2003). For sites where ≥25% of the surface was covered by

stones or ferricrete nodules >5 cm in diameter, substrate texture was based on visual

estimation of the percent surface area covered by different grain sizes, or consolidated

bedrock.

• Bedrock type: For sites with surficial rock, ‘rock’ was identified following Varlet et

al.’s (1977) and Groupement Manantali’s (1979) descriptions; determination of

subsurface geology followed DNGM (1992). Four types of bedrock were observed:

pélites (‘fine-grained sedimentary rocks’), Sandstone 0, Sandstone B, and dolomite

(using Groupement Manantali’s terminology). Ferricrete was also included as a rock

type. Further description of geology and geomorphology are included in the

discussion section, below, where it is relevant for understanding results.

• Slope: Slope angle measurements made with an inclinometer over a distance of c.100

m in the approximate center of the area sampled. No sites were sampled in which

slope varied by >5°. These data were used in their original, quantitative form, or as

categorical data reflecting ranges of values (e.g. ≤5°, >10°).

• Hydrography: These categories were based on: the topographic position of a site

relative to adjacent parts of the landscape; distance between sample plots and

drainage channels; and the presence of permanent water sources directly upslope or

adjacent to sample sites.

• Past use, as described in the following paragraph.

114 All categories for each of these environmental factors are listed in Table 1 (p. 160), which also

shows the number of sites sampled per category.

All sites with evidence of past settlement (e.g. remains of huts) or cultivation (e.g. rock

piles, rock lines, girdled stumps) were classified as ‘disturbed’, while ‘undisturbed’ sites were

those without evidence of past use. For each disturbed site, three types of data revealed past use

(settlement or cultivation) and time since abandonment:

• Interviews of past occupants, their relatives, and/or their descendents were used to

estimate abandonment date and identify site use (settlement or cultivation).

Abandonment dates were estimated by correlating informant life history markers,

changes in site occupation status, and datable events, such as national elections.

Multiple informants were interviewed for each site, to triangulate date estimates

and increase precision (Flowerdew & Martin 1997).

• For some settlement sites, past occupation was dated from historical documents

(Anonymous 1958; de Lannoy de Bissy 1882; Park 1954 [1815]; Projet Inventaire

1990) or aerial photos taken in 1952. These photos also allowed dating use of

some abandoned field sites.

• For three abandoned field sites, the presence of rock piles, rock lines, and girdled

stumps attested past use, although only generalized oral historical evidence

supported this.

If a disturbed site had been occupied at two separate periods, only the most recent abandonment

date was used in analyses. Sites were assigned to 10-year age classes; all settlement sites

abandoned >40 years ago (y.a.), and fallows abandoned >30 y.a., were grouped together. For

115 some analyses, past-use categories were lumped together based on disturbance type (settlement

or cultivation) and time since site abandonment.

A concurrent census of settlement sites in the research area revealed 7 occupied and 80

abandoned settlements (see Chapter 2). All abandoned settlement sites (henceforth ‘ruins’) that

had not been subsequently cultivated (n=50) were sampled. Sampling also included 61

abandoned field sites (henceforth ‘fallows’), none of which included ruins. These 111 disturbed

sites nearly equaled the number of undisturbed sites (n=106).

Vegetation sampling. At each site, ten 2×50m plots (0.1 ha total area) were established

(one site had thirteen plots) following Alwyn Gentry’s methods (cf. Phillips & Miller 2002). All

woody plants, bamboo, and palms rooted in these plots and meeting size requirements were

sampled. Specific data varied by growth form:

• for single-stemmed trees and shrubs ≥2.5 cm diameter at breast height (DBH), DBH

was measured with a diameter tape and recorded;

• for multi-stemmed trees and shrubs with at least one stem ≥2.5 cm DBH, DBH of the

largest stem was measured and recorded;

• for lianas with stems ≥2.5 cm diameter at any visible point, maximum stem diameter

was measured or estimated and recorded;

• for bamboo, the number of live stems ≥2.5 cm DBH was counted and multiplied by

the estimated average DBH of stems ≥2.5 cm DBH (usually 2.5-3.0 cm); and

• for palms having stems (not solely leaves) ≥1.4 m high, basal diameter of the stem,

not including petiole bases, was estimated.

Voucher specimens, deposited at the Missouri Botanical Garden (MO), were collected for most

species encountered. Additionally, qualitative notes on microhabitat characteristics were

116 recorded for each species. Notes included: specific locations where individuals occurred within

patches, relative to geomorphic, geologic, and topographic features; species that occurred in the

same or similar locations; and phenological status of each individual observed in samples.

At each site, sampling began at a randomly established point; subsequent plots usually

began at the end point of the previous plot. The long axis of each plot was oriented in a compass

direction different from those of adjacent plots, although in riparian corridors, plots were nearly

parallel. At least 10 m separated near-parallel plots. No plots overlapped or included more than

one site type. Plot length was determined using a hip chain, while width was estimated from the

researcher’s reach, measured at 1.96 m. At each site, plots were dispersed in an area 200-500 m

× 10-100 m.

Data analysis

Summary statistics. For each site, the following summary calculations were made:

• number of individuals, species, and families, number of individuals per species and

family, and number of species per family;

• density (individuals per unit area) and basal dominance (summed basal areas of all

individuals, based on DBH) per species and for all species;

• diversity, using the Berger-Parker index, which is equal to the proportional

abundance of the most abundant species in a sample, and most strongly reflects

species evenness, rather than richness (Southwood & Henderson 2000).

These summary statistics were used in subsequent analyses, described in the following

paragraphs.

Floristic analyses. Three complementary analyses were conducted using PC-ORD

software (McCune & Mefford 1999): 1) hierarchical cluster analysis interpreted via indicator

117 species analysis (ISA); 2) multi-response permutation procedures (MRPP) of clusters and sites

grouped by environmental factor; and 3) non-metric multi-dimensional scaling (NMS). In all

analyses, between-site distances were calculated using the Sørensen (Bray-Curtis) index

(McCune & Grace 2002; Southwood & Henderson 2000).

First, cluster analysis was used to identify groups of sites with similar vegetation

composition. This classification method has a long history of use in ecology, and many technical

descriptions and research applications have been published (cf. McCune & Grace 2002). For the

present research, the steps performed by PC-ORD in cluster analysis were:

1) a between-site dissimilarity matrix was calculated from proportional abundance

values per site;

2) the sites with the lowest dissimilarity value were linked using the flexible beta

method (β=-0.25);

3) the information lost by creating this new group (i.e. dendrogram scaling) was

calculated using Wishart’s objective function;

4) a new dissimilarity matrix was calculated using the new group; and

5) steps 2-4 were repeated until all sites had been grouped together.

Linkages and associated objective function values were represented graphically as a dendrogram.

Dendrogram pruning was based on ISA, MRPP, and subjective interpretation of the

ecological meaning of clusters. Described by Dufrêne and Legendre (1997), ISA assesses the

fidelity of species to predefined groups of sample sites (i.e. clusters identified through cluster

analysis). The indicator value (IV) for each species per cluster ranges from 0% (no indication) to

100% (perfect indication). Interpreting IVs is easiest when done in comparison with statistics on

within-cluster homogeneity, such as mean distance. Species with high IVs for clusters with low

118 within-cluster homogeneity are generalists, and IVs for these species decrease as within-cluster

homogeneity increases (Devineau 2005). In contrast, IVs for specialist species increase as

within-cluster homogeneity increases (Devineau 2005). However, if the number of sites per

cluster decreases too greatly, IVs are less indicative of generalist or specialist adaptation,

because cluster characteristics become indicative only of conditions in a small number of sites

(Devineau 2005). Thus, IVs are useful in identifying the smallest ecologically meaningful

clusters identified through cluster analysis (McCune & Grace 2002).

For the present research, the steps performed by PC-ORD in ISA were:

1) the proportional abundance (the proportion of all individuals that belong to a species)

of each species in each cluster was calculated relative to its abundance in all clusters;

2) the proportional frequency (the proportion of sample sites in which a species occurs)

of each species in each cluster was calculated; and

3) these two proportions were multiplied then expressed as a percentage, for each

species in each cluster. These percentages are the IVs per species per cluster.

The statistical significance of IVs was assessed by randomly reassigning species to groups 1000

times, then calculating IVs for these random reassignments (McCune & Grace 2002). The 22

most informative clusters were subject to ISA.

IVs provided no clear indication of which clusters should be retained after pruning. To

help determine the optimal set of clusters to retain, MRPP was used to test the statistical

significance of the between-group heterogeneity and within-group homogeneity exhibited by

different sets of clusters. MRPP is a multivariate, nonparametric method of testing the

hypothesis of no difference in species composition between predefined groups of sample units

(e.g. clusters identified through cluster analysis). MRPP has been used regularly in community

119 ecology, especially to assess the influence of disturbance and environmental factors on

vegetation composition (cf. McCune & Grace 2002; Mielke & Berry 2001). Technical

decriptions of MRPP include Biondini et al. (1985), Zimmerman et al. (1985), Mielke and Berry

(2001), and McCune and Grace (2002). MRPP essentially assesses the likelihood that observed

within-cluster homogeneity and between-cluster heterogeneity for predefined clusters are due to

chance, based on randomization of group membership (Mielke & Berry 2001). Specific

procedures used by PC-ORD in MRPP analysis are described by McCune and Mefford (1999).

In the present analysis, distance measures were rank-transformed, which increases sensitivity as

community heterogeneity increases, and makes MRPP results more analogous to those provided

by NMS (McCune & Grace 2002). The distance matrix used abundance per species per site; the

weighted mean within-group distance (δ) used the standard group-weighting equation, Ci=ni/∑ni

(McCune & Grace 2002). For the present research, the 22 most informative clusters from cluster

analysis were subject to MRPP, then the 21 most informative, and so on. Based on interpretation

of IVs and MRPP results, fifteen clusters were retained after pruning as the ecologically most

meaningful (Table 2, p. 162).

Additionally, MRPP was used to assess the significance of these fifteen clusters relative

to site groupings based on environmental factors.

Finally, NMS was used to assess the relationship between environmental factors, past

use, and floristic patterns. Clarke (1993), McCune and Grace (2002), and others describe this

ordination method; published applications of NMS in ecology include Kantvilas and Minchin

(1989), Tuomisto et al. (1995), and Waichler et al. (2001). NMS was used because several

species (especially trees with edible fruit) appeared to have bimodal relationships to disturbance

120 history, being abundant in both ruins and undisturbed forests. The appropriateness of NMS

analysis is not limited by nonlinear species-variable relationships (Clarke 1993).

Three separate NMS ordinations were undertaken: one each using data from all sites,

only undisturbed sites, and only disturbed sites. Ordinations attempted using only sites

belonging to the woodland/bushland cluster, described below, were highly unstable, while others

using only sites belonging to the forest cluster, described below, were marginally more

informative than the undisturbed-site analysis. Analyses proceeded through several iterations

testing different combinations of starting configuration, exclusion of rare species, thoroughness

settings in PC-ORD’s “NMS autopilot mode” (McCune & Mefford 1999), and number of

dimensions. The analyses chosen as final were those with the lowest instability and stress.

Specifically, in all analyses, species occurring in less than 5 sites were eliminated, and the

“medium thoroughness” default settings were used (15 runs with real data, 30 with randomized

data, 200 maximum iterations, and 0.0001 instability criterion: McCune & Mefford 1999). Final

analyses began with the most stable configuration of intermediate trials to avoid local minima.

The all-site and undisturbed-site analyses found 2-D solutions, while the disturbed-site analysis

identified a stable, 3-D solution. Finally, ordination axes were interpreted by overlying

quantitative or categorical variables for environmental factors, including past use.

Results

Vegetation clusters. Cluster analysis produced a dendrogram in which the primary

division splits forest sites from woodland, wooded grassland, and bushland sites (Figure 2, p.

152). Fifteen less inclusive clusters were retained after pruning. MRPP analysis of the two

primary clusters shows that they are very distinct compositionally (T=-82.1, A=0.22, p<0.0001;

see Table 2, p. 162, for explanation of statistics), which is also suggested by strong IVs for each

121 cluster. The woodland/bushland cluster is strongly indicated by the generalist species

Combretum glutinosum and Pterocarpus erinaceus, while the forest cluster is indicated by

Spondias mombin, Oxytenanthera abyssinica (bamboo), and the liane Sarcocephalus latifolius

(Figure 2, p. 152). Average between-site distance in the two primary clusters is high; less

inclusive clusters have lower average between-group distances (Figure 2, p. 152). However, in

the woodland/bushland cluster IVs also mostly decrease in less inclusive clusters, while in the

forest cluster most IVs increase, indicating that specialist species dominate forest vegetation

(Devineau 2005).

Several clusters retained after pruning can be lumped together and retain ecological

meaning (Figure 2, p. 152). However, MRPP shows that between-group heterogeneity and

within-group homogeneity is higher for the 15 clusters than for groupings based on any single

environmental factor (Table 2, p. 162). Grouping sites in an intuitive manner—with disturbed

sites grouped by disturbance type, time since disturbance, and substrate texture, and undisturbed

sites grouped by vegetation structure and substrate texture—produces the second-best result in

terms within-group homogeneity, while the second-greatest between-group heterogeneity is

achieved by grouping sites by the binary variable ‘disturbed’ or ‘undisturbed’ (Table 2, p. 162).

The ecological characteristics of the fifteen clusters retained from cluster analysis are

described in the following paragraphs. Summary statistics for these vegetation types are

presented in Tables 1 and 3-18 (pp. 160 & 163-178).

Clusters 1-3 comprise the forest cluster, and all three consist of sites with moist soil

conditions. Clusters 1 and 2 comprise sites with gallery forest vegetation found in moist

microhabitats along steeply sloped rock outcrops:

122 • Cola cordifolia, Spondias mombin, and Bombax costatum indicate Cluster 1

vegetation. Structurally, no species dominates—this cluster has the highest diversity

of all clusters (Table 3, p. 163)—but several are important (Table 4, p. 164). These

sites occur on scree slopes, where exposed bedrock occupies ≤25% of the surface

(Table 1, p. 160). Most have permanent springs or lie along seasonal drainage

channels.

• Cluster 2 represents outcrop sites with gallery forest vegetation dominated by

Gilletiodendron glandulosum and Hippocratea indica (Table 5, p. 165). Other than

these dominant species, vegetation composition is similar to Cluster 1, but

compositional heterogeneity is extremely low (Table 3, p. 163). Cluster 2 sites occur

in narrow ravines and along cliff tops; in contrast to Cluster 1, all Cluster 2 sites have

≥25% of the surface covered by bare bedrock, uniquely Sandstone B (Table 1, p.

160). Many component species, especially indicator species, grow in the vertical

cracks characteristic of Sandstone B (Jaeger 1950b; Jaeger & Jarovoy 1952; Raynal

& Raynal 1961), in which groundwater remains accessible throughout the year

(DCTD 1990; Groupement Manantali 1979; Varlet et al. 1977).

• Bamboo (Oxytenanthera abyssinica) strongly dominates Cluster 3 vegetation, both

structurally and compositionally (Table 6, p. 166), resulting in low diversity (Table 3,

p. 163). These sites occur mainly in seasonally dry drainage channels (Table 1, p.

160), and their composition includes some species indicative of Clusters 1 and 2 (e.g.

Spondias mombin, Saba senegalensis) as well as woodland indicators (e.g.

Pterocarpus erinaceus). This cluster includes one fallow site abandoned 0-10 y.a., in

which vegetation is structurally not forest.

123 Clusters 4-6 are weakly distinguishable, both individually and as a group (Figure 2, p.

152). Ecologically, these sites are important because Maninka farmers consider most of them

non-arable (see Chapter 3). Their soil is mostly silty or clayey, and many overlie ferricrete

hardpans. In all iterations of cluster analysis, IVs remained low and average within-group

homogeneity high within the higher-level cluster comprising Clusters 4-6. The clusters retained

are those with the highest IVs within the higher-level cluster:

• Cluster 4 sites occur mainly on slopes (Table 1, p. 160). The widespread woodland

species Xeroderris stühlmannii is the best indicator (Figure 2, p. 152). Pterocarpus

erinaceus and Bombax costatum are also important structurally (Table 7, p. 167), but

both have IVs<10% for this cluster.

• Cluster 5 is also dominated by generalists. No species has an IV>20%.

Oxytenanthera abyssinica, Pterocarpus erinaceus, and Combretum glutinosum are

the most important species (Table 8, p. 168). These sites are mainly in high

topographic positions, with silty soil overlying a ferricrete hardpan (Table 1, p. 160).

Most sites have undisturbed vegetation, but some were occupied or cultivated >30

y.a.

• Generalists also dominate Cluster 6 sites; only Combretum nigricans has an IV >20%

(Figure 2, p. 152). These sites have a wide range of environmental characteristics:

disturbed and undisturbed sites with nearly every substrate textural class belong to

this cluster (Table 1, p. 160). Combretum glutinosum and Pterocarpus erinaceus are

important structurally, as are the less widespread species Combretum nigricans and

Hexalobus monopetalus (Table 9, p. 169), both of which are associated with poor,

rocky soil (Arbonnier 2000).

124 Few sites belong to Cluster 7, which is strongly distinct from other clusters and has the

lowest between-site heterogeneity of all clusters (Figure 2, p. 152) and relatively high diversity

(Table 3, p. 163). These sites are all undisturbed, with non-arable silty to clayey soil (Table 1, p.

160). Gardenia ternifolia and Crossopteryx febrifuga strongly indicate Cluster 7 (Table 10, p.

170). Structurally, several widespread species that are uncommon in other clusters have high

importance in Cluster 7.

Terminalia macroptera strongly indicates and dominates Cluster 8 vegetation (Figure 2,

p. 152; Table 11), but the tree is also common in several other clusters. Pterocarpus erinaceus

and Combretum glutinosum are also important structurally. About half of Cluster 8 sites are

undisturbed, and about half are abandoned fields (Table 1, p. 160).

Clusters 9 and 10 both represent bushland vegetation dominated by Pterocarpus lucens

and other Sahelian species. Different, strong associations with substrate texture distinguish these

clusters:

• Exposed Sandstone B bedrock occupies ≥25% of the ground surface in all Cluster 9

sites (Table 1, p. 160). In these sites, patches of exposed bedrock are interspersed

with grassy patches of shallow soil. Nearly all woody plants are rooted in bedrock

fractures, not soil. The best indicator of this cluster is the cactiform succulent

Euphorbia sudanica (Figure 2, p. 152). Many component species also occur in

mesic, Gilletiodendron–Hippocratea gallery forest (Cluster 2); their association with

Sandstone B is observed also in Cluster 9 sites. Amongst these Sandstone B

specialists is Combretum micranthum, which co-dominates Cluster 9 vegetation with

Pterocarpus lucens (Table 12, p. 172). Gilletiodendron glandulosum is moderately

important in Cluster 9 vegetation, especially in downslope portions of these sites.

125 Combretum micranthum and Pterocarpus lucens dominate in the drier, upslope

conditions most characteristic of this cluster. Diversity is fairly high in this cluster

(Table 3, p. 163) because it includes species with both upslope and downslope

affinities.

• Cluster 10 includes all, and only, sample sites with clayey red (i.e. plinthitic) soil

(Table 1, p. 160). These sites occur in naturally eroded areas near seasonal drainage-

channel heads and have almost no herbaceous cover. Pterocarpus lucens and Guiera

senegalensis strongly indicate this vegetation (Figure 2, p. 152), which is dominated

structurally by Pterocarpus lucens and Combretum glutinosum (Table 13, p. 173).

All Cluster 11 sites have disturbed vegetation, and all but one are abandoned settlements

(Table 1, p. 161). Cluster 11 vegetation is physiognomically bushland, indicated and structurally

dominated by Dichrostachys cinerea and Ziziphus mauritiana (Table 14, p. 174), both species

characteristic of disturbed ground (Arbonnier 2000; Devineau 2005; Devineau 2001). Diversity

is low in this cluster (Table 3, p. 163), but several economically important species are important

or indicative, including the introduced, domestic tree Moringa oleifera (33.2%IV, p=0.003).

Clusters 12-15 represent Sudanian woodland vegetation as classically conceived (e.g.

Aubréville 1950; Chevalier 1938). These clusters are distinctive as a group, but are not strongly

different from one another (Figure 2, p. 152). All four clusters are dominated by Pterocarpus

erinaceus, Combretum glutinosum, and Terminalia macroptera, and useful trees are indicative or

important (e.g. Vitellaria paradoxa, Adansonia digitata). Most sites are abandoned fields or

settlements with deep, loamy soil without rock, and gentle slope (Table 1, p. 160). Importantly,

no undisturbed sites were identified with similar soil and slope characteristics, but several

undisturbed sites having shallow, rocky soil or strong slopes also belong to these clusters:

126 • Vegetation in Cluster 12 sites is dominated by Pterocarpus erinaceus and Combretum

glutinosum (Table 15, p. 175), but no species has an IV≥15% (Figure 2, p. 152).

Compositional heterogeneity is moderate, and diversity low. Most sites are

abandoned fields or settlements, and of these most are long-abandoned fields (Table

3, p. 163). However, eight undisturbed sites also belong to Cluster 12. Combretum

micranthum, Acacia ataxacantha, and Hexalobus monopetalus are moderately

important components of this vegetation. All three are characteristic of poor, rocky

soil (Arbonnier 2000), suggesting that soil in these sites has low fertility.

• The importance of Terminalia macroptera, Vitellaria paradoxa, and Piliostigma

thonningii distinguish Cluster 13 vegetation (Table 16, p. 176), although Prosopis

africana is the best indicator (Figure 2, p. 152). All Cluster 13 sites are disturbed,

and nearly all have deep, sandy soil (Table 1, p. 160). Several economically

important species (especially Vitellaria paradoxa) are important in Cluster 13 (and

Cluster 15) vegetation.

• Cluster 14 consists of both disturbed and undisturbed sites, with silty to sandy soil

(Table 1, p. 160). Pteleopsis suberosa and Hymenocardia acida—both small trees

with wind-dispersed fruit—are good indicators of this vegetation (Figure 2, p. 152),

although the generalists Pterocarpus erinaceus and Combretum glutinosum are most

important structurally (Table 17, p. 177). Most Cluster 14 sites are abandoned

settlements, but many undisturbed sites and abandoned fields are also included.

• Finally, Cluster 15 sites are dominated strongly by Pterocarpus erinaceus, which

results in low diversity per site (Table 18, p. 178) and low between-site heterogeneity

(Figure 2, p. 152). Combretum glutinosum and Vitellaria paradoxa are also

127 important structurally. Most Cluster 15 sites are abandoned settlements (Table 3, p.

163). This vegetation is similar to that of Cluster 13, except Terminalia macroptera is

a minor component of Cluster 15 vegetation, and diversity is much lower in Cluster

15 (Table 3, p. 163).

Ordination results. All three final solutions of NMS ordination had low instability but

high stress relative to Kruskal’s (1964) and Clarke’s (1993) guidelines for assessing NMS

results. NMS “stress” is “a measure of departure from monotonicity in the relationship between

the dissimilarity (distance) in the original p-dimensional space and… in the reduced k-

dimensional ordination space” (McCune & Grace 2002: 125-126). The large size of these data

sets is the probable source of the high stress: Kruskal’s and Clarke’s guidelines are based on

analysis of relatively small data sets (Kruskal & Wish 1978; McCune & Grace 2002). Stress

increases for larger data sets, and if contributions to final stress are distributed roughly evenly

across points, high-stress results can be interpreted with moderate reliability (Clarke 1993). In

the present research, examination of the distribution of sites in ordination space and r-values for

each species and all axes suggested that no site or species contributed an inordinately large

portion of final stress.

For the all-site NMS analysis, the most stable solution arranged sample sites along two

axes (final stress=23.29, final instability=0.00051). In ordination space, sample sites and species

occupy two swarms corresponding to the primary division in cluster analysis: forest versus

woodland/bushland sites (Figure 3, p. 154). Clusters 1-3 segregate fairly clearly in ordination

space, but few clusters in the woodland/bushland cluster are distinct. In the all-site ordination,

the first axis explains a small portion of variation (r2=0.155), while the second axis explains

much more (r2=0.403). (NMS ordination does not identify axes in order of strength of

128 correlation: McCune and Grace 2002.) Undisturbed-site ordination was the least stable of the

three NMS analyses (final stress=21.29, final instability=0.0062). The first axis in this analysis

explains a moderate amount of variation (r2=0.239), the second axis much more (r2=0.424)

(Figure 4, p. 156). Forest clusters remain distinct in this ordination, but other vegetation clusters

are less clearly defined in ordination space. Disturbed-site NMS ordination identified three axes

in the most stable solution (final stress=21.30, final instability=0.0002). In this ordination, the

first (r2=0.193), second (r2=0.245), and third (r2=0.232) axes each explain moderate amounts of

variation (Figure 5, p. 158). Ruins and fallows weakly segregate in this ordination.

Correlation coefficients for environmental factors and species distributions in relation to

each of the seven ordination axes range between strongly negative to strongly positive (Figures

3-5, pp. 154-159; Table 19, p. 179; Appendix 1, p. 180). Based on observations of the

microhabitats in which species were observed, as well as published information on species

ecology, the ordination axes appear to relate to:

• All-site axis 1: Species with positive correlation coefficients are mesophytes, while

those with negative correlation coefficients are xerophytes.

• All-site axis 2: Species with positive correlation coefficients are adapted to growth in

rock outcrops, while those with negative correlation coefficients are most abundant in

arable sites.

• Undisturbed-site axis 1: Species with positive correlation coefficients are mesophytes,

while those with negative correlation coefficients are xerophytes.

• Undisturbed-site axis 2: Species with positive correlation coefficients are most

abundant in arable sites, while those with negative correlation coefficients are adapted

to growth in rock outcrops.

129 • Disturbed-site axis 1: Species with positive correlations are most abundant in

undisturbed sites with sandy loam to loamy sand, while those with negative

correlation coefficients are most abundant in disturbed sites with silty loam.

• Disturbed-site axis 2: Species with positive correlations are most abundant in

undisturbed sites with silty loam, while those with negative correlation coefficients

are most abundant in disturbed sites with sandy loam to loamy sand.

• Disturbed-site axis 3: Species with positive correlations are most abundant in old

fallows, while those with negative correlation coefficients are most abundant in new

ruins.

Discussion

The results indicate three main causes of variation in vegetation composition in the

research area: soil moisture, bedrock geology, and disturbance.

Soil moisture. Topography and hydrogeology create habitat patches with permanently

elevated soil moisture conditions. The two axes identified in the all-site and undisturbed-site

ordinations relate to these two different sources of variation in soil moisture content. In both

ordinations, the first axes relate to soil moisture determined by landscape position, as suggested

by the r-values for hydrology (Figures 3-4, pp. 154-157). Also, the species with the highest

positive correlations with these axes (Table 19, p. 179) are those indicative or characteristic of

riparian bamboo forest (Cluster 3), which dominates low-lying sites along drainage channels

with ≤25% of the ground surface covered by rocks >5 cm in diameter (Table 1, p. 160). Sites

that lie toward the right end of this axis (as represented in Figures 3-4, pp. 154-157) have

permanently moist soil conditions. The second axes in these ordinations relate to soil moisture

conditions as determined by groundwater flow, as indicated by high r-values for the factor

130 ‘substrate texture’ (Figure 4, p. 156). Sites with cobbly to bouldery soil are scree slopes below

outcrops, where soil moisture is high because runoff is concentrated at the base of exposed

bedrock (DCTD 1990). Sites with exposed bedrock have fractures where runoff collects or

where perched aquifers are accessible (DCTD Dunne 1990; DCTD 1990). The species with the

strongest, positive correlations to these axes are all characteristic or indicative of vegetation

Clusters 1, 2, or 9 (Tables 4, 5, 12, & 19, pp. 164, 165, 172, & 179), which represent nearly all

sites with extremely coarse substrate texture. High r-values for the factors ‘slope’ and

‘permanent water’ are indicative of spatial correlation for these factors: sites with exposed

bedrock substrate most commonly occur on steeply sloped outcrops, and most permanent water

sources are springs originating from between sedimentary layers in exposed sandstone bedrock

(see Chapter 6). The high r-value for the factor ‘disturbance’ is misleading, because disturbed

sites swarm at one end of the second axes. In contrast, substrate texture explains the position of

vegetation clusters along the entire length of these axes. Hydrogeography and topography both

create permanently moist soil conditions, which is the primary cause of variation in vegetation

composition across the landscape (cf. Breman & Kessler 1995; de Bie et al. 1998; Fournier 1991;

Lawesson 1995), represented by the woodland/bushland versus forest split in the cluster analysis

(Figure 2, p. 152).

Bedrock and vegetation. Sudano-Guinean gallery forest (Clusters 1 and 2) occurs at

seepage areas and on seasonal drainage channels along bedrock outcrops, where mesic

conditions allow hygrophilous plants to survive. Since the 1930s, researchers have recognized

these patches—especially those dominated by Gilletiodendron glandulosum, endemic to the

Manding Plateau—as relict vegetation because many range-restricted or extralimital species

occur in them (e.g. Aubréville 1939; Duong 1947; Jaeger 1956). However, few researchers have

131 noticed that many species characteristic of Gilletiodendron-Hippocratea forest (Table 5, p. 165)

also occur in xeric, rupicolous bushland (Table 12, p. 172). These vegetation types are uniquely

associated with exposed Sandstone B.

Anthropogenic deforestation theory has dominated views of vegetation history in West

Africa, and has until recently discouraged studies of edaphic causes of vegetation variation in the

region (Cole 1986). The dominant view of vegetation history in Mali holds that Gilletiodendron-

Hippocratea forest patches are remnants of the presumed original forest climax that has been

destroyed by allegedly poor African land management (Duvall 2003). To proponents of this

view, forest patches occur only where topography protects vegetation from fire and human

activities (e.g. Jaeger 1956; Jaeger 1968; Schnell 1976). Rupicolous scrub has not been

specifically considered in the context of anthropogenic deforestation theory. While the

inaccessibility of Gilletiodendron-Hippocratea forest sites contributes to their low level of direct

human use (Duvall 2001; Geerling 1985), the present research shows that similarly inaccessible

sites with different types of bedrock do not host Gilletiodendron-Hippocratea forest, but other

types of Sudano-Guinean gallery forest.

These results confirm a longstanding, alternative model of phytogeography in the

Manding Plateau, which is poorly developed in West Africa but well supported by observations

elsewhere. Proponents of this view propose that some vegetation types, especially gallery forest,

are indicators of patchily distributed hydrogeological conditions. In 1917, the geologist Réné

Chudeau proposed that fractures in sandstone bedrock capture and hold surficial runoff, which

slowly seeps to the surface and allows azonal hygrophilous vegetation to survive. He made

similar observations elsewhere in West Africa (e.g. Chudeau 1910; Chudeau 1913), and

Larminat (1927) independently proposed this mechanism for plateaus in central Mauritania.

132 Subsequently, the botanist Paul Jaeger also concluded that sandstone hydrogeology is significant

in the distribution of Gilletiodendron-Hippocratea forest, but he believed that forest vegetation

creates mesic soil conditions, and not that groundwater availability allows forest vegetation to

establish in mesic sites (e.g. Jaeger 1950b; Jaeger & Jarovoy 1952; Jaeger & Winkoun 1962).

Ultimately, Jaeger embraced the anthropogenic theory of vegetation distribution (e.g. Jaeger

1956; Jaeger 1968; Jaeger 1966), which had been promoted by earlier botanists (e.g. Aubréville

1939; Duong 1947). Although the early geological observations have received no attention from

botanists working in the Manding Plateau since Raynal and Raynal (1961), the biogeographic

significance of perched aquifers in sandstone outcrops is well established from research

conducted on other continents (Bowman et al. 1990; Bowman et al. 1988; Danin 1999; Davis

1951; Dunne 1990; Walck et al. 1996).

The Manding Plateau is a geographic unit within the extensive West African sandstone

massif that has distinct cultural and biogeographic features (Chudeau 1921; Jaeger 1959; Jaeger

1966), but it is not geologically distinct from other parts of the massif. Structural features of the

sandstones comprising the massif result in predictably variable hydrogeology and erosion, which

drive landform development (Chudeau 1917; Daveau 1959; Larminat 1927; Michel 1973; Urvoy

1942).

The Manding Plateau comprises three sedimentary series overlying a weakly

metamorphosed granitic basement complex (DNGM 1992; Groupement Manantali 1979; Varlet

et al. 1977). Dolomite intrusions occur throughout the area. At a broad scale, perched aquifers

in rock fractures distinguish the sedimentary rocks from the igneous rocks, which hold little

groundwater internally (DCTD Chudeau 1917; DCTD 1990). However, the sedimentary series

include various rocks with differing structural characteristics, which cause perched aquifers to be

133 distributed in predictable locations across the landscape. Simplistically, these sedimentary rocks

can be divided into three broad groups. Most importantly, there are hard, well-cemented

sandstones—including Sandstone B—that form the distinctive cliffs of the Manding Plateau

(Varlet et al. 1977). These sandstones have low intergranular porosity, but hold much

groundwater in intercrossed bedding planes and strongly expressed vertical fractures (DCTD

1990; Groupement Manantali 1979). Second, in most areas, the hard sandstones overlie massive,

weakly cemented sandstones—including Sandstone 0—with indistinct, horizontal bedding

planes and uniform grain size. Although these sandstones have high intergranular porosity, they

lack well-developed networks of fractures that hold water, and serve mainly as barriers to

vertical throughflow, especially when saturated (Groupement Manantali 1979). Finally, in some

areas, thin layers of fine-grained rocks, mainly siltstone, interpose layers of the two broad types

of sandstone described above. These fine-grained rocks have low porosity and permeability, and

also serve as barriers to throughflow.

In short, barriers to vertical throughflow cause the hard, well-cemented sandstones to

serve as groundwater reservoirs. Groundwater flow at these barriers is horizontal; some water

may penetrate the underlying rock where fractures occur, but such infiltration is generally low

(Groupement Manantali 1979). Instead, the groundwater tends to scour tunnels along preexisting

fractures in the hard sandstone, thus increasing the strength of horizontal flow (cf. Dunne 1990).

Field observations suggest that oblique stratifications in Sandstone B direct groundwater flow to

portions of the rock with horizontal stratifications (cf. Campbell 1973; Dunne 1980; Schick

1965). In several cases, cliff faces were observed where past fragmentation followed vertical

fractures and the upper surface of strongly expressed stratifications, indicating a relative

hardness that would impede throughflow. Indeed, in all sites in the research area where

134 groundwater permanently seeps from Sandstone B outcrops, stratifications are horizontal, and

the strongest flow occurs at the uppermost stratum. Seepage erosion (sensu Dunne 1990) of the

hard sandstones results in steep, angular outcrops due to disaggregation along vertical fractures

and horizontal bedding planes. Many authors have described this geomorphic process

throughout the West African massif (e.g. Daveau 1959; de Chételat 1938; Jaeger & Jarovoy

1952; Larminat 1927; Michel 1973; Schnell 1960; Urvoy 1942). These authors have not

observed that disaggregation is most active at the heads of gorges with groundwater seepage.

Lithological features that concentrate groundwater flow—such as fractures, or horizontal

stratifications amid oblique strata—lead to the extension of spring heads through seepage

erosion, and to the formation of surface drainage networks (Campbell 1973; Dunne 1980). This

distribution indicates that erosion by groundwater has created the deep, narrow gorges in which

Gilletiodendron-Hippocratea forest often occurs (cf. Howard et al. 1988; Kochel et al. 1985).

Rupicolous bushland occurs in adjacent, less steeply sloped areas of exposed bedrock upslope of

a gorge head, where less runoff is available due to topography, but where plants adapted to

Sandstone B can access groundwater via vertical fractures.

Composition varies between Gilletiodendron-Hippocratea forest and rupicolous bushland

because the former hosts mesophytes (e.g. Sarcocephalus latifolius, Spondias mombin) and the

latter xerophytes (e.g. Pterocarpus lucens, Combretum glutinosum) that are not specially adapted

to the physical structure of Sandstone B. Both Axes 1 of the all-site and undisturbed-site

ordinations directly indicate correlation between species distribution and soil-moisture

conditions, because these axes represent floristic variation determined by hydrology (i.e.

landscape position). The second axes in these ordinations indirectly and only partly indicate

species response to soil-moisture availability, but directly indicate specialized adaptation to the

135 most distinct plant habitat across the landscape, Sandstone B outcrops. For instance, many of the

dominant species in Gilletiodendron-Hippocratea forest (Table 5, p. 165) have high, positive r-

values for the second axis of the undisturbed-site ordinations (Table 19, p. 179), even though

these species occur in forest, woodland, and bushland clusters. In contrast, Pterocarpus lucens,

indicative of rupicolous bushland, has a neutral r-value for the same axis (r=-0.006), but a

relatively high r-value for the first axis (r=-0.41), indicating its drought tolerance. Pterocarpus

lucens is not a sandstone specialist, but a species tolerant of dry soil conditions.

The weakly cemented, porous sandstones of the Manding Plateau absorb water in

interstices between grains. During the rainy season, these sandstones darken as they become

saturated: the shade of one Sandstone 0 outcrop in the research area changed from 10 R 6/4 to 10

R 4/4 between 20 May and 25 September 2004. Additionally, fractures and junctures between

strata, in which water may accumulate, are rare in these porous sandstones, as suggested by the

characteristically rounded shape of Sandstone 0 outcrops. Although dolomite is harder and less

porous, its hydrogeology is similar because it does not hold much groundwater in fractures

(DCTD 1990). Thus, there are no sites of permanent groundwater flow associated with dolomite

or Sandstone 0 in the research area. Seasonal seeps occur sparingly in Sandstone 0, but these

have discharge rates sufficient to support only algae, mosses, and small ferns.

Spondias–Cola–Bombax gallery forest (Cluster 1) occurs on colluvial slopes below

outcrops of all bedrock types. These sites also have elevated groundwater levels, but for

different reasons from that described above for Gilletiodendron-Hippocratea forest. Runoff is

channeled along the face of outcrops and collects underground along subsurface portions of the

bedrock, whether the rock is permeable or not (DCTD 1990). Vegetation composition differs

136 between Clusters 1 and 2 because plants that are specialized to the physical structure of

Sandstone B dominate Cluster 2 sites.

Bedrock geology does not account completely for the distribution of the

biogeographically notable species characteristic of Gilletiodendron-Hippocratea forest,

including Gilletiodendron glandulosum. Several sites with Sandstone B bedrock that appear to

be suitable for Gilletiodendron-Hippocratea forest instead host Spondias–Cola–Bombax forest

(Cluster 1). Fire and human activities certainly have roles in shaping the distribution of these

forest types, as do historical and ecological factors such as dispersal, climate variation, and

natural disturbance (Kellman & Meave 1997; Kellman & Miyanishi 1982; McCune & Allen

1985; Morison et al. 1948). However, past research that attributed the patchy distribution of

Gilletiodendron-Hippocratea forest to anthropogenic disturbance has not shown that this

vegetation type, or the species that comprise the vegetation, are singularly restricted to sites

inaccessible to fire and humans, regardless of other characteristics. The present research shows

that indicator species for Gilletiodendron-Hippocratea forest (Figure 2, p. 152) are restricted

almost uniquely to Sandstone B outcrops (Tables 4-18, pp. 164-178). These species occur in

easily accessible and fire-prone rupicolous bushland, and other types of vegetation occupy

inaccessible, narrow gorges in dolomite outcrops and ferricrete crusts.

Floristic patterns of human disturbance. All but one disturbed sites (110 of 111), as well

as 70 of 106 undisturbed sites, belong to the woodland/bushland cluster (Figure 2, p. 152).

Disturbed sites predominate in Clusters 11-15, but only Clusters 11, 13, and 15 are composed

entirely of disturbed sites (Table 1, p. 160). The effects of settlement and cultivation on

vegetation composition in the research area are difficult to characterize for three reasons. First,

the factor ‘human disturbance’ cannot be satisfactorily controlled in sampling design. No sites

137 were discovered that had deep, loamy soil, few rocks, and gentle slope, and no evidence of past

cultivation or settlement. There are probably very few such sites in Sudanian West Africa as a

whole (Schnell 1976; White 1983), and it is unjustifiable to present any Sudanian woodland

vegetation as ‘undisturbed’ without supporting historical evidence. Second, interpreting the

disturbed-site ordination (Figure 5, p. 158), which suggests sources of variation in composition

between disturbed sites, is difficult because the second two axes in this ordination do not

strongly correspond to any measured environmental factor. Finally, there is insufficient

information available on the ecology of many species to make strong conclusions about the

meaning of correlations between species distributions and disturbed-site ordination axes (Table

19, p. 179). Within these constraints, the results suggest that vegetation response to

anthropogenic disturbance is variable across the landscape.

Disturbed sites as a group, or sub-groups based on type of or time since disturbance, are

not homogenous (Table 2, p. 162). Most disturbed sites occupy the lower, central portion of all-

site ordination space, which represents sites with sandy to loamy soil, gentle slope, and mid-

slope topographical position (Figure 3, p. 154). However, disturbed sites are not closely grouped

in ordination space. Similarly, disturbed sites are only broadly grouped in the dendrogram

resulting from cluster analysis (Figure 2, p. 152). Human disturbance does not have a single,

unambiguous effect on vegetation composition.

The results show that the effects of cultivation and settlement on vegetation composition

vary across the landscape, depending on biophysical factors and land-use practices. Cultural

ecologists working in African woodlands have observed three responses to disturbance caused by

cultivation or settlement. All three of these responses are evident in the research area:

138 • Vegetation turnover occurs when disturbance increases the abundance of previously

uncommon species, often increasing plant diversity through intentional or

unintentional plant introduction. This outcome of disturbance has gained prominence

through Fairhead and Leach’s (1996) work in southern Guinea, in which they argue

that farmers purposefully create forest patches at settlement sites by planting or

protecting Guineo-Congolian rainforest species and thus increasing their abundance

relative to Sudano-Guinean woodland species. Amanor (1994) described similarly

teleological vegetation turnover in Ghana, while Avenard et al. (1974) and Spichiger

and Pamard (1973) in Côte d’Ivoire and Duvall in Mali (see Chapter 5) report

unintentional turnover (although the responsible farmers recognize and understand

this process). ‘Bush encroachment’—in which woody shrub abundance increases

relative to grasses in woodlands where early dry season fires become more frequent,

often due to livestock management practices—is also appears a form of vegetation

turnover (cf. Audru 1977; Bassett & Boutrais 2000; Bassett & Koli Bi 2000).

In the research area, vegetation turnover occurs primarily at ruins, but also in

some fallows. Fallows and ruins do not strongly segregate in cluster analysis (Table

1, p. 160; Figure 2, p. 152) or all-site ordination space (Figure 3, p. 154). This is

because fallows and ruins generally occupy environmentally similar sites—those with

relatively deep, fertile soil—where Sudanian woodland species dominate. However,

fallows and ruins weakly segregate in the disturbed-site ordination (Figure 5, p. 158),

because settlement and cultivation differ as ecological disturbances. Of course,

settlement and cultivation varies between individuals and over time, and each

settlement or field site has a distinct history. Thus, vegetation turnover does not

139 occur in all ruins, and may occur in some field sites. Dichrostachys-Ziziphus

bushland (Cluster 11: Table 14, p. 174)—the only cluster composed primarily of

abandoned settlement sites (Table 1, p. 160)—most clearly represents turned-over

vegetation, although vegetation composition in Clusters 12-13 (Tables 15-16, pp.

175-176) also suggests turnover.

Many species associated with vegetation turnover following settlement

disturbance are strongly, negatively correlated to the three axes of the disturbed-site

ordination (Table 19, p. 179), which also correlate strongly to past use (Figure 5, p.

158). Species associated with turnover have one or more of the following traits: a)

edible fruits (e.g. Ximenia americana, Annona senegalensis), b) high economic value

(e.g. Adansonia digitata, Moringa oleifera), or c) general adaptation to disturbance

(e.g. Dichrostachys cinerea, Ziziphus mauritiana: Devineau 2001, 2005; Arbonnier

2000). These traits suggest that two processes, both related to plant dispersal,

contribute to vegetation turnover.

The main cause of turnover is the colonization of disturbed sites by post-

disturbance specialists. Although vegetative reproduction is important in post-

disturbance succession in fallows in semi-arid West Africa (Nyerges 1989), it is less

important in ruins because settlement is a more intense disturbance than cultivation:

a) few trees and no sucker sprouts are spared in settlement clearings, while many

trees and sucker sprouts are maintained in fields; b) soil compaction is higher in

settlements than fields; and c) settlements are occupied for more years and during

more of each year than fields (see Chapter 2). This increases the importance of seed

dispersal in vegetation succession following settlement abandonment. While many

140 widely dispersed, woodland species remain abundant in settlement sites following

abandonment, vegetation in ruins is distinctive because species with high dispersal

potential—those adapted to colonizing woodland canopy openings—increase in

abundance. Colonizing species include anemochores (e.g. Pterocarpus erinaceus,

Combretum glutinosum) and zoochores (e.g. Lannea velutina, Acacia seyal) that have

relatively low economic value.

Second, humans contribute directly to vegetation turnover by increasing the

abundance of economically valuable species in settlement sites. While such species

are often highly visible in and qualitatively indicative of ruins, individually they

comprise only modest components in terms of vegetation structure. Vegetation in

ruins includes introduced, domesticated trees: Moringa oleifera was the only such

species observed in >5 sites, but mango (Mangifera indica) and cashew (Anacardium

occidentale) were also encountered. More important, though, are economically

valuable, African native trees, especially ronier palm (Borassus aethiopum) and

baobab (Adansonia digitata). These valuable, native species are regionally

widespread and do not appear to have narrow habitat preferences (Arbonnier 2000;

Breman & Kessler 1995; Lawesson 1995), but in the research area are rare outside of

settlements (see Chapter 5). The presence of these plants in ruins is due primarily to

human activities; all have human-dispersed seeds and high use values, and are

adapted to the edaphic conditions characteristic of settlement sites (see Chapter 5).

Vegetation turnover is relatively easy to recognize, because it is indicated by

the relative abundance of species that are uncommon elsewhere in the landscape. The

141 other two vegetation responses—homogenization and resilience—are more difficult

to recognize.

• Vegetation homogenization occurs when species number decreases following

disturbance, usually due to the elimination of rare species. This outcome of

disturbance has been recognized for decades (e.g. Aubréville 1947; Chevalier 1928)

and has been used to support the desertification and deforestation discourses that have

dominated aspects of natural resource management in Africa (cf. Bassett & Crummey

2003; Leach & Mearns 1996). Nonetheless, Devineau (2005) has shown

convincingly that homogenization occurs following cultivation in western Burkina

Faso, similar to earlier findings in Sierra Leone (Nyerges 1989), Benin

(Schreckenberg 1999), and Senegal (Lykke 1998).

In the research area, homogenization occurs primarily in agroecologically

marginal fallows, increasing the distinctness of fallows in the disturbed-site

ordination (Figure 5, p. 158). Terminalia-Pterocarpus-Combretum woodland

(Cluster 8: Table 11, p. 171) most clearly exemplifies homogenized vegetation—

although composition in Clusters 4-6 and 15 also suggests this process. Cluster 8 is

composed of eight undisturbed sites and seven fallows (Table 1, p. 160), and has low

plant diversity (Table 3, p. 163). None of the undisturbed sites are arable because of

infertile or shallow soil, while the disturbed sites are arable. That vegetation

composition in these sites is similar despite disturbance history suggests that human

disturbance has reduced plant diversity in the fallow sites (Devineau 2005). While

the fallow sites all have arable soil and gentle slope, they are considered poor for

farming because their soil is fairly shallow, very sandy or silty, or relatively infertile

142 (see Chapter 3). The undisturbed sites have even lower agroecological potential: they

are steeply sloped, and/or have rocky, silty, clayey, or shallow, infertile soil.

Maninka farmers do not generally consider such sites farmable. Many species

associated with vegetation homogenization following settlement are strongly,

positively correlated to the three axes of the disturbed-site ordination (Table 19, p.

179), which also correlate strongly to past use (Figure 5, p. 158). These species are

strongly associated with sandy loam and loamy sand (e.g. Terminalia macroptera,

Albizia malacophylla), or silty loam (e.g. Crossopteryx febrifuga, Strychnos spinosa),

further indicating that sites with poor, but arable, soil are most susceptible to

homogenization following disturbance. Other species indicative of homogenized

vegetation are associated with infertile soil, such as Combretum nigricans, Hexalobus

monopetalus, and Acacia ataxacantha (Arbonnier 2000). Agricultural practices that

deplete soil fertility to a greater degree than customary practice—including farming

on agroecologically marginal sites—may lead to vegetation homogenization (Nyerges

1989). Specific farming practices (e.g. length of cultivation, crops planted) were not

assessed for each fallow site, and would be difficult to assess for many sites.

In most cases, widespread generalists dominate homogenized vegetation (e.g.

Terminalia macroptera, Pterocarpus erinaceus, Combretum glutinosum),

contributing to low plant diversity. Homogenization results from reductions in the

abundance of uncommon or specialist species (Devineau 2005). This does not mean

that past human activities have homogenized all vegetation dominated by widespread

generalists. Large areas of woodland vegetation with no history of settlement or

cultivation occur in sites that are not arable. In the present research, undisturbed

143 woodland sites dominate the distinct, if heterogeneous, vegetation Clusters 4-7 (Table

1, p. 160). These sites (and others labeled ‘undisturbed’ in this paper) are not pristine

because they are exposed to low intensity and low frequency disturbance via hunting,

livestock grazing, wild plant harvesting, and wild honey collection, but such activities

do not move these sites a great distance toward the ‘humanized’ pole of the

disturbance continuum (cf. Vale 2002). The facile labels ‘derived woodland’ and

‘undifferentiated woodland’, which have been applied to Sudanian woodlands to

substitute for recognition of different woodland communities (Lawesson 1994;

Lawesson 1995), mask variability in human impacts on woody vegetation (cf. Laris

2002).

• Vegetation resilience exists when disturbance has little effect on species number.

Resilience has been reported in southern Africa’s Zambezian woodlands, where

species composition remains stable over time despite repeated clearing (Chidumayo

2004; McGregor 1994; Stromgaard 1986). Resilience of woody vegetation to

disturbance has not been reported from Sudanian woodland sites.

The fact that settlement and cultivation have ambiguous and equivocal effects

on vegetation composition suggests that Sudanian woodlands are resilient to

disturbance. While seed dispersal by humans and other vectors alters the abundance

of some species in some sites, the dominance of vegetative reproduction amongst

woodland trees enhances ecosystem memory and allows similar communities to

develop after disturbance as long as disturbance events do not exceed ecosystem-

specific intensity and frequency thresholds (Nyerges 1989). In other words,

144 vegetation turnover and homogenization may be relatively short-lived vegetation

responses to disturbance, at least in some cases.

In the research area, resilience is clearest in the composition of Pteleopsis-

Pterocarpus woodland (Cluster 14: Table 17, p. 177). Several species associated

with resilient vegetation have strong, positive correlation to the third axis of the

disturbed-site ordination (Table 19, p. 179), which correlates to past use (Figure 5, p.

158). Such species include Pterocarpus erinaceus, Bombax costatum, and

Anogeissus leiocarpus, which have long been considered indicative of “climax”

vegetation in the Sudanian woodland zone (e.g. Aubréville 1949a; Aubréville 1949b;

Chevalier 1938; Chevalier 1933; Schnell 1976; White 1965; White 1983). These

species are abundant in both old and new fallows (and ruins) that have resilient

vegetation.

Vegetation resilience is also suggested more generally in the disturbed-site

ordination. Age-classes of ruins and fallows are moderately clustered along the third

axis of this ordination, and the oldest disturbed sites—whether fallows or ruins—tend

to occupy the central portion of ordination space (Figure 5, p. 158).

The effects of settlement and cultivation on vegetation composition are ambiguous and

equivocal, and the present results do not prove that vegetation turnover, homogenization, or

resilience has occurred in any specific sample site or vegetation cluster. Additional research

designed to assess specifically the effects of human disturbance on particular plant communities

will be necessary to describe actual vegetation change in the research area. In contrast, the

present research was meant to contextualize anthropogenic vegetation by situating it in the

broader context of vegetation variation across the focal landscape. Furthermore, these

145 observations do not apply to herbaceous vegetation, which varies independently of woody

vegetation (Devineau 2005).

While much research remains in order to describe actual vegetation change in the

research area, these results underscore that anthropogenic vegetation change in Sudanian West

Africa is more complex than often represented. First, vegetation composition in ruins and

fallows is not identical, because settlement and cultivation differ ecologically as disturbances.

Few cultural ecologists working in West Africa have studied settlement as a distinct land use

(see Chapter 2), and ecological understanding of rural settlements as woody plant habitat lags

behind that of fields and fallows. Second, although specific vegetation clusters are cited above

as indicative of each of the three vegetation responses, compositional data for most clusters that

include many disturbed sites could be interpreted to suggest more than one response to

disturbance. The direction and intensity of anthropogenic vegetation change varies between sites

for social and ecological reasons (Vale 1982), and ecologically similar sites may express

different vegetation responses if socioeconomic factors caused farming or settlement practices to

differ, even subtly, between sites (Nyerges 1989). This aspect of disturbance was not assessed in

the present research. Finally, observation of specific vegetation responses may be time-

dependant, if responses endure only for limited times following disturbance. The present results

suggest that turnover and homogenization may be short-lived, although the length of time a site

shows these responses probably varies between sites.

Keeping in mind the complexity and variability of vegetation response to disturbance

suggested here, the different responses to disturbance previously observed for African woodland

sites may reflect different approaches to studying vegetation change as much as cultural

ecological differences between research sites. Researchers who have observed similar vegetation

146 responses to disturbance in different sites have studied vegetation change in similarly limited

portions of focal landscapes, and have focused on similar groups of species. Studies that have

found vegetation resilience have focused on mean vegetation attributes in fallows (Chidumayo

2004; McGregor 1994; Stromgaard 1986). Mean vegetation attributes are determined largely by

widespread, generalist species. In contrast, newer studies that show or suggest vegetation

homogenization have focused on the abundance of uncommon, specialist species (e.g. Devineau

2005; Lykke 1998; McCune & Grace 2002; Nyerges 1989; Schreckenberg 1999). Finally,

studies that have shown vegetation turnover have relied primarily on sampling in ruins or

intensively managed fields, and not in less intensively disturbed fields and fallows (Amanor

1994; Fairhead & Leach 1996; Spichiger & Pamard 1973). Vegetation response across focal

landscapes is likely more complex than suggested by studies of specific, limited areas. However,

narrowly focused studies are necessary to reduce complexity and allow precise description of

actual vegetation change.

Conclusion

Vegetation in southwestern Mali has interested botanists since the 1930s because the

endemic tree Gilletiodendron glandulosum has been viewed as evidence for recent

anthropogenic deforestation (Duvall 2003). However, this dominant representation of

Gilletiodendron–Hippocratea forest neglected Chudeau’s (1917) earlier explanation that

provided a possible biophysical explanation of Gilletiodendron’s endemism and patchy

distribution. The present research provides reason to resurrect the scientific discourse linking

plant geography and bedrock hydrogeology that has been largely forgotten in the Africa context.

This approach to understanding plant geography provides a fresh perspective for understanding

how humans have affected vegetation composition in West Africa.

147 For instance, the case of Gilletiodendron-Hippocratea forest contrasts clearly with the

better-known case of Polylepis forests in the Andes, another narrowly endemic habitat. The

patchy distribution of Polylepis forest was considered natural, associated with specific types of

terrain, until research in the 1980s showed that these patches are not restricted to a narrow range

of topographic or edaphic situations (Purcell et al. 2004). Instead, biogeographic analysis has

shown that Polylepis patches survive only in sites that are topographically protected from fire,

grazing, and human activities (Fjeldså 2002). In contrast, this research shows that the

biogeographically notable species in Gilletiodendron-Hippocratea forest are restricted to a

narrow range of sites with specific hydrogeological characteristics, and are not uniquely found in

topographically protected sites. Gilletiodendron-Hippocratea forest and rupicolous bushland

should be considered edaphic vegetation types, and not remnants of past deforestation. The

dominant spatial structure of vegetation variation in the research area is attributable to edaphic

variation, especially the hydrogeology of Sandstone B. Settlement and cultivation result in

observable changes in vegetation characteristics, but only in those parts of the landscape with

edaphic and hydrological conditions suitable for agriculture.

The biogeography of Gilletiodendron-Hippocratea forest, and Sudanian West Africa

more generally, must be understood in a global context. The bedrock-vegetation link described in

the present paper helps create high-diversity patches of vegetation around the world, particularly

mesic refugia in semi-arid areas (Bowman et al. 1990; Bowman et al. 1988; Danin 1999; Davis

1951; Walck et al. 1996; Woodford 2000). In short, the protective topography created by sharply

angular, hard sandstone outcrops, and ultimately the resilient hydrogeology of these outcrops,

create small patches of highly stable habitat that allows mesophytes to survive despite climate

desiccation and human disturbance (Larson et al. 2000). Hard, sandstone outcrops must be more

148 generally recognized as important refuges for paleoendemics (Larson et al. 2000)—exemplified

by Wollemi pine (Wollemia nobilis W.G. Jones, K.D. Hill & J.M. Allen), a Tertiary relict

discovered in Australia in 1994 (Woodford 2000). Recognition of the biogeographic importance

of hard sandstone outcrops has significant implications for biodiversity conservation. Other

parts of the West Africa sandstone massif also host a high number of biogeographically notable

and endemic plants (e.g. Jaeger & Winkoun 1962; Porembski & Brown 1995; Schnell 1960).

Management strategies that focus on protecting sites, rather than restricting activities, should be

more effective to conserve these plants and the vegetation that hosts them (Danin 1999; Larson

et al. 2000; Maxted et al. 1997). Better understanding of hydrogeology and biogeography in

hard sandstone massifs is necessary for understanding how dispersed networks of refugial

patches—not just linear riparian forest patches (cf. Meave & Kellman 1994; Meave et al.

1991)—buffer the impacts of climate change on biodiversity.

149

Figures, tables, and appendix for Chapter Three

150 Figure 1. Maps of West Africa, western Mali, and the research area. Map 1 shows West Africa

including the West African sandstone massif (after Jaeger & Winkoun, 1962), and the location of

the area shown in map 2. Map 2 shows western Mali, including the location of the research area.

Map 3 shows the research area, including the location of Solo.

151

152 Figure 2. Dendrogram produced by cluster analysis. Similarity scale given at top. Physiognomic

categories shown at the left of dendrogram follow Lawesson’s (1995:24) definitions. Average

between-site distance given as x. For indicator values (IVs), p < 0.001 unless another value is

given.

153

154 Figure 3. All-site NMS ordination. Cluster numbers (in ordination plot at right) refer to

vegetation clusters represented in Figure 2 (p. 152) and described in Tables 4-18 (pp. 164-178).

155

156 Figure 4. Undisturbed-site ordination. Abbreviations: bushl. = bushland; woodl. = woodland.

For species abbreviations, see Appendix 1 (p. 180).

157

158 Figure 5. Disturbed-site NMS ordination.

159

160 Table 1. Environmental factors. Abbreviations: n=sites per environmental factor;

undist.=undisturbed (no evidence of past clearing); woodl.=woodland; bushl.=bushland;

cult.=cultivated; ya=years ago; sett.=settled. Pélite (French) means ‘fine-grained sedimentary

rock’. In some analyses, factors were simplified by lumping categories. Thus, ‘permanent

water’ lumped the first four ‘landscape position’ categories and maintained the fifth. The

following factors were measured quantitatively: ‘past use’ (by time since abandonment, lumping

the first three categories, and others according to time, not use), ‘substrate texture’ (by estimating

mean grain size for soil or colluvial rock, and considering bedrock the greatest grain size), and

‘slope’ (by degrees). ‘Landscape position’ was quantified by estimating average distance per site

to drainage channel or permanent spring (after IGN 2001 and field data); ‘bedrock’ was

quantified by ranking rocks by increasing hardness (i.e. pélite, ferricrete, Sandstone 0, Sandstone

B, dolomite).

161

Environmental factor n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Forest, undist. 34 13 11 10 Woodl., undist. 52 2 7 9 6 4 8 8 8 Bushl., undist. 20 4 6 10 Cult. ≤10 ya 17 1 1 2 1 2 3 3 4 Cult. 10-20 ya 14 2 1 2 3 6 Cult. 20-30 ya 14 2 2 1 5 4 Cult. ≥30 ya 16 1 1 4 2 1 7 Sett. ≤10 ya 5 3 2 Sett. 10-20 ya 7 3 1 2 1 Sett. 20-30 ya 12 1 5 2 3 1 Sett. 30-40 ya 11 1 3 4 1 1 1

Past

use

Sett. ≥40 ya 15 1 1 1 1 7 1 1 2 Clayey loam 18 2 2 1 10 1 3 Silty loam 34 4 2 6 6 3 2 2 3 6 Loam 30 2 2 1 5 4 4 1 5 6 Sandy loam, sand 87 5 1 2 2 5 2 24 12 15 19 ≥25% cobbles 11 4 2 1 2 1 1 ≥25% boulders 15 7 2 4 1 1 Su

bsra

te te

xtur

e

≥25% bedrock 22 2 11 3 6 Topo. high 97 4 1 2 8 12 10 2 11 6 4 11 2 16 8 Topo. low 54 2 2 4 2 16 7 9 12 Any plot ≤50m from channel

41 2 3 1 1 2 10 2 9 5 1 5

All plots ≤100m from channel

16 2 3 10 1

Hyd

rolo

gy

Any plot ≤100m from spring

9 5 4

dolomite 10 1 1 1 1 2 1 1 ferricrete 79 2 1 2 9 6 4 6 10 5 10 1 15 10 pélite 2 1 3 1 1 Sandstone 0 28 1 3 8 4 2 8 B

edro

ck

Sandstone B 98 10 11 10 6 3 7 6 6 15 8 7 7 ≤2° 132 8 3 8 8 3 8 7 34 12 19 22 ≤5° 31 3 1 3 1 2 3 7 2 1 2 3 3 ≤10° 9 3 1 2 3 Sl

ope

>10° 45 10 10 2 6 3 3 5 1 1 4

162 Table 2. MRPP results. Statistics: T=test statistic, more negative values indicating greater

between-group heterogeneity; A=agreement statistic, within-group homogeneity increases as A

approaches 1; p=statistical significance.

Grouping basis T A P Cluster results -69.07 0.71 <0.001 Binary: disturbed/undisturbed -61.99 0.16 <0.001 Binary: presence/absence of permanent water -53.06 0.14 <0.001 Sites grouped by disturbance type, time since disturbance, and substrate texture; undisturbed sites further grouped by vegetation structure -46.43 0.42 <0.001 Substrate texture (as shown in Table 1, p. 160) -44.48 0.29 <0.001 Landscape position (as shown in Table 1, p. 160) -39.64 0.21 <0.001 Slope categories (as shown in Table 1, p. 160) -31.48 0.15 <0.001 Disturbed sites grouped by time since disturbance; undisturbed sites lumped -30.30 0.18 <0.001 Disturbed sites grouped by time since and type of disturbance; undisturbed sites lumped -25.56 0.21 <0.001 Geological parent material (as shown in Table 1, p. 160) -10.98 0.07 <0.001

163 Table 3. Vegetation cluster characteristics. Abbreviations: med.= median; dist.=between-site

distance (Sørenson [Bray-Curtis] index). Cluster numbers correspond to those shown in Figure 2

(p. 152). Berger-Parker diversity index equals the proportional abundance of the most abundant

species per site.

Cluster Number of

individuals Number of species

Number of families

Berger-Parker diversity index

Mean dist.

Total sites

Total area

All sites

Per site (±SD)

All sites

Per site (±SD)

All sites

Per site (±SD)

Range Med. (m2)

1 1237 95.2 (±37.4)

80 19.5 (±6.2)

34 14.8 (±3.6)

0.12-0.52

0.19 0.27 13 13,300

2 1357 121.4 (±27.6)

53 17.2 (±5.0)

31 13.6 (±3.1)

0.17-0.35

0.23 0.04 11 11,000

3 1096 84.3 (±39.5)

64 13.9 (±5.7)

29 11.2 (±3.7)

0.30-0.82

0.59 0.15 12 12,000

4 339 33.1 (±16.7)

43 11.5 (±4.3)

21 7.5 (±2.5)

0.12-0.46

0.31 0.22 9 9,000

5 679 56.6 (±17.3)

49 17.5 (±2.9)

22 12.0 (±1.8)

0.14-0.47

0.27 0.18 12 12,000

6 457 32.7 (±13.3)

63 12.9 (±3.5)

31 8.9 (±2.7)

0.18-0.39

0.25 0.41 14 14,000

7 210 49.5 (±9.0)

22 12.0 (±1.8)

11 7.0 (±1.4)

0.16-0.26

0.21 0.03 4 4,000

8 1188 81.6 (±41.5)

45 12.9 (±4.4)

22 8.9 (±3.1)

0.23-0.66

0.4 0.07 15 15,000

9 208 32.5 (±14.1)

29 11.7 (±3.9)

20 9.2 (±3.1)

0.17-0.43

0.22 0.14 6 6,000

10 653 66.3 (±15.6)

37 14.6 (±2.1)

16 8.8 (±1.6)

0.27-0.55

0.33 0.08 10 10,000

11 796 81.1 (±33.4)

53 15.8 (±3.7)

27 11.1 (±2.5)

0.21-0.56

0.37 0.14 9 9,000

12 2031 56.4 (±18.1)

80 13.7 (±3.7)

33 9.6 (±2.5)

0.19-0.55

0.30 0.14 36 36,000

13 1071 76.5 (±29.5)

53 15.9 (±3.2)

25 10.9 (±2.4)

0.13-0.40

0.22 0.15 14 14,000

14 2474 91.9 (±33.7)

75 19.5 (±5.0)

33 12.2 (±2.8)

0.13-0.38

0.23 0.10 26 26,000

15 2843 114.2 (±35.7)

64 16.0 (±3.9)

32 11.2 (±2.5)

0.19-0.76

0.44 0.08 25 25,000

164 Table 4. Dominant species for Cluster 1 (Cola cordifolia–Spondias mombin–Bombax costatum

forest). For species abbreviations, see Appendix 1 (p. 180). Proportional abundance=individuals

per species divided by total individuals. Relative frequency=sites per species divided by total

sites. Basal dominance=basal area (m2) per hectare. All species shown having proportional

abundance ≥0.05 or relative frequency ≥0.50. Top five species shown for basal dominance.

Proportional abundance

Relative frequency

Basal dominance

Boco 0.17 Spmo 1.00 Spmo 2.08 Spmo 0.09 Dime 0.92 Oxab 2.04 Dime 0.07 Coco 0.92 Coco 1.37 Oxab 0.07 Comi 0.77 Stku 1.20 Grbi 0.06 Boco 0.77 Comi 0.99 Coco 0.05 Oxab 0.69 Grbi 0.62 Sala 0.62 Stku 0.62 Feap 0.62 Algl 0.54 Maal 0.54

165 Table 5. Dominant species for Cluster 2 (Gilletiodendron glandulosum–Hippocratea indica

forest). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Gigl 0.23 Gigl 1.00 Gigl 11.55 Hiin 0.19 Hiin 1.00 Boco 2.26 Boco 0.09 Grbi 1.00 Spmo 2.25 Grbi 0.08 Comi 0.91 Oxab 1.76 Sase 0.07 Spmo 0.91 Addi 1.48 Comi 0.05 Boco 0.82 Sase 0.82 Sala 0.73 Oxab 0.73 Coto 0.73 Boan 0.73 Diab 0.64 Stsa 0.64 Gyam 0.55 Dime 0.55

166 Table 6. Dominant species for Cluster 3 (Oxytenanthera abyssinica [Bamboo] forest). For

explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Oxab 0.54 Oxab 1.00 Oxab 22.82 Xest 0.05 Sala 0.75 Ersu 0.52 Rasu 0.05 Pter 0.67 Khse 0.31 Fisu 0.58 Rasu 0.27 Khse 0.50 Cepe 0.18 Rasu 0.50 Anle 0.50 Sase 0.50

167 Table 7. Dominant species for Cluster 4 (Xeroderris stühlmannii–Pterocarpus erinaceus–

Bombax costatum wooded grassland). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Xest 0.19 Pter 1.00 Addi 2.15 Boco 0.15 Xest 0.89 Pter 1.83 Pter 0.13 Boco 0.89 Oxab 1.32 Oxab 0.05 Cogl 0.89 Boco 1.14 Tema 0.78 Xest 0.76 Oxab 0.56 Hemo 0.56

168 Table 8. Dominant species for Cluster 5 (Ferricrete wooded grassland). For explanation of table

contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Oxab 0.16 Pter 1.00 Oxab 15.98 Cogl 0.14 Cogl 0.92 Pter 1.31 Pter 0.07 Laac 0.83 Boco 1.03 Acat 0.06 Oxab 0.75 Cogl 0.68 Boco 0.67 Lave 0.68 Demi 0.67 Tema 0.67 Hemo 0.67 Lave 0.58 Acat 0.58 Stsp 0.58 Vima 0.58 Buaf 0.50 Xest 0.50 Doqu 0.50 Selo 0.50

169 Table 9. Dominant species for Cluster 6 (Combretum nigricans–Hexalobus monopetalus

wooded grassland). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Hemo 0.12 Cogl 0.93 Addi 2.60 Coni 0.09 Hemo 0.71 Pter 1.20 Cogl 0.08 Pter 0.64 Anle 0.96 Pter 0.06 Coni 0.50 Hemo 0.38 Tema 0.50 Laac 0.31

170 Table 10. Dominant species for Cluster 7 (Crossopteryx febrifuga–Gardenia ternifolia wooded

grassland). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Tela 0.19 Buaf 1.00 Pter 3.13 Crfe 0.12 Crfe 1.00 Demi 2.74 Gate 0.12 Demi 1.00 Laac 2.70 Cogl 0.10 Enaf 1.00 Tela 2.47 Demi 0.09 Gate 1.00 Buaf 1.44 Ptsu 0.07 Laac 1.00 Pter 0.06 Tela 1.00 Daol 0.05 Cogl 0.75 Daol 0.75 Ptsu 0.75

171 Table 11. Dominant species for Cluster 8 (Terminalia macroptera–Pterocarpus erinaceus

woodland). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Tema 0.46 Tema 1.00 Oxab 4.64 Pter 0.14 Pter 1.00 Tema 3.20 Cogl 0.05 Cogl 0.87 Pter 2.79 Vipa 0.87 Laac 1.38 Xest 0.73 Copi 0.62 Laac 0.60 Stsp 0.53 Boco 0.53

172 Table 12. Dominant species for Cluster 9 (Rupicolous bushland). For explanation of table

contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Comi 0.15 Ptlu 1.00 Ptlu 0.74 Hemo 0.10 Cogl 0.83 Cogl 0.42 Eusu 0.09 Pter 0.83 Gyam 0.35 Cogl 0.09 Boan 0.67 Grbi 0.24 Gigl 0.09 Comi 0.67 Comi 0.16 Pter 0.06 Eusu 0.67 Grbi 0.06 Grbi 0.67 Ptlu 0.06 Hemo 0.67 Hiin 0.05 Boco 0.50 Gigl 0.50 Gyam 0.50 Hiin 0.50 Lami 0.50

173 Table 13. Dominant species for Cluster 10 (Pterocarpus lucens–Guiera senegalensis bushland).

For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Ptlu 0.29 Cogl 1.00 Ptlu 1.45 Cogl 0.16 Ptlu 1.00 Oxab 0.75 Comi 0.11 Acat 0.80 Cogl 0.65 Acat 0.06 Hemo 0.80 Afaf 0.42 Hemo 0.05 Laac 0.80 Xiam 0.35 Boan 0.60 Casi 0.60 Guse 0.60 Pter 0.60 Tela 0.60 Comi 0.50 Coni 0.50 Copi 0.50 Daol 0.50 Stsa 0.50 Xiam 0.50

174 Table 14. Dominant species for Cluster 11 (Dichrostachys cinerea–Ziziphus mauritiana

bushland). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Dici 0.33 Dici 1.00 Addi 4.88 Zima 0.13 Zima 0.89 Oxab 1.44 Cogl 0.10 Cogl 0.78 Boae 1.16 Pter 0.06 Pith 0.78 Zima 0.76 Pith 0.05 Pter 0.78 Fisy 0.58 Alma 0.05 Acse 0.56 Addi 0.56 Alma 0.56 Boae 0.56 Doqu 0.56 Hemo 0.56 Tema 0.56 Vipa 0.56

175 Table 15. Dominant species for Cluster 12 (Acacia ataxacantha–Combretum micranthum–

Hexalobus monopetalus woodland). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Pter 0.30 Pter 1.00 Addi 7.99 Cogl 0.11 Cogl 0.94 Pter 3.83 Zima 0.06 Tema 0.69 Oxab 3.29 Pith 0.05 Vipa 0.67 Anle 1.21 Dici 0.05 Pith 0.56 Pith 0.56 Tema 0.05 Zima 0.50

176 Table 16. Dominant species for Cluster 13 (Terminalia macroptera–Vitellaria paradoxa–

Piliostigma thonningii woodland). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Tema 0.15 Tema 1.00 Addi 5.35 Pter 0.13 Pith 1.00 Oxab 3.00 Cogl 0.10 Cogl 0.93 Vipa 1.78 Vipa 0.07 Pter 0.93 Pter 1.19 Pith 0.06 Vipa 0.93 Khse 0.99 Dici 0.06 Lave 0.86 Alma 0.06 Praf 0.64 Tela 0.05 Alma 0.57 Praf 0.05 Anse 0.57 Boco 0.57 Dici 0.57 Ptsu 0.50 Selo 0.50 Zima 0.50

177 Table 17. Dominant species for Cluster 14 (Pteleopsis suberosa–Hymenocardia acida

woodland). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Pter 0.18 Cogl 1.00 Pter 5.73 Ptsu 0.13 Ptsu 1.00 Oxab 2.55 Cogl 0.11 Pter 1.00 Tema 1.54 Tema 0.09 Lave 0.85 Addi 1.48 Hyac 0.05 Tema 0.85 Vipa 1.44 Hyac 0.81 Vipa 0.81 Crfe 0.65 Demi 0.62 Anse 0.58 Boco 0.58 Buaf 0.58 Pith 0.54 Stsp 0.54 Xest 0.54 Laac 0.50 Tela 0.50

178 Table 18. Dominant species for Cluster 15 (Pterocarpus erinaceus–Vitellaria paradoxa–

Piliostigma thonningii woodland). For explanation of table contents, see Table 4, p. 164.

Proportional abundance

Relative frequency

Basal dominance

Pter 0.36 Cogl 1.00 Pter 10.91 Cogl 0.23 Pter 1.00 Oxab 3.56 Pith 0.06 Vipa 0.84 Vipa 2.34 Vipa 0.05 Lave 0.76 Addi 1.88 Tema 0.76 Cogl 1.82 Pith 0.64 Zima 0.56 Ptsu 0.52 Alma 0.48 Hyac 0.48

179 Table 19. Correlations between species distributions and ordination axes. The highest and

lowest five Pearson’s r correlation coefficients for each axis of the three NMS analyses are

shown. For Pearson’s r correlation coefficients for all species in all ordinations, see Appendix 1

(p. 180).

All-site ordination Undisturbed-site ordination Disturbed-site ordination

Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 3 Ptlu -0.39 Pter -0.42 Cogl -0.58 Grbi -0.69 Laac -0.56 Zima -0.58 Zima -0.51 Hemo -0.38 Cogl -0.37 Ptlu -0.41 Gigl -0.63 Addi -0.40 Dici -0.41 Pith -0.50 Crfe -0.37 Vipa -0.36 Laac -0.40 Hiin -0.61 Hemo -0.36 Addi -0.38 Dici -0.39 Laac -0.35 Lave -0.33 Hemo -0.38 Spmo -0.58 Xiam -0.34 Fisy -0.33 Anse -0.37 Demi -0.31 Zima -0.31 Tela -0.37 Gyam -0.50 Mool -0.32 Acse -0.31 Mool -0.32 Ptsa 0.28 Sala 0.51 Ersu 0.43 Vipa 0.36 Cole 0.29 Crfe 0.44 Boco 0.24 Fisu 0.28 Hiin 0.51 Coto 0.46 Crfe 0.39 Alma 0.36 Demi 0.47 Pter 0.34 Sala 0.33 Gigl 0.54 Sala 0.54 Buaf 0.41 Praf 0.40 Stsp 0.47 Stsp 0.37 Khse 0.34 Grbi 0.63 Oxab 0.55 Tema 0.46 Pith 0.46 Hyac 0.50 Oxab 0.38 Oxab 0.35 Spmo 0.65 Spmo 0.57 Pter 0.53 Tema 0.54 Ptsu 0.56 Anle 0.47

180 Appendix 1. Species abbreviations and correlation coefficients for ordination axes.

Abbreviations: spp.=species; Ax1, etc.=Axis 1, etc.; undist.=undisturbed (no evidence of past

settlement or cultivation). No correlation coefficients are provided for species that occurred in

<5 disturbed or undisturbed sites. Nomenclature follows Hutchinson and Dalziel (1954-1972) or

Geerling (1982).

181

All-site

ordination Undist.-site ordination

Disturbed-site ordination

Spp. Ax1 Ax2 Ax1 Ax2 Ax1 Ax2 Ax3 codes r r r r r r r Full species name Acat -0.29 -0.07 -0.34 0.17 -0.25 0.16 0.09 Acacia ataxacantha DC. Acse 0.17 -0.20 0.07 -0.31 -0.20 Acacia seyal Del. Addi 0.16 -0.21 -0.40 -0.38 -0.22 Adansonia digitata L. Afaf -0.17 -0.03 Afzelia africana Smith ex Pers. Alco 0.11 0.32 0.24 -0.24 Allophyllus cobbe (L.) Raeusch Algl 0.25 0.30 0.36 -0.18 Albizzia glaberrima (Schum. ex Thonn.) Benth. Alma 0.15 -0.23 0.36 -0.10 -0.18 Albizzia malacophylla (A.Rich.) Walp. Anaf 0.13 0.15 Antiaris africana Engl. Anle 0.17 -0.07 0.11 0.15 0.07 -0.15 0.47 Anogeissus leiocarpus (DC.) Guill. & Perr. Anse -0.10 -0.28 -0.15 0.29 0.15 0.30 -0.37 Annona senegalensis Pers. Bamu 0.02 0.06 0.09 -0.04 -0.18 0.00 0.10 Baissea multiflora A.DC. Boae 0.14 -0.20 -0.27 -0.28 -0.27 Borassus aethiopum Mart. Boan -0.17 0.44 -0.02 -0.43 Boscia angustifolia A.Rich. Boco 0.11 0.41 0.33 -0.37 0.23 0.32 0.24 Bombax costatum Pellegr. & Vuillet Brfe 0.15 -0.04 Bridelia ferruginea Benth. Brmi 0.04 0.00 Bridelia micrantha (Hochst.) Baill. Buaf -0.30 -0.15 -0.30 0.41 -0.03 0.42 0.23 Burkea africana Hook. f. Casi -0.25 -0.01 -0.20 0.07 -0.02 0.16 0.20 Cassia sieberana DC. Cepe 0.20 0.31 Ceiba pentandra (L.) Gaertn. Cipo 0.18 0.28 0.31 -0.33 0.16 -0.09 -0.06 Cissus populnea Guill. & Perr. Coco 0.25 0.45 0.42 -0.33 Cola cordifolia (Cav.) R.Br. Cogl -0.12 -0.37 -0.58 0.30 0.07 0.09 -0.01 Combretum glutinosum Perr. ex DC. Cole 0.14 -0.15 0.29 0.01 -0.21 Combretum lecardii Engl. & Diels Comi -0.18 0.36 -0.11 -0.43 Combretum micranthum G.Don Como -0.11 -0.06 Combretum molle R.Br. ex G.Don Comy 0.22 0.15 0.29 -0.11 Cordia myxa L. Coni -0.21 0.14 -0.20 0.01 0.10 0.08 0.20 Combretum nigricans Lepr. ex Guill. & Perr. Copi -0.24 -0.17 -0.34 0.31 -0.22 0.14 0.00 Cordyla pinnata (Lepr. ex A.Rich.) Milne-Redhead Coto 0.27 0.50 0.46 -0.38 Combretum tomentosum G.Don Crfe -0.37 -0.20 -0.34 0.39 -0.10 0.44 0.01 Crossopteryx febrifuga (Afzel. ex G.Don) Benth. Daol -0.21 -0.13 -0.26 0.16 -0.25 0.36 -0.10 Daniellia oliveri (Rolfe) Hutch. & Dalz. Demi -0.31 -0.18 -0.32 0.34 0.05 0.47 -0.03 Detarium microcarpum Guill. & Perr. Diab 0.12 0.39 0.27 -0.36 Diospyros abyssinica (Hiern) F.White Dici 0.18 -0.25 -0.12 -0.41 -0.39 Dichrostachys cinerea (L.) Wight & Arn. Dime 0.23 0.42 0.42 -0.37 Diospyros mespiliformis Hochst ex A.DC. Doqu -0.04 -0.13 -0.07 0.20 -0.14 0.06 -0.01 Dombeya quinqueseta (Del.) Exell. Enaf -0.20 -0.16 -0.33 0.28 -0.16 0.31 -0.08 Entada africana Guill. & Perr. Ersu 0.28 0.37 0.43 -0.18 Erythrophleum suaveolens (Guill. & Perr.) Brenan Eusu -0.16 0.17 -0.16 -0.15 Euphorbia sudanica A.Chev. Feap 0.03 0.26 0.17 -0.24 Feretia apodanthera Del. Fiab 0.14 0.38 0.31 -0.33 Ficus abutifolia (Miq.) Miq. Fico -0.03 0.26 Ficus cordata Thunb. Fidi 0.08 -0.11 Ficus dicranostyla Mildbr. Figl 0.05 0.14 0.09 -0.14 0.04 -0.04 -0.26 Ficus glumosa Del. Fisu 0.28 0.26 0.38 -0.09 Ficus sur Forssk. Fisy 0.27 0.01 0.07 -0.33 -0.15 Ficus sycomorus L. Gate -0.30 -0.16 -0.32 0.30 -0.19 0.13 -0.13 Gardenia ternifolia Schum.

182 Gigl 0.06 0.54 0.25 -0.63 Gilletiodendron glandulosum (Port.) J.Léonard Grbi 0.06 0.63 0.30 -0.69 Grewia bicolor Juss. Grfl 0.00 -0.11 -0.07 0.17 -0.24 -0.01 -0.16 Grewia flavescens Juss. Grla -0.05 -0.02 0.02 0.09 0.02 0.00 0.20 Grewia lasiodiscus K. Schum. Guse -0.24 0.01 -0.28 -0.04 Guiera senegalensis J.F.Gmelin Gyam 0.02 0.43 0.15 -0.50 Gyrocarpus americanus Jacq. Hemo -0.38 0.04 -0.38 0.07 -0.36 0.27 -0.08 Hexalobus monopetalus (A.Rich.) Engl. & Diels Hiin 0.07 0.51 0.24 -0.61 Hippocratea indica Willd. Hyac -0.19 -0.18 -0.21 0.20 0.10 0.50 0.00 Hymenocardia acida Tul. Isto 0.22 0.26 0.32 -0.14 Isoberlinia tomentosa Craib & Stapf Khse 0.34 0.17 0.37 0.00 Khaya senegalensis (Desr.) A.Juss. Laac -0.35 -0.14 -0.40 0.27 -0.56 0.33 -0.03 Lannea acida A.Rich. Lami 0.05 0.26 0.14 -0.20 Lannea microcarpa Engl. & K.Krause Lave -0.10 -0.33 -0.14 0.33 0.07 0.37 -0.06 Lannea velutina A.Rich. Lola 0.16 -0.02 0.11 0.06 0.11 -0.22 0.15 Lonchocarpus laxiflorus Guill. & Perr. Maal 0.18 0.37 0.30 -0.29 Malacantha alnifolia (Bak.) Pierre Mase -0.04 -0.22 -0.01 0.17 -0.20 Maytenus senegalensis (Lam.) Exell. Mool 0.13 -0.17 -0.32 -0.21 -0.32 Moringa oleifera Lam. Oxab 0.35 0.27 0.55 0.09 -0.30 0.00 0.38 Oxytenanthera abyssinica (A.Rich.) Munro Pabi 0.07 -0.11 0.02 -0.03 -0.16 Parkia biglobosa (Jacq.) Benth. Pacu -0.01 -0.10 0.18 0.03 -0.22 Parinari curatellifolia Planch. ex Benth. Pela -0.22 -0.09 -0.15 0.23 Pericopsis laxiflora Harms. Pith 0.12 -0.23 0.04 0.23 0.46 -0.09 -0.50 Piliostigma thonningii (Schum.) Milne-Redhead Praf 0.07 -0.23 -0.12 0.13 0.40 0.17 -0.14 Prosopis africana (Guill. & Perr.) Taub. Psse 0.03 -0.11 Psorospermum senegalense Engl. & Diels Pter 0.03 -0.42 -0.23 0.53 0.15 0.03 0.34 Pterocarpus erinaceus Poir. Ptlu -0.39 0.08 -0.41 -0.06 Pterocarpus lucens Lepr. ex Guill. & Perr. Ptsa 0.28 0.22 0.35 -0.05 Pterocarpus santalinoides L'Hér. ex DC. Ptsu -0.20 -0.25 -0.24 0.31 -0.01 0.56 -0.10 Pteleopsis suberosa Engl. & Diels Rasu 0.26 0.07 0.23 0.09 Raphia sudanica A.Chev. Sala 0.33 0.51 0.54 -0.38 Sarcocephalus latifolius (Smith) Bruce Sase 0.16 0.35 0.28 -0.37 0.01 -0.14 -0.15 Saba senegalensis (A.DC.) Pichon Scbi 0.01 -0.05 0.08 -0.01 0.06 Sclerocarya birrea (A.Rich.) Hochst. Selo -0.15 -0.21 -0.25 0.20 0.22 0.38 -0.10 Securidaca longipedunculata Fresen. Spmo 0.28 0.65 0.57 -0.58 Spondias mombin L. Stku 0.26 0.32 0.39 -0.21 0.20 0.13 -0.01 Stereospermum kunthianum Cham. Stsa -0.07 0.34 0.02 -0.29 Strophanthus sarmentosus DC. Stse -0.18 -0.02 -0.12 0.09 Sterculia setigera Del. Stsp -0.25 -0.15 -0.22 0.30 0.03 0.47 0.37 Strychnos spinosa Lam. Tasp -0.09 -0.12 0.00 -0.06 -0.01 Tapinanthus sp. Tela -0.18 -0.18 -0.37 0.23 0.26 0.34 -0.16 Terminalia laxiflora Engl. Tema -0.12 -0.21 -0.18 0.46 0.54 0.16 0.20 Terminalia macroptera Guill. & Perr. Vido 0.27 0.16 Vitex doniana Sweet Vima -0.19 -0.14 -0.20 0.30 0.04 0.39 0.22 Vitex madiensis Oliv. Vipa 0.04 -0.36 -0.04 0.36 0.09 0.18 -0.08 Vitellaria paradoxa Gaertn. f. Xest -0.01 0.04 0.11 0.22 0.04 0.30 0.01 Xeroderris stühlmannii (Taub.) Mendonça & E.P.Sousa Xiam -0.23 -0.18 -0.31 0.18 -0.34 0.19 -0.12 Ximenia americana L. Zima 0.24 -0.31 -0.16 -0.58 -0.51 Ziziphus mauritiana Lam. Zimu 0.19 0.05 0.14 0.01 Ziziphus mucronata Willd.

183 Chapter Five: Human settlement and baobab distribution in southwestern Mali

Abstract

Researchers have long assumed that human settlement establishment and reproduction of

the baobab tree (Adansonia digitata) are spatially and temporally dependent because baobabs are

abundant in many settlement sites in semi-arid Africa. This paper tests the spatiotemporal

relationship between baobab and settlement distribution.

In a study area of 183 km2, 1240 baobabs were located and mapped, their diameters

measured, and habitat characteristics recorded for each individual. Second, all occupied (n=7)

and abandoned (n=80) settlements were located and mapped, and occupation dates for each site

were determined through interviews. Ethnographic observations revealed local knowledge of

human-baobab interaction. Chi-square tests indicated baobab habitat preferences, and bivariate

point-pattern analysis tested independence of observed point patterns of baobabs and settlements,

including paired point sets consisting only of certain settlement age-classes and baobab size-

classes.

Statistical analyses support local knowledge of baobab-settlement attraction.

Specifically, baobabs and settlements are attracted at most distances and for most baobab size-

class–settlement age-class pairs. This attraction is significant only at distances of less than c.500

m. Young settlements are not significantly associated with large baobabs. Attraction between

small and large baobabs is marginally significant at distances of less than c.500 m, but observed

significance is less than that observed for attraction between baobabs and settlements.

There are five main conclusions: 1) Human settlement and baobab recruitment are

spatially dependant. 2) Settlement leads directly and indirectly to the development of baobab

184 groves at settlement sites. 3) Human activities cannot account for baobab presence in many parts

of the landscape. 4) Although the baobab occurs in a wide range of habitats, its recruitment and

mortality are not evenly distributed across these habitats. Recruitment is strongest in

settlements, fields, and cliffs or steep slopes along rock outcrops, while mortality is highest on

cliffs or steep slopes. 5) These habitat preferences suggest that baobab abundance in settlements

is not caused simply by human seed dispersal, but also by other aspects of settlement practice

that ensure dry, fire-protected settlement sites.

Introduction

Long-term conservation and management of biodiversity resources depend on our ability

to situate human activities within ecosystem processes (Micheli et al. 2001). Due to the urgency

of addressing short-term threats to biodiversity resources, research on anthropogenic

environmental change has focused on human activities having obvious effects at relatively short

timescales, such as years to decades. However, in semi-arid Africa, humans have actively

manipulated plant populations through management practices for millennia (O'Brien & Peters

1998; van der Veen 1999). Through much of the 20th century, recognition of this long history of

resource management by African farmers and herders was expressed in terms that assigned

blame for supposed environmental destruction on the supposed destructiveness of indigenous

practices (Bassett & Crummey 2003; Fairhead & Leach 1998; Leach & Mearns 1996; Richards

1985). Dominant narratives of widespread, anthropogenic deforestation in West Africa have

proven inaccurate or unsupportable (Bassett & Koli Bi 2000; Duvall 2003; Fairhead & Leach

1996; Ribot 1999). Nonetheless, land management practices do alter vegetation (Maranz &

Wiesman 2003; Nyerges 1989; Nyerges 1997; Schreckenberg 1999; Turner 1998a; Turner

185 1998b). The emerging challenge is to develop land-use ecologies that account for observed

variation in vegetation characteristics without resorting to oversimplified or inappropriate

explanatory frameworks (Turner 2000).

Humans are significant factors in the regeneration of many wild plant populations

through activities that affect seed dispersal and germination (Maranz & Wiesman 2003; O'Brien

& Peters 1998). In situations where markets exert strong influence on fruit use, humans may

overharvest fruits and remove seeds from ecosystems, negatively impacting reproduction of

target species and plant-frugivore interactions (Moegenburg 2002; Peres et al. 2003; Pruetz

2002). Where market influence is weaker, humans disperse seeds within ecosystems by

harvesting and consuming wild fruits, which serve as important dietary items for people and

domestic animals. In such situations, wild fruit seeds accumulate at settlement sites (Reid &

Ellis 1995; van der Veen 1999). Fruit processing—including selection, cleaning, cooking, and

digestion—destroys some seeds, but also may promote germination in seeds that are not

destroyed (Esenowo 1991; Johansson 1999; von Maydell 1992). Additionally, people often

select and manage self-sown wild fruit trees in occupied and abandoned settlements and fields

(Assogbadjo et al. 2005; Lovett & Haq 2000; Maranz & Wiesman 2003). As a result of fruit use

and fruit tree management, wild fruit trees often dominate vegetation in fields, fallows, and

settlements. In areas where these practices are combined with relatively intensive land use, in

which most arable land is actively managed and fallow periods are fairly short, ‘savanna

parkland’ vegetation, dominated by economically important trees, develops (Boffa 1999; Etkin

2002; Neumann et al. 1998; Pullan 1974; Raison 1988).

Although regionally widespread and characteristic of much of northern sub-Saharan

Africa, parkland vegetation does not occur in many landscapes where land use has been

186 primarily extensive (Boffa 1999; Pullan 1974). Extensive farming has long been, and remains,

important throughout semi-arid Africa (Raynaut 1997). In landscapes with long histories of

land-extensive farming, the cumulative effects of human activities on indigenous plant

distributions are unclear. There are well-worn assumptions about the destructiveness of land-

extensive agriculture, but these are often poorly founded on empirical assessment of

agroecological dynamics (Dove 1983; Richards 1985). Humans have altered plant distributions

at global, continental, regional, and local scales, but at local scales—across landscapes measured

in tens of kilometers—there is poor understanding of the biogeographic and ecological processes

that lead to human-plant associations (Binggeli 1996; Cronk & Fuller 2001). The present paper

uses point pattern analysis of tree and settlement distribution to assess the role of shifting

settlement on the distribution of the baobab, a highly valued fruit tree, in southwestern Mali.

Focal species

The African baobab (Adansonia digitata L.) occurs in most of tropical Africa’s woodland

areas, and its high utility as a source of food and various raw materials causes it to be one of the

most salient trees for rural Africans (Dhillion & Gustad 2004; Kristensen & Lykke 2003; Owen

1970). Similarly salient to botanists due to its characteristic growth habit, the tree has attracted

scientific attention for centuries; literature on the baobab is extensive (reviews include: Simpson

1995; reviews include: Wickens 1982). Nonetheless, knowledge of the tree’s ecology is

remarkably thin (Assogbadjo et al. 2006; Assogbadjo et al. 2005; Baum 1995; Bowman 1997;

Ebert 2006; Simpson 1995; Wickens 1982). Considering the tree’s large size, including large

flowers and fruit, and gregariousness in many areas, the baobab’s significance in African

ecosystems is certainly underrepresented in present ecological knowledge.

187 The baobab appears to be an important link between the social and ecological processes

that together create the effects of human activities on African ecosystems. Baobab has been part

of agroforestry systems in West Africa for at least 1000 years (Kahlheber 1999; Neumann et al.

1998). Agricultural management practices and uses of baobab fruit, leaves, and seedlings affect

the viability of baobab sub-populations (Assogbadjo et al. 2005; Baumer 1994; Dhillion &

Gustad 2004; Johansson 1999). Indeed, baobabs dominate vegetation in and around settlements

in many parts of semi-arid Africa (Boffa 1999; Enjalbert 1956; Rosevear 1937; Seignobos 1980;

Wickens 1982). Indeed, abandoned settlements are often identifiable as baobab groves (Hobley

1922; Perron 1926; Sikes 1972; Taylor 1960).

Apparent attraction between baobabs and settlements could arise in three ways. First,

humans appear to be an important dispersal vector for the tree (Assogbadjo et al. 2006; Chevalier

1906; Dhillion & Gustad 2004; Guy 1971; Hobley 1922; Ridley 1930), so that the abundance of

baobabs at settlement sites may result from fruit use. Second, humans may choose to establish

settlements at preexisting baobabs for improved access to the resources these trees represent

(Wickens 1982). Place names suggest that this relationship may be widespread. For instance,

dozens of settlements named “Sitakòtò”—a Manding-language locative noun meaning ‘under the

baobab tree’—exist from Senegal to Burkina Faso (Office of Geography 1965a; 1965b; 1965c;

1966; 1965d). Third, settlement-baobab attraction may arise simply because human settlements

and baobabs thrive in edaphically and topographically similar locations: the tree is most common

on deep, well-drained soils that are also good for farming (Barnes 1980; Johansson 1999;

Simpson 1995; Wilson 1988). In any case, researchers have not shown that human-baobab

interactions are, indeed, spatially dependent.

188 Studies of baobab dispersal and reproduction have focused on individuals propagated or

protected by humans in humanized vegetation, particularly settlement, field, and fallow sites

(Dhillion & Gustad 2004; Johansson 1999), or have not explicitly considered land-use

characteristics (Assogbadjo et al. 2006; Assogbadjo et al. 2005; Barnes 1980; Wilson 1988).

Human activities are important in baobab reproduction, but it is necessary to contextualize

human-baobab interactions by studying the tree’s ecology throughout focal landscapes. Scant

information exists on its distribution or abundance in non-humanized vegetation, although

several authors have identified several non-human dispersal agents, including large primates like

chimpanzees (Pan troglodytes) and baboons (Papio spp.) (Ridley 1930; Wickens 1982). The

baobab has no clear habitat or soil preferences, although it is generally most abundant on deep,

sandy, well drained soils in arid to semi-arid woodlands (Simpson 1995; Wilson 1988). The

tree’s aboriginal range may have been only drier parts of sub-Saharan Africa; its presence in

more humid areas may result ultimately from human introduction, although it reproduces without

human intervention and occurs in apparently undisturbed vegetation in semi-arid to sub-humid

areas (Wickens 1982). There is no information on how the tree’s autecology, its interaction with

non-human pollination and dispersal agents, and human activities together create spatial patterns

of abundance in specific landscapes.

Research area

Research was conducted in an area of 183 km2 around Solo village in Mali’s Bafing

Biosphere Reserve (Figure 1, p. 212). The research area approximately represents the area

where residents of Solo, and no other villages, have traditional usufruct (although this customary

tenure has no legal recognition). Solo has been occupied for about 400 years (Samaké et al.

189 1986), and Solo’s current residents retain oral history of settlement for the last c.200 years (see

Chapter 2).

Biophysical setting. The research area lies in Mali’s Manding Plateau, where numerous

sandstone outcrops rise 100-200 m above surrounding lowlands. The research area is bisected

by the edge of a large sandstone plateau, with similar vegetation occurring above and below the

plateau edge (Figure 1, p. 212). Vegetation is characteristic of White’s (1983) Sudanian regional

center of endemism, dominated by woodland but with dispersed patches of forest, scrub, and

grassland. The distribution of most vegetation types is linked to specific edaphic conditions:

forest types occur in sites with moist soil conditions; woodlands and bushlands occur on

relatively dry and deep, arable to non-arable soil; and grasslands occupy shallow soil and

seasonally flooded areas (see Chapter 4 and Breman & Kessler 1995; Lawesson 1995). Human

activities have ambiguous and equivocal effects on vegetation composition, but more clearly

affect the distribution of some species (see Chapter 4). The abundance of useful trees, including

baobab, is positively correlated with disturbance caused by settlement and cultivation.

Elephants, which cause elevated baobab mortality in parts of East Africa (Barnes 1980;

Barnes 1985; Caughley 1976; Weyerhaeuser 1985), were extirpated in the research area in about

1984, and were never abundant in the living memory of local residents (Duvall & Niagaté 1997).

All parts of the landscape are subject to at least low intensity or low frequency

disturbance although evidence of anthropogenic disturbance is not readily apparent in many

areas (Duvall 2001). The indigenous Maninka people settle and cultivate lowland sites with

arable soil and good drainage. Rocky areas, steep slopes, sites with poor or shallow soil, and

seasonally inundated areas are used only for seasonal livestock grazing, wild plant and honey

collection, and hunting (Duvall 2001; Samaké et al. 1987). Around settlements, farmers

190 annually burn most grassland and woodland areas to prevent destructive fires and to prepare

fields (Laris 2002). Wild fruits and other plant products provide important food resources

(Samaké et al. 1987). During field clearing and subsequent management, farmers preserve

individuals of several tree species with edible fruits, including baobab (Koenig & Diarra 1998;

Samaké et al. 1987).

Farming is seasonal and rain-fed. Although farming practices vary between farmers and

over time, individual fields are generally cultivated <10 years before fallowing >10 years

(Samaké et al. 1987). Arable soil is patchily distributed (PIRT 1983), and farmland is limited

immediately around Solo. Thus, many farmers improve their access to farmland by establishing

farming hamlets some distance from Solo, but within Solo’s area of traditional usufruct (see

Chapter 2). Most hamlets are occupied only during the farming season and only for relatively

short time periods—in most cases, <20-30 years—before abandonment (Samaké et al. 1986).

Hamlets are usually occupied by only a small number of related, nuclear families who return to

Solo after a hamlet is abandoned, and often establish other hamlets after some time in Solo. The

practices of hamlet establishment and abandonment represent a shifting settlement system (cf.

Stone 1996). Hamlet farming has probably been practiced in the research area for at least several

hundred years, but has become increasingly important over the last century. Conservationists

working in Mali consider hamlet farming a spatially uniform and destructive threat to natural

habitat in the area (e.g. Caspary et al. 1998; PREMA 1996), but the cultural ecology of hamlet

farming has not been studied.

Methods

191 Data collection. Field research was conducted January-December, 2004, and entailed: 1)

an ethnographic study of baobab-human interaction; 2) a census of baobab individuals, and 3) a

census of settlement sites.

Ethnographic study. Ethnographic research was conducted during January-December

2004 to understand the cultural ecology of settlement around Solo. Participant observation

provided experiential knowledge of human-baobab interactions, while informal interviews

clarified observations (Werner & Schoepfle 1987). Informal interviews were conducted by the

researcher in the Maninka language, while working with farmers in fields, fallows, and

settlements. Ethnographic data was analyzed in a qualitative manner (cf. Cotton 1996).

Baobab census. The baobab census was based on interviews of local residents, and foot

surveys. Only baobabs >1 m high were tallied. Interviews of c.45 people—males and females,

c.10-80 years old—indicated the approximate locations of c.1000 baobabs. With the assistance

of interviewees and other residents, the researcher located these baobabs. Additionally, local

residents assisted the researcher in locating all abandoned settlement sites, which were searched

for baobabs by walking a spiral path from the center of the site to a distance from the center of

about 200 m. In occupied settlements, baobabs were located in collaboration with residents.

Every occupied and abandoned settlement was visited and searched at least twice. Finally, foot

surveys were conducted in areas: a) similar to those where many baobabs had already been

observed; and b) where no baobabs or abandoned settlements were known. These foot surveys

extensively covered the research area, including all drainage channels, edges of bedrock

outcrops, edges of ferricrete hardpans, and known, active or abandoned paths. These efforts did

not locate every baobab in the research area; the smallest DBH classes are certainly

underrepresented. Nonetheless, a conservative estimate is that ≥95% of individuals were

192 located. For statistical analyses, the baobabs identified in the research area are considered a

mapped, rather than sampled, point pattern (Bailey & Gatrell 1995).

The location of each baobab was recorded using a Garmin GPS 12XL unit, and every tree

was observed directly by the researcher. For each tree, the following data were collected:

diameter at breast height (DBH), and habitat. DBH was measured using a diameter tape 5m

long. For individuals with circumference at breast height >5m, circumference was measured

using a 50-m tape, and diameter was determined arithmetically. Each individual >1m DBH

(except those hosting beehives) was measured twice, during a single visit; the mean of the two

measurements was recorded. The DBH of inaccessible individuals (e.g. on cliff ledges) was

visually estimated. ‘Habitat’ was simplified as comprising three environmental factors (Table 1,

p. 226): a) topography, recorded either as slope angle (measured with an inclinometer), or as

drainage channel, for individuals occurring along seasonal or permanent waterways; b) land use

(i.e. occupied settlement or field, abandoned settlement or field, or no history of past use),

determined from observable features or interview data (see Chapter 4); and c) vegetation,

determined by structure (i.e. forest, woodland, scrub, thicket), with occupied fields and

settlements lumped together as a single class. Additional vegetation characteristics (e.g. bamboo

thicket, cliff-side gallery forest) provide specific information about vegetation ecology based on

a concurrent vegetation sampling effort (see Chapter 4).

No age estimates for baobabs were made because: a) DBH measurements were taken

throughout the year, and baobab DBH varies significantly with short-term rainfall variation

(Fenner 1980); b) age estimates from baobab DBH measurements do not appear to be robust in

most cases (Johansson 1999); and c) local informants knew the ages of very few trees

independently of the estimated ages of associated settlements or fields. However, DBH is used

193 as a general proxy for age because small individuals are considered younger than larger

individuals (cf. Bowman 1997).

Settlement census and history. A census of occupied and abandoned settlements was

based mainly on interviews. Abandoned settlements were identified during three group

interviews of Solo’s traditional authorities (chief, land chief, and senior counselors), all men

aged c.40-80 years. Aerial photographs from 1952 and historical documents did not identify any

additional settlement sites. With the assistance of interviewees, the researcher visited all

occupied and abandoned settlement sites, sampled vegetation in sites that had not been

subsequently farmed or occupied (see Chapter 4), and searched each site for baobabs. This data

set is also considered a mapped point pattern (Bailey & Gatrell 1995).

The location of each site was determined using a Garmin GPS-12XL unit, and was

recorded as a point corresponding to the approximate center of the occupied area of each site (as

evidenced by the distribution of huts or remains of hut foundations).

For every site, the following information was collected: site name, estimated dates of

establishment and abandonment, and subsequent use (i.e. cultivation, occupation, or

subsequently undisturbed by cultivation or occupation). Generally, establishment and

abandonment dates were estimated by correlating informant life history markers, changes in site

occupation status, and datable events, such as national elections. Multiple informants were

interviewed to triangulate date estimates and increase precision (cf. Flowerdew & Martin 1997).

In some cases, specific dates of past site occupation were gathered from published documents

(Anonymous 1958; de Lannoy de Bissy 1882; Park 1954 [1815]; Projet Inventaire 1990) or

aerial photos from 1952. Since the goal of the present analysis is to determine if baobabs in a

194 settlement site precede or follow human occupation, only establishment dates were used in

analyses.

Although age estimates were established for the establishment and abandonment of each

settlement, these age estimates were not used precisely in analyses because comparable estimates

were not possible for observed baobabs. Instead, as with baobabs, the settlement data set was

divided into quintiles based on estimated date of settlement establishment (Table 2, p. 227).

Data analysis. Two analyses were used. First, baobab habitat preferences were assessed

using chi-square tests (cf. Bowman 1997). Based on the size of each population quintile (248

individuals) and the number of environmental factor categories (Table 1, p. 226), the expected

and observed numbers of baobabs per quintile per factor were compared (Bailey 1995).

Second, the spatial independence of distribution patterns for baobabs and settlements was

tested using Ripley’s bivariate K function for several subsets of the data. There are numerous

technical descriptions of the bivariate K function, which is calculated from the observed number

of points (such as baobab individuals) in a given distance, h (e.g. Bailey & Gatrell 1995; Diggle

2003; Dixon 2002; Haase 1995). In application, the K function is generally linearized to

stabilize variance and facilitate interpretation, and in this form is called the L function (Dixon

2002). Since the purpose of the present application is to examine the interaction of two point

processes, significance testing was based on random toroidal shifts of the observed point patterns

(Dixon 2002). Similar use of the bivariate K function to test spatial independence of two

distribution patterns include Camarero et al. (2005), Eccles et al. (1999), Pélissier (1998), and

Couteron and Kokou (1997).

Spatial analyses were conducted using the SPLANCS package (version 2.01) in the R

statistical software environment (version 2.2.1). For further description of SPLANCS, see

195 Rowlingson and Diggle (1993), Gatrell et al. (1996), Bivand and Gebhardt (2000), and Diggle

(2003).

Multiple pairs of baobab and settlement point sets were used in bivariate L-function

analyses (Table 2, p. 227). For each pair of baobab and settlement point sets, the bivariate L

function was calculated using the equation L(h)=√((K(h))/π) – h (Diggle 2003), with edge effects

corrected geometrically (Bailey & Gatrell 1995). Values of L(h)>0 indicate attraction between

types of points, while L(h)<0 indicates repulsion (Dixon 2002). There is no method for assessing

significance for empirical L(h) values from non-rectangular study areas using toroidal shifts, and

the present study area is not rectangular. Thus, significance of empirical LBaobab•Settlement(h) values

for the study area as a whole was not determined. Instead, three rectangular areas within the

study area were designated in a post hoc manner to allow significance testing (Figure 2, p. 214).

Bivariate analyses were conducted for each pair of baobab and settlement point sets in these

rectangles; empirical LBS(h) values were compared with an envelope derived from 99 random

toroidal shifts of data points (cf. Bailey & Gatrell 1995; Diggle 2003; Dixon 2002).

Results

Settlement practice and baobabs. Maninka farmers recognize that settlement leads to

increased abundance of wild fruit trees, including baobab, at settlement sites. These increases

are considered to result from interactions between tree species and humans, livestock, and wild

animals. Maninka farmers clearly recognize the consequences of human activities on baobab

abundance, but do not consider increased baobab abundance a planned outcome of settlement

practice.

Human-baobab interaction centers on the high material value of baobab products.

Baobab fruit and leaves are probably the most frequently used wild foods. Women use baobab

196 fruit for flavoring breakfast porridge by dissolving the dry, sour pulp in hot water, then straining

out the seeds and fiber embedded in the pulp. Briefly soaking seeds in boiling water stimulates

germination (Esenowo 1991). Baobab seeds, discarded in rubbish mounds, are extremely

abundant in the soil of occupied settlements and may remain dormant for many years (Baum

1995; Hobley 1922; Palmer & Pitman 1972), as illustrated in Figure 3 (p. 216). During fruiting

season (November-January), successful women collect and store hundreds of fruits. Women also

seasonally collect large quantities of young baobab leaves, which are powdered and used in

preparing sauces for grain dishes, and men collect, harvest, and process baobab bark to make

rope throughout the year. Due to the importance of these uses, men and women individually

know the precise location of dozens of baobab trees, and often manage highly valued trees away

from settlements or fields by clearing grass to reduce a tree’s vulnerability to fire damage.

The baobab’s non-material meanings increase its sociocultural significance. First,

baobabs maintain history by physically representing past settlement (cf. Peluso 1996). Historical

primacy is an important basis for socially granted resource-use rights (cf. Shipton 1994), which

help determine an individual’s access to natural resources (Ribot & Peluso 2003). Baobab trees

may be named for the individuals who originally claimed rights to harvest their produce, usually

by protecting self-sown trees. Baobab groves are recognized as, and called by the name of,

settlements that indicate a familial group’s historical primacy in a given area. Second, baobabs

carry spiritual meanings. Baobabs are associated with beneficent spirits who may help advance a

human’s desires, and thus are places where people may safely invoke spiritual assistance without

exposing one’s wishes to malevolent spirits. Nonetheless, humans must interact cautiously with

baobabs to avoid offending associated, benevolent spirits and risking their retribution.

197 These material, historical, and spiritual meanings all contribute to a strong prohibition

against killing baobabs. When clearing vegetation for fields or settlements, baobab is one of

three species—with Vitellaria paradoxa C.F. Gaertn., and Parkia biglobosa (Jacq.) R.Br. ex

G.Don—that informants describe as never being felled. However, new settlements are rarely

established under preexisting baobabs, because harvesting rights for most large trees in arable

sites have been determined previously. Maninka tree tenure—which has no legal recognition—

is based upon a tree’s value and its origin in relation to human activities. Individual usufruct for

trees of low-value or highly abundant species are generally not recognized, while individual

usufruct for high-value trees are recognized if a person can reasonably claim to have been crucial

to a tree’s establishment. Thus, men and women protect self-sown baobab saplings in fields or

settlements in order to gain heritable harvesting rights. Nonetheless, since baobab trees fruit

abundantly only after several decades (>30 years, according to Baumer 1983)—that is, after its

human ‘owner’, and often his/her children, are able to harvest the fruit—many baobabs are

openly accessible resources even though their historic harvesting rights are respected in many

situations, especially settlement and field establishment. Men establishing settlements avoid

conflicts with preexisting rights to ensure that they will have unquestioned rights to any trees that

sprout in their new settlement.

Maninka settlements are habitat patches where wild fruit seeds accumulate and may find

suitable conditions for germination. Settlement sites are selected based on several biophysical

criteria that are determined through examination of topography, soil, and vegetation

characteristics. The most significant criteria in terms of tree ecology are: a) deep, well drained

soil, ideally sandy loam; b) good surface drainage; and c) the water table is shallow enough to

access via hand-dug wells (i.e. <15-20 m). Sites with shallow or fine-grained soil, or poor

198 drainage are rejected for settlement because they are unsuitable for most crops, and because it is

difficult to maintain mud buildings on moist soil. In establishing a settlement, male occupants

clear all vegetation, except for about one moderately sized tree per family, to provide shade, and

any Vitellaria, Parkia, or baobab individuals. Sites with very large trees are generally avoided,

because they are difficult to fell, can attract pest birds, and pose a potential hazard during

windstorms. During site occupation, people, aided by livestock, continuously clear herbaceous

vegetation (to reduce snake and rodent habitat), and cut volunteers of low-value tree species (to

reduce bird habitat). Volunteers of highly valued trees are spared, but generally not watered or

protected with fencing. Browsing livestock, and leaf and bark harvesting severely injure and

stunt most baobabs near settlements (cf. Arbonnier 2000). Although innumerable baobab seeds

germinate in rubbish heaps each rainy season few survive; successful saplings are those that

happen to grow in protected microenvironments (Figs. 3b, 3c). Informants recognize that baobab

abundance increases after settlement abandonment because trees are no longer subject to human-

and livestock-caused injuries. Other trees, particularly Vitellaria and Borassus aethiopum Mart.,

show increases in abundance similar to baobab, according to informants.

Following site abandonment, baobabs grow quickly to become primarily sources of fruit

rather than leaves or bark. Their growth, along with the growth of other trees surviving site

occupation, initiates interactions between plants and animals that Maninka men, in their

gendered role as hunters, recognize as transforming abandoned settlements into sites with an

abundance of wild fruit trees (Figure 3d, p. 216). First, large trees shade the soil and create

relatively cool and moist microenvironments (cf. Belsky et al. 1993). Second, chimpanzees and

baboons frequently visit fruiting baobabs during the early dry season (November-January).

While foraging, these animals trample the grass under baobabs they visit, which inhibits fire

199 from burning immediately under these trees. As a result, tree seedlings under productive

baobabs are often spared from fires throughout the dry season. Large baobabs serve as nurse

trees (cf. Simpson 1995), especially for plants with seeds dispersed by wild frugivores—

including Ficus spp., Tamarindus indica L., Spondias mombin L., and baobab.

Ethnographic evidence further suggests that chimpanzees and baboons are important

dispersal agents for baobab seeds. Hunters believe that most large baobabs at abandoned

settlement sites are the result of human activities. However, they believe that nearly all small

baobabs outside of occupied settlements and fields sprout from chimpanzee or baboon scat. This

belief is based on the widely shared observations of Maninka hunters that: a) chimpanzees and

baboons more frequently consume baobab fruit than any other wild animal; b) baobab seeds

occur in chimpanzee and baboon feces; and c) small baobabs are abundant in habitats, especially

along cliffs, where chimpanzees and baboons are most frequently seen, and where humans rarely

visit.

Baobab population structure. The baobab census identified 1240 individuals, a density

of 6.69/km2 in the research area (Figure 2, p. 214). This density is similar to those reported

elsewhere in Sudanian Africa: 5.0/km2 for northern Benin (Assogbadjo et al. 2005), 10.7/km2 for

central Mali, and 11.2/km2 for southern Sudan (Wilson 1988). Other published density estimates

range upward to 72.8/km2 (Weyerhaeuser 1985), with the highest densities occurring in northern

Tanzania.

The largest individual observed had a DBH of 455.0 cm, while 52 individuals were

observed with DBH ≤1 cm. Notably, the average DBH of observed trees declined with the

number of trees observed (Figure 4, p. 218), even though observations began in the dry season,

when baobab DBH shrinks due to water loss, and continued through the rainy season, when

200 baobab DBH increases due to water gain (Fenner 1980). A histogram of DBH distribution

(Figure 5, p. 219) suggests a Type III survivorship curve (Deevey 1947), which is characteristic

of r-selected species, including most trees (Barbour et al. 1999; Pianka 1970). Similar size-class

distributions have been reported for sampled baobab populations in Tanzania (Barnes 1980;

Barnes 1985; Weyerhaeuser 1985). Elsewhere, baobab populations have exhibited bell-shaped

size-class distributions (Assogbadjo et al. 2005; Caughley 1976; Johansson 1999; Wilson 1988).

Habitat characteristics. Baobabs were observed in sites having a range of slope,

vegetation, and land-use characteristics. However, baobabs are not evenly distributed with

respect to the observed range of habitats; the tree is most abundant in occupied and abandoned

settlements and fields. Additionally, some size classes—especially the largest and smallest

individuals—are significantly overabundant or underabundant in certain habitats (Table 1, p.

226). Recruitment and mortality are unevenly distributed. In particular, small baobabs are

significantly overabundant in occupied settlements, occupied fields, and cliff faces (Figs. 3, 6),

and underabundant on moderate slopes and in woodland vegetation. Large baobabs are

significantly overabundant in old fields, and underabundant in occupied settlements and on cliff

faces.

Baobab spatial ecology. Habitat preferences revealed through chi-square analysis

account for the patchiness of baobab distribution. Several large clusters occur throughout the

research area, particularly at settlement sites (Figure 2, p. 214). These dense, broadly ovate

clusters contrast clearly with the smaller number of generally linear and sparsely occupied

clusters located along the edges of rock outcrops. Different size classes have different

distributions across the landscape, but all size classes display a similar patchiness (Figure 2, p.

214). There are also several large gaps in baobab distribution that correspond to gaps in

201 settlement distribution, but not all gaps in settlement distribution—such as rock outcrops—

correspond to gaps in baobab distribution.

Large baobabs and small baobabs show attraction over all distances analyzed (Figure 7,

p. 222). However, this attraction is significant only at distances less than c.300 m, and only for

two of the three rectangular sub-regions. Observed significance is marginal in all cases.

Baobab-settlement spatiality. Baobabs and settlements are attracted at nearly all spatial

scales for all pairs of data sets examined (Figure 8, p. 224). However, observed attraction is

significant only at distances less than c.500 m. Strongly significant attraction is observed in the

distribution of all baobabs in relation to all settlements. Decreasingly significant attraction is

evident in the distribution of: a) large baobabs and old settlements; b) small baobabs and young

settlements; and c) small baobabs and old settlements. Finally, the locations of large baobabs

and young settlements displayed essentially no significant attraction; in the Rectangle 3 sub-

region, attraction between these data sets shows minimal significance. The statistical

significance of baobab-settlement attraction is, for most pairs of data sets, considerably stronger

than that observed for attraction between large and small baobabs (Figs. 7, 8).

Finally, spatial relationships between baobabs and settlements vary between rectangular

sub-regions (Figure 8, p. 224). In some portions of the study area, particularly Rectangle 2,

baobabs and settlements display repulsion over intermediate distances. For large baobabs and

young settlements, this repulsion is marginally significant at a distance of about 2 km.

Discussion

Baobab habitat. Baobabs occur in a wide range of habitats in the research area, but

abundance and reproductive success are not uniform across this range (Table 1, p. 226).

Baobabs are most abundant in occupied and abandoned settlements and fields. The tree is also

202 relatively abundant on cliffs and steep slopes (Figure 6, p. 220), even though previous authors

have made only passing mention that it occurs in such habitat (Mullin 1992: 66; Wickens 1982:

188). Of course, a conclusion based primarily on observation of large individuals would be that

baobabs are rare on cliffs and steep slopes: large individuals are significantly underabundant in

such habitats (Table 1, p. 226). Cliffs and steep slopes are geomorphically unstable, and baobab

mortality due to substrate collapse and rock fall appears to be high and positively correlated to

tree size (cf. Larson et al. 2000).

Small trees can be difficult to see in many situations, but time spent searching increases

the rate at which they may be found (Figure 4, p. 218). Indeed, small baobabs are frequently

encountered along cliffs and steep slopes, and cliff-side gallery forest hosts nearly as many small

baobabs as occupied settlements and fields (Table 1, p. 226). Baobab abundance in settlement

and field sites and the overabundance of small baobabs along cliffs and steep slopes indicates: a)

the importance of primates, including humans, as seed dispersers; b) the tree’s adaptation to dry

soil conditions; and c) the possible intolerance of small baobabs to fire.

Non-human primates disperse baobab seeds (Wickens 1982), and baboons and

chimpanzees may account for the tree’s abundance on rock outcrops. In the fruiting season

(November-January), baboons are frequently observed eating fallen baobab fruit, and frequently

travel along and rest on cliffs (Duvall, unpublished data). Chimpanzees and their nests are most

frequently encountered near large, fruiting baobabs and along cliffs from November-January

(e.g. Moore 1985), and baobab seeds are amongst the most common food remains in fecal

samples from this period (see Chapter 6). While other animals certainly disperse baobab seeds,

no other large animals appear to share spatial ecology as closely with the baobab. The

distribution of human settlement also corresponds strongly to baobab distribution, as discussed

203 below. Other human activities, such as cultivation in fields distant from settlements and fruit

consumption while traveling along paths, certainly account for the presence of some baobabs

away from settlement sites, but nearly all areas along cliffs and steep slopes are considered

uncultivable (see Chapter 3) and are nearly inaccessible to humans. Human activities cannot

reasonably account for the abundance of baobabs along cliffs and steep slopes.

Cliffs and steep slopes provide a wide range of habitats in terms of soil moisture (Larson

et al. 2000). Ledges and soil pockets amongst boulders—the microhabitats most frequently

occupied by baobabs (Figure 6, p. 220)—are relatively xeric. While most baobabs along cliffs

and steep slopes occur in cliff-side gallery forest vegetation, which has a high abundance of

mesophytes (Duvall 2001), baobabs do not occur in moist microhabitats. Additionally, only

2.2% of observed individuals occur along drainage channels, though only in drier portions of

channels without riparian gallery forest vegetation (Table 1, p. 226). Settlement sites, where

most small baobabs occur, also seem to represent relatively xeric habitat; dryness is a primary

criterion in Maninka site selection. Vegetative cover is relatively low in settlements, meaning

that insolation and run-off are relatively high (Belsky et al. 1993). As an indication of the

dryness of settlements as tree habitat, several woody xerophytes—particularly Bauhinia

rufescens Lam., Calotropis procera (Aiton) W.T. Aiton, and the domesticated date palm

(Phoenix dactyllifera L.)—occur only in settlements in the study area.

Fenner (1980) shows that baobab is a drought-adapted species, but none have considered

how its physiological ecology may relate to its association with settlements. Although better

knowledge of the characteristics of settlements as tree habitat is necessary, these results suggests

that baobab-habitat association occurs not simply because humans disperse seeds to settlements,

but also because settlement sites are suitably dry for baobabs. Other wild fruit trees with dry

204 habitat preferences—such as Tamarindus (Arbonnier 2000) and Borassus (which requires a high

water table: Sambou et al. 1992)—also increase in abundance at settlement sites during site

occupation, according to ethnographic data and vegetation analysis (Duvall in review a). In

contrast, wild fruit trees that are associated with mesic habitats—such as Spondias mombin and

Cordia myxa L. (Arbonnier 2000; Geerling 1982)—generally increase in abundance only after

site abandonment, once trees surviving human occupation have grown large enough to create

cooler and moister soil conditions (Belsky et al. 1993) and attract non-human frugivores. While

humans may contribute to the dryness of settlement sites through vegetation clearing and

earthworks, non-anthropogenic physical processes are probably the primary cause of the dry soil

conditions that make settlement sites suitable for baobabs.

Additionally, cliffs and settlements are both well protected from fire. Wildfires,

especially intense ones, are unable to reach most portions of cliffs and steep slopes (Larson et al.

2000). Fires lit in and around settlements and in actively cultivated fields are closely controlled;

there is generally insufficient fuel on the ground in settlements or fields to sustain uncontrolled

fires. There has been no study of baobab’s fire tolerance, although based on general fire-

vegetation relationships several authors have concluded that baobabs, especially small ones, are

poorly adapted to frequent or intense fires (Esenowo 1991; Napier-Bax & Sheldrick 1963;

Palmer & Pitman 1972; Simpson 1995; Wilson 1988). While baobab has relatively thin bark,

suggesting vulnerability to fire, it can sprout from its roots and regenerate cambium (Wickens

1982), indicating a capacity to recover from injury. Indeed, Baumer (1983: 67) states that “[t]he

tree is not much affected by bush fires”. Small baobabs are overabundant in occupied

settlements, occupied fields, and along cliffs, and underabundant in old fields and woodland

vegetation. In contrast, large baobabs are overabundant in old fields and woodland, habitats that

205 are generally not protected from fire (Laris 2002; Mbow et al. 2000). These results suggest that

ecological conditions in fire-controlled habitats are optimal for young baobabs, while conditions

in fire-prone habitats are tolerable to large, but less so to small, individuals. Fire protection may

be a significant aspect of settlements as tree habitat that contributes to baobab-settlement

attraction.

Human-baobab biogeography. Settlements and baobabs are attracted at most spatial

scales (Figure 7, p. 222), but there is no single reason for the observed attraction. A portion of

the attraction—especially for larger spatial scales—is due to the fact that the preferred habitat for

human settlement is the same as that in which baobabs are most abundant: areas with deep,

arable soil, dominated by woodland vegetation.

More specifically, Maninka knowledge of baobab-human interaction indicates that

human settlement leads to the development of baobab groves at settlement sites, and that

settlements are rarely established under preexisting baobabs. Point pattern and chi-square

analyses support Maninka knowledge. In particular, large baobabs and young settlements are the

only point sets that do not exhibit significant attraction: young settlements are rarely established

under preexisting, large baobabs. All other point sets, including small baobabs and young

settlements, display significant attraction at short distances: baobab recruitment occurs

significantly more than expected at settlement sites. Additionally, point pattern analysis of the

distributions of large and small baobabs (Figure 7, p. 222) shows that small baobabs are more

significantly associated with settlements than with large baobabs. While non-anthropogenic

baobab recruitment certainly contributes to the significant attraction between small baobabs and

old settlements, settlement-baobab attraction is not simply the result of natural recruitment

associated with large individuals.

206 Repulsion observed between baobabs and settlements at some scales also suggests

dependence, although baobab-settlement repulsion is but marginally significant, only at distances

of c.2 km, and only in the Rectangle 2 sub-region (Figure 7, p. 222). New settlements are most

frequently established by young men seeking access to more arable land than available to them

in their home village. Men establishing settlements often seek sites that are several kilometers

from preexisting, occupied or abandoned settlements in order to avoid conflict over historically

established land or tree usufruct. This aspect of settlement practice creates observable repulsion

between young settlements and baobabs that parallels repulsion between young and old

settlements.

African farmers have profoundly altered plant distributions through migration,

settlement, and cultivation (Maranz & Wiesman 2003; O'Brien & Peters 1998). Baobab groves

created at settlement sites in semi-arid Mali are analogous to “forest islands” farmers create at

settlement sites in sub-humid Guinea (Fairhead & Leach 1996), even though the two vegetation

features are structurally and compositionally different. Although Fairhead and Leach (1996)

describe a teleological process of vegetation enrichment, the creation of baobab groves at

settlement sites in southwestern Mali is an unintentional outcome of settlement—although

farmers clearly recognize, understand, and anticipate this outcome when establishing new

settlements. While baobab fruit collection contributes most visibly to baobab-settlement

attraction, other aspects of settlement practice—particularly site selection and fire protection—

are also important.

Past assessments of human-baobab interactions have relied largely on qualitative analyses

of incomplete censuses of baobabs and settlements in focal landscapes. In many cases,

impressions of landscape characteristics have been the primary ‘data’ used to support the

207 conclusion that baobab and settlement distribution are spatially dependant (Aubréville 1950;

Chevalier 1906; Hobley 1922; Sikes 1972; Taylor 1960). Humans have modified woodland

composition throughout Africa (e.g. Ballouche & Neumann 1995; Boffa 1999; Maranz &

Wiesman 2003; O'Brien & Peters 1998; Pullan 1974), but it is vital to identify clearly how

humans alter vegetation characteristics to avoid inaccurate or oversimplified representations of

human-environment interactions that support socially unjust and ecologically short-sighted

policies (Bassett & Koli Bi 2000; Leach & Mearns 1996; Turner 2000). Human settlement

increases baobab recruitment in parts of the landscape where small baobabs are otherwise

uncommon (i.e. flat areas dominated by woodland vegetation), but this influence is significant

only over distances <c.500 m. Ecological factors other than settlement—such as seed dispersal

by non-human primates, and other human activities—must account for the occurrence of

baobabs in many parts of the landscape, especially along cliffs and steep slopes. At a scale of

tens of kilometers, baobab distribution cannot be attributed entirely to humans, although the

tree’s dispersion and abundance would be lower in the absence of human settlement.

Based on data collected across West Africa, Maranz and Wiesman (2003) propose that

economically important, semi-domesticated trees in African agroecosystems reproduce

independently of human activities, but humans alter gene frequencies by culling self-sown trees

with unfavorable phenotypes. The observed, landscape-scale distribution of baobab is consistent

with Maranz and Wiesman’s model, although their model may underestimate the dependence of

tree reproduction on human activities. Specifically, the overabundance of small baobabs in cliff

habitats indicates that the tree does reproduce independently of humans, but the underabundance

of large (i.e. reproductive) trees along cliffs, along with the overabundance of large trees in

abandoned settlements and fields, shows that the baobab population depends on human activities

208 to maintain a recruitment rate sufficient for population replacement. If baobab reproduction was

predominantly independent of human activities, the tree would be much rarer because few trees

would grow large enough to fruit. However, multi-scale analyses are necessary to understand

more completely how humans affect the reproduction and distribution of economically

important, African trees. For instance, landscape-scale study of the distribution of Vitellaria

paradoxa is necessary to determine how this tree reproduces in landscapes where it has been

introduced in the past 200 years (cf. Kelly et al. 2004; Maranz & Wiesman 2003), while region-

to continent-scale genetic analysis is needed to determine whether humans introduced baobab in

the present study area.

The present results suggest concretely how land-use change—a factor implicated in poor

recruitment for many tree populations in semi-arid Africa (Assogbadjo et al. 2005; Lykke 1998;

Sambou et al. 1992; Schreckenberg 1999; Wilson 1988)—may negatively affect baobab

reproduction. Shifting cultivation and settlement, including the establishment and abandonment

of fields and settlements, has been practiced in semi-arid Africa for centuries (Raynaut 1997;

Richards 1978; Stone 1996). Parkland vegetation, dominated by valued wild fruit trees, is

clearly a long-term result of agroforestry practices that increase the relative abundance of highly

valued trees in farmed and settled parts of landscapes (Boffa 1999; Maranz & Wiesman 2003;

Pullan 1974; Schreckenberg 1999). Parkland vegetation does not occur throughout semi-arid

Africa, but the distribution and abundance of highly valued trees in areas without parkland

vegetation also results from long-term management practices. Most reported cases of poor

recruitment for highly valued wild trees are from areas where land-use intensification is

proceeding—including the expansion of cultivated area paired with the reduction in fallow land,

and increased commodification of non-timber forest products—often at the expense of land-

209 extensive practices (Assogbadjo et al. 2005; Johansson 1999; Lykke 1998; Sambou et al. 1992;

Schreckenberg 1999; Wilson 1988). In the research area, baobab population replacement rate is

linked to the rate of settlement establishment and abandonment: small baobabs are most

abundant in occupied settlements and fields, and large (i.e. reproductive) baobabs are most

abundant in abandoned settlements and fields (Table 1, p. 226). Processes that reduce the rate of

settlement establishment or abandonment will negatively affect recruitment rates of tree species

associated with settlements.

Conservation policies that prohibit shifting cultivation or settlement—such as those

recently enacted in the present research area (see Chapter 2)—represent non-economic processes

that reduce rates of settlement establishment and abandonment. Thus, such policies may

unintentionally reduce recruitment or dispersion of wild fruit trees associated with settlements.

While such policies are unlikely to lead to extinction or local extirpations of effected trees and

may be necessary to address short-term threats to biodiversity resources, declines in wild fruit

tree populations caused by land-use change may have unforeseen consequences for ecosystems

over longer terms if effected tree species are important resources for wildlife (cf. Fimbel 1994a;

Fimbel 1994b; Wilkie & Finn 1990).

Conclusion

The results show that baobab population regeneration and human settlement

establishment are spatially dependent. Human settlements are habitat patches that are suitable

for baobabs, and baobab’s adaptation to human fruit preferences means that the tree has gained a

reliable dispersal agent that allows it to colonize these habitat patches. The tree’s success in

these habitat patches, long recognized through observation of baobab groves at settlement sites,

is not simply a function of seed accumulation at settlement sites. Other aspects of settlement

210 practice—especially the selection of dry, well drained sites with deep soil, and the prevention of

fires in settlements—make settlement sites suitable habitat for baobabs. Baobabs are also

relatively abundant in similarly dry and fire-protected habitats on cliffs and steep slopes,

although these sites are quite unlike settlements in other ways. The tree’s abundance on cliffs

also indicates its adaptation to baboon and chimpanzee feeding behavior. Indeed, these animals

may, over the long term, benefit from increased baobab dispersion and abundance resulting from

human settlement. Other highly valued, wild fruit trees likely have similar relationships with

human settlement and non-human frugivores, and the persistence of these relationships is linked

to long-term patterns of land use.

211

Figures and tables for Chapter Four

212 Figure 1. Baobab range and research area. Map 1: Africa, showing location of Map 2 and

baobab distribution, which is redrawn and simplified from Wickens (1982). Distribution on

islands (except Madagascar) not shown. Map 2: Western Mali, showing location of research

area. Map 3: Research area, showing location of Solo, all occupied settlements, and cliffs in the

research area.

213

214 Figure 2. Baobab and settlement data maps. Abbreviations: B=baobab/s, Q=quintile/s,

S=settlement/s. Dashed rectangles indicate the rectangular sub-areas used to test significance in

point pattern analysis. These sub-areas are referred to by number, shown in a box near the lower,

left-hand corner of each sub-area. Panel 1: Bullet points represent all baobab individuals; grey

circles represent circular areas (500 m diameter) centered on all settlement sites. This diameter

is chosen to provide cartographic representation of the distance at which baobab-settlement

attraction is significant in most cases (see text and Figure 8, p. 224). Panel 2: Triangles represent

all settlements, with shading indicating quintile membership of each settlement. Panels 3-6:

Bullet points and gray circles as described for Panel 1. ‘Smallest baobabs’ and ‘Youngest

settlements’ depict only quintiles 1 and 2 of the baobab and settlement data sets. ‘Largest

baobabs’ and ‘Oldest settlements’ depict quintiles 4 and 5 of these data sets. These panels

represent to the paired point sets described in Table 2 (p. 227), whose observed L-function values

are shown in Figure 8 (p. 224).

215

Longitudinal distance (meters)

Latit

udin

al d

ista

nce

(met

ers)

All baobabs2

3

1

2

3

1

Largest B,Youngest S 2

3

1

Largest B,Oldest S 2

3

1

SQ 1&2SQ 3SQ 4&5

Smallest B,Youngest S 2

3

1

Smallest B,Oldest S 2

3

1

216 Figure 3. Photos of baobabs in settlements. Photo (a): What appear to be pebbles embedded in

the ground surface are often baobab seeds. Arrows indicate three seeds in this photo. Photo (b):

During the rainy season, hundreds of baobab seeds germinate in rubbish mounds, but most die

during the dry season. Photo (c): The few seedlings that survive happen to grow in well-

protected situations, such as under granaries. Arrows indicate three saplings emerging from

under a granary that had been built about seven years earlier. These saplings are stunted as a

result of frequent leaf collection and browsing livestock. Photo (d): Baobabs that outlast human

occupation of a settlement site often thrive to dominate vegetation in abandoned settlements.

Arrows indicate three medium-sized baobabs that are nurse trees for various other wild fruit

trees.

217

a

c

b

d

218

Figure 4. DBH measurement in relation to research effort. Trend line is a linear regression of

DBH measurements for observed baobabs, arranged in order of date encountered.

219 Figure 5. Baobab size-class distribution. Size classes are 10-cm DBH intervals.

220 Figure 6. Photos of baobabs in cliff habitats. Photo 1: Most baobabs on cliffs grow on narrow

ledges, which baboons and chimpanzees use in traveling along rock outcrops. This photo, taken

from the top of a cliff looking down at a ledge about 25 cm wide and 2 m below the cliff top,

shows a baobab sapling (its base is to the right of the arrow) that has partially fallen, turning over

the soil mat in which it is rooted. (The branches to the right are the crowns of trees rooted at the

base of the c.20-m cliff.) Baobab mortality on cliffs appears to be very high due to the

geomorphic instability of this habitat. Photo 2: Baobabs that attain large size on cliffs are those

that happen to grow in relatively flat, rocky patches of soil trapped between boulders. Both of

these cliff microhabitats experience xeric soil moisture conditions although they may be in or

adjacent to mesophytic cliff-side gallery forest. These two medium-sized trees have annular

scars near their bases that indicate humans harvested their bark for rope when the trees were

smaller.

221

a

b

222 Figure 7. Large baobab-small baobab bivariate point-pattern analyses. Observed L-function

values depicted with solid lines; dashed lines depict simulation envelopes derived from 99

random toroidal shifts of data sets. Values >0 indicate spatial attraction between large and small

baobabs; values <0 indicate repulsion. Where observed values lie outside simulation envelopes,

observed attraction/repulsion is significant (p<0.01).

223

224

Figure 8. Baobab-settlement bivariate point-pattern analyses. Observed L-function values

depicted with solid lines; dashed lines depict simulation envelopes derived from 99 random

toroidal shifts of data sets. Values >0 indicate spatial attraction between baobabs and

settlements; values <0 indicate repulsion. Where observed values lie outside simulation

envelopes, observed attraction/repulsion is significant (p<0.01).

225

226 Table 1. Baobab-habitat associations. Symbols used to indicate statistical significance:

+++=overabundant, p<0.001; ++=overabundant, p<0.01; +=overabundant, p<0.1;

°°°=underabundant, p<0.001; °°=underabundant, p<0.01; and °=underabundant, p<0.1.

Abbreviations: aban.=abandoned; indivs.=individuals; occ.=occupied; Q=quintile. Habitat for

each baobab was characterized by selecting one category (e.g. pathside, slope >36°, woodland)

per environmental factor (i.e. land use, topography, vegetation). The categories ‘Abandoned

settlement’ and ‘Occupied settlement’ include only trees found ≤100 m from the center of a

settlement site or ≤20 m from huts found ≥100 m from a settlement center. Other land-use

categories based on observation of current use, or oral historical evidence for past use. For

quintile DBH ranges, see Table 2 (p. 227).

All indivs. Q1 Q2 Q3 Q4 Q5 Number of trees 1240 248 248 248 248 248 Land use Aban. settlement 443 (35.7%) 80 89 107++ 81 86 Aban. field 336 (27.1%) 53° 72 46°°° 74 91+++ No history of past use 331 (26.7%) 59 62 80+ 74 56° Occ. settlement 76 (6.1%) 35+++ 18 9° 9° 5°°° Occ. field 37 (3.0%) 20+++ 3++ 4 6 4 Pathside 17 (1.4%) 1° 4 2 4 6 Topography Slope <5° 881 (71.0%) 181 178 165 174 183 Slope 5-36° 227 (18.3%) 23°°° 34° 53 61++ 56+ Slope >36° 105 (8.5%) 40+++ 27 22 10°°° 6°°° Drainage channel 27 (2.2%) 4 9 8 3 3 Vegetation Woodland 918 (74.0%) 142°°° 192 179 197+ 208+++ Occ. settlement or field 122 (9.8%) 57+++ 22 15°° 18 10°°° Cliff-side gallery forest 116 (9.4%) 45+++ 25 23 10°°° 13°° Bamboo thicket 61 (4.9%) 3°°° 3°°° 21+ 20+ 14 Rupicolous scrub 23 (1.9%) 1°°° 6 10+ 3 3

227 Table 2. Baobab and settlement quintile characteristics, and paired data sets used in point-

pattern analyses. Abbreviations: B=baobab/s; DBH=diameter at breast height; Q=quintile;

S=settlement/s. Section 1: Second and third columns give ranges per quintile of the quantitative

variable determining quintile membership (DBH for baobabs, years since establishment for

settlements). Fourth column gives number of settlements per quintile; membership per quintile

unequal because several settlements have same estimated ages. All baobab quintiles have 248

members. Section 2: Bivariate point-pattern analyses were conducted as described in the text for

each point pair indicated, in the study area as a whole and in three rectangular sub-regions

(Figure 3, p. 216).

Section 1: Section 2:

Quintile characteristics Paired point sets used in bivariate analyses. Q B DBH

(cm) S Age (yrs)

S per Q

Point sets

Ecological meaning

1 ≤1-19.8 4-24 19 All B, All S

Are baobabs associated with settlements?

2 20.0-60.0

29-45 17 BQ 1&2, SQ 1&2

Are small baobabs associated with young settlements?

3 60.5-124.3

47-66 19 BQ 1&2, SQ 4&5

Are small baobabs associated with old settlements?

4 124.5-193.3

69-110 16 BQ 4&5, SQ 4&5

Are large baobabs associated with old settlements?

5 195.0-455.0

120-≥250

18 BQ 4&5, SQ 1&2

Are large baobabs associated with young settlements?

BQ 1&2, BQ 4&5

Are small baobabs associated with large baobabs?

228 Chapter Six: Chimpanzee diet, habitat use, and human settlement in Mali

Abstract

Humans have profoundly altered vegetation composition in Africa through millennia of

settlement and cultivation. In many areas, a long-term effect of settlement has been the

development of vegetation patches at settlement sites, where structure and composition differ

from that in surrounding vegetation. These patches represent potentially valuable habitat for

chimpanzees and other frugivores, because settlement increases the relative abundance of wild

fruit trees at settlement sites. In southwestern Mali, settlement creates baobab groves at

settlement sites, and past observations of chimpanzee behavior suggest that chimpanzees

frequently forage in baobab groves, at least seasonally. The purpose of the present paper is to

determine spatio-temporal characteristics of chimpanzee habitat use in Mali, and particularly to

determine if the animal significantly uses abandoned settlements as habitat. The results show

that chimpanzees use abandoned settlements especially during the time of year when baobab

fruit is an important dietary component. During other times of the year, forest habitats along

sandstone outcrops are more heavily used, because permanent water sources and food patches

are more abundant along outcrops. Chimpanzees are ecologically linked to groundwater stored

in sandstone outcrops, like other rare and biogeographically noteworthy species in southwestern

Mali. Anthropogenic baobab groves increase the abundance and distribution of chimpanzee food

patches, and appear to be especially important during times of the year when fruits produced by

plants characteristic of cliff habitats are not abundant. Recognizing indirect human-chimpanzee

interactions is necessary for reducing unforeseen, long-term consequences of conservation

policies.

229 Key words

chimpanzees; agriculture; conservation; Mali; ecological history

Introduction

In West Africa’s semi-arid, Sudanian woodland zone, human settlement has been

characterized historically as destructive of natural resources, especially woody vegetation

(Aubréville 1949a; Chevalier 1938; Keay 1959). The discourse of deforestation and

desertification has arisen, in part, from the view that rural settlements cause declines in

vegetation density, productivity, and diversity (Bassett & Crummey 2003; Fairhead & Leach

1996; Fairhead & Leach 1998). While urbanization has led to loss of woody cover around cities

(Becker 2001), research on vegetation ecology in rural areas has shown that although the effects

of settlement are variable, settlements are generally not deforested patches. In some areas, where

land-use intensification is proceeding, the density or diversity of woody plants has decreased, or

has the potential to decrease (Lykke 1998; Nyerges 1989; Schreckenberg 1999). However, there

are also examples of afforestation associated with rural settlement: in Guinea and Ghana,

settlements become “forest islands” in a “savanna” matrix due to purposeful management of

settlement sites (Amanor 1994; Fairhead & Leach 1996); in Côte d’Ivoire and Cameroon the

density and abundance of woody species is increasing in woodlands around settlements as an

unintentional outcome of increased livestock ownership (Bassett & Boutrais 2000; Bassett &

Koli Bi 2000); and in Mali, human use of wild fruits and management of settlement sites directly

and indirectly create patches of wild fruit trees in woodlands (see Chapters 5 and 6). Such long-

term, often subtle changes in vegetation structure and composition are indicative of the

230 profoundly anthropogenic character of many types of African vegetation (Lovett & Wasser 1993;

Lovett & Haq 2000; Maranz & Wiesman 2003; O'Brien & Peters 1998).

What effects do gradual, long-term, anthropogenic changes in vegetation characteristics

have on wildlife? The response of wild animals to disturbance depends on the type and degree

of change, as well as a species’ behavioral ecology and body size (Johns & Skorupa 1987;

Plumptre 2001; Plumptre & Reynolds 1994). Drastic, rapid vegetation changes, such as non-

selective logging in rainforest areas, decimate most wildlife populations associated with the

original habitat. Indeed, the drastic land-cover changes that have occurred globally in the past

century have made habitat loss the main threat to plant and animal populations worldwide

(Hilton-Taylor 2000; Walter & Gillett 1998).

This global, relatively recent situation does not describe all cases of vegetation change

caused by human disturbance, especially those that have occurred slowly, over hundreds of

years. Over the course of centuries to millennia, human management of occupied and abandoned

settlement, field, and garden sites has altered vegetation composition throughout the tropics

(Balée 1994; Croll & Parkin 1992; Denevan & Padoch 1988; Fairhead & Leach 1996; Janzen

1998). One aspect of this change has been the development of vegetation patches that are

attractive to wildlife for various reasons, but primarily as food patches (Medellin & Equihua

1998; Naughton-Treves 2002; Thiollay 1995; Thomas 1991). These food patches may

significantly enhance habitat quality for some wildlife species in human-occupied landscapes,

and reflect long-term co-adaptation between humans and wildlife (Bahuchet & Garine 1990;

Fimbel 1994a; Gadgil et al. 1993; Greenberg 1992). Their significance may be muted, however,

by hunting: farmers often depend on abandoned settlement, field, and garden sites to produce

231 meat (Bahuchet & Garine 1990; Denevan et al. 1984; Fairhead & Leach 1996; Naughton-Treves

2002; Nietschmann 1973).

Wildlife response to long-term, anthropogenic vegetation change has not been studied in

the semi-arid tropics, where settlement and agricultural practice, and the composition and

ecology of plant and animal communities, differs strongly from rainforests. Additionally, studies

of wildlife use of abandoned settlement, field, and garden sites have often focused on animals

valued for meat (although see: Fimbel 1994a; Fimbel 1994b), and frequently do not

contextualize wildlife activities in humanized sites by providing comparable data for animal

behavior across entire landscapes, including non-humanized portions.

The present paper looks specifically at how chimpanzee (Pan troglodytes verus)

abundance varies across a Malian landscape in which human settlement over the last c.400 years

has significantly changed the distribution and composition of certain plant communities. In

African rainforests, chimpanzee populations have almost uniformly declined due to short-term

vegetation change, especially clearing for logging or agriculture (Chapman & Lambert 2000;

Kormos et al. 2003; Plumptre 2001; Plumptre & Reynolds 1994). However, in rainforest

settings where agricultural disturbance results in the development of persistent patches of woody

plants with edible fruits—and where hunters do not purposefully target chimpanzees—

chimpanzee abundance has not declined, and the animals use these disturbed patches more

frequently than expected (Fimbel 1994a; Fimbel 1994b; Wilkie & Finn 1990).

Circumstantial evidence suggests that chimpanzees in Mali also rely on vegetation

patches at abandoned settlement sites. Specifically, Moore (1985: 60), after surveying

chimpanzee distribution in southwestern Mali, found that “many of the chimp nests we observed

occurred near fruiting baobabs”. Baobabs (Adansonia digitata L.) dominate vegetation at

232 abandoned settlement sites in many parts of Africa (Wickens 1982), and in southwestern Mali

baobab groves develop at settlement sites directly and indirectly as a result of human activities

(see Chapter 5). However, other findings seem to conflict with this circumstantial evidence. In

particular, humans settle and farm relatively flat areas with deep soil in southwestern Mali (see

Chapters 2 and 4), while chimpanzee abundance is strongly associated with cliffs and steep

slopes (Granier & Martinez 2004; Moore 1985; Pruetz et al. 2002).

The purpose of the present paper is to determine spatio-temporal characteristics of

chimpanzee habitat use in Mali, and particularly to determine if the animal significantly uses

abandoned settlements as habitat. This paper analyzes a range of data, from: 1) systematic

surveys of chimpanzee abundance, 2) studies of chimpanzee nesting and feeding ecology, 3)

maps of the distribution of surface water and chimpanzee food-plant patches, and 4)

ethnographic interviews on local settlement history and chimpanzee behavior. These disparate

but complementary information sources support the view that human settlement creates valuable

habitat patches for chimpanzees in semi-arid West Africa.

Human-chimpanzee interactions in semi-arid West Africa

The northernmost limit of chimpanzee distribution occurs near 13° N in Senegal and Mali

(Butynski 2003). Chimpanzees have been known scientifically in this area for nearly 30 years,

and various researchers have contributed to a relatively small, largely exploratory literature on

Mali’s chimpanzees (Duvall 2000; Gagneux et al. 1999; Granier & Martinez 2004; Moore 1985;

Pavy 1993; Sayer 1977). Chimpanzee ecology in Mali is probably quite similar to that in

southeastern Senegal, where research on the ape’s behavioral ecology has been more abundant

and substantial (Baldwin et al. 1982; Baldwin et al. 1981; Bermejo et al. 1989; Marchant &

McGrew 2005; McBeath & McGrew 1982; McGrew et al. 1981; McGrew et al. 1988; Pruetz et

233 al. 2002). Many researchers have found that chimpanzee abundance is strongly associated with

topography; nests (which chimpanzees build nightly for sleeping) are from eight (Granier &

Martinez 2004) to 800 (Pruetz et al. 2002) times more abundant in forests along cliffs and steep

slopes than in relatively flat, woodland areas. However, around Mt. Assirik, Senegal, observers

have recorded more nests in relatively flat, woodland areas rather than in forests associated with

abrupt topography (Baldwin et al. 1982; McGrew et al. 1981).

Interactions between chimpanzees and humans in semi-arid Africa have been poorly

studied, although three interactions that represent threats to chimpanzees are certain in Senegal

and Mali. First, humans rarely hunt chimpanzees (Carter et al. 2003; Duvall et al. 2003; Granier

& Martinez 2004), because most people consider chimpanzee flesh inedible as food. Limited

hunting exists because some people consume chimpanzee meat for medicinal use (Duvall &

Smith 2005), and there is a small but persistent pet trade sustained by hunting (Carter et al. 2003;

Duvall et al. 2003; Granier & Martinez 2004). Due to the animal’s relatively low population size

in these countries (up to c.1500 individuals in Mali: Duvall et al. 2003) and low reproductive

rate, even a very low rate of hunting could pose a serious threat to the population (Moore 1985).

Although all sources indicate a low absolute level of hunting, this threat remains a crucial issue

for chimpanzee conservation, and better knowledge of chimpanzee population size and the rate

of human hunting of chimpanzees is needed to manage this threat successfully. Second, humans

and chimpanzees compete for wild plant resources, especially fruits. In Senegal, commercial

harvesting of wild fruits for sale in cities has reduced local abundance of chimpanzee food

during certain seasons, leading chimpanzees to consume lower-quality foods (Carter et al. 2003;

Pruetz 2002). Finally, road building, agricultural expansion, and industrial mining all cause

habitat loss at an unknown rate (Carter et al. 2003; Duvall et al. 2003). In particular,

234 conservationists are concerned that indigenous settlement practices in Mali’s Bafing Biosphere

Reserve—especially the establishment and abandonment of dispersed farming hamlets—

represent the expansion of settlement into previously unoccupied areas of wildlife habitat (see

Chapter 2). While there is no frontier-style settlement expansion occurring in the Bafing area

(Chapter 2), the effects of shifting settlement on chimpanzee habitat and behavior are unknown.

Research area

Research occurred in an area of 183 km2 around the village of Solo (12°58' N, 10°26' W)

in Mali’s Bafing Biosphere Reserve (Figure 1, p. 258).

In this area, topographic complexity associated with bedrock outcrops helps create a wide

range microenvironments (Jaeger 1959; Lawesson 1995). In the northern portion of the research

area, sandstone plateaus dominate the landscape, rising 200-300 m above surrounding lowlands

(IGM 2001). The southern portion is relatively flat, with few bedrock outcrops, all of which are

dolomite, not sandstone. Erosion of the sandstone plateaus has formed narrow ravines, rocky

slopes, and plains with relatively infertile sandy and silty soils. Dolomite outcrops erode more

uniformly to form rounded inselbergs surrounded by silty soil. Ferricrete hardpans and bare

bedrock surfaces, with very dry soil conditions, are common throughout the area (Dames &

Moore 1992; Jaeger & Jarovoy 1952; Michel 1973). Groundwater seeps to the surface

permanently or seasonally in some locations where sedimentary layers in the sandstone have

been exposed (DCTD 1990). If seepage occurs in topographically sheltered locations, very

humid microclimates exist (Duvall 2001). Elsewhere, permanently moist habitats are

uncommon, and are primarily deep depressions in seasonal streambeds. Soils in most locations

are driest from April to June, when air temperature and potential evapotranspiration peak (FAO

1984).

235 Seasonal changes in rainfall and potential evapotranspiration control plant phenology and

the availability of surface water. Precipitation is highly seasonal and averages about 1100 mm

per year, with high interannual variation (FAO 1984; Leroux 2001). Most rain falls during June-

September, but brief, intense rainstorms also occur during April-June; rainfall is minimal during

November-March (Dames & Moore 1992; FAO 1984; Leroux 2001). Woody plant phenology

varies between habitats based on the dominant reproductive strategy in each habitat, largely a

function of annual soil moisture variation (Breman & Kessler 1995; de Bie et al. 1998; Rathcke

& Lacey 1985). Many deciduous species (dominant in woodland areas but also present in

forests) rely on stored energy to produce fruit during April-June or during one other period,

while many evergreen species (dominant in forests) flower or fruit throughout the year

(Arbonnier 2000; Breman & Kessler 1995; de Bie et al. 1998).

Woodland vegetation dominates most of this landscape, especially in areas with relatively

deep, fertile soil. Forest patches occur in topographically protected microhabitats with moist soil

conditions along bedrock outcrops. These patches are important chimpanzee habitat (Duvall

2000; Granier & Martinez 2004; Moore 1985; Pavy 1993). Locations with shallow or infertile

soil host patches of edaphic bushland or grassland. Based on woody species composition, fifteen

vegetation types have been described for the area (Table 1, p. 264).

Maninka farmers, who have occupied this landscape for at least 400 years (Samaké et al.

1986), have altered vegetation characteristics. Most sites with arable soil have been farmed in

the past, and all sites have been subject to at least low intensity or frequency disturbance.

Designating different types of vegetation ‘primary’ or ‘secondary’ based on past human

disturbance is not helpful in understanding vegetation ecology, because the effects of human

activities on vegetation are variable, and many types of undisturbed vegetation are

236 compositionally and structurally similar to disturbed vegetation (see Chapter 4). The Maninka

settle and cultivate sites with deep, arable soil and good drainage. Large areas of the landscape

have never been cleared for agriculture or settlement: rocky areas, plateau tops, and sites with

poor soil or drainage are used only for seasonal livestock grazing, wild plant and honey

collection, and hunting (Duvall 2001; Samaké et al. 1987). Many abandoned human settlement

sites host distinctive vegetation dominated by disturbance-adapted shrubs and by trees with

edible fruits (see Chapters 5 and 6). In particular, past settlement has led directly and indirectly

to the development of many baobab groves across the landscape (see Chapter 5).

Data collection and analysis

Five types of data were used: 1) observations of chimpanzees and night nests; 2) contents

of chimpanzee fecal samples; 3) samples of vegetation composition; 4) observations of plant

phenology; 5) mapping of permanent surface water sources and chimpanzee food-plant patches;

and 6) ethnographic interviews of indigenous farmers and hunters.

Chimpanzee and nest observations. During May-July 2003, reconnaissance surveys and

ethnographic interviews of indigenous hunters revealed approximately 20 sites—such as forest

patches and baobab groves—that chimpanzees appeared to use frequently for nesting or

foraging. During January-December 2004, the researcher and six research assistants visited each

of these sites twice weekly for 38 weeks (observations were made on 241 total days) to record

observations of chimpanzees and their night nests. Specifically, each research assistant walked

one of six survey loops (Figure 2, p. 259) that passed through several of the sites that

chimpanzees appeared to use frequently. However, most of the length of each survey loop was

in habitats—such as wooded grassland—where evidence for chimpanzee abundance is low.

Repeated visits to the sites identified in 2003 revealed that chimpanzees did not frequently use

237 many of them. Nonetheless, the layout of survey loops was not modified during 2004, in order

to collect systematic data on variation in chimpanzee abundance, and because increased

familiarity with the research area during 2004 did not reveal any additional sites that chimpanzee

appeared to use frequently.

Two types of observations were recorded when walking survey loops: 1) visual

observations of chimpanzees, and 2) nests constructed since the previous survey. Locations and

times of all observations were determined using Garmin 12-XL GPS units.

For each chimpanzee sighting, the initial activity of observed chimpanzees was recorded,

if possible, and observation sites were searched for feces and evidence of feeding. Observers did

not attempt to follow observed chimpanzees. Multiple observations of a single group may have

been recorded as single observations of multiple groups, but this possibility is unlikely. Only 3

of 48 observations followed an earlier observation on a single survey loop on one day, and

several kilometers separated these same-day observations. Similarly, different survey loops that

were walked on the same day were walked at approximately the same time (approximately 8:00

a.m.-2:00 p.m.) and were separated by 5-20 km; examination of observation times shows that

chimpanzee groups seen on different survey loops on one day could not have been a single

group.

To locate nests, observers walked at c.4-6 km/h, searching from side to side for nests

visible from the survey loop. Upon observing a nest, the observer left the survey loop to

examine the nest and search for other contemporaneous nests belonging to the same nest group.

The number of nests per nest group was counted. ‘Nest groups’ were defined as all

contemporaneous nests within 20 m of one another (cf. Blom et al. 2001). In practice, nearly all

nest groups were clearly discrete, isolated by several hundred meters from contemporaneous

238 nests. ‘Contemporaneous nests’ were defined as those that had been constructed since the

previous survey; the period between surveys per loop was 4-7 days. These nests were considered

‘fresh’ because their leaves remained green, flexible, and not dry, and their construction could be

attributed to the preceding 4-7 days. Data collection began on 31 January 2004, ceased from 16

July to 30 August 2004, and continued until 15 December 2004; for the weeks following 31

January and 30 August, the initial survey of each loop served to identify all nests along the loop,

so that during the second survey all new nests could be recorded and their construction dated.

Each time a fresh nest was identified, the ground immediately below it was searched for ‘fresh’

(i.e. not entirely dry) chimpanzee feces.

Opportunistic observations of nests away from these survey loops (and during initial

visits to survey loops) were similarly recorded if leaves on observed nests were green, flexible,

and not dry. Additional, opportunistic chimpanzee sightings were also recorded.

For most analyses, nest and chimpanzee observations were lumped together, even though

these two types of observation do not indicate precisely the same information about chimpanzee

behavioral ecology (Strier 2003). Nests indicate where chimpanzees sleep overnight, while

diurnal observations indicate where chimpanzees are active during the day, whether feeding,

traveling, resting, or otherwise. Since the present research is meant to assess generalized

chimpanzee distribution—and not specifically the distribution of nesting, feeding, or other

behaviors—in relation to various environmental features, lumping nest and chimpanzee

observations together serves to increase sample size. This is important because the number of

observations was low during some months and in some parts of the research area.

Fecal contents. To estimate chimpanzee diet, fecal samples were collected during

surveys (and opportunistically when found fresh and associated with a fresh nest) and dissected.

239 Following Moreno-Black (1978), McGrew et al. (1988), and Tutin and Fernandez (1993), the

steps in this data collection effort were:

1) Chimpanzee feces identified based on: a) direct observation of defecation, or b) size,

shape, texture, and location, being found fresh below a fresh nest;

2) Contaminating plant debris and insects removed from feces; each sample sealed

individually and stored in plastic bag until dissection;

3) Fecal samples placed individually in 1-mm mesh bag and agitated in water to dissolve

fecal matrix;

4) Items <1 mm diameter (e.g. Ficus seeds) floating in water skimmed out using cloth

strainer (pore size <<1 mm);

5) Fecal material remaining after dissolution placed on metal plate and sorted;

6) Each item in this material identified through: a) prior familiarity, b) comparison with

fresh plant specimens, c) comparison with items previously collected in fecal samples, or

d) identification by taxonomist (for termites only);

7) Abundance of each item per fecal sample based on visual estimation of its volume after

dissolution (cf. Basabose 2004), according to the following scale: 5 = >80% of sample, 4

= 50-80%, 3 = 20-50%, 2 = 1-20%, 1 = <1% (i.e. negligible volume); and

8) Samples of each item collected, dried, and stored for comparison with subsequent

samples.

Additionally, direct observation of chimpanzees or feeding remains, as described above,

indicated consumption of other food items.

Vegetation composition. Vegetation sampling used in this research is described fully in

Chapter 4. To summarize, vegetation sampling occurred at 217 sites with disturbance histories

240 determined primarily through interviews of local residents. At each site, 0.1 ha was sampled

according to Gentry’s methodology (ten 2 (50 m plots: Phillips & Miller 2002). In these plots,

all woody plants ≥2.5 cm diameter at breast height (DBH) were identified to species, measured

(DBH only), and tallied. These tallies served as the bases of a hierarchical clustering analysis

that identified the fifteen vegetation types listed in Table 1 (p. 264; see also Chapter 4).

Relative dominance (basal area per species divided by basal area for all species),

proportional abundance (number of individuals per species divided by total number of

individuals), and relative frequency (number of sites in which a species was present divided by

total number of sites) were calculated for each species per vegetation type. An importance value

(IV) was calculated per species per vegetation type by adding its relative dominance,

proportional abundance, and relative frequency scores.

Plant phenology. The amount of fruits, flowers, and leaves borne by each individual

plant observed during vegetation samples was assessed separately, using the following scale:

0=no fruits, flowers, or leaves; 1=less than 10 ripe fruits or fresh flowers, or less than 25% of the

canopy with fresh leaves; 2=10-50 ripe fruits or fresh flowers, or 25-75% of the canopy with

fresh leaves; 3=greater than 50 ripe fruits or fresh flowers, or greater than 75% of the canopy

with fresh leaves.

Distribution of biophysical features. Mapping permanent surface water sources and

vegetation patches with high abundance of chimpanzee food plants was meant to estimate the

spatial structure of biophysical features presumed important to chimpanzees.

The distribution of permanent surface water was determined through interviews of local

residents and foot surveys. Nine men, resident Maninka farmers and hunters, were interviewed

to identify all locations where surface water is permanently available in the research area.

241 ‘Permanent’ sources were considered those that have never been dry in living memory. The

location of each permanent source was determined with a GPS unit, and the following

characteristics recorded: type of source (i.e. spring, seep, or depression in drainage channel),

abundance of water, vegetation cover, and human use. Foot surveys during the 2004 dry season

(January-June) along all drainage channels and edges of all outcrops did not locate any

additional, permanent water sources.

Second, vegetation patches with a high abundance of chimpanzee food plants were

mapped based on vegetation samples (described above) and a census of baobab trees (Chapter 5).

Specifically, the following sites were mapped as points corresponding to each site’s approximate

center: 1) all patches of forest vegetation (i.e. vegetation types 1 and 2: Table 1, p. 264) larger

than c.1 ha; 2) all sites that have not been occupied or farmed by humans since 1994 where ≥5

baobabs ≥100 cm diameter at breast height occur in an area ≤100 m × ≤100 m; and 3) any other

sites where woody plants with fruits eaten by chimpanzees (as identified through fecal samples

or direct observation) were densely gregarious in an area ≤100 m × ≤100 m. The disturbance

history of all sites was determined through interviews of local residents (see Chapter 2). These

sites provide a highly simplified representation of the distribution of chimpanzee food-plant

patches. This representation is probably grossly accurate, and highly conservative because most

food-plant species are sparse outside of these sites (Table 3, p. 268; see also Chapter 4).

Ethnographic interviews. The researcher interviewed indigenous residents of the

research area to gain information on their knowledge of chimpanzee behavioral ecology.

Interviewees (n=12) were all male hunters between the ages of c.30-70. Interviews were all

informal, primarily conducted during normal conversations, in which the subject of wildlife

behavior was a common subject. Notes on the content of conversations were written during or

242 immediately after conversations. To clarify information provided during interviews, multiple

interviewees were independently asked similar questions about in order to gain multiple

perspectives on specific topics and improve the accuracy of interview results (Werner &

Schoepfle 1987).

Results

Diet composition and seasonality. Thirty-eight food items were identified in 71 fecal

samples, including five items of animal origin (Table 2, p. 266). Observations of chimpanzees or

feeding remains at sites where chimpanzees were observed revealed nine food items; for six of

these items there was no identifiable fecal evidence. The mean number of items per fecal sample

was 3.45±1.32, the range 1-6. Fourteen items were not identifiable to taxa, and the identification

of three items is tentative. Most unidentified items were single seeds, but many fecal samples

included unidentifiable vegetal fiber. Fecal samples were found during February-July and

September-December, but no food item occurred in fecal samples in every month. Most food

items occurred in samples during limited periods of time, with high abundance during these

periods. For fruits, these periods corresponded with the fruiting seasons of relevant species

(Figure 2, p. 259).

The majority of food items identified are of plant origin (Table 2, p. 266). Fruit

remains—whether seeds or identifiable fibers—accounted for 29 of 38 food items, including an

unidentified type of grass seed and eight unidentified types of dicot seeds. Excluding

undifferentiated fiber, two types of leaves—a grass and a dicot, both unidentified—were present

in samples, and fibrous remains of bamboo stems (tentative identification) were seasonally very

abundant. Direct observation and feeding remains of bamboo stem consumption were also

abundant. Two types of flower were found as well: Daniellia oliveri flowers are a seasonally

243 important dietary item, while another, unidentified flower (tentatively determined to

Caesalpiniaceae) occurred in a single sample. Chimpanzees were observed to eat the cambium

of three tree species—primarily Pterocarpus erinaceus, but also P. lucens, and Cordyla

pinnata—although identifiable fecal evidence for this consumption was absent. Notably, local

residents recognize the cambium of C. pinnata as a famine food (K. Dembélé, personal

communication, 3 Dec 2004). Of the five items of animal origin found in fecal samples, only

two—termites of the genus Macrotermes and bee larvae (Apis africana, tentative

identification)—can be positively identified as dietary components. Two of the other three—a

tentatively identified roundworm (Ascarias spp.) and a mass of small, unidentified worms—are

apparently parasites, while hairs observed in one sample may have been from a chimpanzee,

although these were light in color.

Four distinct periods can be identified over the course of the year in terms of chimpanzee

diet (Table 2, p. 266). First, in February-March, most fecal samples were composed primarily of

one of six food items, three of which are not fruits. This period had the lowest availability of

ripe fruits based on phenological observations (Figure 2, p. 259). The mean number of items per

fecal sample for February-March was 3.06±1.11. Second, in April-July, fruit remains dominated

most fecal samples; this period also had fairly high availability of fruits, although few of the

fruits that were abundant in fecal specimens were available (Figure 2, p. 259). The mean number

of items per fecal sample was 4.08±1.50. The number of samples collected during this period—

the hot, dry season—was relatively low, because dry environmental conditions meant that many

observed fecal remains were dry, and thus did not meet the ‘fresh’ standard for collection. No

data was collected in August. Third, in September-October, diet composition is almost entirely

different from that in January-July. Most fecal samples were composed primarily of either

244 bamboo stem fiber, or remains of the fruits of Spondias mombin, Grewia bicolor, or Cissus

populnea. These fruits were highly abundant during September-October (Figure 2, p. 259). The

mean number of items per sample in September-October was 3.04±1.14. Finally, diet

composition in November-December had composition similar to that of both September-October

and February-March. The availability of fruit foods also showed some overlap between these

periods (Figure 2, p. 259). The mean number of items per sample was highest in November-

December (4.21±1.31).

Diet composition certainly varies somewhat from year to year depending on the

abundance of food sources. Local residents considered 2004 a notably poor year for edible wild

fruit productivity generally, and especially for baobab and Hexalobus monopetalus. No

comparative data is available to evaluate this impression. High percentages of the number of

baobabs observed in September-December had ripe fruits. However, H. monopetalus—which

has sweet, moist fruit—occurred in only one fecal sample, and of 232 H. monopetalus

individuals observed during vegetation sampling, only one (with <10 ripe fruits, on 18 Sep 2004)

was observed with fruits. Observed diet composition (Table 2, p. 266) may be more indicative

of chimpanzee behavior in years when the abundance of fruits is relatively low.

Distribution of water sources and food patches. Permanent surface water sources and

chimpanzee food patches are both most abundant along rock outcrops (Figure 2, p. 259). Water

sources along cliffs are all springs or seeps coming from exposed sedimentary layers in

sandstone bedrock. Thus, in the southern portion of the research area there are no permanent

water sources along outcrops, because these are dolomite, whose hydrogeology differs from

sandstone (DCTD 1990). No current human use of water sources along cliffs was observed or

indicated in interviews. However, interviewees stated that past residents of several abandoned

245 settlements heavily used several of these sources prior to about 1940. In the relatively flat areas

away from outcrops, nearly all water sources are depressions in seasonal streambeds. Humans

regularly use most of these waterholes, primarily to provide water to domestic livestock.

Most forest patches (vegetation types 1 and 2) occur along rock outcrops, although two

occur in steep, narrow ravines incised into the edge of ferricrete hardpans, in the southern portion

of the research area (Figure 2, p. 259). Most chimpanzee food patches identified more than

c.100 m from a rock outcrop were baobab groves associated with abandoned settlement sites,

although only a small proportion of abandoned settlement sites host a large number of large

baobab individuals. Most sites have either a small number of large baobabs (≤2), or have only

small individuals. Finally, particularly in the southern portion of the research area, some food

patches occur along seasonal streams where food plants, especially Ficus spp., occur

gregariously in bamboo thicket vegetation.

All vegetation types include species for which evidence was collected of chimpanzee

consumption, but chimpanzee food plants have highest importance values in gallery forests

(vegetation types 1 and 2: Table 3, p. 268). If importance values of all food plants are used to

estimate the abundance of food plants in vegetation types, several types of woodland and

bamboo thicket are nearly as important sources of food plants as gallery forest, primarily because

of the high relative frequency and proportional abundance of Pterocarpus erinaceus, and the

high basal dominance of bamboo in many vegetation types. However, if only plants with fruits

eaten by chimpanzees are retained in this analysis, food plants are clearly most abundant in

gallery forests (Table 3, p. 268). Notably, the vegetation types with the next highest importance

values for fruit-food plants are: 1) a type of woodland vegetation that occurs exclusively at

abandoned settlement sites, and 2) bamboo thicket vegetation.

246 Distribution of nests and chimpanzee observations. A total of 695 nests in 224 nest

groups and 131 chimpanzees in 48 groups were observed throughout the research area (Figure 3,

p. 261). At this scale, chimpanzees and nests were observed in two broad patches—the northern

and the southern parts of the research area—that are separated by a gap several kilometers wide.

This gap corresponds to an area of relatively flat topography without bedrock outcrops. As noted

above, there are topographical and geological differences between the northern and southern

portions of the research area.

The median number of nests per group differs between the northern and southern areas,

for all months and for three of four multi-month periods (Table 4, p. 269). A two-tailed Mann-

Whitney U test (Bailey 1995) shows that the difference in nest group size is significant for the

all-months medians (z=3.02, p<0.01). Difference in nest-group size between these two areas was

also significant (z=2.53, p<0.05) for the September-October period.

Within the broad northern and southern patches, nest groups are distributed patchily in

dense clusters at a limited number of sites where chimpanzees build nests repeatedly throughout

the year (Figures 3 & 4, pp. 261 & 262). Many of these sites occur along cliffs, but others occur

some distance away from cliffs.

The majority of nest groups were observed in vegetation types characteristic of cliff

habitats, although the proportion of nests groups in cliff habitats was lower in the southern area

than in north (Table 5, p. 270). Most other nest groups were observed in woodland habitats,

although in the southern area a substantial minority occurred in bamboo thicket vegetation. The

proportion of nests occurring in different habitats changed during the year. The occupation of

some nest group sites in woodland areas was strongly seasonal, whereas others were occupied

throughout the year (Figure 4). Most nest group sites along cliffs were occupied during all

247 seasons. Notably, most nest groups observed in woodland vegetation were in abandoned human

settlement sites (Table 5, p. 270). Indeed, baobab groves at abandoned settlement sites are the

most abundant type of food patch away from cliffs (Figure 3, p. 261).

Finally, most nest groups were closer to food-plant patches than to permanent surface

water sources (Table 6, p. 271; Figures 3 & 4, pp. 261 & 262). The distances between nest

groups and either food-plant patches or permanent water sources varied somewhat from season

to season. Notably, during the period April-July—the hottest, driest period of the year—a

greater proportion of nest groups were nearer to permanent water sources than during any other

period. The greatest proportion of nest groups nearer to food patches occurred in the period

November-December.

Discussion

Chimpanzee geography. The research area appears to include parts of the home ranges of

two separate chimpanzee groups. The strongest evidence for this is the difference in nest group

size between the northern and southern portions of the research area for all months of

observation (Table 4, p. 269), which likely indicates different chimpanzee social organization in

the two areas (Baldwin et al. 1981). While environmental differences between the northern and

southern portions may contribute to different nesting behavior (cf. Baldwin et al. 1981)—in

particular, large predators, especially lions, are more abundant in the southern portion (Duvall,

unpublished data from 2004; Duvall & Niagaté 1997)—it is unlikely that chimpanzees belonging

to a single group would display such strong behavioral variation in moving from one portion of

the landscape to another (McGrew et al. 1996). Local residents also believe that different groups

occupy these areas, and report that it is very rare to see chimpanzees in the gap between the two

areas.

248 Difference in nest group size between the two areas during the four multi-month periods

displayed no or low significance (Table 4, p. 269). This is probably because the number of

observations per period in the southern area was insufficient to gain an accurate estimate of

median nest group size.

Habitat differences between nest groups in the northern and southern areas (Table 5, p.

270) probably reflect environmental differences between these areas, rather than behavioral

differences between chimpanzee groups. Sandstone outcrops dominate the northern portion of

the research area, while the southern portion is relatively flat with just two small dolomite

outcrops. This geology leads to topographical and, more importantly, hydrogeological

differences between the northern and southern areas. These differences directly affect

chimpanzees through the distribution and abundance of permanent water sources, and indirectly

through the distribution and abundance of vegetation types. There are fewer, more widely

dispersed permanent water sources in the south, and most of these are depressions in seasonal

streambeds, occurring in relatively flat areas away from rock outcrops. Chimpanzee food

patches are more widely dispersed as well, including in bamboo thicket vegetation along

seasonal streams—an uncommon location for food patches in the northern part of the research

area.

Chimpanzees appear to select nesting sites based on the location of food patches because

most nest groups, as well as observed chimpanzees, were nearer to food patches than water

sources (Table 6, p. 271). However, many food patches and water sources occur near one

another along rock outcrops, which means that the cost, in terms of increased distance to water,

of nesting in a cliff-side food patch may be minimal. Indeed, there appear to be more permanent

water sources in the research area than assumed by previous researchers (see Moore 1986; see

249 Moore 1985), meaning that water availability may not constrain chimpanzee ranging behavior as

much as presumed, at least in areas with sandstone outcrops. Elsewhere, such as in the southern

portion of the research area where there are not sandstone outcrops, water availability may affect

chimpanzee distribution more significantly.

Perched aquifers—the water held in sandstone outcrops—is directly important to

chimpanzees as the source of most permanent surface water in the research area, and indirectly

important as a reason why forest patches occur along sandstone cliffs (see Chapter 4). The

Manding Plateau area of southwestern Mali is important biogeographically as an area where

numerous rare and relict plants occur, particularly associated with sandstone outcrops (Jaeger

1959). Cliffs in general, and sandstone cliffs in particular, represent ecologically very stable

habitat where conditions change relatively little over millennia (cf. Larson et al. 2000). As a

result, sandstone outcrops host numerous rare and relict plant and animal populations in arid to

semi-arid areas around the world (Bowman et al. 1990; Bowman et al. 1988; Danin 1999; Jaeger

& Winkoun 1962; Woodford 2000), and in southwestern Mali in particular (Duvall 2001; Jaeger

1956; Jaeger 1959; Jaeger 1966; Jaeger & Jarovoy 1952).

Some chimpanzees in southwestern Mali are strongly linked to sandstone outcrops, and

should be considered a component of the rare and biogeographically distinctive biota of the

Manding Plateau. The abundance of water sources and food patches are both reasons why cliffs

are the habitat chimpanzees most frequently use. First, permanent water sources are

concentrated along sandstone outcrops, which links chimpanzees to these outcrops especially

during the dry season. Permanent surface water away from outcrops occurs primarily in deep

depressions along drainage channels, and humans have occupied or used most of these sites for

decades to centuries. The availability of groundwater to trees adapted to growing in cracks in

250 sandstone outcrops also helps create the conditions necessary for cliff-side forests (see Chapter

4). Microclimate in these forests is cool and humid throughout the year, even in the dry season

(Jaeger 1956). Indeed, chimpanzee and nest observations were most common cliff-side forests

during the dry season, January-July (Table 5, p. 270).

Second, many plants associated with sandstone outcrops are important food sources for

chimpanzees, especially during September-October. Of course, with the present data it is not

possible to say whether chimpanzees frequently use cliff habitats because of the abundance of

food plants these habitats host, or if chimpanzee food plants are abundant in cliff habitats due to

seed dispersal by chimpanzees. Nonetheless, several food plants are strongly abundant in cliff

habitats and rare elsewhere in the landscape. In particular, fruits of the shrub Grewia bicolor are

an important food source (Table 2). This plant dominates the edges of forest patches on

sandstone outcrops (Duvall 2001; Jaeger 1950a; Jaeger 1956), and is strongly associated with

bare sandstone outcrops (Table 3, p. 268; see also Chapter 4). The fruit-food plants Cissus

populnea, Cola cordifolia, Cordia myxa, Diospyros mespiliformis, Erythrophleum suaveolens,

Saba senegalensis, Sarcocephalus latifolius, Sorindeia juglandifolia, and Spondias mombin are

also most abundant in cliff-side gallery forests, but are less restricted to sandstone outcrops

(Table 3, p. 268; see also Chapter 4). Since cliffs provide a wide range of microhabitat

conditions ranging from xeric to mesic (Larson et al. 2000), cliff habitats host the greatest variety

of food plants, including many species that are characteristic of woodland and bushland, and not

just forest, vegetation. Foods from plants characteristic of cliff habitats—especially G. bicolor

and S. mombin—are particularly important in the period September-October (Table 2, p. 266).

Chimpanzees may prefer these foods, or the proximity of most cliff-side food patches to

permanent water sources, because the highest proportion of baobabs with ripe fruit occurred in

251 September-October, at the same time as peaks in fruit availability for G. bicolor and S. mombin

(Figure 2, p. 259), but evidence for baobab fruit consumption did not appear until after G.

bicolor and S. mombin were no longer fruiting (Table 2, p. 266).

While cliff habitats are ecologically stable (Larson et al. 2000), perched aquifers in

sandstone outcrops ultimately depend upon recharge from rainfall (Dunne 1990). Africa is the

least-studied, populated continent with regard to the potential effects of climate change (IPCC

1996). While minor increases in average annual precipitation are predicted for semi-arid West

Africa (Hély et al. 2006), the timing of rainfall is expected to change more substantially

(Caminade et al. 2006). The effects of such change are not known precisely, but forest

vegetation is expected to be most sensitive to changes in rainfall amount and timing (Hély et al.

2006). Additionally, changes in precipitation regimes will substantially change runoff rates, and

thus rates of groundwater recharge (de Wit & Stankiewicz 2006). Considering that West Africa

has experienced increased aridity in the past 160 years (Schöngart et al. 2006), forests reliant

upon groundwater may already face significant climate-change induced stress. Monitoring

groundwater flow rates in sandstone outcrops and the composition, structure, and distribution of

forests on these outcrops should be considered an important aspect of chimpanzee conservation

and management efforts in Mali.

Chimpanzees and humans. Humans and chimpanzees have shared Sudanian West Africa

for thousands of years, and the results show that chimpanzees are ecologically adapted to some

types of agricultural landscape. African farmers have profoundly altered plant distributions

through migration, settlement, and cultivation (O'Brien & Peters 1998). In southwestern Mali,

human settlement has led to the development of baobab groves at settlement sites (see Chapter

5), and to the introduction of other edible fruit-bearing, African trees that were originally absent

252 (Maranz & Wiesman 2003). These baobab groves are important to the tree’s reproduction

because large (i.e. reproductive) individuals are most abundant, and significantly overabundant

relative to other habitat types, in abandoned settlements and fields (see Chapter 5). Other wild

fruit trees are present at abandoned settlement sites, due in part to human activities, but also

because the environmental conditions at these sites are suitable for trees with seeds dispersed by

animals that feed on the fruits of trees, like baobab, that survive settlement occupation (see

Chapter 4). Chimpanzees are amongst those animals that benefit from the density and

abundance of fruit trees at some abandoned settlement sites, and contribute to the further

development of these fruit-tree patches by dispersing seeds to these sites.

Chimpanzee use of abandoned settlements is strongly seasonal, corresponding to the

abundance of food sources in different habitat types (cf. Naughton-Treves et al. 1998). Half

(50.8%) of all nest groups observed during November-December were in abandoned settlement

sites, while during other portions of the year, only 6.0-15.9% were in such sites (Table 5, p. 270).

Chimpanzee use of abandoned settlements corresponds to the period when baobab fruit

comprises a significant portion of the animal’s diet (Table 2); baobabs are most abundant in

abandoned settlements (Table 3, p. 268; see also Chapter 5). During other parts of the year, most

important food items come from plants associated with cliff habitats (Tables 2 & 3, pp. 266 &

268). Notably, though, chimpanzees do not appear to increase their use of baobab groves at

abandoned settlements in direct correspondence to increases in the availability of ripe baobab

fruit. The highest proportion of baobabs with ripe fruit occurred in September-October (Figure

2, p. 259), but sharply increased use of abandoned settlement sites did not occur until November-

December, after two important food plants associated with cliff habitats—Grewia bicolor and

Spondias mombin—were no longer fruiting (Figure 2, p. 259). Similarly, evidence for

253 chimpanzee feeding on figs and termites—both of which represent fallback foods for

chimpanzees, and frugivores more generally, when fruit abundance is low (McGrew et al. 1988;

Terborgh 1986)—increased only after G. bicolor and S. mombin had finished fruiting (Table 2, p.

266), even though figs (and, presumably, termites) were available throughout the year (Figure 2,

p. 259). Baobab groves at abandoned settlement sites may represent a fallback source when fruit

food is not abundant in cliff habitats.

Past observations of chimpanzee ecology in Mali further support these results. Most

published observations have come, coincidentally, from the period November-January, when

observers found high abundance of chimpanzee nests near large baobabs (Granier & Martinez

2004; Moore 1985). In contrast, Pavy (1993), who surveyed the area in February-April 1992,

made no mention of chimpanzee or nest observations near baobabs and had few observations in

woodland areas more generally; he did find a strong correlation between nest distribution and

topography. “[T]hree times out of four” he found nests when encountering a cliff or steep slope

along line transects (Pavy 1993: 19). While he did not describe vegetation, his data on the

identification of trees hosting nests (J.-M. Pavy, personal communication, December 2002)

suggest that at least 60% of nests were observed in vegetation comparable to vegetation types 1

or 2 (Table 1, p. 264).

Local residents also recognize that chimpanzees use abandoned settlements primarily

during baobab fruiting season, which is considered an unintentional outcome of settlement

practice. This knowledge has gained importance because of the influx of chimpanzee

researchers who have visited Solo since 1983. When the primatologist James Moore visited

Mali in 1983 and asked the village of Solo to provide a guide for his search for chimpanzees,

Solo’s leaders agreed because Moore’s proposition seemed to hold promise for future benefits

254 for the village (F. Dembélé, personal communications, March 2004). After discussion amongst

themselves separate from conversations held with Moore and his Malian colleague, Solo’s

hunters decided that the guide should lead him to several baobab groves at abandoned

settlements sites since baobabs were fruiting at the time (F. Dembélé, personal communications,

March 2004). The guide did not tell Moore that these baobab groves were abandoned settlement

sites (F. Dembélé, personal communications, March 2004). The hunter recalls finding fresh

nests at most of these groves, and Moore (1985: 60) reported that “many” nests were observed

“near fruiting baobabs”. While the group also visited other locations, including a bedrock

outcrop south of Solo, the decision of Solo’s hunters to focus search efforts on abandoned

settlement sites was based on their knowledge of the seasonality of human-baobab-chimpanzee

interaction. Similarly, the primatologist Pascal Gagneux visited Solo in November 1995,

seeking chimpanzee nests from which hair samples could be collected (cf. Gagneux et al. 1999).

Since Gagneux’s visit was several weeks earlier in the year, his guide decided to visit primarily

forest patches along the sandstone outcrop north of Solo, but also visited two baobab groves at

abandoned settlement sites (K. Dembélé, personal communications, November 2004). They

found chimpanzees in both types of site, but more abundantly in cliff-side forests (K. Dembélé,

personal communications, November 2004).

Maninka farmers do not aim to attract chimpanzees to settlement sites, because

chimpanzees are not considered a valuable resource, as a source of meat or other products

(Duvall & Smith 2005; Granier & Martinez 2004). However, occupied and abandoned

settlement and field sites are often managed with an explicit goal of attracting certain frugivorous

or omnivorous game animals (cf. Fairhead & Leach 1996), such as duikers (Cephalophinae),

bushbuck (Tragelaphus scriptus), and warthog (Phacochoerus aethiopicus). For instance, fig

255 trees (Ficus spp.) are often maintained in fields and around occupied and abandoned settlement

sites because fallen figs attract these animals; humans generally do not eat wild figs. Few wild

animals consume baobab fruits because the shells are too difficult to break open; baobabs are

maintained because of their utility to humans, in addition to their historical and spiritual

significance (see Chapter 5). Chimpanzees are able to break open baobab fruits (Marchant &

McGrew 2005), and their use of abandoned settlements is an unintended, but clearly understood,

outcome of Maninka settlement practice.

Human and chimpanzee use of baobab fruit represents potential competition between the

species. Residents of Solo do not consider chimpanzees a pest, though, because humans tend to

rely on baobabs in occupied settlements and fields, and abandoned settlements near primary

footpaths. Chimpanzees visit only abandoned settlements, and generally only those distant from

occupied settlements and primary footpaths (Figure 3, p. 261). An aggressive response by

conservationists to potential human-chimpanzee competition for baobab fruit would perhaps

entail prohibition of all baobab fruit collection outside of occupied settlements and fields. This

response would overlook the importance of wild fruit to rural livelihoods, and hinder the cause of

chimpanzee conservation by alienating rural people who have great knowledge of chimpanzee

behavior and the ability to either protect or extirpate Mali’s chimpanzees. Knowledgeable local

residents—indigenous hunters—recognize that humans and chimpanzees share wild fruit

resources by foraging in different parts of the landscape. Most local residents would accept

policies that clearly specify open and closed areas for wild fruit collection if these are developed,

explained, and enacted in a way that reflects indigenous knowledge, and not just the global rarity

of chimpanzees and the conservation significance of Mali’s population.

Conclusion

256 In southwestern Mali, chimpanzees frequently use abandoned settlement sites during the

time of year when baobab fruit is a significant component of their diet. Baobab groves at

settlement sites develop directly and indirectly as the result of human activities, which means

that humans have effectively increased the distribution of chimpanzee food plants across the

landscape, creating seasonally important habitat patches. Baobab is less abundant elsewhere in

chimpanzee range, so it is unclear how widely chimpanzees and humans interact in this way

because. However, similar relationships between humans, plant distributions, and wildlife

probably occur widely in Africa where human settlement practice and history have led to the

development of distinct vegetation patches at abandoned settlements. Indirect effects of human

activities on wildlife certainly have a deep evolutionary history in Africa, and conservationists

should recognize these relationships in order to develop policies that more accurately reflect

land-use ecology, and minimize potential, negative, long-term effects of the forced alteration of

customary land-use practices. While short-term threats to chimpanzee survival must remain a

priority, the biological and, in some aspects, ecological similarity between chimpanzees and

humans may mean that human activities can serve as an asset to the long-term viability of

chimpanzee populations and their habitat. Wildlife managers must recognize this possibility, and

carefully evaluate how human selection and use of wild fruit trees may, over longer timescales,

prove beneficial to chimpanzees.

257

Figures and tables for Chapter Five

258 Figure 1. Western Mali, showing chimpanzee distribution and research area. Inset map of Africa

shows area represented by main map. Chimpanzee distribution from Duvall et al. (2003).

259 Figure 2. Phenology of chimpanzee fruit-food plants. Numbers to the left of graphs show the

percent of all individuals observed per month with any ripe fruit; numbers below graphs show

the number of individuals observed with any ripe fruit over the total number of individuals

observed per species. Individual graphs are shown only for species with fruits eaten by

chimpanzees that composed >20% of >1 fecal samples. The graph “Ficus spp. Fig” lumps

observations for 13 fig species, including F. cordata, the only species for which there is species-

specific evidence for its consumption. The graph “All other confirmed fruit foods” lumps

observations for the 14 species with fruits eaten by chimpanzees that occurred in at least one

fecal sample (i.e. Diospyros mespiliformis, Saba senegalensis, Cordia myxa, Cola cordifolia,

Lannea spp., Sorindeia juglandifolia, Hexalobus monopetalus, Parkia biglobosa, Vitex

madiensis, Tamarindus indica, and Boscia angustifolia), or for which there was direct evidence

for chimpanzee consumption (i.e. Erythrophleum suaveolens). The item Lannea spp. includes

observations of L. acida, L. microcarpa, and L. velutina. The graph “Candidate fruit foods”

lumps observations for 23 other species encountered in vegetation samples that either have fruits

considered edible by local residents, or are congeneric with plants having fruit remains occurring

in fecal samples (i.e. Annona senegalensis, Borassus aethiopum, Cordyla pinnata, Detarium

microcarpum, Diospyros abyssinica, Gardenia erubescens, Grewia flavescens, G. lasiodiscus, G.

venusta, Landolphia heudelotii, Malacantha alnifolia, Manilkara multinervis, Pachystela

brevipes, Parinari curatellifolia, Raphia sudanica, Sclerocarya birrea, Strychnos spinosa,

Trichilia emetica, Vitellaria paradoxa, Vitex doniana, Ximenia americana, Ziziphus mauritiana,

and Z. mucronata).

260

261 Figure 3. Biophysical features in the research area, and observations of chimpanzees and nests.

0 5 km

1

2

3

4

5

6

a) Biophysical features and survey loops

Clif °

Primary footpaths

Seasonal streams

Permanent surface water sources

Chimpanzee sightingsChimpanzee nest groups

Food-plant patches: cliff forests, baobab groves,other (primarily bamboo thickets)

Bafing Reservoir3 Survey loops

Occupied settlements

b) Chimpanzee and nest observations

Solo

262 Figure 4. Seasonal observations of chimpanzees and nests.

263

264

Table 1. Floristic vegetation types present in the research area. For full description of these

vegetation types, as well as explanations of the vegetation type codes and names used below, see

Chapter 4. These vegetation types are based on floristic composition, but most have strong

environmental correlates, indicated in the ‘Habitat description’ column. Vegetation structural

categories (i.e. forest, thicket, woodland, and wooded grassland) reflect Lawesson’s (1995)

definitions.

265 Code Name Habitat description

1 Cola cordifolia-Spondias mombin forest

Topographically sheltered sites, in narrow ravines along rock outcrops. Most sites with permanent surface water and permanently wet soil.

2 Gilletiodendron glandulosum-Hippocratea indica forest

Topographically sheltered sites with extremely rocky (sandstone) soil. Many sites with seasonal surface water, all with seasonally dry soil.

3 Oxytenanthera abyssinica (bamboo) thicket

Strongly dominated by bamboo. Sites along seasonal streams that are not sheltered from fire.

4 Xeroderris stuhlmannii-Bombax costatum wooded grassland

Steep, rocky slopes, in topographically high sites (i.e. away from drainage channels).

5 Ferricrete wooded grassland Topographically high sites with gravelly, silty soil shallowly overlying a ferricrete horizon.

6 Combretum nigricans wooded grassland

Topographically high sites with shallow, infertile, and dry soil.

7 Crossopteryx febrifuga wooded grassland

Sites with silty soil that are seasonally waterlogged due to poor drainage.

8 Terminalia macroptera woodland

Topographically high sites with apparently low fertility.

9 Rupicolous bushland Topographically high sites with very rocky (sandstone) soil.

10 Pterocarpus lucens-Gueira senegalensis bushland

Sites with naturally eroded, clayey loam soil, at seasonal drainage channel heads.

11 Dichrostachys cinerea bushland

Sites disturbed by past settlement. Vegetation dominated by species with edible fruits.

12 Acacia ataxacantha-Combretum micranthum woodland

13 Terminalia macroptera-Vitellaria paradoxa woodland

14 Pteleopsis suberosa-Hymenocardia acida woodland

15 Pterocarpus erinaceus-Vitellaria paradoxa woodland

These four woodland vegetation types share characteristic habitat: sites with deep, arable soil that is neither xeric nor highly mesic. Over 80% of these sites have been disturbed by past human settlement or cultivation.

266 Table 2. Seasonality of chimpanzee diet. Abbreviations: B=bark; Fl=flower; Fr=fruit; L=leaf;

S=seed; St=stem; Unk.=unknown; vol.=volume. Food items are arranged from top to bottom in

the temporal order in which evidence for their consumption was collected. Thus, items that were

first encountered in fecal samples early in 2004 are found toward the top of the table, while items

that were first encountered later in the year are found toward the bottom. Ficus cordata is not

listed in this order, but is placed next to the entry for unidentified Ficus seeds. Tentative

identifications of food items are given in brackets. Gray ovals indicate the volumetric

composition of fecal samples (see key); black diamonds represent direct evidence of chimpanzee

diet, through observations of chimpanzees. Each vertical column of gray ovals represents the

contents of one fecal sample. Direct evidence is indicated in the column of the fecal sample

whose collection date corresponds most closely with the date of the direct observation.

267

268 Table 3. Abundance of chimpanzee food plants in vegetation types. For Vegetation type codes,

see Table 1 (p. 264). Species names have been abbreviated from those listed in Table 2 by

retaining the first two letters of the genus and species names. Values shown are importance

values (IVs) per species for each vegetation type. IVs <0.1 are represented by a tilde ‘~’.

‘Totals’ are the sums of IVs for all species per vegetation type. ‘Fruits’ row sums IVs only for

plants with fruits for which there is evidence of chimpanzee consumption (Table 2, p. 266).

Species Vegetation types 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Ad. di. 0.1 0.1 0.4 0.4 0.9 0.9 0.7 0.2 0.2 Bo. an. 0.4 0.7 0.1 0.1 0.6 0.1 ~ Ci. po. 0.4 0.3 0.1 0.2 0.1 0.1 0.1 ~ 0.1 Co. co. 1.2 0.4 0.1 0.1 Co. my. 0.4 0.1 0.2 0.1 0.1 0.1 Co. pi. 0.1 0.2 0.4 0.2 0.5 0.5 0.5 0.1 0.2 0.7 0.3 0.2 Da. ol. 0.2 0.1 0.3 0.2 0.9 0.1 0.5 0.1 0.7 0.3 0.1 Di. me. 1.5 0.6 0.2 0.2 0.1 ~ ~ ~ Er. su. 0.4 0.2 0.4 Fi. spp. 1.3 1.2 1.0 0.1 0.2 0.3 1.4 0.1 1.1 0.1 0.2 ~ ~ Gr. bi. 0.7 1.1 0.1 0.2 0.8 ~ He. mo. 0.1 0.3 0.6 0.7 0.9 0.3 0.8 0.9 0.6 0.2 0.1 0.4 0.3 La. spp. 0.5 0.3 0.5 1.0 1.5 1.0 1.2 0.9 0.9 0.8 0.8 1.0 1.4 1.1 Ox. ab. 1.0 0.8 2.5 0.7 1.6 0.1 0.4 0.2 0.4 0.2 0.5 0.3 0.3 0.3 Pa. bi. 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.3 0.2 0.1 Pi. th. 0.1 0.3 0.2 0.2 0.3 0.4 0.9 0.6 1.1 0.6 0.8 Pt. er. 0.2 0.7 1.3 1.1 0.8 0.7 1.3 0.9 0.6 0.9 1.5 1.1 1.4 1.7 Pt. lu. 0.1 0.3 0.1 1.4 1.6 0.1 Sa. la. 0.7 0.8 0.8 0.1 ~ ~ Sa. se. 0.5 0.9 0.5 0.1 0.1 0.1 0.1 0.2 0.1 0.1 ~ So. ju. 0.2 0.1 Sp. mo. 1.2 1.0 0.4 0.1 0.2 0.1 ~ Ta. in. 0.1 0.1 ~ ~ Vi. ma. 0.6 0.2 0.4 0.2 0.1 0.1 0.1 0.4 0.1 Totals 11.3 8.6 8.3 5.2 7.1 5.2 3.5 4.8 5.4 6.1 6.4 5.7 6.6 5.6 5.0 Fruits 9.9 7.8 4.9 2.9 3.4 3.8 1.4 2.5 2.9 2.5 5.2 3.4 3.7 3.3 2.7

269 Table 4. Summary statistics for nest group size. Abbreviations: n, number of nest groups

observed; p, statistical significance. Mann-Whitney U test was used to compare median nest-

group size between the northern and southern portions of the research area. Significance shown

only where p<0.05.

All months Jan-Mar Apr-Jul Sep-Oct Nov-Dec

n 224 50 63 37 74 Entire area median 2.5 3 2 2 2

n 198 44 60 32 62 Northern portion median 2 3 2 2 2

n 26 6 3 5 12 Southern portion median 4 4 2 4 4 p <0.01 <0.05

270 Table 5. Nest group characteristics. ‘Cliff’ habitats includes Vegetation types 1, 2, and 9 only

(Table 1, p. 264). ‘Woodland’ habitats include vegetation types 12, 11, 13, and 15 only (Table 1,

p. 264). Observations made in ‘Abandoned settlements’ are a subset of the observations made in

woodland, and occurred ≤100 m from the center of an abandoned settlement site. ‘Thicket’

habitat is vegetation type 3 only (Table 1, p. 264). Nests were not observed in other vegetation

types.

Area Habitat All

months Jan-Mar Apr-Jul Sep-Oct Nov-Dec

Cliff 138 (61.6%)

40 (80.0%)

48 (76.2%)

19 (51.4%)

31 (41.9%)

Woodland 79 (35.3%)

7 (14.0%) 14 (22.2%)

17 (45.9%)

41 (55.4%)

Abandoned settlement

54 (24.1%)

3 (6.0%) 10 (15.9%)

9 (14.3%) 32 (50.8%)

Entire research area (n=224)

Thicket 7 (3.1%) 3 (6.0%) 1 (1.6%) 1 (2.7%) 2 (2.7%) Cliff 135

(68.2%) 39

(88.6%) 46

(76.7%) 19

(59.4%) 31

(50.0%) Woodland 63

(31.8%) 5 (11.4%) 14

(23.3%) 13

(40.6%) 31

(50.0%) Abandoned settlement

49 (24.7%)

3 (6.8%) 10 (16.7%)

9 (12.1%) 27 (43.5%)

Northern portion (n=198)

Thicket 0 0 0 0 0 Cliff 3 (11.5%) 1 (16.7%) 2 (66.7%) 0 0 Woodland 16

(61.5%) 2 (33.3%) 0 4 (100%) 10

(83.3%) Abandoned settlement

5 (19.2%) 0 0 0 5 (41.7%)

Southern portion (n=26)

Thicket 7 (26.9%) 3 (50.0%) 1 (33.3%) 0 2 (16.7%)

271 Table 6. Distances between nest groups and water sources or food patches.

All

months Jan.-Mar. Apr.-Jul. Sep.-Oct. Nov.-

Dec. Nest group observations 224 50 63 37 74 Chimpanzee observations 48 7 18 17 6 Observations nearer to water source than food patch

44 (16.2%)

8 (14.0%)

18 (22.2%)

5 (9.3%)

10 (12.5%)

Observations ≤100 m to both water source and food patch

42 (15.4%)

12 (12.1%)

11 (13.6%)

12 (22.2%)

8 (10.0%)

Observations nearer to food patch than water source

186 (68.4%)

37 (64.9%)

52 (64.2%)

37 (68.5%)

62 (77.5%)

Median distance to nearest food patch (meters)

216 171 255 250 185

Median distance to nearest water source (meters)

578 476 619 656 763

272 Chapter Seven: Conclusion

For decades, researchers have recognized that humans have profoundly shaped African

ecosystems by affecting both the abundance and distribution of plant species. Earlier analyses of

human impacts on African ecosystems tended to underscore ‘negative’ impacts—such as

deforestation or other declines in plant density and/or diversity—while more recent works have

tended to draw attention to ‘positive’ impacts—generally increases in (or the stability of) plant

density or diversity. In both cases, past studies have often overgeneralized the spatial extent of

anthropogenic impacts, either across focal landscapes or more broadly across the continent. In

short, Africa—and West Africa in particular—has often been represented as a place where

anthropogenic disturbance is the primary determinant of ecosystem composition and structure.

In contrast, this dissertation shows that human activities are not necessarily the dominant factor

determining these ecosystem characteristics in rural landscapes. Humans act within biophysical

and sociocultural contexts that create variability in disturbance intensity, and that determine the

significance of human disturbance as a source of ecological heterogeneity and change.

Biogeographers recognize that similar vegetation does not necessarily develop in ecologically

similar sites (McCune & Allen 1985). Likewise, human-environment scholars must recognize

that the intensity and significance of ecosystem response to human disturbance depends on the

specific context in which disturbance occurs. Thus, human environmental impacts vary—across

landscapes as well as between different landscapes—depending on the biophysical and

sociocultural contexts in which disturbance occurs. Many generalized relationships between

human activities and biodiversity resources are accurate in many landscapes, but some of these

generalized relationships may be inaccurate in portions, or all, of some landscapes.

273 The importance of recognizing local context to understand human environmental impacts

is the core of many geographical critiques of conservation practice. This dissertation contributes

to this widely shared geographical argument. More importantly, however, it supports two

conclusions on how human-environment knowledge is created and applied. First, this

dissertation shows that the dominance of certain scientific traditions over others may create an

inaccurate appearance of certainty in scientific assessments of human-environment relationships.

Second, this dissertation shows that the spatially fixed and temporally absolute boundaries

generally used in conservation practice are inadequate for managing human-environment

interactions in strongly heterogeneous environments. These conclusions are discussed separately

in the following two sections.

Scientific knowledge creation and transmittal. Why have past researchers failed to

recognize adequately the importance of context in analyses of anthropogenic environmental

change in West Africa? This dissertation contributes to recent work on the role scientific

discourse has played in the development and maintenance of dominant, but inaccurate,

environmental narratives of human-environment relationships in Africa (e.g. Bassett &

Crummey 2003; Cline-Cole & Madge 2000; Fairhead & Leach 1998; Leach & Mearns 1996).

Many of these challenges to dominant environmental narratives have shown that indigenous

knowledge can offer more accurate representations of biophysical reality than environmental

sciences have in the West African context (Bassett & Koli Bi 2000; Fairhead & Leach 1996;

Richards 1985). Indeed, Chapter 3 of this dissertation shows that Maninka categorization of

‘land cover’, for instance, is considerably more detailed than that of natural resource scientists.

Nonetheless, counterpoising scientific and indigenous knowledge oversimplifies the range of

viewpoints people have on environmental questions (Agrawal 1995; Atran 1990). In particular,

274 political ecologists have often overlooked divergent traditions that exist within the broad

scientific community (Turner 2005). ‘Science’ has been portrayed monolithically in many recent

challenges to dominant environmental narratives in Africa, although some have acknowledged

differences between scientists concerned with particular problems (Bassett & Boutrais 2000;

Beinart 1996; Duvall 2003; Richards 1985). In contrast, this dissertation shows that ‘science’

does not necessarily offer a single, dominant narrative, against which only local knowledge may

be offered as counternarratives. ‘Science’ may include neglected literatures and viewpoints that

can effectively counter dominant narratives.

Many of the findings of this dissertation build upon earlier academic work that has been

neglected in the Malian, if not African, research literature. Two key examples may be drawn

from the findings. First, conservationists in southwestern Mali have focused their efforts on rare

trees associated with sandstone bedrock, and on chimpanzees associated with these trees. To

explain why these trees are associated with sandstone outcrops, conservationists have relied on

the anthropogenic deforestation theory of African vegetation history, which has dominated

natural scientific discourse in Africa for decades (Chapter 4). This theory depicts the patchy

distribution of the endemic tree Gilletiodendron glandulosum and the forest type it dominates as

evidence for past deforestation caused by indigenous land management practices (Duvall 2003).

However, anthropogenic deforestation theory fails to incorporate abundant findings from West

Africa (e.g. Chudeau 1917; Larminat 1927) and worldwide (Larson et al., 2000) that show

clearly how cliff habitats create ecologically very stable microhabitats, and are likely to host rare

species (Chapter 4). Second, Chapter 1 shows that representations of Maninka settlement in the

Bafing area as an expanding frontier neglects not only local settlement history, but also a

275 literature on the wide occurrence of shifting settlement practice in Africa (de Schlippe 1956; e.g.

Murdock 1967; Netting 1993; Richards 1978; Sidikou 1974; Stone 1996).

The dominance of particular scientific viewpoints—such as anthropogenic deforestation

theory, or frontier-style settlement—on the geography of biodiversity has given the appearance

of certainty in understanding how human activities pose threats to natural resources in

southwestern Mali. Yet this dissertation, supported by neglected scientific literature, shows that

biodiverse habitats do not necessarily represent the remains of pre-settlement landscapes humans

have devastated through resource use. Instead, these habitats may reflect ecological conditions

that exist independently of humans (Chapter 4), or they may represent the outcome of human

activities (Chapters 5 and 6). Again, however, context matters. For instance, in the case of

Polylepis forests in the Andes—similarly patchy habitats dominated by a narrowly endemic tree

genus—discursive inertia privileged biophysical conditions over human activities to account for

the patchy distribution of these trees, a viewpoint that has recently been overturned by careful

observations on Polylepis ecology and distribution (Fjeldså 2002; Purcell et al. 2004). ‘Science’

may be inherently political (Forsyth 2003), but it also claims to be scientific and thus can be

evaluated on its own terms. Uncovering the scientific lineage of received ideas also exposes

other scientific ideas, which compose alternate narratives about particular environmental

questions. Human-environment scholars must recognize that much research of current interest

and apparent novelty may have neglected antecedents, and must seek these antecedents in order

to improve understanding of scientific knowledge transmission, as much as human-environment

relationships.

Boundaries and mobility in conservation practice. The most significant contribution this

dissertation makes is to understanding the biogeography of human activities in the semi-arid

276 tropics, and how this affects the appropriateness of conservation practices in different

landscapes. This contribution is grounded on recognition of three aspects of local context,

underscored by this dissertation, that may lead to variability in the intensity or significance of

human disturbance.

First, different land uses—even closely linked land uses, like settlement and cultivation in

agrarian rural landscapes—differ significantly as ecological disturbances because indigenous

knowledge and practice varies according to land use. Specifically, vegetation analysis in

Chapter 4 shows that settlement and cultivation have distinct effects on vegetation composition,

because the goals of vegetation clearing for settlement differ from that practiced for cultivation.

As argued in Chapter 2, settlement must be recognized as a distinct land use in natural resource

conservation and management. Maninka farmers seek to eliminate most woody vegetation from

settlement sites for the duration of site occupation, while in fields the goal is to reduce tree

canopy cover, without killing trees, for the relatively short period of swidden use. These

differences in land management practice favor different plant adaptations to disturbance:

reproduction from seed is more important in settlement sites, while vegetative reproduction is

more important in fields (Chapter 4). Variation in ecological knowledge and practice—as a

function of land-use goals and as a characteristic of difference between individuals and

societies—creates variation in the intensity of environmental impacts caused by humans.

Second, biophysical factors that are significant in biotic variation at regional spatial

scales—such as soil and hydrology in West Africa and the semi-arid tropics more generally—

may limit the significance of human disturbance as a source of ecological heterogeneity in focal

landscapes. Vegetation analysis in Chapter 4 shows that human activities affect vegetation

composition, but also that edaphic characteristics are a more significant source of habitat

277 heterogeneity across the research area. Similarly, analysis of chimpanzee distribution in Chapter

6 shows that chimpanzees use anthropogenic habitat patches, but also that other, non-

anthropogenic types of habitat are more frequently used throughout the year. In both cases, a

spatial framework created by edaphic variation limits settlement and cultivation to specific

portions of the focal landscape. Ecological heterogeneity in other parts of the landscape is not

attributable to human activities. Furthermore, edaphic variation can limit the ecological

significance of disturbance: the composition of disturbed vegetation may not differ significantly

from other vegetation, especially if observed relative to the full range of plant communities

present in the landscape. In other words, recognition of some anthropogenic disturbances—

either as types of event or as specific events—may be meaningful primarily as indications of

human history and not of ecological heterogeneity, depending on which portions of a landscape

and which ecological processes are of interest. Indeed, for Maninka farmers, manyang

(‘fallows’) are distinct because of their history and not necessarily because of vegetation, soil, or

other biophysical characteristics (Chapter 3).

Third, human influence on ecosystem structure and function must be understood in a

long-term temporal context. Awareness of long-term timescales is particularly important in

Africa because of its extremely long human history. Analysis of spatiotemporal relationships

between humans, baobabs, and chimpanzees in Chapters 5 and 6 shows that the ecologies of

these species are closely intertwined, both directly (for humans and baobabs, and for baobabs

and chimpanzees) and indirectly (for humans and chimpanzees, in terms of habitat distribution,

composition, and abundance). These linkages have arisen from a long history of interaction, and

contribute to the emergent structure of the focal ecosystem. This history matters for the long-

term, future ecosystem structure in the research area. As shown in Chapter 2, altering settlement

278 practice alters the spatial distribution of human impacts, and perhaps also the intensity of impacts

in affected areas. Spatial change in settlement practice may directly impact biodiversity

resources—if, for instance, human use of biodiverse habitats increases in areas where the number

of people has increased because other areas are closed to settlement (Chapter 2)—but indirect

effects may be more significant in the long term—if reduced frequency of settlement

establishment and abandonment reduces recruitment for wild fruit trees across the landscape

(Chapter 5). While the effects of specific disturbance events may fade over decades to centuries

(Chapter 4), the anthropogenic disturbance regime—the cumulative effects of individual

disturbance events—over longer timescales may represent a mechanism that contributes to the

maintenance of certain ecosystem characteristics.

Recognizing how these aspects of local context may constraint the spatial and ecological

significance of human activities as environmental disturbances enables assessment of the

appropriateness of spatial and temporal scales implicit in conservation practice. Of all

conservation interventions, spatial and temporal scale is perhaps most strongly inherent in the

establishment of protected-area boundaries. Protected-area boundaries are in virtually all cases

spatially explicit, and generally also delineate absolute breaks in the spatial distribution of

allowed human activities, regardless of temporal variation in human activities or biophysical

conditions. However, these boundaries are created in landscapes where biophysical and

sociocultural processes either are unbounded spatially, or occur within spaces that are not

coterminous with protected areas. In either case, the rate and direction of environmental changes

associated with biophysical and sociocultural processes are not necessarily consistent, so that the

apparent appropriateness of a boundary may change over time. The inadequacy of rigid,

absolute borders to encompass spaces that are ecologically meaningful either for humans or other

279 ecosystem components helps create conservation failures in landscapes where mobility is a

crucial aspect of land use and livelihood security (Turner 2006).

Human mobility has been most widely recognized in landscapes where pastoral livestock

husbandry is practiced. In landscapes where shifting settlement and/or shifting cultivation are

practiced, mobility is as important to land use and livelihoods as in landscapes where pastoral

livestock husbandry is practiced, but the rate of mobility differs in settlement, cultivation, and

pastoral husbandry practices (cf. Stone 1996). Simplistically, time periods over which mobility

is evident in settlement practices are longer than those for cultivation, which are longer than

those for pastoralism—although there are longer-term dynamics in each type of land use that

complicates such simple, generalized comparisons. In any case, conservation practices that

emphasize rigid boundaries and absolute land-use delineations are poorly suited to managing

spatial mobility and variation over time, whether these are associated with humans or other

ecosystem components (Kozakiewicz 1995; Turner 2006). Clearly, short-term observations of

human practices or biophysical conditions—which have been central to the development of

conservation theory and practice in West Africa—are inadequate for understanding the spatial

dynamism of land use and associated environmental changes.

More importantly, the failure of conservationists to recognize long-term mobility in

traditional land management has meant that conservation practitioners are poorly equipped

conceptually to deal with environmental changes that develop over the course of decades, and

not shorter periods. For instance, rigid boundaries are unable to adjust to slow or subtle shifts in

the distribution of human activities or biodiversity resources, while absolute land-use

delineations are unable to adapt to infrequent boundary crossings by humans or wildlife resulting

from relatively short-lived environmental conditions regularly or widely separated in time, such

280 as seasonal change, or natural disasters. Conservationists must accept that: a) the importance of

mobility increases for organisms—including humans—as landscape heterogeneity and

patchiness increases (Kozakiewicz 1995); and b) accept that the distribution of human activities

and biodiversity resources changes over timescales longer than it is feasible to observe before

making conservation decisions. In other words, rigid and absolute boundaries are ecologically

alien to heterogeneous landscapes, and the introduction of such boundaries will sooner or later

disrupt natural and human ecological processes, many of which scientific observers may be

unaware because of their long periodicity. Thus, in markedly heterogeneous landscapes—such

as semi-arid tropical woodlands and grasslands, but not necessarily tropical rainforests—

conservationists should de-emphasize rigid enforcement of protected-area boundaries and

absolute protection of conservation spaces from ‘shifting’ people. Instead, conservationists

should focus on developing durable means through which people—conservationists, land

managers, tourists, researchers, etc.—can negotiate access to resources when this is both

necessary and feasible without precluding potential future uses.

The perceived imperatives of conservation reinforce the short-term focus of conservation

practice. Land-use changes contributing to biodiversity loss have proceeded rapidly and widely

in the last century. The rapidity of these changes in many areas has created the impression that

unless significant efforts are made in the short term, insignificant levels of biodiversity will

survive in the long term. Furthermore, regardless of the soundness of estimates or predictions of

environmental change, short-term threats must logically be reduced if their persistence prevents

the attainment of long-term goals. For instance, the short-term threat of human hunting must be

reduced to ensure the long-term survival of chimpanzees, even though habitat loss is the primary

long-term threat (Kormos & Boesch 2003). Altogether, current understanding of recent and

281 expected environmental changes creates substantial pressure to address and reduce perceived

threats to biodiversity as quickly as possible. Lost in this situation is consideration of the long-

term consequences of conservation interventions. Neglecting long-term planning in conservation

practice because of the perceived urgency of short-term threats is tantamount to wasteful

resource use, in which long-term sustainability is neglected because of perceived resource

abundance.

Partially as a result of the pressure to reduce short-term biodiversity threats, conservation

practitioners frequently address perceived problems by using generalized strategies that have

been successful elsewhere—such as IUCN’s general strategy of establishing “integral protection

zones” (see Chapter 2)—without considering the appropriateness of these strategies in the

specific contexts of particular protected areas. As a result, these strategies often represent blunt

means of achieving conservation goals, perhaps achieving some degree of success in attaining

specific goals, only while creating new problems because of their inappropriateness in a

particular context. This dissertation argues that the application of IUCN’s general strategy of

establishing “integral protection zones” will likely fail long-term, if not short-term, conservation

goals in Mali’s Bafing reserve, because the rigid and absolute boundaries this strategy creates are

inappropriate for the biophysical and socioeconomic contexts of the Bafing area. There are

many comparable cases in which generalized conservation strategies have proven clearly

inappropriate for meeting conservation goals, and have been counterproductive to conservation

success (e.g. Brockington & Homewood 1996; Daniels & Bassett 2002; Ite 2001; Koenig &

Diarra 1998; Neumann 1997; Redford 1991). While there are certainly conservation strategies

that are widely appropriate and not strongly influenced by local conditions—reducing hunting

pressure, for instance, will reduce population decline for hunted animal species—

282 conservationists must place greater emphasis on recognizing and understanding how specific

biophysical, sociocultural, and geographic contexts may limit the appropriateness of

conservation strategies that have proven effective in other contexts. Conservation strategies that

rely on the creation of immobile and inviolable conservation spaces are inappropriate in

landscapes where spatially fixed and temporally absolute land-use boundaries have never

existed.

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