UNIVERSIDAD POLITÉCNICA DE MADRID ESCUELA TÉCNICA...

UNIVERSIDAD POLITÉCNICA DE MADRID

ESCUELA TÉCNICA SUPERIOR DE INGENIEROS DE MONTES

CARBON ALLOCATION IN NORTH-WESTERN

AMAZON FORESTS (COLOMBIA)

DISSERTATION

ELIANA MARÍA JIMÉNEZ ROJAS

FORESTRY ENGINEER, M.SC. IN AMAZON STUDIES

MADRID

2013

DEPARTAMENTO DE PROYECTOS Y PLANIFICACIÓN RURAL

ESCUELA TÉCNICA SUPERIOR DE INGENIEROS DE MONTES

UNIVERSIDAD POLITÉCNICA DE MADRID

CARBON ALLOCATION IN NORTH-WESTERN AMAZON

FORESTS (COLOMBIA)

DISSERTATION

ELIANA MARÍA JIMÉNEZ ROJAS

FORESTRY ENGINEER, M.SC. IN AMAZON STUDIES

ADVISERS:

PH.D. MA ANGELES GRANDE ORTÍZ

FORESTRY ENGINEER, PH.D. IN PROJECTS AND RURAL PLANNING

PH.D. CARLOS A. SIERRA USMA

FORESTRY ENGINEER, M.SC., PH.D. IN FOREST SCIENCE MAX-PLANCK INSTITUTE FOR BIOGEOCHEMISTRY, JENA, GERMANY

MADRID, 2013

iv

© Copyright by Eliana María Jiménez Rojas

September, 2013

All Rights Reserved

v

ACTA DE LECTURA Y DEFENSA DE TESIS

DOCTORAL

(ART. 11 DEL REGLAMENTO DE ELABORACIÓN Y

EVALUACIÓN DE LA TESIS DOCTORAL)

El Tribunal ha sido designado por el Presidente de la Comisión de Posgrado de Doctorado de la

UPM con fecha 28 de Octubre de 2013, y está integrado por los siguientes doctores:

Presidente

D FRANCISCO DIAZ PINEDA

Vocales

D JOSE ALFREDO VICENTE ORELLANA

D LUIS GONZAGA GARCIA MONTERO

D BJÖRN REU

Secretario

D ANTONIO DAMIAN GARCIA ABRIL

Suplentes

D JAVIER VELAZQUEZ SAORNIL

Da MARIA INMACULADA VALVERDE ASENJO

Madrid, a 28 de Octubre de 2013.

El Presidente

Fdo: D FRANCISCO DIAZ PINEDA

El Secretario

Fdo: D ANTONIO DAMIAN GARCIA ABRIL

Vocal

Fdo: D LUIS G. GARCIA MONTERO

Vocal

Fdo: D JOSE A. VICENTE ORELLANA

Vocal

Fdo: D BJÖRN REU

vii

A Lucy y Fare, a mi Querida Familia

ix

AGRADECIMIENTOS

A Sandra Patiño, por quién se inició esta investigación en la Amazonia

colombiana, con el entusiasmo y alegría que la caracterizaban.

A María Cristina Peñuela Mora, quién me acogió en su hogar y lugar de trabajo, y

generosamente lleno mi vida de valiosos consejos y enseñanzas, una Profesora con quién

crecer y ser mejor persona es la lección fundamental en un mundo acomodado a la

injusticia y vanidad.

A Ma. Ángeles Grande Ortiz y Antonio García Abril, por permitir y contribuir en

el desarrollo de este programa de doctorado, por su amabilidad, confianza, ánimo y

apoyo hasta el final. A todos aquellos que me acogieron con gentileza en la E.T.S.I. de

Montes.

A Flavio Moreno, por su colaboración, asesoría y buena actitud para enseñar,

aconsejar y dialogar. Especialmente su valiosa contribución en los capítulos 2 y 3.

A Carlos Sierra, quién gracias a su confianza, interés, contribución, asesoría

permanente y arrojo se unió a este trabajo y, logre terminar este ciclo de mi vida, y

conseguimos aportar al conocimiento e historia natural de los bosques Amazónicos. A

Susan Trumbore por permitir y facilitar mis estancias en el Instituto Max Planck para

Biogeoquímica; Björn Reu, quién también contribuyó y facilitó el trabajo en Alemania;

Kerstin Lohse por su gran amabilidad y colaboración permanentes y, Markus Mueller por

su trabajo para el índice de área foliar y compañía en la oficina. Al Instituto Max Planck

para Biogeoquímica por facilitar mis estancias para el análisis y escritura del manuscrito.

A Jon Lloyd y Oliver Phillips, por su contribución para que esta investigación se

realizará en la Amazonia colombiana. Particularmente, Jon Lloyd asesoró los cálculos del

índice de área foliar y facilitó el código en Fortran, revisó y dio su concepto sobre el

presente manuscrito. A Beto Quesada quién colaboró en campo y contribuyó con la

información de suelos; a Luiz Aragão por su colaboración en la revisión y concepto del

presente manuscrito.

x

Al Grupo de Ecología de Ecosistemas Terrestres Tropicales GEETT, de la

Universidad Nacional de Colombia, quienes de una u otra forma contribuyeron al trabajo

de campo y por el tiempo compartido en el laboratorio de Productos Naturales y

Semillas y en el Zafire. A la Universidad Nacional de Colombia sede Amazonia por

facilitar sus instalaciones para la realización del trabajo de campo y, a todos aquellos que

directa o indirectamente me apoyaron.

El trabajo en la Estación Biológica Zafire, se realizó gracias a la Profesora María

Cristina Peñuela Mora, investigadora principal del “Programa de Monitoreo del Carbono

y la Vegetación” (ZAR plots); muchas personas colaboraron en campo y laboratorio:

Diego Navarrete, Miguel Arcangel Angel, Ever Kuiru, John Javal, Angel Pijachi, Juan

David Turriago, Betty Villanueva, Adriana Aguilar, Luis Rivera, Carolina Rivera, Zulma

Albán, Iván Niño, Ilmer Niño, Edilberto Rivera, Eufrasia Kuyuedo, Arcesio Pijachi y,

muchas más, a todas ellas mis más sinceros agradecimientos.

El trabajo en el Parque Natural Nacional Amacayacu se realizó gracias al apoyo y

colaboración del Director y personal del Parque en especial Alex Alfonso y Jaime Galvin,

a las comunidades indígenas de Palmeras y San Martín; gracias a Adriana Prieto y Agustín

Rudas quienes permitieron y colaboraron con el trabajo en las parcelas permanentes de

Agua Pudre (AGP plots). Agradecimientos especiales a Eugenio Sánchez y Familia,

Magno Vela, Benicio Amado, Simón Amado, Don Pedro, Laureano, Priscilla, Eustorgio,

Estanislao, Basilio, Inés Gregorio, al finado William y a las dos comunidades en general.

Infinitos agradecimientos a mi Querida Familia, mi madre Lucy y hermana Fare,

Abuelita Teresa, mis Tías Irma, Sagrario, Marta, primas Mary, Yaneth, Paola y Mariam, a

los pequeños Saray, Isabella, Jose y Sebitas, de quienes me perdí tantos momentos por

realizar este trabajo, y quienes desde la distancia siempre me dieron mensajes de aliento y

apoyaron con sus oraciones en los momentos más duros de este largo camino. A Arley,

quién de cerca o desde la distancia siempre me apoyó y acompañó para lograr este sueño.

A todos los amigos que con su calidez hicieron más fácil, hermosa y divertida la

vida en los diferentes lugares, en Madrid, la familia de Ma. Angeles, Miguel, sus hijas y

papas, Alejo Baena, Lina González y Álvaro de Cózar; en Jena a Hemma, Federica, Nani,

Corinna, Marta, Rakesh y a todos aquellos que ayudaron a instalar las “multicultural

xi

dinners” en la torre. En Leticia a todos los que contribuyeron a que pudiera realizar este

trabajo y compartieron conmigo malos y buenos ratos. A todos aquellos que siempre me

animaron a terminar ‘gracias totales’.

INSTITUTIONAL SUPPORT

Financial support for field work at Amacayacu National Natural Park was

provided by several grants to RAINFOR, notably from Max-Planck Institute for

Biogeochemistry (census 2004, AGP plots), from the European Union FP5 PAN-

AMAZONIA project (scholarship supporting fieldwork 20052006), NERC (grant to

Oliver Phillips, funding census 20052006) and support from the Gordon and Betty

Moore Foundation (soil sampling at Zafire, census 2009). The field work at Zafire

Biological Station was supported by Maria Cristina Peñuela Mora y Eliana M. Jimenez

from personal funds and projects of the Research Group −GEETT. I received

fellowships from: Fundación Conde del Valle de Salazar (Stays in May to July of 2004

and 2005), Max-Planck Institute for Biogeochemistry (Stays in June to November of

2011 and February to June of 2012) and the German Academic Exchange Service –

DAAD (Stay in July to December of 2013).

xiii

TABLE OF CONTENTS

ABSTRACT 25

RESUMEN 29

1 INTRODUCTION 33

1.1 CARBON ALLOCATION IN AMAZON FORESTS 34

1.1.1 Abiotic factors related to carbon allocation variability 36

1.1.2 Biomass ratios and net primary production partitioning 38

1.2 AIMS AND OUTLINE OF DISSERTATION 39

1.3 REFERENCES 41

2 FINE ROOT DYNAMICS FOR FORESTS ON CONTRASTING SOILS IN

THE COLOMBIAN AMAZON 49

1.1 ABSTRACT 51

2.1 RESUMEN 52

2.2 INTRODUCTION 53

2.3 MATERIALS AND METHODS 54

2.3.1 Study sites 54

2.3.2 Sampling design 56

2.3.2.1 The AMP sampling design 56

2.3.2.2 The ZAR sampling design 56

2.3.3 Core methods 56

2.3.3.1 Ingrowth cores 56

2.3.3.2 Sequential cores 57

2.3.4 Fine root extraction 57

2.3.5 Statistical methods 58

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2.4 RESULTS 60

2.4.1 Standing crop fine root mass and production from ingrowth cores 60

2.4.2 Standing crop fine root mass, production, and turnover rates from sequential soil coring61

2.4.3 Relationship between the standing crop fine root mass and rainfall 62

2.5 DISCUSSION 63

2.5.1 Carbon allocation to production of fine roots 63

2.5.2 Turnover rates of fine roots 66

2.5.3 Temporal variation of standing crop fine root mass 67

2.5.4 The methods of sampling/estimation used 69

2.6 CONCLUSIONS 70

2.7 REFERENCES 71

3 EDAPHIC CONTROLS ON ECOSYSTEM-LEVEL CARBON

ALLOCATION IN TWO OLD-GROWTH TROPICAL FORESTS ON

CONTRASTING SOILS 91

3.1 ABSTRACT 93

3.2 RESUMEN 94

3.3 INTRODUCTION 95


3.4.1 Site description 97

3.4.2 Carbon allocation 98

3.4.2.1 Biomass 99

3.4.2.1.1 Above-ground biomass 99

3.4.2.1.2 Below-ground biomass 100

3.4.2.2 Net primary production 100

3.4.2.2.1 Total above-ground net primary production 101

3.4.2.2.2 Below-ground net primary production 101

3.4.3 Error propagation and statistical analysis 102

3.4.4 Leaf area index 102

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3.5 RESULTS 103

3.5.1 Biomass 103

3.5.2 Net primary production 104

3.5.3 Net primary production partitioning 105

3.5.4 Leaf area index 105

3.6 DISCUSSION 105

3.6.1 Differences in above- and below-ground biomass 106

3.6.2 Differences in above- and below-ground net primary production 107

3.6.3 Biomass ratios and net primary production partitioning 108

3.7 CONCLUSIONS 110

3.8 REFERENCES 110

4 SPATIAL AND TEMPORAL VARIATION IN CARBON ALLOCATION

COMPONENTS AND DYNAMICS IN AMAZON FORESTS WITH

CONTRASTING SOILS 131

4.1 ABSTRACT 133

4.2 RESUMEN 135

4.3 INTRODUCTION 137


4.4.1 Site description 139

4.4.2 Intra-annual variation of above- and below-ground components 140

4.4.3 Inter-annual variation of above-ground biomass increment and forest dynamics 141

4.4.4 Error propagation and statistical analysis 142

4.5 RESULTS 143

4.5.1 Intra-annual variation of above- and below-ground components 143

4.5.1.1 Litterfall production 143

4.5.1.2 Stem growth 144

4.5.1.3 Leaf area index 144

4.5.1.4 Fine-root mass 144

4.5.2 Inter-annual variation of forests 145

xvi

4.5.2.1 Above-ground biomass increment 145

4.5.2.2 Forest dynamics 145

4.6 DISCUSSION 146

4.6.1 Correlation with precipitation 146

4.6.2 Correlation among forests 150

4.7 CONCLUSIONS 150

4.8 REFERENCES 152

5 CONCLUSIONS 175

6 CONCLUSIONES 179

7 APPENDIXES 183

xvii

LIST OF FIGURES

Figure 1-1 The components of (a) forest Net Primary Production –NPP, and (b) NPP*,

the sum of all materials that together represent: (1) the amount of new

organic matter that is retained by live plants at the end of the interval, and

(2) the amount of organic matter that was both produced and lost by plants

during the same interval. CHO = carbohydrates (Clark et al. 2001a). 48

Figure 2-1 Localization of the study sites in the Colombian Amazon (Trapecio

Amazónico, Leticia): Amacayacu National Natural Park (AMP) with two 1–

ha plots: AGP-01 and AGP-02, and Biological Station Zafire (ZAR) with

one 1-ha plot: ZAR-01, in the Forest Reservation of the Calderón river. 78

Figure 2-2 Patterns of monthly and mean monthly precipitation (1973–2006) and mean

temperature from the meteorological station of the Vásquez Cobo airport,

Leticia (Amazonas, Colombia) during the time of the research. Shady areas

show the dry period of each year, the dark one represents the drought

period. Mean monthly precipitation is plotted repeatedly for every year. 79

Figure 2-3 Schematic representation of the time table for establishment and retrieval of

fine root cores. Each colour indicates the establishment of a given set of

cores and their correspondent retrieval sequence considering only the 020

cm depth soil core. Note that for sequential cores there is only retrieval

(blue). 80

Figure 2-4 Fine root production (Mg ha1) in the first 20 cm of soil depth estimated by

the ingrowth core method in two forests with different soil types in the

Colombian Amazon. Cores were established in three times: (1) February of

2004, (2) September of 2004 and, (3) February of 2006. Values are the means

and the standard errors. The shady area is the drought period of the year

2005. *Significant differences (p<0.05) of fine root mass in relation to: (1)

differences between soil depths (010 cm and 1020 cm) per collection date

xviii

in each plot, (2) differences between all soil depths per collection date and

forest type. 81

Figure 2-5 Anomaly of rainfall along the period of study, calculated as the precipitation

of each month minus the mean monthly precipitation (1973–2006) and

relative growth rates (RGR) of fine root mass (year1) for the forests studied.

Dotted vertical lines show the time intervals considered for the estimation of

RGR from ingrowth cores; the portion dashed shows the drought of 2005.

Circles with vertical bars represent the mean and standard errors of RGR;

white and grey circles for plots of forests on clay soils (AGP-01 and AGP-

02, respectively) and black circles for the plot on white sands (ZAR-01).

*RGR for ZAR-01, calculated between the former harvest (July 2005) and

the following harvest (September 2005). 82

Figure 2-6 Temporal variation of fine root mass (Mg ha1) in the 020 cm soil depth in

two forests on different soil types in Colombian Amazon: one forest on clay

soils (plots AGP-01 and AGP-02) and another on white sands (plot ZAR-

01), estimated with the method of sequential cores. Values are averages and

standard deviations. The area dashed shows the drought period in 2005.

Different letters in each plot show significant differences (p<0.05) of fine

root mass (020 cm) between collection dates. *Significant differences

(p<0.05) of fine root mass in each collection date between depths 010 and

1020 cm. 83

Figure 2-7 Fine root mass (Mg ha1) at soil depths: 010, 1020, and 020 cm, in plots of

two forests on different soils in Colombian Amazon: one forest on clay loam

soil (plots AGP-01 and AGP-02) and other forest on loamy sand (plot ZAR-

01), estimated by the sequential core method. *Significant differences

(p<0.05) of fine root mass in each collection date among plots. 84

Figure 3-1 Above- and below-ground biomass (dry weight) of two old-growth forests in

the north-western Amazon. 117

xix

Figure 3-2 Above- and below-ground components of the net primary production of two

old-growth forests in the north-western Amazon. 118

Figure 3-3 Total, above- and below-ground net primary production (NPP) and leaf area

index of two old-growth forests in the north-western Amazon. 119

Figure 4-1 Rainfall anomaly for the sites studied in the north-western Amazon

(Colombia), for 2004–2012 from the meteorological station at the Vásquez

Cobo airport in Leticia (04° 11’ 36’’ S, 69° 56’ 35’’). Anomaly rainfall

calculated as the observed minus the average for 1973–2012. The dashed

lines show the standard deviation (1π, 2π), and the shade areas represent

periods where the driest months can occur, including the dry season of 2005

(June to September with rainfall <100 mm month1). 157

Figure 4-2 Soil texture classification for the forest plots of this study, adapted from Soil

Textural Triangle of USDA Classification (Thien, 1979): (1) Clay-soil terra-

firme forests (Cl, AGP-01 and AGP-02 plots); (2) Clay-loam black-water

seasonally flooded forest (ClLo, ZAR-02); (3) Sandy-clay-loam terra-firme

forest (SaClLo, ZAR-03); (4) Sandy-loam terra-firme forest (SaLo, ZAR-04);

(5) Loamy-sand terra-firme forest (LoSa, ZAR-01). 158

Figure 4-3 Litterfall intra-annual variation of five Amazon forests. The measurements

were taken from October 2005 to December 2006. The shade areas

represent periods where the driest months can occur, including the dry

season of 2005 (June to September with rainfall <100 mm month1). 159

Figure 4-4 Correlation between litterfall and rainfall with different lags (months). Cross-

correlation above the dotted lines indicates significant correlation. 160

Figure 4-5 Litterfall anomaly of Amazon forests for 2005 and 2006. The continuous

horizontal line represents the mean; the anomaly is the difference between

the observed and the mean of litterfall (2005-2006), the dashed lines show

the standard deviation (1σ, 2σ). The shade areas represent periods where the

xx

driest months can occur, including the dry season of 2005 (June to

September with rainfall <100 mm month1). 161

Figure 4-6 Stem growth measured with dendrometer bands in individuals of the different

Amazon forests plots for 2005 and 2006. The shade areas represent periods

where the driest months can occur, including the dry season of 2005 (June to

September with rainfall <100 mm month1). 162

Figure 4-7 Intra-annual variation of leaf area index, litterfall production, and fine-root

mass of two forests on contrasting soils in the Amazon Basin. The shade

areas represent periods where the driest months can occur, including the dry

season of 2005 (June to September with rainfall <100 mm month1). 163

Figure 4-8 Correlation between fine-root mass and rainfall with different lags (months).

Cross-correlation above the dotted lines indicates significant correlation. 164

Figure 4-9 Above-ground biomass increment for different Amazon forests plots. Points

represent the median, and lines the mean standard deviation. 165

Figure 4-10 Intra-annual variation of stem increments of individuals for different

Amazon forest plots, for 20042012 years. 166

Figure 4-11 Intra-annual variation of biomass increments of individuals for different

Amazon forest plots, for 20042012 years. 167

Figure 4-12 Annual mortality and recruitment rates of Amazon forests (loam-soil forests

group on Table 4.1), for 20042012 years. 168

Figure 4-13 Proportion of deaths as recorded in field measurements: standing death,

broken top, uprooted or missed from inventory. Data for 4 forests 1-ha

plots in the loam-soil forests group (Table 4.1), for 20042012 years. 169

xxi

LIST OF TABLES

Table 2.1 Main characteristics of the study sites (Colombian Amazon): Amacayacu

National Natural Park (AMP), and Biological Station Zafire (ZAR). 85

Table 2.2 Fine root production (Mg ha1 yr

1) in the first 20 cm of soil depth in two

forests with different soil types in the Colombian Amazon, estimated from

ingrowth cores established in three times: (1) February of 2004, (2)

September of 2004 and, (3) February of 2006. 86

Table 2.3 Fine root mass (Mg ha1), production (Mg ha

1 yr1) and turnover rates (yr

1),

in two forests with different soil types in the Colombian Amazon: one forest

on clay soils (two 1-ha plots: AGP-01 and AGP-02), and other on white

sands (one 1-ha plot: ZAR-01), estimated by the sequential core method. 87

Table 2.4 Standing crop fine root mass (SFR), production (FRP) and turnover rates

(FRT) of fine roots (<2 mm) in forests of the Amazon basin. 88

Table 2.5 Above- and below-ground productivity in two old-growth forests with

different soil types in the Colombian Amazon: one forest on clay soil in the

Amacayacu National Natural Park (two 1-ha plots: AGP-01 and AGP-02),

and other on white sands in the Biological Station Zafire (one 1-ha plot:

ZAR-01). 89

Table 2.6 Spearman coefficients (rs) for the standing crop fine root mass in two forests

with different soil types in the Colombian Amazon, correlated with the mean

precipitation per day (PD) of the last 7, 15, 30, 60, 90, 100 and 120 days until

the date of collection and, with the mean precipitation per day with a time

lag (TL) of 7, 15, 30, 120 and 150 days from the date of collection with fixed

intervals of time of 15, 30, 60 and 90 days. 90

Table 3.1 Above- and below-ground biomass (dry weight, Mg C ha1) of trees and palms

in a clay-soil and a white-sand forests in the north-western Amazon (the

xxii

average of the biomass medians for 2004, 2005 and 2006 years ± standard

error), and their contribution to total biomass (p). 120

Table 3.2 Above- and below-ground subcomponents of net primary productivity (NPP,

Mg C ha1 yr

1), partitioning with respect to NPP (p, unitless) and leaf area

index (LAI, m2 m2) in a clay-soil forest and a white-sand forest in the north-

western Amazon between the years 2004 and 2006. 121

Table 4.1 Description of the plots located in the Amacayacu National Natural Park

(AGP) and in the Zafire Biological Station (ZAR) in the north-western

Amazon (Colombia). 170

Table 4.2 Correlation coefficients of above- and below-ground carbon allocation

components, forest dynamics, and leaf area index (LAI) of Amazon forests

with precipitation. Missing data correspond to unavailable measurements for

some plots or very small measurement periods not suitable for calculation of

correlation coefficients. 171

Table 4.3 Correlation matrices of the litterfall production and the stem growth of

individuals among different Amazon forest plots. 172

Table 4.4 Correlation matrix of the leaf area index and the fine-root mass between two

Amazon forests on contrasting soils. 173

Table 4.5 Correlation matrices of above-ground biomass increment, the annual mortality

and recruitment rates among different Amazon forest plots (loam-soil

forests group, ZAR plots on Table 4.1). 174

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LIST OF APPENDICES

Appendix 3-1 Supporting Online Material for Edaphic controls on ecosystem-level

carbon allocation in two old-growth tropical forests on contrasting soils.

This includes: 1) Photos A. Soil-clay forest. B. White-sand forest. 2)

Table A1. Allometric equations selected to estimate above- and below-

ground biomass (dry weight, Mg ha1) for trees and palms of tropical

evergreen forests. d= diameter (cm), ρ= wood specific gravity (g cm3).

3) References. 122

Appendix 7-1 Jiménez, E.M., Moreno, F.H., Peñuela, M.C., Patiño, S., Lloyd, J., 2009.

Fine root dynamics for forests on contrasting soils in the Colombian

Amazon. Biogeosciences 6, 2809-2827.

www.biogeosciences.net/6/2809/2009/ 184

Appendix 7-2 Contribution of this dissertation to other researches in the Amazon Basin.

204

25

CARBON ALLOCATION IN NORTH-WESTERN AMAZON FORESTS (COLOMBIA)

ABSTRACT

Studies of carbon allocation in forests provide essential information for

understanding spatial and temporal differences in carbon cycling that can inform models

and predict possible responses to changes in climate. Amazon forests play a particularly

significant role in the global carbon balance, but there are still large uncertainties

regarding abiotic controls on the rates of net primary production (NPP) and the

allocation of photosynthetic products to different ecosystem components; and how the

carbon allocation components of Amazon forests respond to extreme climate events.

The overall objective of this thesis is to examine the carbon allocation

components in old-growth tropical forests on contrasting soils, and under similar climatic

conditions in two sites at the Amacayacu National Natural Park and the Zafire Biological

Station, located in the north-western Amazon (Colombia). Measurements of above- and

below-ground carbon allocation components (biomass, net primary production, and its

partitioning) at the ecosystem level, and dynamics of tree mortality and recruitment were

done along eight years (20042012) in six 1-ha plots established in five Amazon forest

types on different soils (clay, clay-loam, sandy-clay-loam, sandy-loam and loamy-sand) to

address specific questions detailed in the next paragraphs.

In Chapter 2, I evaluated the hypothesis that as soil fertility increases the amount

of carbon allocated to below-ground production (fine-roots) should decrease. To address

this hypothesis the standing crop mass and production of fine-roots (<2 mm) were

estimated by two methods: (1) ingrowth cores and, (2) sequential soil coring, during 2.2

years in the most contrasting forests: the clay-soil forest and the loamy-sand forest. We

found that the standing crop fine-root mass and its production were significantly

different between forests and also between soil depths (0–10 and 10–20 cm). The loamy-

sand forest allocated more carbon to fine-roots than the clay-soil forest, with fine-root

26

production in the loamy-sand forest twice (mean ± standard error = 2.98 ± 0.36 and 3.33

± 0.69 Mg C ha−1 yr

−1, method 1 and 2, respectively) as much as for the more fertile clay-

soil forest (1.51 ± 0.14, method 1, and from 1.03 ± 0.31 to 1.36 ± 0.23 Mg C ha−1 yr

−1,

method 2). Similarly, the average of standing crop fine-root mass was three times higher

in the loamy-sand forest (5.47 ± 0.17 Mg C ha1) than in the more fertile soil (from 1.52

± 0.08 a 1.82 ± 0.09 Mg C ha1). The standing crop fine-root mass also showed a

temporal pattern related to rainfall, with the production of fine-roots decreasing

substantially in the dry period of the year 2005. These results suggest that soil resources

may play an important role in patterns of carbon allocation of below-ground

components, not only driven the differences in the biomass and its production, but also

in the time when it is produced.

In Chapter 3, I assessed the three components of stand-level carbon allocation

(biomass, NPP, and its partitioning) for the same forests evaluated in Chapter 2 (clay-soil

forest and loamy-sand forest). We found differences in carbon allocation patterns

between these two forests, showing that the forest on clay-soil had a higher above-

ground and total biomass as well as a higher above-ground NPP than the loamy-sand

forest. However, differences between the two types of forests in terms of stand-level

NPP were smaller, as a consequence of different strategies in the carbon allocation of

above- and below-ground components. The proportional allocation of NPP to new

foliage production was relatively similar between the two forests. Our results of above-

ground biomass increments and fine-root production suggest a possible trade-off

between carbon allocation to fine-roots versus wood growth (as it has been reported by

other authors), as opposed to the most commonly assumed trade-off between total

above- and below-ground production. Despite these differences among forests in terms

of carbon allocation components, the leaf area index showed differences between forests

like total NPP, suggesting that the leaf area index is more indicative of total NPP than

carbon allocation.

In Chapter 4, I evaluated the spatial and temporal variation of carbon allocation

components and forest dynamics of Amazon forests as well as their responses to climatic

fluctuations. I evaluated the intra- and inter-annual variation of carbon allocation

components and forest dynamics during the period 2004−2012 in five forests on

27

different soils (clay, clay-loam, sandy-clay-loam, sandy-loam and loamy-sand), but

growing under the same local precipitation regime in north-western Amazonia

(Colombia). We were interested in examining if these forests respond similarly to rainfall

fluctuations as many models predict, considering the following questions: (i) Is there a

correlation in carbon allocation components and forest dynamics with precipitation? (ii)

Is there a correlation among forests? (iii) Are temporal responses in leaf area index (LAI)

indicative of variations of above-ground production or a reflection of changes in carbon

allocation patterns among forests?. Overall, the correlation of above- and below-ground

carbon allocation components with rainfall suggests that soils play an important role in

the spatial and temporal differences of responses of these forests to rainfall fluctuations.

On the one hand, most forests showed that the above-ground components are

susceptible to rainfall fluctuations; however, there was a forest on loamy-sand that only

showed a correlation with the below-ground component (fine-roots). On the other hand,

despite the fact that north-western Amazonia is considered without a conspicuous dry

season (defined as <100 mm month−1), litterfall and fine-root mass showed high

seasonality and variability, particularly marked during the drought of 2005. Additionally,

forests of the loam-soil group showed that litterfall respond to time-lags in rainfall as well

as and the fine-root mass of the loamy-sand forest. With regard to forest dynamics, only

the mortality rate of the loamy-sand forest was significantly correlated with rainfall (77%).

The observed inter-annual variability of stem and biomass increments of individuals

highlighted the importance of the mortality in the above-ground biomass increment.

However, mortality rates and death type proportion did not show clear trends related to

droughts. Interestingly, litterfall, above-ground biomass increment and recruitment rates

of forests showed high correlation among forests, particularly within the loam-soil forests

group. Nonetheless, LAI measured in the most contrasting forests (clay-soil and loamy-

sand) was poorly correlated with rainfall but highly correlated between forests; LAI did

not reflect the differences in the carbon allocation components, and their response to

rainfall on these forests. Finally, the forests studied highlight that north-western Amazon

forests are also susceptible to climate fluctuations, contrary to what has been proposed

previously due to their lack of a pronounced dry season.

29

ASIGNACIÓN DEL CARBONO EN BOSQUES DEL

NOROESTE AMAZÓNICO (COLOMBIA)

RESUMEN

Los estudios sobre la asignación del carbono en los ecosistemas forestales

proporcionan información esencial para la comprensión de las diferencias espaciales y

temporales en el ciclo del carbono de tal forma que pueden aportar información a los

modelos y, así predecir las posibles respuestas de los bosques a los cambios en el clima.

Dentro de este contexto, los bosques Amazónicos desempeñan un papel particularmente

importante en el balance global del carbono; no obstante, existen grandes incertidumbres

en cuanto a los controles abióticos en las tasas de la producción primaria neta (PPN), la

asignación de los productos de la fotosíntesis a los diferentes componentes o

compartimentos del ecosistema (aéreo y subterráneo) y, cómo estos componentes de la

asignación del carbono responden a eventos climáticos extremos.

El objetivo general de esta tesis es analizar los componentes de la asignación del

carbono en bosques tropicales maduros sobre suelos contrastantes, que crecen bajo

condiciones climáticas similares en dos sitios ubicados en la Amazonia noroccidental

(Colombia): el Parque Natural Nacional Amacayacu y la Estación Biológica Zafire. Con

este objetivo, realicé mediciones de los componentes de la asignación del carbono

(biomasa, productividad primaria neta, y su fraccionamiento) a nivel ecosistémico y de la

dinámica forestal (tasas anuales de mortalidad y reclutamiento), a lo largo de ocho años

(20042012) en seis parcelas permanentes de 1 hectárea establecidas en cinco tipos de

bosques sobre suelos diferentes (arcilloso, franco-arcilloso, franco-arcilloso-arenoso,

franco-arenoso y arena-francosa). Toda esta información me permitió abordar preguntas

específicas que detallo a continuación.

En el Capítulo 2 evalúe la hipótesis de que a medida que aumenta la fertilidad del

suelo disminuye la cantidad del carbono asignado a la producción subterránea (raíces

finas con diámetro <2 mm). Y para esto, realicé mediciones de la masa y la producción

30

de raíces finas usando dos métodos: (1) el de los cilindros de crecimiento y, (2) el de los

cilindros de extracción secuencial. El monitoreo se realizó durante 2.2 años en los

bosques con suelos más contrastantes: arcilla y arena-francosa. Encontré diferencias

significativas en la masa de raíces finas y su producción entre los bosques y, también con

respecto a la profundidad del suelo (010 y 1020 cm). El bosque sobre arena-francosa

asignó más carbono a las raíces finas que el bosque sobre arcillas. La producción de raíces

finas en el bosque sobre arena-francosa fue dos veces más alta (media ± error estándar =

2.98 ± 0.36 y 3.33 ± 0.69 Mg C ha1 año

1, con el método 1 y 2, respectivamente), que

para el bosque sobre arcillas, el suelo más fértil (1.51 ± 0.14, método 1, y desde 1.03 ±

0.31 a 1.36 ± 0.23 Mg C ha1 año

1, método 2). Del mismo modo, el promedio de la masa

de raíces finas fue tres veces mayor en el bosque sobre arena-francosa (5.47 ± 0.17 Mg C

ha1) que en el suelo más fértil (de 1.52 ± 0.08 a 1.82 ± 0.09 Mg C ha

1). La masa de las

raíces finas también mostró un patrón temporal relacionado con la lluvia, mostrando que

la producción de raíces finas disminuyó sustancialmente en el período seco del año 2005.

Estos resultados sugieren que los recursos del suelo pueden desempeñar un papel

importante en los patrones de la asignación del carbono entre los componentes aéreo y

subterráneo de los bosques tropicales; y que el suelo no sólo influye en las diferencias en

la masa de raíces finas y su producción, sino que también, en conjunto con la lluvia, sobre

la estacionalidad de la producción.

En el Capítulo 3 estimé y analicé los tres componentes de la asignación del

carbono a nivel del ecosistema: la biomasa, la productividad primaria neta PPN, y su

fraccionamiento, en los mismos bosques del Capítulo 2 (el bosque sobre arcillas y el

bosque sobre arena-francosa). Encontré diferencias significativas en los patrones de la

asignación del carbono entre los bosques; el bosque sobre arcillas presentó una mayor

biomasa total y aérea, así como una PPN, que el bosque sobre arena-francosa. Sin

embargo, la diferencia entre los dos bosques en términos de la productividad primaria

neta total fue menor en comparación con las diferencias entre la biomasa total de los

bosques, como consecuencia de las diferentes estrategias en la asignación del carbono a

los componentes aéreo y subterráneo del bosque. La proporción o fracción de la PPN

asignada a la nueva producción de follaje fue relativamente similar entre los dos bosques.

Nuestros resultados de los incrementos de la biomasa aérea sugieren una posible

31

compensación entre la asignación del carbono al crecimiento de las raíces finas versus el

de la madera, a diferencia de la compensación comúnmente asumida entre la parte aérea y

la subterránea en general. A pesar de estas diferencias entre los bosques en términos de

los componentes de la asignación del carbono, el índice de área foliar fue relativamente

similar entre ellos, lo que sugiere que el índice de área foliar es más un indicador de la

PPN total que de la asignación de carbono entre componentes.

En el Capítulo 4 evalué la variación espacial y temporal de los componentes de la

asignación del carbono y la dinámica forestal de cinco tipos e bosques amazónicos y sus

respuestas a fluctuaciones en la precipitación, lo cual es completamente relevante en el

ciclo global del carbono y los procesos biogeoquímicos en general. Estas variaciones son

así mismo importantes para evaluar los efectos de la sequía o eventos extremos sobre la

dinámica natural de los bosques amazónicos. Evalué la variación interanual y la

estacionalidad de los componentes de la asignación del carbono y la dinámica forestal

durante el periodo 2004−2012, en cinco bosques maduros sobre diferentes suelos

(arcilloso, franco-arcilloso, franco-arcilloso-arenoso, franco-arenoso y arena-francosa),

todos bajo el mismo régimen local de precipitación en la Amazonia noroccidental

(Colombia). Quería examinar sí estos bosques responden de forma similar a las

fluctuaciones en la precipitación, tal y como pronostican muchos modelos. Consideré las

siguientes preguntas: (i) ¿Existe una correlación entre los componentes de la asignación

del carbono y la dinámica forestal con la precipitación? (ii) ¿Existe correlación entre los

bosques? (iii) ¿Es el índice de área foliar (LAI) un indicador de las variaciones en la

producción aérea o es un reflejo de los cambios en los patrones de la asignación del

carbono entre bosques?. En general, la correlación entre los componentes aéreo y

subterráneo de la asignación del carbono con la precipitación sugiere que los suelos

juegan un papel importante en las diferencias espaciales y temporales de las respuestas de

estos bosques a las variaciones en la precipitación. Por un lado, la mayoría de los bosques

mostraron que los componentes aéreos de la asignación del carbono son susceptibles a

las fluctuaciones en la precipitación; sin embargo, el bosque sobre arena-francosa

solamente presentó correlación con la lluvia con el componente subterráneo (raíces

finas). Por otra parte, a pesar de que el noroeste Amazónico es considerado sin una

estación seca propiamente (definida como <100 mm meses−1), la hojarasca y la masa de

raíces finas mostraron una alta variabilidad y estacionalidad, especialmente marcada

32

durante la sequía del 2005. Además, los bosques del grupo de suelos francos mostraron

que la hojarasca responde a retrasos en la precipitación, al igual que la masa de raíces finas

del bosque sobre arena-francosa. En cuanto a la dinámica forestal, sólo la tasa de

mortalidad del bosque sobre arena-francosa estuvo correlacionada con la precipitación (ρ

= 0.77, P <0.1). La variabilidad interanual en los incrementos en el tallo y la biomasa de

los individuos resalta la importancia de la mortalidad en la variación de los incrementos

en la biomasa aérea. Sin embargo, las tasas de mortalidad y las proporciones de

individuos muertos por categoría de muerte (en pie, caído de raíz, partido y

desaparecido), no mostraron tendencias claras relacionadas con la sequía. Curiosamente,

la hojarasca, el incremento en la biomasa aérea y las tasas de reclutamiento mostraron una

alta correlación entre los bosques, en particular dentro del grupo de los bosques con

suelos francos. Sin embargo, el índice de área foliar estimado para los bosques con suelos

más contrastantes (arcilla y arena-francosa), no presentó correlación significativa con la

lluvia; no obstante, estuvo muy correlacionado entre bosques; índice de área foliar no

reflejó las diferencias en la asignación de los componentes del carbono, y su respuesta a la

precipitación en estos bosques. Por último, los bosques estudiados muestran que el

noroeste amazónico es susceptible a fenómenos climáticos, contrario a lo propuesto

anteriormente debido a la ausencia de una estación seca propiamente dicha.

33

CARBON ALLOCATION IN NORTH-WESTERN

AMAZON FORESTS (COLOMBIA)

1 INTRODUCTION

Amazon forests have provided enormous and diverse services throughout human

history at local and global scales. It is currently understood that forests and human uses

of forests provide important climate forcing and feedbacks and that climate change may

adversely affect ecosystem functions (Bonan 2008). Moreover, emerging evidence

suggests that the Amazon operates like a system in biophysical transition (Davidson et al.

2012). In a recent review, Davidson et al. (2012) highlighted that: “Changes in

Amazonian ecosystems must be viewed in the context of the natural variation in climate

and soils across the region, as well as natural cycles of climatic variation and extreme

events”. Nevertheless, it is clear that we need to improve our knowledge of Amazon

forest ecosystem processes and also determine and understand the major and most

fundamental aspects of the change in the tropical forest ecosystems (Malhi 2012).

Although Amazon forests are crucial in the global carbon cycle, they are a poorly

understood component (Phillips et al. 2009). Furthermore, our understanding of the

causes of carbon cycle variation among forests is still poor (Malhi 2012), and the basin-

wide carbon balance remains uncertain (Davidson et al. 2012). In order to understand its

forest ecosystem carbon cycling, it is necessary to assess carbon allocation (Litton et al.

2007), i.e. the assignment of the products of photosynthesis between respiration and

biomass production, ephemeral and long-lived tissues, and above- and below-ground

components or compartments.

34

This dissertation is focused on the analysis of carbon allocation components in

different forest types on contrasting soils, but growing under similar climatic conditions

in the north-western Amazon (Colombia). This chapter presents a background on the

topic of carbon allocation in Amazon forests, the factors that have been explored to

explain the spatial and seasonal variability in its components, and subsequently the aims

and the outline of this dissertation.

1.1 CARBON ALLOCATION IN AMAZON FORESTS

Carbon allocation at the ecosystem level comprises three main components

(Litton et al. 2007): (1) biomass, the mass of any or all organic components within an

ecosystem (e.g. above- and below-ground biomass; Mg C ha1), (2) flux, the rate at which

carbon moves to or from a particular component of the forest ecosystem per unit ground

area per unit time (e.g. Net Primary Production –NPP, Mg C ha1 yr

1) and (3) partitioning,

the flux of carbon to a particular compartment as a fraction of total photosynthesis

(Gross Primary Production GPP), expressed either as a percentage (%) or a proportion

(0–1, unitless).

Few studies have addressed integrally all components of carbon allocation in

Amazon forests (Malhi et al. 2009, Malhi 2012, Doughty et al. 2013). Most of them have

emphasized on biomass (carbon stocks) or NPP, and only recently partitioning has been

addressed as NPP fraction into above- and below-ground components and

subcomponents for Amazon forests (Aragão et al. 2009, Girardin et al. 2010, Malhi et al.

2011, Malhi 2012).

Biomass is the most studied component of carbon allocation in Amazon forests;

however there are few studies that involve above- and below-ground biomass

measurements (Fittkau and Klinge 1973, Klinge and Rodriguez 1973, Klinge et al. 1975,

Klinge and Herrera 1983, Saldarriaga et al. 1988, Brown and Lugo 1992, Fearnside et al.

1993, Salomao et al. 1996, De Castro and Kauffman 1998, da Silva 2007). In particular, a

large body of literature is available for above-ground biomass; direct and indirect

estimations exists for forests types (Jordan and Uhl 1978, McWilliam et al. 1993,

35

Laurance et al. 1999, Mackensen et al. 2000), regions (Brown et al. 1995, Kauffman et al.

1995, Alves et al. 1997, Laurance et al. 1999, Cummings et al. 2002, Nascimento and

Laurance 2002, Baker et al. 2004, de Castilho et al. 2006, Feldpausch et al. 2006, Asner et

al. 2010, Baraloto et al. 2011, Salimon et al. 2011) and the entire Amazon basin (Brown

and Lugo 1992, Houghton et al. 2001, Malhi et al. 2006, Saatchi et al. 2007, Nogueira et

al. 2008).

On the contrary, for the below-ground compartment both biomass and NPP are

the least understood carbon allocation components (Fittkau and Klinge 1973, Klinge and

Herrera 1978, Silver et al. 2000, Clark et al. 2001a, 2001b). For instance, while there are

several allometric models for above-ground coarse wood biomass of tropical forests

(Návar 2009), fewer equations are available for coarse root biomass (da Silva 2007, Sierra

et al. 2007, Kenzo et al. 2009, Moser et al. 2010, Niiyama et al. 2010, Ryan et al. 2011).

And even though fine-root biomass and below-ground net primary production are well

recognized as an important component in the carbon cycling (Jackson et al. 1996, Jackson

et al. 1997), below-ground components (Figure 1-1) are often ignored or are estimated as

some theoretical proportion of above-ground values (Clark et al. 2001a).

The net primary production (Figure 1-1) is considered by Clark et al. (2001a) as

the sum of above-ground biomass increment, fine litterfall, above-ground losses to

consumers, emissions of biogenic volatile organic compounds (VOCs), above-ground

losses of leached organic compounds, net increments in biomass of coarse and fine-roots,

dead coarse and fine-roots, root losses to consumers, root exudates, carbohydrates

exported by plants to their mycorrhizal or nodule symbionts, and any net increases in

stores of non-structural carbohydrates. In practice, few NPP components are measured

in field studies in forest ecosystems, most frequently, measurements are restricted to fine

litterfall and above-ground biomass increment (Clark et al. 2001a, 2001b).

The NPP considered here was (nomenclature according to Clark et al. 2001a and

Litton et al. 2007): the total above-ground net primary production ANPPtotal = ANPPfoliage

+ ANPPwood, where ANPPfoliage is the new foliage, small twig and flower/fruit production

rate (assumed equal to the “soft-litterfall”), and ANPPwood is the above-ground biomass

increment as defined in Clark et al. (2001a); and the below-ground net primary

production in roots BNPProot = BNPPfineroot + BNPPcoarseroot, where BNPPfineroot is the fine-

36

root production and BNPPcoarseroot the below-ground coarse wood production. Losses to

consumers, VOCs, leached organic compounds, root exudates, carbohydrates exported

and increases in stores of non-structural carbohydrate were not considered here owing to

their contribution to total NPP which is marginal (Sierra et al. 2007).

Furthermore, biomass assessments are frequently not comparable. For above-

ground biomass the differences in data sets, areas (number and size of plots, sampling

design), methods of estimation and, components that are considered (size of vegetation,

growth form, dead stand coarse debris, and others) limits the reliability of comparisons

(Fearnside et al. 1993, Brown et al. 1995). Similarly, for below-ground biomass and net

primary production, the differences in methods and components measured (fine and

coarse root diameter, live or dead roots) complicate the comparisons (Vogt et al. 1998);

and consequently, increases the level of uncertainty in total biomass estimates and their

production rates.

Additionally, most of the biomass studies in the Amazon basin have been carried

out in Brazil. Above-ground biomass estimates for forests in the Colombian Amazon are

few but have been increasing (Saldarriaga et al. 1988, Overman et al. 1990, Ballesteros

1996, Anaya et al. 2009, Londoño Vega 2011, Phillips et al. 2011, Asner et al. 2012),

mainly due to interests in emerging carbon markets such as the program for Reducing

Emissions from Deforestation and Forest Degradation (REDD). In fact, Colombian

Amazon forests have been identified as a major gap in the assessment of biomass and net

primary production for the Amazon basin (Malhi et al. 2006).

1.1.1 Abiotic factors related to carbon allocation variability

Three main factors have been explored to explain the variation of the above-

ground biomass and total above-ground net primary production, across the Amazon

Basin: climate, soils and stand-related variables (Laurance et al. 1999, Malhi et al. 2004,

Baraloto et al. 2011, Quesada et al. 2012). Despite much research, above-ground biomass

estimates in tropical forests vary two-fold, with little consensus on the relative

importance of these variables explaining spatial patterns (Baraloto et al. 2011).

37

Regarding the factors controlling the natural variability in forest biomass, some

studies have shown that soils play an important role in the variation of above- and below-

ground biomass, and consequently in total biomass. Laurence et al. (1999) found that

above-ground biomass increases on clayey soils with larger concentrations of nitrogen,

organic matter, and exchangeable bases, and declines in sandy soils with higher

aluminium saturation. These authors also suggest that soils could affect tree biomass in at

least two ways: first, soils may influence species composition and second, trees could

simply grow bigger on the most fertile substrates, regardless of species composition,

which was also point out by other authors (Brown et al. 1992). Similarly, it has been

suggested that soils play an important role in the variability of below-ground biomass

across Amazon forests (Silver et al. 2000, Metcalfe et al. 2008), because texture, nutrients,

and soil moisture can affect root biomass and its production.

Meanwhile, Malhi et al. (2004) proposed a positive relationship between wood

productivity and soil fertility, with wood production varying by up to a factor of three

across Amazonian forests in a gradient of soil fertility from the poorest soils in central

and eastern Amazonia to richer soils in the west (Andean foothills). In a subsequent

analysis of NPP for ten Amazon forests, Aragão et al. (2009) noticed that the trend of

increased wood production in more fertile sites, as suggested by Malhi et al (2004), was

not obvious in their studied forests; however, they reported an increase in the total NPP

with soil phosphorus and leaf nitrogen status. Additionally, Aragão et al. (2009) showed

that above- and below-ground productivity increased as total NPP increased; similarly as

Litton et al (2007), who found that carbon fluxes increase with increasing GPP regardless

of forest type, gradients in resource supply, tree density, or stand age. However, Litton et

al. (2007) also indicated that increasing GPP does not increase all component fluxes

proportionately.

Subsequently, Chave et al. (2010) examined litterfall production and its seasonality

in tropical South America including a large dataset of Amazon forests. They found that

litterfall does not vary consistently with soil type, except in the poorest soils (white sands)

where litterfall is significantly lower than in other soil types, and that the seasonality of

litterfall is significantly and positively correlated with rainfall seasonality. In addition, they

suggested that the allocation to photosynthetic organs is prioritized over that to

38

reproduction on poor soils. However, the analysis of Litton et al. (2009) suggests that all

components of carbon allocation are likely to first receive some proportion of GPP to

satisfy basic needs. The authors therefore, concluded that their data do not support this

concept of priorities in carbon investment.

1.1.2 Biomass ratios and net primary production partitioning

Predicting ecosystem and biomass responses to climate change is one of the main

interests in terrestrial ecosystem modelling (Fisher et al. 2010). Nonetheless, due to the

scarce available data for above- and below-ground biomass, the total biomass

assessments in Amazon forests are frequently derived from above-ground estimates,

which imply the use of fractions, percentages or ratios for the missing components

(Fearnside et al. 1993, Nogueira et al. 2008). Moreover, in most cases root biomass is

assumed as a percentage of above-ground biomass, between 10 and 50 % (with an

average of 17%) of above-ground biomass can be applied to estimate root biomass in

many tropical moist forests (Brown and Lugo 1992). Consequently, determining patterns

and generalizations about the contribution of roots to forest biomass is difficult (Brown

and Lugo 1992), and this also hinder the improvement of fractions or ratios inputs that

should be used in the ecosystem models for Amazon forests types.

Recently, Malhi et al. (2011) highlighted the importance of allocation of net

primary productivity to canopy, woody tissue and fine-roots as an important descriptor

of the functioning of an ecosystem, and an important feature to correctly represent in

terrestrial ecosystem models.

The use of partitioning coefficients is necessary for carbon allocation schemes in

terrestrial ecosystem models (including Net Primary Production). In the case of global

NPP models, the Lea Area Index –LAI– mostly generated from satellite images, is an

indirect measurement of NPP. Net Primary Production, its partitioning and LAI are key

inputs for many global terrestrial NPP ecosystem models (Nemani et al. 2003, Zhao and

Running 2010). However, NPP partitioning used in many models is based on few studies

available for Amazon forests, which have created a large degree of uncertainty about the

distribution of NPP in tropical forests. On the one hand, there is no consensus about the

39

role of the Amazon forests on the global NPP patterns over time and how they respond

to climate events (Nemani et al. 2003, Saleska et al. 2007, Samanta et al. 2010, Zhao and

Running 2010, Medlyn 2011, Samanta et al. 2011). On another hand, LAI and its

dynamics is one of the key parameters for modelling forest growth and, by extension

quantifying the role of forests in the global carbon cycle (Clark et al. 2008). However,

there is much uncertainty about the role of LAI as a good proxy for estimating total

NPP.

An incomplete understanding of carbon allocation currently limits the capacity to

model forest ecosystem metabolism and accurately predict the effects of global change

on carbon cycling (Litton et al. 2007). This dissertation will contribute to some of these

questions addressed for tropical forests in general.

1.2 AIMS AND OUTLINE OF DISSERTATION

The overall objective of this thesis is to examine the carbon allocation

components in old-growth tropical forests on contrasting soils, but growing under similar

climatic conditions in the north-western Amazon (Colombia). Measurements of above-

and below-ground carbon allocation components (biomass, net primary production, and

its partitioning) at ecosystem level were done along eight years in five different Amazon

forest types to address specific questions that are detailed in the next sections.

This dissertation consists of three main independent, nonetheless connected

chapters. The content of each of the chapters is briefly presented below:

In Chapter 2, I address the below-ground compartment, through a detailed study

of fine-root (≤2 mm) biomass and productivity during 2.2 years in two old-growth terra-

firme forests located on contrasting soils in the Colombian Amazon. The research

examines the questions: (1) How different are the standing crop fine-root mass and

production between these forest types? (2) How do these variables change with soil depth

(0–10 and 10–20 cm) at each forest and between them? (3) Is there any temporal

variation in the standing crop fine-roots mass? And if so, is it related to variations in

precipitation? Because during the period of data collection in 2005 occurred an unusually

40

strong dry period, we added one more question: (4) Did the Amazon drought of 2005

affect the production of fine-roots at our sites? This chapter is already published in

Biogeosciences (Jiménez, E.M., Moreno, F.H., Peñuela, M.C., Patiño, S. and Lloyd, J.,

2009. Fine root dynamics for forests on contrasting soils in the Colombian Amazon.

Biogeosciences 6, 2809-2827).

In Chapter 3, I analyse above- and below-ground carbon allocation components of

the forests studied in the chapter 2 (the clay-soil forest and the loamy-sand forest). I test

possible soil-related controls on the three components of carbon allocation (biomass, net

primary production, and its partitioning) and in the leaf area index between these two

forest ecosystems growing under the same climatic conditions, but differing substantially

in their soil resource availability and plant community composition. The objectives were:

(i) to tests whether allocation to biomass was similar than allocation to net primary

production in the two forests studied; (ii) to explore possible relations among component

fluxes; particularly, testing whether component fluxes were proportionally larger in the

nutrient-rich clay site than in the nutrient-poor sandy site; (iii) to explore the

correspondence between leaf area index and net primary productivity and allocation. This

chapter is ready for submission to a journal.

In Chapter 4, I examine the temporal and spatial variation in the carbon allocation

components and forest dynamics in five forest types monitored during eight years

(20042012), and test the following questions: (i) Does carbon allocation components

and forest dynamics respond to temporal climatic fluctuations? (ii) Is the response

between above- and below-ground components and forest dynamics similar for these

forests? (iii) Are temporal responses in the leaf area index indicative of variations in the

net primary production or simply a reflection of changes in carbon allocation patterns?.

Finally, Chapters 5 and 6 presented the general conclusions in English and

Spanish, respectively. And the Chapter 7, are the appendixes with the published papers

from this research and the contribution of this dissertation to others research networks in

the Amazon Basin.

41

1.3 REFERENCES

Alves, D., J. V. Soares, S. Amaral, E. Mello, S. Almeida, O. F. Da Silva, and A. Silveira.

1997. Biomass of primary and secondary vegetation in Rondônia, Western

Brazilian Amazon. Global Change Biology 3:451-461.

Anaya, J. A., E. Chuvieco, and A. Palacios-Orueta. 2009. Aboveground biomass

assessment in Colombia: A remote sensing approach. Forest Ecology and

Management 257:1237-1246.

Aragão, L. E. O. C., Y. Malhi, D. B. Metcalfe, J. E. Silva-Espejo, E. Jiménez, D.

Navarrete, S. Almeida, A. C. L. Costa, N. Salinas, O. L. Phillips, L. O. Anderson,

T. R. Baker, P. H. Goncalvez, J. Huamán-Ovalle, M. Mamani-Solórzano, P. Meir,

A. Monteagudo, M. C. Peñuela, A. Prieto, C. A. Quesada, A. Rozas-Dávila, A.

Rudas, J. A. Silva Junior, and R. Vásquez. 2009. Above- and below-ground net

primary productivity across ten Amazonian forests on contrasting soils.

Biogeosciences Discuss. 6:2441-2488.

Asner, G. P., J. K. Clark, J. Mascaro, G. A. Galindo García, K. D. Chadwick, D. A.

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48

Figure 1-1 The components of (a) forest Net Primary Production –NPP, and (b) NPP*,

the sum of all materials that together represent: (1) the amount of new organic matter

that is retained by live plants at the end of the interval, and (2) the amount of organic

matter that was both produced and lost by plants during the same interval. CHO =

carbohydrates (Clark et al. 2001a).

a. NPP b. NPP* (THE MEASUREMENT)

49

2 FINE ROOT DYNAMICS FOR FORESTS ON

CONTRASTING SOILS IN THE COLOMBIAN AMAZON

Based on: Jiménez, E.M., Moreno, F.H., Peñuela, M.C., Patiño, S., Lloyd, J., 2009.

Fine root dynamics for forests on contrasting soils in the Colombian Amazon.

Biogeosciences 6, 2809-2827. www.biogeosciences.net/6/2809/2009/ (Appendix 7-1).

E.M. Jiménez et al.: Fine root dynamics for Amazon forests

51

1.1 ABSTRACT

It has been hypothesized that as soil fertility increases, the amount of carbon

allocated to below-ground production (fine roots) should decrease. To evaluate this

hypothesis, we measured the standing crop fine-root mass and the production of fine

roots (<2 mm) by two methods: (1) ingrowth cores and, (2) sequential soil coring, during

2.2 years in two lowland forests growing on different soils types in the Colombian

Amazon. Differences of soil resources were defined by the type and physical and

chemical properties of soil: a forest on clay loam soil (Endostagnic Plinthosol) at the

Amacayacu National Natural Park and, the other on white sand (Ortsteinic Podzol) at the

Zafire Biological Station, located in the Forest Reservation of the Calderón River. We

found that the standing crop fine-root mass and the production were significantly

different between forests and also between soil depths (0–10 and 10–20 cm). The loamy

sand forest allocated more carbon to fine roots than the clay loam forest with the

production in loamy sand forest twice (mean ± standard error = 3.0 ± 0.4 and 3.3 ± 0.7

Mg C ha−1 yr

−1, method 1 and 2, respectively) as much as for the more fertile loamy soil

forest (1.5±0.1, method 1, and from 1.0 ± 0.3 to 1.4 ± 0.2 Mg C ha−1 yr

−1, method 2).

Similarly, the average of standing crop fine-root mass was higher in the white-sands

forest (5.5 ± 0.2 Mg C ha1) as compared to the forest on the more fertile soil (from 1.5

± 0.1 a 1.8 ± 0.1 Mg C ha1). The standing crop fine root mass also showed a temporal

pattern related to rainfall, with the production of fine roots decreasing substantially in the

dry period of the year 2005. These results suggest that soil resources may play an

important role in patterns of carbon allocation to the production of fine roots in these

forests as the proportion of carbon allocated to above- and below-ground organs is

different between forest types.

Keywords: Fine root, biomass, net primary productivity, tropical forests, below-

ground productivity, terra firme forests, white-sand forests, Amacayacu National Natural

Park, Zafire Biological Station.


52

2.1 RESUMEN

Se ha planteado la hipótesis de que a medida que aumenta la fertilidad del suelo

disminuye la cantidad del carbono asignado a la producción subterránea (raíces finas con

diámetro <2 mm). Para evaluar esta hipótesis, medimos la masa (SFR) y producción de

raíces finas (FRP), mediante dos métodos: (1) los cilindros de crecimiento y, (2) los

cilindros secuenciales, durante 2.2 años en dos bosques sobre suelos contrastantes en la

Amazonía colombiana. Las diferencias en los recursos del suelo fueron definidas por el

tipo de suelo y las propiedades físico-químicas de los mismos: un bosque sobre suelo

franco arcilloso (Endostagnic Plinthosol) en el Parque Natural Nacional Amacayacu y,

otro sobre arenas blancas (Ortsteinic Podzol) en la Estación Biológica Zafire, ambos

sitios en el Noroeste Amazónico. SFR y FRP fue significativamente diferente entre los

bosques y también entre profundidades del suelo (010 y 1020 cm). El bosque sobre

arenas blancas asignó más carbono a las raíces finas que el bosque sobre el suelo franco

arcilloso; la FRP fue dos veces más alta en el bosque de arenas blancas (media ± error

estándar = 3.0 ± 0.4 y 3.3 ± 0.7 Mg C ha1 año

1, con los métodos 1 y 2,

respectivamente), que en el bosque sobre el suelo franco arcilloso (método 1: 1.5 ± 0.1

Mg C ha1 año

1 y método 2: 1.0 ± 0.3 a 1.4 ± 0.2 Mg C ha1 año

1). Así mismo, el

promedio de la SFR fue mayor en el bosque de arenas blancas o sobre arena-francosa (5.5

± 0.2 Mg C ha1) que en el bosque sobre suelo más fértil, el franco-arcilloso (de 1.5 ± 0.1

a 1.8 ± 0.1 Mg C ha1). Adicionalmente, la SFR mostró estacionalidad relacionada con la

precipitación, la FRP disminuyó sustancialmente en el período seco del año 2005. Estos

resultados sugieren que los recursos del suelo desempeñan un papel importante en los

patrones de la asignación del carbono para la producción de raíces finas en estos bosques,

así como en la proporción de carbono asignado a los componentes aéreos y subterráneos.

Palabras claves: raíces finas, biomasa, productividad primaria neta, bosques

tropicales, producción subterránea, bosques de tierra firme, bosques de arenas blancas,

Parque Natural Nacional de Amacayacu, Estación Biológica Zafire.


53

2.2 INTRODUCTION

Tropical forests play a central role in the global carbon cycle (Dixon et al., 1994;

Vogt et al., 1996; Brown, 2002), and this has encouraged a long on-going interest in the

study of various components of their net primary productivity, NPP (Clark et al., 2001a;

Vogt et al., 1996; Malhi et al., 2004). However, understanding of NPP in many

ecosystems, including tropical forests, is still poor due to the scarcity of information on

several of its components, especially in the below-ground component.

Moreover, fine root dynamics have usually not been measured despite their

importance for the plant carbon economy and overall ecosystem functioning. Excluding

the below-ground portion of NPP could produce significant biases in the quantification

of carbon fluxes in ecosystems (Woodward and Osborne, 2000). For example, it has been

estimated that about 0.33 of annual global NPP is used to produce fine roots (Jackson et

al., 1997).

Fine root dynamics are particularly important for tropical forests, where biomass

and rates of production and decomposition of fine roots are high (Silver et al., 2005). The

apparent paradox of the exuberance and large size of tropical humid forests growing on

intensively leached soils, suggests that fine roots play an important role in optimizing

nutrient acquisition and maintaining a closed nutrient cycle in these forests (Gower,

1987). However, understanding the dynamics of biomass and the factors controlling fine

root productivity in tropical forests, including in Amazonia, remains poor (Vogt et al.,

1998; Clark et al., 2001b; Silver et al., 2005; Trumbore et al. 2006, Metcalfe et al., 2007,

2008; Arãgao et al., 2009).

Hendricks et al. (1993) summarize two contrasting hypotheses proposed to

explain the control of soil resources on carbon allocation and NPP. The first one is called

the “differential allocation hypothesis”, and states that total NPP increases with the

increase in the availability of resources, and that allocation between above- and below-

ground components is differential, with a higher allocation to foliage and wood than to

fine roots on richer sites (Gower et al., 1992; Albaugh et al., 1998). The other hypothesis

is the “constant allocation hypothesis”, also proposes an increase in total NPP with the


54

increase in the availability of soil resources, but the relative allocation of NPP to above-

and below-ground organs remaining relatively constant (Aber et al., 1985; Nadelhoffer et

al., 1985; Raich and Nadelhoffer, 1989).

This study evaluates below-ground productivity (fine roots_2 mm) in two mature

forests of Terra firme developing on contrasting soils in the Colombian Amazon. The soil

underneath one forest was a relatively fertile Plinthosol with loam clay texture and a

higher nutrient content than a nearby loamy sand textured Podzol. In particular, we

aimed to answer the following questions: (1) How different are the standing crop fine

root mass and production between these forest types? (2) How do these variables change

with soil depth (0–10 and 10–20 cm) in each forest and between them? (3) Is there any

temporal variation in the standing crop fine roots mass? And if so, is it related variations

in precipitation? As the period of data collection in 2005 occurred an unusually strong dry

period, we added one more question: (4) Did the Amazon drought of 2005 affect the

production of fine roots at our sites? To answer these questions, we estimated the

standing crop fine root mass (SFR), production (FRP), the relative growth rates (RGR)

and the turnover rates of fine roots.

This study is orientated by the differential allocation hypothesis, which has been

one of the most accepted for tropical forests (Albaugh et al., 1998; Gower et al., 1992;

Keyes and Grier, 1981). As a consequence, we predicted a decrease of standing crop fine

root mass and production with the increase of soil resources (review in Hendricks et al.,

1993). We also predicted that the pattern of FRP would be the opposite to results

obtained for wood production by Malhi et al. (2004). They found a positive relationship

between wood productivity and soil fertility in the Amazonian basin.

2.3 MATERIALS AND METHODS

2.3.1 Study sites

Three 1 ha old-growth forest plots were used to study fine roots in the

Colombian Amazon (Trapecio Amazónico) (Figure 2-1, Table 2.1) between September 2004


55

and December 2006. Two plots, AGP-01 and AGP-02 were sampled in Amacayacu

National Natural Park (AMP) as part of the RAINFOR-NPP project (www.rainfor.org).

One plot, ZAR-01 was sampled at the Zafire Biological Station (ZAR), Calderón River

Forest Preserve as part of Grupo de Ecología de Ecosistemas Terrestres Tropicales Net Primary

Productivity Project (http://www.imani.unal.edu.co/).

In general, the Trapecio Amazónico shows a mean monthly rainfall of 278mm with a

drier period from June to September (mean monthly rainfall of 190 mm), and a rainy

season from October to May (mean monthly rainfall of 324 mm) (data from the Vásquez

Cobo airport of Leticia for the period 1973–2006). Mean temperature is about 26°C and

does not fluctuate greatly through the year (Figure 2-2). Relative humidity is high, with a

yearly average of 86%. In 2005, in the middle of the measurements described here, an

extended and unusually dry period occurred from June to September (“the 2005 Amazon

drought” see for example Phillips et al., 2009). The rainfall in 2005 was thus only 2873

mm, substantially lower than the previous and subsequent year (3250 in 2004 and 3710 in

2006), and than the multi-annual average (3335 mm).

AMP belongs to the geologic unit named Pebas or Solimoes Formation; the terrain

is slightly undulated and uniform, with soils moderately deep, well drained, and strongly

acidic with moderately fine textures (Herrera, 1997). Soils from ZAR belong to the

Terciario Superior Amazónico unit (Herrera, 1997; PRORADAM, 1979), probably originating

from the Guiana Shield (Hoorn, 1994, 2006), and composed mainly by quartz. The

terrain is flat and uniform.

Vertical profiles in exchangeable cations, carbon density and particle size

distribution have been illustrated for the soils two of the three plots sampled here (AGP-

02 and ZAR-01) by Quesada et al. (2010) with the physical and chemical nature of these

soils also specifically discussed in that paper. Of particular note is the presence in ZAR-

01, classified according to the World Reference Base (2006) as an Ortsteinc Podzol

(Oxyaquic) of a layer at approximately 1m deep in which aluminium concentration

increases abruptly as does carbon and to some extent clay content. This reflects the

composition of the defining ortsteinic layer where Al-humus chelates act as cementing

substances and despite the sandy nature of the soil above, can be regarded as a form of


56

“hardpan” both limiting root distribution and impeding drainage. On the other hand,

although both AGP-01 and AGP-02 were classified in Quesada et al. (2010) as being an

Endostagnic Plinthosol (Alumic, Hyperdystric), the plinthic layer was considered relatively

permeable exerting only moderate constraints on drainage (Quesada et al., 2011). Data

from Table 2.1 clearly show that AGP-01 and AGP-02 are more fertile than ZAR-01,

especially in terms of “total exchangeable phosphorus”, a measure as defined by Quesada

et al. (2011) and also being shown by Quesada et al. (2012) to be one of the best

indicators of Amazon soil fertility because of its dominating influence on above-ground

wood productivity.

2.3.2 Sampling design

2.3.2.1 The AMP sampling design

AGP-01 and AGP-02 are two 20×500m plots. We selected 13 areas every 40m

along the plots (Figure 2-1). Ingrowth cores were established on three dates: (1) February

2004, (2) September 2004, and (3) February 2006 (Figure 2-3). Ingrowth cores were

located 1–2m from trees ≥10 cm in diameter at breast height (DBH) to avoid coarse

roots next to trees; cores were also 0.20–1m apart from each other.

2.3.2.2 The ZAR sampling design

ZAR-01 is a plot composed of two rectangular blocks of 40×140m and 40×130m

(Figure 2-1). We established ingrowth cores in 14 areas (following the same criteria as for

AMP).

2.3.3 Core methods

2.3.3.1 Ingrowth cores

In AMP soil cores were extracted using a root auger 8 cm-diameter and 20 cm-

length; in ZAR we used a soil core sampler 5 cm-diameter and 15 cm-length. Differences


57

in core size between sites occurred because the two sampling programs started as

independent research projects. Soil cores at both sites were 20 cm long. Once extracted

from the ground cores were divided into two parts: from 0–10 cm and from 10–20 cm

depth. The soil from each part was sieved twice (through 6 and 1mm mesh size,

respectively), and all roots and fragments were extracted by hand using forceps. Finally,

this root-free soil was sown back in the same hole and depth level of the original sample.

In this method only a portion of the initially installed cores were retrieved each sampling

date, so the total amount of time that the cores were in the ground increased with each

successive sampling.

2.3.3.2 Sequential cores

In the same areas where ingrowth cores were planted we collected undisturbed

cores whenever ingrowth cores were planted and extracted (Figure 2-3). Handling and

processing of samples was the same as described for ingrowth cores.

2.3.4 Fine root extraction

In all cases, the first collection of ingrowth cores was done 5–7 months after

establishment; subsequent collections were done at 2–4 month intervals (Figure 2-3).

Selection of the time interval for the first collection was based on reports of mean fine

roots life time for several tropical forests, which ranges from 6 to 12 months (Priess et al.,

1999), previous studies having shown that starting collection after a shorter period than

this is too early to allow representative root growth into the root-free soil cores.

After extraction, soil cores were packed in labelled polythene bags and

transported to field stations where they were washed and sorted in nearby streams.

Samples were then air dried before transportation to the laboratory, where they were

washed with deionised water, sieved (mesh size 0.1 mm), and remaining roots manually

extracted with forceps. Roots were packed in paper bags and oven-dried for 24 h at 80_C,

and then weighed (0.001 g precision).


58

2.3.5 Statistical methods

Tests for differences between forest types, time and soil depths were carried out

with one way ANOVA. Data had been previously checked for normality of distributions

with the Kolmogorov-Smirnov and Shapiro-Wilk tests and, for homogeneity of variances

with the test of Levene (Dytham, 2003). When ANOVA was significant (p<0.05), we

used the post hoc test of Tukey to compare means. When the requirements of ANOVA

were not met, we used non-parametrical tests, such as the test of Kruskal-Wallis followed

by the test U of Mann-Whitney between pairs of data until the differences of the entire

group were evaluated. Statistical analyses were done with the software SPSS 11.5.0 (6

September 2002, LEAD Technologies, Inc).

FRP (Mg ha−1 yr

−1) was calculated as the time between ingrowth core installation

(time zero) and the subsequent 6–10 months, scaled to a yearly basis (Vogt et al., 1998).

In this way, for the first establishment in the loamy soil forest, calculation of yearly

production was based on growth between February and December 2004; for the

establishment 2, on growth between September 2004 and April 2005; and for the loamy

sand forest, on growth between September 2004 and July 2005; for the establishment 3 in

both forest types, production was based on growth between February and December

2006.

To compare FRP in standard units between forests and time intervals, we

calculated the relative growth rate (RGR), defined by Fogg (1967) and Kozlowski et al.

(1991) as:

(1)

where, log represents the natural logarithm; W1 and W0 are the final and initial dry weight

of fine roots, respectively and t is the time between the two collections in days.

Due to the occurrence of a strong drought period in the middle of our sampling

in 2005 (Figure 2-2), we tested for its effect on root production through the comparison

of RGR before (September–December 2004), during (April–July 2005), and after drought

t

WWRGR

)log(log(= 01 -)


59

(September–December 2006). For the loamy sand forest, we also analysed the RGR in

the time interval between the installation and the harvest 370 days later, and between

measurements separated by 89 days, this being with the purpose of having an estimate of

RGR during drought, between July and September 2005. We considered for the

estimation of RGR the time elapsed between the establishment and the retrieval time of

cores, trying to compare similar periods of growth.

Annual production of fine roots (Mg ha−1 yr

−1) was also estimated from the data

of sequential cores as the difference between maximum and minimum biomass measured

in one year (Vogt et al., 1998). To analyse the same time intervals for all plots, even

though the length of monitoring was different, we selected two 12 month periods (April

2005–2006 and December 2005–2006) for this analysis. The initial period, from

September 2004 to April 2005 was not used for calculations because the sharp seasonality

of SFR observed in the clay loam soil forest during this period could introduce biases in

the estimations.

Turnover rate was calculated as the FRP divided into the average SFR for that

year; carbon content in fine roots was assumed to be equal to 50% of dry mass (Silver et

al., 2005). To evaluate the association between SFR (Mg ha−1) –from data of sequential

cores– and mean daily rainfall (mm day−1), we used the Spearman’s correlation

coefficient, rS (Dytham, 2003).

Several works have correlated the production and the standing crop fine roots

mass with rainfall (Gower et al., 1992; Kavanagh and Kellman, 1992; Vogt et al., 1998;

Yavitt and Wright, 2001). However, the speed of the response and its temporal scale is

unknown. It is presumed that this response can be variable depending on soil conditions

and rainfall regimes (Yavitt and Wright, 2001). For this reason, we explored a wide range

of time intervals with respect to rainfall, examining the average daily rainfall of the

previous 7, 15, 30, 60, 90, 100, and 120 days before the sampling day. We also explored

the existence of a lagged response of SFR to rainfall; for this purpose we considered the

average daily rainfall for fixed time periods of 15, 30, 60, and 90 days with time lags of 7,

15, 30, 120, and 150 days from the sampling date.


60

2.4 RESULTS

In total we collected 207, 138, and 175 cores from AGP-01 and AGP-02 and

ZAR-01 respectively with the ingrowth cores method and 110 and 82 soil cores during

the monitoring period from AGP-01 and AGP-02 respectively and with 234 samples

from ZAR-01 with the sequential core. Figure 2-3 shows details of the establishment and

retrieval timetable for the two methods and plots.

2.4.1 Standing crop fine root mass and production from ingrowth cores

The standing crop for each collection date and soil depth (0–10, 10–20, and 0–20

cm) did not show significant differences (p>0.05) between the two clay loam soil plots

(AGP-01 and AGP-02). For this reason, these plots were considered as a unique site in

subsequent analyses and were significantly different (p<0.05) from the loamy sand forest

plot (ZAR-01).

SFR and FRP were higher in the 0–10 cm than in the 0–20 cm soil depth for all

establishment dates and forests (Figure 2-4 and Table 2.2). SFR in the clay loam soil

forest showed significant differences (p<0.05) between soil depths (0–10 cm and 10–20

cm) in most collection times and establishment dates, the exception being the collection

of April 2005 for the second establishment. Similarly, in the loamy sand forest, SFR

showed significant differences between soil depths in most collection dates, but

differences between depths were not significant for the first collection dates.

Figures of FRP were higher in the forest on loamy sand forest than in the clay

loam forest in all depths and establishment dates (Table 2.2). Differences of FRP in the

0-20 cm layer were significant (p<0.05) in establishments 2 and 3; however, in

establishment 2 differences between forest types were not significant (p>0.05) when

evaluated independently at each soil depth (0–10 cm and 10–20 cm). In establishment 3,

we found significant differences (p<0.05) of FRP between forest types at all soil depths.

Results for SFR had similar trends: establishments 2 and 3 showed significant differences

(p<0.05) between the two forest types at each soil depth in most dates of collection, the


61

except being for establishment 2 in April 2005 for which differences were not significant

between sites at any soil depth, and in April 2006 at the 10–20 cm depth. Nevertheless,

for this collection date, the other depths (0–10 cm and 0–20 cm) showed significant

differences (p<0.05) between the two forests.

The rates of FRP in the first 20 cm of soil ranged from 1.60 Mg ha−1 yr

−1 in the

forest growing on the clay loam soil to 6.00 Mg ha−1 yr

−1 on sandy soil, both rates

obtained for establishment 3 (Table 2.2). Likewise, mean FRP was lower for the clay

loam forest (3.02 Mg ha−1 yr

−1) than in the loamy sand forest with an estimate of 5.97 Mg

ha−1 yr

−1 being obtained (Table 2.2).

Relative growth rates (RGR) for the three periods evaluated were higher for the

loamy sand forest than for the clay loam forest (Figure 2-5). RGR in the clay loam forest

both before and after drought were also higher than during drought in 2005. Before the

drought RGR was 4.47 yr−1 and 3.94 yr

−1 for AGP-01 and AGP-02, respectively, and after

drought it was 1.21 yr−1 for AGP-01. RGR during the drought period were lower: −1.00

and −0.72 yr−1 for AGP-01 and AGP-02, respectively. RGR before and after drought in

the loamy sand forest were similar: 2.94 and 2.08 yr−1, respectively, while the RGR

estimated during the final part of the drought period were much lower (−0.77 yr−1),

similar to those obtained for the forest on clay loam soil.

2.4.2 Standing crop fine root mass, production, and turnover rates from

sequential soil coring

Similar to results obtained for the ingrowth cores, SFR was significantly higher

(p<0.05) at 0–10 cm depth than at 10–20 cm (Figure 2-5 and Table 2.3). Temporal

variation of SFR along the monitoring period also showed significant differences among

collection dates for all plots: AGP-01 (F8,101=4.754, p<0.01), AGP-02 (X2=23.130,

D.F.=6, p=0.001), and ZAR-01 (X2=49.258, D.F.=8, p=0.000) (Figure 2-6). December

2005 had the highest value of SFR (5.04 Mg ha−1) in AGP-01. In AGP-02 September

2004, April and December 2005 showed higher values (3.90, 4.27 and 5.04 Mg ha−1,


62

respectively) whereas July 2006 showed the lowest value (2.44 Mg ha−1). For ZAR-01,

September 2004, July 2005, and December 2006, showed significant differences (Figure

2-6). In the clay loam forest (plots AGP-01 and AGP-02) the standing crop increased

between September and December, while for the loamy sand forest the increase occurred

between March and July.

The standing crop also showed significant differences between plots (p<0.05) at

each collection date and at all soil depths (0–10, 10–20 and 0–20 cm), but differences

between plots of clay loam forest (AGP-01 and AGP-02) were not significant (p>0.05)

for most sampling dates (Figure 2-7), the exception being April 2005. The loamy sand

forest (ZAR-01) showed values significantly higher (p<0.05) than the plots on clay loam

soil for almost all collection dates.

By contrast, SFR measured for the entire monitoring time (2.2 years) showed

significant differences (p<0.05) between plots at all soil depths considered (Table 2.3).

The average SFR for over the monitoring period was almost three times higher in the

loamy sand forest plot than in plots of clay loam forest (10.94 Mg ha−1 in ZAR-01 and

3.04 and 3.64 Mg ha−1 for AGP-01 and AGP-02, respectively). Likewise, when the two

years were evaluated independently, SFR was higher in the loamy sand forest (8.92 and

4.41 Mg ha−1 yr

−1 for years 1 and 2, respectively) than in the clay loam forest (for AGP-

01: 2.77 and 2.67 Mg ha−1 yr

−1 for years 1 and 2, respectively, and 2.05 Mg ha−1 yr

−1 for

AGP-02 in year 1) (Table 2.3).

Turnover rates (yr−1) estimated from sequential cores for each year (Table 2.3),

varied between 0.53–0.84 in the clay loam forest, and between 0.51–0.81 in the loamy

sand forest. Averages per plot were 0.84 and 0.53 for AGP-01 and AGP-02, and 0.66 for

ZAR-01.

2.4.3 Relationship between the standing crop fine root mass and rainfall

We found a significant correlation between SFR and rainfall (mm day−1) in both

forest types (Table 2.6). For plots in the clay loam soil forest (AGP-01 and AGP-02), the


63

correlation was positive and significant between SFR and mean daily rainfall with and

without time lag. Rainfall variables that showed a positive correlation (R between 0.1884

and 0.2397) in AGP-01 were average daily rainfall of the previous 90 days, average rainfall

of the previous 60 and 90 days with time lags of 7 and 15 days, and average rainfall of the

previous 60 days with a time lag of 30 days. In AGP-02 we obtained a higher number of

rainfall variables with positive and higher correlations (0.2397–0.4702); variables with

significant correlations were average daily rainfall of last 60, 90, 100 and 120 days, as well

as rainfall with time lags of 7 days in all the fixed periods considered (15, 30, 60 and 90

days), rainfall with time lag of 15 days for fixed periods of 30 and 60 days, and rainfall

with time lag of 30 days with a fixed period of 15 days. Rainfall with the longest time lags

(120 and 150 days) and almost all fixed periods considered, showed negative correlation

with SFR in plots of clay loam forest (0.2593 to 0.3719).

In the loamy sand forest plot SFR showed negative correlations with daily

averages of rainfall of the previous 7, 15, 30, 60, 90, 100 and 120 days without time lag,

and with rainfall for fixed periods of 15, 30 and 60 days with time lags of 7 and 30 days,

and finally, with rainfall of the previous 30 days with time lag of 120 days. Contrary to the

results in the clay loam soil plots, correlations for long time lags were positive:

correlations varied from 0.1657 to 0.1943 for a time lag of 150 days and fixed periods of

30 and 60 days.

2.5 DISCUSSION

2.5.1 Carbon allocation to production of fine roots

The forest growing on the Podzol soil had a much higher average of SFR (10.94

Mg ha−1) than the forest on the clay loam textured Plinthosol (3.04 and 3.64 Mg ha

−1).

This agrees with several reviews, which show that higher values of SFR in tropical forests

occur in soils with low nutrient content, such as Caatinga (Cavelier, 1992; Vogt et al.,

1996). These results are inside the range reported in the Amazon basin and other similar

forests in Venezuela (Table 2.4), which varied between 2.18 and 39.50 Mg ha−1 for a


64

forest on clays (Acrisols/Ferralsols) in Brazil and a transition between well drained and

Caatinga forest (Acrisols/Podzols) in Venezuela, respectively.

Values of SFR reported in Table 2.4 for forests on Podzols (ranging from 4.98 to

20.00 Mg ha−1) are generally higher than those for forests on most Acrisols/Ferralsols

(from 2.18 to 3.64 Mg ha−1), which were close to the values obtained for forests on the

same soil type of this study; however, the value for forests classified by Duivenvoorden

and Lips (1995) as belonging to well-drained soils (probably Acrisols/Alisols) in the

middle Caquetá (Colombian Amazon) are much higher than this general range (12 Mg

ha−1) as estimates for Ferralsol/Acrisol soils in Eastern Brazil taken from data reported

by Metcalfe et al. (2008). It is, however, important to note that even within the one soil

group, significant variations in nutrient contents can be observed. For example, Quesada

et al. (2010) found 0–30 cm total exchangeable phosphorus concentrations to vary from

27 to 68 mg kg−1 across a range of 13 ferralsols sampled as part of Amazon Basin wide

study and to vary from 8 to 91 mg kg−1

for the seven acrisols sampled as part of the same

work. It is thus difficult to make generalisations about likely differences in soil fertility

based on a simple knowledge of (usually only approximately and often incorrectly

identified) soil classifications.

Our estimates of FRP for the loamy sand forest are high compared with the range

shown in Table 2.4 (5.97 Mg ha−1 yr

−1 with the ingrowth core method and 6.67 Mg ha−1

yr−1 with sequential cores), while estimations for the clay loam forest are intermediate

(3.02 Mg ha−1 yr

−1 with ingrowth cores and 2.05 Mg ha−1 yr

−1, 2.72 Mg ha−1 yr

−1 with

sequential cores).

The decrease of SFR and FRP with soil depth found here is a general trend

reported in tropical forests (Cavelier, 1992; Duivenvoorden and Lips, 1995; Klinge, 1973;

Pavlis and Jeník, 2000; Silver et al., 2000) due to the proliferation of fine roots near the

surface. These roots are considered important for resource acquisition because they allow

the direct cycling of nutrients from organic matter, which probably is an adaptation to the

low nutrient supply in infertile soils (Sayer et al., 2006).


65

Both SFR and FRP were significantly higher in the loamy sand forest than in the

clay loam forest. These results show that in loamy sand forest, with lower nutrient

contents, below-ground mass allocation is higher than for other forests. This result has

been found in other forests on soils with low nutrient availability and content (Cavelier,

1992; Priess et al., 1999), and specifically in sites such as the mountains in Guiana (Priess

et al., 1999) and elsewhere in Amazonia (Klinge and Herrera, 1978).

Differences found here between forest types support our hypothesis about the

decrease of stocks and production of fine roots with the increase of soil resources and

agree with other hypotheses proposing the increase of SFR and carbon allocation below-

ground with the decrease of site quality, nutrient availability or under more xerophytic

conditions (Shaver and Aber, 1996, Landsberg and Gower, 1997). The investment in leaf

compounds, such as tannins, to retard litter decomposition and hence slow down the rate

of nutrient cycling, could result in an increase of below-ground productivity to improve

the supply of nutrients to the plant (Fischer et al., 2006). These authors found that FRP

was highly correlated with leaf tannin content and the genetic composition of individual

trees, which suggests a potential genetic control of the compensatory growth of fine

roots in response to the accumulation of secondary compounds of foliage in the soil.

This is a factor that could be evaluated as a potential mechanism of allocation to below-

ground productivity, particularly in sand forests which tend to contain high amounts of

tannins in the foliage.

Several studies show that soil plays an important role in the carbon allocation to

below-ground production (Block et al., 2006; Cavelier, 1992; Haynes and Gower, 1995;

Yavitt and Wright, 2001), and Malhi et al. (2004) found that soil is an important factor

affecting above-ground NPP with Quesada et al. (2012) showing soil available

phosphorus availability to be the likely driving variable. Haynes and Gower (1995)

analysed how soil fertility affected carbon allocation to below-ground productivity in a

plantation of Pinus resinosa Ait. on fertilized and unfertilized soils, and found that

fertilization decreased the relative carbon allocation to below-ground production. Gower

et al. (1992), analysed how the availability of water and nutrients affected the NPP in a

coniferous forest (Pseudosuga menziesii var. galuca), and found a negative relationship


66

between water and nutrient availability and carbon allocation to below-ground organs. In

the case of the forests studied, the fact that they are subject to the same climatic regime,

we conclude that soil is the factor playing the principal role on the amount of carbon

allocated to roots. In this way, both SFR and FRP decreased with the increase of soil

fertility, which is opposite to the results of Malhi et al. (2004) for above-ground NPP.

On the other hand, integrating above- and below-ground productivity of the

forests studied (Table 2.5) suggests that allocation of NPP between above (wood and

foliage)- and below-ground (fine roots) is differential, just as suggested by the differential

allocation hypothesis proposes. Even though carbon allocation to the above-ground

portion was higher than that to fine roots, this difference is more accentuated in the clay

loam forest than in the forest growing on loamy sand. Indeed, differences in the total

productivity (above plus below-ground) between the two forests were not high (between

8.66 and 8.76 Mg C ha−1 yr

−1 for the clay loam forest and, 7.12 Mg C ha−1 yr

−1 for the

loamy sand forest). These results on above- and below-ground productivity show the

large variation of Amazonian forests at smaller scales than that presented by Malhi et al.

(2004), which reflects the importance of soil and widen the knowledge about the

allocation to above- and below-ground productivity in different forest types and soils of

the Amazon region (see Aragão et al., 2009).

2.5.2 Turnover rates of fine roots

Turnover rates of this study (0.51–0.84 yr−1) are similar to values reported for

other Amazonian forests (0.14 and 0.70 yr−1) (Table 2.4). Nevertheless, the average

turnover rate for the plot AGP-01 in the loamy soil forest was comparatively high (0.84

yr−1). Fine roots are tissues energetically expensive to build (Yavitt and Wright, 2001) and

their longevity is critical for the functionality of the root system. Short longevity supposes

higher energetic demands for the formation of new roots to replace dead roots and to

maintain the concomitant absorption surface. Aber et al. (1985) propose that turnover

rates of fine roots are higher on rich soils than on poor ones. However, turnover rates in

both forest types showed similar values (0.53–0.84 yr−1 for the clay loam forest, and 0.51–


67

0.81 yr−1 for the forest on white sands). The large variability of turnover rates in each

forest type could mask differences between them.

2.5.3 Temporal variation of standing crop fine root mass

Several authors have correlated environmental variables with biomass or

production of fine roots (Gower et al., 1992; Kavanagh and Kellman, 1992; Vogt et al.,

1998; Yavitt and Wright, 2001). Among these variables, rainfall has been found to be one

of the most influential factors affecting SFR and its longevity in tropical forests (Green et

al., 2005). Standing crop fine root mass showed a clear temporal variation during the

monitoring period, a result in line with numerous studies showing that for certain periods

of the year a higher growth rate of fine roots occurs in response to specific climatic

events (Vogt et al., 1986). Though results suggest that differences in carbon allocation to

production of fine roots between forest types are governed by the availability of soil

resources, patterns of temporal variation of SFR are explained by their correlation with

rainfall, which has been reported for other tropical forests (Green et al., 2005; Kavanagh

and Kellman, 1992; Yavitt and Wright, 2001).

Though SFR responded to the average daily rainfall in both forest types, these

responses were of a contrasting nature. For the loamy soil forest soil plot SFR increased

with rainfall of the last three months and decreased with rainfall occurring over long time

lags (120–150 days). In the forest on white sands SFR decreased with rainfall of the

previous 4 months, and increased with the rainfall observed at longer time lags (up to 5

months). Differences of the response of both forest types to rainfall might be explained

by differences in their soils: the loamy sand forest contained a hard pan at 90–100 cm

depth, which inevitably produces water logging in the soil above during rainy season and

likely impeding growth of fine roots; this is shown by the negative correlation of SFR

with rainfall of last 4 months. This phenomenon does not occur in the clay loam soil

forest which responds positively to the increase of rainfall.

On the other hand, the effect of rainfall on FRP was evident in the drought

season of 2005. RGR during the drought showed that both forests responded in similar


68

way, because both showed a decrease in FRP. However, for the loamy sand forest this

decrease was more obvious over the last months of the drought (Figure 2-5). The

impermeable ortsteinic layer of the Podzol soil probably plays an important role in the

soil water content of this forest by causing water retained above it during the rainy season

to only be slowly released during the dry season, and therefore delaying the forest

response to the drought. The general behaviour during drought suggests that both forest

types are susceptible to strong changes of rainfall. However, the main difference between

them is the speed of the response of each forest: the clay loam forest showed a faster

decrease of FRP as a response to drought than the loamy sand forest. Due to this

differential response of two forest types to drought we cannot compare FRP between

forests to dry season.

Likewise, in both forest types RGR before drought were higher than after

drought. This probably is related with the higher rainfall of last months before the first

collections (year 2004), than that after the drought, in 2006 (Figure 2-4). In the clay loam

forest of AGP-01, the periods between collections that showed an increase of SFR were

October–December 2005, and September–December 2006, which coincided with the

rainy season. Also in AGP-02 October-December 2005 was the period of increase of

SFR. In both plots the SFR was higher in December 2005 than in the same month of

2006, which could be explained by the higher rainfall of the two previous months in 2005

than in 2006 (Figure 2-2).

In the loamy sand forest the periods of increase of the SFR occurred between

April–July 2005, and March–July 2006, when rainfall decreased and we suggests because

the water logging of soil caused by the hardpan also decreased. On the other hand, SFR

was higher in July 2005 than in July 2006, which can be explained by the decrease in the

soil water logging in July 2005 when the first months of drought occurred, which allowed

an increase of SFR. The two preceding months to July 2006 showed a mean rainfall

higher than in 2005, which suggests that water logging conditions of soil were greater at

this time than in 2005, which was expressed in a lesser SFR.

Our results thus show that rainfall plays a crucial role in the seasonal variation of

fine root growth in both forests; in the clay loam soil forest the pattern accords with


69

reports for other well drained forests (Green et al., 2005; Metcalfe et al., 2008; Priess et

al., 1999; Silver et al., 2005), where SFR increased in the rainy season and decreases

during the dry season. The loamy sand forest showed a different pattern, similar to that

of flooded forests. This behaviour is apparently conditioned by the hardpan (ortsteinic

layer) that causes water logging during the rainy season thus limiting growth of fine roots

and lagging the timing of fine root growth in response to rainfall.

2.5.4 The methods of sampling/estimation used

The selection of methods for the estimation of FRP and its controlling factors is

tremendously important and has raised great interest nowadays (Hendricks et al., 2006;

Lauenroth et al., 1986; Majdi et al., 2005; Makkonen and Helmisaari, 1999; Metcalfe et al.,

2007; Vogt et al., 1986, 1998). Hendricks et al. (2006) used a wide range of common

methods to estimate FRP in three types of ecosystems in a gradient of soil humidity, with

different soil characteristics and resource availability. They found that FRP was not

negatively correlated with the availability of soil resources. Their results support in some

cases the hypothesis of differential resource allocation and in some others the constant

allocation hypothesis. With respect to the methods used in the present study – sequential

cores and the ingrowth cores –these authors mention, as well as others (Madji et al., 2005;

Vogt et al., 1986, 1998), that they probably underestimate FRP; however, they seem to be

the most appropriate to compare sites and to evaluate the temporal variation of FRP and

SFR (Vogt et al., 1998; Makkonen and Helmisaari, 1999).

We acknowledge that sampling differences have the potential to bias the results

of this study. On the one hand, sample sizes might not have a significant effect because

they were almost identical in both forest types (22 vs. 26 in the establishment 2, and 1 3

vs. 13 in the establishment 3 in the loamy sand forest and clay loam forest, respectively).

On the other hand, the main potential limitation of our sampling scheme is the effect on

FRP of different core sizes in the ingrowth experiment. The use of two independent

methods to estimate FRP (ingrowth and sequential cores), allowed us to crosscheck our

results. Sequential cores are samples of fine root mass under the natural conditions of the


70

site and their results are expected to be little affected by the diameters of cores, as

expected in ingrowth cores due to the differential colonization of roots.

Results of FRP and its temporal variation did not show large differences between

the two methods used here in each forest type. The clay loam forest showed similar

results of SFR between the two methods, but differences in the loamy sand forest were

marked: SFR estimated by the ingrowth cores was about 5.00 Mg ha−1, while by the

sequential cores was about twice that value (10.94 Mg ha−1). This result suggests that

probably more important than the diameter of auger, there is a strong effect of the

changed physical properties of soil on root growth in loamy sands and that those soils

require a longer time to reach the original root density after the disturbance implied by

the ingrowth method.

Despite the different results of FRP obtained with the different methods as

widely documented by several authors (Hendricks et al., 2006; Vogt et al., 1998), all of

them continue being used because of the lack of consensus about the most appropriate

one to study the dynamics of fine roots. For these reasons, the combination of different

methods used here seems to be a good strategy for the estimation of FRP.

2.6 CONCLUSIONS

Carbon allocation to production of fine roots was different between forest types.

As expected in a gradient of availability of soil resources, the clay loam soil forest, with

less limitation in soil resources, showed a lower carbon allocation to production of fine

roots than the loamy sand forest, which had more limitations in soil resource availability.

SFR and FRP also showed differences with soil depth, with higher values in the first 10

cm than in the 10–20 cm layer of soil.

Temporal variation of SFR was correlated with mean daily rainfall; however, this

inverse relationship was between forest types: in the clay loam soil forest SFR increased

with the increase in rainfall of the previous three months; in the loamy sand forest SFR

decreased with the increase in rainfall of the previous four months. Likewise, RGR of


71

fine roots were different before, during, and after the drought period. Both forest types

showed lower RGR during drought, which suggests that severe changes of rainfall could

strongly affect both forest types.

In summary, from results shown here we can say that this study: (1) that the

amount and fraction of carbon allocated to fine roots was different between plots, (2)

that there are differences in net primary production allocation at these plots, (3) suggests

that probably total NPP (above plus below-ground) was not very different between the

plots, and (4) a strong cautionary warning is provided against assuming that patterns of

total ecosystem net primary production can be adequately understood/studied solely

from above-ground net primary production.

Finally, this study shows that variation in the functioning of Amazonian

ecosystems at small spatial and time scales is large; it also shows that both rainfall

patterns and soils act in different ways on the carbon allocation to production of fine

roots in these forests and that understanding how Amazonian ecosystems can respond to

these factors is fundamental considering the events expected by climate change.

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Figure 2-1 Localization of the study sites in the Colombian Amazon (Trapecio

Amazónico, Leticia): Amacayacu National Natural Park (AMP) with two 1–ha plots:

AGP-01 and AGP-02, and Biological Station Zafire (ZAR) with one 1-ha plot: ZAR-01,

in the Forest Reservation of the Calderón river.

ZAR


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Figure 2-2 Patterns of monthly and mean monthly precipitation (1973–2006) and mean

temperature from the meteorological station of the Vásquez Cobo airport, Leticia

(Amazonas, Colombia) during the time of the research. Shady areas show the dry period

of each year, the dark one represents the drought period. Mean monthly precipitation is

plotted repeatedly for every year.

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J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D

Tem

per

atu

ra (

oC

)

Pre

cip

itaci

ón

(m

m)

2004 2005 2006

Precipitacion mensualPrecipitacion prom.Temperatura prom.


80

Figure 2-3 Schematic representation of the time table for establishment and retrieval of fine root cores. Each colour indicates the establishment of

a given set of cores and their correspondent retrieval sequence considering only the 020 cm depth soil core. Note that for sequential cores there

is only retrieval (blue).

Method Site Action

Establishment 105 63 39

25 12 13 13 13 13 6 10

12 13 13 13 12

13 13 13

Establishment 79 59

19 12 13 13 11 6 2 3

10 13 13 13 3 7

Establishment 136 39

26 26 28 28 14 14

13 13 13

AGP-01 13 13 13 13 13 6 13 13 13

Sequential AGP-02 13 12 13 13 13 5 13

ZAR-01 124 14 14 14 14 14 14 13 13


Retrieval

2004 2005 2006

Number of cores

Ingrowth

AGP-01Retrieval

AGP-02Retrieval

ZAR-01Retrieval


81

Figure 2-4 Fine root production (Mg ha

1) in the first 20 cm of soil depth estimated by the ingrowth core method in two forests with different soil types in the Colombian Amazon. Cores were established in three times: (1) February of 2004, (2) September of 2004 and, (3) February of 2006. Values are the means and the standard errors. The shady area is the drought period of the year 2005. *Significant differences (p<0.05) of fine root

mass in relation to: (1) differences between soil depths (010 cm and 1020 cm) per collection date in each plot, (2) differences between all soil depths per collection date and forest type.

1

0,0

1,0

2,0

3,0

4,0

5,0

6,0

E F M A M J J A S O N D E F M A M J J A S O N D E F M A M J J A S O N D2004 2005 2006

* *

*

*

*

Monitoring time (months)

Forest on white sands

Accu

mu

late

d f

ine r

oo

t m

ass

(M

g h

a-1)

1)

2)

3)

Forest on clay soils

0,0

1,0

2,0

3,0

4,0

5,0

6,0


0-10 cm10-20 cm0-20 cm

* *

* *

*

* * *

0,0

1,0

2,0

3,0

4,0

5,0

6,0


*

*

*

*

*

*

*

*

0,0

1,0

2,0

3,0

4,0

5,0

6,0


*

*

*

0,0

1,0

2,0

3,0

4,0

5,0

6,0


*

*

*

*

*

0,0

1,0

2,0

3,0

4,0

5,0

6,0


0-10 cm10-20 cm

0,0

1,0

2,0

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4,0

5,0

6,0


0-10 cm10-20 cm

0,0

1,0

2,0

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4,0

5,0

6,0


0-10 cm10-20 cm

0,0

1,0

2,0

3,0

4,0

5,0

6,0


0-10 cm10-20 cm

0,0

1,0

2,0

3,0

4,0

5,0

6,0


0-10 cm10-20 cm


82

Figure 2-5 Anomaly of rainfall along the period of study, calculated as the precipitation of

each month minus the mean monthly precipitation (1973–2006) and relative growth rates

(RGR) of fine root mass (year1) for the forests studied. Dotted vertical lines show the

time intervals considered for the estimation of RGR from ingrowth cores; the portion

dashed shows the drought of 2005. Circles with vertical bars represent the mean and

standard errors of RGR; white and grey circles for plots of forests on clay soils (AGP-01

and AGP-02, respectively) and black circles for the plot on white sands (ZAR-01). *RGR

for ZAR-01, calculated between the former harvest (July 2005) and the following harvest

(September 2005).

-3

-2

-1

0

1

2

3

4

5

6

-200

-100

0

100

200

300

400

E F MA M J J A S O N D E F MA M J J A S O N D E F MA M J J A S O N D

TC

R (t h

a-1

añ

o-1

)

An

om

alía e

n la p

reci

pit

aci

ón

men

sual

(m

m)

2004 2005 2006

AnomaliaTCR

RG

R (y

ear

1 )

An

om

aly

of

mo

nth

ly r

ain

fall

(m

m)

-3

-2

-1

0

1

2

3

4

5

6

-200

-100

0

100

200

300

400

J F MA M J J A S O N D J F MA M J J A S O N D J F MA M J J A S O N D

TC

R (t h

a-1

añ

o-1

)

An

om

aly

of

ía e

n la p

recip

itació

n m

en

sual

(mm

)

2004 2005 2006

Anomaly

RGR

-3

-2

-1

0

1

2

3

4

5

6

-200

-100

0

100

200

300

400

J F MA M J J A S O N D J F MA M J J A S O N D J F MA M J J A S O N D

TC

R (t h

a-1

añ

o-1

)

An

om

aly

of

ía e

n l

a p

reci

pit

aci

ón

men

sual

(mm

)

2004 2005 2006

AnomalyRGR


83

Figure 2-6 Temporal variation of fine root mass (Mg ha1) in the 020 cm soil depth in

two forests on different soil types in Colombian Amazon: one forest on clay soils (plots

AGP-01 and AGP-02) and another on white sands (plot ZAR-01), estimated with the

method of sequential cores. Values are averages and standard deviations. The area dashed

shows the drought period in 2005. Different letters in each plot show significant

differences (p<0.05) of fine root mass (020 cm) between collection dates. *Significant

differences (p<0.05) of fine root mass in each collection date between depths 010 and

1020 cm.

1 Monitoring time (months)

Fin

e r

oo

t m

ass

(M

g h

a-1)

0,0

1,0

2,0

3,0

4,0

5,0

6,0

7,0

E FMAM J J A S OND E FMAM J J A S OND E FMAM J J A S OND

2004 2005 2006

Bosque sobre arcillas - AME

0-10 cm10-20 cm0-20 cm

a

b

a a a* a

a a

ab

*

*

* *

*

* * * *

0,02,04,06,08,0

10,012,014,016,018,020,0


2004 2005 2006

Bosque sobre arenas blancas - ZAB

a

c

ab

ab

ac

ab*

ab

ab

b

*

*

*

*

* *

*

* *

0,0

1,0

2,0

3,0

4,0

5,0

6,0

7,0


2004 2005 2006

Bosque sobre arcillas - AMU

a

ab

a

a

ab ab

b

* *

* *

*

* *

0,0

1,0

2,0

3,0

4,0

5,0

6,0

7,0

e f m am j j a s o n d e f m am j j a s o n d e f m am j j a s o n d

2004 2005 2006

Forest on clay soils - AME0-10 cm

10-20 cm

0-20 cm

0,0

1,0

2,0

3,0

4,0

5,0

6,0

7,0

E FMAMJ J AS ONDE FMAMJ J AS ONDE FMAMJ J A S OND

2004 2005 2006

Forest on clay soils - AMU

0,02,04,06,08,0

10,012,014,016,018,020,0

E FMAM J J A S ONDE FMAM J J A S ONDE FMAM J J A S OND

2004 2005 2006

Forest on white sands - ZAB

-3

-2

-1

0

1

2

3

4

5

6

-200

-100

0

100

200

300

400


TC

R (t ha-

1 a

ño

-1)

Anom

aly

of

ía e

n la

pre

cipit

ació

n m

ensu

al (

mm

)

2004 2005 2006

Anomaly

RGR

-3

-2

-1

0

1

2

3

4

5

6

-200

-100

0

100

200

300

400


TC

R (t ha-

1 a

ño

-1)

Anom

aly

of

ía e

n la

pre

cipit

ació

n m

ensu

al (

mm

)

2004 2005 2006

Anomaly

RGR

-3

-2

-1

0

1

2

3

4

5

6

-200

-100

0

100

200

300

400


TC

R (t ha-

1 a

ño

-1)

Anom

aly

of

ía e

n la

pre

cipit

ació

n m

ensu

al (

mm

)

2004 2005 2006

Anomaly

RGR

Forest on clay soils – AGP–01

Forest on clay soils – AGP–02

Forest on white sands – ZAR–01


84

Figure 2-7 Fine root mass (Mg ha1) at soil depths: 010, 1020, and 020 cm, in plots of

two forests on different soils in Colombian Amazon: one forest on clay loam soil (plots

AGP-01 and AGP-02) and other forest on loamy sand (plot ZAR-01), estimated by the

sequential core method. *Significant differences (p<0.05) of fine root mass in each

collection date among plots.

1

20

15

10

5

0

Monitoring date

Fin

e r

oo

t m

ass

(M

g h

a

1 )

20

15

10

5

0

30

25

20

15

10

5

0

1313141414141414123 1351313131213 13131361313131313N =

FECHA

9,008,00

7,006,00

5,004,00

3,002,00

1,00

MA

SATO

TA

30

20

10

0

-10

Plots

AME: clay soils

AMU: clay soils

ZAB: white sands

425

358

371

70

64

51179

110109

120121108

95

AGP-01: clay soils

AGP-02: clay soils

ZAR-01: white sands


85

Table 2.1 Main characteristics of the study sites (Colombian Amazon): Amacayacu

National Natural Park (AMP), and Biological Station Zafire (ZAR).

a Codes follow those published in Patiño et al. (2009). b World Reference Base for Soil Resources (2006). c Quesada, et al. (2009b).

Characteristics AMP ZAR

Principal Investigator A. Rudas and A. Prieto M. C.Peñuela and E.

Álvarez

Plot Codea AGP-01 AGP-02 ZAR-01

Latitude 3.72 3.72 4.01

Longitude 70.31 70.30 69.91

Altitude (m) 105 110 130

Forest type Terra firme Caatinga

Soil typeb Endostagnic Plinthosol (Alumic,

Hyperdystric) - clay loam Ortsteinc Podzol

(Oxyaquic) - loamy sand

Chemical properties (depth 0-30 cm)c

pH 4.50 4.29 4.27

Resin extractable P (mg kg1) 1 1 12

Total extractable P (mg kg1) 131 123 22

Mean N (%) 0.15 0.16 0.11

Mean C (%) 1.2 1.4 2.4

C/N 8 8 27

Ca (mmolc kg1) 6 5 3

Mg (mmolc kg1) 3 3 2

K (mmolc kg1) 1 1 1

Na (mmolc kg1) 0 0 1

Al (mmolc kg1) 52 52 1

SB (mmolc kg1) 10 10 6

CIC (mmolc kg1) 6.21 6.26 0.71

Al Saturation (%) 84 84 10

Base saturation (%) 16 16 90

Physical propertiesc

Sand (%) 21 19 75

Clay (%) 42 43 1

Silt (%) 37 38 25

Main root depth (cm) 20 20 10

Total root depth (cm) 50 50 100

Available water capacity, cm water per cm depth

0-30 cm 3.75 3.51 2.82

Vegetation

Richness (species ha1) 225 244 25

Mean height of crown (m) 30 30 20

Mean stem diameter (cm) 17.3 21 15

Stem density (ha1) 647 606 866

Above-ground biomass (Mg ha1) 281 276 161


86

Table 2.2 Fine root production (Mg ha1 yr

1) in the first 20 cm of soil depth in two

forests with different soil types in the Colombian Amazon, estimated from ingrowth

cores established in three times: (1) February of 2004, (2) September of 2004 and, (3)

February of 2006.

Soil depth Clay loam forest Loamy sand forest

N Mean (SE) N Mean (SE)

Establishment 1 (0.83 years)

010 cm 24 3.082 (0.196) --- ---

1020 cm 25 1.153 (0.144) --- ---

020 cm 24 4.215 (0.307) --- ---

Total C 2.108

Establishment 2 (0.52 and 0.77 years, respectively)

010 cm 22 2.104 (0.357) a 26 3.530 (0.520) a

1020 cm 22 1.243 (0.212) a 26 2.404 (0.414) a

020 cm 22 3.346 (0.472) a 26 5.934 (0.773) b

Total C 1.680 2.967

Establishment 3 (0.82 and 0.81 years, respectively)

010 cm 13 1.210 (0.178) a 13 3.910 (0.990) b

1020 cm 13 0.390 (0.078) a 13 2.091 (0.589) b

020 cm 13 1.600 (0.203) a 13 6.001 (1.388) b

Total C 0.800 3.001

Mean

020 cm 3.022 5.968

Total C 1.511 2.984

In parenthesis the time elapsed between the establishment and the retrieve of cores. Different letters shows significant differences (p<0.05) in the production between forests.


87

Table 2.3 Fine root mass (Mg ha1), production (Mg ha

1 yr1) and turnover rates (yr

1), in

two forests with different soil types in the Colombian Amazon: one forest on clay soils

(two 1-ha plots: AGP-01 and AGP-02), and other on white sands (one 1-ha plot: ZAR-

01), estimated by the sequential core method.

Clay loam forest Loamy sand forest

AGP-01 AGP-02 ZAR-01

Mean mass by soil depth a

0–10 cm 2.331 (0.114) a 2.758 (0.148) b 7.861 (0.240) c

10–20 cm 0.711 (0.053) a 0.879 (0.056) b 3.077 (0.174) c

0–20 cm 3.043 (0.151) a 3.637 (0.181) b 10.938 (0.327) c

Mean mass by year (0–20 cm)

Apr 2005–2006 3.30 (0.24) 3.85 (0.24) 10.94 (0.65)

Dec 2005–2006 3.17 (0.23) --- 8.69 (0.46)

Maximum and minimum values of annual stocks

Apr 2005–Apr 2006 5.042 (0.547) 5.196 (0.648) 16.710 (1.288)

2.273 (0.327) 3.145 (0.258) 7.794 (1.156)

Dec 2005–Dec 2006 5.042 (0.547) --- 10.943 (1.046)

2.374 (0.403) --- 6.530 (0.824)

Production b

Apr 2005–Apr 2006 2.769 2.051 8.916

Dec 2005–Dec 2006 2.668 --- 4.413

Mean Biomass 2.719 2.051 6.665

Mean C 1.359 1.026 3.332

Turnover rates

Apr 2005–Apr 2006 0.84 0.53 0.81

Dec 2005–Dec 2006 0.84 --- 0.51

Mean 0.84 0.53 0.66

Standard errors in parenthesis. a Mean Fine root mass for the whole monitoring time (2.2 years). b Difference between the maximum and minimum fine root mass measured during a year. Different letters shows significant differences (p<0.05) between forests.


88

Table 2.4 Standing crop fine root mass (SFR), production (FRP) and turnover rates

(FRT) of fine roots (<2 mm) in forests of the Amazon basin.

TF: Terra firme. * Fine root diameter <5 mm. ** The FRT as calculated from production and SFR reported for each

forest. 1 The FRP was estimated in the same site using rhizotrons with different methods to convert root length into

root mass per unit ground area. 2 They make reference to method used to estimate the production: a maximum-minimum, b decision matrix, c flow compartment, and d decomposition model. 3 The values of SFR are the averages for different forests for landscape unit. 4 They make reference to method used to estimate the production: a. ingrowth cores and b. maximum-minimum. 8 This sites were included due the similar conditions with the study sites.

Forest type Soil Depth

(cm)

SFR

(Mg ha1)

FRP

(Mg ha1 yr1)

FRT

(yr1) Reference

Brazil

Campina on humus (Podzol) 29 4.98 --- --- Klinge (1973)

TF Forest (Ferralsol) 27 5.33 --- --- Klinge (1973)

TF Forest (Ferralsol) “clay plot” 1 30 --- 11.40 --- Metcalfe et al. (2007)




TF Forest (Acrisol)”sand plot” 30 14.00 4.00 0.29 Metcalfe et al. (2008)**

TF Forest (Acrisol) “dry plot” 30 10.00 3.00 0.30 Metcalfe et al. (2008)**

TF Forest (Ferralsol) “clay plot” 30 15.00 4.00 0.27 Metcalfe et al. (2008)**

TF Forest (Archao-Anthroposol) “fertile plot” 30 11.00 7.00 0.64 Metcalfe et al. (2008)**

Forest on clay soils (Ferralsol) - year 1 10 2.18 2.04 0.70 Silver et al. (2005)

Forest on clay soils (Ferralsol) - year 2 10 2.18 1.57 0.69 Silver et al. (2005)

Forest on sandy loam soils (Acrisol) - year 1 10 2.92 2.54 0.57 Silver et al. (2005)

Forest on sandy loam soils (Acrisol) - year 2 10 2.92 1.49 0.39 Silver et al. (2005)

Mature forest (Acrisol/Ferralsol)2a 10 2.60 0.35 0.14 Trumbore et al. (2006)**

Mature forest (Acrisol/Ferralsol)2b 10 2.60 0.92 0.35 Trumbore et al. (2006)**

Mature forest (Acrisol/Ferralsol)2c 10 2.60 1.18 0.45 Trumbore et al. (2006)**

Mature forest (Acrisol/Ferralsol)2d 10 2.60 0.52 0.20 Trumbore et al. (2006)**

Secondary forest of 17 years old (Acrisol/Ferralsol)2d 10 3.42 0.85 0.25 Trumbore et al. (2006)**

Colombia

Flooded forest on well drained soils (Cambisol)3* 20 10.00 --- --- Duivenvoorden & Lips (1995)

TF Forest on well drained soils (Acrisol/Alisol)3* 20 12.00 --- --- Duivenvoorden & Lips (1995)

TF Forest on white sands (Podzol)3* 20 20.00 --- --- Duivenvoorden & Lips (1995)

Secondary forest of 18 years old in low terraces in the Caquetá river 20 12.82 --- --- Pavlis & Jeník (2000)



Mature forest in low terraces in the Caquetá river 20 30.61 --- --- Pavlis & Jeník (2000)

TF Forest on clay soils (Plinthosol)4a 20 --- 3.02 --- Present study

TF Forest on white sands/Caatinga (Podzol)4a 20 --- 5.97 --- Present study

TF Forest on clay soils (Plinthosol)4b 20 3.04 2.72 0.84 Present study

TF Forest on clay soils (Plinthosol)4b 20 3.64 2.05 0.53 Present study

TF Forest on white sands/Caatinga (Podzol)4b 20 10.94 6.67 0.66 Present study

Venezuela8

High Caatinga --- --- 1.20 --- Cuevas & Medina (1988)

TF Forest (Acrisol/Ferralsol)* --- --- 2.01 --- Jordan & Escalante (1980)

TF Forest (Acrisol/Ferralsol)* --- --- 11.17 --- Jordan & Escalante (1980)

High forest (Ferralsol) 20 11.40 3.12 0.27 Priess et al. (1999)**

Medium forest (Ferralsol) 20 12.20 3.06 0.25 Priess et al. (1999)**

Low forest (Ferralsol) 20 9.63 4.20 0.44 Priess et al. (1999)**

TF Forest 30 13.80 --- --- Rev. in Cavelier (1992)

Bana 30 15.70 --- --- Rev. in Cavelier (1992)

Transitional forest Caatinga/Bana (Podzol) 30 15.70 --- --- Rev. in Cavelier (1992)

Caatinga 30 17.90 --- --- Rev. in Cavelier (1992)

Transitional forest TF/Caatinga (Podzol) 30 39.50 --- --- Rev. in Cavelier (1992)

TF Forest 10 --- 15.4 --- Rev. in Nadelhoffer & Raich (1992)

TF Forest 10 --- 1.90 --- Sanford (1990)

TF Forest 10 1.00 --- Sanford (1990)

TF Forest 10 --- 1.00 --- Sanford (1990)

89

Table 2.5 Above- and below-ground productivity in two old-growth forests with different soil

types in the Colombian Amazon: one forest on clay soil in the Amacayacu National Natural

Park (two 1-ha plots: AGP-01 and AGP-02), and other on white sands in the Biological

Station Zafire (one 1-ha plot: ZAR-01).

Productivity (Mg C ha1 yr1) Clay loam forest Loamy sand forest


Above-ground productivity

Wood productivity a 3.35 3.84 1.32

Litterfall production b 3.87a 3.65a 2.67b

Total 7.22 7.49 3.99

Below-ground productivity (fine roots) c

Ingrowth cores 1.51 2.94

Sequential soil coring 1.36 1.03 3.33

Mean 1.44 1.27 3.14

Total productivity 8.66 8.76 7.12 a E. M. Jiménez and M. C. Peñuela (data not publ.). b Navarrete (2006). c Mean production, results from the present study. Different letters show significant differences (p<0.05).

90

Table 2.6 Spearman coefficients (rs) for the standing crop fine root mass in two forests with different

soil types in the Colombian Amazon, correlated with the mean precipitation per day (PD) of the last 7,

15, 30, 60, 90, 100 and 120 days until the date of collection and, with the mean precipitation per day

with a time lag (TL) of 7, 15, 30, 120 and 150 days from the date of collection with fixed intervals of

time of 15, 30, 60 and 90 days.

Mean precipitation (mm day1) Clay loam forest Loamy sand forest


PD-7 –0.0844 –0.0244 –0.2581 **

PD-15 –0.0560 0.0691 –0.2872 **

PD-30 –0.0118 0.1700 –0.1414 *

PD-60 0.1479 0.3052 ** –0.2857 **

PD-90 0.2169 * 0.3422 ** –0.1612 *

PD-100 0.1721 0.2397 * –0.1544 *

PD-120 0.1492 0.2397 * –0.1294 *

TL7-15 0.1835 0.4666 ** –0.3223 **

TL7-30 0.0455 0.3548 ** –0.1502 *

TL7-60 0.2190 * 0.4702 ** –0.2769 **

TL7-90 0.2397 * 0.2845 ** –0.1222

TL15-15 0.0068 0.1314 0.0555

TL15-30 0.0142 0.2494 * –0.0240

TL15-60 0.2015 * 0.3422 ** –0.1168

TL15-90 0.1884 * 0.1820 –0.1100

TL30-15 0.1417 0.2512 * –0.1563 *

TL30-30 0.1195 0.1941 –0.2261 **

TL30-60 0.2060 * 0.1820 –0.1652 *

TL30-90 0.1831 0.1820 –0.0601

TL120-15 –0.2813 ** –0.2653 * –0.0779

TL120-30 –0.3719 ** –0.2805 * –0.1562 *

TL120-60 –0.3260 ** –0.2603 * –0.1283

TL120-90 –0.2775 ** –0.3871 ** 0.0923

TL150-15 –0.3369 ** –0.0254 0.1179

TL150-30 –0.2834 ** –0.2777 * 0.1943 **

TL150-60 –0.2627 ** –0.3449 ** 0.1657 *

TL150-90 –0.2593 ** –0.4480 ** 0.1120

* Significance level p<0.05. ** Significance level p<0.01

91

3 EDAPHIC CONTROLS ON ECOSYSTEM-LEVEL

CARBON ALLOCATION IN TWO OLD-GROWTH

TROPICAL FORESTS ON CONTRASTING SOILS

E.M. Jiménez et al.: Edaphic controls on carbon allocation in two tropical forests

92


93

3.1 ABSTRACT

Studies of carbon allocation in forests provide essential information for

understanding spatial and temporal differences in carbon cycling that can inform models

and predict possible responses to changes in climate. Amazon forests play a particularly

significant role in the global carbon balance, but there are still large uncertainties

regarding abiotic controls on the rates of net primary production (NPP) and the

allocation of photosynthetic products to different ecosystem components. We evaluated

three components of stand-level carbon allocation (biomass, NPP, and its partitioning) in

two amazon forests on different soils (nutrient-rich clay soils versus nutrient-poor sandy

soils), but otherwise growing under similar conditions. We found differences in carbon

allocation patterns between these two forests, showing that the forest on clay-soil had a

higher above-ground and total biomass as well as a higher above-ground NPP than the

loamy-sand forest. However, differences between the two types of forests in terms of

stand-level NPP were smaller, as a consequence of different strategies in the carbon

allocation of above- and below-ground components. The proportional allocation of NPP

to new foliage production was relatively similar between the two forests. Our results of

above-ground biomass increments and fine-root production suggest a possible trade-off

between carbon allocation to fine-roots versus wood growth (as it has been reported by

other authors), as opposed to the most commonly assumed trade-off between total

above- and below-ground production. Despite these differences among forests in terms

of carbon allocation components, the leaf area index showed differences between forests

like total NPP, suggesting that the leaf area index is more indicative of total NPP than

carbon allocation.

Key words: Carbon allocation, biomass, net primary productivity, partitioning,

above-ground productivity, below-ground productivity, Amazon forests, terra firme

forests, white-sand forests, Amacayacu National Natural Park, Zafire Biological Station,

Colombia.


94

3.2 RESUMEN

Los estudios sobre la asignación del carbono en los ecosistemas forestales

proporcionan información esencial para la comprensión de las diferencias espaciales y

temporales en el ciclo del carbono, y pueden aportar información a los modelos y

contribuir en la predicción de las posibles respuestas a los cambios en el clima. Los

bosques amazónicos desempeñan un papel particularmente importante en el balance

global del carbono; no obstante, hay grandes incertidumbres en cuanto a los controles

abióticos sobre la producción primaria neta (NPP) y la asignación de los productos de la

fotosíntesis a los diferentes componentes del ecosistema. Se evaluaron los tres principales

componentes de la asignación del carbono a nivel del rodal (biomasa, NPP, y su

fraccionamiento) en dos bosques de la Amazonia colombiana sobre diferentes suelos

(suelos arcillosos ricos en nutrientes en comparación con suelos arenosos pobres en

nutrientes), pero que crecen bajo condiciones similares de clima. Se encontraron

diferencias significativas en los patrones de la asignación del carbono entre estos dos

bosques. El bosque sobre arcillas presentó una biomasa aérea y total más alta, así como la

NPP, que el bosque sobre suelo arenoso. No obstante, las diferencias entre los dos tipos

de bosques con respecto a la productividad total fueron más pequeñas, como

consecuencia de las diferentes estrategias en la asignación del carbono en los

componentes aéreo y subterráneo de los bosques. El fraccionamiento de la NPP para la

producción de hojas fue relativamente similar entre los dos bosques. Nuestros resultados

de los incrementos de la biomasa aérea sugieren una posible compensación entre la

asignación del carbono al crecimiento de las raíces finas versus el de la madera, a

diferencia de la compensación comúnmente asumida entre la parte aérea y la subterránea

en general. A pesar de estas diferencias entre los bosques en los componentes de la

asignación del carbono, el índice de área foliar mostró diferencias entre los bosques

como la PPN total, lo que sugiere que el índice de área foliar es más un indicador de

PPN total que de la asignación de carbono entre compartimentos del bosque.

Palabras claves: asignación del carbono, biomasa, productividad primaria neta,

fraccionamiento de la productividad, productividad aérea, productividad subterránea,

bosques Amazónicos, bosques de tierra firme, bosques de arenas blancas, Parque Natural

Nacional de Amacayacu, Estación Biológica Zafire, Colombia.


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3.3 INTRODUCTION

Three different components of the forest carbon cycle –biomass, flux, and

partitioning– are key to understanding forest ecosystem metabolism and accurately

predicting the effects of global change on forest carbon cycling (Litton and others, 2007;

Chapin and others, 2011). Terrestrial ecosystem models require information on the

partitioning of carbon among different forest compartments, but there are few studies

that have actually measured all components of the forest carbon budget that allow

accurate estimation of these partitioning coefficients (Litton and others, 2007). Even in

the world’s most extensive tropical forest ecosystem, the Amazon forest, most previous

studies have focussed on quantifying biomass or net primary production with very few

attempts at a more comprehensive analysis of carbon allocation (Malhi and others, 2009;

Girardin and others, 2010).

Amazon forests are highly heterogeneous in terms of their structure and resource

environment. Most soil groups are represented within the Amazon Basin with

considerable variations in soil fertility and potential physical limitation to plant growth,

even within the same soil order (Quesada and others, 2011). Therefore, below-ground

resource availability may vary considerably across the Basin, with potential consequences

for resource acquisition, carbon allocation, and consequently ecosystem carbon balances.

There have been important attempts to understand Basin-wide patterns of some carbon

allocation components controlled by the soil environment (Malhi and others, 2004;

Aragão and others, 2009; Malhi and others, 2009), but given that not only soils but also

climate, plant community composition, and levels of human influence co-vary, it is

complex to obtain clear patterns of carbon allocation controlled only by the soil

environment.

Although the allocation of NPP to different above- and below-ground

components might be expected to show systematic patterns that vary with soil resource

availability, reports for Amazon forests on contrasting soils (in relation to available

phosphorus [P]a as indicator of soil nutrient status), can display surprisingly similar NPP

allocation strategies: Aragão and others (2009) showed that in both the most

phosphorus-rich fertile site (an anthroposol terra preta forest) and the most nutrient


96

depauperate site (a white-sand forest) NPP partitioning was ~0.5 to above- and below-

ground components. Despite this variation in NPP partitioning when comparing

individual sites, Malhi and others (2011) suggested a roughly equal three-way split of

NPP for tropical forests as a whole, with 34 ± 6% for foliage, 39 ± 10% for wood, and

27 ± 11% for fine-roots. They suggested that the dominant allocation trade-off in

Amazon forest may be a “fine-root versus wood” (Dybzinski and others, 2011), as

opposed to the expected “root-shoot” trade-off.

Current terrestrial ecosystem models vary widely in their assumed NPP allocation

patterns. Allocation fractions for the dominant tropical plant functional types range from

10 to 45% for foliage, 16-77% for wood, and 4-39% for fine-root production (Malhi and

others, 2011). Although this variation encompasses the narrower range observed for

Amazon forests (Aragão and others, 2009): 33.5 ± 1.5% for foliage, 21.3 ± 2.2% for

wood and 31.4 ± 3.5% for fine-root production; the NPP allocation schemes of some

terrestrial ecosystem models are clearly far removed from those actually observed (Malhi

and others 2011). In addition, biomass ratios are often used as proxies to infer NPP

partitioning in some terrestrial ecosystems models (Litton and others, 2007; Malhi and

others, 2011; Wolf and others, 2011a). In contrast, Litton and others (2007) showed that

biomass cannot be used to infer either flux or partitioning in forests, because trees

accumulate biomass in both long- and short-lived tissues, and flux and partitioning are

not proportional to retention.

To calculate NPP many ecosystem models also require estimates of leaf area

index, LAI (Malhi and others, 2011). Nevertheless, the relationship between the satellite-

based indices of seasonal greenness and ecosystem productivity remains an unresolved

focus of debate (Davidson and others, 2012). Thus, although time-series of LAI from

remote sensing products can be of significant relevance to analyse fluctuations in light

absorption that translate into temporal and spatial dynamics of carbon metabolism and

allocation at different scales, the fidelity of these observations and their means of

extrapolation remain highly controversial (Nemani and others, 2003; Saleska and others,

2007; Samanta and others, 2010; Zhao and Running, 2010; Medlyn, 2011; Samanta and

others, 2011).


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In the present contribution, we aimed to assess possible soil-related impacts on

the three components of carbon allocation (biomass, net primary production, and its

partitioning) and in the leaf area index of two Amazon forest ecosystems growing under

the same climatic conditions, but differing substantially in their soil resource availability

(a nutrient rich clay soil and a nutrient poor soil on white sands) and plant community

composition. First, we were interested in testing whether allocation to biomass was

different than allocation to net primary production in the two forests studied. Second, we

explored possible relations among component fluxes; particularly, testing whether

component fluxes were proportionally larger in the nutrient-rich clay site than in the

nutrient-poor sandy site. Third, we explored the correspondence between leaf area index,

net primary productivity, and allocation.


3.4.1 Site description

The study was conducted in the north-western Amazon (Colombia), in two types

of old-growth mature terra firme forests. We established two 1-ha clay soil forests plots

located in the Amacayacu National Natural Park (AGP-01: 3°43'10.5"S-70°18'25.8"W,

AGP-02: 3°43'20.2"S-70°18'25.8"W) and a 1-ha white-sand plot in the Zafire Biological

Station (ZAR-01: 4°0’20.9”S-69°53´55.2”W). The sites are between 105110 and 130 m

a.s.l., respectively.

The plots are separated approximately 50 km apart and are under similar climatic

conditions, typical of a perhumid lowland equatorial climate; with 3342 mm (± 320 mm)

of mean annual precipitation and 279 mm (± 73 mm) of monthly mean (data from the

meteorological station at the Vásquez Cobo airport in Leticia 04°11’36’’S-69°56’35’’W,

period 1973-2008). This region have been described without dry season (defined as

rainfall <100 mm month1 by Malhi and others 2004); the driest month being July (164

mm) and the wettest April (368 mm). Mean annual temperature is 26°C (2427°C

minimum and maximum) and with a mean annual relative humidity of 86%.


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The plots were established on contrasting soils: clay soils (plots AGP-01 and

AGP-02, with 43% clay content) and sandy soil (plot ZAR-01, with 75% sand content).

These soils are classified according to the World Reference Base as Endostagnic

Plinthosol (Alumic, Hyperdystric) and Ortsteinic Podzol (Oxyaquic), respectively. A

detailed soil description of these plots is provided in Quesada and others (2011, soil

profiles for AGP-02 and ZAR-01 therein). Physical and chemical characteristics of these

soils are also discussed in Quesada and others (2010). In general, the clay soils have

higher [P]a, [N], and especially a higher effective cation exchange capacity (CEC) than the

sandy soils ([P]a: 25.4 vs. 14.4 mg kg1, [N]: 0.16 vs. 0.11 %, CEC: 6.2 vs. 0.7 mmolc kg

1,

respectively). In the clay soils the plinthic layer was considered relatively permeable thus

exerting only moderate constraints on drainage, but the sandy soil (hydromorphic

Podzol) contains a hardpan at approximately 1 m depth that causes periodic water

stagnation in the layers above it. This hydromorphic podzol is in an advanced stage of

soil development, with most clay, iron oxides, and cations elluviated to a Bh horizon

(hardpan layer) 1 m below the surface (Sierra et al., 2013).

As a result of differences in resource availability, these two soil types are

associated with a distinctive flora (Photo in Appendix 3-1). The forest on sandy soils is

known as “white-sand forest” and it is dominated by few species that only grow on this

particular soil type (Tuomisto and others, 2003; Fine and others, 2004; Fine and others,

2006; Fine and others, 2010; Stropp and others, 2011; Peñuela-M. and others 2013).

Furthermore, the white-sand forests have substantially lower species richness than the

forest on clay soils, even though the stem density is higher (889 stems ha1 with diameter

at breast height, d≥ 10 cm) than in the clay-soil forest (618 stems ha1).

3.4.2 Carbon allocation

We estimated the three components of carbon allocation at the ecosystem level

(Litton and others, 2007): biomass, flux (NPP), and partitioning. We considered

partitioning as the fractional NPP allocated to the above- and below-ground components

(similarly as in Aragão and others 2009, Malhi and others 2011, Wolf and others 2011b).


99

3.4.2.1 Biomass

3.4.2.1.1 Above-ground biomass

Above-ground biomass –AGB, was calculated from stem diameter measurements

of all trees and palms with d≥ 10 cm every year (2004, 2005, 2006). During these annual

censuses we also registered dead stems as well as the ingrowth of the new individuals that

grew during the interval between the censuses and reached d minimum (10 cm).

We developed a methodology to account for the uncertainty related to the fact

that there are no local biomass equations for either of the two forest types. AGB was

calculated as the median of biomass estimates from different allometric models selected

from the literature. For this selection of equations we considered several criteria such as

differences in biomass allometry and growth patterns of palms and trees (Brown, 1997;

Clark and others, 2001); palms contributed about 9 and 5% of the stems per hectare in

the clay-soil forest and white-sand forest, respectively. Therefore, we estimated AGB

separately for trees and palms. We selected 28 models, 25 were from harvested trees and

three from palms (Appendix 3-1 in Supplementary material). Other criteria used were:

biomass estimation from whole tree or palm, differences between height-diameter

relationships by geographic regions (Feldpausch and others, 2011, 2012), d minimum of

harvested individuals (at least 1 cm), and the type of forest (old growth, terra firme and

dense forests). Specifically, for palms, we chose equations of the same species or with

similar architectural characteristics. Consequently most of the equations selected were

generated from Amazon forests although we included other models broadly used in the

literature. For the models that include wood density, we used the database of Chave and

others (2006), and also for the white-sand forest we used wood density values directly

measured in this forest (Agudelo, 2005).

The advantage of this approach, as opposed to selecting one single biomass

equation for the tropical biome, is that we can account for the uncertainty associated to

selecting a biomass equation. We report this uncertainty with our biomass and NPP


100

estimations (see error propagation section) and therefore account for possible differences

in allometric patterns at the local level.

3.4.2.1.2 Below-ground biomass

Below-ground biomass was defined as BGB = BGBfineroot + BGBcoarseroot; where

BGBfineroot is fine-root biomass and BGBcoarseroot the coarse root biomass. Although the

importance of below-ground carbon stocks in tropical forests is well recognized (Jackson

and others, 1996; Cairns and others, 1997; Vogt and others, 1998; Clark and others,

2001), allometric models to estimate BGBcoarseroot are rare. Similarly as for AGB, there are

not local allometric models to estimate BGBcoarseroot for our sites; notwithstanding we

found five allometric equations for trees and one for palms harvested from tropical

forests (Table A1 in Appendix 3-1). We therefore followed the same procedure used to

calculate AGB, using all available models for BGBcoarseroot and taken the median of the

predictions.

The methodology to estimate BGBfineroot (root diameter ≤2 mm) is described in

Jiménez and others (2009), who used the sequential root coring method (Vogt and

others, 1998) to estimate fine-root mass at 0.2 m depth of soil samples taken every three

to four months from September 2004 to December 2006. We used the average fine-root

mass observed during the entire monitoring period to calculate stand level fine-root

biomass (Table 3 in Jiménez and others, 2009).

3.4.2.2 Net primary production

In practice, the net primary production that can be estimated is defined as the

total new organic matter produced during a specified interval (Clark and others, 2001),

i.e. the sum of the net increments in above- and below-ground live biomass plus losses.

We estimated net primary production as: NPP = ANPPtotal+ BNPProot; where ANPPtotal is

the total above- and BNPProot the below-ground net primary production in roots (Clark

and others, 2001; Litton and others, 2007). We assumed dry organic matter to be 50%


101

carbon. Other components of NPP (see section 3.1) such as emissions of volatile organic

compounds and root exudates were not quantified here; however, their contribution to

total NPP is marginal (Sierra et al., 2007) and of minor importance for comparisons

among forest types.

3.4.2.2.1 Total above-ground net primary production

This component was estimated as ANPPtotal = ANPPfoliage + ANPPwood, where

ANPPfoliage is the new foliage, small twig and flower/fruit production rate (assumed equal

to the “soft-litterfall”), and ANPPwood is the above-ground biomass increment defined as

in Clark et al. (20001a). We estimated ANPPfoliage using litter traps (25 traps per hectare

with dimensions of 1 m x 0.5 m, 1 mm mesh with a concavity to allow enough material

to accumulate, and 1 m above the forest floor). We collected fine litterfall (leaves,

flowers, fruits, twigs with diameter ≤2 cm and indeterminate material) every 15 days

from October 2005 until December 2006. The organic matter collected was dried for 48

hours at 70 ºC and then weighed. We calculated ANPPwood as the increments of surviving

trees or palms (difference between its estimated biomass at the beginning of the year and

end of the year) plus increments of ingrowth viz. the difference between its estimated

biomass at the end of the year and the minimum d measured (10 cm) (Clark and others,

2001; Malhi and others, 2004).

3.4.2.2.2 Below-ground net primary production

We estimated below-ground net primary production as BNPProot = BNPPfineroot +

BNPPcoarseroot, where BNPPfineroot is the new fine-root production and BNPPcoarseroot the

below-ground coarse wood production rate. In some studies BNPPcoarseroot is assumed as

21 ± 3% of above-ground coarse wood productivity (Aragão and others, 2009; Malhi and

others, 2009). We estimate BNPPcoarseroot as the sum between the increments in coarse

root biomass of surviving trees or palms plus increments of ingrowth –similarly as for

ANPPwood due to our use of coarse root biomass equations. Ingrowth cores at 0.20 m

depth were used to estimate BNPPfineroot (Jiménez and others, 2009), being established


102

three times in February and September of 2004 and on February of 2006. The ingrowth

cores were left in situ for periods of 5-7 months for the first collection and subsequent

samplings were done at 2-4 months intervals. In our analysis, we calculated BNPPfineroot as

the mean fine-root production of the three measurement intervals (Table 2.2 in Chapter

2).

3.4.3 Error propagation and statistical analysis

We estimated uncertainties in estimated biomass and fluxes through error

propagation. Our measure of uncertainty includes the uncertainty associated with the

biomass estimates from different allometric models (allometric uncertainty, Appendix 3-

1, Table A1) and the spatial variation within the plots (Chave and others, 2004). We

determined the individual AGB and BGBcoarseroot for each model and year; then we

aggregated them by subunits (20 m x 20 m) within the plots (25 ha1) for each year, then

calculating the median for each subunit for all equations per year, and subsequently

estimating the biomass median for all subunits per year and plot. Lastly, for each forest

we calculated the mean for AGB and BGBcoarseroot for all years.

Standard errors were calculated as the square of the median absolute deviation

divided by the square of sample size depending of the situation (n= 25 subunits per ha or

n=3 years per forest). Errors were propagated as the sum or average of the squared

standard errors, depending on the operations performed on the means. For statistical

comparisons, we used a Z- or t-test to compare means depending on the size or types of

samples compared. We merged the two plots on clay soils (AGP-01 and AGP-02) in all

analyses. All calculations and statistical tests were performed in the R environment for

statistical computing (R Development Core Team, 2012).

3.4.4 Leaf area index

Leaf area index (LAI) was also measured during the same regular field trips using

hemispherical photographs (26 points ha1) using a digital camera (Nikon, Cool Pix 990),


103

and a fish-eye converter (Nikon, FC-E8). Images were recorded under overcast

conditions with the camera placed on a tripod above 1 m above the ground following the

same protocol (http://www.rainfor.org/en/manuals). Photographs were analysed using

the Hemiview Canopy Analysis Software (Version 2.1 SR1, Delta-T Devices Ltd, UK.)

and following the recommendations of Keeling and Phillips (2007) for calculating LAI.

3.5 RESULTS

3.5.1 Biomass

We found differences between the two forests in terms of both their above- and

below-ground biomass as well as in their total biomass (Figure 3-1 and Table 3.1).

Specifically, AGB was twice as large for the clay-soil forest (120 ± 14 Mg C ha1) than for

the white-sand forest (60 ± 7 Mg C ha1). BGB was also higher in the clay-soil forest

than in the white-sand forest, being estimated at 24 ± 4 and 16 ± 2 Mg C ha1,

respectively. However, it is important to consider that BGB must be examined carefully

due to the of lack of local BGBcoarseroot allometric equations, particularly for the white-

sand forest, which showed a higher contribution of below-ground to total biomass (21%)

as opposed to the clay-soil forest (15%). BGBfineroot was three times larger in the white-

sand forest than in the clay-soil forest (6 ± 0.2 vs. 2 ± 0.1 Mg C ha1, respectively).

Total biomass was twice as large for the clay-soil forest (145 ± 15 Mg C ha1)

than for the white-sand forest (77 ± 8 Mg C ha1). Trees contributed 94 and 85% of total

biomass in the clay-soil forest and in the white-sand forest, respectively. Tree above-

ground biomass alone accounted for 81% of total biomass in the clay-soil forest and

73% in the white-sand forest. Palms accounted for 5 and 8% of total biomass in the clay-

soil forest and the white-sand forest, respectively. Nevertheless, uncertainties for palm

biomass estimates were high, suggesting that palms should be considered as an important

component of total biomass that requires specific and more accurate methods for its

estimation. Fine roots contributed with 1 and 7% to the total biomass for the clay-soil

forest and the white-sand forest, respectively.


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3.5.2 Net primary production

We found differences in ANPPtotal and its components, with ANPPfoliage larger in

the clay-soil forest than in the white-sand forest (4 ± 0.3 vs. 3 ± 0.3 Mg C ha1 yr

1;

Figure 3-2 and Table 3.2). For the clay-soil forest, ANPPwood was three times larger than

for the white-sand forest (3 ± 0.4 vs. 1 ± 0.1 Mg C ha1 yr

1), and consequently ANPPtotal

was twice for the clay-soil forest than the white-sand forest (7 ± 0.5 vs. 3 ± 0.3 Mg C

ha1 yr

1; Table 3.2). For both forests, litterfall production was the major contributor to

total above-ground net primary production, accounting for 60% of ANPPtotal for the

clay-soil forest and 80% for the white-sand forest.

We found differences in fine- and coarse root production rates between forests

(Table 3.2), with BNPPfineroot for the white-sand forest being twice that of the clay-soil

forest (3 ± 0.5 vs. 1.5 ± 0.2 Mg C ha1 yr

1). As a consequence, BNPProot was higher in

the white-sand forest than in the soil-clay forest (3 ± 0.5 vs. 2 ± 0.2 Mg C ha1 yr

1; Table

3.2). Fine-root production was the major contributor to below-ground net primary

production, accounting for 96% of BNPProot for the white-sand forest and 70% of

BNPProot for the clay-soil forest.

When adding the individual terms together, we found that NPP was 29% larger

in the clay-soil forest (9 ± 0.5 Mg C ha1 yr

1) than in the white-sand forest (6 ± 0.6 Mg

C ha1 yr

1). Above- and below-ground production varied largely between forests, such

that ANPPtotal in the white-sand forest was less than half that in the clay-soil forest, while

BNPProot of white-sand forest was higher than in the clay-soil forest. In both forests, the

productivity of fine material components (viz. ANPPfoliage and BNPPfineroot) was larger than

woody components (viz. ANPPwood and BNPPcoarseroot). Additionally, we found differences

between most above- and below-ground NPP subcomponents within the forests.


105

3.5.3 Net primary production partitioning

The two forests exhibited different NPP partitioning between above- and below-

ground components (Figure 3-3 and Table 3.2). For the clay-soil forest they were 0.8 and

0.2 respectively; while for the white-sand forest these ratios were similar at ca. 0.5.

The allocation of NPP into ANPPfoliage was similar between forests

(ANPPfoliage/NPP ~ 0.4), but with the partitioning to above-ground biomass increment

differing, ANPPwood/NPP being much higher in the clay-soil forest (~0.3) than in the

white-sand forest (~0.1). However, partitioning to fine-root production showed the

opposite pattern, being much higher in the white-sand forest (BNPPfineroot/NPP ~0.5)

than in the clay-soil forest (BNPPfineroot/NPP ~0.2). This same pattern applies to total

below-ground net primary production, with partitioning to below-ground components

higher in the white-sand forest (BNPProot ~ 0.5) than in the clay-soil forest (BNPProot ~

0.2).

3.5.4 Leaf area index

Leaf area index (LAI) was slightly higher for clay-soil forest with 4.5 ± 0.1 m2 m2

than for the white-sand forest with 4.3 ± 0.1 m2 m2 (Figure 3-3), and presented

significant differences between forests (P = 0.045, t = 1.697, d.f. = 726).

3.6 DISCUSSION

Analyses of forest ecosystems at local scales provide the opportunity to tease

apart edaphic controls versus climatic controls on the allocation of carbon. The forests

studied here are under the same climate conditions, yet the different measures of carbon

allocation we found varied as much as what has been observed across all sites studied to

date within the Amazon Basin (Malhi and others, 2004; Aragão and others, 2009; Chave

and others, 2010; Quesada and others, 2012). Given that our study sites did not present

variation in climatic factors (total precipitation and its seasonal distribution), and that


106

carbon allocation was assessed with the same methods and over the same time interval

(20042006), the results suggest that edaphic factors likely play a strong role in

determining NPP and allocation patterns. These differences were observed for the three

components of carbon allocation: biomass, net primary production, and its partitioning.

These are discussed in detail in the next sections.

3.6.1 Differences in above- and below-ground biomass

The values of above- and below-ground biomass of the forests studied here were

among the lowest of the range of values reported for Amazon forests. The white-sand

forest AGB (60 Mg C ha1) was within the lowest values previously reported for other

forests on podzols (Klinge and Herrera, 1983). Meanwhile, the clay-soil forest AGB was

close to the average for Amazon forests (143 ± 38 Mg C ha1) (Saldarriaga and others,

1988; Brown and others, 1989; Overman and others, 1990; Fearnside and others, 1993;

Brown and others, 1995; Carvalho and others, 1998; Laurance and others, 1999;

Chambers and others, 2000; Chave and others, 2001; Cuevas, 2001; Keller and others,

2001; Nascimento and Laurance, 2002; Malhi and others, 2006; Nogueira and others,

2008; Malhi and others, 2009; Girardin and others, 2010; Londoño Vega, 2011). The

BGB range compiled here (average of 40 ± 28 Mg C ha1) showed a large variability in

Amazon forests (Klinge and Herrera, 1978, 1983; Saldarriaga and others, 1988; Fearnside

and others, 1993; Salomao and others, 1996; Chave and others, 2001; Cuevas, 2001;

Keller and others, 2001; Nogueira and others, 2008; Girardin and others, 2010), varying

from the white-sand forest studied here (16 Mg C ha1) to two forests on podzols, a Tall

and Low Caatinga in San Carlos del Rio Negro, with 98 and 168 Mg C ha1, respectively

(Klinge and Herrera, 1978, 1983); the clay-soil forest BGB was located below the

average.

Comparing the two sites, there were large differences in total biomass between

the forests (50%), with even larger differences in terms of above- and below-ground

components analysed separately. AGB was almost a factor of 2 higher in the clay-soil


107

forest than in the white-sand forest, and conversely fine-root mass in the white-sand

forest was more than a factor of 3 higher than in the clay-soil forest.

Our results suggest that differences in soils are playing an important role in the

observed differences of above- and below-ground biomass, as well as in its

subcomponents, as expressed by the differences in above-ground and fine-root biomass

observed between the forests. These differences also suggest that conclusions on the

factors that contribute to differences in biomass allocation may be very different

depending on whether the above- and below-ground components are analysed

independently or in combination.

3.6.2 Differences in above- and below-ground net primary production

The two forests exhibited differences in net primary production (NPP). They

differed more in their total standing biomass than in their NPP, and differences in the

below-ground pools mainly contributed to these differences in the components of

carbon allocation (biomass and NPP).

Total, above- and below-ground net primary production were within the lowest

values reported for Amazon forests (Aragão and others, 2009; Girardin and others,

2010). The white-sand forest had a lower NPP than the clay-soil forest, both below the

average for Amazon forests (12 ± 3 Mg C ha1). Although both forests had lower total

biomass and net primary production in comparison to other Amazon forests, there were

significant differences between them, highlighting the enormous site-to-site variation

within the Amazon Basin.

Differences in total biomass between forests were larger (50%) compared to the

differences in NPP (Clay-soil forest 29% higher than the white-sand forest NPP).

However, differences in net primary production between forests are much larger when

the above- and below-ground components are analysed separately. For example, above-

ground coarse wood and fine-root production exhibited the larger differences. ANPPwood

was a factor of 3 larger in the clay-soil forest than in the white-sand forest, and

BNPPfineroot was twice as high in the white-sand forest than in the clay-soil forest.


108

Correspondingly as biomass, the differences in the above- and below-ground NPP

between the forests studied suggest that conclusions on the factors that contribute to

differences in NPP may be very different when the above- and below-ground

components are analysed separately than in combination.

One important topic in carbon allocation analyses is to evaluate whether

component fluxes are related (Litton and others, 2007), i.e. if the increase in the total

amount of NPP involve a proportional increase in the above- and below-ground flux

components. For Amazon forests, it has been reported that larger values of NPP

typically involve larger values of both above- and below-ground NPP (Aragão and

others, 2009). Conversely, the forests studied here showed that NPP was higher in the

clay-soil forest than in the white-sand forest, but above and below-ground production

were not proportionally larger in the clay-soil forest.

3.6.3 Biomass ratios and net primary production partitioning

Biomass ratios have been used widely for total biomass estimations in the

Amazon basin, especially for root biomass. Some indirect assessments of total biomass at

large spatial scales use an average of root biomass directly measured in the field, or an

overall mean for forests (17% by Brown & Lugo 1992, 19% by Jackson and others 1996,

24% by Cairns and others 1997). Cuevas (2001) showed that below-ground contributions

for terra firme forests can vary from 9 to 25%, while Klinge and Herrera (1983)

registered by direct measurements a wide below-ground biomass range, between 18 to

59% for podzols in San Carlos de Rio Negro. The below-ground contribution to total

biomass for our sites was 15 and 21% for clay-soil forest and white-sand forest,

respectively. Taken together, our observations and reports from the literature suggest

that biomass ratios are not constant in Amazon forests, and the use of basin-scale ratios

to predict the contribution of below-ground biomass to total biomass may lead to

significant errors.

Furthermore, biomass ratios have been used to infer either flux or partitioning of

NPP in forests. Our results show that despite the differences in the total amount of


109

biomass and net primary production, both higher in the clay-soil forest than in the white-

sand forest; the differences between them in the fractional distribution of biomass and

the NPP partitioning of above- and below-ground components were certainly marked.

These results confirm those found by Litton and others (2007) who revealed that

biomass was poorly related to carbon flux and to partitioning of photosynthetically

derived carbon, and should not be used to infer either. Likewise, Wolf and others

(2011b) showed that stand biomass is weakly related to NPP partitioning, and other

factors that impact NPP such as soils or climate may have a stronger influence on

partitioning than does biomass per se.

Our estimations showed that the NPP fractions allocated to short-lived

structures (foliage and fine-roots) were the main contributors to above- and below-

ground components. Partitioning to foliage was rather similar for both forests, while

partitioning to fine-roots and wood was largely different (Table 3.2). These results are

consistent with the idea that ANPPfoliage is relatively constant across different forests, but

what varies most among sites is how the remaining NPP is allocated between wood and

fine-root production (Malhi and others, 2004; Litton and others, 2007; Malhi and others,

2011; Wolf and others, 2011b). This trade-off in partitioning between wood and below-

ground production (wood to fine-roots) may be associated with differences in soil

resources, the main abiotic factor that strongly differs between our sites.

Although litterfall production and LAI have been also proposed as a good proxy

for above and total NPP (Malhi and others, 2004; Zhao and Running, 2010; Malhi and

others, 2011; Nunes and others, 2012); ANPPfoliage and LAI in the two forests studied

seems to be a less sensitive indicator of differences in the total NPP and its components

between them. Differences in fine-root production suggest that by measuring ANPPtotal

alone, total NPP can be underestimated by 20 and 50% in the clay-soil forest and the

white-sand forest, respectively.


110

3.7 CONCLUSIONS

We found important edaphic controls on carbon allocation of two Amazon

forests under similar climatic conditions on contrasting soils. In particular, we found (i)

biomass ratios between above- and below-ground components differed from the

partitioning of total NPP to above- and below-ground components. In general, biomass

ratios for the components did not reflect the fraction of NPP allocated to them. (ii)

Component fluxes in these forests were not related. High values of total NPP in the clay-

soil forest did not correspond to high values for all NPP components. Differences in the

soil environment (nutrients and water availability) are probably responsible for the large

values of below-ground NPP observed in the white-sand forest. (iii) Our results of

above-ground biomass increments suggest a possible trade-off between carbon allocation

to fine-roots versus wood growth, as opposed to the most commonly assumed trade-off

between total above- and below-ground production. (iv) The leaf area index showed

slight the differences between the two forests like total NPP, but not reflecting the

differences in carbon allocation components.

Furthermore, our analysis may have important implications for ecosystem

modelling. In particular, (1) land-surface models that require parameters on NPP

partitioning, but only use data on biomass fractions may incur in important errors for

predicting total NPP and component fluxes, and (2) models in which NPP is

proportional to LAI may miss important differences in internal ecosystem responses

caused by edaphic factors.

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117

Figure 3-1 Above- and below-ground biomass (dry weight) of two old-growth forests in

the north-western Amazon.

Clay-soil forest White-sand forest

Above-ground biomass

AG

B (M

g C

ha1 )

020

4060

8010

014

0


Below-ground biomass

BG

B (M

g C

ha1 )

020

4060

8010

014

0


Coarse root biomass

BG

B coa

rser

oot (

Mg

C ha

1 )

05

1015

2025

30


Fine root mass

BG

B fin

eroo

t (M

g C

ha1 )

05

1015

2025

30


118

Figure 3-2 Above- and below-ground components of the net primary production of two

old-growth forests in the north-western Amazon.


Foliage production

Pro

duct

ivity

(Mg

C h

a1 y

r1 )

01

23

4


Coarse wood production

Pro

duct

ivity

(Mg

C h

a1 y

r1 )

01

23

4


Fine-root production

Pro

duct

ivity

(Mg

C h

a1 y

r1 )

01

23

4


Coarse root production

Pro

duct

ivity

(Mg

C h

a1 y

r1 )

01

23

4


119

Figure 3-3 Total, above- and below-ground net primary production (NPP) and leaf area

index of two old-growth forests in the north-western Amazon.


Above-ground NPP

Pro

duct

ivity

(Mg

C ha

1 yr

1 )

02

46

810


Below-ground NPP

Pro

duct

ivity

(Mg

C ha

1 yr

1 )

02

46

810


Total NPP

Pro

duct

ivity

(Mg

C ha

1 yr1 )

02

46

810


Leaf area index

LAI (

m2 m

2 )

02

46

810


120

Table 3.1 Above- and below-ground biomass (dry weight, Mg C ha1) of trees and palms

in a clay-soil and a white-sand forests in the north-western Amazon (the average of the

biomass medians for 2004, 2005 and 2006 years ± standard error), and their contribution

to total biomass (p).

Component Growth form Clay-soil forest

White-sand forest

Median ± se p Median ± se p

Above-ground −AGB

Trees 114.93 ± 14.29 0.79 55.70 ± 7.07 0.73

Palms 5.21 ± 2.11 0.04 4.44 ± 2.35 0.06

Total 120.15 ± 14.45 0.83 60.14 ± 7.45 0.79

Below-ground −BGB

Trees* 20.86 ± 4.05 0.14 9.36 ± 1.76 0.12

Palms* 1.95 ± 1.00 0.01 1.55 ± 1.07 0.02

Fine root mass 1.67 ± 0.09 0.01 5.47 ± 0.17 0.07

Total 24.48 ± 4.17 0.17 16.38 ± 2.07 0.21

Total biomass 144.62 ± 15.04 1 76.52 ± 7.73 1

*BGBcoarseroot. Statistical differences: AGB of trees: P <0.01 (t = 37, d.f. = 3), AGB of palms: P <0.01 (t

= 25, d.f. = 4), total AGB: P< 0.01 (t = 38, d.f. = 3), BGBcoarseroot of trees: P< 0.01 (t = 37, d.f. =

2), BGBcoarseroot of palms: P< 0.01 (t = 17, d.f. = 2), BGBfineeroot: P< 0.01 (Z = 11), total BGB: P

<0.01 (Z = 4), and total biomass: P< 0.01 (Z = 21).


121

Table 3.2 Above- and below-ground subcomponents of net primary productivity (NPP,

Mg C ha1 yr

1), partitioning with respect to NPP (p, unitless) and leaf area index (LAI,

m2 m2) in a clay-soil forest and a white-sand forest in the north-western Amazon

between the years 2004 and 2006.


Median ± se % p Median ± se % p

Foliage 3.75 ± 0.30 0.6 0.42 2.48 ± 0.25 0.8 0.39

AGB increment 2.99 ± 0.36 0.4 0.34 0.73 ± 0.08 0.2 0.12

Above-ground NPP 6.74 ± 0.47 1.0 0.76 3.21 ± 0.26 1.0 0.51

Fine roots 1.53 ± 0.16 0.7 0.17 2.98 ± 0.54 0.96 0.47

Coarse roots 0.58 ± 0.11 0.3 0.07 0.13 ± 0.02 0.04 0.02

Below-ground NPP 2.11 ± 0.19 1.0 0.24 3.11 ± 0.54 1.0 0.49

NPP 8.85 ± 0.51 6.31 ± 0.60

LAI 4.45 ± 0.06 4.25 ± 0.07

Statistical differences between forests: ANPPfoliage: P <0.1 (Z = 1.79), AGB increment (Above-ground

biomass): P< 0.01 (t = 44.97, d.f. = 2), ANPPtotal: P< 0.01 (Z = 4.21), BNPPfineroot: P <0.05 (t =

-4.7, d.f. = 3), BNPPcoarseroot: P< 0.01 (t = 18.92, d.f. = 2), NPP: P< 0.05 (Z = 2.31), LAI: P =

0.045 (t = 1.697, d.f. = 726).


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Appendix 3-1 Supporting Online Material for Edaphic controls on ecosystem-level

carbon allocation in two old-growth tropical forests on contrasting soils. This includes: 1)

Photos A. Soil-clay forest. B. White-sand forest. 2) Table A1. Allometric equations

selected to estimate above- and below-ground biomass (dry weight, Mg ha1) for trees

and palms of tropical evergreen forests. d= diameter (cm), ρ= wood specific gravity (g

cm3). 3) References.


123

1) Photos A. Soil-clay forest. B. White-sand forest. More details on Table 2.1 (Jiménez and others, 2009)

.


124


125

2) Table A1. Allometric equations selected to estimate above- and below-ground biomass (dry weight, Mg ha1) for trees and palms of tropical

evergreen forests. d= diameter (cm), ρ= wood specific gravity (g cm3).

Country Model Reference

Tree above-ground biomass (AGB, whole tree)

Amazon

Brazil Ln (AGB)= -2.55 + 2.65 Ln d Araújo and others (1999), recalculated by Chave and others (2001, p. 85)

Brazil Ln (AGB)= 0.6 (4.06 d1.76) Araújo and others (1999, p. 47)

Brazil Ln (AGB)= -2.26 + 2.66 Ln d Brown (1997), recalculated by Chave and others (2001, p. 85)

Brazil Ln (AGB)= -2.43 + 2.57 Ln d Brown (1997), recalculated by Chave and others (2001, p. 85)

Brazil Ln (AGB)= -4.898 + 4.512 Ln d - 0.319 (Ln d)2 Chambers and others (2000, p. 5)

Brazil Ln (AGB)= 0.58 (2.2737 d1.9156) da Silva (2007, p. 50)

Brazil Ln (AGB)= -2.00 + 2.55 Ln d Higuchi and others (1998), recalculated by Chave and others (2001, p. 85)

Brazil Ln (AGB)= -1.716 + 2.413 Ln d Nogueira and others (2008, p. 1859)

Brazil Ln (AGB)= 600 exp [3.323 + 2.546 Ln (d/100) ] Santos (1996), cited by Carvalho and others (1998, p. 13200)

Colombia Ln (AGB)= -1.97 + 2.48 Ln d Overman and others (1994), recalculated by Chave and others (2001, p. 85)

Colombia Ln (AGB)= -0.906+1.177 Ln d2 ρ Overman and others (1994, p. 214)

Colombia Ln (AGB)= -1.6028 + 2.4242 Ln d Rodríguez (1991), cited by Londoño (2011, p. 120)

Various Ln (AGB)= ρ/0.67 exp [ 0.33 Ln d + 0.933 (Ln d)2 - 0.122 (Ln d)3 -0.37] Baker and others (2004, p. 552)

Andes

Colombia Ln (AGB)= -2.2862 + 2.4709 Ln d Zapata and others (2003)

Central America

Costa Rica Ln (AGB)= -1.81 + 2.32 Ln d Brown (1997), recalculated by Chave and others (2001, p. 85)

Puerto Rico Ln (AGB)= -2.749 + 2.634 Ln d Crow (1980, p. 43)

Puerto Rico Ln (AGB)= -2.399 + 2.475 Ln d Scatena and others (1993, p. 17)

Guiana Shield

French Guiana Ln (AGB)= -2.8762 + 2.7248 Ln d Lescure and others (1983, p. 246)


126

French Guiana Ln (AGB)= -2.19 + 2.54 Ln d Chave and others (2001, p. 87)



Guiana French Ln (AGB)= ρ exp [ -1.499 + 2.148 Ln d +0.207 (Ln d)2 - 0.0281 (Ln d)3 ] Chave and others (2005, p. 92)

Guiana French Ln (AGB)= ρ exp [ -1.239 + 1.980 Ln d + 0.207 (Ln d)2 - 0.0281 (Ln d)3 ] Chave and others (2005, p. 93)

Tropical forests

Colombia Ln (AGB)= 2.406 - 1.289 Ln d + 1.169 (Ln d)2 - 0.122 (Ln d)3 + 0.445 Ln ρ Alvarez and others (2012, p. 303)

Colombia Ln (AGB)= 3.103 - 1.794 Ln d + 1.290 (Ln d)2 - 0.128 (Ln d)3 + 0.819 Ln ρ Alvarez and others (2012, p. 303)

Colombia Ln (AGB)= 2.789 - 1.414 Ln d + 1.178 (Ln d)2 - 0.118 (Ln d)3 + Ln ρ Alvarez and others (2012, p. 303)

Colombia Ln (AGB)= -0.983 + 2.350 Ln d + Ln ρ Alvarez and others (2012, p. 303)

Various Ln (AGB)= -2.134 + 2.53 Ln d Brown (1997, p. 11)

Tree coarse root biomass (BGBcoarseroot)

Amazon

Brazil Ln (BGBcoarseroot)= [ 0.0469 (d 2.4754) ] 0.58 da Silva (2007, p. 50)

Andes

Colombia Ln (BGBcoarseroot) = exp (-4.394 + 2.693 Ln d) Sierra and others (2007, p. 303)

Tropical forests

Taiwan Ln (BGBcoarseroot)= exp (2.609 Ln d - 4.233) Lin and others (2006), cited by Kenzo and others (2009, p. 379)

Malaysia Ln (BGBcoarseroot)= 0.02186 d 2.487 Niiyama and others (2005), cited by Kenzo and others (2009, p. 379)

Malaysia Ln (BGBcoarseroot)= 0.026 d 2.49 Niiyama and others (2010, p. 276)

Palm above-ground biomass (AGB, whole palm)

Colombia Ln (AGB)= -3.956 + 3.106 Ln d Alvarez (1993), cited by Restrepo and others (2003, p. 139), palm species Euterpe precatoria Mart. and Iriartea deltoidea Ruiz & Pav.

Colombia Ln (AGB)= -3.956 + 1.553 Ln d2 Alvarez (1993), cited by Londoño (2011, p. 120), palms from upland and floodplain Amazon forests.

Colombia Ln (AGB)= -3.017 + 3.013 Ln d Restrepo and others (2003, p. 139), palm specie E. precatoria.

Palm total below-ground biomass (BGBcoarseroot)

Colombia Ln (BGBcoarseroot)= -4.742 + 3.083 Ln d Restrepo and others (2003, p. 136), palm specie E. precatoria.


127

3) References

Alvarez, E., 1993. Composición florística, diversidad, estructura y biomasa de un bosque

inundable en la Amazonia Colombiana., Departamento de Biología. Universidad

de Antioquia, Medellín, Antioquia.

Alvarez, E., Duque, A., Saldarriaga, J., Cabrera, K., de las Salas, G., del Valle, I., Lema,

A., Moreno, F., Orrego, S., Rodríguez, L., 2012. Tree above-ground biomass

allometries for carbon stocks estimation in the natural forests of Colombia.

Forest Ecology and Management 267, 297-308.

Araújo, T.M., Higuchi, N., de Carvalho Júnior, J.A., 1999. Comparison of formulae for

biomass content determination in a tropical rain forest site in the state of Pará,

Brazil. Forest Ecology and Management 117, 43-52.

Baker, T.R., Phillips, O.L., Malhi, Y., Almeida, S., Arroyo, L., Di Fiore, A., Erwin, T.,

Killeen, T.J., Laurance, S.G., Laurance, W.F., Lewis, S.L., Lloyd, J., Monteagudo,

A., Neill, D.A., Patiño, S., Pitman, N.C.A., M. Silva, J.N., Vásquez Martínez, R.,

2004. Variation in wood density determines spatial patterns in Amazonian forest

biomass. Global Change Biology 10, 545-562.

Brown, S., 1997. Estimating biomass and biomass change of tropical forests: a primer.

FAO Forestry Paper. Forest Resources Assessment publication, Rome.

Carvalho, J.A., Jr., Higuchi, N., Araújo, T.M., Santos, J.C., 1998. Combustion

completeness in a rainforest clearing experiment in Manaus, Brazil. J. Geophys.

Res. 103, 13195-13199.

Chambers, J.Q., Santos, J.d., Ribeiro, R.J., Higuchi, N., 2000. Tree damage, allometric

relationships, and above-ground net primary production in central Amazon

forest. Forest Ecology and Management 152, 73-84.

Chave, J., Andalo, C., Brown, S., Cairns, M.A., Chambers, J.Q., Eamus, D., Fölster, H.,

Fromard, F., Higuchi, N., Kira, T., Lescure, J.P., Nelson, B.W., Ogawa, H., Puig,

H., Riéra, B., Yamakura, T., 2005. Tree Allometry and Improved Estimation of

Carbon Stocks and Balance in Tropical Forests. Oecologia 145, 87-99.

Chave, J., Riéra, B., Dubois, M.-A., 2001. Estimation of Biomass in a Neotropical Forest

of French Guiana: Spatial and Temporal Variability. Journal of Tropical Ecology

17, 79-96.

Crow, T.R., 1980. A Rainforest Chronicle: A 30-Year Record of Change in Structure and

Composition at El Verde, Puerto Rico. Biotropica 12, 42-55.

da Silva, R.P., 2007. Alometria, estoque e dinâmica da biomassa de florestas primárias e

secundárias na região de Manaus (AM). Universidade Federal do Amazonas -

UFAM - Instituto Nacional de Pesquisas da Amazônia - INPA, Manaus.


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Higuchi, N., Santos, d.J., Ribeiro, R.J., Minette, L., Biot, Y., 1998. Biomassa da parte

aérea da vegetação da floresta tropical úmida de Terra-firme da Amazônia

Brasileira. Acta Amazonica 28, 153-166.

Kenzo, T., Ichie, T., Hattori, D., Itioka, T., Handa, C., Ohkubo, T., Kendawang, J.J.,

Nakamura, M., Sakaguchi, M., Takahashi, N., Okamoto, M., Tanaka-Oda, A.,

Sakurai, K., Ninomiya, I., 2009. Development of allometric relationships for

accurate estimation of above- and below-ground biomass in tropical secondary

forests in Sarawak, Malaysia. Journal of Tropical Ecology 25, 371-386.

Lescure, J.P., Puig, H., Riera, B., Leclerc, D., Beekman, A., Beneteau, A., 1983. La

phytomasse epigee d'une foret dense en Guyane francaise. Acta Oecologica-

Oecologia Generalis 4, 237-251.

Lin, K., Duh, C., Huang, C., Wang, C., 2006. Estimate of Coarse Root Biomass and

Nutrient Contents of Trees in a Subtropical Broadleaf Forest in Taiwan. Taiwan

Journal of Forest Science 21, 155-166.

Londoño Vega, A.C., 2011. Flora and dynamics of an upland and a floodplain forest in


Niiyama, K., Kajimoto, T., Matsuura, Y., Yamashita, T., Kassim, A.R., Ripin, A., Noor,

N.S.M., 2005. Allometric relationship between stem diameter, tree height, leaf,

stem and root biomass in Pasoh Forest Reserve. In: Okuda, T., Kondo, T. (Eds.),

Annual report of NIES/FRIM/UPM joint project tropical ecology and

biodiversity Alles Druck Inc., Tsukuba., pp. 22–36.

Niiyama, K., Kajimoto, T., Matsuura, Y., Yamashita, T., Matsuo, N., Yashiro, Y., Ripin,

A., Kassim, A.R., Noor, N.S., 2010. Estimation of root biomass based on

excavation of individual root systems in a primary dipterocarp forest in Pasoh

Forest Reserve, Peninsular Malaysia. Journal of Tropical Ecology 26, 271-284.

Nogueira, E.M., Fearnside, P.M., Nelson, B.W., Barbosa, R.I., Keizer, E.W.H., 2008.

Estimates of forest biomass in the Brazilian Amazon: New allometric equations

and adjustments to biomass from wood-volume inventories. Forest Ecology and

Management 256, 1853-1867.

Overman, J.P.M., Witte, H.J.L., Saldarriaga, J.G., 1994. Evaluation of Regression Models

for Above-Ground Biomass Determination in Amazon Rainforest. Journal of

Tropical Ecology 10, 207-218.

Restrepo, D.E., Benjumea, J.F., Orrego, S.A., del Valle, J.I., Moreno Hurtado, F., 2003.

Ecuaciones de biomasa para palmas del subdosel y sotobosque. In: Orrego, S.A.,

del Valle, J.I., Moreno Hurtado, F. (Eds.), Medición de la captura de carbono en

ecosistemas tropicales de Colombia. Universidad Nacional de Colombia Sede

Medellín, Departamento de Ciencias Forestales, Medellín, pp. 121-143.


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Rodríguez, L.V.A., 1991. Biomasa y reserva de nutrientes en un ecosistema de tierra

firme en la región de Araracuara. Fundación Tropenbos-Colombia, Bogotá.

Santos, J.d., 1996. Análise de modelos de regressão para estimar a fitomassa da floresta

tropical úmida de terra-firme da amazônia brasileira. Universidade Federal de

Viçosa, Viçosa.

Scatena, F.N., Silver, W., Siccama, T., Johnson, A., Sanchez, M.J., 1993. Biomass and

Nutrient Content of the Bisley Experimental Watersheds, Luquillo Experimental

Forest, Puerto Rico, Before and After Hurricane Hugo, 1989. Biotropica 25, 15-

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Sierra, C.A., del Valle, J.I., Orrego, S.A., Moreno, F.H., Harmon, M.E., Zapata, M.,

Colorado, G.J., Herrera, M.A., Lara, W., Restrepo, D.E., Berrouet, L.M., Loaiza,

L.M., Benjumea, J.F., 2007. Total carbon stocks in a tropical forest landscape of

the Porce region, Colombia. Forest Ecology and Management 243, 299-309.

Zapata, M., Colorado, G.J., del Valle, J.I., 2003. Ecuaciones de biomasa aérea para

bosques primarios intervenidos y secundarios. In: Orrego, S.A., del Valle, J.I.,

Moreno, F.H. (Eds.), Medición de la captura de carbono en ecosistemas

forestales tropicales de Colombia: contribuciones para la mitigación del cambio

climático. Universidad Nacional de Colombia, Sede Medellín-Centro Andino para

la Economía en el Medio Ambiente (CAEMA), Panamericana, Bogotá,

Colombia, pp. 87-144.

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4 SPATIAL AND TEMPORAL VARIATION IN CARBON

ALLOCATION COMPONENTS AND DYNAMICS IN

AMAZON FORESTS WITH CONTRASTING SOILS

Jimenez et al. Spatial and temporal variation in carbon allocation components in Amazon forests

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4.1 ABSTRACT

The spatial and temporal variation of carbon allocation components and forest

dynamics of Amazon forests and their responses to extreme events in climate are very

relevant for the global carbon cycle and other biogeochemical processes. These dynamics

are also important to assess the effects of drought or extreme events on the natural

variability of Amazon forests. We evaluated the intra- and inter-annual variation of

carbon allocation components and forest dynamics during the period 20042012 in five

forests on different soils (clay, clay-loam, sandy-clay-loam, sandy-loam and loamy-sand),

but growing under the same local precipitation regime in north-western Amazonia

(Colombia). We were interested in examining if these forests respond similarly to rainfall

fluctuations as many models predict, considering the following questions: (i) Is there a

correlation in carbon allocation components and forest dynamics with precipitation? (ii)

Is there a correlation among forests? (iii) Are temporal responses in leaf area index (LAI)

indicative of variations of above-ground production or a reflection of changes in carbon

allocation patterns among forests?. Overall, the correlation of above- and below-ground

carbon allocation components with rainfall suggests that soils play an important role in

the spatial and temporal differences of responses of these forests to rainfall fluctuations.

On the one hand, most forests showed that the above-ground components are

susceptible to rainfall fluctuations; however, there was a forest on loamy-sand that only

showed a correlation with the below-ground component (fine-roots). On the other hand,

despite the fact that north-western Amazonia is considered without a conspicuous dry

season (defined as <100 mm month−1), litterfall and fine-root mass showed high

seasonality and variability, particularly marked during the drought of 2005. Additionally,

forests of the loam-soil group showed that litterfall respond to time-lags in rainfall as well

as and the fine-root mass of the loamy-sand forest. With regard to forest dynamics, only

the mortality rate of the loamy-sand forest was significantly correlated with rainfall

(77%). The observed inter-annual variability of stem and biomass increments of

individuals highlighted the importance of the mortality in the above-ground biomass

increment. However, mortality rates and death type proportion did not show clear trends

related to droughts. Interestingly, litterfall, above-ground biomass increment and

recruitment rates of forests showed high correlation among forests, particularly within


134

the loam-soil forests group. Nonetheless, LAI measured in the most contrasting forests

(clay-soil and loamy-sand) was poorly correlated with rainfall but highly correlated

between forests; LAI did not reflect the differences in the carbon allocation components,

and their response to rainfall on these forests. Finally, the forests studied highlight that

north-western Amazon forests are also susceptible to climate fluctuations, contrary to

what has been proposed previously due to their lack of a pronounced dry season.

Key words: Above-ground productivity, above-ground biomass increment,

litterfall, fine-root mass, leaf area index, mortality, recruitment, precipitation, drought,

Colombia.


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4.2 RESUMEN

La variación espacial y temporal de los componentes de la asignación del carbono

y la dinámica forestal de los bosques amazónicos y sus respuestas a fenómenos extremos

en el clima son muy relevantes en el ciclo global del carbono y los procesos

biogeoquímicos en general. Estas variaciones son así mismo importantes para evaluar los

efectos de la sequía o eventos extremos sobre la dinámica natural de los bosques

amazónicos. Se evaluó la variación interanual y la estacionalidad de los componentes de

la asignación del carbono y la dinámica forestal durante el periodo 20042012, en cinco

bosques sobre diferentes suelos (arcilloso, franco-arcilloso, franco-arcilloso-arenoso,

franco-arenoso, arena-francosa), todos bajo el mismo régimen local de precipitación en la

Amazonia noroccidental (Colombia). Se quería examinar sí estos bosques responden de

forma similar a las fluctuaciones en la precipitación, tal y como pronostican muchos

modelos. Se consideraron las siguientes preguntas: (1) ¿Existe una correlación entre los

componentes de la asignación del carbono y la dinámica forestal con la precipitación? (2)

¿Existe una correlación entre los bosques? (3) ¿Es el índice de área foliar (LAI) un

indicador de las variaciones en la producción aérea o es un reflejo de los cambios en los

patrones de la asignación del carbono entre bosques?. En general, la correlación entre los

componentes aéreo y subterráneo de la asignación del carbono con la precipitación

sugiere que los suelos juegan un papel importante en las diferencias espaciales y

temporales de las respuestas de estos bosques a las variaciones en la precipitación. Por un

lado, la mayoría de los bosques mostraron que los componentes aéreos de la asignación

del carbono son susceptibles a las fluctuaciones en la precipitación; sin embargo, el

bosque sobre arena-francosa solamente presentó correlación con la lluvia con el

componente subterráneo (raíces finas). Por otra parte, a pesar de que el noroeste

Amazónico es considerado sin una estación seca propiamente (definida como <100 mm

meses−1), la hojarasca y la masa de raíces finas mostraron una alta variabilidad y

estacionalidad, especialmente marcada durante la sequía del 2005. Además, los bosques

del grupo de suelos francos mostraron que la hojarasca responde a retrasos en la

precipitación, al igual que la masa de raíces finas del bosque sobre arena-francosa. En

cuanto a la dinámica forestal, sólo la tasa de mortalidad del bosque sobre arena-francosa


136

estuvo correlacionada con la precipitación (ρ = 0.77, P <0.1). La variabilidad interanual

en los incrementos en el tallo y la biomasa de los individuos resalta la importancia de la

mortalidad en la variación de los incrementos en la biomasa aérea. Sin embargo, las tasas

de mortalidad y las proporciones de individuos muertos por categoría de muerte (en pie,

caído de raíz, partido y desaparecido), no mostraron tendencias claras relacionadas con la

sequía. Curiosamente, la hojarasca, el incremento en la biomasa aérea y las tasas de

reclutamiento mostraron una alta correlación entre los bosques, en particular dentro del

grupo de los bosques con suelos francos. Sin embargo, el índice de área foliar estimado

para los bosques con suelos más contrastantes (arcilla y arena-francosa), no presentó

correlación significativa con la lluvia; no obstante, estuvo muy correlacionado entre

bosques; índice de área foliar no reflejó las diferencias en la asignación de los

componentes del carbono, y su respuesta a la precipitación en estos bosques. Por último,

los bosques estudiados muestran que el noroeste amazónico es susceptible a fenómenos

climáticos, contrario a lo propuesto anteriormente debido a la ausencia de una estación

seca propiamente dicha.

Palabras claves: Productividad aérea, incremento en biomasa aérea, hojarasca,

masa de raíces finas, índice de área foliar, mortalidad, reclutamiento, precipitación,

sequia, Colombia.


137

4.3 INTRODUCTION

Forests ecosystems are susceptible to different climate extremes; with carbon

allocation components (biomass and fluxes) being strongly affected by these extreme

events (Reichstein et al., 2013). Among the climatic factors affecting tropical lowland

rainforests, drought has received the most attention (Corlett, 2011), and it has been

reported as the most widespread factor affecting their carbon balance (Reichstein et al.,

2013). The effect of droughts on Amazon forests has received particular concern due its

role in the global carbon cycle (Davidson et al., 2012, Meir and Ian Woodward, 2010,

Meir et al., 2013, Zhou et al., 2013). According to Davidson et al. (2012), the

susceptibility of the Amazon Basin to drought likely varies regionally, depending on the

climate (total precipitation and its seasonal distribution) and soil water storage properties

(soil texture and depth) to which the existing vegetation types are physiologically adapted.

Moreover, drought experiments (Brando et al., 2008, da Costa et al., 2010, Nepstad et al.,

2007, Nepstad et al., 2002) and permanent plots (Phillips et al., 2009, Phillips et al., 2010,

Williamson et al., 2000, Lewis et al., 2011) have showed high mortality and substantial

decreases in the rates of carbon allocation to above-ground components (biomass and

net primary production) due to drought events; but with less clear trends below-ground

(Brando et al., 2008).

The response of Amazon forests to drought have been very controversial from

different approaches such as remote sensing, and permanent plot monitoring (Saleska et

al., 2007, Malhi et al., 2009, Phillips et al., 2009, Brando et al., 2010, Rammig et al., 2010,

Lewis et al., 2011, Samanta et al., 2012, Saatchi et al., 2013). Furthermore, many studies

have emphasized the differences in the effects of drought on forests with long versus

marked dry seasons in the east and southeast regions of the Amazon Basin (Brando et al.,

2008, da Costa et al., 2010, Meir et al., 2013); although with less data from forests of the

north-western Amazon. Recently, Saatchi et al. (2013), using microwave observations of

rainfall and canopy backscatter, showed than 70 million hectares of forests in the western

Amazon experienced a strong water deficit during the dry season of 2005, and even a

decrease in canopy structure persisted until the next drought in 2010.


138

Analyses of the intra- and inter-annual fluctuations of carbon allocation

components and forest dynamics (mortality and recruitment rates) in relation with

rainfall are limited by data availability. On the one hand, most of the approaches to study

carbon allocation at larger scales come from time-series of leaf area index –LAI, the

fraction of absorbed photosynthetically active radiation (fAPAR), or the enhanced

vegetation index (EVI), all of them remote sensing products which analyse fluctuations

in light absorption that translate in temporal and spatial dynamics of carbon metabolism

and allocation at different scales. The fidelity of these observations and their means of

extrapolation remain highly controversial, especially in terms of the role of Amazon

forest productivity (Nemani et al., 2003, Saleska et al., 2007, Samanta et al., 2010, Zhao

and Running, 2010, Medlyn, 2011, Samanta et al., 2011). Despite these uncertainties, LAI

is generally used to calculate NPP in many ecosystem models (Malhi et al., 2011), even

though the relationship between the satellite-based indices of seasonal greenness and

ecosystem productivity remains an unresolved focus of debate (Davidson et al., 2012).

However, from field measurements, Brando et al. (2008) found in a throughfall exclusion

experiment that LAI declined in response to drought, with a strong positive linear

relationship with volumetric water content and total above-ground net primary

production. On the other hand, in terms of forest dynamics, it has been showed the

importance of mortality in the above-ground biomass change and its possible impacts on

the global carbon balance (Phillips et al., 2009). However, long-term monitoring data

from mature forests in the Amazon basin is still necessary to understand the adaptation

of these forests to seasonal or multi-year droughts and to better understand their natural

level of variability.

Therefore, we aim to contribute to evaluate the intra- and inter-annual variation

of carbon allocation components and forest dynamics of five forest types on different

soils, but growing under the same local precipitation regime. We were interested to

examine if these north-western Amazon forests respond similarly to rainfall fluctuations

as many models predict, considering the following questions: (i) Do carbon allocation

components and forest dynamics respond to temporal climatic fluctuations? (ii) Is the

response between above- and below-ground components and forest dynamics similar for

the different forests? (iii) Are temporal responses in the leaf area index indicative of


139

variations in the net primary production or simply a reflection of changes in carbon

allocation patterns?.


4.4.1 Site description

We carried out this research in the north-western Amazon (Colombia) in five old-growth

mature forests under different soils. We worked in six 1-ha plots located in the

Amacayacu National Natural Park (AGP plots) and in the Zafire Biological Station (ZAR

plots), a description of the plots is presented in the Table 4.1.

The study sites are separated approximately 50 km apart and experience similar

climatic conditions, typical of a perhumid lowland equatorial climate. Annual

precipitation ranges from 2561 to 3902, with an average of 3351 mm yr1 (± 323 mm

yr1), according to data for 1973–2012 from the meteorological station at the Vásquez

Cobo airport in Leticia (04 11’ 36’’ S, 69 56’ 35’’). The rainfall monthly mean for this

period was 279 mm (± 75 mm), and in most years there is no dry season (defined as

rainfall <100 mm mo1; Malhi et al. 2004). Although some sporadic years showed one or

two months with rainfall <100 mm between June and September; there was a marked dry

season in 2005 from June to September with a rainfall between 52 and 98 mm, which

was not registered before during the 1973–2012 period (Rainfall anomaly of the study

period is presented in the Figure 4-1). This drought also affected most of the Amazon

Basin (Zeng et al., 2008, Davidson et al., 2012). Mean annual temperature is 26°C (min

24°C, max 27°C) with little variation among seasons and years. Mean annual relative

humidity is 86%.

The six 1-ha plots were established in five different forests with contrasting soils

(Figure 4-2 and Table 4.1). In a clay content gradient the plots are in decreasing order as

follow: (i) Clay-soil: AGP-01 and AGP-02 plots, (ii) Clay-loam: ZAR-02 plot, (iii) Sandy-

clay-loam: ZAR-03 plot, (iv) Sandy-loam: ZAR-04 plot, (v) Loamy-sand: ZAR-01 plot. A


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detailed soil description of these permanent plots is provided in Quesada et al. (2011, soil

profiles for AGP-02, ZAR-02 and ZAR-01 therein), and in Quesada et al. (2010). In

general, the clay soils have higher concentrations of available phosphorus [P]a, nitrogen

[N], and especially a higher effective cation exchange capacity (CEC) than the ‘loam-soil

forests group’ (ZAR plots on Table 4.1). There are drainage differences as well, four

plots are non-flooded upland vegetation called terra-firme, one is located on a floodplain

of black or clear water known as “igapó” (ZAR-02), and the loamy-sand plot

(hydromorphic Podzol, ZAR-01) presents a hardpan at approximately 1 m depth that

causes periodic water stagnation in the layers above it.

These forests show differences in composition and physiognomy. The upland

vegetation or terra-firme forests exhibit more structural and floristic similarities among

them than with the black-water seasonally flooded forest or the loamy-sand terra-firme

forest. The clay-loam black-water flooded forest (igapó) represents a specialized

ecosystem, a vegetation with xeromorphic adaptations growing mainly on sandy soils

and, which is distinct from white-water floodplains (várzea) and the terra-firme forests

(see introduction in Parolin et al. 2004). The loamy-sand forest is known as “white-sand

forest”, and it is also a particular ecosystem dominated by species that only grow on this

specific soil type (Peñuela-Mora et al., 2013, Fine et al., 2004, Fine et al., 2006, Tuomisto

et al., 2003, Stropp et al., 2011, Fine et al., 2010).

4.4.2 Intra-annual variation of above- and below-ground components

We assessed the intra-annual variation or seasonality among plots of above- and

below-ground carbon allocation components by making detailed measurements of fine

litterfall, stem growth, fine-root mass and also the leaf area index. Measurements were

done every three or four months from 2004 to 2006. Fine litterfall (leaves, flowers, fruits

and twigs with diameter ≤20 mm and indeterminate material) was collected bi-weekly,

from 25 mesh traps (0.5 m2) per plot. We calculated the litterfall anomaly as the

difference between the observed and the mean of the monitoring time. Stem growth was

monitored using dendrometer bands installed on 90% of stems with diameter ≥10 cm (d


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at 1.3 m or above buttresses). An adjustment period of six months was allowed for the

dendrometer bands to settle before making the first measurement; diameter increment

was registered using a calliper. We measured the leaf area index (LAI) and the fine-root

mass in the most contrasting forests: the clay-soil forest (AGP-01 and AGP-02 plots) and

the loamy-sand forest (ZAR-01 plot). LAI was estimated indirectly using hemispherical

photographs, which were analyzed with the Hemiview Canopy Analysis Software

(Version 2.1 SR1, Delta-T Devices Ltd, UK), photographs were taken in the same points

(26 per plot). Fine-root mass (root diameter ≤ 2 mm) was estimated from soil samples

taken at 0.2 m depth using the sequential root coring method (Vogt et al., 1998); which is

described in Jiménez et al. 2009 (Chapter 2). We used the average of fine-root mass for

each monitoring period (Figure 2-6). More details about the methods for litterfall and

LAI are presented in Chapter 3 (section 3.4).

4.4.3 Inter-annual variation of above-ground biomass increment and forest

dynamics

We evaluated the inter-annual variation of above-ground biomass increment and

forest dynamics in the ‘loam-soil forests group’ at the Zafire Biological Station (ZAR

plots on Table 4.1). We monitored the above-ground biomass and net primary

production of these forests between 2004 and 2012. Above-ground biomass was

calculated from stem diameter measurements of all trees and palms with d ≥10 cm. We

estimated the above-ground biomass using allometric equations, due to the fact that there

are no local biomass equations for the forests studied, we developed a methodology to

account for this uncertainty (see description Chapter 3, section 3.4.2). Above-ground

biomass was estimated as the median of biomass estimates from allometric models

selected from the literature (Table A1 in Appendix 3-1), and we estimated the biomass

separately for trees and palms.

Above-ground biomass increment, defined as in Clark et al. (2001), was

calculated as the increments of surviving trees or palms (difference between its estimated

biomass at the beginning of the interval and the end of the interval) plus increments of


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ingrowth viz. the difference between its estimated biomass at the end of the interval and

the minimum d measured (10 cm) (Clark et al., 2001). We assumed dry organic matter to

be 50% carbon.

Forest dynamics were assessed through the calculation of mortality and

recruitment rates. During the censuses, dead trees or palms identified as dry or dead

wood were registered in four death categories (Gale and Barfod, 1999): standing dead,

snapped, uprooted, and missed (disappeared stems previously tagged). We also registered

the ingrowth of the new individuals that grew during the interval between the censuses

and reached d minimum (10 cm). We calculated the stem mortality and recruitment rates

following Nepstad et al. (2007).

4.4.4 Error propagation and statistical analysis

We estimated uncertainties in estimated above-ground biomass and production

through error propagation. We considered the uncertainty associated with the biomass

estimates from allometric models selected and the spatial variation within the plots

(Chave et al., 2004). This error propagation procedure is well described in Chapter 3

(section 3.4.3). Standard errors were calculated as the square of the median absolute

deviation divided by the square of sample size depending of the component analysed.

Errors were propagated as the sum or average of the squared standard errors, depending

on the operations performed on the means. The intra-annual and inter-annual

correlations of the above- and below-ground carbon allocation components among

forests was tested through a correlation matrix for litterfall, stem growth, above-ground

biomass increment, leaf area index and fine-root mass; and for forest dynamics a

correlation matrix for mortality and recruitment rates. Also, we evaluated the correlation

between rainfall and every component or rate. We evaluated the correlation of carbon

allocation components and forest dynamic rates with the current precipitation registered

during the measurements. Additionally, using a cross-correlation test, we evaluated the

correlation of litterfall and fine-root mass with time-lags in rainfall. All calculations and


143

statistical tests were performed in the R environment for statistical computing (R

Development Core Team, 2012).

4.5 RESULTS

4.5.1 Intra-annual variation of above- and below-ground components

4.5.1.1 Litterfall production

Overall, the intra-annual variation of litterfall (Figure 4-3) was poorly correlated

with the precipitation without time-lags (Table 4.2); only one plot on clay-soil showed

significant correlation with the current precipitation ( = 0.49, P <0.05). In contrast,

most forests from the loam-soil group (ZAR plots on Table 4.1) except the loamy-sand

forest, exhibited a significant correlation with the lagged rainfall of one or two previous

months (Figure 4-4). In addition, these forests were significantly correlated among them

(Table 4.3); the clay-soil forest plots were significantly correlated between them

(correlation coefficient of 54%), but not with the loam-soil forests group; while these

forests were significantly correlated among them, varying from 67 to 92% correlation

(Table 4.3).

Litterfall exhibited a seasonal pattern (Figure 4-3), particularly the loam-soil

forests group. In addition, forests displayed an anomaly especially marked in the dry

season of 2005 (Figure 4-5). Litterfall in the clay-soil forest plots exhibited a very

different intra-annual variation pattern compared to the loam-soil forest group. The clay-

soil forest plots exhibited a decrease in litterfall during the dry season of 2005, followed

by a spike thereafter. On the contrary, the most correlated forests, the loam-soil forests

group showed an increase in litterfall in the transition between the dry and the wet

season, highlighted in 2005.


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4.5.1.2 Stem growth

Stem growth of the individuals in the forests studied also showed some

seasonality (Figure 4-6) and growth in some of the plots was highly correlated with

rainfall (Table 4.2). The clay-loam black-water flooded and the sandy-clay-loam forests

were significantly correlated with rainfall (97% and 82%, respectively). Contrary, as for

litterfall production, stem growth was poorly correlated among forest types (Table 4.3).

The strongest correlation between forests was among those plots with high clay content,

the clay-soil plots (88%) and the clay-loam black-water flooded forest with the sandy-

clay-loam forest (78%).

4.5.1.3 Leaf area index

The simultaneous measurements of leaf area index –LAI, in the clay-soil forest

plots and the loamy-sand forest (‘white-sand forest’) indicated that LAI showed higher

correlation among forests than between forests and rainfall (Figure 4-7, Tables 4.2 and

4.4). Overall, the magnitude of the correlation with rainfall was low, and there were

differences within the same forest type. For instance, despite the fact that plots on clay-

soils are considered the same forest type (separated only 300 m apart), one plot on clay

soil exhibited the highest correlation with rainfall ( = 0.34, P = 0.1) while the other

showed a very low positive and not significant correlation ( = 0.06).

4.5.1.4 Fine-root mass

The fine-root mass from the two most contrasting soils considered (clay and

loamy-sands), showed only a significant correlation for the loamy-sand forest with one

month lagged of precipitation (Figure 4-8 and Table 4.2). However, the intra-annual

variation of fine-root mass showed differences between forests (Figure 4-7). The loamy-

sand forest displayed the highest values of fine-root mass at the starting of the dry

season, particularly marked in 2005, while the clay-soil forest plots exhibited the highest


145

values at the starting of the wet season (December of 2005). On the other hand, the

correlation among forests (Table 4.4) clearly separated the clay-soil forest plots with a

significant correlation (72%), in contrast to the uncorrelated pattern of the loamy-sand

forest.

4.5.2 Inter-annual variation of forests

4.5.2.1 Above-ground biomass increment

The inter-annual variation evaluated in the loam-soil forests group (ZAR plots on

Table 4-1), showed significant correlation between the plots with high clay content and

precipitation (Table 4.2). The most correlated plots with rainfall were the clay-loam

flooded forest and the sandy-clay-loam forest (88% and 76%, respectively). These forests

displayed the lowest values of above-ground biomass increment for the periods when the

annual rainfall was lower (Figure 4-9), including the 2005 and 2011 driest years for the

studied sites (Figure 4-1). The loamy-sand forest, the plot with the lowest correlation

with rainfall, also showed a decrease in above-ground biomass increment in 20062007

and 20072008, both very wet years (Figures 4-1 and 4-9). However, the stem and

biomass increment of individuals did not show obvious changes over time (Figures 4-10

and 4-11).

The above-ground biomass increment of most forests were highly correlated

among the forests with similar basal area, with correlation coefficients varying from 88%

to 92% (Table 4.5), except the loamy-sand forest.

4.5.2.2 Forest dynamics

The annual mortality and recruitment rates showed that both process are highly

variable among forests and over the years (Figures 4-12 and 4-13). Overall, the forest

dynamics variables were poorly correlated with rainfall (Table 4.2); only the mortality


146

rates of the loamy-sand forest exhibited a high correlation with rainfall ( = 0.77, P

<0.1); which displayed the highest mortality rates in the period 20092012 as 2.8%. This

period included 2011, a dry year for the studied sites (Figure 4-1). Particularly, for the

20042005 census, forests did not exhibit predominantly high annual mortality rates like

should be expected due to the 2005 drought, even some forests showed the lowest

annual mortality rates during this period, the loamy-sand forest and the clay-loam black-

water flooded forest with 0.6% and 0.8%, respectively.

With respect to the correlation among forests, they were correlated in terms of

recruitment rates, but not mortality rates (Table 4.5). The high and significant correlation

of the recruitment rates among forests is showed in the trend over the years (Figure 4-

13); the forests exhibited the highest recruitment rates between 2008 and 2009, varying

from 1% to 3.6% for the sandy-loam and the loamy-sand forests, respectively; whereas

the low rates were more variable over time. In terms of mortality rates, the most

correlated forests were the loamy-sand and the sandy-clay-loam with a correlation

coefficient of 66% (P = 0.15). Overall, we did not observe marked mortality or

recruitment trends associated with the dry event in 2005 or in the period 2009–2012, or

particular trends were not registered in the proportion of death stems by category for

these dry years (Figure 4-11).

4.6 DISCUSSION

4.6.1 Correlation with precipitation

Overall the correlation of carbon allocation components with rainfall exhibited

high variation among forest types. The correlation of above- and below-ground carbon

allocation components with rainfall suggests that soils play an important role in the

differences of responses of these forests to rainfall fluctuations. Previously, it has been

highlighted the importance of soil water storage properties (texture and depth) in relation

to the susceptibility of Amazon forests to drought (Davidson et al., 2012), and the

possible impacts of this extreme events on water availability that can affect plant


147

physiology, phenology, and carbon allocation patterns (Reichstein et al., 2013). Despite

the forests studied grow under similar conditions of climate, in terms of total

precipitation and its seasonal distribution, their carbon allocation components exhibited

spatial and temporal differences in the responses to fluctuations in rainfall. On the one

hand, some forests showed that some above-ground and belowground components were

susceptible to fluctuations in rainfall. For most forests of the loam-soil group, litterfall,

stem growth and above-ground biomass increment showed correlation with rainfall,

except the loamy-sand forest, which displayed a correlation with rainfall only with fine-

root mass. On the other hand, litterfall and fine-root mass showed susceptibility to

fluctuations with the precipitation of one or two months previously (Figure 4-4),

indicating important differences in the response among forests. For the clay-soil forest

plot, litterfall was significantly correlated with the current precipitation while most forests

from the loam-soil group were correlated with the lagged precipitation. Additionally,

litterfall and fine-root mass showed seasonality and differences in the patterns between

forests on clay-soils and the loam-soil forest group (Figures 4-3 and 4-7). Fine-root mass,

as described in Chapter 2, showed important differences in seasonal patterns among

forests.

Drought experiments in Amazon forests have also pointed out that wood

production is the net primary productivity component more affected by the exclusion of

rainfall or changes in soil moisture availability (Brando et al., 2008, da Costa et al., 2010).

In contrast, in a throughfall exclusion experiment, litterfall responses to rainfall were less

clear and diverged from the responses of stem growth or above-ground coarse wood

production (Brando et al., 2008). Chave et al. (2010), showed that litterfall seasonality was

significantly and positively correlated with rainfall seasonality. Although our sites are

located in the north-western region of the Amazon basin, considered less vulnerable to

changes in rainfall regimes (Malhi et al., 2009), litterfall in these forests exhibited marked

responses to rainfall seasonality and with important differences among them (Figures 4-3,

4-4 and 4-5). The clay-soil forest showed a decrease during the dry season of 2005, while

the loam-soil forests group showed an increase in litterfall rates.


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The inter-annual variability of above-ground biomass increment also suggests

notable differences in the responses of forests to both dry and wet years (Figures 4-1 and

4-9). The lowest rates of above-ground biomass increment in most forests coincided with

the driest years for the sites (2005 and 2011, Figure 4-1). However, the loamy-sand forest

displayed also a low above-ground biomass increment during the period 20062007,

registered as very wet years (Figure 4-1), and this can be related to the fact that these soils

remain waterlogged during the wet season due to the hard pan. This is a very particular

effect of rainfall for this site and highlights the importance of the variable responses to

different extreme events. Nevertheless, for all forests above-ground biomass increment

increased after low wood production rates, which indicates that forests recovered rapidly

after dry or wet events along the time period studied.

Contrary to above- and below-ground carbon allocation components, the leaf

area index –LAI– did not display strong correlation with rainfall for these forests. This

suggests that LAI did not reflect the variation of above-ground carbon allocation

components which fluctuate with rainfall (litterfall, stem growth and above-ground

biomass increment). Although Brando et al. (2008) found that LAI declined in response

to drought with little effect on litterfall under experimental conditions, our results do not

support such a decline in LAI under episodic drought events. Furthermore, LAI and

litterfall measurements during the 2005 drought did not show the greenness trend

proposed for some parts of the Amazon Basin (Saleska et al., 2007), and suggested that

the role of LAI as an indirect measure of changes in above-ground production or canopy

greenness should be taken cautiously. Also, our results confirm, as it has been discussed

previously, that approaches from remote-sensing products need to be corroborated

comprehensively with ground data before general conclusion are drawn (Samanta et al.,

2012).

The forest dynamics variables showed little susceptibility in relation to the rainfall

fluctuation for these forests. Only the mortality rate in the loamy-sand forest was

significantly correlated with rainfall. This forest showed highest mortality rates during the

20092012 censuses, period that included a marked dry season in 2011 (Figure 4-1). This

year was drier for the study sites than 2010 reported as a basin-wide drought (Lewis et al.,


149

2011, Potter et al., 2011). Conversely, the forests did not show high mortality rates for

the period 20042005, despite a major drought that affected largely the Amazon Basin

(Phillips et al., 2009).

According to drought experiments, Amazon forests can show high mortality

rates caused by rainfall exclusion (Nepstad et al., 2007, da Costa et al., 2010, Brando et

al., 2008). However, in our case it is difficult to attribute tree mortality of these mature

old-growth forests to extreme events reported for the Amazon Basin. This would

demand improving our knowledge about the natural variability of Amazon forest

mortality. We do not know with certainty the natural range of variability of mortality

(Davidson et al., 2012); therefore it hinders the evaluation of extent in which recent

extreme droughts have affected mortality rates. Even our analysis of death type did not

show a clear pattern about possible physiological failures related to drought. Overall,

forests did not exhibit differences in the type of death over years that can be related to

some extreme event (Figure 4-13). Considerable variations of death type proportions for

each forest showed that standing death is the most common death type registered,

particularly for the terra-firme forests, but also death trees had broken tops or were

uprooted, which may indicate other causes of death different than drought-related.

Despite of this, the variability of the stem and biomass increments over years

(Figures 4-10 and 4-11) highlighted that mortality has an important effect on above-

ground biomass increment, and this can be observe by the inter-annual variation of the

above-ground biomass increment (Figure 4-9). This has been also shown for throughfall

experiments (Brando et al., 2008, da Costa et al., 2010), or from permanent plots (Phillips

et al., 2009). These north-western Amazon forests seem susceptible to changes in rainfall;

and their responses can differ among forests under different soil types, and also

depending on the above- and below-ground carbon allocation component analysed.

Forest dynamics suggests that other factors in addition to annual precipitation might be

affecting mortality and recruitment rates. These forests, particularly in the context of

their different soil properties, could also respond in different ways to extreme events that

can affect the net uptake of carbon regionally.


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4.6.2 Correlation among forests

Interestingly, despite important differences among forests in terms of soil

properties, vegetation structure and species composition, they were highly correlated for

some carbon allocation components. Litterfall was highly correlated within the loam-soil

forests group (ZAR plots on Table 4.1), followed by above-ground biomass increment

with the exception of the loamy-sand forest, reflecting the differences in net primary

production among them (discussed in detail in Chapter 3). The leaf area index was also

considerably correlated between the clay-soil forest and the loamy-sand forest, the most

contrasting forests in terms of soil texture and vegetation structure, suggesting that LAI

did not reflect differences between forest types (also discussed in Chapter 3). While fine-

root mass was poorly correlated between the clay-soil forest plots and the loamy-sand

forest. However, the clay-soil forest plots were highly correlated among them in terms of

litterfall and fine-root mass.

Mortality rates were not correlated among forests, while there was a high

correlation among forests in terms of recruitment rates, suggesting that there are other

factors different than soils, controlling annual mortality rates.

4.7 CONCLUSIONS

Five forests from the north-western Amazon basin on different soils and with

differences in forest structure but growing under the same local climatic conditions

showed differential responses to rainfall and its seasonality in terms of their above- and

below-ground carbon allocation components. In terms of the dynamics of these forests,

only mortality displayed a significant correlation with rainfall for the loamy-sand forest.

The magnitude of correlation for the same carbon allocation components or the

mortality rates was highly variable among forests, and even within the same forest type.

These differences in correlation between above- and below-ground carbon allocation

components with rainfall suggest that soils play an important role in the different

responses of these forests to climatic fluctuations.


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Although these north-western Amazon forests are generally considered as

without a marked dry season, above- and below-ground carbon allocation components

showed some seasonality and high intra-annual variability related to rainfall. Short-live

structures such as foliage and fine-roots showed high intra-annual variability. On the one

hand, litterfall displayed high seasonality, particularly marked in the dry season of 2005;

with different responses among the clay-forest plots and the loam-soil forests group, and

significant correlation with the precipitation from previous months. On the other hand,

fine-root mass exhibited also different responses between the most contrasting soils (clay

and loamy-sand), and for the loamy-sand forest a significant correlation with the

precipitation from previous months. The inter-annual variability of forest showed that

for the loam-soil forests group, above-ground biomass increment displayed low values

for the periods that included the droughts reported for the Amazon Basin (2005 and

2010). Meanwhile, mortality rates did not show clear trends related to the droughts or

even in the proportions of death type assumed related to drought as standing dead.

Most forests showed high correlation among them in terms of litterfall

production, which was markedly correlated within the loam-soil forests group despite

their differences in composition, structure, and belowground resources. For above-

ground biomass increment, only forests with similar basal area were correlated among

them. Interestingly, recruitment rates were highly correlated for all the forests in the

loam-soil group.

The leaf area index (LAI) showed low correlation with precipitation, but high

correlation between forests, even among the most contrasting forest types (clay-soil and

loamy-sand forests). LAI did not reflect the observed variations in above-ground carbon

allocation components neither the differences between forests. This suggests high

uncertainty of LAI as a measure of changes in the above-ground carbon allocation

components related to rainfall fluctuations, and to characterize differences in their

responses to changes in the environment.


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157

Figure 4-1 Rainfall anomaly for the sites studied in the north-western Amazon (Colombia), for 2004–2012 from the meteorological station at the

Vásquez Cobo airport in Leticia (04° 11’ 36’’ S, 69° 56’ 35’’). Anomaly rainfall calculated as the observed minus the average for 1973–2012. The dashed lines show the standard deviation (1π, 2π), and the shade areas represent periods where the driest months can occur, including the dry

season of 2005 (June to September with rainfall <100 mm month1).

2004 2006 2008 2010 2012

-300

-200

-100

010

020

030

0

Year

Rai

nfal

l ano

mal

y (m

m)


158

Figure 4-2 Soil texture classification for the forest plots of this study, adapted from Soil

Textural Triangle of USDA Classification (Thien, 1979): (1) Clay-soil terra-firme forests

(Cl, AGP-01 and AGP-02 plots); (2) Clay-loam black-water seasonally flooded forest

(ClLo, ZAR-02); (3) Sandy-clay-loam terra-firme forest (SaClLo, ZAR-03); (4) Sandy-

loam terra-firme forest (SaLo, ZAR-04); (5) Loamy-sand terra-firme forest (LoSa, ZAR-

01).

Cl

SiCl

SaCl

ClLo SiClLo

SaClLo

Lo

SiLoSaLo

SiLoSaSa

10

20

30

40

50

60

70

80

90

10

20

30

40

50

60

70

80

90

10

20

30

40

50

60

70

80

90

[%] Sand 50-2000 m

[%]C

lay

0-2

m

[%] S

ilt2-50

m

AGP-01AGP-02ZAR-02ZAR-03ZAR-04ZAR-01


159

Figure 4-3 Litterfall intra-annual variation of five Amazon forests. The measurements

were taken from October 2005 to December 2006. The shade areas represent periods

where the driest months can occur, including the dry season of 2005 (June to September

with rainfall <100 mm month1).

2005 2006 2007

0.0

0.2

0.4

0.6

0.8

Clay-soil forest (AGP-01)Li

tterfa

ll pr

oduc

tion

(Mg

ha1 )

2005 2006 2007

0.0

0.2

0.4

0.6

0.8

Clay-soil forest (AGP-02)

2005 2006 2007

0.0

0.2

0.4

0.6

0.8

Loamy-sand forest (ZAR-01)

Litte

rfall

prod

uctio

n (M

g ha

1 )

2005 2006 2007

0.0

0.2

0.4

0.6

0.8

Clay-loam flooded forest (ZAR-02)

2005 2006 2007

0.0

0.2

0.4

0.6

0.8

Sandy-clay-loam forest (ZAR-03)

Year

Litte

rfall

prod

uctio

n (M

g ha

1 )

2005 2006 2007

0.0

0.2

0.4

0.6

0.8

Sandy-loam forest (ZAR-04)

Year


160

Figure 4-4 Correlation between litterfall and rainfall with different lags (months). Cross-

correlation above the dotted lines indicates significant correlation.


161

Figure 4-5 Litterfall anomaly of Amazon forests for 2005 and 2006. The continuous

horizontal line represents the mean; the anomaly is the difference between the observed

and the mean of litterfall (2005-2006), the dashed lines show the standard deviation (1σ,

2σ). The shade areas represent periods where the driest months can occur, including the

dry season of 2005 (June to September with rainfall <100 mm month1).

2005 2006 2007

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3


Litte

rfall

anom

aly

(Mg

C h

a1 )

2005 2006 2007

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3


2005 2006 2007

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3


Litte

rfall

anom

aly

(Mg

C h

a1 )

2005 2006 2007

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3


2005 2006 2007

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3


Year

Litte

rfall

anom

aly

(Mg

C h

a1 )

2005 2006 2007

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3


Year


162

Figure 4-6 Stem growth measured with dendrometer bands in individuals of the different

Amazon forests plots for 2005 and 2006. The shade areas represent periods where the

driest months can occur, including the dry season of 2005 (June to September with

rainfall <100 mm month1).

2005 2006 2007

0.0

0.1

0.2

0.3

0.4


Ste

m g

row

th (c

m in

d1 d

ay1 )

2005 2006 2007

0.0

0.1

0.2

0.3

0.4


2005 2006 2007

0.0

0.1

0.2

0.3

0.4


Ste

m g

row

th (c

m in

d1 d

ay1 )

2005 2006 2007

0.0

0.1

0.2

0.3

0.4


2005 2006 2007

0.0

0.1

0.2

0.3

0.4


Year

Ste

m g

row

th (c

m in

d1 d

ay1 )

2005 2006 2007

0.0

0.1

0.2

0.3

0.4


Year


163

Figure 4-7 Intra-annual variation of leaf area index, litterfall production, and fine-root

mass of two forests on contrasting soils in the Amazon Basin. The shade areas represent

periods where the driest months can occur, including the dry season of 2005 (June to

September with rainfall <100 mm month1).

02

46

8

Leaf

are

a in

dex

(m2 m

2 )

0.0

0.2

0.4

0.6

0.8

Litte

rfall

prod

uctio

n (M

g ha

1 )

Clay -soil f orest (AGP-01)Clay -soil f orest (AGP-02)Loamy -sand f orest (ZAR-01)

05

1015

Fine

root

mas

s (M

g ha

1 )

Year

Rai

nfal

l (m

m)

010

020

030

040

050

060

070

0

Year

2005 2006 2007

1015

2025

30

Tem

pera

ture

(ºC

)


164

Figure 4-8 Correlation between fine-root mass and rainfall with different lags (months).

Cross-correlation above the dotted lines indicates significant correlation.


165

Figure 4-9 Above-ground biomass increment for different Amazon forests plots. Points

represent the median, and lines the mean standard deviation.


166

Figure 4-10 Intra-annual variation of stem increments of individuals for different

Amazon forest plots, for 20042012 years.


167

Figure 4-11 Intra-annual variation of biomass increments of individuals for different

Amazon forest plots, for 20042012 years.


168

Figure 4-12 Annual mortality and recruitment rates of Amazon forests (loam-soil forests

group on Table 4.1), for 20042012 years.

04-05 05-06 06-07 07-08 08-09 09-12

Ann

ual m

orta

lity

rate

(%)

01

23

4a) Loamy-sand forest (ZAR-01)

04-05 05-06 06-07 07-08 08-09 09-12

Ann

ual r

ecru

itmen

t rat

e (%

)

01

23

4

04-05 05-06 06-07 07-08 08-09 09-12

Ann

ual m

orta

lity

rate

(%)

01

23

4

b) Clay-loam flooded forest (ZAR-02)

04-05 05-06 06-07 07-08 08-09 09-12

Ann

ual r

ecru

itmen

t rat

e (%

)

01

23

4

04-05 05-06 06-07 07-08 08-09 09-12

Ann

ual m

orta

lity

rate

(%)

01

23

4

c) Sandy-clay-loam forest (ZAR-03)

04-05 05-06 06-07 07-08 08-09 09-12

Ann

ual r

ecru

itmen

t rat

e (%

)

01

23

4

05-06 06-07 07-09 09-12

Year

Ann

ual m

orta

lity

rate

(%)

01

23

4

d) Sandy-loam forest (ZAR-04)

05-06 06-07 07-09 09-12

Year

Ann

ual r

ecru

itmen

t rat

e (%

)

01

23

4


169

Figure 4-13 Proportion of deaths as recorded in field measurements: standing death,

broken top, uprooted or missed from inventory. Data for 4 forests 1-ha plots in the

loam-soil forests group (Table 4.1), for 20042012 years.


170

Table 4.1 Description of the plots located in the Amacayacu National Natural Park (AGP) and in the Zafire Biological Station (ZAR) in the north-

western Amazon (Colombia).

Forest typea Plot code

Soil classificationb Coordinates Altitude

(m) [P]a [N] CEC

Stem density

(ha1)

Basal area

(m2)

Clay-soil terra-firme

AGP-01 Endostagnic Plinthosol (Alumic, Hyperdystric)

3.72 S, 70.31 W 105 25,36 0,15 6,21 625 27.75

AGP-02 3.72 S, 70.30 W 110 25,43 0,16 6,26 574 27.78

Loamy-sand terra-firme

ZAR-01 Ortsteinc Podzol (Oxyaquic)

4.01 S, 69.91 W 130 14,36 0,11 0,71 873 16.87

Clay-loam black-water seasonally flooded

ZAR-02 Haplic Gleysol (Alumic, Hyperdystric)

4.00 S, 69.90 W 120 23,16 0,16 3,51 624 26.16

Sandy-clay-loam terra-firme

ZAR-03 Haplic Cambisol (Alumic, Hyperdystric, Clayic)

3.99 S, 69.90 W 135 16,22 0,21 1,93 685 28.78

Sandy-loam terra-firme

ZAR-04 Haplic Alisol (Alumic, Hyperdystric)

4.01 S, 69.90 W 120 8,6 0,10 2,60 658 27.82

a Forest type based on soil texture differences (USDA Classification), see Figure 4-2. b World Reference Base soil classification system, more details of plot soils in Quesada et al.

(2010, 2011). [P]a: available phosphorus (mg kg1), [N]: nitrogen (%), CEC: effective cation exchange capacity (cmolc kg1).


171

Table 4.2 Correlation coefficients of above- and below-ground carbon allocation components, forest dynamics, and leaf area index (LAI) of

Amazon forests with precipitation. Missing data correspond to unavailable measurements for some plots or very small measurement periods not

suitable for calculation of correlation coefficients.

Forest type Plot code

Above-ground NPP

LAI

Below-ground NPP

Forest dynamic rates

Litterfall

Stem growth

AGB increment

Fine-root mass

Mortality Recruitment

Clay-soil forest AGP-01 0.4894** -0.3818 -- 0.0562 -0.3804 -- --

AGP-02 0.1790 -0.1373 -- -0.3427* -0.0512 -- --

Loamy-sand forest ZAR-01 -0.1596 0.5853 0.3277 0.0342 -0.2867 0.7673* 0.1777

Clay-loam flooded forest ZAR-02 0.0199 0.9651*** 0.8787** -- -- -0.0532 -0.0525

Sandy-clay-loam forest ZAR-03 -0.1598 0.8221* 0.7582* -- -- 0.1972 -0.0607

Sandy-loam forest ZAR-04 -0.2251 -- -0.1819 -- -- 0.5230 0.2096

Bold values are significant at ***P <0.01, **P <0.05, *P ≤0.1. AGB: above-ground biomass.


172

Table 4.3 Correlation matrices of the litterfall production and the stem growth of individuals

among different Amazon forest plots.

Clay-soil forest

Loamy-sand forest

Clay-loam

flooded forest

Sandy-clay-loam

forest

Sandy-loam forest

AGP-01 AGP-02 ZAR-01 ZAR-02 ZAR-03 ZAR-04

Litterfall production

Clay-soil forest AGP-01 1

AGP-02 0.5448** 1

Loamy-sand forest ZAR-01 0.2252 0.1502 1

Clay-loam flooded forest ZAR-02 0.0553 0.3352 0.7386*** 1

Sandy-clay-loam forest ZAR-03 0.1433 0.2714 0.6730*** 0.7858*** 1

Sandy-loam forest ZAR-04 0.1743 0.3072 0.7061*** 0.7814*** 0.9174*** 1

Stem growth of individuals

Clay-soil forest AGP-01 1

AGP-02 0.8855* 1

Loamy-sand forest ZAR-01 0.2976 0.4087 1

Clay-loam flooded forest ZAR-02 0.6784 0.3425 0.4192 1

Sandy-clay-loam forest ZAR-03 0.5935 0.5301 0.5885 0.7754* 1 --

Bold values are significant at ***P <0.01, **P <0.05, *P ≤0.1.


173

Table 4.4 Correlation matrix of the leaf area index and the fine-root mass between two

Amazon forests on contrasting soils.

Clay-soil forest

Loamy-sand forest


Leaf area index

Clay-soil forest AGP-01 1 AGP-02 0.8996*** 1

Loamy-sand forest ZAR-01 0.6374* 0.7061* 1 Fine-root mass

Clay-soil forest AGP-01 1 AGP-02 0.7235* 1

Loamy-sand forest ZAR-01 0.1868 -0.3007 1



174

Table 4.5 Correlation matrices of above-ground biomass increment, the annual mortality

and recruitment rates among different Amazon forest plots (loam-soil forests group,

ZAR plots on Table 4.1).

Loamy-sand forest

Clay-loam flooded forest

Sandy-clay-loam forest

Sandy-loam forest

ZAR-01 ZAR-02 ZAR-03 ZAR-04

Above-ground biomass increment

Loamy-sand forest ZAR-01 1

Clay-loam flooded forest ZAR-02 0.5604 1

Sandy-clay-loam forest ZAR-03 0.2673 0.9153*** 1

Sandy-loam forest ZAR-04 0.3850 0.9243* 0.8794* 1

Mortality rates


Clay-loam flooded forest ZAR-02 -0.0164 1

Sandy-clay-loam forest ZAR-03 0.6619 -0.2589 1

Sandy-loam forest ZAR-04 0.5565 -0.2242 0.7045 1

Recruitment rates


Clay-loam flooded forest ZAR-02 0.8463** 1

Sandy-clay-loam forest ZAR-03 0.6772* 0.8813* 1

Sandy-loam forest ZAR-04 0.8992* 0.9012* 0.8912* 1


175

5 CONCLUSIONS

Overall I can conclude that soils play an important role on carbon allocation

strategies in different old-growth forest types in the north-western of the Amazon Basin.

Despite of these forests are growing under similar climatic conditions (total precipitation

and its seasonal distribution) they showed differences in the above- and below-ground

carbon allocation components and their responses to rainfall. These differences highlight

also that the patterns in total biomass and net primary production could change when the

below-ground compartment (roots) is considered.

In particular, the above- and below-ground carbon allocation components

measured in the forests on clay-soils and loamy-sand showed that the biomass ratios

between above- and below-ground components differed from the partitioning of total

net primary production to above- and below-ground components. On the other hand,

component fluxes in these forests were not related. High values of total net primary

production in the clay-soil forest did not correspond to high values for all net primary

production components. Differences in the soil environment (nutrients and water

availability) are probably responsible for the double amount of below-ground net primary

production of fine-roots observed in the loamy-sand forest. Our results of above-ground

biomass increments suggest a possible trade-off between carbon allocation to fine-roots

versus wood growth, as opposed to the most commonly assumed trade-off between total

above- and below-ground production.

176

The contribution of fine-roots to total biomass and net primary production,

particularly for forests with high limitation of soil resources, confirms its importance in

the allocation of carbon in forests and the overall carbon cycling of these ecosystems.

The loamy-sand forest (‘white-sand forest’) showed that carbon allocation to fine-roots

could reach 50% of total primary production. In comparison, this contribution in the

clay-soil forest was 24%, which had less limitation in soil resource availability.

Regarding above-ground carbon allocation components, the five forests studied

during 2004 to 2012 in the north-western Amazon basin on different soils and with

differences in forest structure, but growing under the same local climatic conditions,

showed differential responses to rainfall and seasonality. In terms of forest dynamics,

only mortality displayed a significant correlation with rainfall for the loamy-sand forest.

The magnitude of correlation for the same carbon allocation component or the mortality

rates was highly variable among forests, and even within the same forest type. These

differences in correlation between above- and below-ground carbon allocation

components with rainfall suggest that soils play an important role in the different

responses of these forests to precipitation fluctuations. On the one hand, there were

differences in the above- and below-ground components with respect to the

susceptibility to rainfall fluctuations; for most forests of the loam-soil group, litterfall,

stem growth, and above-ground biomass increment showed correlation with rainfall,

except the loamy-sand forest, which displayed a correlation with rainfall only with fine-

root mass. On the other hand, litterfall and fine-root mass showed susceptibility to

fluctuations with the precipitation of one or two months previously, indicating important

differences in the response among forests; for one clay-soil forest plot, litterfall was

significantly correlated with the current precipitation, while most forests from the loam-

soil group were correlated with the lagged precipitation.

Despite of these north-western Amazon forests are considered without dry

season, litterfall and fine-root mass showed seasonality and differences in the patterns

between forests on clay-soils and the loam-soil forests group. Additionally, litterfall and

fine-root mass showed particular variation in the dry season of 2005 reported as a

strong drought for the Amazon Basin; with remarkably different responses among the

clay-soil forest plots and the loam-soil forests group.

177

Additionally, the above-ground biomass increment showed high correlation with

rainfall, except for the forest on loamy-sand. Therefore, forests showed low values of

above-ground biomass increment for the periods which included the droughts reported

for the Amazon basin, 2005 and 2010. However, mortality rates and the proportion of

death types did not show clear trends associated with these dry years.

Most forests showed high correlation among them in terms of litterfall

production, which was markedly correlated within the loam-soil forests group despite

their differences in composition, structure, and below-ground resources. For above-

ground biomass increment, only forests with similar basal area were correlated among

them. Interestingly, recruitment rates were highly correlated for all forests in the loam-

soil group.

The leaf area index showed low correlation with precipitation, but high

correlation among forests, even among the most contrasting forest types (clay-soil and

loamy-sand forests). This suggests high uncertainty of leaf area index as a measure of

changes in the above-ground carbon allocation components related to rainfall

fluctuations, and to characterize differences in their responses to changes in the

environment.

Furthermore, this analysis may have important implications for ecosystem

modelling. In particular, (i) land-surface models that require parameters on net primary

production partitioning, but only use data on biomass fractions may incur in important

errors for predicting total net primary production and component fluxes, and (ii) models

in which net primary production is proportional to the leaf area index may miss

important differences in internal ecosystem responses caused by edaphic factors.

179

6 CONCLUSIONES

En general, puedo concluir que los suelos juegan un papel importante en la

asignación del carbono en diferentes bosques maduros en el noroeste de la cuenca

Amazónica. A pesar de que estos bosques crecen bajo condiciones climáticas similares

(precipitación total y su distribución estacional), mostraron diferencias en los

componentes aéreo y subterráneo de la asignación del carbono y en sus respuestas a las

fluctuaciones en la precipitación. Además, estas diferencias resaltan que los patrones en la

biomasa total y la producción primaria neta pueden cambiar cuando el compartimento

subterráneo (raíces) es incluido en las estimaciones.

En particular, los componentes aéreo y subterráneo de la asignación del carbono

en los bosques sobre el suelo arcilloso y arena-francosa, mostraron que las fracciones

entre los compartimentos aéreo y subterráneo de la biomasa difieren de la fracción de la

productividad primaria neta total asignada a estos compartimentos. Por otra parte, los

componentes de la productividad primaria neta en estos bosques no estuvieron

relacionados, i.e. valores altos de la producción primaria neta total en el bosque sobre el

suelo arcilloso no correspondieron a valores altos para todos los componentes de la

producción primaria neta. Las diferencias en los recursos del suelo (nutrientes y

disponibilidad de agua) son probablemente la causa de la doble producción de raíces

finas observada en el bosque sobre arena-francosa (también llamado como “bosque de

arenas blancas”). Las diferencias en las fracciones de la productividad primaria neta

asignadas a los componentes (aéreo y subterráneo) y subcomponentes (hojarasca,

180

incremento en la biomasa aérea, raíces gruesas y finas) entre los bosques se debió

principalmente a la variación en el carbono asignado al crecimiento de la biomasa aérea y

a la productividad de raíces finas, mientras que la fracción asignada a la producción de

hojarasca fue similar entre los bosques; lo que sugiere que a nivel del bosque existe una

compensación el incremento en la biomasa aérea (posiblemente en la producción de los

tallos, como otros autores han sugerido) y la producción de raíces finas, en lugar de un

balance entre vástago-raíz, como se ha propuesto anteriormente.

La contribución de las raíces finas a la biomasa total y productividad primaria

neta total, particularmente para los bosques con limitación de recursos en el suelo,

confirma su importancia en la asignación y el ciclo del carbono en estos ecosistemas

forestales. El bosque de arenas blancas (arena-francosa) mostró que la asignación del

carbono a las raíces finas puede alcanzar un 50% de la productividad primaria neta total,

comparado con el 24% que alcanzó el bosque sobre el suelo arcilloso y, que tiene menos

limitaciones en la disponibilidad de recursos en el suelo.

En cuanto a los componentes aéreos de la asignación del carbono, los cinco tipos

de bosque estudiados (suelo arcilloso, franco-arcilloso, franco-arcilloso-arenoso, franco-

arenoso y, arena-francosa) durante 2004−2012 mostraron diferencias en las respuestas a

la precipitación y, en su estacionalidad para algunos de los subcomponentes de la

productividad primaria neta. En cuanto a la dinámica forestal, solamente el bosque sobre

arena-francosa presentó una correlación significativa entre la precipitación y las tasas de

mortalidad. La magnitud de la correlación para el mismo componente de la asignación

del carbono o la tasa de mortalidad fue muy variable entre bosques e incluso dentro de

un mismo tipo de bosque. Estas diferencias en la correlación entre los componentes

aéreo y subterráneo de la asignación del carbono con la precipitación sugieren que los

suelos juegan un papel importante en las diferentes respuestas de estos bosques a las

fluctuaciones en la precipitación. Por un lado, hubo diferencias en los componentes

aéreo y subterráneo con respecto a la susceptibilidad a las variaciones en la precipitación.

Para la mayoría de los bosques del grupo de suelos francos, la hojarasca, el crecimiento

de los tallos, y el incremento de la biomasa aérea mostró correlación con la precipitación,

excepto el bosque sobre arena-francosa, que solo mostró correlación con la precipitación

para la masa de raíces finas. Por otro lado, la hojarasca y la masa de raíces finas

181

mostraron susceptibilidad a la precipitación de uno o dos meses previos, lo que indica

diferencias importantes en la respuesta de estos bosques a la lluvia. En una parcela sobre

el suelo arcilloso, la hojarasca se correlacionó significativamente con la precipitación

actual, mientras que la mayoría de los bosques del grupo sobre suelos francos se

correlacionaron con retrasos en la precipitación.

A pesar de que estos bosques del noroeste Amazónico han sido considerados sin

una estación seca propiamente dicha (definida como meses con <100 mm), la

producción de hojarasca y de raíces finas mostró una estacionalidad alta, con una

variación particular en la estación seca del año 2005, reportado como un año de sequía

para la cuenca Amazónica. Así mismo, durante este periodo seco se presentaron

diferencias en las respuestas de la hojarasca y las raíces finas entre los bosques sobre

suelo arcilloso y el grupo de bosques sobre suelos francos.

Adicionalmente, el incremento de la biomasa aérea presentó una alta correlación

con la precipitación, exceptuando el bosque sobre arena-francosa. Los bosques por lo

tanto mostraron valores bajos de la producción aérea para los periodos que incluyeron

los años reportados como secos para la cuenca Amazónica, el 2005 y 2010. No obstante,

las tasas de mortalidad y la proporción de individuos muertos por categoría de muerte no

mostraron tendencias claras relacionadas con estos periodos secos.

En términos de los componentes aéreos de la asignación del carbono, pese a las

diferencias en composición, estructura y recursos en el suelo entre los bosques

estudiados, éstos estuvieron altamente correlacionados entre ellos. La producción de

hojarasca, el crecimiento de los tallos y el incremento en la biomasa aérea estuvieron

marcadamente correlacionados dentro del grupo de bosques sobre suelos francos.

El índice de área foliar presentó una baja correlación con la precipitación, sin

embargo, mostró una alta correlación entre los bosques sobre suelo arcilloso y arena-

francosa, incluso siendo estos los bosques más contrastantes en términos de la diferencia

entre los recursos del suelo. Esto sugiere una alta incertidumbre del índice de área foliar

como una medida de los cambios en los componentes aéreos de la asignación del

carbono relacionados con las fluctuaciones en la precipitación, y para caracterizar las

diferencias en las respuestas de estos componentes a cambios en el ambiente.

182

Por otra parte, este análisis puede tener implicaciones importantes para la

modelización de los ecosistemas terrestres. En particular, (i) los modelos de la superficie

terrestre que requieren parámetros sobre la fracción de la productividad primaria neta,

pero que utilizan las fracciones de biomasa, pueden estar incurriendo en errores

importantes para predecir la productividad primaria neta total y los flujos de carbono

entre componentes o compartimentos (aéreo y subterráneo), y (ii) los modelos en los que

la producción primaria neta es proporcional al índice de área foliar puede sugerir que se

están omitiendo diferencias importantes en las respuestas internas del ecosistema

causadas por factores edáficos.

183

7 APPENDIXES

184

Appendix 7-1 Jiménez, E.M., Moreno, F.H., Peñuela, M.C., Patiño, S., Lloyd, J., 2009.

Fine root dynamics for forests on contrasting soils in the Colombian Amazon.

Biogeosciences 6, 2809-2827. www.biogeosciences.net/6/2809/2009/

http://www.biogeosciences.net/6/2809/2009/

Biogeosciences, 6, 2809–2827, 2009www.biogeosciences.net/6/2809/2009/© Author(s) 2009. This work is distributed underthe Creative Commons Attribution 3.0 License.

Biogeosciences

Fine root dynamics for forests on contrasting soils inthe Colombian Amazon

E. M. Jimenez1, F. H. Moreno2, M. C. Penuela1, S. Patino1,3, and J. Lloyd3,*

1Grupo de Ecologıa de Ecosistemas Terrestres Tropicales, Universidad Nacional de Colombia Sede Amazonia, InstitutoAmazonico de Investigaciones-Imani, km. 2, vıa Tarapaca, Leticia, Amazonas, Colombia2Grupo de Bosques y Cambio Climatico, Universidad Nacional de Colombia Sede Medellın, Apartado Aereo 1779,Medellın, Colombia3Earth and Biosphere Institute, School of Geography, University of Leeds, LS2 9JT, UK* previously at: Max Planck Institute fuer Biogeochemie, Jena, Germany

Received: 9 January 2009 – Published in Biogeosciences Discuss.: 30 March 2009Revised: 29 September 2009 – Accepted: 2 November 2009 – Published: 3 December 2009

Abstract. It has been hypothesized that as soil fertility in-creases, the amount of carbon allocated to below-groundproduction (fine roots) should decrease. To evaluate thishypothesis, we measured the standing crop fine root massand the production of fine roots (<2 mm) by two meth-ods: (1) ingrowth cores and, (2) sequential soil coring,during 2.2 years in two lowland forests growing on dif-ferent soils types in the Colombian Amazon. Differencesof soil resources were defined by the type and physicaland chemical properties of soil: a forest on clay loam soil(Endostagnic Plinthosol) at the Amacayacu National Natu-ral Park and, the other on white sand (Ortseinc Podzol) atthe Zafire Biological Station, located in the Forest Reser-vation of the Calderon River. We found that the stand-ing crop fine root mass and the production was signifi-cantly different between soil depths (0–10 and 10–20 cm)and also between forests. The loamy sand forest allocatedmore carbon to fine roots than the clay loam forest withthe production in loamy sand forest twice (mean±standarderror=2.98±0.36 and 3.33±0.69 Mg C ha−1 yr−1, method 1and 2, respectively) as much as for the more fertile loamysoil forest (1.51±0.14, method 1, and from 1.03±0.31 to1.36±0.23 Mg C ha−1 yr−1, method 2). Similarly, the av-erage of standing crop fine root mass was higher in thewhite-sands forest (10.94±0.33 Mg C ha−1) as compared tothe forest on the more fertile soil (from 3.04±0.15 to3.64±0.18 Mg C ha−1). The standing crop fine root massalso showed a temporal pattern related to rainfall, with the

Correspondence to:E. M. Jimenez([email protected])

production of fine roots decreasing substantially in the dryperiod of the year 2005. These results suggest that soil re-sources may play an important role in patterns of carbon al-location to the production of fine roots in these forests as theproportion of carbon allocated to above- and below-groundorgans is different between forest types. Thus, a trade-offbetween above- and below-ground growth seems to existwith our results also suggesting that there are no differencesin total net primary productivity between these two forests,but with higher below-ground production and lower above-ground production for the forest on the nutrient poor soil.

1 Introduction

Tropical forests play a central role in the global carbon cy-cle (Dixon et al., 1994; Vogt et al., 1996; Brown, 2002), andthis has encouraged a long ongoing interest in the study ofvarious components of their net primary productivity, NPP(Clark et al., 2001a; Vogt et al., 1996; Malhi et al., 2004).However, understanding of NPP in many ecosystems, includ-ing tropical forests, is still poor due to the scarcity of infor-mation on several of its components, especially in the below-ground component.

Moreover, fine root dynamics have usually not been mea-sured despite their importance for the plant carbon economyand overall ecosystem functioning. Excluding the below-ground portion of NPP could produce significant biases inthe quantification of carbon fluxes in ecosystems (Woodwardand Osborne, 2000). For example, it has been estimated thatabout 0.33 of annual global NPP is used to produce fine roots(Jackson et al., 1997).

Published by Copernicus Publications on behalf of the European Geosciences Union.

http://creativecommons.org/licenses/by/3.0/

2810 E. M. Jimenez et al.: Fine root dynamics for Amazon forests

Calderón river

Peru

AMP

Leticia

Brazil

ZAB

AGP-01

ZAR-01

AGP-02

Brazil Peru

Colombia

300 m

Fig. 1. Localization of the study sites in the Colombian Amazon (Trapecio Amazonico, Leticia): Amacayacu National Natural Park (AMP)with two 1-ha plots: AGP-01 and AGP-02, and Biological Station Zafire (ZAB) with one 1-ha plot: ZAR-01, in the Forest Reservation ofthe Calderon river. The circles show the areas to sampling fine roots.

Fine root dynamics are particularly important for tropicalforests, where biomass and rates of production and decom-position of fine roots are high (Silver et al., 2005). The ap-parent paradox of the exuberance and large size of tropicalhumid forests growing on intensively leached soils, suggeststhat fine roots play an important role in optimizing nutrientacquisition and maintaining a closed nutrient cycle in theseforests (Gower, 1987). However, understanding the dynam-ics of biomass and the factors controlling fine root productiv-ity in tropical forests, including in Amazonia, remains poor(Vogt et al., 1998; Clark et al., 2001b; Silver et al., 2005;Trumbore et al. 2006, Metcalfe et al., 2007, 2008; Aragao etal., 2009).

Hendricks et al. (1993) summarize two contrasting hy-potheses proposed to explain the control of soil resourceson carbon allocation and NPP. The first one is called the“differential allocation hypothesis”, and states that total NPPincreases with the increase in the availability of resources,and that allocation between above- and below-ground com-ponents is differential, with a higher allocation to foliageand wood than to fine roots on richer sites (Gower et al.,1992; Albaugh et al., 1998). The other hypothesis is the“constant allocation hypothesis”, also proposes an increasein total NPP with the increase in the availability of soil re-sources, but the relative allocation of NPP to above- andbelow-ground organs remaining relatively constant (Aber etal., 1985; Nadelhoffer et al., 1985; Raich and Nadelhoffer,1989).

This study evaluates below-ground productivity (fineroots≤2 mm) in two mature forests ofTerra firmedevelop-ing on contrasting soils in the Colombian Amazon. Thesoil underneath one forest was a relatively fertile Plinthosol

with loam clay texture and a higher nutrient content than anearby loamy sand textured Podzol. In particular, we aimedto answer the following questions: (1) How different are thestanding crop fine root mass and production between theseforest types? (2) How do these variables change with soildepth (0–10 and 10–20 cm) in each forest and between them?(3) Is there any temporal variation in the standing crop fineroots mass? And if so, is it related variations in precipita-tion? As the period of data collection in 2005 occurred anunusually strong dry period, we added one more question:(4) Did the Amazon drought of 2005 affect the productionof fine roots at our sites? To answer these questions, we es-timated the standing crop fine root mass (SFR), production(FRP), the relative growth rates (RGR) and the turnover ratesof fine roots.

This study is orientated by the differential allocation hy-pothesis, which has been one of the most accepted for tropi-cal forests (Albaugh et al., 1998; Gower et al., 1992; Keyesand Grier, 1981). As a consequence, we predicted a decreaseof standing crop fine root mass and production with the in-crease of soil resources (review in Hendricks et al., 1993).We also predicted that the pattern of FRP would be the op-posite to results obtained for wood production by Malhi etal. (2004). They found a positive relationship between woodproductivity and soil fertility in the Amazonian basin.

2 Material and methods

2.1 Study sites

Three 1 ha old-growth forest plots were used tostudy fine roots in the Colombian Amazon (Trapecio

Biogeosciences, 6, 2809–2827, 2009 www.biogeosciences.net/6/2809/2009/

E. M. Jimenez et al.: Fine root dynamics for Amazon forests 2811

Table 1. Main characteristics of the study sites (Colombian Amazon): Amacayacu National Natural Park (AMP) and Biological StationZafire (ZAB).

Characteristics AMP ZAB

Principal Investigator A. Rudas and A. Prieto M. C.Penuela and E.AlvarezPlot Codea AGP-01 AGP-02 ZAR-01Latitude −3.72 −3.72 −4.01Longitude −70.31 −70.30 −69.91Altitude (m) 105 110 130Forest type Terra firme CaatingaSoil typeb Endostagnic Plinthosol Orteinic Podzol (Oxyaquic) –

(Alumic, Hyperdystric) – loamy sandclay loam

Chemical properties (depth 0 – 30 cm)c

pH 4.50 4.29 4.27Resin extractable P (mg kg−1) 1 1 12Total extractable P (mg kg−1) 131 123 22Mean N (%) 0.15 0.16 0.11Mean C (%) 1.2 1.4 2.4C/N 8 8 27Ca (mmolc kg−1) 6 5 3Mg (mmolc kg−1) 3 3 2K (mmolc kg−1) 1 1 1Na (mmolc kg−1) 0 0 1Al (mmolc kg−1) 52 52 1SB (mmolc kg−1) 10 10 6CIC (mmolc kg−1) 6.21 6.26 0.71Al Saturation (%) 84 84 10Base saturation (%) 16 16 90

Physical propertiesc

Sand (%) 21 19 75Clay (%) 42 43 1Silt (%) 37 38 25Main root depth (cm) 20 20 10Total root depth (cm) 50 50 100

Available water capacity, cm water per cm depth0–30 cm 3.75 3.51 2.82

Vegetationd

Richness (species ha−1) 225 244 25Mean height of crown (m) 30 30 20Mean stem diameter (cm) 17.3 21 15Stem density (ha−1) 647 606 866above-ground biomass (Mg ha−1) 281 276 161

a Codes follow those published in Patino et al. (2009).b World Reference Base for Soil Resources (2006).c Quesada et al. (2009b).d

RAINFOR (http://www.rainfor.org).

www.biogeosciences.net/6/2809/2009/ Biogeosciences, 6, 2809–2827, 2009

http://www.rainfor.org


39

820 Fig. 2. 821 822

22

23

24

25

26

27

28

0

100

200

300

400

500

600

700

E F M A M J J A S O N D E F M A M J J A S O N D E F M A M J J A S O N D

Tem

per

atu

re (oC)

Rain

fall (m

m)

2004 2005 2006


Fig. 2. Patterns of monthly and mean monthly precipitation (1973–2006) and mean temperature from the meteorological station of theVasquez Cobo airport, Leticia (Amazonas, Colombia) during the time of the research. Shady areas show the dry period of each year, thedark one represents the drought periods. Mean monthly precipitation is plotted repeatedly for every year.

Amazonico) (Fig. 1, Table 1) between September 2004 andDecember 2006. Two plots, AGP-01 and AGP-02 weresampled in Amacayacu Natural National Park (AMP) aspart of the RAINFOR-NPP project (www.rainfor.org). Oneplot, ZAR-01 was sampled at the Zafire Biological Station(ZAB), Calderon River Forest Preserve as part ofGrupo deEcologıa de Ecosistemas Terrestres TropicalesNet PrimaryProductivity Project (http://www.imani.unal.edu.co/).

In general, theTrapecio Amazonicoshows a mean monthlyrainfall of 278 mm with a drier period from June to Septem-ber (mean monthly rainfall of 190 mm), and a rainy seasonfrom October to May (mean monthly rainfall of 324 mm)(data from the Vasquez Cobo airport of Leticia for the pe-riod 1973–2006). Mean temperature is about 26◦C and doesnot fluctuate greatly through the year (Fig. 2). Relative hu-midity is high, with a yearly average of 86%. In 2005, inthe middle of the measurements described here, an extendedand unusually dry period occurred from June to September(“the 2005 Amazon drought” see for example Phillips et al.,2009). The rainfall in 2005 was thus only 2873 mm, substan-tially lower than the previous and subsequent year (3250 in2004 and 3710 in 2006), and than the multi-annual average(3335 mm).

AMP belongs to the geologic unit namedPebas orSolimoesFormation; the terrain is slightly undulated and uni-form, with soils moderately deep, well drained, and stronglyacidic with moderately fine textures (Herrera, 1997). Soilsfrom ZAB belong to theTerciario Superior Amazonicounit

(Herrera, 1997; PRORADAM, 1979), probably originatingfrom the Guiana Shield (Hoorn, 1994, 2006), and composedmainly by quartz. The terrain is flat and uniform.

Vertical profiles in exchangeable cations, carbon densityand particle size distribution have been illustrated for thesoils two of the three plots sampled here (AGP-02 and ZAR-01) by Quesada et al. (2009b) with the physical and chemicalnature of these soils also specifically discussed in that paper.Of particular note is the presence in ZAR-01, classified ac-cording to the World Reference Base (2006) as anOrtseinicPodzol (Oxyaquic)of a layer at approximately 1 m deep inwhich aluminium concentration increases abruptly as doescarbon and to some extent clay content. This reflects thecomposition of the defining ortseinic layer where Al-humuschelates act as cementing substances and despite the sandynature of the soil above, can be regarded as a form of “hard-pan” both limiting root distribution and impeding drainage.On the other hand, although both AGP-01 and AGP-02 wereclassified in Quesada et al. (2009b) as being anEndostag-nic Plinthosol (Alumic, Hyperdystric), the plinthic layer wasconsidered relatively permeable exerting only moderate con-straints on drainage (Quesada et al., 2009a). Data from Ta-ble 1 clearly show that AGP-01 and AGP-02 are more fer-tile than ZAR-01, especially in terms of “total exchangeablephosphorus”, a measure as defined by Quesada et al. (2009a)and also being shown by Quesada et al. (2009c) to be oneof the best indicators of Amazon soil fertility because of itsdominating influence on above-ground wood productivity.


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PAGE 5, Figure 3.

Method Site Action

Establishment 105 63 3925 12 13 13 13 13 6 10

12 13 13 13 1213 13 13

Establishment 79 5919 12 13 13 11 6 2 3

10 13 13 13 3 7Establishment 136 39

26 26 28 28 14 1413 13 13

AGP-01 13 13 13 13 13 6 13 13 13AGP-02 13 12 13 13 13 5 13ZAR-01 124 14 14 14 14 14 14 13 13


Number of cores

RetrievalAGP-01

RetrievalAGP-02

Retrieval

Retrieval

ZAR-01

Ingrowth

Sequential

Fig. 3. Schematic representation of the time table for establishment and retrieval of fine root cores. Each colour indicates the establishmentof a given set of cores and their correspondent retrieval sequence considering only the 0–20 cm depth soil core. Note that for sequential coresthere is only retrieval (blue).

2.2 Sampling design

2.2.1 The AMP sampling design

AGP-01 and AGP-02 are two 20×500 m plots. We selected13 areas every 40 m along the plots (Fig. 1). Ingrowth coreswere established on three dates: (1) February 2004, (2)September 2004, and (3) February 2006 (Fig. 3). Ingrowthcores were located 1–2 m from trees≥10 cm in diameter atbreast height (DBH) to avoid coarse roots next to trees; coreswere also 0.20–1 m apart from each other.

2.2.2 The ZAB sampling design

ZAR-01 is a plot composed of two rectangular blocks of40×140 m and 40×130 m (Fig. 1). We established ingrowthcores in 14 areas (following the same criteria as for AMP).

2.3 Core methods

2.3.1 Ingrowth cores

In AMP soil cores were extracted using a root auger 8 cm-diameter and 20 cm-length; in ZAB we used a soil core sam-pler 5 cm-diameter and 15 cm-length. Differences in coresize between sites occurred because the two sampling pro-grams started as independent research projects. Soil cores atboth sites were 20 cm long. Once extracted from the groundcores were divided into two parts: from 0–10 cm and from10–20 cm depth. The soil from each part was sieved twice(through 6 and 1 mm mesh size, respectively), and all rootsand fragments were extracted by hand using forceps. Finally,this root-free soil was sown back in the same hole and depthlevel of the original sample. In this method only a portionof the initially installed cores were retrieved each samplingdate, so the total amount of time that the cores were in theground increased with each successive sampling.

2.3.2 Sequential cores

In the same areas where ingrowth cores were planted wecollected undisturbed cores whenever ingrowth cores wereplanted and extracted (Fig. 3). Handling and processing ofsamples was the same as described for ingrowth cores.

2.4 Fine root extraction

In all cases, the first collection of ingrowth cores was done5–7 months after establishment; subsequent collections weredone at 2–4 month intervals (Fig. 3). Selection of the timeinterval for the first collection was based on reports of meanfine roots life time for several tropical forests, which rangesfrom 6 to 12 months (Priess et al., 1999), previous studieshaving shown that starting collection after a shorter periodthan this is too early to allow representative root growth intothe root-free soil cores.

After extraction, soil cores were packed in labelled poly-thene bags and transported to field stations where they werewashed and sorted in nearby streams. Samples were then air-dried before transportation to the laboratory, where they werewashed with deionised water, sieved (mesh size 0.1 mm), andremaining roots manually extracted with forceps. Roots werepacked in paper bags and oven-dried for 24 h at 80◦C, andthen weighed (0.001 g precision).

2.5 Statistical methods

Tests for differences between forest types, time and soildepths were carried out with one way ANOVA. Data hadbeen previously checked for normality of distributions withthe Kolmogorov-Smirnov and Shapiro-Wilk tests and, forhomogeneity of variances with the test of Levene (Dytham,2003). When ANOVA was significant (p<0.05), we used thepost hoctest of Tukey to compare means. When the require-ments of ANOVA were not met, we used non-parametricaltests, such as the test of Kruskal-Wallis followed by the testU of Mann-Whitney between pairs of data until the differ-ences of the entire group were evaluated. Statistical analyses



were done with the software SPSS 11.5.0 (6 September 2002,LEAD Technologies, Inc).

FRP (Mg ha−1 yr−1) was calculated as the time betweeningrowth core installation (time zero) and the subsequent 6–10 months, scaled to a yearly basis (Vogt et al., 1998). In thisway, for the first establishment in the loamy soil forest, cal-culation of yearly production was based on growth betweenFebruary and December 2004; for the establishment 2, ongrowth between September 2004 and April 2005; and for theloamy sand forest, on growth between September 2004 andJuly 2005; for the establishment 3 in both forest types, pro-duction was based on growth between February and Decem-ber 2006.

To compare FRP in standard units between forests andtime intervals, we calculated the relative growth rate (RGR),defined by Fogg (1967) and Kozlowski et al. (1991) as:

RGR=log(W1)−log(W0)

t(1)

where, log represents the natural logarithm;W1 andW0 arethe final and initial dry weight of fine roots, respectively andt is the time between the two collections in days.

Due to the occurrence of a strong drought period in themiddle of our sampling in 2005 (Fig. 2), we tested for its ef-fect on root production through the comparison of RGR be-fore (September–December 2004), during (April–July 2005),and after drought (September–December 2006). For theloamy sand forest, we also analysed the RGR in the time in-terval between the installation and the harvest 370 days later,and between measurements separated by 89 days, this be-ing with the purpose of having an estimate of RGR duringdrought, between July and September 2005. We consideredfor the estimation of RGR the time elapsed between the es-tablishment and the retrieval time of cores, trying to comparesimilar periods of growth.

Annual production of fine roots (Mg ha−1 yr−1) was alsoestimated from the data of sequential cores as the differencebetween maximum and minimum biomass measured in oneyear (Vogt et al., 1998). To analyse the same time intervalsfor all plots, even though the length of monitoring was dif-ferent, we selected two 12 month periods (April 2005–2006and December 2005–2006) for this analysis. The initial pe-riod, from September 2004 to April 2005 was not used forcalculations because the sharp seasonality of SFR observedin the clay loam soil forest during this period could introducebiases in the estimations.

Turnover rate was calculated as the FRP divided into theaverage SFR for that year; carbon content in fine roots wasassumed to be equal to 50% of dry mass (Silver et al., 2005).

To evaluate the association between SFR (Mg ha−1) –from data of sequential cores- and mean daily rainfall (mmday−1), we used the Spearman’s correlation coefficient,rS(Dytham, 2003).

Several works have correlated the production and thestanding crop fine roots mass with rainfall (Gower et al.,

1992; Kavanagh and Kellman, 1992; Vogt et al., 1998; Yavittand Wright, 2001). However, the speed of the response andits temporal scale is unknown. It is presumed that this re-sponse can be variable depending on soil conditions and rain-fall regimes (Yavitt and Wright, 2001). For this reason, weexplored a wide range of time intervals with respect to rain-fall, examining the average daily rainfall of the previous 7,15, 30, 60, 90, 100, and 120 days before the sampling day.We also explored the existence of a lagged response of SFRto rainfall; for this purpose we considered the average dailyrainfall for fixed time periods of 15, 30, 60, and 90 days withtime lags of 7, 15, 30, 120, and 150 days from the samplingdate.

3 Results

In total we collected 207, 138, and 175 cores from AGP-01 and AGP-02 and ZAR-01 respectively with the ingrowthcores method and 110 and 82 soil cores during the monitor-ing period from AGP-01 and AGP-02 respectively and with234 samples from ZAR-01 with the sequential core. Figure 3shows details of the establishment and retrieval timetable forthe two methods and plots.

3.1 Standing crop fine root mass and production fromingrowth cores

The standing crop for each collection date and soil depth (0–10, 10–20, and 0–20 cm) did not show significant differences(p>0.05) between the two clay loam soil plots (AGP-01 andAGP-02). For this reason, these plots were considered asa unique site in subsequent analyses and were significantlydifferent (p<0.05) from the loamy sand forest plot (ZAR-01).

SFR and FRP were higher in the 0–10 cm than in the0–20 cm soil depth for all establishment dates and forests(Fig. 4 and Table 2). SFR in the clay loam soil forestshowed significant differences (p<0.05) between soil depths(0–10 cm and 10–20 cm) in most collection times and estab-lishment dates, the exception being the collection of April2005 for the second establishment. Similarly, in the loamysand forest, SFR showed significant differences betweensoil depths in most collection dates, but differences betweendepths were not significant for the first collection dates.

Figures of FRP were higher in the forest on loamy sandforest than in the clay loam forest in all depths and estab-lishment dates (Table 2). Differences of FRP in the 0-20 cmlayer were significant (p<0.05) in establishments 2 and 3;however, in establishment 2 differences between forest typeswere not significant (p>0.05) when evaluated independentlyat each soil depth (0–10 cm and 10–20 cm). In establish-ment 3, we found significant differences (p<0.05) of FRPbetween forest types at all soil depths. Results for SFR hadsimilar trends: establishments 2 and 3 showed significant



41

826 Fig. 4. 827

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J F M AM J J A S OND J F M AM J J A S OND J F M AM J J A S OND J F M AM J J A S OND J F M AM J J A S OND J F M AM J J A S OND

J F M AM J J A S OND J F M AM J J A S OND J F M AM J J A S OND

Fig. 4. Fine root production (Mg ha−1) in the first 20 cm of soil depth estimated by the ingrowth core method in two forests with differentsoil types in the Colombian Amazon. Cores were established in three times: (1) February of 2004, (2) September of 2004 and, (3) Februaryof 2006. Values are the means and the standard errors. The shady area is the drought period of the year 2005.∗Significant differences(p<0.05) of fine root mass in relation to: (1) differences between soil depths (0–10 cm and 10–20 cm) per collection date in each plot, (2)differences between all soil depths per collection date and forest type.

differences (p<0.05) between the two forest types at eachsoil depth in most dates of collection, the except being forestablishment 2 in April 2005 for which differences were notsignificant between sites at any soil depth, and in April 2006at the 10–20 cm depth. Nevertheless, for this collection date,the other depths (0–10 cm and 0–20 cm) showed significantdifferences (p<0.05) between the two forests.

The rates of FRP in the first 20 cm of soil ranged from1.60 Mg ha−1 yr−1 in the forest growing on the clay loamsoil to 6.00 Mg ha−1 yr−1 on sandy soil, both rates obtainedfor establishment 3 (Table 2). Likewise, mean FRP waslower for the clay loam forest (3.02 Mg ha−1 yr−1) than inthe loamy sand forest with an estimate of 5.97 Mg ha−1 yr−1

being obtained (Table 2).

Relative growth rates (RGR) for the three periods evalu-ated were higher for the loamy sand forest than for the clayloam forest (Fig. 5). RGR in the clay loam forest both beforeand after drought were also higher than during drought in



Table 2. Fine root production (Mg ha−1 yr−1) in the first 20 cm of soil depth in two forests with different soil types in the ColombianAmazon, estimated from ingrowth cores established in three times: (1) February of 2004, (2) September of 2004 and, (3) February of 2006.

Soil depthClay loam forest Loamy sand forest

N Mean N Mean

Establishment 1 (0.83 years)0–10 cm 24 3.082 (0.196) – –10–20 cm 25 1.153 (0.144) – –0–20 cm 24 4.215 (0.307) – –Total C 2.108

Establishment 2 (0.52 and 0.77 years, respectively)0–10 cm 22 2.104 (0.357) a 26 3.530 (0.520) a10–20 cm 22 1.243 (0.212) a 26 2.404 (0.414) a0–20 cm 22 3.346 (0.472) a 26 5.934 (0.773) bTotal C 1.680 2.967

Establishment 3 (0.82 and 0.81 years, respectively)0–10 cm 13 1.210 (0.178) a 13 3.910 (0.990) b10–20 cm 13 0.390 (0.078) a 13 2.091 (0.589) b0–20 cm 13 1.600 (0.203) a 13 6.001 (1.388) bTotal C 0.800 3.001

Mean0–20 cm 3.022 (0.279) a 5.968 (0.721) bTotal C 1.511 (0.140) 2.984 (0.361)

In parenthesis the time elapsed between the establishment and the retrieve of cores. Standard errors in parenthesis. Different letters showsignificant differences (p<0.05) in the production between forests.

2005. Before the drought RGR was 4.47 yr−1 and 3.94 yr−1

for AGP-01 and AGP-02, respectively, and after drought itwas 1.21 yr−1 for AGP-01. RGR during the drought periodwere lower: −1.00 and−0.72 yr−1 for AGP-01 and AGP-02, respectively. RGR before and after drought in the loamysand forest were similar: 2.94 and 2.08 yr−1, respectively,while the RGR estimated during the final part of the droughtperiod were much lower (−0.77 yr−1), similar to those ob-tained for the forest on clay loam soil.

3.2 Standing crop fine root mass, production, andturnover rates from sequential soil coring

Similar to results obtained for the ingrowth cores, SFR wassignificantly higher (p<0.05) at 0–10 cm depth than at 10–20 cm (Fig. 5 and Table 3). Temporal variation of SFR alongthe monitoring period also showed significant differencesamong collection dates for all plots: AGP-01 (F8,101=4.754,p<0.01), AGP-02 (X2=23.130, D.F.=6, p=0.001), andZAR-01 (X2=49.258,D.F.=8, p=0.000) (Fig. 6). Decem-ber 2005 had the highest value of SFR (5.04 Mg ha−1) inAGP-01. In AGP-02 September 2004, April and December2005 showed higher values (3.90, 4.27 and 5.04 Mg ha−1,respectively) whereas July 2006 showed the lowest value(2.44 Mg ha−1). For ZAR-01, September 2004, July 2005,

and December 2006, showed significant differences (Fig. 6).In the clay loam forest (plots AGP-01 and AGP-02) thestanding crop increased between September and December,while for the loamy sand forest the increase occurred be-tween March and July.

The standing crop also showed significant differences be-tween plots (p<0.05) at each collection date and at all soildepths (0–10, 10–20 and 0–20 cm), but differences betweenplots of clay loam forest (AGP-01 and AGP-02) were notsignificant (p>0.05) for most sampling dates (Fig. 7), the ex-ception being April 2005. The loamy sand forest (ZAR-01)showed values significantly higher (p<0.05) than the plotson clay loam soil for almost all collection dates.

By contrast, SFR measured for the entire monitoring time(2.2 years) showed significant differences (p<0.05) betweenplots at all soil depths considered (Table 3). The average SFRfor over the monitoring period was almost three times higherin the loamy sand forest plot than in plots of clay loam forest(10.94 Mg ha−1 in ZAR-01 and 3.04 and 3.64 Mg ha−1 forAGP-01 and AGP-02, respectively). Likewise, when the twoyears were evaluated independently, SFR was higher in theloamy sand forest (8.92 and 4.41 Mg ha−1 yr−1 for years 1and 2, respectively) than in the clay loam forest (for AGP-01:2.77 and 2.67 Mg ha−1 yr−1 for years 1 and 2, respectively,and 2.05 Mg ha−1 yr−1 for AGP-02 in year 1) (Table 3).



42

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J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S O N D

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Fig. 5. Anomaly of rainfall along the period of study, calculatedas the precipitation of each month minus the mean monthly precip-itation (1973–2006) and relative growth rates (RGR) of fine rootmass (yr−1) for the forests studied. Dotted vertical lines show thetime intervals considered for the estimation of RGR from ingrowthcores; the portion dashed shows the drought of 2005. Circles withvertical bars represent the mean and standard errors of RGR; whiteand gray circles for plots on clay loam forest (AGP-01 and AGP-02, respectively) and black circles for the plot on loamy sands for-est (ZAR-01). ∗RGR for ZAR-01, calculated between the formerharvest (July 2005) and the following harvest (September 2005).

Turnover rates (yr−1) estimated from sequential cores foreach year (Table 3), varied between 0.53–0.84 in the clayloam forest, and between 0.51–0.81 in the loamy sand forest.Averages per plot were 0.84 and 0.53 for AGP-01 and AGP-02, and 0.66 for ZAR-01.

3.3 Relationship between the standing crop fine rootmass and rainfall

We found a significant correlation between SFR and rain-fall (mm day−1) in both forest types (Table 6). For plots inthe clay loam soil forest (AGP-01 and AGP-02), the corre-lation was positive and significant between SFR and meandaily rainfall with and without time lag. Rainfall variablesthat showed a positive correlation (Rbetween 0.1884 and0.2397) in AGP-01 were average daily rainfall of the previ-ous 90 days, average rainfall of the previous 60 and 90 dayswith time lags of 7 and 15 days, and average rainfall of theprevious 60 days with a time lag of 30 days. In AGP-02 weobtained a higher number of rainfall variables with positiveand higher correlations (0.2397–0.4702); variables with sig-nificant correlations were average daily rainfall of last 60,90, 100 and 120 days, as well as rainfall with time lags of7 days in all the fixed periods considered (15, 30, 60 and 90days), rainfall with time lag of 15 days for fixed periods of30 and 60 days, and rainfall with time lag of 30 days with afixed period of 15 days. Rainfall with the longest time lags

Page 3 of 7


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Clay loam forest (AGP-01)

Clay loam forest (AGP-02)

Loamy sand forest (ZAR-01)

Fig. 6. Temporal variation of fine root mass (Mg ha−1) in the 0–20 cm soil depth in two forests on different soil types in ColombianAmazon: one forest on clay loam soils (plots AGP-01 and AGP-02) and another on loamy sands (plot ZAR-01), estimated with themethod of sequential cores. Values are averages and standard devia-tions. The area dashed shows the drought period in 2005. Differentletters in each plot show significant differences (p<0.05) of fineroot mass (0–20 cm) between collection dates.∗Significant differ-ences (p<0.05) of fine root mass in each collection date betweendepths 0–10 and 10–20 cm.

(120 and 150 days) and almost all fixed periods considered,showed negative correlation with SFR in plots of clay loamforest (−0.2593 to−0.3719).



Table 3. Fine root mass (Mg ha−1), production (Mg ha−1 yr−1), and turnover rates (yr−1) in two forests with different soil types in theColombian Amazon: one forest on clay loam soils (two 1-ha plots: AGP-01 and AGP-02) and other on loamy sands (one 1-ha plot: ZAR-01), estimated by the sequential core method.

Clay loam forest Loamy sand forest


Mean mass by soil deptha

0–10 cm 2.331 (0.114) a 2.758 (0.148) b 7.861 (0.240) c10–20 cm 0.711 (0.053) a 0.879 (0.056) b 3.077 (0.174) c0–20 cm 3.043 (0.151) a 3.637 (0.181) b 10.938 (0.327) c

Mean mass by year (0–20 cm)April 2005–2006 3.30 (0.24) 3.85 (0.24) 10.94 (0.65)December 2005–2006 3.17 (0.23) – 8.69 (0.46)

Maximum and minimum values of annual stocksApril 2005–April 2006 5.042 (0.547) 5.196 (0.648) 16.710 (1.288)

2.273 (0.327) 3.145 (0.258) 7.794 (1.156)December 2005–December 2006 5.042 (0.547) – 10.943 (1.046)

2.374 (0.403) – 6.530 (0.824)

Productionb

April 2005–April 2006 2.769 (0.546) 2.051 (0.621) 8.916 (2.102)December 2005–December 2006 2.668 (0.574) – 4.413 (1.256)Mean Biomass 2.719 (0.452) 2.051 (0.621) 6.665 (1.377)Mean C 1.359 (0.226) 1.026 (0.310) 3.332 (0.689)

Turnover ratesApril 2005–April 2006 0.84 0.53 0.81December 2005–December 2006 0.84 – 0.51Mean 0.84 0.53 0.66

Standard errors in parenthesis.a Mean Fine root mass for the whole monitoring time (2.2 years).b Difference between maximum andminimum fine root mass measured during a year.

In the loamy sand forest plot SFR showed negative corre-lations with daily averages of rainfall of the previous 7, 15,30, 60, 90, 100 and 120 days without time lag, and with rain-fall for fixed periods of 15, 30 and 60 days with time lags of 7and 30 days, and finally, with rainfall of the previous 30 dayswith time lag of 120 days. Contrary to the results in the clayloam soil plots, correlations for long time lags were positive:correlations varied from 0.1657 to 0.1943 for a time lag of150 days and fixed periods of 30 and 60 days.

4 Discussion

4.1 Carbon allocation to production of fine roots to fineroots

The forest growing on the Podzol soil had a much higheraverage of SFR (10.94 Mg ha−1) than the forest on the clayloam textured Plinthosol (3.04 and 3.64 Mg ha−1). Thisagrees with several reviews, which show that higher values

of SFR in tropical forests occur in soils with low nutrientcontent, such asCaatinga(Cavelier, 1992; Vogt et al., 1996).These results are inside the range reported in the Amazonbasin and other similar forests in Venezuela (Table 4), whichvaried between 2.18 and 39.50 Mg ha−1 for a forest on clays(Acrisols/Ferralsols) in Brazil and a transition between welldrained andCaatingaforest (Acrisols/Podzols) in Venezuela,respectively.

Values of SFR reported in Table 4 for forests on Podzols(ranging from 4.98 to 20.00 Mg ha−1) are generally higherthan those for forests on most Acrisols/Ferralsols (from 2.18to 3.64 Mg ha−1), which were close to the values obtained forforests on the same soil type of this study; however, the valuefor forests classified by Duivenvoorden and Lips (1995) asbelonging to well-drained soils (probably Acrisols/Alisols)in the middle Caqueta (Colombian Amazon) are much higherthan this general range (12 Mg ha−1) as estimates for Ferral-sol/Acrisol soils in Eastern Brazil taken from data reportedby Metcalfe et al. (2008). It is, however, important to note



44

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Fig. 7. Fine root mass (Mg ha−1) at soil depths: 0–10, 10–20, and 0–20 cm, in plots of two forests on different soils in Colombian Amazon:one forest on clay loam soil (plots AGP-01 and AGP-02) and other forest on loamy sand (plot ZAR-01), estimated by the sequential coremethod.∗Significant differences (p<0.05) of fine root mass in each collection date among plots.

that even within the one soil group, significant variations innutrient contents can be observed. For example, Quesadaet al. (2009b) found 0–30 cm total exchangeable phospho-rus concentrations to vary from 27 to 68 mg kg−1 across arange of 13 ferralsols sampled as part of a Amazon Basin-wide study and to vary from 8 to 91 mg kg−1 for the seven

acrisols sampled as part of the same work. It is thus difficultto make generalisations about likely differences in soil fer-tility based on a simple knowledge of (usually only approxi-mately and often incorrectly identified) soil classifications.

Our estimates of FRP for the loamy sand forestare high compared with the range shown in Table 4



Table 4. Standing crop fine root mass (SFR), production (FRP) and turnover rates (FRT) of fine roots (<2 mm) in forests of the Amazonbasin.

Foresttype Soil Depth SFR FRP FRT Reference(cm) (Mg ha−1) (Mg ha−1 yr−1) (yr−1)

BrazilCampina onhumus (Podzol) 29 4.98 – – Klinge (1973)TF Forest (Ferralsol) 27 5.33 – – Klinge (1973)TF Forest (Ferralsol) “clay plot”1 30 – 11.40 – Metcalfe et al. (2007)TF Forest (Ferralsol) “clay plot”1 30 – 5.00 – Metcalfe et al. (2007)TF Forest (Ferralsol) “clay plot”1 30 – 5.60 – Metcalfe et al. (2007)TF Forest (Ferralsol) “clay plot”1 30 – 2.10 – Metcalfe et al. (2007)TF Forest (Acrisol) “sand plot” 30 14.00 4.00 0.29 Metcalfe et al. (2008)∗∗

TF Forest (Acrisol) “dry plot” 30 10.00 3.00 0.30 Metcalfe et al. (2008)∗∗

TF Forest (Ferralsol) “clay plot” 30 15.00 4.00 0.27 Metcalfe et al. (2008)∗∗

TF Forest (Archao-Anthroposol) “fertile plot” 30 11.00 7.00 0.64 Metcalfe et al. (2008)∗∗

Forest on clay soils (Ferralsol) – year 1 10 2.18 2.04 0.70 Silver et al. (2005)Forest on clay soils (Ferralsol) – year 2 10 2.18 1.57 0.69 Silver et al. (2005)Forest on sandy loam soils (Acrisol) – year 1 10 2.92 2.54 0.57 Silver et al. (2005)Forest on sandy loam soils (Acrisol) – year 2 10 2.92 1.49 0.39 Silver et al. (2005)Mature forest (Acrisol/Ferralsol)2a 10 2.60 0.35 0.14 Trumbore et al. (2006)∗∗

Mature forest (Acrisol/Ferralsol)2b 10 2.60 0.92 0.35 Trumbore et al. (2006)∗∗

Mature forest (Acrisol/Ferralsol)2c 10 2.60 1.18 0.45 Trumbore et al. (2006)∗∗

Mature forest (Acrisol/Ferralsol)2d 10 2.60 0.52 0.20 Trumbore et al. (2006)∗∗

Secondary forest of 17 years old (Acrisol/Ferralsol)2d 10 3.42 0.85 0.25 Trumbore et al. (2006)∗∗

ColombiaFlooded forest on well drained soils (Cambisol)3∗ 20 10.00 – – Duivenvoorden and Lips (1995)TF Forest on well drained soils (Acrisol/Alisol)3∗ 20 12.00 – – Duivenvoorden and Lips (1995)TF Forest on white sands (Podzol)3∗ 20 20.00 – – Duivenvoorden and Lips (1995)Secondary forest of 18 years old in low terraces in the Caqueta river 20 12.82 – – Pavlis and Jenık (2000)Secondary forest of 25 years old in low terraces in the Caqueta river 20 11.24 – – Pavlis and Jenık (2000)Secondary forest of 37 years old in low terraces in the Caqueta river 20 16.87 – – Pavlis and Jenık (2000)Mature forest in low terraces in the Caqueta river 20 30.61 – – Pavlis and Jenık (2000)TF Forest on clay soils (Plinthosol)4a 20 – 3.02 – Present studyTF Forest on white sands/Caatinga(Podzol)4a 20 – 5.97 – Present studyTF Forest on clay soils (Plinthosol)4b 20 3.04 2.72 0.84 Present studyTF Forest on clay soils (Plinthosol)4b 20 3.64 2.05 0.53 Present studyTF Forest on white sands/Caatinga(Podzol)4b 20 10.94 6.67 0.66 Present study

Venezuela5

High Caatinga – – 1.20 – Cuevas and Medina (1988)TF Forest (Acrisol/Ferralsol)∗ – – 2.01 – Jordan and Escalante (1980)TF Forest (Acrisol/Ferralsol)∗ – – 11.17 – Jordan and Escalante (1980)High forest (Ferralsol) 20 11.40 3.12 0.27 Priess et al. (1999)∗∗

Medium forest (Ferralsol) 20 12.20 3.06 0.25 Priess et al. (1999)∗∗

Low forest (Ferralsol) 20 9.63 4.20 0.44 Priess et al. (1999)∗∗

TF Forest 30 13.80 – – Rev. in Cavelier (1992)Bana 30 15.70 – – Rev. in Cavelier (1992)Transitional forestCaatinga/Bana(Podzol) 30 15.70 – – Rev. in Cavelier (1992)Caatinga 30 17.90 – – Rev. in Cavelier (1992)Transitional forest TF/Caatinga(Podzol) 30 39.50 – – Rev. in Cavelier (1992)TF Forest 10 – 15.4 – Rev. in Nadelhoffer and Raich (1992)TF Forest 10 – 1.90 – Sanford (1990)TF Forest 10 1.00 – Sanford (1990)TF Forest 10 – 1.00 – Sanford (1990)

TF: Terra firme∗ Fine root diameter<5 mm∗∗ FRT as calculated from production and SFR reported for each forest.1 FRP was estimated in the same site using rhizotrons with different methods to convert root length into root mass per unit ground area.2 They make reference to the method used to estimate production:a maximum-minimum,b decision matrix,c flow compartment, andd decomposition model.3 Values of SFR are the averages for different forests per landscape unit.4 They make reference to the method used to estimate production:a ingrowth cores andb maximum-minimum.5 These sites were included due their similar conditions with our study sites.



(5.97 Mg ha−1 yr−1 with the ingrowth core methodand 6.67 Mg ha−1 yr−1 with sequential cores), whileestimations for the clay loam forest are interme-diate (3.02 Mg ha−1 yr−1 with ingrowth cores and2.05 Mg ha−1 yr−1, 2.72 Mg ha−1 yr−1 with sequentialcores).

The decrease of SFR and FRP with soil depth found hereis a general trend reported in tropical forests (Cavelier, 1992;Duivenvoorden and Lips, 1995; Klinge, 1973; Pavlis andJenık, 2000; Silver et al., 2000) due to the proliferation offine roots near the surface. These roots are considered impor-tant for resource acquisition because they allow the direct cy-cling of nutrients from organic matter, which probably is anadaptation to the low nutrient supply in infertile soils (Sayeret al., 2006).

Both SFR and FRP were significantly higher in the loamysand forest than in the clay loam forest. These resultsshow that in loamy sand forest, with lower nutrient contents,below-ground mass allocation is higher than for other forests.This result has been found in other forests on soils with lownutrient availability and content (Cavelier, 1992; Priess etal., 1999), and specifically in sites such as the mountainsin Guiana (Priess et al., 1999) and elsewhere in Amazonia(Klinge and Herrera, 1978).

Differences found here between forest types support ourhypothesis about the decrease of stocks and production offine roots with the increase of soil resources and agree withother hypotheses proposing the increase of SFR and car-bon allocation below-ground with the decrease of site qual-ity, nutrient availability or under more xerophytic conditions(Shaver and Aber, 1996, Landsberg and Gower, 1997). Theinvestment in leaf compounds, such as tannins, to retard lit-ter decomposition and hence slow down the rate of nutrientcycling, could result in an increase of below-ground produc-tivity to improve the supply of nutrients to the plant (Fischeret al., 2006). These authors found that FRP was highly cor-related with leaf tannin content and the genetic compositionof individual trees, which suggests a potential genetic controlof the compensatory growth of fine roots in response to theaccumulation of secondary compounds of foliage in the soil.This is a factor that could be evaluated as a potential mecha-nism of allocation to below-ground productivity, particularlyin sand forests which tend to contain high amounts of tanninsin the foliage.

Several studies show that soil plays an important role inthe carbon allocation to below-ground production (Block etal., 2006; Cavelier, 1992; Haynes and Gower, 1995; Yavittand Wright, 2001), and Malhi et al. (2004) found thatsoil is an important factor affecting above-ground NPP withQuesada et al. (2009c) showing soil available phosphorusavailability to be the likely driving variable. Haynes andGower (1995) analysed how soil fertility affected carbon al-location to below-ground productivity in a plantation ofPi-nus resinosaAit. on fertilized and unfertilized soils, andfound that fertilization decreased the relative carbon allo-

cation to below-ground production. Gower et al. (1992),analysed how the availability of water and nutrients affectedthe NPP in a coniferous forest (Pseudosuga menziesiivar.galuca), and found a negative relationship between water andnutrient availability and carbon allocation to below-groundorgans. In the case of the forests studied, the fact that theyare subject to the same climatic regime, we conclude thatsoil is the factor playing the principal role on the amount ofcarbon allocated to roots. In this way, both SFR and FRPdecreased with the increase of soil fertility, which is oppositeto the results of Malhi et al. (2004) for above-ground NPP.

On the other hand, integrating above- and below-groundproductivity of the forests studied (Table 5) suggests thatallocation of NPP between above (wood and foliage)- andbelow-ground (fine roots) is differential, just as suggested bythe differential allocation hypothesis proposes. Even thoughcarbon allocation to the above-ground portion was higherthan that to fine roots, this difference is more accentuatedin the clay loam forest than in the forest growing on loamysand. Indeed, differences in the total productivity (above plusbelow-ground) between the two forests were not high (be-tween 8.66 and 8.76 Mg C ha−1 yr−1 for the clay loam forestand, 7.12 Mg C ha−1 yr−1 for the loamy sand forest). Theseresults on above- and below-ground productivity show thelarge variation of Amazonian forests at smaller scales thanthat presented by Malhi et al. (2004), which reflects the im-portance of soil and widen the knowledge about the alloca-tion to above- and below-ground productivity in different for-est types and soils of the Amazon region (see Aragao et al.,2009).

4.2 Turnover rates of fine roots

Turnover rates of this study (0.51–0.84 yr−1) are similarto values reported for other Amazonian forests (0.14 and0.70 yr−1) (Table 4). Nevertheless, the average turnoverrate for the plot AGP-01 in the loamy soil forest was com-paratively high (0.84 yr−1). Fine roots are tissues energeti-cally expensive to build (Yavitt and Wright, 2001) and theirlongevity is critical for the functionality of the root system.Short longevity supposes higher energetic demands for theformation of new roots to replace dead roots and to main-tain the concomitant absorption surface. Aber et al. (1985)propose that turnover rates of fine roots are higher on richsoils than on poor ones. However, turnover rates in bothforest types showed similar values (0.53–0.84 yr−1 for theclay loam forest, and 0.51–0.81 yr−1 for the forest on whitesands). The large variability of turnover rates in each foresttype could mask differences between them.

4.3 Temporal variation of standing crop fine root mass

Several authors have correlated environmental variables withbiomass or production of fine roots (Gower et al., 1992; Ka-vanagh and Kellman, 1992; Vogt et al., 1998; Yavitt and



Table 5. Above- and below-ground productivity (Mg C ha−1 yr−1) in two forests with different soil types in the Colombian Amazon: oneforest on clay loam soil in the Amacayacu National Natural Park (two 1-ha plots: AGP-01 and AGP-02), and other on loamy sands in theBiological Station Zafire (one 1-ha plot: ZAR-01).

Productivity (Mg C ha−1 yr−1)Clay loam forest Loamy sand forest


Above-ground productivityWood productivitya 3.35 3.84 1.32Litterfall productionb 3.87a 3.65a 2.67bTotal 7.22 7.49 3.99

Below-ground productivity (fine roots)c

Ingrowth cores 1.51 2.94Sequential soil coring 1.36 1.03 3.33Mean 1.44 1.27 3.14

Total productivity 8.66 8.76 7.13

a E. M. Jimenez and M. C. Penuela (data not published).b Navarrete (2006).c Mean production, results from the present study. Differentletters show significant differences (p<0.05).

Wright, 2001). Among these variables, rainfall has beenfound to be one of the most influential factors affecting SFRand its longevity in tropical forests (Green et al., 2005).Standing crop fine root mass showed a clear temporal vari-ation during the monitoring period, a result in line with nu-merous studies showing that for certain periods of the year ahigher growth rate of fine roots occurs in response to specificclimatic events (Vogt et al., 1986). Though results suggestthat differences in carbon allocation to production of fineroots between forest types are governed by the availabilityof soil resources, patterns of temporal variation of SFR areexplained by their correlation with rainfall, which has beenreported for other tropical forests (Green et al., 2005; Ka-vanagh and Kellman, 1992; Yavitt and Wright, 2001).

Though SFR responded to the average daily rainfall inboth forest types, these responses were of a contrasting na-ture. For the loamy soil forest soil plot SFR increased withrainfall of the last three months and decreased with rainfalloccurring over long time lags (120–150 days). In the foreston white sands SFR decreased with rainfall of the previous4 months, and increased with the rainfall observed at longertime lags (up to 5 months). Differences of the response ofboth forest types to rainfall might be explained by differencesin their soils: the loamy sand forest contained a hard pan at90–100 cm depth, which inevitably produces water loggingin the soil above during rainy season and likely impedinggrowth of fine roots; this is shown by the negative correla-tion of SFR with rainfall of last 4 months. This phenomenondoes not occur in the clay loam soil forest which respondspositively to the increase of rainfall.

On the other hand, the effect of rainfall on FRP was evi-dent in the drought season of 2005. RGR during the drought

showed that both forests responded in similar way, becauseboth showed a decrease in FRP. However, for the loamy sandforest this decrease was more obvious over the last monthsof the drought (Fig. 5). The impermeable orstenic layer ofthe Podzol soil probably plays an important role in the soilwater content of this forest by causing water retained aboveit during the rainy season to only be slowly released duringthe dry season, and therefore delaying the forest responseto the drought. The general behaviour during drought sug-gests that both forest types are susceptible to strong changesof rainfall. However, the main difference between them isthe speed of the response of each forest: the clay loam for-est showed a faster decrease of FRP as a response to droughtthan the loamy sand forest. Due to this differential responseof two forest types to drought we can not compare FRP be-tween forests to dry season.

Likewise, in both forest types RGR before drought werehigher than after drought. This probably is related with thehigher rainfall of last months before the first collections (year2004), than that after the drought, in 2006 (Fig. 4). In the clayloam forest of AGP-01, the periods between collections thatshowed an increase of SFR were October–December 2005,and September–December 2006, which coincided with therainy season. Also in AGP-02 October-December 2005 wasthe period of increase of SFR. In both plots the SFR washigher in December 2005 than in the same month of 2006,which could be explained by the higher rainfall of the twoprevious months in 2005 than in 2006 (Fig. 2).

In the loamy sand forest the periods of increase of the SFRoccurred between April–July 2005, and March–July 2006,when rainfall decreased and we suggests because the waterlogging of soil caused by the hardpan also decreased. On



Table 6. Spearman coefficients (rs ) for the standing crop fine root mass in two forests with different soil types in the Colombian Amazon,correlated with the mean precipitation per day (PD) of the last 7, 15, 30, 60, 90, 100 and 120 days until the date of collection and, with themean precipitation per day with a time lag (TL) of 7, 15, 30, 120 and 150 days from the date of collection with fixed intervals of time of 15,30, 60 and 90 days.

Mean precipitation (mm day−1)Clay loam forest Loamy sand forest


PD-7 −0.0844 −0.0244 −0.2581∗∗

PD-15 −0.0560 0.0691 −0.2872∗∗

PD-30 −0.0118 0.1700 −0.1414∗

PD-60 0.1479 0.3052∗∗−0.2857∗∗

PD-90 0.2169∗ 0.3422∗∗−0.1612∗

PD-100 0.1721 0.2397∗ −0.1544∗

PD-120 0.1492 0.2397∗ −0.1294∗

TL7-15 0.1835 0.4666∗∗−0.3223∗∗

TL7-30 0.0455 0.3548∗∗−0.1502∗

TL7-60 0.2190∗ 0.4702∗∗−0.2769∗∗

TL7-90 0.2397∗ 0.2845∗∗−0.1222

TL15-15 0.0068 0.1314 0.0555TL15–30 0.0142 0.2494∗ −0.0240TL15–60 0.2015∗ 0.3422∗∗

−0.1168TL15-90 0.1884∗ 0.1820 −0.1100

TL30-15 0.1417 0.2512∗ −0.1563∗

TL30-30 0.1195 0.1941 −0.2261∗∗

TL30-60 0.2060∗ 0.1820 −0.1652∗

TL30-90 0.1831 0.1820 −0.0601

TL120-15 −0.2813∗∗−0.2653∗

−0.0779TL120-30 −0.3719∗∗

−0.2805∗−0.1562∗

TL120-60 −0.3260∗∗−0.2603∗

−0.1283TL120-90 −0.2775∗∗

−0.3871∗∗ 0.0923

TL150-15 −0.3369∗∗−0.0254 0.1179

TL150-30 −0.2834∗∗−0.2777∗ 0.1943∗∗

TL150-60 −0.2627∗∗−0.3449∗∗ 0.1657∗

TL150-90 −0.2593∗∗−0.4480∗∗ 0.1120

∗ Significance levelp<0.05.∗∗ Significance levelp<0.01

the other hand, SFR was higher in July 2005 than in July2006, which can be explained by the decrease in the soil wa-ter logging in July 2005 when the first months of droughtoccurred, which allowed an increase of SFR. The two pre-ceding months to July 2006 showed a mean rainfall higherthan in 2005, which suggests that water logging conditionsof soil were greater at this time than in 2005, which was ex-pressed in a lesser SFR.

Our results thus show that rainfall plays a crucial role inthe seasonal variation of fine root growth in both forests; inthe clay loam soil forest the pattern accords with reports forother well drained forests (Green et al., 2005; Metcalfe etal., 2008; Priess et al., 1999; Silver et al., 2005), where SFR

increased in the rainy season and decreases during the dryseason. The loamy sand forest showed a different pattern,similar to that of flooded forests. This behaviour is appar-ently conditioned by the hardpan (orstenic layer) that causeswater logging during the rainy season thus limiting growthof fine roots and lagging the timing of fine root growth inresponse to rainfall.

4.4 The methods of sampling/estimation used

The selection of methods for the estimation of FRP and itscontrolling factors is tremendously important and has raisedgreat interest nowadays (Hendricks et al., 2006; Lauenrothet al., 1986; Majdi et al., 2005; Makkonen and Helmisaari,



1999; Metcalfe et al., 2007; Vogt et al., 1986, 1998). Hen-dricks et al. (2006), used a wide range of common methodsto estimate FRP in three types of ecosystems in a gradientof soil humidity, with different soil characteristics and re-source availability. They found that FRP was not negativelycorrelated with the availability of soil resources. Their re-sults support in some cases the hypothesis of differential re-source allocation and in some others the constant allocationhypothesis. With respect to the methods used in the presentstudy – sequential cores and the ingrowth cores – these au-thors mention, as well as others (Madji et al., 2005; Vogt etal., 1986, 1998), that they probably underestimate FRP; how-ever, they seem to be the most appropriate to compare sitesand to evaluate the temporal variation of FRP and SFR (Vogtet al., 1998; Makkonen and Helmisaari, 1999).

We acknowledge that sampling differences have the poten-tial to bias the results of this study. On the one hand, samplesizes might not have a significant effect because they werealmost identical in both forest types (22 vs. 26 in the estab-lishment 2, and 1 3 vs. 13 in the establishment 3 in the loamysand forest and clay loam forest, respectively). On the otherhand, the main potential limitation of our sampling schemeis the effect on FRP of different core sizes in the ingrowthexperiment. The use of two independent methods to estimateFRP (ingrowth and sequential cores), allowed us to cross-check our results. Sequential cores are samples of fine rootmass under the natural conditions of the site and their resultsare expected to be little affected by the diameters of cores, asexpected in ingrowth cores due to the differential coloniza-tion of roots.

Results of FRP and its temporal variation did not showlarge differences between the two methods used here in eachforest type. The clay loam forest showed similar results ofSFR between the two methods, but differences in the loamysand forest were marked: SFR estimated by the ingrowthcores was about 5.00 Mg ha−1, while by the sequential coreswas about twice that value (10.94 Mg ha−1). This resultsuggests that probably more important than the diameter ofauger, there is a strong effect of the changed physical proper-ties of soil on root growth in loamy sands and that those soilsrequire a longer time to reach the original root density afterthe disturbance implied by the ingrowth method.

Despite the different results of FRP obtained with the dif-ferent methods as widely documented by several authors(Hendricks et al., 2006; Vogt et al., 1998), all of them con-tinue being used because of the lack of consensus about themost appropriate one to study the dynamics of fine roots.For these reasons, the combination of different methods usedhere seems to be a good strategy for the estimation of FRP.

5 Conclusions

Carbon allocation to production of fine roots fine roots wasdifferent between forest types. As expected in a gradient of

availability of soil resources, the clay loam soil forest, withless limitation in soil resources, showed a lower carbon allo-cation to production of fine roots than the loamy sand forestwhich had more limitations in soil resource availability. SFRand FRP also showed differences with soil depth, with highervalues in the first 10 cm than in the 10–20 cm layer of soil.

Temporal variation of SFR was correlated with mean dailyrainfall; however, this inverse relationship was between for-est types: in the clay loam soil forest SFR increased withthe increase of rainfall of the previous three months; in theloamy sand forest SFR decreased with the increase of rain-fall of the previous four months. Likewise, RGR of fine rootswere different before, during, and after the drought period.Both forest types showed lower RGR during drought, whichsuggests that severe changes of rainfall could strongly affectboth forest types.

In summary, from results shown here we can say that thisstudy: (1) that the amount and fraction of carbon allocatedto fine roots was different between plots, (2) that there aredifferences in NPP allocation at these plots, (3) suggests thatprobably Total NPP (above plus below-ground) was not verydifferent between the plots, and (4) a strong cautionary warn-ing is provided against assuming that patterns of total ecosys-tem NPP can be adequately understood/studied solely fromabove-ground NPP.

Finally, this study shows that variation in the functioningof Amazonian ecosystems at small spatial and time scales islarge; it also shows that both rainfall patterns and soils act indifferent ways on the carbon allocation to production of fineroots in these forests and that understanding how Amazonianecosystems can respond to these factors is fundamental con-sidering the events expected by climate change.

Acknowledgements.This paper is a product of the RAINFORnetwork and Pan-Amazonia project from University of Leeds.RAINFOR is currently supported by the Gordon and Betty MooreFoundation. This work was supported also by the Amazon Droughtproject of the University of Leeds, the Zafire Biological Stationand the project “Saber y Gestion Ambiental Amazonica” of theUniversidad Nacional de Colombia Sede Amazonia. We alsowish to thank to E.Alvarez, D. Navarrete and O. Phillips for theircontribution to this research, A. Prieto and A. Rudas who madeit possible to work in the plots in Amacayacu Natural Park andespecially C. A. Quesada who generously shared his informationon soils of the sites studied. We also thank to the indigenouscommunities of Palmeras and San Martın of Amacayacu and thestaff of the Amacayacu National Natural Park and the crew of theZafire Biological Station, especially Eufrasia Kuyuedo, ArcesioPijachi, Edilberto Rivero, Angel Pijachi, Ivan Nino, Ilmer Nino,Zulma Alban and Magnolia Restrepo, and the Universidad Na-cional de Colombia Sede Amazonia by permitting and facilitatingthe fieldwork. Finally, we thank to D. Metcalfe, S. Vasconcelosand P. Meir for valuable suggestions and comments to improve themanuscript.

Edited by: P. Meir



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Appendix 7-2 Contribution of this dissertation to other researches in the Amazon Basin.

1) ter Steege, H., Pitman, N.C.A., Sabatier, D., Baraloto, C., Salomão, R.P., Guevara, J.E.,

Phillips, O.L., Castilho, C.V., Magnusson, W.E., Molino, J.-F., Monteagudo, A.,

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Piedade, M.T., Araujo-Murakami, A., Arroyo, L., Gribel, R., Fine, P.V.A., Peres,

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T.W., Maas, P., Petronelli, P., Stropp, J., Zartman, C.E., Daly, D., Neill, D.,

Silveira, M., Paredes, M.R., Chave, J., Lima Filho, D.d.A., Jørgensen, P.M.,

Fuentes, A., Schöngart, J., Cornejo Valverde, F., Di Fiore, A., Jimenez, E.M.,

Peñuela Mora, M.C., Phillips, J.F., Rivas, G., van Andel, T.R., von Hildebrand, P.,

Hoffman, B., Zent, E.L., Malhi, Y., Prieto, A., Rudas, A., Ruschell, A.R., Silva,

N., Vos, V., Zent, S., Oliveira, A.A., Schutz, A.C., Gonzales, T., Trindade

Nascimento, M., Ramirez-Angulo, H., Sierra, R., Tirado, M., Umaña Medina,

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205

Lezama, A., Urrego Giraldo, L.E., Zagt, R., Alexiades, M.N., Hernandez, L.,

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A., Arroyo, L., Chao, K.J., Dezzeo, N., Erwin, T., di Fiore, A., Higuchi, N.,

Honorio Coronado, E., Jimenez, E.M., Killeen, T., Lezama, A.T., Lloyd, G.,

López-González, G., Luizão, F.J., Malhi, Y., Monteagudo, A., Neill, D.A., Núñez

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