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UTRECHT UNIVERSITY

Impacts of selective logging

on tree aboveground biomass and carbon stocks

in tropical forests, French Guiana

Edited by Alexandra Mitsiou, MSc student

Supervised by

Dr. Pita Verweij & Drs. ing. Vijko Lukkien,

Utrecht University

Partnership:

Utrecht University & Copernicus Institute

WWF-France

Trésor Foundation & Association Trésor

French agricultural research centre for development, in French “Centre de coopération Internationale

en Recherche Agronomique pour le Développement” (CIRAD)

French national forests office, in French “Office National des Forêts” (ONF)

Funding Institutions:

Van Eeden-fonds Foundation

KF Hein Foundation

Miquel Foundation

Alberta Mennega Foundation

Cover design and photo © 2011 Alexandra Mitsiou

Utrecht University, December 2011

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CONTACT LIST

Alexandra Mitsiou (Editor)

MSc student in Environmental Biology, track Ecology & Natural Resource Management,

Utrecht University

Telephone: +31.6.25.21.87.41

Email: a.mitsiou@students.uu.nl

Dr. Pita Verweij (Supervisor)

Research Coordinator at Copernicus Institute, Utrecht University

Telephone: +31.30.25.37.605

Email: p.a.verweij@uu.nl

Dr. Vijko Lukkien (Supervisor)

Research Coordinator & Project Manager Trésor Foundation

Department of Biology/ Trésor Foundation, Utrecht University

Telephone: +31.30.25.37.436

Email: v.p.a.lukkien@uu.nl

Trésor Foundation website: www.tresorrainforest.org

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PREFACE

This report is based on an MSc research project on Kaw Mountains in French Guiana conducted by

the author, Alexandra Mitsiou together with my fellow colleague Ariane Laport-Bisquit. The project

included 4 months field work (February-May 2011) and tree measurements in Trésor reserve and

forest sites under the management of the French national forests office (ONF). This study is a follow

up project, initiated in summer 2010 by two masters‟ students in Utrecht University, Anna Duden and

Ineke Roeling (Duden & Roeling, 2011). Certain data produced from the study of Duden & Roeling

(2011) were incorporated to be used as replicates in the data analysis for the present study. The aim

of the study is to investigate the impacts of selective logging on tree aboveground biomass and

carbon stocks in the tropical rainforest as well as investigate if there is any variation in tree

aboveground biomass among primary forest sites in French Guiana and what are the factors that

could explain this variation. This research project is connected to the voluntary carbon market

implementation under the REDD (Reduced Emissions from Deforestation and forest Degradation)

mechanism in French Guiana and contributes to carbon stock databases and forest inventories that

will set the foundations for the implementation of an REDD mechanism in French Guiana. In addition,

this study promotes the conservational importance of the French Guyanese region. Towards the same

direction are several research institutes in French Guiana that gave support for the accomplishment of

this project. Thus, an MOU agreement was signed among: WWF France (WWF-FR), the French

research institute for agricultural research for development (CIRAD), the French national forests

office (ONF), Utrecht University and in particular the Department of Environmental Biology, group

Ecology & Biodiversity and the Copernicus Institute, Trésor Natural Reserve Association (Association

Réserve Naturelle Régionale Trésor) and Trésor Foundation, the Netherlands. This agreement

solidified the cooperation and data exchange among participants for the coming four years 2010-2014

and opens opportunities for collaboration in the long term. This cooperation will contribute towards the

establishment of a research plan on carbon stocks for the Guianas region together with WWF-FR and

Utrecht University.

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ACKNOWLEDGEMENTS

This research project could not have been possible without the help and cooperation among different

people from organisations and institutes in French Guiana and the Netherlands. That is why I would

like to express my gratitude to a number of people who contributed in this research project

French Guiana

1. Trésor Association

I own a big thank to all people from Trésor Association, especially the conservator of Trésor

Association Hélène Guillen, for her help with practical matters and her hospitality and the two forest

guards, Benoît Villete and Jean-François Szpigel for all the enjoyable time they devoted to this project

and for all their help during field work. They shared their passion for nature and their knowledge which

added more to the educational part of this project but most important to the experience of the French

Guyanese rainforest. Moreover, I would like to thank the president of Trésor Association, Olivier

Tostain for his advice and support.

2. WWF-FR

I would like to thank WWF France (WWF-FR) and especially the Director of WWF-FR, Laurent Kelle

and his colleague Romain Taravella for the support and cooperation in the carbon research in the

French Guyanese rainforest. Moreover, I would like to thank them for their indirect contribution to this

project by financing the two forest guards of Trésor reserve; Benoît Villete and Jean-François Szpigel

who were indispensable during field work.

3. CIRAD and ECOFOG

The people from CIRAD and ECOFOG contributed substantially in the scientific part of this project by

providing help and advice with the methodology of data collection and data analysis. In particular, I

would like to express my gratitude to both Lilian Blanc and Christopher Baraloto for their constant

guidance during the project in French Guiana as well as for their data sharing. Moreover, I am very

grateful to the PhD student, Quentin Molto for his indispensable help with the data analysis and his

kind patience to share his knowledge on statistics and R program. Moreover, I would like to express

my gratitude to the two botanists Pascal Petronelli and Petrus Naisso for the time they spend with us

in the field and their valuable help with the taxonomic identification.

4. French national forests office (ONF)

The ONF office offered valuable information on the geology and topography of the Kaw Mountains

and contributed in one of the most important parts of the research which was the determination of

forest sampling sites. I would like to thank the responsible in the research and development

department of ONF in French Guiana, Stephan Guitet for his help and information sharing; and

Bernard Perrin for his time and help with the sampling sites determination.

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The Netherlands

1. Financial Support

This project could not have been feasible without the substantial financial support provided by different

funding organisations based in the Netherlands. In particular, I would like to acknowledge Van Eeden-

fonds Foundation, K.F.Hein Foundation, Alberta Mennega Foundation and Miquel Foundation for their

generous financial support that was enough to cover the travel expenses, equipment and subsistence

costs for the research in French Guiana.

2. Supervision

I would like to express my appreciation to my two supervisors from Utrecht University, Dr. Pita Verweij

and Drs.ing. Vijko Lukkien for all their support and guidance during this research project while in

French Guiana and in the Netherlands.

3. Support

Lastly, I owe a big thank you to the two graduate masters‟ students of Utrecht University, Anna Duden

and Ineke Roeling for all their help, data sharing and cooperation throughout the preparation and

realisation of this research project. Moreover, their advice on practical issues concerning the life in the

tropical forest as well as on methodology and data analysis was very important. Additionally, I would

like to thank Dr. Heinjo During from the Department of Plant Ecology in Utrecht University, for his help

and advice with the interpretation of the principal component analysis results.

Last but not least, I would like to express a big thank you to my family and all my friends for their

substantial support and patience. Moreover, I would like to thank my fellow colleague and friend

Ariane Laport-Bisquit for the excellent cooperation throughout this research project and for the great

time we had in the tropical forest of French Guiana.

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LIST OF CONTENTS

Preface 3

Acknowledgements 5

Acronyms 9

Summary 11

1. INTRODUCTION 13

1.1. Climate change and Kyoto protocol 13

1.2. From Kyoto protocol to REDD 13

1.3. REDD in Guiana Shield 14

1.4. REDD implementation 14

1.5. Sustainable forest management 15

1.6. Research topic 16

1.7. Study area 16

2. METHODOLOGY 21

2.1. Sampling sites selection 21

2.2. Sampling design 21

2.3. Tree allometry 23

2.4. Forest stand and climatic variables 23

2.5. Data analysis 24

2.6. Tree aboveground biomass recovery 26

2.7. Financial analysis 26

3. RESULTS 27

3.1. Overview of aboveground biomass 27

3.2. Regional variation in big tree aboveground biomass 28

3.3. Variables explaining tree aboveground biomass variation 31

3.4. Impact of selective logging 34

3.5. Forest recovery estimation 5 years after selective logging 38

3.6. Forest carbon finance vs. timber market 39

4. DISCUSSION 42

4.1. Regional variation in primary forest sites 42

4.2. Impact of selective logging on carbon stocks 42

4.3. Forest recovery estimation 43

4.4. Financial approach 43

4.5. Limitations-suggestions for improvement 44

4.6. Suggestions for future study 44

4.7. Conclusion 45

REFERENCES 46

APPENDICES 51

Appendix 1: Study area 51

Appendix 2: Details on sampling methods 53

Appendix 3: Formulas for tree aboveground biomass calculation 54

Appendix 4: Statistical analysis tables 55

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ACRONYMS

AAU Assigned amount units, referring to carbon emissions

AGB Aboveground biomass of living woody plants

ARNT Association réserve naturelle Trésor (Trésor natural reserve association)

BA Basal area

CER Certified emissions reduction

CIA Central intelligence agency

CIRAD Centre de coopération Internationale en recherche agronomique pour le

développement (French research institute for agricultural research for development)

CO2 Carbon dioxide

CO2e Carbon dioxide equivalent of emissions

DAGB Dead aboveground biomass, referring to the dead fallen trees biomass

DBH Diameter at breast height, referring to the diameter of each individual measured at

1.30m above the base of trunk.

DOM Département d‟Outre Mer (Overseas department)

DSI Dry season index

ECOFOG Ecologie des forêts de Guyane (Joint research unit ecology of Guiana forests)

EU European Union

FAO Food and agriculture organisation

FCU Forest carbon units

FSC Forest stewardship council

GHG Greenhouse gas

GOFC-GOLD Global observation of forest and land cover dynamics

ha hectare (1 ha=104 m

2)

IPCC Intergovernmental panel on climate change

ITTO International Tropical Timber Organisation

Mg Megagram aka metric ton (t) (1 Mg=106 g)

MRV Monitoring, reporting, verifying, referring to deforestation and forest degradation

monitoring

ONF Office national des forêts (French national forests office)

PCA Principle component analysis

PEFC Programme for the endorsement of forest certification

REDD Reduced emissions from deforestation and forest degradation mechanism

RIL Reduced-impact logging

RNF Réserves naturelles de France (Natural reserves of France)

RNR Réserve naturelle régionale (Regional natural reserve)

SFM Sustainable forest management

SD Stem density, referring to the number of stems/trees per hectare

ST Stump

UNEP United Nations environment programme

UNESCO United Nations educational, scientific and cultural organisation

UNFCCC United Nations framework convention on climate change

UTM Universal transverse Mercator

WCMC World conservation monitoring centre

WSG Wood specific gravity

WWF World wide fund for nature

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SUMMARY

Under the current pressure of climate change due to CO2 emissions that are highly probable to be

caused by human activity, different mechanisms and policies have been proposed in order to mitigate

this problem. One of these mechanisms is REDD (Reduced Emissions from Deforestation and forest

Degradation) that aims to the mitigation of CO2 emissions provoked by deforestation and forest

degradation mainly observed in the tropics. However, basic precondition for the application of this

mechanism is the existence of forest carbon inventories and monitoring processes of forest cover.

The broader aim of this study was to contribute to the database on carbon stocks from primary

forest sites and investigate the possibilities of an REDD mechanism application in French Guiana that

would be based on a non regulated carbon market as long as the regulated market is not applicable to

French Guiana (Annex I country). More specifically, this study aims at assessing the regional variation

in tree aboveground biomass and carbon stocks across primary terra firme forest sites in French

Guiana. Additionally, the study aims at assessing the impact of selective logging activity on tree

aboveground biomass and carbon stocks in recently selectively logged terra firme forest with a mean

logging intensity of 2-6 trees/ha. Moreover, an estimation of forest regeneration was done based on

the minimum timber volume extracted from the forest due to selective logging and regeneration rates

found in literature. Finally, an estimation of the financial benefits generated from timber market and the

REDD carbon market was realised.

The study was conducted on Kaw Mountains that are located at the coastal region of French

Guiana. More specifically, primary terra firme forest was sampled in Trésor reserve whereas for the

study of selective logging impact, ONF terra firme forest that had been selectively logged 5 years

before and ONF primary terra firme forest (control) were sampled. Moreover, data from Trinité

reserve, Nouragues reserve, Laussat conservation area and Regina primary terra firme forest sites in

French Guiana were used for the regional variation study (data acquired from Baraloto, C. & Blanc, L.,

2011). The sampling of forest sites was done using a modified Gentry plot of 0.5ha named “transect”

where diameter at breast height (DBH) and height measurements were taken of small (2.5 ≤ DBH <

10 cm) and big trees (DBH ≥ 10 cm) (Baraloto et al., 2010; 2011). Moreover, taxonomic identification

was done by experienced botanists assigned by CIRAD-EcoFog in order to use the wood specific

gravity of trees for tree aboveground biomass calculations (Zanne et al., 2009).

The main findings of this study can be summarised under three main points. There was no

significant regional variation in tree aboveground biomass (AGB) among the five primary terra firme

forest sites in French Guiana (Kaw Mountains, Trinité reserve, Nouragues reserve, Laussat

conservation area and Regina), even though there was high local variation in tree AGB in all forest

sites. Selective logging activity influenced tree AGB and carbon stocks, mainly of big trees (DBH ≥ 10

cm). In particular, the AGB of big trees in the selectively logged forest was 54 Mg/ha lower than in the

primary forest site. The forest structure analysis showed that the percentage of carbon present in the

class (DBH ≥ 70cm) was reduced by 5% as compared to the primary forest. On the other hand, the

carbon percentage within medium DBH classes of (40-70 cm) was found higher in the selectively

logged forest maybe due to regeneration (Blanc et al., 2009). However this increase could not

compensate for the AGB and carbon losses due to the removal of big trees. Estimates on forest

recovery showed that the tree AGB loss of 36.3 Mg and 13.6 Mg would recover by 14% and 38%

respectively within a period of 5 years after selective logging, indicating that forest recovery highly

depends on logging intensity. The lack of significance in all results apart from the forest structure

analysis showed that more replicates are needed to support the conclusions. Moreover, the results on

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forest regeneration represented only an estimate and no final conclusions could be drawn.

Additionally, more research on the environmental variables that affect tree AGB is needed.

From the forest carbon perspective, the mean tree aboveground carbon stock in the sampled

sites was 212.35 Mg/ha in Trésor reserve and 206.74 Mg/ha in the ONF primary forest site. On the

contrary, in the ONF forest site that had been selectively logged 5 years before the mean tree

aboveground carbon content was 181.03 Mg/ha. In terms of financial value when applying the REDD

finance, the primary terra firme forest in Trésor reserve would be worth €3179.63/ha ($4286.26/ha)

and the same forest type in ONF forest site would be worth €3095.66/ha ($4173.07/ha). On the

contrary, the ONF forest that had been selectively logged 5 years before would have a lower value of

€2710.67/ha ($3654.09/ha). Thus the preservation of these primary forest sites is important not only

for sustainable financial profits but also for biodiversity and habitat conservation. On the other hand,

timber market was estimated to be more profitable than the application of an REDD finance scheme

for the standing forest. In more detail, it was estimated that the 108.33 m3 standing timber extracted

from the ONF forest due to selective logging activity had generated an economic benefit of €1841.61

($1366.18) and €5416.5 ($4018.18) in the local timber market in French Guiana and open timber

market, respectively (CCIG, 2008; personal comm. Guitet, S., 2011). This profit can be generated

once every 65 years according to the cutting cycle currently applied in French Guiana (Blanc et al.,

2009; ONF, 2010). Alternatively, if this tree aboveground biomass was retained as standing forest

would have generated the financial profit of €524.61 ($707.19) assuming that the REDD carbon

finance would have been applied for 65 years (Watson, 2009; Diaz et al., 2011). However, taking into

consideration the co-benefits from forest conservation and the inevitable forest degradation due to

selective logging activities in the long term, it is concluded that measures over sustainable logging

practices would be an option to be soon taken into account in French Guiana in order to achieve both

habitat conservation and financial benefits from the primary forest sites when applying the REDD

carbon finance.

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1. INTRODUCTION

1.1 Climate change and Kyoto protocol

Climate change is one of the most pressing environmental concerns of the 21st century. It is widely

accepted that the release of greenhouse gases (GHG) (United Nations framework convention on

climate change [UNFCCC], 1998) into the atmosphere over the last decades due to human activity it

is highly probable to be the main driver to this phenomenon (Lashof & Ahuja, 1990; Schroeder, 1992;

Lasco & Cardinoza, 2005; Intergovernmental panel on climate change [IPCC], 2007). In order to set a

limit to the GHG emissions and mitigate climate change, the Kyoto protocol was introduced in 1998

(UNFCCC, 1998). This protocol set the financial incentives for the limitation of carbon dioxide (CO2)

emissions (Kyoto Protocol, Article 3) as it is considered the most responsible GHG for climate change

(Lashof & Ahuja, 1990; UNFCCC, 2010).

The Kyoto protocol proposes that the developed Annex I Parties1 (Developed nations and

nations with economies in transition i.e. Australia, Germany, France) will reward through carbon

credits, the Annex II Parties2 (Developing countries i.e. Brazil, Cameroon, Costa Rica) for the

reduction of GHG emissions (UNFCCC, 1998; World Bank, 2008; World Bank, 2011a). These carbon

credits (aka Kyoto Units) represent an emission reduction equal to one metric tonne of CO2 (or 1Mg

CO2) (UNFCCC, 1998). In particular, Kyoto protocol allows Annex I Parties to trade carbon credits

with other Parties in order to add or subtract emissions amounts from their initial assigned emissions

amount (assigned amount units [AAUs]), thus raise or lower the level of their allowed emissions over a

commitment period (UNFCCC, 2008). Overall, the Kyoto protocol in 1998 set the basis of a carbon

trade for the mitigation of climate change due to fossil fuel related carbon emissions (CO2 emissions

due to industry etc.). However, it does not take into account the carbon emissions resulting from other

anthropogenic activities, such as deforestation and forest degradation (Fearnside, 2001).

1.2. From Kyoto protocol to REDD

Forests play an important role in climate change mitigation (Food and agriculture organisation [FAO],

2010). They absorb carbon dioxide (CO2) and store in their biomass large amounts of carbon (FAO,

2010). Thus, when a forest is cut down on the one hand carbon is released back into the atmosphere

(i.e. trough decomposition or burning of the wood) and on the other hand CO2 absorption capacity is

decreased, resulting in a total CO2 emission due to deforestation and forest degradation (Verweij et

al., 2009). The most important terrestrial carbon sinks are tropical forests, which cover almost the 17%

of the earth‟s land surface (Olson et al., 1983; Buchmann, 1997; IPCC, 2007), representing a

considerable terrestrial carbon sink. For example, the Brazilian rainforest alone accounts for almost

the 15% of all terrestrial carbon (Keller et al., 1997; Houghton et al., 2001; Verweij et al., 2009). Thus,

deforestation and forest degradation in the tropics have been estimated to account for approximately

20% of total global carbon emissions annually and therefore represent one of the major human

activities responsible for contributing to climate change (Fearnside, 2000; Malhi & Grace, 2000; Rudel,

2001; DeFries et al., 2002; Fearnside & Laurance, 2003, 2004; Asner et al., 2005; Houghton, 2005;

Gibbs et al., 2007; Holmgren et al., 2007; Angelsen, 2008; Olander et al., 2008).

1 The complete list of Annex I Parties can be consulted on UNFCCC website:

http://unfccc.int/parties_and_observers/parties/annex_i/items/2774.php

2 The complete list of Annex II Parties can be consulted on UNFCCC website:

http://unfccc.int/parties_and_observers/parties/non_annex_i/items/2833.php

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Consequently, the United Nations framework convention on climate change (UNFCCC) in

2005, initiated international discussions to introduce financial incentives for the reduction of carbon

emissions due to deforestation and forest degradation (REDD) (DeFries et al., 2002; Buchmann,

1997; UNFCCC, 2011a). The REDD mechanism is focusing mostly on Annex II Parties as they own

the largest part of the tropical forests globally (tropical forest countries). The high poverty levels in

these countries and the non-sustainable land use practices (uncontrollable logging, mining, etc.) are

posing a great risk to the forest (Guiana Shield Initiative, 2011). In fact, the REDD mechanism initiates

a different type of carbon market based on forest carbon units (FCUs) where the developing countries

(Annex II Parties) can exchange their (FCUs) to receive financial benefits and invest on human

development, improvement of living standards and development towards more sustainable economies

(UNFCCC, 1998; Gibbs et al., 2007; Angelsen, 2008; Verweij et al., 2009; United Nations environment

programme [UNEP], 2010; World Bank, 2011b). In turn, the Annex I Parties (developed countries) will

need these credits to compensate for their carbon emissions that cannot be reduced in their country

(Verweij et al., 2009). On the whole, a REDD mechanism apart from contributing to human

development and poverty alleviation, would enable the preservation of tropical forest carbon sinks as

well as the diversity of tropical fauna and flora related to these forests (Verweij et al., 2009; UNEP,

2010).

1.3. REDD in Guiana Shield

The Annex I countries cannot be credited for the future regulated REDD carbon market described

above. Such an example is French Guiana, an overseas department of France (Annex I country)

where the present study was conducted. Alternatively, these counties can take part in a voluntary

REDD carbon market. Public and private organisations, companies or industries etc. provide financial

support for the voluntary market in order to mitigate their CO2 emissions (Verweij et al., 2009; Peters-

Stanley et al., 2011; World Bank, 2011c). Moreover, possibilities for the development of a REDD type

of mechanism within the European Union that will be applicable to the overseas departments of EU

countries is currently being under discussion (personal comm. Verweij, P. & Lukkien, V., 2011). This

creates another opportunity for the application of an REDD type mechanism in French Guiana. The

application of an REDD voluntary carbon market or an EU REDD mechanism in French Guiana as

well as in the Guiana Shield could contribute towards the mitigation of logging activity and the

development of more sustainable logging practices that have the minimum effect on carbon stocks

(Mazzei et al., 2010). In addition, this mechanism is expected to provide the incentives for biodiversity

conservation in tropical rainforest (Verweij et al., 2009).

1.4. REDD implementation

The implementation of such a mechanism requires scientific knowledge and decisions based on

carbon inventories of the tropical forest countries. Forest carbon inventories will serve as a reference

to monitor the forest cover changes in the future (Intergovernmental panel on climate change [IPCC],

2003, 2006; Wertz-Kanounnikoff et al., 2008; Global observation of forest and land cover dynamics

[GOFC-GOLD], 2009). According to IPCC (2003) figures on five carbon pools are needed to estimate

the emissions from deforestation and forest degradation. These are the aboveground biomass (AGB),

below ground biomass, dead wood or dead aboveground biomass (DAGB), litter and soil organic

carbon (Wertz-Kanounnikoff et al., 2008). Although logging can result in significant emissions from

litter or dead wood, the most practical way to assess carbon emissions due to deforestation and forest

degradation is the monitoring of the aboveground biomass changes (IPCC, 2003; Wertz-Kanounnikoff

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et al., 2008). However, this results in a minimum estimation of the carbon stocks in a forest area

(“conservative” estimate).

Monitoring, reporting and verifying (MRV) deforestation and forest degradation requires a

combination of field based carbon stock measurements (forest carbon inventories) and remote

sensing data (DeFries et al., 2006, 2007; IPCC, 2006; Gibbs et al., 2007; Olander et al., 2008; Wertz-

Kanounnikoff et al., 2008; Pluggle et al., 2010; UNFCCC, 2011b). The methodology to create forest

carbon inventories is classified in Tiers which are levels of increasing accuracy from Tier 1to Tier 3.

The classification is done according to the accuracy of tools (i.e. equations, parameter values, remote

sensing maps etc.) and the level of analytical complexity used for the creation of the inventory (IPCC,

2003, 2006; Penman et al., 2003; Wertz-Kanounnikoff et al., 2008; GOFC-GOLD, 2009). The more

accurate the forest inventory is (i.e. Tier 3), the more likely to be credited for the REDD. This

classification underlines the importance of a high quality and detailed forest carbon inventories that

meet the requirements for forest carbon reporting at a global scale (Holmgren et al., 2007; Olander et

al., 2008).

1.5. Sustainable forest management

The deforestation intensity may vary from clear-cut deforestation (i.e. Brazilian Amazon and

Indonesia) to controlled deforestation through sustainable logging practices that can be described as

forest degradation. The sustainable forest management (SFM) is the use of forests in a way and

intensity that preserves their biodiversity, productivity, regeneration capacity and vitality in the long

term (FAO, 2000). In other words, the SFM aims at the balance between the increasing demand for

forest products and the preservation of forests integrity and biodiversity. As a result, SFM is important

for the carbon stocks preservation and thus critical for the application of the REDD mechanism.

It has been shown that the conventional logging practices are unfavourable for carbon

retention in the forest after logging (Putz, 1993, 2008; Healey et al., 2000). Thus, reduced-impact

logging (RIL) techniques have been developed in order to preserve the forest, soil and wildlife

whereas at the same time serve the demand for tropical timber (Tropical Forest Foundation, 2009).

Reduced-impact logging is a tool for sustainable forest management and includes a set of carefully

planned timber harvesting operations to mitigate the environmental impact such as minimise collateral

damage to non-harvest trees, protection of streams, soil and wildlife (Tropical Forest Foundation,

2009; International tropical timber organisation [ITTO], 2011). Some of the management activities that

should be applied to ensure a reduced-impact logging practice are the following (ITTO, 2011): (1) Pre-

harvest inventory and mapping of individual crop trees, (2) Pre-harvesting planning of roads and trails

to minimize soil disturbance (Putz & Pinard, 1993), (3) Establishment of stream buffer zones and

watershed protection areas to prevent water ways pollution, (4) Pre-harvest vine cutting if heavy vines

connect tree crowns (Vidal et al., 1997), (5) Use of appropriate felling techniques (i.e. directional

felling, cutting stumps low to the ground) to reduce waste (Putz & Pinard, 1993; Feldpausch et al.,

2005), (6) Use of improved technologies to reduce soil and nearby vegetation damage caused by the

log extraction, (7) Conduct post-harvest assessment and evaluate the degree to which the RIL

guidelines were successfully applied.

The impact of logging on forest aboveground biomass and regeneration rate after logging

varies and depends on the forest management scheme applied as well as the extent to which these

RIL rules are being applied successfully. According to Boltz et al. (2001) when the timber volume of

19.74 m3/ha was extracted from the Brazilian Amazon forest through RIL and applying all the

necessary management practices above mentioned, the post-logging growth rate would vary between

0.01 cm/year for the first year after logging and 1.39 cm/year as shown by the dynamic model the

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researchers developed. A more recent study by Sist and Ferreira (2007) showed that the RIL

practices in the Eastern Amazon were not sustainable. In details, the logging intensity of 6 trees/ha

accounting for 21 m3/ha of timber combined with the post-logging growth rate of 0.05 cm/year and the

cutting cycle of only 30 years were not compatible to sustainable forest management. Under this

felling regime, only silvicultural treatment, growth rate of 0.04-0.05 cm/year and felling cycle of 40

years could ensure a logging intensity of 3-4 trees/ha (10-13 m3/ha of timber) 40 years after logging

(Sist & Ferreira, 2007). Thus, it is clear that attention should be given when referring to reduced-

impact logging practices and extensive post-logging study should be done in order to prove whether a

forest management scheme can be sustainable in the long term.

1.6. Research topic

Objective

The broader aim of this study was to contribute to the database on carbon stocks from primary forest

sites and investigate the possibilities of a REDD mechanism application in French Guiana that would

be based on a non regulated carbon market as long as the regulated market is not applicable to

French Guiana (Annex I country). More specifically, this study aims at assessing the regional variation

in tree aboveground biomass and carbon stocks across primary terra firme forest sites in French

Guiana. Moreover, this study aims at assessing the effect of low-impact logging activity on tree

aboveground biomass and carbon stocks in recently logged terra firme forest.

Research Questions

1. Is there any regional variation in tree aboveground biomass when comparing primary terra firme

forest in French Guiana?

2. What is the effect of low-impact logging practices on forest structure and carbon stocks in terra

firme forest that had been recently logged?

Hypotheses

1. Previous studies done by Baraloto et al. (2010, 2011) have shown that there is no significant

variation in tree aboveground biomass (AGB) and carbon content among terra firme forest from

different geographic regions. Thus it is expected that there will be no significant variation in tree AGB

when comparing primary terra firme forest sites in French Guiana.

2. It is assumed that the selectively logged forest will contain less carbon and more small trees (2.5 ≤

DBH <10 cm) than the primary forest site. Thus, it is supposed that 5 years are not enough for the

recovery of carbon stocks in the forest. Even though, more small trees will grow in the forest after low-

impact logging, they cannot compensate for the carbon lost from the big trees (DBH > 10 cm) removal

according to previous studies (Mazzei et al., 2010; Rutishauser et al., 2010).

1.7. Study area

To investigate the above questions, study was conducted in French Guiana and the sampling areas

included terra firme forest sites in Trésor reserve and the French national forests office (ONF) forest

both located on Kaw Mountain in the north-east of French Guiana (Appendix1).

French Guiana

French Guiana (French: Guyane française or officially: Guyane) is an overseas department of France

(French: Département d‟Outre Mer or DOM) located at the northern Atlantic coast of South America at

17

geographical coordinates 4.00N and 53.00W (Central intelligence agency [CIA], 2011). French Guiana

borders with Brazil at the East and Suriname at the West with two natural boarders (Oyapock River

and Maroni River, respectively). At the South, the county is covered with dense forest that can only be

accessed by rivers and streams from south to north. French Guiana is part of the Guiana Shield, one

of the three cratons of the South America plate (Goodwin, 1996). The geological term “shield” implies

a large area of exposed old Precambian rock basement (2.1 billion years old) that is not subject to

large extent changes due to modern geological activity (tectonic or volcanic) (Press & Siever, 1982;

Gibbs & Barron, 1993; Goodwin, 1996; Hammond, 2005). This profile describes the geology of Kaw

Mountains that has an important role in forest structure and forest dynamics on the Mountains.

Tropical forest in French Guiana

Due to this geological background, Guiana Shield and thus French Guiana, is characterised by poor

soils (Ter Steege, 2000; Olson et al., 2001; Hammond, 2005). However, it supports a vast surface of

continuous primary tropical forest in the world with particularly high biodiversity (Guiana Shield

Initiative, 2011). French Guiana is first on the list for its forest cover per land area, with its tropical

forest accounting for the 98% of the total land area, according to the Global Forest Resources

Assessment main report of 2010 (FAO, 2010). The most interesting is that its tropical forest is mostly

undisturbed. The primary forest in French Guiana covers a vast area of 7.690 million hectares that

represents the 95% of the total forest cover in the country (8.082 million hectares), according to the

same report (FAO, 2010). Unfortunately, the high poverty levels of a large part of the population are

leading to uncontrollable and non-sustainable activities such as illegal logging and gold mining that

threaten the ecosystem with degradation (Guiana Shield Initiative, 2011). Thus, forest monitoring and

sustainable forest management plan are crucial for the tropical forest conservation in the region.

Kaw Mountains

Kaw Mountains are located at the coastal region of French Guiana, south east of the capital of

Cayenne (Appendix 1) between the communities of Roura and Kaw (Trésor Foundation, 2011). Its

total length is 121 km and its altitude ranges from 2 m (“Marais de Kaw”) to 390 m (“Camp Caiman”)

covering 19,860 hectares of mixed tropical forest and a variety of bio-habitats (ONF, 1994).

Geology and soils

Kaw Mountains are characterised for their lateritic geological profile and poor soils (ONF, 1994). The

90% of the Mountains surface is laterite that can reach 20 m depth (ONF, 1994). Especially, on the

top of the Kaw Mountains regosols are found (Ek et al., 2000). Regosols are soils of unconsolidated

parent material on which not significant soil forming processes have been taken place due to dry or

cold climatic conditions (FAO-UNESCO, 1981). Due to this thin soil profile, during the rainy season the

soil is getting quickly inundated, whereas during the dry season it dries due to its low water capacity.

In addition, the low depth of this type of soil prevents trees over 60 cm of diameter at breast height

(DBH) to develop an adequate rooting system.

Climate

Kaw Mountains are situated in one of the most humid regions in French Guiana (Appendix 1). The

mean annual precipitation on Kaw Mountains is estimated to be around 4000 mm yr -1

(Météo-France

& CIRAD, 2010), whereas in other regions in French Guiana (i.e. Laussat Conservation Area) the

mean annual precipitation is almost the half (~ 2400 mm yr -1

) (Météo-France & CIRAD, 2010). The

combination of thin soils (inadequate rooting system for big trees) and high precipitation values results

18

in a lot of fallen trees and thus canopy gaps (French: “chablies”) after the rain season (ONF, 1994).

This phenomenon influences the forest structure on the Mountains.

Biodiversity

Kaw Mountains exhibit variation in biotopes from terra firme forest on the top of the Mountains, to

swamp forest at the “Marais de Kaw” (ONF, 1994). The terra firme forest is characterised by an almost

continuous canopy that is composed by high trees of all diameter classes (ONF, 1994). Overall, the

particular geological profile makes Kaw Mountains interesting not only for floristics (i.e. high diversity

in plant species) but also for the diversity of animal species (i.e. the bird Rupicola rupicola or common

name: Cock of the rock) (ONF, 1994). In addition, Kaw Mountains holds a great variety of endemic

species from amphibians and reptiles to birds and mammals as well as insects. Thus, it is considered

as a high priority area for nature conservation (ONF, 1994).

Anthropogenic impacts on Kaw Mountains

The greatest area of Kaw Mountains is covered by primary forest; however certain parts of the forest

have been submitted to exploitation. In the past, gold mining activities affected with deforestation an

extended forest area where today is located the Camp Caiman for ecotourism. In addition, the

construction of Kaw road (Departmental n° 6 road, D6) destructed an extended area of primary forest.

The road is located on the top of Kaw Mountains, covering the distance from Roura to “Marais de

Kaw”. The construction of Kaw road created an easy access to the forest not only for ecotourism but

also for exploitation. Nowadays, the most important threats for the tropical forest on the Mountains

are: illegal logging activity in certain areas, illegal gold mining and hunting. In addition, the French

national forests office (ONF) is responsible for the low-impact legal logging activity in specific forest

sites on the Mountains. Almost all timber extracted is directed to the sawmill industry and use within

the French Guianese borders (CCIG, 2008). Timber exportation to Europe and Antilles is limited due

to the competition with the Brazilian timber and the lack of infrastructure.

Trésor Regional Natural Reserve (RNR Trésor)

Trésor reserve is located on the North side slope of Kaw Mountains, occupying a 2640 hectares area

of tropical forest (Trésor Foundation, 2011). It is bordered by Kaw road at the Northeast and Oparu

River at the Southwest (Trésor Foundation, 2006). Terra firme forest covers a large surface of the

slopes in the reserve, whereas at the plain that is flooded during the rainy season, there are wet

savannas and swamp forest (Trésor Foundation, 2006). The reserve contains a large variety of

habitats with high biological diversity of tropical fauna and flora. In particular, the reserve is divided in

7 different biotopes that are basically all in primary state, a fact that classifies the reserve in a high

priority for conservation. The variety in biotopes creates a diverse environment favourable to host a

great variety of animals. In numbers, the Trésor reserve represents only a small area of the total land

surface of French Guiana. However, 22% of plants, 45% of amphibians, 37% of reptiles, 45% of birds,

60% of mammals and 59% of bat species in French Guiana can be found in this area (Figure 1). The

ownership, management and preservation of the reserve are controlled by the Trésor Foundation,

founded in 1995. In 2010, Trésor reserve was classified as a nature reserve of France and it was

given the status of “Réserve Naturelle Régionale Trésor” (RNR Trésor or RNR124) (Trésor

Foundation, 2010; RNF, 2011). The new status made official the nature preservation in the particular

tropical forest area and activities such as logging, mining and hunting were banned from the region

(Trésor Foundation, 2010).

19

Figure 1. Location map of Trésor reserve and its biodiversity © 2010 Trésor Foundation (Trésor

Foundation, 2011).

ONF forest

The French national forests office (ONF) is responsible for the forest management in French Guiana

(Journal officielle de la gouvernement française, 1967). On Kaw Mountains, the forest area that is

managed by the ONF accounts for 19.860 ha and is divided into 44 parcels (KAW1-KAW44) (Figure

2). Even though the forest on Kaw Mountains is situated close to the main infrastructure axes of the

country and to principal population centres with agricultural, industrial and urban activities, the largest

part of the ONF forest is protected for its ecological value and remains unexploited (ONF primary

forest) (ONF, 2011a). The protection series include parcel KAW6, 23, 24, 27, 30, 37-44 accounting for

the 55% of the total ONF forest area on Kaw Mountains. Thought there are some parts of the forest

that either were exploited 30 years before or data are not clear on whether there had been exploitation

(i.e. parcel KAW5) and thus they are also considered as primary forest by the ONF. The rest forest

parcels that compose the production series are submitted to low-impact logging activity. According to

the year of exploitation the parcels can be distinguished in parcels where selective logging activity

occurred 5 years before (i.e. parcel KAW2), 10 years before etc. The certification schemes that have

been adopted by the ONF for these activities are ISO 9001 and ISO 14001, although ONF aims to

achieve the PEFC certification standards in the future (ONF, 2007).

ONF forest under exploitation

The management of the ONF forest under exploitation is concentrated on the monitoring of the main

and secondary roads that lead to the forest, control of the users and set of barriers to the forest.

Moreover, ONF takes into account the importance of detailed inventories for the forest resources

mapping (ONF, 1994; Guitet, 2005; Guitet et al., 2007). Additionally, ONF applies the practice of

“undesirable” tree species removal to promote the growth of commercial species, although this

forestry method can seriously affect carbon stocks and biodiversity. The forest sites under exploitation

are divided into parcels and forest inventories indicating the availability of commercial tree species in

each parcel, are available. The division of forest area into parcels facilitates the control of timber

volumes extracted from the forest under exploitation (ONF, 1994). Before 1995, data on the exact

timber volumes extracted from the forest as well as the geographical position of the felled trees are

inadequate. However, since 1995 considerable attention has been given on keeping track of the

amount of timber extracted from each of the parcels. The period 1986-1994, forest parcels KAW1-

20

KAW39 (totalling 13.400 ha of forest) were being exploited by the ZWAHLEN industry (permit no

1/ROURA, 28 August 1986) and for the period 1995-1998 only the forest within the zone of parcels

KAW4, 5, 7 and 17, 20 and 21 was being exploited under the “Convention of management and

exploitation” whose beneficiary was again the ZWAHLEN industry (ONF, 1994). Since 1998, the

logging activity depends on the customer‟s demand for particular tree species and quantity and thus

timber is sold in a form of private sale or supply contracts (ONF, 1994). Moreover, considerable

attention has been given on the application of reduced-impact logging methods for timber extraction.

In details, ONF has recognised the importance of appropriate felling techniques such as directional

logging, the necessity of pre-harvesting planning of roads and trails to minimize the disturbance of the

delicate poor soils in the Guianese region and the use of GPS to monitor the position of felled trees as

well as of the trails in the forest (Guitet, 2005). Additionally, ONF underlined that the collaboration of

all stakeholders involved in the exploitation procedure was very important as well as the post-harvest

monitoring of the forest cover via remote sensing technology is crucial for the estimation of

environmental impact of the logging methods applied (Guitet, 2005; Guitet et al., 2007).

The annual timber extraction volume since 2007 has been 25 m3/ha, although the average

timber extraction volume over the last 10 years has been 13-14 m3/ha (Guitet, 2005; ONF, 2010). The

majority of timber extracted (60%) is comprised by Dicorynia guianensis (aka Angelique) and Qualea

rosea (aka Gonfolo) (ONF, 1994). Once a parcel is selectively logged then the forest should be remain

untouched for 65 years (Blanc et al., 2009; ONF, 2010). Thus in the production series a part of the

parcels is being exploited and another part contains forest parcels that have been left untouched to

recover. The exploitation criterion is the tree diameter that should be over 55 cm rendering the trees

with DBH > 70 cm the most attractive target for selective logging practices (ONF, 1994; Guitet, 2005).

Figure 2. Map of the Kaw Mountains where the forest parcels KAW1-KAW44 are located. These parcels have been set by the French national forests office (ONF) with the scope to facilitate and make more efficient the forest management in the region (© 2011 ONF). Source: ONF, 2011b

21

2. METHODOLOGY

2.1. Sampling sites selection

The criteria used for the sampling sites selection were: 1. Lateritic geological profile. 2. Similar forest

density. 3. Similar forest structure (i.e. number of lianas, small trees etc.). 4. Same forest type (terra

firme forest). 5. No steep slopes. This was done to produce comparable results among forest sites.

The tools used for this selection were: geological information maps, aerial photos, satellite images of

the three forest sites (ONF database 2011) as well as in situ identification of the sampling sites. The

slope was estimated using Garmin GPSMAP® 60CSx (© Garmin technologies). Moreover, a minimum

distance of at least 300 m between replicates was selected.

2.2. Sampling design

The sampling was done between March and April 2011. Terra firme forest was sampled from three

forest sites: Trésor reserve, ONF primary forest and ONF forest selectively logged 5 years before

(ONF logged). The sampling unit used was a modified Gentry plot of 0.5 ha (Baraloto et al., 2010)

called “transect” (Appendix 2). In Trésor reserve, transect T6 was settled as a replicate to T1 and T2

(Duden & Roeling, 2011) (Table 1). In ONF primary forest transects T10 and T11 were settled as

replicates to transect T5 (Duden & Roeling, 2011) (Table 1). In this study and the one of Duden &

Roeling (2011), similar methodology for the aboveground biomass inventories in French Guiana was

used, proposed by Baraloto C. and Blanc L. (Baraloto et al., 2010, 2011). For the regional variation

study, data from terra firme primary forest sites in Laussat conservation area in the northwest, Trinité

reserve in the southwest, Nouragues reserve in the southeast and Regina in the northeast of French

Guiana (Appendix 1) were used (data acquired from Baraloto, C. & Blanc, L., 2011) (Table 2).

For the study of low-impact logging effects on tree aboveground biomass (AGB) and carbon

stocks, the sampling was done in the ONF parcel 2 (KAW2) where selective logging had occurred 5

years before. According to the ONF management report, this parcel had been selectively logged in

2006 when 4 m3/ha of timber (2.4 Mg/ha tree AGB) was removed from the forest as well as in 2007

when 3.8 m3/ha of timber (2.28 Mg/ha tree AGB) was removed from the forest, totalling 7.8 m

3/ha of

timber (4.68 Mg/ha tree AGB) for both years. It is important to note that the majority of timber included

big trees of DBH > 60 cm (ONF, 2011b). Comparing this volume with the mean volume of timber

extracted from the 15 parcels the period 1995-2009, which was 7.5 m3/ha of timber, parcel 2 had been

submitted to a considerable logging intensity. Since then, the forest has been left undisturbed for the

rest 65 years in order to recover (Blanc et al., 2009). For the sampling of this forest site, transects T7,

T8 and T9 were settled (Table 1). The replicates were separated by at least 300m between any two

transects. The UTM points of each transect were acquired using Garmin GPSMAP® 60CSx (©

Garmin technologies) and maps showing the place of transects were created (Figure 3).

22

Figure 3. Location of transects in Trésor reserve and ONF forest site on Kaw Mountains (© 2011 ONF). Blue line: transects in Trésor reserve, red line: transects in ONF forest that had been selectively logged 5 years before and purple line: transects in ONF primary forest.

Table 1. Sampling sites overview. Trésor reserve (n=3), ONF primary forest (n=3) and ONF forest that had been selectively logged 5 years before (n=3). Trésor reserve and ONF primary forest sites represented the Kaw Mountains region in the regional variation study whereas the ONF logged site together with ONF primary forest (control) were used for the impact study of selective logging.

Kaw Mountains

Transect Location Reference

T1 Trésor reserve Duden & Roeling, 2011; Mitsiou & Laporte-Bisquit, 2011

T2 Trésor reserve Duden & Roeling, 2011

T6 Trésor reserve Mitsiou & Laporte-Bisquit, 2011

T5 ONF primary forest Duden & Roeling, 2011

T10 ONF primary forest Mitsiou & Laporte-Bisquit, 2011

T11 ONF primary forest Mitsiou & Laporte-Bisquit, 2011

T7 ONF selectively logged 5 years before Mitsiou & Laporte-Bisquit, 2011

T8 ONF selectively logged 5 years before Mitsiou & Laporte-Bisquit, 2011

T9 ONF selectively logged 5 years before Mitsiou & Laporte-Bisquit, 2011

23

Table 2. Overview of data collection used for the regional variation study. For the location of these forest sites refer to Appendix 1.

French Guiana

Size of sample (n) Location Reference

6 Kaw Duden & Roeling, 2011; Mitsiou & Laporte-Bisquit, 2011

6 Regina Baraloto et al. 2011

7 Laussat Baraloto et al. 2011

7 Nouragues Baraloto et al. 2011

6 Trinité Baraloto et al. 2011

2.3. Tree allometry

Within each subplot the stems were mapped taking coordinates information and the diameter was

measured at 1.30 m height from the ground (diameter at breast height [DBH]) (Hughes et al., 2000;

Baraloto et al., 2011). For stems with irregularities, buttressed roots or prop roots, the stem diameter

was measured at a higher point above irregularities (Hughes et al., 2000; Baraloto et al., 2011) and

the point of measurement was marked with paint. If it was impossible to reach a point above

irregularities, the diameter was estimated by two trained persons.

For the aboveground biomass (AGB) study of big trees, all individuals with stem diameter at

breast height (DBH) ≥ 10 cm (Hughes et al., 2000) and rooted within 10 10x50 m subplots, totalling a

surface of 0.5ha forest area, were included (Baraloto et al., 2010; Baraloto et al., 2011). The

individuals were considered inside the subplot if the centre of their trunk base lay within the

boundaries of the subplot (Boyle, 1996). Trees having more than one stems, were considered as the

same individual, however, the diameter and height of each stem were measured separately. Tree

height was measured using a laser hypsometer. All trees of DBH ≥10 cm were given a code and

labelled using Simplex Blanche ® labels (© Signe Nature). The code was a triple name containing

information on transect, subplot and tree number (i.e.T6A11: transect T6, subplot A, tree number 11).

For the AGB study of small trees, all individuals of 2.5 ≤ DBH <10 cm encountered within 1m

on either side of the middle-line of the subplot, totalling a 10 2x50 m subplots of 0.1ha forest area,

were included (Baraloto et al., 2010; Baraloto et al., 2011). For the lianas biomass study, the DBH

measurement was also done at 1.30 cm, although the height was not measured. For the dead

aboveground biomass (DAGB) study, all dead fallen (DF) and dead standing (DS) trees of stem

diameter at breast height (DBH) ≥ 10cm found within 10 10x50 m subplots, were included. The height

of the dead standing or alternatively the length of the dead fallen trees was also measured.

Additionally, the number of stumps (ST) found within the 10 10x50 m subplots of transects in the

selectively logged forest sites was calculated for an estimation of the logging intensity.

2.4. Forest stand and climatic variables

The forest stand variables involve tree size (DBH and height), wood specific gravity (WSG), basal

area (BA) and stem density (Baraloto et al., 2011). The basal area was calculated per hectare for all

trees (small and big) using the common equation BA=pi * DBH2/4. The stem density was calculated as

the count of trees per hectare. Mean annual rainfall and dry season index (DSI) (Baraloto et al., 2011)

values for the five primary forest sites in French Guiana were used in order to examine the correlation

of these climatic variables with the aboveground tree biomass variation in the primary forest sites

(Table 3). The mean annual rainfall represents a calendar year average of precipitation and the DSI a

mean value of the dry season length (Baraloto at al., 2011). The dry season length is the maximum

24

number of successive days that receive less than 10 mm of precipitation (Baraloto et al., 2011). The

climatic data for all sites were acquired from CIRAD and in particular the data for Kaw Mountains

originate from Camp-Caiman meteorological station located on Kaw Mountains (Météo-France &

CIRAD, 2010).

Table 3. Mean annual rainfall and mean dry season length in the five primary forest locations studied in French Guiana. Data by: Baraloto, 2011; Météo-France & CIRAD, 2010

Location Rainfall (mm/year) DSI (days/year)

Kaw 4075 25.70

Regina 4421 23.90

Laussat 2471 36.80

Nouragues 3472 25.45

Trinité 2584 23.70

2.5. Data analysis

The largest part of data analysis was done in the R environment for statistical computing, version

2.13.0 (R Core Development Team, 2011, Vienna), using FactoMineR package for multivariate

analysis (Le et al., 2008), vegan 1.17-10 for statistics in Ecology and multcomp packages.

Additionally, SPSS® Statistics 18 and Microsoft Excel 2007 for Windows were used.

Aboveground biomass calculation

Aboveground biomass of living trees

The living tree aboveground biomass (AGB) was calculated using the equation for living trees

proposed by Baraloto et al. (2010). This equation is based on Chave et al. (2005) equation that

includes Diameter at Breast Height (DBH) in centimetres (cm), height in meters (m) and wood specific

gravity (WSG) in g/cm3 per binomial (see Wood Density): AGB = 0.0509 * (WSG) * (DBH)

2 * (height)

The AGB values from this equation are calculated in kg/0.5ha and then converted to Mg/ha. This is

the formula and parameters used for the final results and conclusions of this study. However, AGB

was also calculated using the same formula as a basis and changing parameters such as height and

wood density to check for differences in the final result (Appendix 3).

Wood density

The tree individuals identified at a species level were assigned with their wood specific gravity (WSG)

value (Chave et al., 2009) derived from the Global Wood Density Database (Zanne et al., 2009). For

the unknown individuals, the mean wood density of the WSG values in the database was assigned

(mean wood density = 0.66 g/cm3). The tree species identification was done in situ by experienced

botanists appointed by the French research institute for agricultural research for development

(CIRAD). Herbarium vouchers were collected for the unknown species encountered and the species

determination is still underway. It is important to note that the actual identified trees account for less

than 40% of the total individuals.

Aboveground biomass of lianas

The aboveground biomass for lianas was calculated in Mg/ha using the equation for lianas proposed

by Schnitzer et al. (2006): AGB = exp * [-1.484 + 2.657 * ln (DBH)]

25

Aboveground biomass of dead trees (DAGB)

The aboveground biomass of dead trees was calculated using the equation for dead trees proposed

by Baker et al., 2005: DAGB = [0.0509 * (0.4 * (DBH) 2 * (height)] * 0.5

The factor of 0.5 is the percentage of dead wood that it is estimated to remain after decomposition 5

years after logging had occurred (Baker et al., 2005).

Carbon content

The carbon content was calculated in Mg/ha, using the fraction of tree aboveground biomass for

tropical and subtropical forests proposed by IPCC Guidelines for National Greenhouse Gas

Inventories (2006). For big trees (DBH ≥ 10 cm) the fraction 0.49 was used (Hughes et al., 2000)

whereas for small trees (2.5 ≤ DBH <10 cm) the fraction 0.46 was used (Hughes et al., 2000).

Statistical analysis

The normality of data distribution was checked in both SPSS and R environment for statistics (R Core

Development Team, 2009). The Kolmogorov-Smirnov and Shapiro-Wilk tests were conducted in

SPSS to check if the data were distributed normally. The tests showed that the data were not

distributed normally. However, this is the limitation with large data sets where small deviations from

normality can lead to significant results concerning the difference of data sample from a normal

distribution. That is why in this case plotting of the data was important. Boxplots were created in the R

environment, to illustrate the distribution of the data in the samples from Kaw Mountains‟ sampling

sites: ONF primary, ONF selectively logged forest and Trésor reserve. Additionally, the distribution

was checked in the data set for the regional variation study that contained the primary terra firme

forest sites data from Kaw Mountains, Laussat conservation area, Trinité reserve, Nouragues reserve

and Regina (Baraloto et al., 2011). As long as the data distribution in the boxplots was normal,

analysis of variance ANOVA was conducted in the R environment to compare the mean tree

aboveground biomass (AGB) values from ONF primary and ONF selectively logged forest. The same

test was used to compare the mean tree AGB values among primary forest sites in French Guiana. In

addition “Tukey” post hoc test was conducted to compare pairwise all the different combinations of

sampling sites for both the regional variation study and the impact study of selective logging activity on

tree above ground biomass and carbon stocks.

For the forest structure analysis, independent Pearson‟s chi-square tests were conducted in

SPSS to check the degree of relationship between the two variables: DBH classification and selective

logging activity. In detail, the percentage of trees per DBH classes (DBH classification) in ONF

selectively logged forest and ONF primary forest was compared with respect to selective logging

activity. Moreover, Phi and Cramer‟s V test was used to check the strength of association between the

two categorical variables of DBH classification and selective logging activity. The Phi test is more

suitable for 2x2 contingency tables. Thus, in this study most suitable was the Cramer‟s V test as long

as the categorical variable of DBH classification has more than two categories. Additionally,

Goodman-Kruskal‟s lambda was used to test the proportional reduction in error achieved when one

membership of a category of one variable is used to predict the membership of a category of the other

variable. Due to the weakness of lambda test of getting a value of 0 when two variables cannot predict

each other, the Goodman-Krauskal Tau test was used to produce a more robust result.

Finally, principal component analysis (PCA) was executed in the R environment to test the

correlation between forest stand variables, climate variables and aboveground biomass (AGB) of

small (2.5 ≤ DBH < 10 cm) and big trees (DBH ≥ 10 cm) in the five primary forest sites (Kaw

Mountains, Laussat conservation area, Trinité reserve, Nouragues reserve and Regina) in French

26

Guiana. The PCA was chosen as the variables that describe the individual-trees are quantitative (Le

et al., 2008). Moreover, the PCA could provide an overview of the correlation of the different primary

forest sites in French Guiana in terms of the tree AGB values reported in each forest site.

2.6. Tree aboveground biomass recovery

The tree aboveground biomass (AGB) recovery study in the selectively logged sites was based on

field data and regeneration rates found in literature. According to Mazzei et al. (2010) in a tropical

forest that had been submitted to reduced impact logging with logging intensity of 6 trees/ha, the tree

AGB accumulation rate will be equal to 2.6 Mg ha-1

yr-1

, the second until the fifth year after logging.

This rate was used to calculate the tree AGB that has been accumulated in the forest 5 years after

logging. The first year after logging was not taken into account in the calculations because during this

year the net AGB accumulation is negative mainly due to the high death rate (Mazzei et al., 2010).

Moreover, the tree AGB that had been removed due to selective logging activity from the selectively

logged ONF forest sites was calculated using the diameter of stumps found in the sampling site and

an approximation of height of the felled trees based on a relation of diameter and height of the

standing trees in the forest site. The outcome of tree AGB accumulation 5 years after selective logging

was then compared to the tree AGB removed from the forest due to selective logging to give an

estimation of the tree AGB and carbon stocks recovery in the logged forest 5 years after selective

logging. It is important to underline that no death rate after logging was included in the calculations for

tree AGB recovery and no AGB losses due to collateral damage during selective logging were taken

into account.

2.7. Financial analysis

Forest carbon finance

The carbon content in Mg/ha in all forest sites was converted to CO2 equivalent (CO2e) of emissions

by multiplying the forest carbon values by the factor 3.67 which represents the ration of the molecular

weight of CO2 to the atomic weight of carbon (44/12) (Watson, 2009). In order to calculate the profits

when applying the REDD finance, the CO2 equivalent price in 2010 (Diaz et al., 2010) was used which

is equal to $5.5 per Mg CO2e and thus €4.08 per Mg CO2e according to the exchange rate of $ =

€0.74184 in November 2011.

Timber market

The estimation of the financial profits generated from the sale of timber that was removed from the

ONF selectively logged forest 5 years before, was based on the prices for standing timber for both the

local market in French Guiana (€17/m3 standing timber) and the open market (€50/m

3 standing timber)

(CCIG, 2008; personal comm. Guitet, S., 2011). For the conversion of the tree aboveground biomass

into timber volume the equation p=m/V was used where p=mean wood density=0.66 g/cm3

and

m=tree AGB that had been removed from the forest due to selective logging.

27

3. RESULTS

3.1. Overview of aboveground biomass

Preliminary analysis was done to investigate the aboveground biomass contribution of the three

growth forms: lianas, small trees (2.5 ≤ DBH < 10 cm) and big trees (DBH ≥ 10 cm) in the three forest

sites sampled (Figure 4). The analysis showed that big trees have the largest contribution to the total

biomass (93%) in all forest sites whereas small trees and lianas contribute the minimum to the total

aboveground biomass in the terra firme forest sites studied, a fact that has been also supported by

previous studies (Baraloto et al., 2011; Duden & Roeling, 2011). In particular, small trees contributed

only 2% in the primary forest sites (Trésor reserve and ONF primary forest) and 2.5% in the ONF

selectively logged forest site. On the other hand, lianas contributed less than 1% to the total AGB in

the primary forest sites and less than 0.5% in the ONF selectively logged forest. However, the

standard error (SE) of the mean values of big tree aboveground biomass showed that there is high

variability especially in Trésor reserve primary forest site as well as ONF selectively logged forest

(Table 4). On the contrary, the SE for the mean values of aboveground biomass of small trees and

lianas was lower indicating that there is lower variability in AGB for these growth forms in each forest

site. The aboveground biomass of dead trees (DAGB) was calculated as well, expecting larger DAGB

in the selectively logged forest comparing to the primary forest, although such a pattern was not

observed. Based on the results of this preliminary analysis, the AGB of small and especially big trees

was used for the regional variation study as well as for the impact study of selective logging on the

aboveground biomass and carbon stocks in terra firme forest.

28

Figure 4. Mean aboveground biomass (AGB) in Mg/ha of the three growth forms studied (lianas, small trees and big trees) and mean aboveground biomass of dead trees (DAGB) in Tresor primary forest (Transects: T1, T2 and T6; n=3), ONF primary (Transects: T5, T10 and T11; n=3) and ONF selectively logged forest (Transects: T7, T8 and T9; n=3). DBH 2.5-10: small trees of diameter at breast height (DBH): 2.5 ≤ DBH <10 cm; DBH ≥ 10 cm: big trees of DBH ≥ 10 cm. Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011). Table 4. Mean aboveground biomass values of big trees, small trees and lianas as well as aboveground biomass of dead trees (DAGB) in Trésor reserve, ONF primary forest and ONF selectively logged forest site. SE: standard error of the mean values.

Mean AGB (±SE)

Location Big trees

(DBH ≥ 10 cm) Small trees

(2.5 ≤ DBH < 10 cm) Lianas DAGB

Trésor reserve (n=3)

424.41 ± 52.13 9.55 ± 0.38 3.13 ± 2.81 16.71 ± 4.27

ONF primary forest (n=3)

414.43 ± 11.52 7.95 ± 4.29 3.34 ± 2.49 17.28 ± 6.08

ONF logged forest (n=3)

360.19 ± 33.08 9.87 ± 1.65 1.76 ± 0.05 16.24 ± 3.71

3.2. Regional variation in big tree aboveground biomass

For the regional variation study in big tree (DBH ≥ 10 cm) aboveground biomass (AGB), primary terra

firme forest in Trésor reserve and ONF primary forest sites were sampled on Kaw Mountains (Duden

& Roeling, 2011; Mitsiou & Laporte-Bisquit, 2011). Preliminary analysis showed that there was no

significant variation in big tree (DBH ≥ 10 cm) AGB between Trésor reserve and ONF primary forest

sites. This outcome permitted the incorporation of the two datasets, in order to represent the same

forest site (Kaw) in the regional variation study. Then, the data from Kaw were analysed together with

data from five primary terra firme forest sites in French Guiana (Baraloto et al., 2011). The five primary

terra firme forest sites included: Laussat conservation area, Trinité reserve, Nouragues reserve and

Regina (Appendix 1). The small tree (2.5 ≤ DBH < 10 cm) AGB was not included in the regional

variation study as small tree AGB represented a minor percentage (2%) of the total AGB (Figure 4)

and thus could not affect strongly the variation in the total AGB.

Big tree aboveground biomass on Kaw Mountains

The big tree (DBH ≥ 10 cm) aboveground biomass (AGB) study in both primary terra firme forest sites

in ONF forest and Trésor reserve showed big tree AGB values close to the mean AGB for the eastern

Amazon equals to 410 Mg/ha (Mazzei et al., 2010). However, the median big tree aboveground

biomass in Trésor reserve was higher (440.73 Mg/ha) than the median big tree AGB value in ONF

primary forest (406.1 Mg/ha) (Table 5). Although the median big tree AGB in Trésor reserve was

higher, it was interesting to observe the large variation in big tree AGB in Trésor reserve forest as

illustrated in Figure 5. In particular, in transect 6 (T6) was observed big tree AGB equal to 327 Mg/ha,

that was almost equal to big tree AGB values observed in selectively logged forest sites. On the

contrary, big tree AGB values in the ONF primary forest were grouped within a range of 400 Mg/ha to

437 Mg/ha exhibiting less variability in the sample which was also confirmed from the relatively low

standard error of the mean (Table 5). The analysis of variance ANOVA between the two primary forest

sites on Kaw Mountains showed no significant results, F (1, 4) = .03, p = .86 (n=6) (Appendix 4). On

the one hand, the sample size was not sufficient. On the other hand, the close distance between

these forest sites might have be responsible for the non significant variation, taking into account that

the same forest type was sampled in both sites. Considering this outcome, the data from ONF primary

29

forest and Trésor reserve were incorporated to represent the same forest site (Kaw) in the regional

variation study.

Figure 5. Variation in big tree (DBH ≥ 10 cm) aboveground biomass (AGB) in terra firme primary forest sites on Kaw Mountains. The sampling sites include ONF primary forest (Transects: T5, T10 and T11; n=3) and Trésor reserve (Transects: T1, T2 and T6; n=3). The bold line represents the median value of the sample. Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011). Table 5. Big tree (DBH ≥ 10 cm) aboveground biomass (AGB) in Mg/ha per transect in Trésor reserve primary terra firme forest and ONF primary terra firme forest. Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011).

Location Transect AGB (Mg/ha) Location Transect AGB (Mg/ha)

Trésor reserve

T1 505.43

ONF primary forest

T5 406.11 T2 440.73 T10 400.06 T6 327.07 T11 437.20

Median 440.73 Median 406.1 Mean 424.41 ± 52.13 Mean 414.43 ± 11.52

Big tree aboveground biomass in French Guiana

For the regional variation study in French Guiana the aboveground biomass (AGB) of big trees (DBH

≥ 10 cm) was studied. The comparison among the five terra firme primary forest sites in French

Guiana showed a variation in big tree aboveground biomass (AGB) into a range of 200 Mg/ha to 600

Mg/ha (Figure 6). The extreme median big tree AGB values were observed in Trinité and Regina

forest sites. Trinité had the higher median big tree AGB value of 502 Mg/ha, whereas Regina had the

30

lower median big tree AGB value of 355 Mg/ha (Table 6). However, Laussat and Nouragues forest

sites had almost the same median of 398 Mg/ha and 397 Mg/ha, respectively. The variance in big tree

AGB values among transects was high in Nouragues, Regina and Trinité whereas Laussat and Kaw

demonstrated less variance across transects. It is particularly interesting the case of Kaw Mountains

where big tree AGB values were distributed normally and close to the median, although big tree AGB

values from two transects were outliers. The outlier at the lower extreme was transect T6 (327 Mg/ha)

and the outlier at the higher extreme was transect T1 (505 Mg/ha), both transects located in Trésor

reserve. However, the ANOVA test was not significant, F (4, 27) = .94, p = .45, showing that the

variance in the total sampling size was not sufficient to explain significant differences in big tree

aboveground biomass among terra firme primary forest sites in French Guiana (Appendix 4).

Additionally, t-tests that were conducted for the pairwise comparison of sampling sites showed that

none of the sites was significantly different (p > .05) (Appendix 4). In details, the results showed that

big tree AGB content in Regina and Laussat was different by 66 Mg/ha, though this difference was not

significant (p = 1). On the contrary, big tree AGB content in Trinité and Nouragues differed the most

(AGB difference of 99.14 Mg/ha), although the t-test was not significant (p = .36) (Appendix 4).

Figure 6. Regional variation in big trees (DBH ≥ 10 cm) aboveground biomass (AGB) across five terra firme primary forest sites in French Guiana; Laussat: n=7, Nouragues: n=7, Regina: n=6, Trinité: n=6 and Kaw: n=6. The Kaw Mountains forest sites include ONF primary forest (Transects: T5, T10 and T11; n=3) and Trésor reserve (Transects: T1, T2 and T6; n=3). The bold line represents the median value of the sample. Data by Duden & Roeling (2011); Mitsiou & Laporte-Bisquit (2011) (Kaw Mountains) and Baraloto et al. (2011) (Laussat, Nouragues, Regina and Trinité).

31

Table 6. Overview of the mean aboveground biomass (AGB) of big trees in Mg/ha per transect in the five primary forest sites in French Guiana. SE: standard error of the mean value. Data by Duden & Roeling (2011); Mitsiou & Laporte-Bisquit (2011) (Kaw Mountains) and Baraloto et al. (2011) (Laussat, Nouragues, Regina and Trinité).

Location Laussat

(n=7) Nouragues

(n=7) Regina (n=6)

Trinité (n=6)

Kaw (n=6)

AGB (Mg/ha)

364.70 397.64 374.41 390.02 505.43 345.35 505.46 529.05 548.67 400.06 398.42 420.04 487.17 604.87 437.20 474.24 484.10 302.43 495.17 440.73 407.09 246.19 336.88 509.55 406.11 486.53 208.12 325.46 244.27 327.07 335.55 302.47 - - -

Median 398.42 397.64 355.65 502.36 421.65 Mean ± SE 458.70 ± 22.58 366.29 ± 43.82 392.56 ± 38.14 465.43 ± 52.85 419.43 ± 23.98

3.3. Variables explaining tree aboveground biomass variation

The correlation study between forest stand variables (tree size, wood specific gravity, basal area and

stem density), climatic variables (rainfall and dry season index) and tree above ground biomass (AGB)

was done using the principal component analysis. The above ground biomass dataset included small

trees (2.5 ≤ DBH < 10 cm) and big trees (DBH ≥ 10 cm) AGB from Kaw Mountains (Duden & Roeling,

2011; Mitsiou & Laporte-Bisquit, 2011), Laussat conservation area, Trinité reserve, Nouragues

reserve and Regina (Baraloto et al., 2011) primary terra firme forest sites.

The principal component analysis (PCA) showed that the large variation in the dataset was

explained mainly by two groups of factors, relatively independent from each other (Figure 7). The first

group included: basal area (BA), density of big trees (Stems DBH ≥ 30), total tree AGB (AGB total)

and big tree AGB (AGB DBH ≥ 30). This group of variables were positively and highly correlated with

Axes1 (Dim1) explaining the 43.55% of variance (Figure 7). In particular, big tree AGB was correlated

by 0.938 to the first dimension (Dim1) explaining the most of the variance in the sample together with

total tree AGB (correlation: 0.937), density of big trees (correlation: 0.85) and basal area (correlation:

0.82) (Appendix 4). The second group contained: small tree AGB (AGB DBH 2.5-10) and density of

small trees (Stems DBH 2.5-10) and it is strongly correlated with Axes 2 (Dim2), explaining the

25.41% of variance (Figure 7). In details, the degree of correlation of these variables with the second

dimension (Dim2) was 0.90 for small tree AGB and 0.83 for the density of small trees (Appendix 4).

On the whole, the graph showed that the sample consisted of mixed forest communities because the

arrows of the two groups were not opposite but mostly perpendicular to each other. Moreover, it was

concluded that small trees variation was independent of the variation in big trees. However, the total

tree AGB was strongly related to big tree AGB and this pattern was observed in all regions.

Diameter at breast height (DBH) and height variables were well explained in the PCA

ordination diagram (Figure 7). In particular, DBH and height both compromised to the first group of

variables and showed a small positive relation. Generally, these two variables tended to have positive

relationship with dimension 1 (Dim1) and negative with dimension 2 (Dim2). More specifically, the

degree of correlation for DBH with Dim1 and Dim2 was 0.62 and -0.65, respectively (Appendix 4).

Similarly, the degree of correlation for height with Dim1 and Dim2 was 0.71 and -0.40, respectively.

Outcome which was expected as DBH and height were highly responsible for tree AGB values and

thus were the ones to explain a large percentage of tree AGB variation in the sample. It is interesting

to mention that the variable of height showed a stronger relationship with Dim1 than DBH which

underlines the importance of including the variable of height in the formulas for tree AGB calculation.

Accordingly, wood specific gravity (WSG) was shown to correlate more with dimension 1 (Dim1) and

32

less with dimension 2 (Dim2), meaning that big trees tend to have higher WSG whereas small trees

tend to have less WSG. Moreover, the positive correlation of wood specific gravity with Dim1

(correlation: 0.49) indicated that this variable influences tree AGB and thus WSG should be taken into

consideration for tree AGB calculation. On the other hand, the climatic factors included in the analysis,

rainfall and dry season index (DSI), did not exhibit correlation with any of the dimensions of the graph

(Figure 7). More specifically, the variable of rainfall exhibited negative correlation with both dimensions

meaning that more rainfall results in less tree aboveground biomass. Similarly, the dry season index

showed negative correlation with dimension 2 (Dim2), whereas its correlation with dimension 1 (Dim1)

was positive but low and equal to 0.08. This gives an indication that climatic factors such as

precipitation in the tropics might not have an important role in the tree AGB accumulation, although

the length of dry season (DSI) might be more important in tree AGB accumulation than precipitation.

Figure 7. Principal component analysis (PCA) ordination diagram illustrating the correlation cycle of forest stand (green) and climatic variables (red) with tree aboveground biomass (AGB) in five primary terra firme forest sites, French Guiana; Laussat: n=7, Nouragues: n=7, Regina: n=6, Trinité: n=6 and Kaw: n=6. Stems DBH (10-30): density of trees at DBH 10-30 cm; AGB DBH (2.5-10): small tree AGB; Stems DBH (2.5-10): density of small trees; BA: basal area; Stems DBH ≥ 30 cm: density of big trees; AGB total: AGB of small and big trees; AGB DBH ≥ 30: big tree AGB; wsg: wood specific gravity; DBH: diameter at breast height. Data by Duden & Roeling (2011); Mitsiou & Laporte-Bisquit (2011) (Kaw Mountains) and Baraloto et al. (2011) (Laussat, Nouragues, Regina and Trinité).

33

Figure 8. Principal component analysis (PCA) ordination diagram illustrating the coordinates of transects from the five primary terra firme forest sites in French Guiana; Laussat (Lau.): n=7; Nouragues (Nour.): n=7; Regina (Reg.): n=6; Trinité (Tri): n=6 and Kaw: n=6. The square sign represents the mean tree AGB value in the specific forest site. Data by Duden & Roeling (2011); Mitsiou & Laporte-Bisquit (2011) (Kaw Mountains) and Baraloto et al. (2011) (Laussat, Nouragues, Regina and Trinité).

Examining the regional variation in tree aboveground biomass (AGB) of both small and big

trees among the different primary forest sites, there was not strong evidence that the regions differ

(Figure 8). The tree AGB distribution per forest site did not follow any particular pattern, although it

exhibited a pretty dispersed and mixed distribution. Taking into consideration the mean tree AGB

values of each forest site, relative higher mean tree AGB content was observed in Trinité reserve,

whereas in Nouragues lower mean tree AGB value was found. Regina and Laussat seemed to be

clustered together with Kaw around similar mean tree AGB values. It is interesting to note that

transect 6 (T6) that was set in Trésor reserve on Kaw Mountains and exhibited the lowest tree AGB

value for the primary forest sites, was the only transect from Kaw that was correlated with dimension 1

(Dim1) at a degree of 6.5 when the other transects on Kaw correlated with dimension 1 (Dim1) at a

degree less than 0.9. It was observed that transects on Kaw Mountains correlated mainly with

dimension 2 (Dim2), whereas transects that correlated with dimension 1 (Dim1) at a degree of 10 to

13 are two transects in Nouragues and one transect in Trinité reserve. In Trinité reserve the variation

in tree AGB could be explained by both dimension 1 (Dim1) and dimension 2 (Dim2), although this

was not the case for Nouragues. On the opposite, Kaw Mountains were oriented towards the centre of

the distribution meaning that the variation in tree AGB in the region could not be explained by any of

the two principal dimensions. In the case of Kaw Mountains, different dimensions or variables (i.e.

34

geological data) might explain the variance in aboveground biomass. Concerning the amount of small

and big trees in each forest site some differences among sites were observed, although the most

interesting pattern was observed in Trinité reserve where there seemed to be an equal amount of

small and big trees.

3.4. Impact of selective logging

Impact of selective logging on tree aboveground biomass and forest structure

Taking into consideration that big trees had the largest contribution (Figure 4) in the total AGB values

in the forest, the study of selective logging impact was focused on big tree (DBH ≥ 10 cm)

aboveground biomass (AGB). Initially, a boxplot analysis was conducted to explore the variation in big

tree aboveground biomass (AGB) in ONF primary forest and ONF forest that had been selectively

logged 5 years before (Figure 9). The ONF primary forest exhibited higher median aboveground

biomass value of big trees than the ONF selectively logged forest. In the ONF primary forest, the

mean AGB values of big trees in the three transects were grouped in the upper quartile (25% of

scores) meaning that big tree AGB values in the majority of transects were superior to the median

(406 Mg/ha). On the other hand, in the ONF selectively logged forest big tree AGB values

demonstrated a normal distribution across the three transects that could probably be attributed to the

different logging intensity observed in each transect. This variation is also reflected by the high

standard error of the mean AGB value of big trees in the selectively logged forest site (Table 7).

However, the analysis of variance ANOVA was not significant, F (2, 6) = .91, p = .45 meaning that

either selective logging could not explain big tree AGB variation between selectively logged and

primary forest or the replicates were not enough to support the results (Appendix 4).

35

Figure 9. Variation in aboveground biomass (AGB) in Mg/ha of big trees (DBH ≥ 10 cm) in ONF forest that had been selectively logged 5 years before (Transects: T7, T8 and T9; n=3) and ONF primary forest (Transects: T5, T10 and T11; n=3). The bold line represents the median value of the sample. Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011). Table 7. Aboveground biomass (AGB) values of big trees (DBH ≥ 10 cm) in Mg/ha per transect in ONF forest that had been selectively logged 5 years before and in ONF primary forest. SE: standard error of the mean value. Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011).

Location Transect AGB (Mg/ha) Location Transect AGB (Mg/ha)

ONF forest selectively logged 5

years before

T7 415.86

ONF primary forest

T5 406.11

T8 301.37 T10 400.06 T9 363.33 T11 437.20

Median 363.33 Median 406.1 Mean 360.19 ± 33.08 Mean 414.43 ± 11.52

36

Figure 10. Impacts of selective logging activity on forest structure comparing ONF forest that had been selectively logged 5 years before (Transects: T7, T8 and T9; n=3) and ONF primary forest (Transects: T5, T10 and T11; n=3). The carbon values were calculated as the aboveground biomass (AGB) fraction of 0.46 for small trees (2.5 ≤ DBH < 10 cm) and 0.49 for big trees (DBH ≥ 10 cm) (Hughes et al., 2000). Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011).

Table 8. Percentage of carbon and tree counts in each DBH class in ONF selectively logged forest 5 years before (Transects: T7, T8 and T9; n=3) and ONF primary forest (Transects: T5, T10 and T11; n=3). Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011).

Location DBH

classes Tree

counts/ha %

Carbon Location

DBH classes

Tree counts/ha

% Carbon

ONF selectively

logged forest 5 years before

2.5 - 5 4040 0.73

ONF primary forest

2.5 - 5 2170 0.38

5 - 10 1690 1.92 5 - 10 1130 1.36

10-20 812 6.90 10-20 1036 8.82

20-30 232 8.67 20-30 228 9.27

30-40 130 10.83 30-40 128 10.03

40-70 152 38.60 40-70 160 33.92

≥ 70 30 32.33 ≥ 70 54 36.21

Moreover, chi-square tests were conducted to test the association of selective logging activity

with the distribution of small and big trees in DBH classes. Then, the counts of trees per DBH class

with the percentages of carbon content per DBH class (Table 8) were incorporated in graphs to

illustrate the impact of selective logging on forest structure (Figure 10). The carbon content of trees

was calculated as the aboveground biomass (AGB) fraction of 0.46 for small trees (2.5 ≤ DBH < 10

cm) and 0.49 for big trees (DBH ≥ 10 cm) (Hughes et al., 2000). The chi-square tests showed that

there was significant association between selective logging activity and the distribution of trees in DBH

37

classes χ2 (6) = 84.46, p < .001 (n=6) (Appendix 4). This result provided important statistical power to

the observation of forest structure change due to selective logging. In details, in the selectively logged

forest there was a decline in the number of big trees (DBH ≥ 10 cm) by 250 trees/ha comparing to the

primary forest (1606 trees/ha) probably due to selective logging activity. It is important to notice that

there was a significant decline in the number of trees especially in the larger DBH class (DBH ≥ 70

cm). In details, 54 trees of DBH ≥ 70 cm where found in the primary forest site whereas only 30 trees

of DBH ≥ 70 cm were found in the selectively logged forest site. The removal of big trees at DBH > 60

cm due to selective logging activity might be the main reason for this result.

On the contrary, the number of small trees (2.5 ≤ DBH <10 cm) in the selectively logged forest

was higher (5730 trees/ha) in comparison to the primary forest site where 3300 small trees/ha were

observed. The significance of this result was strengthened by the standardised residuals for the

smallest DBH class (DBH 2.5-5 cm) in both forest sites which were significant at p < .001 (n=6). For

the DBH class 10-20 cm the number of trees was reduced from 1036 trees in the primary forest to 812

trees in the selectively logged forest. The standardised residuals for this DBH class in both forest sites

were significant at p < .001 (n=6) which stated the strong effect of this class on the total result.

Although the high significance of the Pearson‟s chi-test indicated that there was a relationship

between selective logging and forest structure, the relatively low Cramer‟s statistic value (Cramer‟s V=

.19, p < .001) indicated that this relationship was not very strong (Appendix 4).

Concerning the carbon distribution in DBH classes, it was concluded that the largest

percentage of carbon was stored in big trees in both forest sites. In particular, the big trees stored the

98% and 97% of the total carbon in the primary forest and the selectively logged forest, respectively

(Table 8). However, a significant fall in carbon stock occured in the DBH class (≥ 70 cm) from 36% in

the primary forest to 32% in the selectively logged forest, probably due to the removal of the largest

trees due to selective logging. However in the selectively logged forest, it was observed an increase of

5% in carbon stocks in the DBH class (40-70 cm). It is interesting to mention that between the classes

DBH (40-70 cm) and DBH (≥ 70cm) a redistribution of carbon was observed when comparing the

forest before and after selective logging. In details, the DBH class (≥ 70cm) contained 38% of the total

carbon in the forest before selective logging when DBH class (40-70 cm) contained 33% of the total

carbon. After selective logging, 38% of carbon was contained in class DBH (40-70 cm) whereas in

DBH class (≥ 70cm) the carbon percentage was reduced to 32%. Moreover, in the smaller DBH

classes of (2.5-10 cm) it was observed a slight increase in carbon by 1%. This finding indicates that

the increase in the number of small trees after logging did not influence much the carbon storage and

thus illustrates again the low contribution of small trees to the carbon stocks in the forest.

Impact of selective logging on carbon stocks

The analysis of carbon stocks showed lower carbon content in the ONF terra firme forest that had

been selectively logged 5 years before (181.03 Mg/ha; n=3) comparing to the ONF terra firme primary

forest (206.74 Mg/ha; n=3) (Figure 11). This difference of 25.71 Mg/ha of carbon between the two

forest sites possibly resulted from the selective felling of big trees (DBH ≥ 10 cm). In particular, big

trees mean carbon content in the primary forest and in the forest 5 years after selective logging was

203.08 Mg/ha and 176.49 Mg/ha, respectively (Table 9). On the contrary, the mean carbon content of

small trees (2.5 ≤ DBH <10 cm) was slightly increased in the selectively logged forest (Table 9).

However, this small increase in carbon could not recompensate for the carbon losses due to the

removal of large trees, especially the individuals of DBH > 110 cm (Mazzei et al., 2010).

38

Figure 11. Mean carbon stock in Mg/ha in ONF primary forest (Transects: T5, T10 and T11; n=3) and ONF forest that had been selectively logged 5 years before (Transects: T7, T8 and T9; n=3). The mean carbon values were calculated as the mean aboveground biomass (AGB) fraction of 0.46 for small trees and 0.49 for big trees (Hughes et al., 2000). DBH 2.5-10: small trees of Diameter at Breast Height (DBH) 2.5 ≤ DBH < 10 cm; DBH ≥ 10 cm: big trees of DBH ≥ 10 cm. Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011).

Table 9. Mean carbon stock in Mg/ha in Trésor reserve (n=3) and ONF primary forest sites (n=3) as well as in the ONF forest that had been selectively logged 5 years before (n=3). SE: standard error of the mean values. Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011).

Mean carbon stock ± SE (Mg/ha)

Location Big trees

(DBH ≥ 10 cm) Small trees

(2.5 ≤ DBH < 10 cm) Total

Trésor reserve 207.96 ± 25.54 4.39 ± 0.17 212.35 ONF primary forest 203.08 ± 5.64 3.66 ± 1.97 206.74 ONF logged forest 176.49 ± 16.21 4.54 ± 0.76 181.03

3.5. Forest recovery estimation 5 years after selective logging

The calculation of the aboveground biomass (AGB) as well as carbon content contained in the trees

that had been removed from the ONF selectively logged forest areas gave an impression of the

minimum AGB and carbon that can be considered lost due to selective logging (Table 10). These

values were the minimum because it was not possible to estimate the extra AGB losses due to

collateral damage while selective logging activity.

39

Table 10. Minimum aboveground biomass (AGB) and carbon values in Mg/0.5ha removed from the ONF selectively logged forest site 5 years before. The calculations were based on the diameter of stumps found in transects (T7, T8 and T9) and on estimation of the felled tree height. Carbon values were calculated as the AGB fraction of 0.49 (Hughes et al., 2000). Data by Mitsiou & Laporte-Bisquit (2011).

Min Aboveground Biomass & carbon removed

Transect (0.5 ha)

DBH (cm) Estim. Height (m) AGB

(Mg/0.5ha) C

(Mg/0.5ha)

T7 Stump 1 103.5 30 21.6 10.6

T7 21.6 10.6

T8 Stump 1 75 25 9.4 4.6

T8 Stump 2 50 25 4.2 2.1

T8 13.6 6.7

T9 Stump 1 33.7 25 1.9 0.9

T9 Stump 2 70 25 8.2 4

T9 Stump 3 114 30 26.2 12.8

T9 36.3 17.8

Taking into account that the net AGB accumulation rate was equal to 2.6 Mg ha-1

yr-1

(± 4.6)

for the second until the fifth year after selective logging (Mazzei et al., 2010), the total AGB amount

that would have been accumulated in the selectively logged forest 5 years after logging would be

equal to 10.4 Mg/ha (5.2 Mg/0.5ha). Comparing this number with the tree AGB amount that had been

removed from the ONF forest due to selective logging, it was observed that tree AGB would not

recover in any of the forest sites within 5 years after selective logging. More specifically, tree AGB in

transect 7 (T7) was expected to recover by 24%, in T8 by 38%, whereas in T9 only the 14% of the

tree AGB removed was expected to recover within 5 years after selective logging. It is interesting to

highlight that in T7 only one tree had been felled in 0.5ha, whereas in T8 and T9 two and three trees,

respectively. This observation underlines the fact that tree dimensions and not only the number of

felled trees are important factors to affect the post-logging AGB recovery in the forest. Moreover, it is

worth mentioning that the felled tree in T7 had been a Manilkara bidentata (common name: Balata

franc) which is one of the most exploited tree species in French Guiana (ONF, 1994).

3.6. Forest carbon finance vs. timber market

The tree aboveground biomass and carbon data were used to create an estimation of the financial

value that could be assigned to the primary forest sites when applying the REDD finance.

Additionally, the amount of carbon that had been removed from the forest due to selective logging was

used to create an estimation of the financial loss due to selective logging activity. For these

estimations, the amount of carbon in Mg was multiplied by 3.67 to be transformed into CO2 equivalent

(Watson, 2009) and this equivalent was multiplied by the CO2e price in 2010 which is $5.5/Mg CO2e

(€4.08/Mg CO2e in November 2011) (Diaz et al., 2010). The numbers in table 11, indicate the financial

losses owing to logging activity when comparing primary forest sites and the selectively logged forest.

In details, the primary terra firme forest sites in Trésor reserve and ONF primary forest were attributed

a financial value within the range of €3000-3180/ha ($4170-4290). On the contrary, the tree

aboveground biomass and thus carbon removal due to selective logging, resulted in a lower financial

value of €2710/ha ($3654/ha) for the ONF selectively logged forest.

40

Table 11. Financial analysis based on mean carbon in Mg/ha in Trésor reserve and ONF primary forest sites as well as in the ONF forest that has been selectively logged 5 years before. CO2e price in 2010: $5.5/Mg CO2e (Diaz et al., 2010) (€4.08/Mg CO2e in November 2011). Data by Duden & Roeling (2011) and Mitsiou & Laporte-Bisquit (2011).

Location Mean carbon

(Mg/ha) CO2 equivalent

(Mg/ha) €/ha $/ha

Trésor reserve 212.35 779.32 3179.63 4286.26

ONF primary forest 206.74 758.74 3095.66 4173.07 ONF logged forest 181.03 664.38 2710.67 3654.09

Moreover, a comparison was done between the financial profits produced from timber sale

and the ones that could be produced by keeping the forest standing and applying the REDD finance.

For this comparison it was considered that the REDD finance will be applied for a period of 65 years

which is the rotation cycle in French Guiana so that onetime benefits would be generated from both

timber market and REDD finance within this time period. The tree aboveground biomass (AGB)

estimated to have been removed from the ONF selectively logged forest site equal to 71.5 Mg was

used (see Table 10). This tree AGB value accounts for the standing timber volume of 108.33 m3

(p=0.66 g/cm3). In French Guiana, almost the 80% of the produced timber is directed towards the local

market and it is used mainly as building material and for woodworks (CCIG, 2008). Thus, a two way

approach was realised in order to estimate the profits produced by the local timber market and by the

open market for standing timber (Figure 12). Taking into consideration that €17/m3 is the price for

standing timber in the local market in French Guiana and €50/m3 is the price for standing timber in the

open market (CCIG, 2008; personal comm. Guitet, S., 2011), the profits from selling the standing

timber had been €1841.61 ($1366.18) and €5416.5 ($4018.18) in the local and open market,

respectively (Figure 12). Alternatively, if this tree AGB had been retained in the forest, the avoided

CO2 emissions would have accounted for 128.58 Mg. Thus, the financial benefit when applying the

REDD finance would have been €524.61 ($707.19) for the period of 65 years (Figure 13).

Figure 12. Financial profits produced by selling the tree aboveground biomass (AGB) removed from the ONF forest due to selective logging 5 years before, as standing timber in the local market in French Guiana and the open market (CCIG, 2008; personal comm. Guitet, S., 2011). 0.66 g/cm

3:

wood specific gravity. Currency exchange rate of $= €0.74184, in November 2011

41

Figure 13. Financial profits produced when applying the REDD finance on the tree aboveground biomass (AGB) removed from the ONF forest due to selective logging 5 years before. 0.49: carbon fraction of AGB (Hughes et al., 2000); 3.67 factor for the conversion of carbon to CO2 equivalent (CO2e) (Watson, 2009); $5.5/Mg the price of CO2e in 2010 (Diaz et al., 2010); €4.08/Mg CO2e according to the currency exchange rate of $= €0.74184, in November 2011

42

4. DISCUSSION

4.1. Regional variation in primary forest sites

The regional variation study showed that there was no significant variation in tree aboveground

biomass (AGB) among primary terra firme forest sites across different regions in French Guiana,

although considerable local variation was observed among transects within a specific forest site. This

is an interesting result as it confirms once again that in the tropics, large variation may be observed

within a particular forest area, although this variation cannot be observed when examining forest

regions at a larger scale such as French Guiana (Baraloto et al., 2010; 2011). Moreover climatic

factors such as precipitation and length of dry season were not likely to shape differences of tree AGB

among primary forest sites in the tropics. Concerning the forest structure and specifically the

composition of small and big trees some differences were observed among forest sites, although the

climatic factors studied could not explain this variation. Alternatively, other variables or a combination

of other variables might be responsible for the variation observed (i.e. geological profile of forest sites,

etc.). For example, the precipitation patterns on Kaw Mountains in combination with the special

geological profile (poor soils and lateritic geological profile) might be the cause of the observed

differences in forest structure in Trésor reserve. These differences result from the high frequency of

forest gaps on Kaw Mountains due to the shallow soil profile that makes big trees unstable. However

this study could not provide evidence that this special geological profile was responsible for the

differences in forest structure.

However, when referring to primary forest sites, cases of relatively undisturbed forest that it is

considered as primary forest should be taken into account. In the case of relatively undisturbed forest,

differences in tree AGB might not be the result of environmental factors influence but rather the

outcome of human disturbance. In the present study, such cases were Trésor reserve and probably

Nouragues forest site. Trésor reserve in the 1950s had been an important spot for gold mining and

had suffered from illegal logging especially in the area close to where today is located the “Botanical

path” and relatively close to the site where transect 6 (T6) was set. This fact could lead to the

conclusion that the low tree AGB value observed in T6 might be the result of past human disturbance

and not an indication of environmental impact on tree aboveground biomass. This history might also

explain the great variation in tree AGB that was observed in Trésor reserve, even though the mean

tree AGB value for the reserve was high. Likewise, Nouragues forest site might not be accessible

through infrastructure, although a research centre is present in the primary forest site and its impact to

the surrounding forest is unclear. Thus, attention should be given when referring to primary forest sites

especially when there is no detailed forest management record which was the case in French Guiana.

4.2. Impact of selective logging on carbon stocks

This study revealed an important impact of selective logging activity on tree aboveground biomass

(AGB) and carbon stocks mainly due to the removal of big trees (DBH ≥ 10 cm). On the contrary, the

mean carbon content of small trees (2.5 ≤ DBH <10 cm) was slightly higher in the selectively logged

forest probably due to regeneration and recruitment of young trees in the resently logged forest that

was enhanced by the openings created due to the felling of big trees (Blanc et al., 2009). However, on

the one hand this increase in AGB of small trees could not compensate for the AGB loss due to the

removal of big trees and on the other hand it might be temporal and occurring only during the first

years after selective logging. Though, it is encouraging the fact that in French Guiana the logging

intensity is relatively low according to field observation (1-3 felled trees/0.5ha) and the mean timber

volume of 7.8 m3/ha that was reported to had been extracted 5 years before from parcel KAW2 where

43

the sampling was conducted (ONF, 2011b). To provide a comparison, in the Brazilian Amazon have

been reported cases of logging practices where almost 20 m3/ha of timber was being removed from

the forest due to logging (Boltz et al., 2001; Sist & Ferreira, 2007). However, progress is still needed

towards a sustainable logging practice regime in French Guiana that will aim to the mitigation of

collateral losses of AGB. First of all, reduced-impact logging (RIL) felling methods should be soon

applied in French Guiana and timber certification schemes are required for both carbon stocks and

biodiversity conservation. The current certification schemes and the PEFC certification that ONF

targets to apply in the future are market orientated without ensuring that the methods for timber

extraction are sustainable. Thus, it is proposed that a better certification scheme for the Guianas

would be the FSC instead of PEFC as the former is less trade orientated and includes principles that

ensure biodiversity and ecosystem conservation (Credible Forest Certification, 2006).

4.3. Forest recovery estimation

The regeneration analysis in the forest that had been selectively logged 5 years before indicated that

the size of felled trees (m3 of timber) might be more important for the estimation of tree aboveground

biomass (AGB) losses due to selective logging than the actual number of trees that had been cut.

However, final conclusions could not be reached because the mortality rate and the recruitment

patterns in the selectively logged forest were not taken into consideration in this study. Additionally,

the tree AGB accumulation rate used was only a speculation of the actual tree AGB recovery rate in

the selectively logged forest sites. In particular, this recovery rate applies to forest that had been

submitted to a logging intensity of 2-6 trees/ha, although in our study site the maximum logging

intensity was 3 trees/ha. Taking into account that greater opening may influence differently the

regeneration rate, possibly the growth rate for our study site is lower than 2.6 Mg/ha yr (Gourlet-Fleury

et al., 2005). Lastly, the collateral damage while selective logging was not taken into account and thus

it should be considered that the actual tree AGB values removed from the forest due to selective

logging were greater than the one calculated according to stumps dimensions. Consequently, it is

estimated that more time would be required for tree aboveground biomass and carbon stocks

recovery in the ONF selectively logged forest sampled.

4.4. Financial approach

The comparison of financial benefits from timber sale and carbon market is important to be taken into

consideration when referring to the application of the REDD finance. For this comparison it was

considered that the REDD finance would be applied for a period of 65 years which is the rotation cycle

in French Guiana so that onetime benefits would be generated from both timber market and REDD

finance within the selected time period. The financial approach showed that, the profits from selling

108.33 m3

standing timber had been €1841.61 ($1366.18) and €5416.5 ($4018.18) in the local and

open market, respectively (CCIG, 2008; personal comm. Guitet, S., 2011), whereas the financial

benefits by keeping this volume as standing forest would have been €524.61 ($707.19) (Watson,

2009; Diaz et al., 2011) by applying the REDD finance for the period of 65 years.

These numbers clearly show that timber trade either in the local or open market would

generate more than double the profits than the REDD finance within the 65 years period. More

specifically, the low carbon price could be one of the constraints towards the application of REDD

mechanism. Additionally, the investment risk is prevalent due to the lack of guarantee that the forest

will remain standing even after the exclusion of logging activity (i.e. forest fires etc.) (Carlson &

Curran, 2009). However, it should be taken into account that with every logging cycle there is a certain

amount of tree aboveground biomass loss that cannot recover with regeneration. Thus, in the long

44

term a decline in profits from timber sale will occur together with forest degradation. Moreover, the

financial aspect should not overshadow the co-benefits from retaining the forest intact. Forest

products used for food, medicines, fibres etc., watershed services and biodiversity conservation are

some of the most important benefits from keeping the forest standing. Moreover, social, environmental

and economic benefits from tropical forest are very important to local communities and indigenous

people (Viana, 2009). Consequently an REDD+ mechanism that links forest conservation with other

services apart from carbon sequestration could be more effective towards tropical forest conservation.

In any case, it is important that tropical forest conservation under the REDD mechanism is coupled

with effective monitoring in order to avoid negative effects such as leakage which imply the increase of

deforestation outside the protected forest areas (Viana, 2009).

4.5. Limitations-suggestions for improvement

In general, this study produced some interesting results concerning the impact of selective logging on

tree aboveground biomass (AGB) and carbon stocks in the tropical forest, although there is high

uncertainty surrounding these outcomes as the statistic tests showed no significant results apart from

the chi-square tests on the impact of selective logging on forest structure. Consequently, more

replicates and further study are needed in order to strengthen the observed results and conclusions.

Furthermore, the great percentage (60%) of missing taxonomic data resulted in incomplete

information on the wood specific gravity used for big tree (DBH ≥ 10 cm) AGB calculation. Additionally,

due to this information gap the investigation of possible correlation between carbon stocks and

biodiversity was not feasible. Moreover, the assumptions over tree AGB regeneration in ONF

selectively logged forest can only be considered as an estimate and no final conclusions can be driven

based on these results. Thus, long term research is needed in order to conclude on the time period

that is necessary for the selectively logged forest regeneration. Finally, the primary forest sampled in

this study could have been relatively unexploited in certain forest sites. This problem was prevalent

during the selection stage of the sampling sites as the exploitation record for certain sites was not

clear especially when referring to illegal logging activity.

4.6. Suggestions for future study

A meta-analysis could be done using the data produced from this study and comparable data

from French Guiana but also from other regions in South America in order to analyse further the

variation in tree aboveground biomass and carbon stocks among primary forest sites.

More research is needed in order to understand the influence of environmental factors (i.e.

soil characteristics, geological profile etc.) on tree aboveground biomass (AGB) as well as carbon

stocks in primary terra firme forest sites in the Guiana Shield (Takyu et al., 2002; Hammond, 2005).

Previous studies have shown that soil quality and composition can affect forest structure in terra firme

forest in Indonesia (Paoli et al., 2008). Thus, soil data could be incorporated in principle component

analysis (PCA) to investigate whether these environmental variables affect the variability in tree AGB.

The completion of taxonomic determination could spark more discussion on a possible

connection between carbon stocks and biodiversity as well as investigate possible effects of selective

logging on biodiversity. This could be very important for the REDD mechanism as forest sites with

high biodiversity are considered of first priority sites for forest conservation (UNEP-WCMC, 2008).

More carbon sinks (i.e. soil carbon), apart from aboveground carbon content in trees could be

investigated with a view to determine more precisely the total carbon stocks in a tropical forest

ecosystem (Verweij et al., 2009). Moreover, this approach could reveal possible effects of selective

logging on other carbon sinks, apart from tree aboveground carbon stocks.

45

Finally, the ground based carbon data produced from this study could be incorporated with

remote sensing data for the creation of bio-habitat GIS maps that will promote forest cover and carbon

stocks monitoring in the French Guianese region (Olson et al., 2001; Baraloto et al., 2011).

4.7. Conclusion

The present study showed that there was no significant regional variation in tree aboveground

biomass (AGB) among primary terra firme forest sites in French Guiana and climatic factors such as

rainfall and length of dry season were not adequate to explain the distribution of tree AGB among

primary forest sites. Thus, it was concluded that further research is needed on the environmental

variables that affect tree aboveground biomass content in primary terra firme forest sites. Moreover,

this study illustrated the serious impacts of selective logging on tree aboveground biomass and carbon

stocks, underlying the importance of primary forest conservation. More specifically, it was illustrated

that selective logging activity had an important impact on tree AGB and carbon stocks especially of big

trees (DBH > 10 cm). Estimates on forest recovery showed that tree AGB loss of 36.3 Mg and 13.6

Mg would recover by 14% and 38% respectively within a period of 5 years after selective logging.

These findings underlined that carefully planned logging scheme and forest management is needed in

order to conserve tree AGB and forest carbon stocks. Moreover, reduced-impact logging schemes

and specialised certification over timber extraction should soon be applied in French Guiana in order

to achieve a more sustainably managed forest. To summarise, the mean carbon content of trees in

the primary terra firme forest sites sampled was 212.35 Mg/ha in Trésor reserve and 206.74 Mg/ha in

the ONF primary forest site. On the contrary, in the ONF forest site that had been selectively logged 5

years before the mean carbon content of trees was 181.03 Mg/ha. In terms of financial value when

applying the REDD finance, the primary terra firme forest in Trésor reserve would be worth

€3179.63/ha ($4286.26/ha) and the same forest type in ONF forest site would be worth €3095.66/ha

($4173.07/ha). On the contrary the ONF forest that had been selectively logged 5 years before would

have a lower value of €2710.67/ha ($3654.09/ha). At the same time, the trade of 108.33 m3 standing

timber had generated €1841.61 ($1366.18) and €5416.5 ($4018.18) in the local and open timber

market, respectively (CCIG, 2008; personal comm. Guitet, S., 2011). Alternatively, if this volume was

retained as standing forest, it would have had the value of €524.61 ($707.19) (Watson, 2009; Diaz et

al., 2011). Considering the co-benefits of forest conservation as well as the permanent loss of a

certain amount of tree aboveground biomass due to selective logging activity resulting in forest

degradation in the long term, it is urgent to consider the preservation or wise forest management of

the tropical forests in French Guiana in order to conserve the value and services that these

ecosystems provide.

46

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APPENDICES APPENDIX 1: Study area

Figure S1. Map of the northern coastal part of French Guiana where the place of Kaw Mountains with Trésor nature reserve is illustrated. The location of the capital Cayenne and its relative distance from the sampling site is shown as well (© 2006 Trésor Foundation) (source: Trésor Foundation, 2011).

Figure S2. Precipitation map, French Guiana (ORSTOM, 1979) (acquired from the report of Duden & Roeling, 2011)

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Figure S3. The location of the primary forest sites in French Guiana. Clockwise from north to south: Laussat conservation area, Kaw Mountains, Regina, Nouragues reserve and Trinité reserve (original map source: mapsof.net, edited by Alexandra Mitsiou, 2011).

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APPENDIX 2: Details on sampling methods

Transect: the modified Gentry plot

The modified Gentry plot developed by Baraloto et al. (2010) was used for the forest sites sampling.

The protocol proposes a flexible design for the sampling of a 0.5 ha forest area with the possibility to

change the place of the subplots as well as the direction of the baseline to acquire a more

representative sample of the mosaic tropical forest within a 2 ha area. The core design of this modified

Gentry plot (transect) consists of a baseline of 190 m along which 10 10x50 m subplots, totalling 0.5

ha forest area, are orientated alternatively perpendicular to the baseline. Between the middle-line of

each subplot there is 20 m separation (Baraloto et al., 2010).

Figure S4. Transect design proposed by Baraloto et al., 2010. Ten 10x50 m subplots are oriented alternatively perpendicular along a 190 m baseline.

Methodology in the field

Initially the baseline was set by laying down 100 m tape and marking with warning tape every 20 m

starting from 5 m and finishing at 185 m (Figure S4). Each point corresponded to the start of the

middle-line of subplot. The direction of the baseline was noted and GPS coordinates at 5 m and 185 m

were marked using Garmin GPSMAP® 60CSx (© Garmin technologies). The direction of subplots was

determined using a compass and the middle-line was set using a 50 m tape. A 10 m tape was laid

perpendicular to the middle-line to mark the limits of subplot. Coordinates of position were taken for all

the individuals, using randomly the subplot middle-line as the x axis and the perpendicular tape as y

axis (- y on the one side and + y on the other side of the middle-line).

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APPENDIX 3: Formulas for tree aboveground biomass calculation

Figure S5. Discrepancy in big tree (DBH ≥ 10 cm) aboveground biomass (AGB) values per transect when using different formulas. The formula used in this study is F3 that contains both height and wood specific gravity (WSG) per binomial (Chave et al., 2005). The scatter plot depicts the discrepancy in big tree (DBH ≥ 10 cm) AGB values per transect when

using four different formulas for big tree AGB calculation (Figure S5). All formulas were different

expressions of the Chave et al. (2005) formula used for the data analysis of the present study and that

corresponds to formula F3 in the above plot: AGB = 0.0509 (WSG) (DBH) 2

(height). Formulas F2 and

F4 did not contain the variable of height, whereas formulas F1 and F2 contained the mean wood

specific gravity value. In the case of F1 and F2, all individuals were assigned with the mean WSG =

0.66 gr/cm3 calculated from the global wood density database (Zanne et al., 2009). Whereas, in the

case of F3 and F4, the known individuals were assigned with the WSG of their binomial and the

unknown individuals were assigned with the mean WSG = 0.66 gr/cm3. It is clear from the graph that

when height was not included in the formula, big tree AGB was overestimated. However, no particular

difference was observed in big tree AGB when using the wood specific gravity per binomial instead of

the mean wood specific gravity value. On the one hand, this result might mean that the WSG per

binomial did not influence much the big tree AGB value. On the other hand, it is important to consider

that due to many missing species, the actual identified trees account for less than 40% of the total

individuals. Consequently, only the 40% of the individuals had been assigned with their specific WSG,

whereas the rest 60% had been assigned with the mean WSG value as in formulas F1 and F2. Thus,

there is a possibility that with more species known we could have seen differences in the big tree AGB

values.

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APPENDIX 4: Statistical analysis tables

TableS1. Summary of the results from Chi-square tests analysis on the forest structure between ONF forest site that had been selectively logged 5 years before and ONF primary forest site. The chi-square tests done were: Pearson Chi-Square test, Lambda, Goodman & Kruskal tau, Phi and Cramer's V test. The analysis was done using the SPSS software.

Chi-Square Tests, ONF LOGGED - ONF PRIMARY Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 84.466a 6 .000

Likelihood Ratio 85.398 6 .000 Linear-by-Linear Association

45.448 1 .000

N of Valid Cases 2414

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 20.23.

Directional Measures

Value Asymp. Std. Error

a

Approx. Tb Approx.

Sig.

Nominal by Nominal

Lambda Symmetric .059 .014 4.212 .000

Treatment Dependent

.134 .030 4.212 .000

DBH Classification Dependent

.000 .000 .c .

c

Goodman and Kruskal tau

Treatment Dependent

.035 .007 .000d

DBH Classification Dependent

.012 .003 .000d

a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. c. Cannot be computed because the asymptotic standard error equals zero. d. Based on chi-square approximation

Symmetric Measures

Value Approx. Sig.

Nominal by Nominal

Phi .187 .000

Cramer's V .187 .000 Contingency Coefficient .184 .000

N of Valid Cases 2414

Standardised Residuals

DBH class ONF LOGGED ONF PRIMARY

2.5 – 5 4.6 -4.8 Std.Residual > ± 1.96, significant at p < .05

5 – 10 1.9 -2.0 Std.Residual > ± 2.58, significant at p < .01 10 – 20 -3.3 3.5 Std.Residual > ± 3.29, significant at p < .001 20 – 30 -1.6 1.7 30 – 40 -.2 .2 40 – 70 -.5 .6

70 and up -1.5 1.5

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TableS2. Summarising tables of the ANOVA statistical test results that was done to compare the mean aboveground biomass values in: (1) ONF forest site that had been selectively logged 5 years before and ONF primary forest site, (2) Trésor reserve primary forest and ONF primary forest site, (3) the primary forest sites Nouragues, Laussat, Trinité, Regina and Kaw Mountain in French Guiana. The analysis was done in the R environment for statistical computing.

ANOVA results, ONF LOGGED - ONF PRIMARY

Model df Sum of

Squares Mean Square F Pr (> F)

Regression 2 7169.6 3584.8 0.9088 0.4521

Residual 6 23668.3 3944.7

Total 8 30837.9 7529.5

Coefficients

Linear Hypothesis Estimate Std. Error t Pr (> │t│)

ONFL - ONFP == 0 54.271 51.282 1.058 0.571

ANOVA results, TRESOR - ONF PRIMARY

Model df

Sum of Squares Mean Square F Pr (> F)

Regression 1 148.7 148.7 0.0348 0.8611

Residual 4 17099.3 4274.8

Total 5 17248 4423.5

Coefficients

Linear Hypothesis Estimate Std. Error t Pr (> │t│)

ONFP - TRESOR == 0 9.956 53.384 0.186 0.861

ANOVA results, French Guiana

Model df Sum of

Squares Mean Square F Pr (> F)

Regression 4 34449 8612.3 0.9424 0.4546

Residual 27 246733 9138.3

Total 281182 17750.6

Coefficients

Linear Hypothesis Estimate Std. Error t Pr (> │t│)

Nour. - Lau. -35.41 51.1 -0.693 0.956

Reg. - Lau. -9.13 53.18 -0.172 1

Tri. - Lau. 63.73 53.18 1.198 0.752

Kaw - Lau. 17.73 53.18 0.333 0.997

Reg. - Nour. 26.28 53.18 0.494 0.987

Tri. - Nour. 99.14 53.18 1.864 0.36

Kaw - Nour. 53.15 53.18 0.999 0.853

Tri. - Reg. 72.86 55.19 1.32 0.681

Kaw - Reg. 26.86 55.19 0.487 0.988

Kaw - Tri. -45.99 55.19 -0.833 0.918

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TableS3. Overview of the principal component analysis (PCA) results: eigenvalues, correlation of variables to dimensions and contribution of variables to the dimensions.

PCA - eigenvalues

eigenvalue percentage of variance cumulative percentage of variance

comp 1 4.3900065253 3.658339e+01 36.58339 comp 2 2.5736619327 2.144718e+01 58.03057 comp 3 1.6511095303 1.375925e+01 71.78982 comp 4 1.2370902871 1.030909e+01 82.09890 comp 5 0.7401385239 6.167821e+00 88.26672 comp 6 0.5147733374 4.289778e+00 92.55650 comp 7 0.3452901681 2.877418e+00 95.43392 comp 8 0.2403690089 2.003075e+00 97.43392 comp 9 0.1857783008 1.548153e+00 98.98515 comp 10 0.1094123358 9.117695e-01 99.89692 comp 11 0.0122872615 1.023938e-01 99.99931 comp 12 0.0000827882 6.899016e-04 100.00000

PCA - Correlation of variables to dimensions

Dim.1 Dim.2 Dim.3 Dim.4 Dim.5

Rainfall -0.2112532 -0.1593915 0.81842053 0.25298166 0.05693826

DSI 0.0820992 -0.1142238 -0.80004186 -0.40386771 -0.05921802 Wsg 0.4973420 -0.2543148 0.19296429 -0.48986448 0.55055008 Height 0.7133671 -0.4041421 0.01958608 -0.02796016 0.32067328 DBH 0.6249709 -0.6511176 -0.03356710 0.22415404 -0.11143397 BA 0.8167244 0.3411307 -0.15431700 0.38240145 -0.09783004 Stems 10-30 -0.1532280 0.3501462 -0.39608283 0.65857461 0.49360006

Stems 2.5-10 0.1093315 0.9011851 0.04578913 -0.20078922 0.16803218

Stems sup30 0.8456109 0.2736166 -0.09814905 0.17515920 -0.08501860

AGB sup30 0.9379346 0.1773016 0.17832881 -0.08791628 -0.11430870

AGB 2.5-10 -0.1269234 0.8296459 0.22610985 -0.23235573 -0.03198098

AGB total 0.9372559 0.1937186 0.16472022 -0.07950170 -0.11077698

PCA - Contribution of variables

Dim.1 Dim.2 Dim.3 Dim.4 Dim.5

Rainfall 1.0165800 0.9871402 40.56739765 5.1734074 0.4380215

DSI 0.1535369 0.5069458 38.76587019 13.1849008 0.4737996 Wsg 5.6343656 2.5129958 2.25516327 19.3977121 40.9525212 Height 11.5920705 6.3462431 0.02323375 0.0631943 13.8935280 DBH 8.8972215 16.4727989 0.06824200 4.0615495 1.6777304 BA 15.1944818 4.5215789 1.44228689 11.8205494 1.2930982

Stems 10-30 0.5348242 4.7637310 9.50158720 35.0597303 32.9183004

Stems 2.5-10 0.2722861 31.5556027 0.12698395 3.2589627 3.8148011

Stems sup30 16.2883064 2.9089307 0.58344012 2.4800733 0.9765959

AGB sup30 20.0391783 1.2214445 1.92604807 0.6247945 1.7654099

AGB 2.5-10 0.3669595 26.7444757 3.09644282 4.3642074 0.1381881

AGB total 20.0101895 1.4581128 1.64330408 0.5109183 1.6580057