e c o l o g i c a l m o d e l l i n g 2 2 0 ( 2 0 0 9 ) 522–532

avai lab le at www.sc iencedi rec t .com

journa l homepage: www.e lsev ier .com/ locate /eco lmodel

Balancing fuelwood and biodiversity concerns in rural Nepal

Morten Christensena,∗, Santosh Rayamajhia,b, Henrik Meilbya

a University of Copenhagen, Forest & Landscape Denmark, Rolighedsvej 23, 1958 Frederiksberg C, Denmarkb Tribhuvan University, Institute of Forestry, Pokhara, Nepal

a r t i c l e i n f o

Article history:

Received 24 June 2008

Received in revised form

23 October 2008

Accepted 24 October 2008

Published on line 6 December 2008

Keywords:

Agent-based modelling

Community-based management

Firewood

Polypore

a b s t r a c t

An agent-based model is developed to explore the pattern of fuelwood collection in an

1178 ha forest area in rural mountainous Nepal. The model relates fuelwood collection inten-

sity and amount of dead wood available for collection to the diversity of polypore species, a

group of strictly dead wood dependent fungi which can be used as indicators of the biodi-

versity associated with dead wood. By analysing scenarios of increased collection the model

shows that the relative impact on polypore diversity is rising more rapidly than the time

used for collection. This indicates that better market access in the future could potentially

imply a major threat to biodiversity associated with dead wood.

To assess the potential for biodiversity conservation we evaluated the effect of protect-

ing areas with high values of polypore diversity. The simulation results showed such area

protection strategies to be effective for short-term protection of polypore diversity only in

the event of a dramatic increase in the local market price of fuelwood. In case of smaller

changes in fuelwood prices a collection quota system appeared to be the most suitable pro-

tection strategy. However, area protection is an important strategy for long-term protection

of biodiversity associated with dead wood and, therefore, we conclude that a combination

of small-protected zones and collection quotas seems to be the most promising strategy for

protection of the forest.

wood left in the forest will eventually be decomposed. The type

1. Introduction

The balance between protection of biodiversity and the localuse of natural resources is a challenge for most develop-ing countries (Millennium Ecosystem Assessment, 2005) andexploration of new sustainable solutions to the dilemma isessential for rural development. Fuelwood for cooking andheating is one of the most important products harvested fromthe forests of most developing countries (Arnold et al., 2006;Cooke et al., 2008). In many areas a major source of fuelwood isdead wood generated by natural disturbances or natural com-

petition, for which less restrictive legislation applies than forfelling of living trees (Cooke et al., 2008). Dead wood is also avery important habitat for biodiversity in forests (e.g. Huston,

∗ Corresponding author.E-mail address: moc@life.ku.dk (M. Christensen).

0304-3800/$ – see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.ecolmodel.2008.10.014

© 2008 Elsevier B.V. All rights reserved.

1996; Lonsdale et al., 2007) and supports a wide range of organ-isms. Recent figures from boreal forests indicate that as muchas one third of all forest-living organisms depend on deadwood in some, or all, stages of their lifecycle (Siitonen, 2001;Jonsson et al., 2006).

Dead wood in a forest is generated in several ways(Christensen et al., 2005). In forest ecosystems without humandisturbance dead wood is generated by the competitionbetween the trees and by natural disturbances, such as windthrow, fire, flooding, ice break or attack by pathogens. Dead

and speed of decomposition is specific to each tree species andhighly dependent on macro- and microclimatic conditionslike temperature and humidity (Stokland, 2001). Polypores

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within 122 plots in November 2006, immediately before themain season for fuelwood collection. We therefore assumethe amount measured to represent the maximum availableamount of fuelwood in a year. Standing trees and snags taller

e c o l o g i c a l m o d e l l i n

onstitute an important group of wood-decaying fungiften used as indicators for biodiversity conservation value

Norstedt et al., 2001; Christensen et al., 2004; Junninen andouki, 2006; Similä et al., 2006). Most species are strictlyependent on certain amounts and types of dead wood andemoving dead wood completely from the forest floor is there-ore a potential threat to them. The diversity of polyporess also known to represent the diversity of other dead-woodependent organisms rather well (Similä et al., 2006). Com-ared to other categories of fungi polypores are useful as

ndicators because of their persistent fruit bodies, recogniz-ble for a long period of the year.

In rural areas of Nepal wood from natural forests is onef the most important sources of fuelwood. Per capita annualuelwood consumption varies between 400 and 1500 kg fresheight, mainly depending on altitude (Metz, 1994; Amacher

t al., 1999; Stræde and Treue, 2006). In many forests fuel-ood is primarily collected from pools of dead wood andying trees and the large consumption poses a direct threato the important part of the biodiversity depending on theead-wood habitat. Community-based management of for-st resources may potentially motivate sustainable fuelwoodollection (Cooke et al., 2008). For the last two decades anmportant strategy for conservation of biodiversity in Nepalas been the establishment of integrated conservation andevelopment projects (ICDPs) where local user groups are

nvolved in forest management. The oldest ICDP is the non-overnmental Annapurna Conservation Area Project (ACAP)hat has been in place since 1986 (Baral et al., 2007).

Agent-based models where individual agents interact withach other and their environment to exploit a natural resources a recently developed approach to explore scenarios ofhange (see Bousquet and Le Page, 2004; Matthews et al.,007 for reviews). The total behaviour of a system dependsn the aggregated results of individual decisions made byach agent (Matthews et al., 2007). So far, only few agent-ased models address the direct impact of human behaviourn biodiversity (e.g. Linderman et al., 2005). In the presenttudy each household acts as one agent who makes ratio-al decisions regarding collection area, thereby minimisinghe time allocated to fuelwood collection. Their decisions areased on dynamic information on spatial distribution of deadood (potential fuelwood). The diversity of wood-inhabitingolypores is used as a proxy for biodiversity related to deadood. The model is used to assess the potential effects of

wo different fuelwood collection restrictions on conservationffectiveness and the economic consequences of such restric-ions for forest users.

. Methods

.1. Research site

model was developed to describe fuelwood collectionehaviour and dead wood distribution in part of Lete and

unjo Village Development Committees (VDC, an administra-ive unit) of Mustang District, Central Nepal (83.58◦E–83.66◦E;8.61◦N–28.66◦N). The mountain forest within the study areaovers 1178 ha (Fig. 1). The altitude ranges from 2200 to 3000 m,

0 ( 2 0 0 9 ) 522–532 523

the average annual precipitation is 1242 mm (1971–2002) andthe rainfall peaks in June to September. The yearly averagetemperature is 11.7 ◦C (1976–1986) and the monthly averageranges from a maximum in July of 18 ◦C to a minimum of 4 ◦Cin February (information from Department of Hydrology andMeteorology).

The area is dominated by temperate coniferous andmixed forest. Pinus wallichiana, Tsuga dumosa and Rhododen-dron arboreum are main tree species. Cupressus torulosa, Abiesspp., Ilex dipyrena, Taxus baccata, Betula alnoides and Acer spp.are less dominant or restricted to smaller parts of the forestarea. Many species of smaller trees occur in the area. Amongthem particularly Coriaria nepalensis, Hippophae salicifolia, Lau-raceae spp., and Viburnum erubescens are important sources offuelwood.

The forest area is used by inhabitants of eight small vil-lages within the two VDCs. The total number of householdsis 220 and the total population is 1065. The western part ofthe area is strongly influenced by a major trekking and trans-port trail connecting low-lying parts of Nepal with remoteareas near the Tibetan border. Twenty major tourist hotelsoperate in Lete. In 2006 these hotels served approximately26,000 over-night visitors. All private households use fuelwoodmainly for their cooking and heating. Some households and allhotels supplement fuelwood with liquefied petroleum (LP) gas,kerosene and electricity. Forest management is organised andimplemented by two local functional conservation commit-tees, one in each VDC, representing the local users in all eightsettlements. The conservation committees are supervised bya ranger from ACAP.

2.2. Fuelwood resource mapping

In total 123 permanent 20 m × 25 m sample plots were dis-tributed using stratified random sampling and a MaxiMinstrategy, implemented as a ‘Coffee-House’ strategy withineach forest stratum (Müller, 2001), ensuring that plots weredistributed evenly to all parts of each stratum (Fig. 1, seeMeilby et al., 2006 for details). Dead wood was measured

Fig. 1 – Study area. Small squares are permanent sampleplots. Black dots are human settlements (villages).

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522–532

Table 1 – Applied decay classes based on Stevens (1997), Kruys et al. (1999), Fraver et al. (2002) and Ódor et al. (2006).

Decay class Bark Twigs, needles andbranches

Softness, texture,penetrability

Surface Shape of log Shape of stump Standing deadtree/snag

1 Intact for all species Twigs, needles andbranches present

Wood is sound(hard) and cannot bepenetrated withthumbnail and aknife penetrate1-2 mm only

Covered by bark,outline intact

Circular Cylindrical With twig andbranches

2 Missing especiallywhen sun exposed;Intact for Picea,Pinus, Cupressus,Betula

Twigs and needlesabsent. Branches(>3 cm) present.branch stubs arefirmly attached

Hard or partly soft,knife penetrate lessthan 1 cm

Smooth, outlineintact

Circular Cylidrical, bark oftenlost

Without twigsand branches

3 Missing or partlyintact for mostspecies; Intact forBetula

Branches absent andbranch stubs pullout easily

Begins to soften,thumbnailpenetrates readily,knife penetrates1–5 cm

Smooth or crevicespresent and smallpieces lost, outlineintact

Circular Cylindrical but withcrevices, Easy toturn over

Very soft, easy toturn over

4 Missing for mostspecies; Partly intactfor Betula

Absent Soft, thumbnails andknife penetratereadily, often blockypieces

Large crevices, smallpieces missing,wood fragmentsoften lost so theoutline of the trunkis deformed

Circular or elliptic Conical, very soft Not present(fallen)

5 Missing Absent Soft and powdery Large piecesmissing, outlinedeformed

Flat elliptic, partlyburried in the soil

Hardly visible Not present(fallen)

6 Missing Absent Soft and powdery,partly reduced tomould, only core ofwood

Outline hard todefine

Flat elliptic coveredby soil

Not visible Not present(fallen)

g 2 2

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e c o l o g i c a l m o d e l l i n

han 1.3 m, lying stems and fallen branches with diameter10 cm were measured within each 20 m × 25 m plot. Stand-

ng and fallen stems, and branches with diameter 4–9.9 cmere measured within an interior 10 m × 15 m sub-plot, andranches with diameter 2–3.9 cm were measured within a0 m × 5 m sub-plot. For standing dead trees the breast heightiameter and the height of the remaining part of the treeere measured. Fallen branches and stems were divided into

ections of maximum 2 m and the exact length and the diam-ter at each end were recorded. Fallen stem/branch fragmentsess than 1 m long were not considered. We distinguished sixecay classes defined by a set of criteria regarding the occur-ence and state of bark, branches, twigs and foliage, texturend surface characteristics of the wood. Definitions of the sixlasses are presented in Table 1 and for classes relevant forogs (fallen trees), stumps and dead standing trees (snags) theisual appearance is sketched in Fig. 2. Analysis of fuelwoodamples collected from the villages shows that the wood den-ity of most fuelwood used for cooking and heating is similaro that of dead wood in decay classes 1–2. In the present studye therefore included dead wood in these two decay classesnly. Conversion from dead-wood volume in the forest to ovenry weight was based on mean densities estimated for eachecay class. The number of wood samples collected for theve decay classes was 272. For decay classes 1 and 2 the aver-ge densities were 446 kg/m3 (n = 57, S.D. = 82) and 331 kg/m3

n = 73, S.D. = 108), respectively.

.3. Biodiversity measures

olypores were surveyed within 57 of the permanent plots.lots not included in the polypore survey were without woodyegetation or inaccessible in the rainy season. Moreover, 25lots had not yet been established by the time that the surveytarted and therefore were not included. The plots were visitedve times over 2 years (2005–2006) within the main fungal sea-on (April, June, September, October and November). The fruitodies were searched on fallen logs and standing dead and liv-

ng trees, and on fallen branches and other smaller debris. Theefinition of polypores is according to Núnez and Ryvarden

2000, 2001) and only wood-inhabiting species is included.dentification was mainly based on Núnez and Ryvarden (2000,

001). Voucher specimens are deposited in Japan (TNS) andathmandu (KATH). The biodiversity index (D) was defined as

he total number of species observed within a 20 m × 25 m ploturing the observation period (2005–2006).

ig. 2 – Visual classification of dead wood (see also Table 1).

0 ( 2 0 0 9 ) 522–532 525

2.4. Inventory of collection and consumption

The private forest in the study area is negligible and almostall fuelwood is gathered in the common property forest forwhich the model was developed. Fuelwood is mainly gath-ered in the dry winter season when the wood is relativelydry and light and almost all fuelwood collected is dead wood.According to the household survey further described below,86% of the fuelwood is collected from September to March.Additional informal information suggests that collection takesplace almost exclusively from December to March due to thetiming of agricultural and tourist activities. Extraction and pro-cessing in the forest is done by axe, hand saw and sickle.Transportation is almost exclusively done by humans, car-rying loads with an average stated weight of 38 kg (n = 62,S.D. = 2.5 kg) corresponding to 24.7 kg dry weight (see below).

In a sample of 42 randomly selected households fromall eight settlements and all 20 major hotels/guesthousesfuelwood consumption was measured in four quarters of2005–2006 (December, March, June and September). In eachquarter fuelwood consumtion was measured over a periodof 7 days. Sample households would use fuelwood from apre-weighed stack only, and the remaining wood in the stackwas reweighed every 24 h (e.g. Benjaminsen, 1993). The yearlyfuelwood consumption per household ranged from a mini-mum of 2607 kg stored weight (corresponding to 2173 kg ovendry weight) to a maximum of 8825 kg stored weight (corre-sponding to 7354 kg oven dry weight). The average fuelwoodconsumption across all households in the study area was5914 kg stored weight per household per year (correspond-ing to 4929 kg oven dry weight) (n = 62, S.D. = 1304 kg for storedweight, and S.D. = 1186 kg for dry weight) or 1222 kg storedweight per inhabitant (corresponding to 1018 kg oven dryweight). In the present study we used separate estimates foreach of the eight settlements to avoid bias due to differencesin consumption patterns.

To enable conversion of the weight of fuelwood stored inhouseholds to dry weight we measured the water content of586 samples from stored wood in stacks. Samples were ran-domly selected from the 62 intensively studied householdsduring March, June and September 2006. Samples were takento represent all major fuelwood species used in the study area.All samples were packed in sealed polybags and dried in anoven for 24 h at 100 ◦C. The water content in stored wood was20% (n = 586, S.D. = 8%). The water content of dead wood whengathered in the forest was taken from the literature (Metz,1994) and set to 35% of fresh weight on average. In the modelall weight measures are converted to oven dry weight.

2.5. Modelling

A spatial model was developed that included information onaltitude, amount of dead wood and diversity of polyporesfor square 50 m × 50 m grid cells (0.25 ha) located in a spatialdomain covering 8.2 km × 5.2 km. Two subsets of such cells,c, are defined: FOR includes the 4663 cells that are located

in forest areas (1165.75 ha, i.e. slightly less than the mapped1178 ha); RES are cells located in the forest for which fuelwoodcollection is restricted by a suggested conservation scheme(RES ⊆ FOR). For each cell the diversity of polypores, denoted

i n g

526 e c o l o g i c a l m o d e l l

by D, was estimated using a regression equation relating therichness of polypores to the amount of dead wood potentiallyavailable for fuelwood (kg dry matter per ha). The regressionwas prepared on the basis of the 57 plots that had been visitedfive times within the main fungal seasons of 2005 and 2006.Effects of altitude and aspect were tested but turned out not tobe significant in regression models also including dead woodvolume. To measure the consequences of particular exploita-tion patterns, i.e. their spatial distribution and intensity, wedefine a percentage measure of human impact (P) on the diver-sity of polypores. For the forest area as a whole the impactmeasure (PFOR) is defined as

PFOR = 100% ×∑

c ∈ FORDc− −

∑c ∈ FOR

Dc+∑c ∈ FOR

Dc−(1)

where Dc− is the predicted diversity index for polypores in cellc, had no fuelwood collection taken place, and Dc+ is the pre-dicted diversity index when fuelwood collection is considered.

Each of the 220 households in the study area is consid-ered one agent in the model. Availability and accessibilityare important variables determining the spatial patterns offuelwood collection in the forest. Availability is in our studydefined as the amount of potential fuelwood per hectare.Accessibility is defined as travelling time for the agent fromvillage to collection site. The total time needed to extract aload of fuelwood (Ttotal, h), i.e. walk to a particular place in theforest, collect a load of fuelwood and walk home with the load,depends on accessibility and availability and can be expressedas

Ttotal = (2 + a)Twalk + Tmin + Tabundance (2)

where Twalk is the average time needed to walk to or fromthe site (half a round trip), a = 1 is a parameter expressing theassumed increase of time consumption when walking with aload, Tmin = 0.25 h is the assumed minimum time required tocollect a load of fuelwood, i.e. the time needed at a site witha very high (∞) abundance of fuelwood, and Tabundance is theadditional time needed due to lower availability.

Based on our experienced travelling time to the 123 per-

manent forest plots in the research area we developed thetravelling time regression (n = 123):

Twalk = 23S + 8A

60(3)

Fig. 3 – Maps of (A) available fuelwood, 103 kg/ha and (B

2 2 0 ( 2 0 0 9 ) 522–532

where Twalk is walking time in hours, S is horizontal distancefrom house to collection site in kilometres, and A is the alti-tudinal difference in hectometres. Tabundance (h) was modelledas

Tabundance = B

1 + M/1000(4)

where B is a parameter controlling the steepness of the func-tion, and M is abundance of potential fuelwood in kg ovendry weight/ha. Thus, Tabundance = B when M = 0 and convergesasymptotically towards zero when M increases.

To enable prediction of fuelwood volume and polyporediversity for all 50 m × 50 m cells in the spatial grid weused kriging (Fig. 3). In both cases the empirical variogramsuggested that an exponential variogram model would be suit-able. In these models the estimated range parameters were726 m for potential fuelwood and 247 m for polypore diversity.Predictions were based on local kriging including the 50 near-est points. The standard error of the kriging estimates rangedbetween 725 and 6837 kg/ha for potential amount of fuelwood(average S.E. was 2923 kg/ha) and between D values of 0.35 and1.90 for polypore diversity (average S.E. 1.67).

We simulated the annual extraction of fuelwood by i = 1,. . ., 220 households (agents). The exact spatial location of eachhousehold was not known and therefore the centre of each vil-lage was used to approximate the location of all householdsin the village. For each household the annual consumption offuelwood was specified as a number of loads Ji. This numberwas determined as an average amount per household for eachof the villages. For two smaller settlements the sample sizewas too small to allow estimation of separate consumptionaverages. In these cases the consumption values were esti-mated using a regression based on the consumption patternsobserved in all eight settlements. The regression includedinformation on household size, landholding and value of otherfinancial assets.

The simulation was done as follows (Fig. 4): One by onethe households were allowed to collect one load of fuelwood;when all households had collected one load they were allowed,one by one, to collect their second load, their third load, theirfourth, etc. When all households had collected all the loads (J )

ithat they needed the simulation terminated. Every time a load(38 kg fresh weight) of fuelwood was extracted from a cell theabundance of dead wood within the cell, M, was reduced by98.8 kg/ha corresponding to 98.8 kg/ha × 0.25 ha = 24.7 kg dry

) diversity of polypores: D = species per 500 m2 plot.

e c o l o g i c a l m o d e l l i n g 2 2 0 ( 2 0 0 9 ) 522–532 527

Fig. 4 – Flow chart of the simulation model. Symbols: HH is a household; c is a 50 m × 50 m cell; M is the abundance of deadwood available in the cell; Twalk is the time required for a return trip to cell c; Tabundance is the time required to gather a loado o anr

wttsawhts

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evaluated with reference to current levels of fuelwood collec-tion and biodiversity without assessing the consequences ofcurrent extraction levels. In other words, the present fuelwoodcollection, the abundance of deadwood and the associated

f fuelwood in cell c; Ttotal is the total time consumption to gequired; Cmin is the cell for which Ttotal = Tmin.

eight. This would lead to an increase in the time neededo collect additional loads within the cell (Tabundance) and, dueo the relationship between amount of deadwood and diver-ity of polypores, a reduction of the predicted diversity, D, andn increase of the impact index PFOR. Each load, ji = 1, . . ., Ji,as gathered in that particular cell which allowed the house-old to collect the load with the least possible effort, subjecto previous extraction from the cell. Thus for each load, ji, theimulation solved:

minc ∈ FOR\RES

{Ttotal(i, ji, c)

}

= minc ∈ FOR\RES

{(2 + a)Twalk(i, c) + Tmin + Tabundance(i, ji, c)

}(5)

here Tabundance(i,ji,c) depends on the initial abundance (Mc) inand the number of loads previously gathered in c, whereas

walk(i,c) remains unchanged throughout the simulation.For each load of fuelwood the horizontal and vertical dis-

ances from the house, the time consumption (Ttotal(i,ji)), theead-wood abundance at the time of collection (Mc,i,ji

) and thestimated biodiversity (Dc) were recorded and used for calcu-ation of summary statistics. After completing the simulationhe impact measure (Eq. (1)) was computed for the forest as ahole (PFOR).

A range of alternatives for the value of B was tested (Fig. 5).n 2006 the average market price of one load of fuelwood was0 Nepalese Rupees (approx. 1.00 USD). The average wage rate

or unskilled labour in the area was approx. 25 Rupees perour, indicating that one load can be collected in less thanh (70/25 = 2.8). Assuming that each household maximizes itstility a B value of 10 is applied in the simulations as it implies

d pick up a load in cell c; Tmin is the minimum total time

that the mean time consumption per load of fuelwood is 2.7 h,thereby matching the labour opportunity cost (Fig. 5).

2.6. Scenario analysis

In the present dataset effects of altitude, distance to villageand abundance of deadwood on polypore diversity are con-founded. Since no true reference areas exist, conservation is

Fig. 5 – Distribution of time consumption per load for thetotal annual extraction simulated using three differentvalues of the steepness parameter B (see Eq. (4)).

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3.5. Modelling conservation strategies

Increased extraction on fuelwood will lead to increasedmarginal time consumption per load as availability decreases.

Fig. 6 – Results of simulations for different extractionscenarios, ranging from 0.1 to 5 times the presentextraction level. Top: Mean and marginal time

528 e c o l o g i c a l m o d e l l

diversity of polypores are assumed to be in equilibrium. How-ever, the demand for fuelwood is likely to increase in the nearfuture as Lete and Kunjo are about to be connected to mar-kets further up and down the valley through a road presentlyunder construction. In these markets the price of fuelwood is140–180 Rupees, i.e. more than twice as much as in the studyarea, so fuelwood export is likely to become profitable once theroad has been completed and transportation costs reduced.Improved road access is known to have increased the pres-sure on forest resources in many developing countries (e.g.Wilkie et al., 2000; Geist and Lambin, 2002), and has also beendocumented in Nepal (Gautam et al., 2004).

The consequences of improved road access for extractionof fuelwood and other resources within the study area are yetto be seen. The simulation model is therefore used to predictthe likely impact of various hypothetical extraction levels onbiodiversity (PFOR) and, as proxies for extraction cost, averageand marginal time consumption for fuelwood collection. Inaddition, as a step towards identifying suitable conservationschemes, various restrictions on the area where fuelwood canbe collected are introduced and the consequences for timeconsumption and human impact on biodiversity are exam-ined.

3. Results

3.1. Dead wood

The study area houses areas of remote virgin forest and agradient in the amount of dead wood from 0 up to morethan 400 m3/ha. The average amount of dead wood usefulfor fuelwood was 12.2 × 103 kg dry weight per ha or approx-imately 14,500 × 103 kg in total. A geographical analysis showsa clear relation between the amount of dead wood and the dis-tance to the nearest village, with high volumes only in remoteplaces (Fig. 3a). Pinus wallichiana dominates the dead wood andaccounts for more than 60% of the volume. Tsuga dumosa con-tributes 21% and Cupressus torulosa 8% of the volume of deadwood. Other important species are Rhododendron spp. (2%),Taxus baccata (<2%) and various deciduous trees and shrubs.

3.2. Biodiversity

A total of 50 species of wood-inhabiting polypores wereidentified within the 57 plots. The average number of poly-pore species observed per plot was 2.8 (S.D. = 2.2) and in sixplots none were found. The most frequent species were Het-erobasidion insulare, Postia undosa, Trichaptum abietinum, andGloeophyllum cf. abietinum. Less frequent species included Laeti-porus sulphureus sl., Polyporus badius, Trametes versicolor, andBondarzewia montana, which are used by local people for foodand medicinal purpose.

The number of polypore species per plot is positively cor-related with the amount of dead wood. When estimating theimpact of fuelwood collection on polypore diversity (P ) we

FOR

used a regression of polypore diversity index (D, species/500 m2) on fuelwood abundance (M, kg/ha): ln D = −0.2696 +0.1760 ln M. The number of valid observations for this regres-sion (M > 0, D > 0) was 36. Two outliers were excluded so the

2 2 0 ( 2 0 0 9 ) 522–532

number of plots used in the regression was 34, the slopeparameter was significant at the 1% level, the residuals werehomogeneously distributed, and their normality could not berejected (R2 = 0.21, RMSE = 0.55).

3.3. Fuelwood consumption

From December 2005 to November 2006 the total annualconsumption of fuelwood in the study area was estimatedat 1408 × 103 kg stored weight in stacks, corresponding to1126 × 103 kg oven dry weight. This corresponds to less thanten percent of the total available amount of dead wood.

3.4. Modelling of fuelwood and biodiversity

The spatial distribution of the plots, the location of the villagesand the altitude of the terrain are shown in Fig. 1. Villages aregenerally located in valleys and on plateaus. As will appearfrom the predicted abundance of potential fuelwood (Fig. 3A)areas close to the villages are in most cases characterised bylow availability compared to areas further away. Areas withparticularly high abundances are found in the eastern andsouth-western parts of the research area. The greatest diver-sity of polypores is found closer to the villages (Fig. 3B). Withinlarge parts of the forest the estimated diversity of polypores(D) is about 3.

consumption per load. Bottom: Impact on biodiversity.Right-hand axes show impact and marginal time per loadin per cent of present mean time consumption and presentimpact, respectively.

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Fig. 7 – Results of simulations when areas with polypore diversity D ≥ 4 and D ≥ 3 are protected. The unprotected case is included for comparison. Top: Marginal timeconsumption per load vs. extraction. Bottom left: Impact on biodiversity vs. extraction. Bottom right: Impact vs. marginal time consumption. Right-hand axes showimpact and marginal time per load in per cent of present mean time consumption and present impact, respectively. The protection strategy threshold is indicated by anasterisk. Dotted lines connect marginal time consumption, impact on polypore diversity and annual extraction at the threshold.

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We analysed the effect of extraction of up to 5 times thecurrent extraction level and found that the marginal timeconsumption rises from 3.7 h for the last load at the presentextraction level to 6.3 h at 5 times the current extraction (Fig. 6).To make this time consumption attractive at the present wagerate of 25 Rupees per hour the fuelwood price should riseto 158 Rupees. In the present-day scenario high transporta-tion costs prevent fuelwood from reaching markets outsidethe study area. However, the ongoing road construction willreduce the price of transportation dramatically and the studyarea will become connected to suburban and urban marketsat lower altitudes where the fuelwood price is about 140–180Rupees per load. The relative effect of increased extraction onbiodiversity (PFOR) is greater than the relative effect on timeconsumption (Fig. 6, right-hand axes), and in the scenario with5 times the current extraction the impact on diversity is 5.5times the present impact, whereas the marginal time con-sumption per load is only 2.3 times the present average timeconsumption. This indicates that market mechanisms will notbe able to conserve biodiversity if improved road connectionto markets and reduced transport costs lead to increased col-lection.

Quotas could be a possible way to regulate the extractionand protect biodiversity. An alternative strategy is to restrictthe areas where fuelwood can be collected, prioritising areaswith the highest D values. We simulated two scenarios of areaprotection, D ≥ 4 (corresponding to 120 ha or 10% of the forestarea) and D ≥ 3 (corresponding to 349 ha or 30% of the for-est area). The unrestricted scenario was used as baseline forthis analysis. The area restriction implies that the marginaltime consumption per load increases considerably (Fig. 7).Assuming that the market price of fuelwood determines themaximum time that can be spent on collecting a load, itemerges that for a marginal time per load of less than 5.5 h(twice the present average time per load) the area restrictionhas no effect on the impact measure PFOR. However, if the mar-ket price is high enough to allow collectors to spend more than5.5 h per load, the area protection will be an efficient strat-egy. A time consumption of 5.5 h per load, corresponding to aprice of about 140 Rupees, can be seen as a threshold betweenalternative biodiversity protection strategies. Below this valuea quota system would be the only effective strategy, whereasabove 5.5 h area protection seems most attractive due to thelower cost of implementation. Another important character-istic of the area protection strategy is that in the long run itcan be expected that D values in protected areas will increase,gradually leading to a decreasing impact on polypore diversityin the area (PFOR).

4. Discussion

4.1. Implication for protection of biodiversity

Our model shows the complex interactions between extrac-tion of natural resources and conservation of biodiversity.

Today fuelwood extraction in Lete and Kunjo is exclusivelydetermined by the local demand as transport is prohibitivelycostly. With increased road access connection will be made tomarkets in fuelwood-scarce areas in the south and north. We

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conclude that given dramatic changes in access to markets,leading to increased incentives for fuelwood collection, somesort of regulation will be necessary to protect the biodiver-sity related to this economically important natural resource.With regard to the fuelwood in Lete and Kunjo the conse-quences for biodiversity are exacerbated because the woodresource is found in a relatively small and accessible area,possibly making almost total extraction economically attrac-tive.

Establishment of protected areas is a common strategy forprotecting biodiversity. Our simulation results show, however,that in the short-term area-based protection may potentiallyhave little effect on the impact on biodiversity. This is becauseit induces increased fuelwood collection in areas with lowdensities of dead wood where small changes in dead-woodamount will have negative impacts on biodiversity. However,most likely the increasing marginal cost of collection causedby the area restriction will make fuelwood collection lessattractive, thereby reducing the impact on biodiversity. Thearea protection only showed to be an efficient short-term toolfor protection in case that the market price rises to a levelhigher than about 140 Rupees per load.

The actual effect of fuelwood collection on polypore diver-sity is obviously more complex than our simple regressionbetween amount of dead wood and species richness. Thestudy area currently includes a few remote and inaccessi-ble areas that could serve as potential refuges for polypores.Should dead-wood availability increase after a period ofheavy extraction polypores would be able to recover rela-tively quickly due to their good dispersal abilities, at leastover relatively short distances. Therefore, protection of smallareas with high D-values may prove effective in the longterm.

Maintaining the current level of biodiversity in a scenarioof increased extraction of fuelwood will only be possible witha combination of restricted areas and quotas. Although arearestriction was observed to have little effect on overall humanimpact on biodiversity (PFOR) in the short term, area protectionmay result in stable or improved conditions for polypores inthe long term as the amount of dead wood increases in therestricted areas. A combination of area restriction and quotashas been suggested by Stefansson and Rosenberg (2005), butmay be difficult to implement because of the cost of monitor-ing extraction.

4.2. Modelling and conservation strategies

Agent-based modelling as presented in this paper can be animportant tool in understanding the processes influencing theinteraction between use and sustainability in areas with inten-sive extraction of natural resources.

Lack of information on dead-wood accumulation is a mainproblem in our model and without time series it is not possibleto address the sustainability of the extraction scenarios. Decayrates were not directly investigated in the present study butexplored during fieldwork and discussed with local assistants.

The rates differ between tree species. Tsuga, Pinus and Abiesdecay relatively fast, whereas Cupressus and Taxus decay moreslowly. Apart from the variation between species, the size ofthe dead wood and the microclimatic conditions are also very

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mportant for the speed of decay. To address these topics its necessary to collect information over longer time whereepeated measurement of dead wood would allow estimationf the average speed of decay.

In this study we applied a simple model describingxpected time consumption for fuelwood collection but mostikely the real figures would show considerable variation. Ded-cated fuelwood collectors can do the collection faster thannexperienced ones and a strong man can collect more woodaster than women and children. Fuelwood collection com-ined with other activities is also relatively important andan reduce the perceived time (cost) of collecting the fuel-ood. We do not know exactly how much fuelwood is gatheredhile also doing other activities but during our fieldwork webserved collection of fuelwood combined with herding andushroom collection. Given the large amount of fuelwood

ollected in the area, however, it is obvious that a consider-ble part of the fuelwood collection takes place as a primaryctivity.

Another important limitation of the present study is theimplified assumptions regarding household behaviour andhe nature of the fuelwood market. It is likely that faced withncreased marginal time consumption for collection of fuel-ood households will gradually reduce their consumption of

uelwood and partly switch to alternative fuel types, such aserosene, LP-gas or electricity.

.3. The role of community-based forest management

he model has contributed towards an understanding of thenteraction between fuelwood use and biodiversity that canuide future conservation efforts by local communities. Manyocal forest users are positive towards the protection of forestiodiversity and have in-depth knowledge about the condi-ions of their forest (Mehta and Heinen, 2001). Identificationf valuable areas for protection of dead wood could be basedn local knowledge, as could monitoring of the conditionssing a participatory approach (Widmann, 2003; Subedi, 2006).onitoring of polypores is relatively simple and could be sup-

orted by a short introductory training, booklets and posters.n support of conservation of biodiversity associated with deadood designation of areas for protection of dead wood coulde made a mandatory component of management plans. Aombination of locally designated protected areas and addi-ional fuelwood quotas could lead to a reduction of the humanmpact on biodiversity and would imply local ownership andromote awareness of the importance of biodiversity conser-ation.

. Conclusion

he present study developed a model describing fuelwoodollection behaviour, dead-wood distribution and biodiver-ity patterns in a Nepalese mountain forest. The interactionetween the natural resource and the human society is com-

lex by nature and determined by its spatial heterogeneitynd the economic behaviour of users. Conservation strate-ies for protection of biodiversity associated with dead woodust take this complexity into account. In the present study

0 ( 2 0 0 9 ) 522–532 531

area, a combination of area restrictions and quotas was shownmost promising in terms of biodiversity conservation, whileexclusively focusing on area restrictions did not lead to posi-tive effects in most cases. The future market for fuelwood isunpredictable and will most likely be influenced by infrastruc-tural changes. In case of major long-term changes in the localprices of fuelwood an area-protection strategy seems robust.

Acknowledgements

We would like to acknowledge Sanjeeb Bhattarai, ShivaDevkota, Giri Joshi, Arun Rijal, Somesh Das, and Basanta Pantfor field work assistance, the people of Kunjo and Lete forproviding information, ACAP for granting permission to workin the area, Jacob Heilmann-Clausen and Tumatso Hattori forassistance in polypore species identification, Helle O. Larsenfor valuable comments during the writing process, DANIDAand ComForM for funding, and two anonymous reviewers foruseful comments.

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