linking species richness, litter chemical diversity...

LINKING SPECIES RICHNESS, LITTER CHEMICAL DIVERSITY AND SOIL CARBON DYNAMICS IN THE ATLANTIC FOREST, BAHIA, BRAZIL

By

KIMBERLY Y. EPPS

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2009

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© 2009 Kimberly Y. Epps

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To Vera Jean and Omar Jamal, in whose hearts no bitterness could take root

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ACKNOWLEDGMENTS

Little can be accomplished in solitude. From its inception to its completion, this work is

beholden to a cast of thousands. Foremost, I thank my mother, Yvonne D. Epps, for her

steadfast encouragement and unconditional love, my family consisting of the Shareef-Epps-

Minter-Perkins-Justice-Runge Units, and its drafted members Joyce Dow, Toni Greene-Garner,

and Edith Yamanoha.

My life of science began at my kindergarten discovery that stars were fiery balls of gas.

Since then I have been fortunate to have been influenced by phenomenal teachers and mentors

along the years. I express sincere gratitude to Mrs. Rosenthal (3rd grade) and Mrs. Millet (4th

grade) of P.S. 95Q; Mrs. Proimos (6th grade) and Mr. Ytuarte (7th grade) of the Immaculate

Conception School of Jamaica; Mme. Gerton (French), Mr. Gordon (Physics), Mr. Greenberg

(Geometry), and Mr. Kelly (English) of Bronx Science; Prof. Shirlynn Spacapan (Psychology)

and Prof. Art Benjamin (“Fun Math”) of HMC; Dr. Paul Marcotte (International Agricultural

Development) , Dr. Bill Rains (Agronomy), and Dr. Kate Scow (Soil Microbiology) of U.C.

Davis. For training my eyes, ears, head and hands, I thank Sheila Pinkel (Photography,

Pomona), Marti Kanin and Julie Hochman (‘Cello). For unparalleled training in field grit and

economy, I am indebted to Dr. Eliska Rejmankova of U.C. Davis.

Here at my current home in Gator Country, I thank my doctoral committee for expanding

my horizons while tightening my scientific investigation skills: Wendell Cropper for introducing

me to the wonders of open source code and to the genius of Ramon Margalef; Willie Harris for

being a model thinker and professor; Quintino Araújo for my immersion in the scientific and

artistic culture of cacao; and P.K. Nair and Nigel Smith for grounding my inquiries in application

and human need. Adding to the embarrassment of riches have been my “behind-the-scenes”

advisors, Jim Reeves (USDA); Nairam Barros (UFV), Jack Ewel (UF); Al Medvitz (UCD);

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Mark Van Horn (UCD); Arlicélio de Queiroz Paiva (UESC); Rosana Higa (EMBRAPA

Floresta), George Sodrê (CEPLAC); Yuncong Li (UF); and Virupax Baligar (USDA) with whom

it would be a pleasure and honor to consider future research plans.

For bringing me up to speed on soil microbiology and the wonders of Biolog, I thank Cory

Krediet and Max Teplitski. For my crash course in the Atlantic Rain Forest I am grateful for the

technical orientation of André M. Amorim and José Limão da Paixão of the CEPLAC Herbarium

as well as to my dear friend, right hand, and mateiro superior, Agnaldo Oliveira da Silva. I

acknowledge the support of the community of the Assentamento Frei Vantuy and in particular,

the leadership of Maïsa Fontana and Marlene Rebouças. The expertise and daily efforts of Dr.

Helena Serôdio, José Nelson Machado, Celso, Claudionou, Osmario of CEPLAC-CEPEC;

Edilson Fernando da Silva, “Seu” José Alberto, Luis Claudio de Almeida Barbosa, Antônio Lelis

Pinheiro, Verónica, and Aldemir at the Federal University of Viçosa were indispensible to my

work, as were the time and tutelage of the inimitable DRIFTS-Squad, Barry Francis and Tanesha

Simmons of USDA-EMBUL.

For their willingness to brave dangers ranging from vigilante wasps and busted boots to

siever’s lung, pipettor’s thumb and dishpan hands, I thank my Brothers and Sisters in Soil past

and present: Dalton Abdala, Kahlil Apuzen-Ito, Elena Azuaje, Christine Bliss, Lizette Borges

Gómez, Oldair Vinhas Costa, Eric Carvalho, Joy Futrell, Antônio and Emanuela Gama-

Rodrigues, Sarah Johnson, Francisco Lópes Neto, Isabel Lopez-Zamora, Melissa Martin, Edson

Márcio Matiello, Marina Morales, Deoyani Sarkhot, Shinjiro Sato, Sharon Schnabel, Lauren

Serra, Lisa Stanley, Aja Stoppe, Dashuai Sun, Ken van Rees, Marcela Quintero, and especially,

Barbara Cade-Menun for leading the way down the dirt path.

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For reminding me that life is not a one-note samba, but rather a frevo, forró and at times a

choro, I thank Abby Lóces; Ana Amelia Guimarães Araujo; Anthony Heric; Arceli Trindade da

Silva; Ayesha Nibbe; Carol Souza; Charlotte Norris; David Giles; David Healy; Flora Piasentin;

Francisco “Chico” de Vale; Glória Fidelis da Paixão; Jina Kim; Justine Di Fiore; Katie Painter;

Kipp Sutton; Larry Dieterich; the “Rainha das Aguas”, Maria Apareçida; Maria Mariano; Maura

Barros; Michelle Young; Morena Maia; Neyde Alice Pereira; “ O Paulozão”; Prashanth Ak;

Rozeli Shiroma; Scott Looney; and Sylvia Ward. I am grateful for those who kept my heart and

belly fed – Renata Santiago, Judite de Ilhéus; Regina Alvez Ferreira; Jean Rissman; Karen van

Epen; Danielle Calin; Paty Guerra; Gustavo Dantas; Ana Elisa Del’Arco; Cecilia and Wilton

Carvalho; Halter Maia; Ivani de Brito; Sônia Sampaio, and Gina Gonçalves dos Santos.

This work was performed under the authorization of CNPq and IBAMA Permit No. RMC -

004/05. I am grateful to the kind attentiveness of Dr. Francisco Guerra who led us along the way

to authorization. For their financial support, I thank the generous contributions of the University

of Florida Alumni Fellowship; the Graduate Women in Science-Nell I. Mondy Award; the Ford

Foundation Pre-Doctoral Diversity Fellowship and the Epps-Tadross-Minter-Painter Student

Emergency Funds.

Above all, I welcome the opportunity to once again express my deep gratitude for the

otherworldly patience, good humor, cheerleadership and translation abilities of my advisor and

role model, Nicholas B. Comerford.

Lastly, for giving me the home of my spirit, a second mother tongue, and a very, very fine

dog, I thank Bahia, herself.

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TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...............................................................................................................4

LIST OF TABLES...........................................................................................................................9

LIST OF FIGURES .......................................................................................................................11

ABSTRACT...................................................................................................................................13

CHAPTER

1 INTRODUCTION ..................................................................................................................15

2 CHEMICAL DIVERSITY – HIGHLIGHTING A SPECIES RICHNESS AND ECOSYSTEM FUNCTION DISCONNECT.........................................................................17

Introduction.............................................................................................................................17 Materials and Methods ...........................................................................................................22

Characterization of Leaf Tissue.......................................................................................22 Mathematical Treatment of Spectral Data.......................................................................23

Results and Discussion ...........................................................................................................24 Experimental Ramifications of Chemical Diversity........................................................27

Conclusion ..............................................................................................................................30

3 THE EFFECT OF CHEMICAL IDENTITY AND CHEMICAL DIVERSITY ON THE DECOMPOSITION OF TROPICAL LEAF MIXTURES.....................................................38


Leaf Material Collection and Characterization ...............................................................41 Calculation of Chemical Diversity ..................................................................................42 Incubation Study..............................................................................................................43 Microbial Community-Level Physiological Profiling.....................................................45 Statistics...........................................................................................................................47

Results.....................................................................................................................................49 Species Identity on Carbon Mineralization of Mixtures .................................................49 Chemical Identity on Carbon Mineralization of Mixtures ..............................................50 Identification of Functional Chemical Traits ..................................................................52 Microbial Functional Diversity and Non-additive Effects ..............................................53

Discussion...............................................................................................................................54 Importance of Species Identity and Chemical Identity on Carbon Mineralization .........54 Which Traits? ..................................................................................................................57 Microbial Underpinnings of Non-Additive Effects.........................................................59

Conclusion ..............................................................................................................................60

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4 TREE SPECIES DIVERSITY AND NUTRIENT CYCLING POTENTIALS OF SMALL-SCALE CACAO PRODUCTION SYSTEMS AND SECONDARY FOREST FRAGMENTS IN SOUTHERN BAHIA, BRAZIL ..............................................................91


Site Selection and Characterization.................................................................................94 Species Inventory and Tree Diversity .............................................................................95 Forest Floor .....................................................................................................................96 Litterfall Production ........................................................................................................96 Soil Nutrient Status..........................................................................................................96 Statistical Analysis ..........................................................................................................97

Results and Discussion ...........................................................................................................98 Tree Density and Species Inventory................................................................................98 Litter Production, Standing Biomass.............................................................................102 Nutrient Inflows through Litterfall ................................................................................103 Soil Nutrient Status........................................................................................................104

Conclusion ............................................................................................................................106

5 OVERVIEW AND SYNTHESIS.........................................................................................127

APPENDIX

A USING INFRARED SPECTROSCOPY TO DETERMINE THE RELATIVE CONTRIBUTION OF ORGANIC INPUTS TO SURFACE SOIL ORGANIC MATTER..............................................................................................................................132

Introduction...........................................................................................................................132 Materials and Methods .........................................................................................................133

Study Site and Sampling ...............................................................................................133 Single-species stands..............................................................................................133 Mixed-species stands..............................................................................................134

Sample Preparation........................................................................................................135 Spectral Analysis and Isotopic Carbon Analysis...........................................................135 Statistical Analysis ........................................................................................................135

Results and Discussion .........................................................................................................136 Relative Importance of Above- and Belowground Inputs.............................................136 Interpretation .................................................................................................................138

Conclusion ............................................................................................................................140

LIST OF REFERENCES.............................................................................................................147

BIOGRAPHICAL SKETCH .......................................................................................................165

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LIST OF TABLES

Table page 2-1 Published mineral nutrition studies reporting total nutrient concentrations used to

investigate the chemical diversity (CDQ) and species richness relationship. ....................32

3-1 Initial foliar total nutrient concentrations of 10 tropical tree species used in the incubation study. ................................................................................................................64

3-2 Species composition and abbreviation codes of the 21 leaf mixtures observed in the incubation study. ................................................................................................................65

3-3 Chemical diversity indices of leaf mixtures according to the mode of chemical characterization of material: total nutrient concentration (Total), mid-infrared (MIR) and near-infrared spectroscopy (NIR). ..............................................................................66

3-4 Kendall’s Tau correlation coefficient of total nutrient concentrations with decomposition responses of leaf-mixtures.........................................................................67

3-5 Summary of repeated measures ANOVA for effects of mixture composition on net respired CO2 of 21 leaf mixtures at Day 80.......................................................................68

3-6 Summary of factorial ANOVA for effects of mixture composition on the carbon mineralization rate constant, a, of 21 leaf mixtures...........................................................68

3-7 Expected and observed carbon mineralization rates and non-additive effect of paired litter treatments in incubation study...................................................................................69

3-8 Summary of factorial ANOVA for effects of mixture composition on interactions of net CO2, I net CO2, and the C mineralization rate constant, I a, of 21 leaf mixtures............70

3-9 Summary of selected successful models† relating chemical identity (1/λ) to net evolved CO2 and C mineralization rate of 21 leaf mixtures. .............................................71

3-10 Summary of successful models† relating chemical identity (1/λ) and chemical diversity (CDQ) on C mineralization rate of 21 leaf mixtures. ..........................................71

3-11 Summary of successful models† of the relationship of chemical identity and chemical diversity on the decomposition responses of INGA leaf mixtures (n=7). ........................72

3-12 Summary of successful models† of the relationship of chemical identity and chemical diversity on the decomposition responses of EMBA leaf mixtures (n=7).........................73

3-13 Summary of successful models† of the relationship of chemical identity and chemical diversity on the decomposition responses of PAUM leaf mixtures (n=7).........................73

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3-14 Discriminant analysis of non-additive effects observed in the C mineralization rates of 21 leaf mixtures, performed on the first four PCA factor scores of the spectra of key and companion species................................................................................................74

3-15 Spectral traits of key species (MIR) contributing to successful models of leaf-mixture decomposition processes and their potential physiochemical interpretation. ....................75

3-16 Kendall’s Tau correlation between microbial functional diversity (Gini coefficient) and mean substrate use of 31 EcoPlate substrates and the decomposition responses of mixed leaf treatments.. .......................................................................................................76

4-1 Soil chemical properties and particle size distribution of cacao and secondary forest study sites in the Assentamento Frei Vantuy, Ilhéus, Bahia, Brazil. ...............................109

4-2 Summary of the floristic diversity of four cabruca sites (0.25 ha) located in the Frei Vantuy Settlement, Ilhéus, Bahia, Brazil.........................................................................110

4-3 Summary of the floristic diversity of four secondary forest sites (0.25 ha) located in the Frei Vantuy Settlement, Ilhéus, Brazil.......................................................................111

4-4 Phytosociological parameters (excluding cacao) of cabrucas in southern Bahia observed in other studies..................................................................................................112

4-6 Tree species (≥ 5 cm DBH) identified in the selected study areas of cabruca and secondary forest in the Assentamento Frei Vantuy, Ilhéus, Bahia, Brazil. .....................114

4-7 Mean total nutrient concentrations of surface soil (0-10cm) and litter and total annual input of nutrients through litterfall in traditional cacao systems and adjacent secondary forest sites in the Assentamento Frei Vantuy, Ilhéus, Bahia, Brazil. .............118

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LIST OF FIGURES

Figure page 2-1 Chemical diversity (CDQ) as a function of species richness of eight litter species

native to the Atlantic Forest region of southern Bahia. .....................................................33

2-2 The effect of the removal of species from a mixture on chemical diversity under assumption of equal species abundance, and DRIFTS characterization of the assemblage in Fig. 2-1. ......................................................................................................34

2-3 The relationship of foliar CDQ diversity with species richness of two different temperate forest communities. . ........................................................................................35

2-4 The idiosyncrasy of additive studies..................................................................................36

3-1 Diffuse reflectance infrared spectra of 10 litter species.....................................................77

3-2 Chemical diversity (normalized to the maximum value) of all possible mixtures of 10 selected tropical species as a function of species richness, as characterized by total nutrient concentrations. ....................................................................................................78

3-3 Chemical diversity (normalized to the maximum value) of all possible mixtures of 10 selected tropical species as a function of species richness, as characterized by mid-infrared spectroscopy (MIR). ............................................................................................79

3-4 Chemical diversity (normalized to the maximum value) of all possible mixtures of 10 selected tropical species as a function of species richness, as characterized by near-infrared spectroscopy (NIR). ............................................................................................80

3-5 Carbon mineralization of ten individual tropical species and the control treatment. . ......81

3-6 The effect of key species on decomposition responses after 80 days................................82

3-7 Dynamic nature of non-additive effects on cumulative evolved CO2 between litter pairs over the course of incubation. ...................................................................................83

3-8 Interaction effects of nitrogen-fixing and non nitrogen-fixing leaf mixtures.. ..................84

3-9 Predicted versus observed interactions occurring in C mineralization rate constant and net evolved CO2 (Day 80) of the 21 mixed-litter treatments. ...................................85

3-10a Chart of spectral traits, one each from key and companion species, contributing to successful models of the form response ~ 1/λ key + 1/λ comp where response is net evolved CO2 (Day 30).. ......................................................................................................86

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3-10b Chart of spectral traits, one each from key and companion species, contributing to successful models of the form response ~ 1/λ key + 1/λ comp where response is net evolved CO2 (Day 80).. ......................................................................................................87

3-11 Chart of spectral traits, one each from key and companion species, contributing to successful models of the form response ~ 1/λ key + 1/λ comp where response is the carbon mineralization rate constant, a. ..............................................................................88

3-12 Principal components analysis of the single-substrate utilization profile of the 31 microbial communities litter treatments extracted from the final time step the incubation study. ................................................................................................................89

3-13 Mean substrate use (AWCD) and functional diversity (GINI) of microbial communities extracted from 21 litter-pair treatments grouped by key species. ................90

4-1 Location of study area among land use types in the municipality of Ilhéus, Bahia, Brazil................................................................................................................................119

4-2 Approximate locations of observed traditional cacao and secondary forest study sites in the Assentamento Frei Vantuy, Ilhéus, Bahia, Brazil..................................................120

4-3 Example of forest structure of secondary forest fragments.. ...........................................121

4-4 Example of forest structure of cabruca systems. .............................................................122

4-5 Diameter class distribution of stems in two secondary forest fragments.........................123

4-6 Monthly litterfall in cacao and secondary forest collected from the period September 2005 to August 2006........................................................................................................124

4-7 Standing biomass on the forest floor of forested plots in May and November, 2005.. ...125

4-8 Total organic matter content and organic matter distribution among particle size fractions of cacao and secondary forest sites.. .................................................................126

A-1 Sum of squared differences from of organic inputs plotted against each other at the 0-5 cm and 5-10 cm depths. . ..........................................................................................143

A-2 Isotopic carbon signatures of 18 single-species plots. .....................................................144

A-3 Sum of squared differences between forest floor litter and root litter in a multi-species forested system atop a clayey soil, based on non-combusted and combusted (cleaned) spectra. .............................................................................................................145

A-4 Sum of squared differences between forest floor litter and root litter in a multi-species forested system atop a sandy soil, based on non-combusted and combusted (cleaned) spectra. .............................................................................................................146

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

LINKING SPECIES RICHNESS, LITTER CHEMICAL DIVERSITY,

AND SOIL CARBON DYNAMICS IN THE ATLANTIC FOREST, BAHIA, BRAZIL

By

Kimberly Y. Epps

May 2009

Chair: Nicholas B. Comerford Major: Soil and Water Science

The high productivity and high species diversity of tropical forests amidst the supposed

nutrient limitation of tropical soils prompt the question, “Does the botanical diversity of tropical

forests assist in the maintenance of nutrient bioavailability?” The primary objective of this

research was to establish the relationship, if any, between plant-litter diversity and litter

decomposition employing tree species of the biodiversity hotspot of the Atlantic Rain Forest of

southern Bahia, Brazil. The central hypothesis was that the chemical diversity of leaf mixtures is

a predictor of microbially mediated processes such as leaf litter decomposition. Two aspects

central to this work were (1) the development of a chemical diversity index of leaf mixtures and

(2) the use of infrared spectroscopy to chemically characterize each species.

Field and laboratory studies targeted the impact of plant litter diversity on soil properties

and processes. The objective of the field study was to evaluate and compare cocoa production

systems and adjacent areas of secondary forest for tree species diversity, inflows of leaf litter

mass and nutrients, and stocks of nutrients in surface soils. Results indicated that despite slightly

lowered tree density and tree diversity compared to secondary forest, traditional cocoa

production systems constituted an agroforestry system that closely resembles secondary forest in

terms of biomass production and carbon and nutrient inputs to surface soil.

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A laboratory incubation study was conducted to test the ability of the chemical diversity

index and chemical identity—the comprehensive chemical fingerprint of individual species by

infrared spectroscopy—to predict rates of carbon mineralization of species mixtures. Infrared

spectral regions were identified that explained as much as 86% of the variation in carbon

mineralization of leaf mixtures. Chemical diversity as a solo parameter showed no correlation

with mixture decomposition, but in conjunction with chemical traits, predicted rates of carbon

mineralization when mixtures were grouped by the presence of a “key” species. Results suggest

that chemical diversity may be most effective in predicting the decomposition of mixtures

dominated in quantity or behavior by a single species.

CHAPTER 1 INTRODUCTION

The transformation of plant litter into soil organic matter and the attendant liberation of

nutrients form the backbone of the terrestrial biogeochemical cycle. Organic matter

decomposition is a complex series of interchanges between plant material, mineral substrate and

the decomposer community that is the precursor to an array of ecosystem functions such as the

recycling of nutrients from dead to living matter that drives ecosystem productivity and carbon

sequestration. Confident as we have become in predicting the degradation of litter derived from

single species using litter quality parameters such as initial nitrogen (N) content, or lignin:N

ratios, our predictions fail when we apply the same relationships to the decay of mixed organic

substrates. The departure of mixtures from the mean response of their individual constituents,

known as “non-additive” effects, signals a gap in our understanding of the mechanisms operating

in the breakdown of multiple and single substrates. Given the prevalence of mixed litter

decomposition, either in the context of multi-species assemblages or differently aged litterfall of

the same species, soil organic carbon models that do not incorporate the phenomenon of non-

additive effects of diverse litter mixtures are inadequate to predict soil C dynamics and potential

modifications brought by climate change.

The aim of this work was to predict the non-additive response of leaf mixtures and to

identify the initial chemical traits correlated with them. The effort to define leaf mixture

diversity in terms of leaf chemistry led to supporting investigations in the application of diffuse

reflectance infrared Fourier transform spectroscopy (DRIFTS) on the relationship between the

chemistry of organic matter inputs to soil and soil properties. Lastly, live systems of contrasting

tree species diversity of similar climate, soil and annual rates of organic biomass to soil through

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litterfall provided the basis of a field-level comparison of the effect of litter chemical diversity on

soil properties.

The role of the definition of diversity on the ability to establish firm connections between

litter mixture diversity and decomposition was addressed in Chapter 2. A new index of chemical

diversity based was introduced based on the chemical composition of constituents within the

plant assemblage.

Chapter 3 relates an incubation study directed at (i) modeling the decomposition of

mixtures constructed from the leaves of 10 tropical tree species, using either the chemical traits

of mixture constituents, the chemical diversity of the mixture, or both and (ii) determining the

relationship between the non-additive effects of leaf mixtures and the functional diversity of their

associated microbial communities.

In Chapter 4, the tree species diversity and soil nutrient status of traditional cocoa

production systems, representing a “low diversity” land use was compared to that of proximal

secondary forests within the “biodiversity hotspot” of the Atlantic Rain Forest in Bahia, Brazil.

Lastly, Chapter 5 highlights the key findings of the work, notes the inherent limitations of

the approaches taken, recommends ways of overcoming them and suggests investigatory

pathways to further elucidate the role of plant chemical diversity in terrestrial biogeochemical

cycling.

CHAPTER 2 CHEMICAL DIVERSITY – HIGHLIGHTING A SPECIES RICHNESS AND ECOSYSTEM

FUNCTION DISCONNECT1

Introduction

The spatial distribution of tree species, fungal hyphal networks, intersecting root systems

and successions of litterfall ensure that litter interactions are the rule rather than the exception in

forest ecosystems. It is well established that the chemical nature of individual leaf litters affects

rates of decomposition, the primary engine of nutrient recycling in terrestrial ecosystems (Swift

et al. 1979). However, the role of plant litter diversity on litter decomposition and nutrient

mineralization continues to be the subject of inquiry in natural and planned ecosystems of

tropical and temperate climate zones (Finzi & Canham, 1998; Rothe &Binkley, 2001; Zimmer

2002; Gama-Rodrigues et al., 2003). To date, studies of diversity effects on varied aspects of

nutrient turnover have recognized the “non-additive effect” of mixed litter interactions, but have

failed to generalize either the magnitude or direction of the interactions in relation to either the

number of species present or to functional group richness (Gartner & Cardon, 2004;

Hättenschwiler et al., 2005). In fact, the recurrent outcome of these works suggests that species

identity surpasses species diversity as the main driver of decomposition-related processes

(Wardle et al., 1997; Gastine et al., 2003; de Deyn et al., 2004). Furthermore, recent works have

shown mixture interactions among even intra-species litters, suggesting that genetic variations

within species leading to litter quality differences may be of equal or greater significance than

species designations (Madritch & Hunter, 2005; Schweitzer et al., 2005).

The hypothesis that litter interactions during decomposition stem, in part, from the effects

of resource heterogeneity on fungal, bacterial and arthropod composition and activity is not new

1 This chapter was published in Oikos Volume 116, No. 11, pp. 1831-1840, and is cited as Epps et al., 2007 in the remainder of the text.

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(Seastedt, 1984; Chapman et al., 1988; Blair et al., 1990). However, the principal approach

employed thus far to test this hypothesis—generating mixtures by varying species or functional

group richness—may be partly responsible for the current failure to validate the hypothesis.

Two assumptions encumber these experiments. First, species richness is made equivalent to

“difference” in the trait or traits pertinent to the observed activity and, second, this difference is

presumed to increase systematically with richness. Containing little explicit information of litter

quality, diversity values based on taxonomy or growth habit provide little basis for the prediction

of biochemically mediated ecosystem functions such as decomposition and mineralization.

In the few experiments that define litter mixtures by initial nutrient concentrations

(Hoorens et al., 2003), the problems persist. The selection of the grouping characteristic

assumes that the characteristic is a driving factor of the ecosystem function under investigation.

Furthermore, the criteria used to assign mixtures into categories based on the chosen nutrient

(e.g. high, medium and low concentrations or low-contrast versus high-contrast mixtures) are

arbitrary. While bearing more relevant information than taxonomic categorizations, simple

classification schemes are nonetheless problematic because they are limited to characterization

via a single nutrient. The articulation of a clear relationship between litter diversity and nutrient

cycling processes—if it exists—requires a continuous variable representative of compositional

heterogeneity that is founded on multiple attributes of litter quality.

With the future goal of linking plant biodiversity with ecosystem processes related to

nutrient cycling, I explicitly define the functional diversity of a litter (or foliar) mixture as the

degree of compositional heterogeneity between its constituents. In this paper, I present an index

of chemical diversity (CD) to quantify the compositional heterogeneity of litter mixtures as

constructed from three main components: 1) the traits used to describe the composition of the

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members of the set, 2) a coefficient of dissimilarity based on these traits, and 3) a summary

measure of the trait dissimilarity between members.

The use of foliar chemical and physical attributes as functional traits is an appropriate and

rational choice when the function to be described is decomposability. Yet, the selection of traits

is not trivial. Within a given climate regime, univariate characteristics such as total nitrogen (N),

phosphorus (P), or lignin:N ratios have been correlated to the decomposition of single-species

litters (Melillo et al., 1982; Nwoke et al., 2004; Palm & Sanchez ,1991, respectively). However,

no single litter quality variable can predict even monospecific decomposition behavior under all

conditions (Smith et al., 1998). Similarly, the traits that give rise to litter interactions may vary

according to environmental conditions, making a priori trait selection to compute a relevant

chemical diversity index a gamble. Further complicating the issue, the time and cost of the

discrete analyses commonly employed in plant tissue characterization discourage the undertaking

of exhaustive chemical inventories for a large number of species.

As a solution, I explore diffuse reflectance infrared Fourier transform spectroscopy

(DRIFTS), as a rapid and cost-effective alternative to a battery of wet chemistry assays. Infrared

spectra contain qualitative and quantitative information of inorganic and organic compounds and

improved calibration techniques enable its increasingly effective use to determine nutrient

contents and carbon profiles of feeds, manures and soils (Reeves, 1998), and even to predict

decomposition patterns of single species and organic residues (Shepherd et al., 2005).

Furthermore, DRIFTS is a non-destructive technique that can preserve potentially key

information that may be lost in traditional digests. Coupled with multivariate techniques, such as

principal components analysis and advanced linear or non-linear modeling approaches, DRIFTS

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may facilitate both the post hoc weighting of suites of traits that contribute most to chemical

diversity, and the identification of those that exert more control over litter interactions.

The choice is similarly challenging in deriving a single value to express the diversity of the

selected traits. Recently proposed continuous measures of functional diversity able to

accommodate multiple traits are functional attribute diversity, FAD2, (Walker et al., 1999);

average functional attribute diversity (Heemsbergen et al., 2004); FD (Petchey & Gaston, 2002)

and; quadratic entropy (Rao, 1982; Botta-Dukát, 2005). The advantages of quadratic entropy

that recommend it as the basis of a chemical diversity index are its ability to incorporate relative

abundance and pairwise trait differences, its flexibility to integrate multiple traits, and its

insensitivity to the number of species.

Rao’s Quadratic Entropy is defined as the average dissimilarity dij between two randomly

selected species i and j from a pool of R species with replacement. The chemical diversity index,

CDQ, is interpretable, therefore, as the mean chemical heterogeneity of a mixture, and is

calculated as

(2-1) ijj

R

i

R

jiQ dCD ρρ∑∑

= =

=1 1

where ρi and ρj are the relative abundances by biomass of species i and j, respectively, and

dij, the dissimilarity coefficient representing the compositional difference between them. I adopt

a standardized Euclidean distance dij, as a dissimilarity coefficient,

( )∑=

−=n

kjkikij tt

nd

1

21 (2-2)

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where tik and tjk are the values of chemical trait k of species i and j, respectively in

chemical trait space of dimension n. The square root, sum of squared trait differences, is divided

by the number of traits, n, such that the index is not directly affected by the number of traits

(Botta-Dukát 2005).

At once, it is necessary to consider the following contested properties of quadratic entropy

that warrant close attention to its application as a measure of functional diversity:

i) The maximum value of diversity is not necessarily reached at uniform frequency

distribution of species (evenness);

ii) The elimination of species can result in an increase in the diversity value.

The mathematical properties of quadratic entropy that delineate its behavior are well

treated in recent works by Shimatani (2001), Izsák and Szeidel (2002), Pavoine et al. (2005) and

Ricotta (2005). In summary, the metric used to calculate dissimilarity, dij, dictates the

conformity or nonconformity of quadratic entropy to accepted diversity index axioms. The ideal

metric for a well-behaved index founded on Rao’s quadratic entropy is one that is ultrametric

(see Pavoine et al., 2005). Commonly used dissimilarity measures in ecology based on rooted

trees, such as taxonomic distinctness (Clarke & Warwick, 1998), or on gene frequencies, as in

Nei’s genetic distance (Nei, 1972), are ultrametric. However, the options of dissimilarity metrics

conducive to continuous, quantitative variables such as nutrient concentrations— Euclidean

distance, Bray-Curtis coefficient (Bray & Curtis, 1957), and the Gower distance (Gower, 1971),

for example — are not. The unavoidable adoption of a dissimilarity metric that leads to

unconventional patterns of functional diversity with species richness need not discount its

validity. Chemical diversity, expressed as a weighted mean of compositional dissimilarity, lends

it great interpretive power of the description of litter mixtures per unit biomass.

21

The aim of the current study was to observe how chemical diversity changes with species

richness and how this relationship varies under differing environments or assemblages. In brief,

the study shows that the relationship between chemical diversity and species richness suggests

that detritivore resource heterogeneity, as represented by chemical diversity, varies within levels

of species richness, revealing the weakness of number of species as a predictor of litter-quality-

dependent processes. Moreover, it is shown that species identity can be confounded with

chemical diversity such that the presence of a species with unique composition can alter the

heterogeneity of a mixture. Lastly, it is also confirmed that the relationship between species

diversity and chemical trait diversity for identical species assemblages changes in response to the

environmental context. The points discussed below illustrate why predictive patterns of

decomposition behavior as a function of richness have remained elusive, and how chemical

diversity may yield new insight into the nutrient-supplying function of plant communities.

Materials and Methods

Characterization of Leaf Tissue

The examples illustrated are drawn from published and recently gathered data from

tropical and temperate forest-systems (Table 2-1). For each dataset of R species an R x n trait

matrix was constructed and standardized by subtracting the mean trait value and dividing by the

mean absolute deviation of each trait before the calculation of the dissimilarity matrix. Leaf

traits were designated as the total tissue concentrations of nitrogen (N), phosphorus (P),

potassium (K), calcium (Ca), and magnesium (Mg) expressed as percent of total dry weight.

These analyses were chosen for an initial investigation of the behavior of chemical diversity

because they are the most widely published in forest mineral nutrition studies, of which very few

provide information at a species level.

22

Two methods of chemical characterization, total nutrient analysis and diffuse reflectance

infrared Fourier transform spectroscopy, were performed on recently gathered material from the

Atlantic Forest region of southern Bahia, to assess their relative practicality. Freshly fallen leaf

litter from 35 year-old, replicated plots of eight native species were collected from the arboretum

of the Ecological Station Pau Brasil in Porto Seguro, Brazil (16º 23′ S 39º 11′ W). The site is

situated on Ultisols derived from Tertiary sediments and experiences mean annual temperature of

23ºC and mean annual precipitation of 1696 mm. Leaves were dried at 60ºC for 48 hours and

ball milled in a SPEX 8000M Mixer/Mill (SPEX SamplePrep LLC, Metuchen, NJ). Total N was

analyzed by dry combustion and total tissue concentrations of P, K, Ca and Mg were determined

by HCl digestion after combustion in a muffle furnace for 5 hours at 500º C (Miller, 1998).

Total P was determined by colorimetry using the molybdate blue method, and total cation

concentrations were measured by atomic absorption spectroscopy. For characterization via

DRIFTS, spectra were obtained from non-diluted ground samples using a Digilab FTS-7000

spectrophotometer (Varian, Inc., Walnut Creek, CA) equipped with a KBr beam-splitter, DTGS

detector, and an AutoDIFF autosampler (Pike Technologies, Madison, WI). Spectral grade KBr

was used as a background. Spectra were collected as interferograms at a resolution of 4 cm-1 in

the mid-infrared range (4000 to 400 cm-1). Sixty-four scans were averaged to form each spectral

signature and expressed in units of pseudo-absorbance (log reflectance-1).

Mathematical Treatment of Spectral Data

Before statistical analyses, raw spectral data were subjected to baseline and multiplicative

scatter correction followed by first derivative transformation to minimize variations arising from

optical effects due to particle size (Martens & Næs, 1989). Multiplicative scatter correction

compensates for additive and/or multiplicative effects in spectral data. Normalization (sample-

wise scaling) of data was not used in order to preserve information on the absolute and relative

23

amounts of chemical constituents. Each baseline-corrected spectral point was treated as a “trait”

and the Euclidean distance between the average spectra of each species was calculated as

described above. Clustering was performed by the unweighted pair-group clustering method

using arithmetic averages (UPGMA). All calculations and graphical output were performed

using the R environment for statistical computing (R Development Core Team, 2006) and code

written by the author.

Results and Discussion

The CDQ of all possible litter combinations generated from eight species native to the

Atlantic Forest characterized by total nutrient concentrations (a) and DRIFTS (b) are represented

in Fig. 2-1. Each point represents a unique combination of species drawn from the total pool, R,

at each level of species richness, S, with the number of combinations appearing at each richness

level determined by the combinatorial R!/S! (R-S)!. In all mixtures, species are present at

uniform distribution. Gray points represent mixtures containing the most chemically distinct

species, as determined by the last clustered species. To permit comparison between the two

techniques, the CDQ values were scaled to a 0-to-1 range through division by the maximum

value.

Juxtaposition of the two forms of characterization demonstrates the effect of trait selection

on CDQ. Imbiruçu (Bombax macrophyllum L.), a waxy, broadleaf species, is the most distinct

species with respect to total elemental concentrations. In contrast, using DRIFTS distinguishes

vinhático (Plathymenia foliolosa L.), a legume. DRIFTS is particularly attractive for chemical

characterization for several reasons. First, its adoption reduces the risk of neglecting critical

traits that govern litter interactions. Total nutrient concentration profiles do not contain explicit

information on carbon, whose forms, including cellulose, lipids and secondary compounds,

affect leaf decomposability. Furthermore, in a single “snapshot”, an infrared spectrum can

24

supply a time-dependent biochemical definition of substrate availability to decomposers that

takes into account the shifting importance of litter quality parameters over the course of

decomposition (Joffre et al., 2001). DRIFTS also facilitates the use of post hoc statistical

regression techniques to identify and assign weights to spectral regions (chemical traits) most

accountable for response variables, permitting an optimization process that can determine the

maximum contribution of chemical diversity to the observed response. Advantageously, this can

be accomplished without prior knowledge of the driving traits or the covariance between them.

Lastly, the inclusion of a greater number of traits, inherent in the employment of DRIFTS, also

has the effect of stabilizing computed dissimilarities between species (Ehrlich, 1964) and,

consequently, the geometry of the relationship.

One of the anticipated features of the chemical diversity versus species richness curve is

the occurrence of maximum diversity before maximum species richness is reached. This implies

that the addition of a species to a collection may reduce the overall chemical diversity of the

mixture and, conversely, that species removal may increase chemical diversity (Fig. 2-2). In its

relevance to the CDQ, the loss or gain in species is equivalent to a change in relative abundance

because functional diversity is computed across units of biomass and not species, per se.

Analogously, the attainment of maximum heterogeneity can occur at abundances outside of

evenness as a result of the dissimilarity matrix. This contentious behavior of quadratic entropy

reflects a desirable property of the CDQ in terms of exhibiting “trait dilution” of the mixture.

The occurrence of overlapping composition of foliar material means that even the inclusion of a

chemically distinct species will contribute to the homogenization of the assemblage. While not a

startling observation (the possibilities of leaf and litter chemistries are constrained by their

function as terrestrial plant tissue), the CDQ maximum suggests an upper limit to diversity effects

25

caused by compositional differences. The strength of this feature is that it enables the valid

comparison of mixtures across values of species richness and across species assemblages

because CDQ is a summary of the chemical heterogeneity per unit biomass.

The relative abundance of species combined with species characteristics determines the

overall substrate heterogeneity as viewed by decomposers (Dangles & Malmqvist, 2004). The

addition of an identical species results in a reduction of CDQ because of the increase in that

species’ relative abundance. Predictably, mixtures that approximate monocultures earn low CDQ

values. It should be noted, however, that low values of CDQ occur between similar, high-quality

species mixes (or high-quality monocultures) as it does between similar low-quality species

mixes (or low-quality monocultures). Thus, the index has the capacity to isolate the effect of

mixture heterogeneity from that of mean mixture quality on observed processes, especially at

low values of chemical diversity.

Another recognizable aspect of the CDQ-richness curve is that of Huston’s “variance

reduction effect” (Huston, 1997)—the decrease in the range of diversity values with increasing

richness as a consequence of overlapping species pools. Because the rate of this reduction is not

the same for all mixtures, it may be interpreted as a characteristic of the assemblage in question.

Figure 2-3 contrasts the envelopes of two temperate forest communities as described by total

nutrient concentrations. A slow reduction of the variation in CDQ suggests strong clustering

among the member-species such that within-group CDQ is very low but across-group

heterogeneity is high. Compact curves indicate a more gradual differentiation of species (less

clustering). Therefore, the curve is a restatement of the topology of the cluster dendrogram.

Areas worthy of further exploration include the investigation of the behavior of CDQ under

other dissimilarity metrics, as well as the comparison of CDQ with measures of molecular

26

diversity or complexity of litter mixtures. The establishment and monitoring of species

assemblages of varying CDQ in litter-mix experiments is the first call to action to test its

correlation with quality-dependent ecosystem processes.

Experimental Ramifications of Chemical Diversity

The variation of functional trait diversity with species richness is fundamental in

establishing the species diversity-ecosystem function relationship (Petchey & Gaston, 2006).

The relationship between species richness and chemical diversity consistently shows that species

diversity cannot act as a proxy for functional diversity (Naeem & Wright, 2003). Therefore,

attempts to predict ecosystem functions such as decomposition and mineralization using the

number of species are inherently doomed.

The range in chemical diversity within levels of species richness reveals the weakness of

species number to describe litter diversity in the context of detritivore-mediated processes. This

range within species richness mimics the wide error bars associated with the responses observed

in many litter-mix experiments. Because logistical constraints discourage the inclusion of all

possible combinations of species mixtures, an idiosyncratic response to increased richness is

highly probable when mixtures are chosen at random. Even studies based on additive designs do

not offer a failsafe solution because the successive addition of species is not guaranteed to

correspond with proportional increases in substrate heterogeneity. As a result, investigations of

mixtures relying on species richness are encumbered by both the chemical diversity within

richness levels and the non-uniform directionality of chemical diversity with increasing richness.

Negative, positive and idiosyncratic relationships between chemical diversity and species

richness are all possible from additive series of assemblages generated from the same pool (Fig.

2-4a). Assuming that resource heterogeneity elicits a positive (or negative), linear response of

litter decomposition, the results seen to date in litter-mix studies may be the direct consequence

27

of the choice of a richness gradient as the independent variable. The use of a monotonic

independent variable such as chemical diversity has the potential to yield a consistent

unidirectional response of decomposition with litter functional diversity.

The categorical nature of species richness also favors the conclusion that species identity,

rather than species diversity, dictates mixture behavior. As illustrated, combinations containing

the most chemically distinct species tend to exhibit greater chemical diversity, showing that

species identity helps to define mixture diversity. While unique traits may not be synonymous

with unique behavior, the fact that the addition or removal of a key species may simultaneously

result in altered mixture heterogeneity underscores the importance of appropriate experimental

design to gauge the relative influence of species diversity and species identity on nutrient release

processes.

Monitoring Potential of the Chemical Diversity Index

Foliar and litter chemical composition are routinely used as predictors of single-species

behavior under specific environmental conditions. As a summary of plant community chemical

makeup, the chemical diversity index is, by nature, the product of multiple abiotic and biotic

interactions, including but not limited to, inter-species genetic differences, inter-specific

competition, inherent soil nutrient bioavailability, climate, and, even, herbivory. Beyond the

realm of litter decomposition studies, an immediate and compelling application of the chemical

diversity index is that of the monitoring of the chemical diversity of vegetation communities in

conjunction with standard biodiversity inventories. The addition of chemical diversity in

diversity assessments permits the comparison of the functional potential of assemblages over

time and over environmental and climatic gradients, which species lists cannot provide. Figure

2-5 illustrates the patterns in chemical diversity versus richness of identical species assemblages

28

occurring over a soil chronosequence (Fig. 2-5a) and of different but proximate forest types (Fig.

5b). Figure 2-5a demonstrates that chemical traits and by extension, functional traits, are not

conserved across environments. Therefore, identical communities, as defined by taxonomical

character, are not likely to exert the same control on ecological processes in all environments

because their relative functions are defined by the very environments in which they are

encountered.

When mapped on the landscape, such information may help reveal broad scale patterns of

resource heterogeneity that may impact related ecosystem properties such as detritivore and

herbivore communities and activity (Armbrecht et al., 2004; Dehlin et al., 2006; Swan & Palmer,

2006). While mean foliar or litter nutrient concentrations may appear equal across systems, how

these nutrients are packaged and distributed among its member species has the potential to

explain trophic responses inasmuch as they are driven by resource heterogeneity. The value of

the chemical diversity index may lie in its ability to monitor the biochemical variability of plant

tissue within communities that simultaneously determines both consumer preference and

decomposition (Pastor & Cohen, 1997) (See Fig. 5b).

The mechanisms that bring about the co-existence of species may also determine how

species express themselves in the environment (Cardinale et al., 2000; Mouquet et al., 2002).

Aboveground species diversity and productivity already show firm positive relationships under

conditions of niche differentiation (Tilman, 1999). The effect of the diversity-productivity

relationship on foliar and litter chemical composition and relative species abundance may play

an important role in linking aboveground species diversity to belowground diversity and

processes. Because litter and foliar properties (species, abundance and chemistry) of plant

communities vary ontogenetically and seasonally, such aboveground-belowground linkages are

29

necessarily contextually dependent (Blomqvist et al., 2000). The chemical diversity index is a

dynamic variable that awaits broad-scale testing and application in varied systems under multiple

conditions.

The current limitation of the index is the lack of sufficient data with which to determine the

statistical significance of diversity differences between mixtures. Due to the variability of

composition within species and the propagation of error in the calculation of the index, it is

difficult to discriminate levels of chemical diversity between assemblages with confidence. This

is especially true with increased species richness as the pair-wise error in trait differences is

compounded during the computation of the index (Fig. 2-4b). However, for low species richness

it is possible to confirm significant differences between mixtures. Yet, the variation of

litter/foliar composition within species and even within individuals need not compromise the

viability of the chemical diversity index. Rather, it calls for careful and systematic sampling

techniques in order to first make credible claims to species differences and later to predict

responses that may occur as a result.

Conclusion

The poor predictability of litter species diversity on decomposition processes may be a

consequence of our current failure to appropriately measure litter functional diversity. A

measure of functional diversity based on species chemical composition, the chemical diversity

index, CDQ, is shown to vary within species richness levels, which may account, in part, for the

idiosyncratic and species-dependent responses observed in early litter-mix studies. The potential

for chemical diversity to correlate with species interactions as they occur in litter-mix studies is

tenable because it is derived from variables known to predict the behavior of individual species.

Because of its ability to distill community patterns of foliar nutrient distribution under

different environmental conditions, the chemical diversity index may also prove a valuable

30

31

monitoring tool, supplementing existing indices based on taxonomy. Whether chemical diversity

can be used to predict how plant diversity influences environmental processes on a larger scale

remains to be established.

Table 2-1. Published mineral nutrition studies reporting total nutrient concentrations used to investigate the chemical diversity (CDQ) and species richness relationship.

Region Leaf form/ context foliage (f); litter (l)

Reference

Brazilian Atlantic Forest l, arboretum, tropical lowland rain forest

(Montagnini et al., 1995)

New Hampshire* f, temperate forest (NERC, 2006) Estonia f, meadow (Niinemets & Kull, 2003) Jamaica f, tropical montane rain forest (Tanner et al., 1977) Hawai’i f, tropical montane rain forest (Vitousek et al., 1995) * Data from the “MAPBG Project” was selected where all five nutrient concentrations (N, P, K, Ca and Mg) were reported and more than three samples were listed. Nutrient concentration values were averaged within each species.

32

33

A B

Figure 2-1. Chemical diversity (CDQ) as a function of species richness of eight litter species

native to the Atlantic Forest region of southern Bahia as characterized by A) total nutrient concentrations, N, P, K, Ca and Mg, and by B) diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) under equal species distribution. Gray points are assemblages containing the most chemically distinct species as illustrated in their respective dendrograms. The most chemically distinct species shifts from imbiruçu (Bombax macrophyllum L.) to vinhático (Plathymenia foliolosa L.) employing DRIFTS. Assemblages are shown under assumption of equal species abundance (This study, 2007).

-20% -15% -10% -5% 0% 5%

Amescla

Arapaçu

Arapatí

Gindiba

Imbiruçu

Jatobá

Sapucaia

Vinhático

Spec

ies

Rem

oved

Relative Change From CDQFinal

Figure 2-2. The effect of the removal of species from a mixture on chemical diversity under

assumption of equal species abundance, and DRIFTS characterization of the assemblage in Fig. 2-1.

34

35

Figure 2-3. The relationship of foliar CDQ diversity with species richness of two different

temperate forest communities. A) Estonia, wooded meadow (Niinemets & Kull, 2003); B) New Hamphire, conifer forest (NERC, 2006).

A A B

36

Figure 2-4. The idiosyncrasy of additive studies. A) Negative, positive and idiosyncratic

relationships between chemical diversity and species richness are possible from three series of additive mixtures derived from the same species pool. B) The same series depicted with error bars (Montagnini et al., 1995).

B

CD

Q

0

0.1

0.2

0.3

0.4

0.5

0.6

0 4 8 12 16 20 24

Species Richness

NegativePositiveIdiosycratic

-0.2

0

0.2

0.4

0.6

0.8

1

0 4 8 12 16 20 24

Species Richness

CD

Q

A

37

A

B

Figure 2-5. Potential of CDQ as a monitoring tool across communities. A) Identical species assemblages across a soil chronosequence (Vitousek et al., 1995). B) Different species assemblages in different environments (Tanner, 1977). Assemblages are shown under assumptions of equal species abundance.

CHAPTER 3 THE EFFECT OF CHEMICAL IDENTITY AND CHEMICAL DIVERSITY ON THE

DECOMPOSITION OF TROPICAL LEAF MIXTURES

Introduction

Decomposition of plant litter releases nutrients from dead organic matter and makes them

potentially available again for uptake by plants and is a primary conduit of carbon into the soil

(Berg & McClaugherty, 2008). In the latter half of the 20th century, concern over accelerated

rates of loss of plant diversity and shift in species composition in vegetation communities fueled

research into the role of individual species and plant diversity on ecosystem processes including

soil carbon dynamics (Hobbie, 1992; Silver et al., 1996; Hooper & Vitousek, 1998; Díaz et al.,

2004; Rodríguez-Loinaz et al., 2008) Gleixner et al., 1995; Bunker, 2005; Sauer et al., 2007;

Gnankambang et al., 2008; Schulp et al., 2008). Although the basic factors governing litter

decomposition—litter quality, environment, and the decomposer community—have been

recognized for nearly a century (Tenney &Waksman 1929), the decay of mixtures comprising

multiple species, which typify most ecosystems, has proven more resistant to generalization

(Gartner & Cardon, 2004 ). Understanding the processes involved in the breakdown of mixed-

species litter is important in understanding the role of species diversity in natural ecosystem

functioning and in designing agronomic ecosystems that cycle nutrients and sequester carbon

effectively. The usual approach in attempting to understand mixed-species decay involves

observation of mixtures of species, defined by species or functional group richness. This study

took another approach that defined individual and mixtures of foliar material by chemical

identity and chemical diversity based on infrared spectroscopy. Subjugating taxonomic identity

for chemical composition, I sought to predict the respiration rates of leaf mixtures.

The enigma of mixed-substrate decay persists, in part, because of our incomplete

understanding of single-substrate decomposition. The principal controls of litter decomposition

38

are climate, soil decomposer community, soil fertility and litter physical and chemical properties.

Under constant environmental conditions of climate and soil mineralogy, litter biochemistry is

the determinant of litter decomposition (Swift et al., 1979; Coûteaux et al., 1995). Controlling

for physical properties such as particle size that contribute to “quality” (Rovira & Vallejo, 2002),

litter chemistry has been shown to dictate litter mass loss (Preston & Trofymow, 2000; Pérez-

Harguindeguy et al., 2001; Raich et al., 2007), nutrient mineralization rates (Nwoke et al., 2004)

and the composition and activity of the decomposer community (Salamon et al., 2006).

Chemical traits shown to predict single-litter decomposition rates include C:N, lignin:N,

polyphenolic content, and N, P, Ca, and, recently Mn concentrations (Aber et al., 1990; Palm &

Sanchez, 1991; Hättenschwiler & Vitousek, 2000; Trofymow et al., 1995; Nwoke et al., 2004;

Berg et al., 2007). However, the relative importance of chemical traits and their relationship

with litter decomposition vary with environmental context and, in particular, with site fertility

(Vanlauwe et al., 1997; Seneviratne, 1999). Hence, generalizations of the relationship between

litter chemistry and decomposition rates of single-species litter remain understandably coarse.

The phenomenon of “non-additive effects” of mixed-litter decomposition is the

observation that when two species decay together, the rate of decomposition of the resultant

mixture is not the mean of the rates as weighted by their appearance in the mixture. At times

positive and at others negative, the commonness of non-additive effects warns that patterns of

individual species are insufficient to predict the decay of litter mixtures (Gartner & Cardon,

2004; Hättenschwiler et al., 2005). Contrasting litter chemistries has been proposed to control

the interactions observed in mixed litter decomposition (Seastedt, 1986; Chapman et al., 1988)

and the roster of initial litter characteristics purported to drive mixed-litter decomposition—

lignin:N, Ca, P and N concentrations—mirror those that predict single-litter decay (Hu et al.,

39

2006; Liu et al., 2007; Amatangelo, 2008; Gnankambary et al., 2008; Pérez-Harguinedeguy et

al., 2008).

Characterization of litter mixtures by contrasting chemical qualities (high versus low C:N

or initial N concentrations), as in the studies by Smith and Bradford (2003) and Hoorens et al.

(2003), offer representations of quality heterogeneity of litter mixtures. However, the

shortcomings of this approach are the a priori selection of quality characteristics that may not be

relevant to the response variable under the conditions of the study and the arbitrary classification

of “high” and “low” nutrient levels.

A potential solution to the advance selection of chemical traits is diffuse reflectance

infrared Fourier transform spectroscopy (DRIFTS). The infrared absorption spectrum of a

sample provides a summary of its mineral and organic composition, as molecular bonds absorb

energies in proportion to their concentration (Stuart, 1997). Extensive applications of infrared

spectroscopy in the analysis of organic compounds has led to the identification of spectral

regions and their associated chemical moieties, including compounds relevant to leaf chemistry

and decomposition, such as lignin and cellulose (Crawford & Crawford, 1980; Card et al., 1988;

Reeves, 1993). Infrared spectroscopy as the basis for the characterization of leaf species and a

means for the determination of the compositional diversity of leaf mixtures may reduce the risk

of omitting litter quality parameters critical to the analysis. Furthermore, the use of infrared

spectroscopy in conjunction with the chemical diversity index, a summary of the compositional

heterogeneity of species assemblages (Epps et al., 2007), enables a continuous, quantitative

metric of the diversity of leaf mixtures.

The purpose of this study was to predict rates and non-additive effects of microbial

respiration on leaf mixtures using chemical identity and chemical diversity and to identify traits

40

or suites of traits that consistently correlated with mineralization rates. In seeking to understand

the underlying microbial interactions that give rise to litter interactions, I tested the hypothesis

that positive non-additive effects (elevated microbial respiration) were correlated with greater

microbial functional diversity as defined by community-level physiological profiling.


Leaf Material Collection and Characterization

Fresh leaves were collected from mature trees of 15 species common to the Atlantic Forest of

southern Bahia and the “Zona da Mata” of Minas Gerais in the arboretum of the Federal

University of Viçosa in the state of Minas Gerais, Brazil, (20° 46' S, 42° 52' W; 651 m mean

altitude, 21°C MAT, 1450-1800 mm MAP) between February 2007 and May 2007. Samples

were gathered from one to five individuals, typically from the lower canopy, and composited, in

order to gain sufficient material for subsequent analyses. Leaves were discarded if they were

very young, very old or showed signs of excessive herbivory or other damage. To remove dust,

insects, and foreign matter, leaves were immersed quickly in a mild detergent solution and the

surface residues were loosened by gentle rubbing. Afterward, leaves were quickly submerged in

deionized water to rinse away residues; blotted dry; loosely packed in large brown paper bags;

and dried in a convection oven at 65°C for 72 hours, then ground to 10 mesh (< 1mm). Petioles

were removed before grinding, but the rachis and petiolules of compound leaves were included.

Ground leaf material was stored in paper envelopes or small brown paper bags within sealed

plastic bags and refrigerated at 4°C until analyzed.

Total nutrient concentrations of foliar material were determined using combustion

followed by an HCl-digest (Miller et al., 1998). Total P was determined by the ascorbic acid

reduction molybdate-blue procedure and read for blue color development at 880nm in a 1-cm

cell. Total K was determined using atomic absorption spectroscopy (Perkin Elmer AA200,

41

Perkin Elmer, Inc., Watham, MA) with an oxygen-acetylene flame. Total Ca and Mg

concentrations were also determined by atomic absorption spectroscopy using a nitrous oxide-

acetylene flame after the addition of 0.25% LaO3 at 1:10 (w:v). Total carbon and nitrogen were

determined by gas combustion using a Shimadzu TOC-VCPH Analyzer (Shimadzu Scientific,

Columbia, MD). Analyses were performed in quintuplicate with the inclusion of NIST standard

reference material (SRM) 1545 (peach leaves) and SRM 1515 (apple leaves).

Foliar material was also characterized using diffuse reflectance infrared Fourier transform

spectroscopy in the near-infrared (NIR: 1000 – 2500 nm) and the mid-infrared (MIR: 2500 –

25000 nm) regions. Spectra were obtained on a DigiLab FTS-7000 FTIR instrument (Varian,

Inc., Walnut Creek, CA) equipped with a Pike AutoDiff 60-cup autosampler (PIKE

Technologies, Madison, WI), and either a KBr beam-splitter and a deuterated trigylcine sulfate

(DTGS) detector (MIR) or a quartz beam-splitter and indium antinomide (InSb) detector (NIR).

Spectra were collected as interferograms normalized against reference spectra of KBr and sulfur

in the mid- and near-IR, respectively, in order to remove the effects of the detector and the

environment (moisture, CO2). Each spectrum resulted from the average of 64 scans taken at a

resolution of 4 cm-1 and were recorded in units (1/log reflectance). Spectra were baseline

corrected using standard normal variate correction (Martens & Næs, 1989). In addition to the ten

species, 21 leaf mixtures, prepared by homogenizing the ground foliage of two species in equal

proportions of dry mass (see Incubation Study), were also scanned for a “composite” spectrum.

Calculation of Chemical Diversity

Using three separate approaches of chemical characterization of leaf material (total nutrient

concentrations, MIR and NIR spectroscopy), the chemical diversity index, CDQ, a measure of

chemical heterogeneity of a mixture (Epps et al., 2007) was calculated for 21 leaf mixtures (see

42

Incubation Study). The chemical diversity of a mixture is defined as the mean dissimilarity in

chemical traits of two randomly selected species from a mixture of R species

ijj

R

i

R

jiQ dCD ρρ∑∑

= =

=1 1

(3-1)

where ρi is the relative abundance by dry weight of species i and the dissimilarity, dij, is the

Euclidean distance between all n traits describing species i and j (3-2). For this experiment R = 2.

( )∑=

−=n

kjkikij ttd

1

2 (3-2)

Incubation Study

In February 2008, a decomposition experiment was conducted to measure the rate of carbon

mineralization of leaf mixtures. Ten tropical tree species common to the secondary forests of the

Atlantic Rain Forest region in southern Bahia (Table 3-1) were selected for this study from a

preliminary study of 15 species. In the preliminary study, 0.5 g of each foliage type was

incubated alone in 25 g of soil at 60% field capacity in the dark for 30 days at 28°C. Treatments

were replicated four times. Evolved CO2 was assayed at regular intervals using the base trap

method to gauge the relative microbial respiration rates associated with each species. Inga

affinis (= INGA), and Balfourodendron reidelianum (= PAUM) exhibited lowest and highest

mineralization rates o the 15 species and were. Cecropia sp. (EMBA), displayed a middle

response. The remaining seven species were selected in order to maximize the representation of

species among vegetation families and to span the broadest possible range of litter chemical

composition as determined by the chemical diversity index of pairs based on MIR

characterization. Only one of the species chosen was an exotic species. Artocarpus heterophylla

(= JAQU) was included because of its dominance in many secondary forest systems in southern

Bahia (Mori et al., 1983; Sambuichi et al., 2008). Based on these preliminary findings (not

43

reported), 32 treatments were generated: 10 single-species treatments, 21 litter-pairs and a

control with no leaf additions (Table 3-2). The paired treatments were formed on the basis of the

three “key” species, each one of which was paired with seven “companion” species.

Ground leaves were mixed with acid-washed and rinsed sand (100 mesh) in sterilized,

sealed plastic containers in the ratio of 0.500 (± 0.001) g litter to 25.000 (± 0.005) g sand,

equivalent to 2.0% OM by dry weight. Quartz sand was used in order to minimize the effects of

sorption of nutrients (especially phosphorus). Leaf mixtures consisted of equal dry mass of each

species. To each container, 2.0 ml of inoculant was added (8% gravimetric water content or

approximately 60% field capacity). The inoculant was prepared from a suspension made by

incubating 10 g of topsoil with 1 L of sterilized, deionized water for three days in the dark at

28°C. The suspension was vacuum-filtered through a 0.45 μm filter, and the resultant filtrate

was diluted 1:10 with sterilized deionized water. A control treatment (sand plus inoculant, no

foliar material), and a blank treatment (no sand, tissue or inoculant) were included. All

treatments were replicated four times. A completely randomized design was used for the

placement of microcosms in the incubator. Microcosms were incubated in the dark at 28°C, in a

forced-air convection incubator (Isotemp, Thermo Fisher Scientific, Waltham, MA).

Microbially respired CO2 was measured by the alkaline trap method at 11 intervals over 80 days

(2, 4, 10, 14, 22, 30, 40, 50, 60, 70, and 80 days). A scintillation vial containing 20.0 ml of 0.5

M NaOH was placed in each container. At each time interval, the exposed base traps were

removed and capped and the vials placed in sealed containers until titration. Cumulative evolved

CO2 was expressed per gram of added carbon, calculated as the total dry weight of added

substrate multiplied by the loss on ignition.

44

Specific carbon mineralization rates were obtained by fitting the model (3-3) to the

change in cumulative CO2 evolved over time, t, measured in days.

tatCOCum ln)(2 = (3-3)

The time-dependent rate, k, is equal to the rate constant a divided by the time t. Because the

quantity, ln t, is a constant at a specified time interval and the same for all treatments, the value

of the rate constant, a, was used interchangeably with the carbon mineralization rate.

Microbial Community-Level Physiological Profiling

Potential catabolic activity of the microbial community associated with each of the leaf

treatments investigated in the above incubation study was assessed using Biolog EcoPlatesTM.

Each plate contains 96 wells consisting of three replicates of 31 single carbon substrates and a

water blank. The wells also contain tetrazolium dye which produces formazan, a blue

compound, in proportion to microbial catabolic activity upon the substrate. The catabolic

profiles of the incubated treatments were determined as follows:

At the end of the 80-day incubation period, a bulked tissue/ sand sample was formed by

combining equal dry weight of material from all replicates. Under aseptic conditions, 1.0 g of

equivalent dry weight of composited litter/sand material into a 50 ml centrifuge tube to which

was added 10.0 mL of sterilized 50 μmol phosphate buffer solution adjusted to pH 6.5 (the mean

pH of foliar extracts used in the study). Each tube was subjected to two vortex/ sonication

cycles. This suspension was immediately passed through a sterilized Whatman GF/C filter to

remove large particulate matter and dissolved organic carbon and again through a Whatman 0.22

μm filter to concentrate the bacteria onto the filter. The filter was transferred into a sterilized

centrifuge tube and eluted with 15 ml of the same sterilized, pH-adjusted buffer solution. Using

this extraction approach, the introduction of carbon sources with the inoculant was greatly

45

reduced. Consequently, the absorbance values read from each well could be attributed more

faithfully to the microbial use of the intended substrate rather than the use of plant-derived,

added substrate.

Wells were inoculated with 250μl of microbial extracts. Color development in the wells

was quantified using a Victor3 Multilabel Plate Reader (Perkin Elmer, Inc., Waltham, MA) to

record absorbance at 590nm. Plates were incubated in the dark at 28°C over seven days and read

at time intervals of 0, 16, 40, 64, 88, and 160 hours. Key species, control, and blank treatments

were duplicated.

Differences in the microbial biomass supported by a food resource are a valid effect of

resource quality. Optical density of the control (water) well was used as an estimate of inoculum

density. To standardize treatment responses by microbial biomass, substrate absorbance values

were normalized of by that of the water control at each time interval (Compton et al., 2004).

Individual carbon-source utilization was determined at each time interval as the mean of

replicated normalized absorbance values. Because the rate of substrate utilization is also a

critical indicator of the microbial community, the time course of color development for each

substrate was incorporated by calculating total substrate use as the trapezoidal area under the

absorbance versus time curve for each carbon source (Guckert et al., 1996).

Two measures, average well color development (AWCD) and the Gini coefficient (GINI)

were used as summaries of microbial activity that incorporated all substrates. Average well

color development for each treatment was calculated for all time intervals as

wii

aAWCD a931 93

1−= ∑

=

(3-4)

where a is the absorbance at 590 nm for the ith well, āw is the mean absorbance of the water blank

and 93 is the total number of wells (Garland & Mills, 1991). Microbial functional diversity was

46

quantified using the Gini coefficient, a measure of the unequal use of carbon sources. Following

Harch et al. (1997), the coefficient was calculated as

∑∑= =

−=N

i

N

jjia

aNGINI

1 12 a

21 (3-5)

where ai and aj are the mean absorbance values of wells i and j, respectively, and ā is the

AWCD. A Gini value of zero represents and even use of substrates or perfect equality, whereas

a Gini value of 1 is perfect inequality (only one substrate consumed). Greater inequality in

substrate use is synonymous with decreased functional diversity or preferential resource

utilization. Thus, a smaller Gini coefficient indicates greater functional diversity.

Statistics

Leaf chemical traits were either discrete analyte concentrations of the selected nutrients or,

in the case of spectral characterization, absorbance values at individual wavelengths. Spectral

functional traits, or simply “spectral traits”, refer to absorbance at a particular energy level

designated by wavenumber units or cm-1. Spectral traits are henceforth abbreviated in the text as

1/λ.

The effects of species identity and mixture composition on initial nutrient parameters;

cumulative evolved CO2; C mineralization rates; inoculum density; AWCD; and single-carbon

substrate utilization were tested with one way ANOVA followed by pair-wise comparisons of

means using Tukey’s HSD or, where appropriate, Tukey’s HSD test for unequal n. Correlations

between leaf chemical traits and decomposition responses (cumulative evolved CO2, C

mineralization constant, net CO2 interaction and mineralization rate interaction) were evaluated

using Kendall’s Tau.

Interactions, or non-additive effects, of the two response variables net evolved CO2 and C

mineralization rate, were calculated as the relative difference between the expected and the

47

observed responses (Wardle et al., 1997). To estimate the variance of expected values, the

average response of all possible combinations of the four replicates of both mixture constituents

was calculated. Two-tailed t-tests were used to evaluate the difference between the expected and

observed values from zero. Interactions occurring in substrate utilization profiles were

calculated and tested in the same manner.

A generalized linear model was used to determine the significance of chemical

composition and chemical diversity on the decomposition of single and mixed treatments.

Continuous response variables were final net evolved CO2 after 80 days (net CO2), the carbon

mineralization rate coefficient, a, and their respective non-additive effects, I net CO2 and Ia.

Mixture composition was defined either categorically by the identities of the key and companion

species, or by continuous variables vis-à-vis one of the three chemical characterization

approaches. All possible one- and two-parameter combinations of chemical identity and

chemical diversity were generated and incorporated into linear regression models of the form

( )21 , traittraitfy = (3-6)

or ( )QCDtraitfy ,1= . (3-7)

When chemical identity and chemical diversity were included in the same model, both

parameters employed the same characterization method (i.e. only the NIR-derived diversity

index was included in a model with NIR spectral traits). To reduce computational time,

regressions were performed on spectra that had been smoothed by averaging every fourth data

point. In this manner, NIR spectra were reduced from 3113 points to 778 points and MIR from

1868 points to 467 points. “Successful” models were defined as those in which all model

parameters were significant at p<0.05 in univariate analysis and the whole model adjusted R2

exceeded 0.65.

48

Principal components analysis was performed separately on the covariance matrix of the

spectral profiles of single-species litters in the near- and mid-IR regions. Discriminant analysis

using the PCA factor scores of the spectra of key and companion species comprising the 21 litter

pairs was performed to determine whether spectral information was adequate to classify mixture

non-additive effects as antagonistic, additive, or synergistic. Principal components analysis was

also conducted on the carbon-source utilization profiles of all 31 litter treatments in order to

observe the relationships between their respective microbial communities.

Discriminant analysis was performed on the substrate-use profiles to determine whether

potential microbial catabolic activity could differentiate the litter interactions observed in the

incubation study.

Where necessary, response variables were natural log-transformed in order to meet the

assumptions of statistical tests. Normality was determined using the Kolmogorov-Smirnov test

for normality and homoskedasticity was tested with the Brown and Forsythe homogeneity of

variance. When transformation did not succeed in meeting the assumptions, the Kruksal-Wallis

non-parametric test for the comparison of multiple means was used.

All statistical analyses were performed at significance level α=0.05 and were conducted in

R (R Foundation for Statistical Computing, 2005) and Statistica 7 (StatSoft, Inc., 2004).

Results

Species Identity on Carbon Mineralization of Mixtures

The ten tropical species selected for this study represented seven families and included

leguminous and non-leguminous species and expressed a wide range of initial nutrient

composition (Table 3-1, Fig 3-1) and carbon mineralization rates (Fig 3-5). Each mode of

characterization captured a different portrait of chemical quality and, by extension, chemical

diversity (Table 3-3, Figs. 3-2 through 3-4). Leaf mixtures with the most contrasting

49

compositions differed by characterization—Balfourodendron reidelianum and Plathymenia

fololiosa (PVIN) by total nutrient composition; Cecropia sp. and Cabralea canjerana (ECANJ)

by MIR; and B. reidelianum and Chorisia speciosa (PAPAI) by NIR (Table 3-3).

Identity of the “key” species was a strong predictor of leaf-mixture respiration rates (Table

3-6) and the C mineralization of mixtures mirrored the trend observed in the mineralization of

individual key species (PAUM>EMBA>INGA) (Fig. 3-6). Interactions between litters were

common. Of the 21 litter-pairs, 11 experienced significant interactions in C mineralization rates,

eight of them positive (Table 3-7). Non-additive effects in net evolved CO2 changed in both

direction and magnitude over time, declining along the course of decomposition (Fig. 3-7).

As with whole-mixture mineralization rates, interactions were also correlated with the

presence of key species. Mixtures containing B. reidelianum showed greater synergistic

interactions than mixtures based on Cecropia sp. or I. affinis (Fig. 3-6).

Residues of nitrogen-fixing species have been demonstrated to promote the decomposition

of litters of “poorer” quality (Fyles & Fyles, 1993; Rothe & Binkley 2001; Xiang & Bauhus,

2007). Two of the ten base species were nitrogen-fixers, I. affinis and P. fololiosa (10 pairs).

Despite the fact that the nitrogen fixers generated the least net microbial respiration and the

slowest C mineralization rates, the ten pairs containing N-fixers exhibited stronger positive non-

additive effects than those without (Fig. 3-8).

Chemical Identity on Carbon Mineralization of Mixtures

A summary of successful models of the 21 litter pairs appear in Tables 3-9 and 3-10.

Individual spectral traits from key species and companion species explained as much as 87% of

the variation in both net evolved CO2 and C mineralization rate of the 21 leaf mixtures (Table 3-

9). Models that included interaction terms between the key and companion spectral traits were

not successful according to the criteria. No models based solely on the spectral traits of one

50

mixture component were successful. By contrast, lignin:N of leaf mixtures, based on the ratio of

associated spectral peaks from 1515-1500cm-1 (lignin) and 1680-1630cm-1 (proteins) (Reeves,

1993), explained only 71% of mixture carbon mineralization (Data not shown).

Chemical diversity alone supplied no significant relationships to mineralization rate or net

respired CO2. Models incorporating chemical identity and mixture chemical diversity as

dependent variables showed clear relationships for net CO2 and C mineralization rates of all 21

leaf pairs (Table 3-10). Under characterization by total nutrient concentrations, a factorial

regression model incorporating chemical heterogeneity and the total phosphorus concentration of

the key species accounted for 88% of the variability in net CO2 evolved from leaf mixtures.

Only the MIR region provided significant spectral traits in conjunction with chemical diversity to

explain 67% of variation in C mineralization rate of mixtures (Table 3-10).

The same model-building approach was applied to subsets of the 21 leaf mixtures, grouped

by key species (n=7). When the presence of key species was incorporated into the model, the

chemical identity of the companion species and mixture chemical diversity explained as much as

98% of the variation in all four decomposition response variables, including the strength and

magnitude of interactions (Tables 3-11 to 3-13). Figure 3-9 shows a summary of the predicted

versus observed decomposition response patterns of each pair-group in which the regression

results of all successful spectral traits–chemical diversity pairings are featured in one plot for a

given response variable, for succinctness.

Discriminant analysis on the PCA factor scores of the spectra of the key and companion

species comprising the mixture successfully classified the 21 litter pairs into three distinct groups

by non-additive effect, with a confidence interval of 95% (Table 3-14). Both spectral regions

51

provided sufficient information for discrimination by non-additive effect, whereas mineral

concentration profiles were inadequate.

Identification of Functional Chemical Traits

The search for spectral traits that yielded successful models highlighted distinct regions of

the mid- and near-infrared spectra that correlated with leaf mixture decomposition. Considerable

overlap of the spectral traits correlated with single and mixed litter decomposition occurred, but

traits unique to each litter context (single vs. mixed), and each process, were apparent and, in the

case of the dynamic variable, cumulative CO2 released, the spectral traits contributing to

successful models and the regression coefficient associated with the model changed with time

(Data not shown). Table 3-15 lists the complete inventory of significant spectral traits

contributing to the model y = f(1/λ key, 1/λ comp) and their associated molecular functional groups.

As defined earlier, a spectral trait refers to an absorbance value at a particular location in the

infrared spectrum that represents the vibrational frequency of molecular bonds. However, it

should be noted that outside of pure compounds, there is no unique correlation between a

spectral location and a particular functional group due to overlapping; thus, the functional groups

listed here represent the most probable contributors to the absorbance feature.

However, when the mixtures were grouped by key species, models formed from

companion spectral traits and mixture chemical diversity consistently explained 93%-99% of the

variation in net respired CO2 and C mineralization rate (Tables 3-11 through Table 3-13). The

spectral traits contributing to successful models varied for each decomposition response, but

most importantly, for each key species. For example, using CDQ as the index of mixture

diversity, net respired CO2 was explained with spectral traits 1/λcomp =1258.5 cm-1, 1683 cm-1

and 1143 cm-1 in mixtures of INGA, EMBA and PAUM, respectively.

52

Whether using condensed spectral information as represented by latent vectors in

discriminant analysis or individual spectral traits, this study provides first evidence that non-

additive effects can be predicted using litter chemical traits.

Microbial Functional Diversity and Non-additive Effects

On a per substrate basis, fewer than half of the 31 substrates exhibited significant

correlations with net evolved CO2 at 80 days or the C mineralization rate constant. Interestingly,

significant non-additive responses in the utilization of individual substrates were also observed.

That is, for a mixture comprised of litter A and litter B, the resultant microbial community of the

mixture did not behave as the mean response of the original microbial communities, A and B,

plus the inoculum. Principal component analysis of the substrate utilization profiles of litter

pairs showed strong clustering of mixed-litter communities around that of their “key” species

(Figure 3-12). Discriminant analysis of single substrate use was unable to categorize treatments

by the non-additive effects.

Mean carbon utilization (AWCD) of microbial communities of litter treatments was not

significantly correlated with C mineralization rate and net evolved CO2 of the incubation study.

The Gini coefficient of microbial communities associated with each litter treatment was

negatively correlated with mean substrate use (Table 3-16) suggesting that greater functional

diversity of the microbial community corresponded with low carbon substrate utilization. The

Gini coefficient also showed significant, positive correlation with C mineralization rate and net

evolved CO2 of the leaf-mixtures incubation study, as well as their non-additive effects (Table 3-

16). Thus, decreasing microbial functional diversity was associated with greater mineralization

response and positive non-additive effects. In contrast to litter incubation responses, mean

carbon utilization was greater in litter pairs associated with the slowly respiring I. affinis than

with either Cecropia sp. or B. reidelianum for nearly all time intervals. Pairs containing I. affinis

53

also exhibited significantly greater substrate-use diversity than Cecropia sp. or B. riedelianum at

64 and 88 hours (Figure 3-13).

Discussion

Decomposition of mixed species is the rule in agricultural and natural environments. The

non-additive behavior of heterogeneous substrates has been documented for well over a century

(Heal et al., 1995) and although it is accepted that the simple weighted average of the

decomposition response of component species are inadequate to describe the behavior of

mixtures, the solution to the mixed-species effect is forthcoming. This experiment sought to

model patterns of microbial respiration – the outcome of substrate preference and substrate-use

efficiency – in response to mixed substrates.

Given that the richness level of the mixtures observed in this study was constant at R=2,

the effect of species richness on decomposition response could not be investigated. However,

the wide variation in microbial respiration in response to substrates that was observed within this

richness level (over 200% from the slowest and fastest rates) underscores the weakness in using

richness to differentiate mixture behavior when it is evident that composition will play a pivotal

role.

Importance of Species Identity and Chemical Identity on Carbon Mineralization

Mixed litter behavior was well determined by the decomposition patterns of their key

species (Fig. 3-6), with key species accounting for 7% to 73% of the total sum of squares (Tables

3-5 and 3-6). The experiment was designed in anticipation of this result, based on the

assumption that under additive effects the mean response of the three groups would be separated

by the difference in response generated by the three base-species. However, as stated earlier in

the description of the experimental design, the individual performance of Cecropia sp. and B.

riedelianum in this experiment did not differ significantly, contrary to the results of a trial run

54

conducted in another soil medium in which C mineralization rate and net evolved CO2 of B.

riedelianum was greater. Yet, the behavior of leaf mixtures founded on B. riedelianum differed

significantly from those based on Cecropia sp. Both the mineralization rate and net evolved CO2

of PAUM-mixtures was significantly greater than those of EMBA. Thus, while additive effects

of species may be similar due to their express decomposition trajectories, their non-additive

effects can significantly diverge.

The results corroborate those of Ball et al. (2008) which state that species identity dictates

both additive and non-additive effects. From this, it can be inferred that species associated with

non-additive effects should possess quality characteristics responsible for such effects. Thus it is

likely that the underlying cause of species identity or mixture composition on the behavior of

litter mixtures is the chemical nature of the species.

While very few published works have used a quantitative measure of the chemical

similarity of litter mixtures, a couple of studies have notably generated mixtures with the

awareness of litter quality in mind. Chapman et al., 2007, in creating mixtures of four temperate

tree species comprising conifers and broadleaf species, found the strongest synergisms to occur

between most similar pairings (conifers). Using diverse organic substrates ranging from wood

chips to feces, Dehlin et al. (2006) found no significant differences between expected and

observed values for basal microbial respiration of organic matter pairs of obvious contrasting

quality.

In this experiment, chemical heterogeneity, by itself, showed no significant correlation

with mixture decomposition response. The litter pair most similar in composition as assessed by

CDQ in all modes of chemical characterization contained the species Inga affinis and

Plathymenia foliolosa (IVIN), which, while showing significant, synergisms in both net CO2 and

55

C mineralization rate, did not display the most dramatic interactions. The next most similar

pairing was that of EPAI (neither of these leguminous species) which showed only additive

effects. Thus, the finding of Chapman et al. (2007) that positive non-additive effects are more

common among more homogenous mixtures is not generalizable. The mismatch between mass

loss as was followed in the Chapman study, and microbial respiration, as followed here may

prevent the direct comparison of results. Microbial respiration is indicative of the rate with

which the decomposing soil community utilizes the substrate. Because the amount of CO2

respired per unit of carbon incorporated is a matter of microbial use efficiency, without direct

measure of microbial growth and energy expenditure through production of exudates and

enzymes the two cannot be directly correlated. However, in studies in which both mass loss and

microbial respiration were monitored during the decomposition of mixed litters, where mass -

loss yielded non-additive results, the same was true of microbial respiration, i.e. synergies in

mass loss corresponded to synergies in microbial respiration (Briones & Ineson, 1996; Bardgett

& Shine, 1999).

In the present study, chemical diversity and chemical identity explained 67% of the

variation in net respired CO2. However, two parameters of chemical identity proved better

predictors than one spectral trait and chemical diversity (R2=0.86). This is a similar but stronger

relationship than that uncovered by to Pérez-Harguinedeguy et al. (2008) who also constructed

significant models of mixed-litter decomposition using chemical diversity in conjunction with a

litter quality trait. In that study, chemical heterogeneity was defined as the coefficient of

variation in initial nitrogen and fiber contents, lignin, cellulose, and hemicellulose, between

mixture components. The linear model incorporating nitrogen content and chemical

heterogeneity accounted for 29% of the decomposition response of 26 litter mixtures containing

56

two to five species under field conditions (litterbags, unground). Their success in constructing a

significant model using un-ground foliar material under field conditions provides further

evidence that mixture chemical diversity and chemical identity are critical functional traits that

contribute to determining mixture behavior.

While not providing successful models for mixed-litter respiration, total nutrients K, Ca

and Mg significantly correlated with net CO2, and C mineralization rate and their interactions

(Table 3-4). In their investigation of mixed temperate species Briones and Ineson (1996) found

evidence of transfer of Ca and Mg during the decomposition of eucalyptus when paired in

mixtures with oak or ash, both pairs exhibiting enhanced respiration. In this experiment, K

correlated significantly with mineralization rate interaction which suggests that elevated transfer

of K between species may correspond with increased respiration rate.

Which Traits?

Over 250 spectral traits were found to be predictors of C mineralization rates,

corresponding to polysaccharides, proteins and even water content. The generalization of the

predictive capacity of pairs of spectral traits that formed successful models in this experiment to

mixtures of other species is undetermined. While the pair of spectral traits 1644 cm-1 and 1428

cm-1 of leaf mixtures provided greater explanation of net respired CO2 than lignin:N in this

experiment, further studies are required to determine whether these two traits, or others, can be

used reliably in other contexts. Context dependence of trait significance is restricted not only to

conditions of the physical environment (Jonsson & Wardle, 2008) but also to the resource

environment. This is to say that while most of the traits are present in some quantity, whether in

single or mixed litter situations, for some spectral traits, their relative importance is enhanced by

the presence of other traits. That the sets of spectral traits relevant to net evolved CO2 and

mineralization rate were not identical, may distinguish quality characteristics linked to

57

palatability (recalcitrance) from quality characteristics associated with the bioavailability of

supporting nutrients. Recalcitrance may cause a lag in substrate use as enzymes are produced,

consequently suppressing mineralization rates, whereas nutrients availability may allow the

greater substrate utilization, which in turn can stimulate microbial growth and, ultimately, CO2

production.

The same is certainly true of spectral traits that are predictors in conjunction with the

chemical diversity index. In the case of leaf mixtures grouped by key species, the suite of

critical, mixture-defining traits of the companion species varied with each key species,

suggesting species-specific diversity effects. It is interesting to note that for a given spectral

trait, the model yielded a positive or negative coefficient. The full implications of this are not

yet clear. Yet, it can be speculated that depending on the chemical moiety represented by the

spectral trait, the mechanism underlying the decomposition response may be positively affected

by chemical heterogeneity. For example, given a spectral trait which by itself is a strong

predictor, associated with an inhibitor, (a negative coefficient), a model showing the significant

contribution of heterogeneity may mean that the effects of inhibitory compounds are mitigated

(or intensified) by increased (decreased) complexity. This might be considered a dampening or

dilution effect. For B. reidelianum, the relationships between the companion spectral trait and

mixture diversity and net CO2 were negative suggesting that any deviation of the companion

species away from the independently rapidly mineralizing, B. reidelianum was likely to result in

lowered microbial activity.

In contrast, where interactions arise from the transfer of nutrients or the use of available

carbon sources to aid in the degradation of more recalcitrant carbon forms or nutrient poor litters

(priming or nurse effect), a positive coefficient may appear. Knowledge of the physiochemical

58

quality associated with the spectral trait as well as the sign of the interaction term between the

spectral trait and diversity index as portrayed in the model may provide clues as to the nature of

the mechanism. Because more than one spectral trait can create a successful model, it can be

inferred that more than one mechanism may be underway (Hättenschwiler, 1995).

Within the scope of this study, more cannot be concluded about specific litter quality traits

or specific mechanisms triggered from the microbial interactions resulting from these qualities.

However, these results hold promise for the ability to demystify the underpinnings of mixed

substrate behavior in the immediate future.

Microbial Underpinnings of Non-Additive Effects

In this experiment, the use of ground leaves and its setting in a controlled laboratory

environment focused on the activity of the microbial community as the sole agents of litter

decomposition. “Litter” interactions are, in fact, the result of interactions occurring in the

microbial community. The response of a community to food resource quality, of which diversity

may play a part, can include changes in microbial species composition, relative abundance and

trophic interactions (Little et al., 2008).

Plant species identity can also play a significant role in the composition of the microbial

community via their influence on the quantity and quality of carbon resource (Carney & Matson,

2006). As observed in the behavior of net evolved CO2 and C mineralization rate of the 31 litter

treatments, the overall patterns of single-C-source utilization were strongly dependent on the key

species. Microbial responses to food resource diversity were non-additive. Based on the

assumption that microbial substrate activity is a direct consequence of microbial species

demographics, the response of microbial community composition to litter mixtures was also non-

additive. The initial microbial communities of a mixture comprised of species A and B were the

community residing on the foliage of species A, plus that of species B, as well as the inoculum

59

derived from the forest soil. In natural environments, this should be expected, each litter

bringing with it its associated phyllosphere community and the surface soil community

introducing its own members. In this study, microbial composition, as proxied by activity,

tended more toward that of the key species (Fig. 3-12). These two observations that plant

species dictate microbial composition and that substrate diversity affects functional diversity of

the microbial community are examples of top down effects of resource on the decomposer food

web.

In all, these and other results indicate a greater complexity to the microbial response to

substrate heterogeneity which may be partitioned into the separate but related effects of litter

colonization and resource use. Results of microbial community analysis should be interpreted

with extreme caution. Conclusions were drawn from a single Biolog plate for most treatments

and where treatments were duplicated, as in the case of key species and the blank differences in

the response of some wells. The high variation of response observed within the same plate also

advises replication of treatments at the plate-level. Also, the microbial communities drawn from

litter mixtures was extracted at the final stage of the litter incubation study and therefore may be

identical nor perform analogously to the community present at time zero. Lastly, Biolog results

are the consequence of culturable bacteria and therefore may not be representative of the

communities responsible for actual observed activities on litters.

Conclusion

The influence of forest species diversity on soil-centered ecosystem processes has taken on

increased importance because of the rate at which species composition and distribution of

vegetation communities are rapidly changing. Until now, our ability to predict these changes has

limited by the fact that the functional traits driving litter interactions have not been identified

even while these interactions are commonly observed.

60

Number of species or taxonomic measures of diversity may be valid for assessing resource

use but not resource supply. Interspecies differences and phenotypic differences emerge such

that a given species assume different litter traits and characteristics under differing

environmental conditions, including aboveground plant diversity. Moreover, should species

richness prove a good predictor in a particular setting, it may not be applicable to other groups of

species. Functional traits to describe individual species and species mixtures relevant to the

process being observed are desirable.

I hypothesized that a strictly quantitative approach of characterizing species identity

(chemical identity), as well as mixture diversity (chemical diversity), would enable the

elucidation of clear, and potentially transferable relationships between litter makeup and

decomposition behavior. The exploration of simple models constructed solely on chemical

identity – the infrared spectral fingerprint – and chemical diversity yielded robust relationships

with all four process variables. The incorporation of species identity, chemical identity and

chemical diversity of mixtures enabled the prediction the strength and magnitude of interactions.

The main questions of the study and their conclusions are:

• Can interactions and net mixture decomposition patterns be predicted by comprehensive initial leaf chemistry profiles or mixture chemical diversity?

Microbial respiration in response to leaf mixtures was successfully modeled by leaf chemistry of

component species and by the foliar chemistry of one species in conjunction with the chemical

diversity of the mixture. The use of leaf chemistries (chemical identity) of both mixture

constituents provided a model with greater explanatory power than the model constructed with

only one species and the mixture diversity. However, when the identity of the “key” species of

the pair was taken into account, C mineralization rate, net evolved CO2 and, most importantly,

the deviations of these values from the expected were also modeled with the chemical identity of

61

only one member of the mixture plus mixture chemical diversity. This is the first time that the

direction and magnitude of mixed-litter interactions have been strongly correlated with leaf

mixture chemistry.

• Which diversity measure and which traits can best model mixture trajectories?

All three analytical methods employed to chemically characterize individual leaf species and

mixture diversity (total nutrient concentrations and near- and mid-infrared spectroscopy)

provided successful models of CO2 evolution rates and interactions. However, the flexibility and

capacity for calibration of both carbon forms and mineral nutrients of mid-infrared diffuse

reflectance spectroscopy recommends it over the other modes.

• Do microbial interactions mirror litter interactions over the course of decomposition?

Single-substrate use patterns of microbial communities extracted from leaf-mixture treatments

did not provide ready interpretation of litter interactions. In general, catabolic profiles of

microbial communities derived from litter mixtures clustered around those of the single key

species as was observed in the respiration of the litter mixtures, themselves. I hypothesized that

strong positive interactions would be significantly correlated with high functional diversity.

Where correlations were significant, the reverse was true: lower catabolic diversity signifying

specialization in substrate use, correlated with greater net CO2 evolution.

The implication of this work is that the potential exists for chemical diversity to be used in

conjunction with vegetation chemistry to model carbon dynamics in real systems. More explicit

representation of litter “quality” in complex litter systems is possible. This can prove a valuable

asset in investigating tropical forest systems with high plant species diversity and chemical

composition (Hättenschwiler et al., 2008; Townsend et al., 2008) and even more possibilities

arise in landscape level analysis with the advent of new technologies to capture snapshots of

forest canopy chemical composition as in satellite-infrared analysis (Huber et al., 2008). The

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63

representation of litter quality using infrared spectra in to model litter decomposition and N

mineralization has already been established (Gillon et al., 1999; Bruun et al., 2005; Shepherd et

al., 2005). The ability to efficiently analyze litter chemistry with DRIFTS along the course of

decomposition with minimal sample preparation and alteration recommends this approach to the

investigations of the dynamic aspects of microbial response to shifting litter composition. As

with other chemometric analyses, the calibration of non-additive effects is plausible, in which the

decomposition trajectory of a set of litter mixtures can be monitored under the required

environmental conditions and used as a “training” set for unknown, experimental mixtures.

Lastly, the interactions of biochemical constituents that drive the interactions in rates

which in turn may lead to greater understanding of the microbial impetus and general food web.

The effect of extrinsic factors the perception of litter quality should follow the approach of

Rodriguez-Loinaz et al. (2008) in which soil enzyme activity is investigated with vegetation

diversity since enzymes control rates of nutrient cycling processes.

Table 3-1. Initial foliar total nutrient concentrations of 10 tropical tree species used in the incubation study.

Scientific name Family Abbrev. P (%)

K (%)

Ca (%)

Mg (%)

Ash (%)

Alchornea sp. Euphorbeaceae DOCE 0.18f 0.98ab 1.21cd 0.04b 6.17b Artocarpus heterophylla Moraceae JAQU 0.13c 1.68c 1.41d 0.06c 12.21f Balfourodendron reidelianum Rutaceae PAUM 0.18f 3.05f 0.96bc 0.21f 10.74d Cabralea canjerana (Vell.) Mart. Meliaceae CANJ 0.11a 1.08b 2.04f 0.09c 8.56c Cassia ferruginea Caesalpinaceae CANA 0.14cd 0.97a 2.23g 0.05b 9.20c Cecropia sp. Cecropiaceae EMBA 0.16e 2.59e 1.39d 0.11d 11.32de Chorisia speciosa Bombaceae PAIN 0.14cd 1.99d 1.98e 0.11d 10.12d Inga affinis Mimosaceae IGA 0.13c 0.96a 0.78b 0.02a 5.40b Lecythis pisonis Lecythidaceae SAPU 0.14cd 0.96a 1.42d 0.19e 9.01c Plathymenia fololiosa Mimosaceae VINHA 0.12b 0.88a 0.28a 0.03a 3.17a

Different lower-case letters indicate significant differences in composition between species (p=0.05, Tukey’s HSD).

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Table 3-2. Species composition and abbreviation codes of the 21 leaf mixtures observed in the incubation study.

Companion “Key” species species Inga affinis Cecropia sp. Balfourodenron

reidelianum

Alchornea sp. IDOC EDOC PDOC Artocarpus heterophylla IJAQ EJAQ PJAQ Cabralea canjerana ICANJ ECANJ PCANJ Cassia ferruginea ICANA ECANA PCANA Chorisia speciosa IPAI EPAI PAPAI Lecythis pisonis ISAP ESAP PSAP Plathymenia fololiosa IVIN EVIN PVIN

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Table 3-3. Chemical diversity indices of leaf mixtures according to the mode of chemical characterization of material: total nutrient concentration (Total), mid-infrared (MIR) and near-infrared spectroscopy (NIR).

Treatment CDQ Total MIR NIR

ECANA 0.464 0.462 0.375 ECANJ 0.547 0.846 0.441 EDOC 0.472 0.539 0.697 EJAQ 0.305 0.433 0.644 EPAI 0.262 0.295 0.317 ESAP 0.414 0.675 0.335 EVIN 0.727 0.537 0.576 ICANA 0.463 0.548 0.272 ICANJ 0.472 0.526 0.463 IDOC 0.333 0.382 0.215 IJAQ 0.455 0.473 0.320 IPAI 0.521 0.570 0.738 ISAP 0.499 0.458 0.572 IVIN 0.205 0.252 0.157 PAPAI 0.512 0.538 0.858 PCANA 0.729 0.618 0.465 PCANJ 0.779 0.529 0.468 PDOC 0.634 0.524 0.347 PJAQ 0.583 0.625 0.440 PSAP 0.507 0.368 0.671 PVIN 0.864 0.439 0.412

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Table 3-4. Kendall’s Tau correlation coefficient of total nutrient concentrations with decomposition responses of leaf-mixtures. Bold values are significant at p<0.05.

Response P (%) K (%) Ca (%) Mg (%) Ash

Net CO2 0.200 0.454 0.273 0.303 0.428 a 0.222 0.424 0.303 0.402 0.432 I Net CO2 -0.068 0.231 0.167 0.145 0.249 I a -0.054 0.262 0.235 0.176 0.335 Net CO2 = net respired CO2 at 80 days; a = C mineralization rate constant; I Net CO2 = interaction of Net CO2; Ia = interaction of C mineralization rate where interaction is calculated as the percent difference from expected value.

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Table 3-5. Summary of repeated measures ANOVA for effects of mixture composition on net respired CO2 of 21 leaf mixtures at Day 80.

Net CO2 df SS MS F p

Intercept 1 2258969 2258969 7452.203 0.000000Key 2 86277 43139 142.311 0.000000Companion 6 16497 2749 9.070 0.000000Key x Companion 12 6900 575 1.897 0.0517Residuals 63 19097 303 Whole model R2

adj = 0.92 (F = 18.09; df = 20; p = 0.00).

Table 3-6. Summary of factorial ANOVA for effects of mixture composition on the carbon mineralization rate constant, a, of 21 leaf mixtures.

a df SS MS F p

Intercept 1 145304.0 145304.0 10695.78 0.000000Key 2 6988.8 3494.4 257.22 0.000000Companion 6 918.5 153.1 11.27 0.000000Key x Companion 12 849.3 70.8 5.21 0.000006Residuals 63 828.7 13.6

Whole model R2adj = 0.89 (F = 32.5; df = 20; p = 0.00).

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Table 3-7. Expected and observed carbon mineralization rates and non-additive effect of paired litter treatments in incubation study. Values presented are means ± standard error, in parentheses.

Treatment apair expected apair observed

Additive = No interaction

ISAP 22.5 (3.3) 24.8 (3.6) ICANA 24.5 (2.3) 29.4 (3.8) IDOC 26.3 (2.5) 27.2 (0.7) ICANJ 30.6 (2.9) 30.3 (2.0) IJAQ 31.7 (8.6) 31.7 (0.4) EDOC 41.9 (2.2) 38.4 (6.0) ECANJ 46.2 (2.6) 42.9 (6.3) PCANJ 47.5 (3.6) 47.4 (1.7) EPAI 53.8 (2.5) 52.9 (4.8)

Antagonistic = Negative interaction

IPAI 38.2 (2.8) 34.8 (0.8) PAPAI 53.5 (6.2) 48.8 (2.8)

Synergistic = Positive interaction IVIN 19.6 (2.9) 25.2 (3.6) EVIN 35.2 (2.6) 39.9 (2.3) PVIN 36.1 (4.2) 44.1 (2.0) ESAP 38.1 (3.0) 52.4 (5.6) PSAP 38.8 (4.8) 49.7 (2.9) ECANA 40.1 (1.9) 52.1 (4.3) PCANA 41.3 (3.4) 51.1 (4.0) PDOC 42.7 (4.1) 55.2 (1.8) PJAQ 44.7 (15.6) 54.6 (3.6) EJAQ 45.9 (11.2) 54.4 (5.0) Non-additive designations were determined by comparison of the observed and expected values by two-tailed t-tests at p < 0.05.

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Table 3-8. Summary of factorial ANOVA for effects of mixture composition on interactions of net CO2, I net CO2, and the C mineralization rate constant, I a of 21 leaf mixtures.

I net CO2 df SS MS F p

Intercept 1 23731.5 23731.52 66.90 0.000000Key 2 23572.3 11786.15 33.23 0.000000Companion 6 12100.6 2016.76 5.69 0.000017Key x Companion 12 8161.6 680.14 1.92 0.033843Residuals 209 74133.3 354.70

I a df SS MS F p

Intercept 1 2993.6 2993.6 26.82 0.000230Key 2 300.7 150.4 1.35 0.296687Companion 6 3004.9 500.8 4.49 0.013059Residuals 12 1339.6 111.6

Whole model (I net CO2) R2adj = 0.31 (F = 6.2; df = 20; p = 0.000).

Whole model (I a) R2adj = 0.52 (F = 3.7; df = 8; p = 0.021).

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Table 3-9. Summary of selected successful models† relating chemical identity (1/λ) to net evolved CO2 and C mineralization rate of 21 leaf mixtures.

Estimate Std. error t-value p Whole model

Net CO2

Intercept -410.3 68.9 -5.95 1.2 e-05 F = 27.1 1/λ key (1644 cm-1) 292.1 28.5 10.26 6.0 e-09 df = 2,18 1/λ comp (1428 cm-1) 157.1 52.7 2.98 0.008 p =1.6 e-08 R2 = 0.86 R2

adj = 0.85

a

Intercept -960.69 92.99 -10.33 5.4 e-09 F = 60.2 1/λ key (2315 cm-1) -886.60 84.17 -10.53 4.0 e-09 df = 2,18 1/λ comp (611 cm-1) 28.61 9.31 3.07 0.0066 p =1.1 e-08 R2 = 0.87 Radj

2 = 0.86 † Model: response = trait1 + trait 2. Table 3-10. Summary of successful models† relating chemical identity (1/λ) and chemical

diversity (CDQ) on C mineralization rate of 21 leaf mixtures. Estimate Std. error t-value p Whole model

apair Intercept -170.1 62.6 -2.72 0.0146 F = 11.3 1/λ key (2585 cm-1) -338.3 99.1 3.42 0.0033 df = 3,17 CDQ (MIR) 270.0 126.9 2.13 0.0482 p = 0.0026 1/λ key * CDQ 421.9 198.3 2.13 0.0483 R2 = 0.67 Radj

2 = 0.61 † Model: response = trait1 * CDQ.

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Table 3-11. Summary of successful models† of the relationship of chemical identity and chemical diversity on the decomposition responses of INGA leaf mixtures (n=7).


Net CO2

Intercept -2126 262 -8.10 0.0039 F = 105 1/λ key (1258.5 cm-1) 1865 223 8.36 0.0036 df = 3, 3 CDQ (MIR) 4675 553 8.46 0.0035 p = 0.0016 1/λ key * CDQ -3880 470 -8.26 0.0037 R2 = 0.99 R2

adj = 0.98

apair Intercept -355.5 65.2 -5.46 0.012 F = 31.9 1/λ key (1174 cm-1) 312.0 54.5 5.73 0.011 df = 3, 3 CDQ (MIR) 769.1 136.2 5.65 0.011 p = 0.0089 1/λ key * CDQ -623.3 114.1 -5.46 0,012 R2 = 0.97 R2

adj = 0.94

I net CO2 Intercept 3508 438 8.00 0.0041 F = 36.3 1/λ key (1258.5 cm-1) -2948 372 -7.92 0.0042 df = 3, 3 CDQ (MIR) -7199 923 -7.80 0.0044 p = 0.0074 1/λ key * CDQ 6061 784 7.73 0.0045 R2 = 0.97 R2

adj = 0.95

I a Intercept 371.5 94.8 3.92 0.030 F = 76.8 1/λ key (2531cm-1) 444.9 138.6 3.21 0.049 df = 3, 3 CDQ (MIR) -1110.5 192.1 -5.78 0.010 p = 0.0025 1/λ key * CDQ -1391.2 277.9 -5.01 0.015 R2 = 0.99 R2

adj= 0.97 † Model: response = trait1 * CDQ.

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Table 3-12. Summary of successful models† of the relationship of chemical identity and chemical diversity on the decomposition responses of EMBA leaf mixtures (n=7).


Net CO2

Intercept -269.4 46.9 -5.74 0.01050 F = 116 1/λ key (1683 cm-1) 501.6 49.3 10.17 0.00203 df = 3, 3 CDQ (MIR) 1307.2 103.1 12.68 0.00106 p = 0.0013 1/λ key * CDQ -1450.4 109.8 -13.21 0.00094 R2 = 0.99 R2

adj = 0.98

I net CO2 Intercept -54.8 40.8 -1.34 0.272 F = 32.7 1/λ key (3271.75 cm-1) 194.8 49.3 3.95 0.029 df = 3, 3 CDQ (MIR) -555.3 115.2 -4.82 0.017 p = 0.0086 1/λ key * CDQ 433.7 120.2 3.61 0.037 R2 = 0.97 R2

adj =0.94 † Model: response = trait1 * CDQ. Table 3-13. Summary of successful models† of the relationship of chemical identity and

chemical diversity on the decomposition responses of PAUM leaf mixtures (n=7). Estimate Std. error t-value p Whole model

Net CO2

Intercept 1752 210 8.34 0.0036 F = 26.4 1/λ key (1143 cm-1) -1308 171 -7.66 0.0046 df = 3, 3 CDQ (MIR) -2607 371 -7.03 0.0059 p = 0.0177 1/λ key * CDQ 2192 298 7.35 0.0052 R2 = 0.96 R2

adj = 0.93

apair Intercept 520.7 54.8 9.50 0.0025 F = 36.2 1/λ key (1135 cm-1) -394.4 44.6 -8.84 0.0031 df = 3, 3 CDQ (MIR) -772.9 93.0 -8.31 0.0037 p =0.0074 1/λ key * CDQ 649.2 75.2 8.63 0.0033 R2 = 0.97 R2

ad j = 0.95 † Model: response = trait1 * CDQ.

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Table 3-14. Discriminant analysis of non-additive effects observed in the C mineralization rates of 21 leaf mixtues, performed on the first four PCA factor scores of the spectra of key and companion species in the mid- (MIR) and near-infrared (NIR).

Region = MIR ADD SYN

SYN D = 7.015 F(6,13) = 3.999 p = 0.017

ANT D = 15.945 F(6,13) = 3.141 p = 0.039

D = 26.292 F(6,13) = 5.747 p = 0.006

Region = NIR ADD SYN

SYN D = 5.711 F(6,13) = 3.256 p = 0.035

ANT D = 17.920 F(6,13) = 3.529 p = 0.027

D = 26.278 F(6,13) = 5.272 p = 0.006

Distances are the squared Mahalanobis distance. ADD= additive; SYN=synergistic; ANT=antagonistic.

Table 3-15. Spectral traits of key species (MIR) contributing to successful models of litter-pair decomposition processes and their potential physiochemical interpretation.

Trait (cm-1)

Functional group

Litter quality

Trait (cm-1)

Functional group

Litter quality

Trait (cm-1)

Functional group

Litter quality

Trait (cm-1)

Functional group

Litter quality

3649.5 2462 1706 1413 δCH2, δCH3

(1450-1375) alkanes

3642 2454 1698 1405 3634 2446 1690.25

νC=O (1705-1685)

aliphatics, unsaturated

1397 3588 2438.75 1683 1390 3580 2431 1675 1382 3572.25 2423 1667 1374 3565 2415.25 1659.75 1366.5 3557 2408 1652 1359 3549 2400 1644 1351 3541.75 2392 1636.25 1297 3534 2385 1629 1289 3526 2377 1621

νC=O (1680-1630) δN-H (1650-1640) δN-H (1640-1550) δN-H, (1640-1550)

amides

1282 3518.25 2369 1613 1066 3511 2361.5 1582.5 1019.25 3503 2354 1575 1012

νC-O-C (1300-1050) νC-O (1200-1020)

esters, alcohols

3495

νC-O, νNH (3600-3200)

alcohols, phenols, amides, amines

2346 1567 1004 2546.75 2338 1521 973 2531 2331 1513 965.25 2523.5 2323 1505

(1509) lignin 958

2516 2315 1498 950 2508 2307.5 1490 765

2500 2300 1482

757 2492.75 2292

νC≡C, νC≡N, νS-H, (2600-2100)

alkynes, nitriles, thiols

1459 749.5 2485 1775 1451 742 2477 1767.75 1428 734 2469.25

νOH (3400-2500) νC≡C, νC≡N, νS-H, (2600-2100)

carboxylic acid

1760 1420.5

δCH2, δCH3 (1450-1375) alkanes

726

δCH, γCH (1000-650) γCH (900-650)

alkenes, aromatics

Values in plain text are spectral traits shared by C mineralization rate and Net CO2. Bold values are significant contributors to models of Net CO2 only. Bold, italicized values indicate spectral traits significant for C mineralization rate only. Adapted from Barbosa (2007) Table 3-1.

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Table 3-16. Kendall’s Tau correlation between microbial functional diversity (Gini coefficient) and mean substrate use of 31 EcoPlate substrates and the decomposition responses of mixed leaf treatments. Bolded values denote significance at p < 0.05.

AWCD Rate Constant, a

Net CO2 I a I Net CO2

Gini -0.588 0.432 0.505 0.242 0.500

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B

A

Figure 3-1. Diffuse reflectance infrared spectra of 10 litter species. A) Mid-infrared spectra; B)

Near-infrared spectra.

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B A

Figure 3-2. Chemical diversity (normalized to the maximum value) of all possible mixtures of 10 selected tropical species as a function of species richness, as characterized by total nutrient concentrations. Each point represents a potential litter mixture. Points in blue are mixtures that contain the most chemically distinct species. Nested dendrograms show the clustering of species by their compositional similarity as characterized by the approach.

78

B A

Figure 3-3. Chemical diversity (normalized to the maximum value) of all possible mixtures of 10 selected tropical species as a function of species richness, as characterized by mid-infrared spectroscopy (MIR). Each point represents a potential litter mixture. Points in blue are mixtures that contain the most chemically distinct species. Nested dendrograms show the clustering of species by their compositional similarity as characterized by the approach.

79

80

Figure 3-4. Chemical diversity (normalized to the maximum value) of all possible mixtures of 10 selected tropical species as a function of species richness, as characterized by near-infrared spectroscopy (NIR). Each point represents a potential litter mixture. Points in blue are mixtures that contain the most chemically distinct species. Nested dendrograms show the clustering of species by their compositional similarity as characterized by the approach

B A

Figure 3-5. Carbon mineralization of ten individual tropical species and the control treatment

(no litter addition). Error shown is ± SE.

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B

D C

A

Figure 3-6. The effect of key species on decomposition responses after 80 days. A) net evolved CO2; B) C mineralization constant, a; C) percent deviation from expected evolved net CO2 (net CO2 interaction) and; D) percent deviation from expected C mineralization rate constant (rate interaction).

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Figure 3-7. Dynamic nature of non-additive effects on cumulative evolved CO2 between litter pairs over the course of incubation A) after 30 days; B) after 80 days. Asterisks (*) denote significant interactions at confidence limit = 0.95.

83

B A

Figure 3-8. Interaction effects of nitrogen-fixing and non nitrogen-fixing leaf mixtures. A) Net CO2 interactions; B) C mineralization rate interactions.

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Figure 3-9. Predicted versus observed interactions occurring in C mineralization rate constant and net evolved CO2 (Day 80) of the 21 mixed-litter treatments. Predicted values are the result of linear regression analysis of the form (response ~ 1/λ comp *CD) when separated by key species. The predicted values represent all successful models for the respective decomposition response variable. A) C mineralization rate constant interaction; B) Net CO2 (Day 80) interaction.

85

C

A

B

Figure 3-10a. Chart of spectral traits, one each from key and companion species, contributing to successful models of the form response ~ 1/λ key + 1/λ comp where response is net evolved CO2 (Day 30). A) The intersection of key-species and companion-species spectral traits; B) Spectral traits of the key species and the coefficient of determination (R2) of the associated model; C) Spectral traits of the companion species and the coefficient of determination (R2) of the associated model.

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Figure 3-10b. Chart of spectral traits, one each from key and companion species, contributing to

successful models of the form response ~ 1/λ key + 1/λ comp where response is net evolved CO2 (Day 80). A) The intersection of key-species and companion-species spectral traits; B) Spectral traits of the key species and the coefficient of determination (R2) of the associated model; C) Spectral traits of the companion species and the coefficient of determination (R2) of the associated model.

C

B

A

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B

C

A

Figure 3-11. Chart of spectral traits, one each from key and companion species, contributing to successful models of the form response ~ 1/λ key + 1/λ comp where response is the carbon mineralization rate constant, a. A) The intersection of key-species and companion-species spectral traits; B) Spectral traits of the key species and the coefficient of determination (R2) of the associated model; C) Spectral traits of the companion species and the coefficient of determination (R2) of the associated model.

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Figure 3-12. Principal components analysis of the single-substrate utilization profile of the 31 microbial communities litter treatments extracted from the final time step the incubation study.

89

90

Figure 3-13. Mean substrate use (AWCD) and functional diversity (GINI) of microbial

communities extracted from 21 litter-pair treatments grouped by key species, as determined by Biolog Ecoplate community-level physiological profiles analysis. A) 64 hours; B) 88 hours; C) 160 hours.

B

C

A

CHAPTER 4 TREE SPECIES DIVERSITY AND NUTRIENT CYCLING POTENTIALS OF SMALL-

SCALE CACAO PRODUCTION SYSTEMS AND SECONDARY FOREST FRAGMENTS IN SOUTHERN BAHIA, BRAZIL

Introduction

Cacao production is limited to three principal growing regions in the world – Africa, Latin

America and Southeast Asia – and occurs nearly exclusively in the world’s zones of high

floristic and faunal diversity, the so-called biodiversity hotspots (Myers et al., 2000). The area

of south Bahia, imbedded in the Atlantic Rain Forest, may be the most environmentally

significant region of cacao production in the world due to its high plant and animal endemism

and its rapidly shrinking area (Donald, 2004; Thomas et al., 1998). According to some accounts,

less than eight percent of the original cover of the Atlantic Forest remains and these persist as

isolated fragments in an agricultural and increasingly urbanized landscape (Morellato & Haddad,

2000). While forest fragmentation can adversely affect tree species and structure through

geometry, isolation and disturbance (Hill & Curran, 2003), the floristic diversity of Atlantic

Forest remnants remains high (Tabanez & Viana, 2000; Thomas, 2003). Despite the

encroachment of urban and agricultural development, 300 new species and five new genera were

reported between 1978 and 1980 (Dean, 1995). Within the heart of Brazil’s “Cacao Coast” a

“hot zone” of floristic diversity was recently recognized as supporting the second greatest tree

species density in the world (Martini et al., 2007) and discoveries of new plant species remain

unabated (Barneby & Grimes, 1994; Thomas, 1997; Amorim, 2002; de Oliveira et al., 2004;

Goldenberg & Amorim, 2006).

Traditional cacao production, locally known as cabruca, an agroforestry system

characterized by selective thinning of the forest understory prior to the establishment of cacao

seedlings (Rice & Greenberg, 2000) comprises roughly 70% of cacao production in southern

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Bahia (Araújo et al., 1998). Since the economic blows dealt to the region by declining cocoa

prices in the world market and the introduction of Moniliophthora pernciosa, the fungal

pathogen causing witch’s broom disease, the grand plantation system of cacao that had shaped

the culture and economy of southeastern Bahia gave way. Large fazendas were abandoned,

converted to pasture or sold to the government as part of a plan of land reform (da Fonseca et al.,

2003). The result is that today, small-scale producers with average land-holdings of < 10 ha

significantly outnumber larger plantations (Landau et al., 2003). Cabruca’s role as the destroyer

or preserver of forest cover has been the focus of many discussions centered on regional forest

conservation strategies (Vinha & Silva 1982; Alger 1998; Greenberg, 1998; da Fonseca et al.,

2003). Yet, sufficient scientific evidence to support the claims of sustainability and biodiversity

of cabrucas, especially in the context of increasingly balkanized land-holding is still lacking.

At the same time, secondary forests are increasingly the most prevalent forest type around

the globe (Brown & Lugo, 1990). The FAO estimates that degraded secondary and heavily

logged forests constitute 60% of the world’s forest cover (FAO, 2005). Since the arrival of the

Portuguese to Porto Seguro in southern Bahia, the Atlantic Rain Forest of Brazil has been

reduced to 2-8% of its original cover (CIT). In 1974, primary forest accounted for 7.03% of total

land use whereas secondary forest and cabruca comprised 12.62% and 3.84%, respectively, of

land use in southern Bahia (CEPLAC, 1976). Recent geographical assessments of the district of

Ilhéus, the center of Brazil’s cacao producing region, attribute 68.3 thousand ha (40%) of its land

cover to cacao, and 26.75% to areas of conservation or preservation (Santana et al., 2003).

Excluding environmental protection zones, natural forest fragments in the district account for a

meager 5% of land cover, as estimated by optical methods (Saatchi et al., 2001). Even as the

concept of pristine native forests is being challenged, the diminishing area of “native forests”

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necessitates the better understanding of the interactions occurring in the broad array of

ecosystems termed “secondary” forests.

Efforts to conserve endangered species and to restore vanishing forest cover in the

Brazilian Atlantic Forest highlight the role that economically significant activities, such as

traditional shade-cacao production, can play in restoring and maintaining ecosystem services

formerly supplied by native forest (da Fonseca et al., 2003). While several studies have been

recently undertaken on the structure and diversity of shade-cacao systems in southeastern Bahia

(Schulz et al., 1994; Sambuichi, 2002; Sambuichi & Haridasan 2007) few studies have focused

on small-holder cacao systems which comprise the majority of cacao production (see Machado et

al., 2005) and even fewer on the status of the secondary forests in their proximity (see Lobão,

2007). Given the dominance of secondary forests in the landscape over primary forest in the

region, the monitoring of secondary forests is essential in order to provide a reference for more

managed agroforest systems and to question the validity of the use of secondary forests as such a

reference. Studies of species diversity and nutrient cycling of both systems at the level of local

management are integral to inform conservation strategies of both secondary forests and

cabrucas to deliver ecosystem services such as soil conservation, carbon and nutrient storage and

habitat afforded by floristic biodiversity, as well as economic services generated by cacao and

non-timber forest products.

The objectives of this study were: 1) to assess differences in floristic composition between

cabrucas and secondary forest 2) to determine the overall inputs to and stocks of nutrients and to

the soil and lastly, 3) to compare the tree diversity and nutrient cycling status of forests

fragments with primary forests as depicted in the literature.

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Site Selection and Characterization

The study sites were located within the Projeto Assentamento Frei Vantuy, an agricultural

settlement occupied by a former landless community, in the district of Ilhéus, Bahia, (14º 48′ 09″

S, 39º 07′ 54″ W) situated alongside the principal highway connecting Ilhéus and Itabuna (Fig. 4-

1). The native vegetation of the area has been categorized as lowland Southern Bahian Wet

Forest and displaying the classic rain forest structure of emergent, canopy, understory and

herbaceous layers (Thomas, 2003). A mean annual temperature of 23.3°C and rainfall between

1800-2000 mm (CEPLAC, 2006), without a distinct dry season classify the region as Af under

the Köppen climate system. The settlement resides on a patchwork soils and contain the Itabuna

and CEPEC soil units classified in the Brazilian and U.S. taxonomic systems as Alissolos

Crômicos (Typic and Orthoxic Hapludults) and Luvissolos Crômicos (Typic Hapludalfs),

respectively, and in the low lying areas Neossolos Flúvicos (Typic Tropofluvents) (Santana et

al., 2003). With the exception of the Fluvents, these soils are unusually high in inherent fertility

relative to other soil types in the district and as such are prime areas for cacao production.

The Frei Vantuy Settlement was established in 2003 by 33 families as a part of the national

program of agrarian reform. The settlement, totaling 479 ha comprises 159 non-contiguous

hectares of forest reserve, of which, approximately 80 ha is forest in various stages of

regeneration. The land is partitioned among the families. As decreed by law, each landholder

must set aside a percentage of his or her holdings to forest cover. The primary appeal of this site

was the nature of the community – small-scale, family-owned cacao systems, in contrast to

larger, plantation systems. Furthermore, the proximity of the community to the research and

extension activities of the campus of the State University of Santa Cruz (UESC) as well as that

of the Executive Commission on the Cacao Management Plan (CEPLAC) suggested that the

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growers have been exposed to and sensitized to programs on the environmental education, the

importance of the maintenance of forest cover and diversity. As such, the secondary forests in

this study were assumed to be representative of “best-case management scenarios”.

Eight paired plots were chosen to encompass a range of soil properties (Table 4-1) and

topographies occurring in the site with the stipulation that forest and cacao plots were as close as

possible and reasonably mature (> 50 years old) (Fig. 4-2). Plot history and management were

determined from interviews with the landholders and corroborated with long-term residents and

workers from the area. Individual study plots of 50 m x 50 m were demarcated within the forest

and cabruca sites (Figs. 4-3, 4-4) to investigate tree community composition, nutrient delivery

through litterfall and soil nutrient stocks. Plots were subdivided into 25-10 m x 10 m sub-plots

to facilitate the inventory and sampling processes.

Species Inventory and Tree Diversity

The common name and occurrence of trees and palms with stems ≥ 5 cm DBH (height =

1.3m) within each site were first identified with the aid of a knowledgeable local farmer raised

and residing on the grounds of the settlement. Tree cuttings were collected and used to identify

trees to the family, genus and, where possible, to the species level, with the consultation of the

herbarium of CEPLAC. Voucher specimens were not catalogued. Trees classified as “unknown”

were used only in stem density counts and not in the enumeration of species richness. For two of

the forest fragment plots, JR and VR, the family and stem size of all individuals was recorded to

determine tree horizontal structure. The complete area (2500 m2) of VR was surveyed for

diameter class distribution but, due to time constraints, only 6 of the 25 subplots in JR (600 m2)

were measured.

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Forest Floor

Standing organic matter was harvested in April/ May 2005 and again in November 2005.

Forest floor was sampled by a random toss method. For each plot, ten subplots were chosen

beforehand by a random number generator. A 50 cm x 50 cm metal frame was thrown within

each selected subplot and all matter within the frame was collected with the aid of a knife.

Material was dried at 60ºC for 48 hours, weighed and averaged to calculate the dry weight of

standing biomass per hectare for each plot. The removal and burning of floor material in May is

common practice in cacao production systems in preparation for cacao harvest. Forest floor

measures were taken prior to clearing in all plots except one (VIC).

Litterfall Production

Wooden collection boxes of internal area 1.0 m2 were constructed with nylon mesh

bottoms with openings of 1 mm2, resting 20 cm above the ground. Ten boxes were evenly

distributed in a regular grid formation in each 0.25 ha plot and rotated every two weeks to

maximize area coverage. Material was collected bi-weekly, dried at 60ºC for 48 hours and

weighed.

Soil Nutrient Status

Soil was sampled within each of the 25- 10 m x 1 0m subplots at each site. Ten soil

samples were taken with a soil corer from the 0-10 cm depth and bulked to create a single sample

for each subplot. Composite soil samples for each site were generated by combining aliquots of

equal weight from the 25 subplot samples after air-drying and sieving to < 2 mm.

For composite-plot samples, particle size distribution was determined by the hydrometer

method (Gee & Bader, 1986); pH was determined in H2O (1:2 soil to solution ratio) and 1.0M

KCl (1:2 ratio) using a glass electrode pH meter; and exchangeable cations by ammonium-

acetate extraction (CSIRO 1985). Gravimetric water content was determined by drying the soil

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at 105°C for 24 hours. Total nutrient concentrations (P, K, Ca, Mg, Zn) were determined at the

sub-plot and whole plot levels using the sulfuric acid - peroxide digest method (Gasparatos &

Haidouti, 2001). Phosphorus in the extracts was determined colorimetrically by the vanadium

blue method, and the absorbance of the blue color was read at 725nm (Olsen & Sommers, 1982).

Calcium and magnesium were determined by atomic absorption (Perkin Elmer AAnalyst 200,

Perkin Elmer, Inc., Waltham MA), using an acetylene-nitrous oxide flame after addition of

0.25% w:v La2O3. Potassium concentrations were determined using atomic absorption

spectrometry with an oxygen-acetylene flame. For each study plot, a 1-kg composited soil

sample was formed from all 25 subplots. In triplicate, a 250g subsample was sieved into four size

classes (<53μm, 53-150μm, 150-250μm and 250μm-2 mm) by dry-sieving for 5 minutes using a

mechanical shaker. Each size class was weighed for size-class distribution and further analyzed

for total P and loss on ignition (LOI). LOI was determined by combustion at 400ºC for 16 hours

in order to remove organic matter. The low temperature was set to minimize the

dehydroxylation of clay minerals (Nelson & Sommers, 1996) during the combustion of organic

matter. Total C and N of whole, composited soils were determined by dry combustion on a

Shimadzu TOC-V total carbon and nitrogen analyzer equipped with an SSM-5000A solid sample

combustion unit (Shimadzu Scientific Instruments, Columbia, MD).

Statistical Analysis

Within plot species diversity as described by Margalef’s diversity (DMG), and the Shannon

information index (H′) were employed in order to assist the comparison of species diversity

across studies. While imperfect in compensating for differing sampling intensities and areas

(Magurran, 2003), both measures incorporate richness and number of individuals and provide a

better alternative to the direct comparison of richness values. Jaccard’s similarity coefficient was

used to compare the similarity of species composition within cabruca systems and secondary

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forest plots. Differences between the eight study plots in soil and litter nutrients, and litterfall

and standing biomass were determined by one-way analysis of variance (ANOVA) followed by

Tukey’s Honestly Significant Differences test (HSD). Comparisons of measured parameters

between the two land uses were performed using the two-tailed, paired Student’s t-test. Where

necessary, data were log-transformed to conform to the assumptions of normality and

homogeneity of variance; if transformed data failed to meet the required assumptions, the

Kruksal-Wallis ANOVA and median test were used to distinguish differences between plots and

the Wilcoxon signed rank test was used to separate differences between land uses. Significant

differences in monthly litterfall rates and seasonal standing biomass within plots and within land

uses were also determined using dependent t-tests. All statistical analyses were performed at a

95% confidence interval using STATISTICA 7 (StatSoft, Inc., 2004).


Tree Density and Species Inventory

Stem density, species richness and diversity of all cabruca and forest plots are summarized

in Tables 4-2 and 4-3. In all, 733 individuals representing 41 species in 23 families were

identified within the four cacao systems observed. Cabrucas were dominated by Moraceae,

Araliaceae, Anacardaceae, Fabaceae and Urticaceae. Within the secondary forest sites, 1108

individuals were surveyed, comprising 96 species from 35 families. Excluding palms, Moraceae,

Araliaceae, Lauraceae, Lecythidaceae and Fabaceae dominated secondary forest assemblages,

with the species Arthocarpus sp., Didymopanax morototonii and Eschweilera ovata constituting

37% of the individuals encountered. Stem density in the cabruca averaged 183±13 and 44±10

individuals ≥ 5cm DBH per 0.25 ha with and without cacao, respectively, and 277±33

individuals in secondary forest. Given the same sampling efforts in both land uses, cabrucas

presented one-third the number of species found in adjacent forest fragments. These findings are

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consistent with those of Rolim and Chiarello (2004) which showed depressed species richness in

areas of cabruca relative to lesser disturbed forest. Of the 38 native species observed within the

cabruca systems, 30 coincide with species observed in the combined plots of secondary forest, or

approximately 30% of native species encountered in secondary forest sites. This result has

implications for cabruca systems acting as a potential seed source for adjacent secondary forests.

Compared to published reports of other cacao production systems in the region (Table 4-

6), the cabrucas observed here are typical. On a per hectare basis, the tree density encountered in

these systems was over twice that as in cabrucas reported by Hummel (1995) and Sambuichi

(2002) with the same size limit. However, in a survey of cabrucas located approximately 40 km

from the study site, 35 to 133 ind. ha-1 ≥ 10 cm DBH excluding cacao were observed (Alves,

1990 as cited in Sambuichi, 2002), suggesting that the stem density of cabrucas in the present

study were sparse in comparison. With regard to tree species diversity, the cabrucas observed in

this study were relatively impoverished, as measured with and without the presence of cacao

H′avg = 2.64 ± 0.09 and H′avg = 1.17± 0.08, respectively. According to recent literature, the least

floristically diverse cabruca system in the region is an abandoned cabruca site (O1) with a

Shannon diversity value, H′= 3.31 (Sambuichi & Haridasan, 2007). Cabrucas established in

another former landless community settlement, potentially similar in parcel size as those

experienced in this study still demonstrated higher diversity values with the inclusion of cacao,

where Shannon diversity H′ = 1.86 (Machado et al., 2005). Among themselves, the four

cabrucas were dissimilar in floristic composition. High variability in tree density and species

diversity and composition are trademarks of agroforestry systems and reflect not only

environmental controls such as geography and climate, but also land-owner preference and land

management history (Schroth & Harvey, 2007).

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The distribution of plant families in cacao systems differ markedly from that of typical

Atlantic Forest. Myrtaceae, Sapotaceae, Caesalpinaceae, Lauraceae and Chrysobalanaceae

comprise the five most important species in native south Bahian moist forest in terms of stem

density and basal area (Mori et al., 1983). In an assessment of three local cabruca systems,

Fabaceae, Moraceae and Lecythidaceae emerged as the most important families by stem density

and basal area (Lobão, 2007). Dominance by Moraceae due to Ficus spp. as observed in the sites

of this study is common to disturbed areas (Sambuichi & Haridasan, 2007). However the

reestablishment of Myrtaceae, Sapotaceae and Chrysobalanaceae and their attendant shade-

tolerant species in abandoned cabrucas suggest that cabrucas have the capacity to regenerate and

revert to a state closer to that of “primary” forest with time (Sambuichi & Haridasan, 2007).

Several measures indicate that the secondary forest sites observed in this study were under

or recovering from disturbance (Table 4-3). Total stem density (1108 ind. ha-1) was lower than

primary forest in the region but within the range of other secondary forests and fragmented

systems of lowland humid Atlantic forest. Native stands, an example of which is a site in the

Serra do Conduru State Park, held as many as 2450 ind. ha-1 (Martini et al., 2007) in the same

diameter class limit. Based on the size distribution of the two forest fragments JR and VR (Fig.

4-5), the frequencies of stems exceeding 10 cm DBH were 138 and 148 ind. 0.25 ha-1, (or 550

and 598 ind. ha-1) respectively, compared to 891.26 ha in the nearby Una Biological Reserve

(Mori et al., 1983), and 493-842 ind. ha-1 in montane tropical forests in upper Amazonia (Gentry,

1988). Other secondary forests of the Atlantic rain forest exhibit densities in the range of 800

ind. ha-1 (Elias Jr., 1998). Declines in stem density from interior forest to fragmented forest to

forest edge to secondary forest correlate with increased forest disturbance (Santos et al., Oliveira

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et al., 2008). The relative sparseness of the observed secondary sites relative to interior primary

forests in the region alludes to a history of disturbance in the secondary sites.

In the currency of species richness, the observed secondary forest sites (44-57 species ≥ 5

cm DBH per 0.25 ha; H′avg = 2.74 ± 0.18, DMGavg = 8.45 ± 1.22) were impoverished relative to

other patches of secondary forest. In the same diameter class limit, Elias Jr. (1998) catalogued

106 individuals in 800 species (H′ =3.96); at ≥ 15 cm DBH fragments in middle and advanced

stage exhibited tree diversity indices 3.16 and 3.77, respectively. In contrast, a site of primary

forest within the Serra Grande National Park, less than 50 km away from the study sites, held

2450 stems ≥ 5cm DBH and 458 species in one hectare (Thomas et al., 2008) and in the Una

Reserve, 178 species in 600 sampled individuals were catalogued in one hectare (Mori et al.,

1983). These two native sites render Margalef diversity indices of 58.6 and 27.7, dwarfing the

most floristically diverse of the secondary forest sites observed in this study (DMG=10.26). The

crowning glory of the region is the Serra do Conduru State Park where 144 tree species ≥ 5 cm

DBH were inventoried in 0.1 hectare. (Martini et al., 2007).

Considerable overlap in the composition of forest species encountered in this work and in

the published species rosters of secondary and primary forests in the region cited above was

evident. Despite their proximity, secondary forest sites exhibited low floristic similarity (Table 4-

3) a phenomenon regularly observed in native stands. Spatial heterogeneity of vegetation species

is the outcome of multiple factors including history and geography which determine the regional

species pool, as well as the interactions between plant and animal species (Ricklefs, 1987).

Human intervention also plays a part. The presence of exotic plantation trees would be expected

in cabrucas, due to their historical economic importance in the region; however the impact of

introduced species on un-managed forested systems was striking. Exotic species appearing in all

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secondary forest areas were dendê palm (Elaeis guineensis N. J. Jacquin), and jackfruit

(Artocarpus heterophyllus Linn.). Jackfruit accounted for the majority of all stems in three of

the four secondary forest plots, accounting for 20-26 % of the individuals counted

Jussara palm (Euterpe edulis Mart.) was the most dominant species, appearing in all four

forest sites, and comprising between 6% and 23% of stems. Didymopanax morototoni, was the

second most frequently occurring species and alongside other pioneers, Tapirira guianensis and

Pterocarpus violaceaus, are indicative of early succession stands (Santos et al., 2008). Of the

four secondary sites, JR situated in the largest forest fragment appeared to be of least disturbance

as evidenced by the greater presence of shade tolerant species such as Sloanea obtusifolia, Virola

gardneri, Mabea occidentalis (Santos et al., 2008), the high density of jussara palm, an

economically lucrative and ecologically threatened species (Matos & Bovi, 2004), as well as the

relatively low frequency of introduced species. In contrast, the site VR appeared to be the most

disturbed site with 51% of its basal area dominated by jackfruit.

Litter Production, Standing Biomass

Mean monthly litterfall produced from September 2005 to August 2006, which included

leaves, branches, flowers, fruits and seeds, was 0.81 ± 0.32 Mg ha-1 in areas of cacao production,

and 0.80 ± 0.32 Mg ha-1 in secondary forest. The peak period of litterfall in cabruca systems

corresponded to the leaf-drop cycle of cacao trees in October. Forest systems also experienced

peak litterfall from October through December, in accordance with the observations of native

forests by Mori et al., (1983). On an annual basis, the amount of litterfall was statistically

insignificant between cabruca and secondary forest, but rates of monthly deposition differed

between the land uses (Fig. 4-6). Annual litterfall rates for the two systems were 9.45 ± 0.25 Mg

ha-1 and 9.42 ± 0.25 Mg ha-1 in cabruca and forest, respectively. Equal rates of organic inputs

within the two systems are interesting in light of the fact that the stem density of cabrucas was

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significantly lower than forests. These rates are within the range observed for lowland tropical

rain forests, annual 5.5 to 14.4 Mg ha-1 (Greenland et al., 1992) and cacao production systems in

Brazil, 9.0 and 14.0 Mg ha-1 when cacao was shaded with Erythrina or Ficus (de Oliveira Leite

& Valle, 1990).

The amount of standing litter material in areas of cabruca and secondary forest areas was

5.6 ± 0.3 Mg ha-1 and 5.9 ± 0.8 Mg ha-1, respectively for the month of May 2005 and 5.4 ± 1.1

Mg ha-1 and 4.9 ± 1.7 Mg ha-1 in November (Fig. 4-7). These two periods represent six months

and one month after the peak leaf-drop period. With the exception of one plot (JC), no

significant seasonal differences in standing biomass appeared within individual plots and no

significant differences between land-uses were found.

Nutrient Inflows through Litterfall

Litterfall is one of the principal mechanisms for the transfer of nutrients into the soil. While

nutrients are extracted from cabruca systems through seed harvest, much is returned through

litter. Litter nutrient concentrations of both cacao and secondary forest sites reflect the relatively

high nutrient status of the soils on which they are situated (Table 4-7). Total P and K

concentrations of secondary forest foliar litter were two to three times that of the mean total P

and K concentrations of native species assemblages on coastal tabuleiro soils (Montagnini et al.,

1995; Silva 1990). Cabruca litterfall was nutrient rich in comparison to secondary forest sites for

elements P, K, and Mg. However, in conjunction with litter biomass, only annual rates of P

addition through litterfall showed significant differences between the two systems. Again,

cabrucas exceeded total P inputs relative to secondary forests at a rate of 6.2± 0.3 kg ha-1 yr-1

versus 4.4 ± 0.3 kg ha-1 yr-1. Because litter nutrients were assessed only once with material

gathered over the course of a year, it was not possible to observe the probable seasonal

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differences in inputs which may occur as a function of both litter biomass and litterfall

concentrations.

Soil Nutrient Status

Total nutrient concentrations in surface soil from the 0 to 10cm depth differed

significantly between cabruca and secondary forest systems with respect to P, Ca and Mg. Bulk

densities of paired plots were assumed equivalent and concentrations directly comparable. This

could be attributed to the addition of fertilizers to boost cacao production. The most commonly

used fertilizers are superphosphate or triple superphosphate which would augment both P and Ca

levels. Liming is also recommended at the rates 2,000 kg ha-1 yr-1 for clayey soils and 1,000 ha-1

yr-1 for sandy soils (Chepote et al., 2003). However, according to the land proprietors, no

additions of fertilizer or lime were applied after the establishment of cacao seedlings and each

plot is at least 50 years old. Calcium and magnesium which did not exhibit significant

differences in annual inputs through litter between the two land uses, nonetheless exhibited

greater accumulation in cabruca soils, suggesting a history of the use of the calcareous

amendments.

Despite the possibility of fertilization at low levels within the cabruca systems, it is also

likely that the “extra” P supplied to cabrucas derives from the litter of cacao itself, as supported

by the greater quantities entering into the soil as litter. Phosphorus sufficiency is commonly

observed in cacao agroforestry systems (Ofori-Frimpong et al., 1999). The difference in Mg

levels observed between the land uses may also arise from the surface deposition of cacao leaves.

Annan-Afful et al. (2004) found significantly greater concentrations of Mg in cacao leaves and

of exchangeable Mg in cacao-plantation topsoils than in other land uses along a toposequence

which included primary forest, fallow, mixed cropping, and traditional rice farming.

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Significantly greater organic matter concentrations were also demonstrated cabrucas

relative to secondary forest. Despite the observation that total inputs of OM through litterfall do

not differ on an annual basis between the two systems, the concentration of soil organic matter in

the whole soil and in all soil particle size fractions of cabruca systems surpassed that of

secondary forest. Figure 4-8 illustrates the distribution of organic material among soil

aggregates of varying size. For both land uses, the greatest quantity of organic material resided

in the largest size fraction (250μm to 2.0 mm), while the highest concentrations consistently

appeared in the smaller fractions (not shown), a common trend. Higher soil Ca content may

facilitate OM sequestration and the formation of Ca-humic compounds in cabruca soils.

Proper design and management of agroforestry systems, including cabruca, can make them

effective carbon sinks (Montagnini & Nair, 2004). Based upon the rate of carbon inputs through

litterfall, standing biomass and the total quantity of carbon in the soil, cabruca as managed in this

region is as effective at C sequestration as secondary forest. In cacao systems of southern

Cameroon, soil carbon levels exceeded that of secondary forest (Duguma et al., 2001). Similar

trends were documented in West African cacao plantations that after 25 years approached the

litterfall production rates and soil carbon stores of primary forest (Annan-Afful et al., 2005; Isaac

et al., 2005).

However, as important as the quantity of carbon stored is, so is its long-term stability. Soil

organic matter is afforded physical or chemical protection from microbial degradation via three

principle mechanisms—its inherent biochemical recalcitrance, associations with surface

minerals, and occlusion within soil aggregates (Sollins et al., 1996; Six et al., 1999). Assuming

for a moment, similar litterfall chemistries between the two land uses, the greater partitioning of

SOM to smaller soil size fractions in secondary forests may mean greater stores of more stable

105

carbon. Even in forested sandy soils where the formation of aggregates is weak compared to

soils richer in clay, showed that with decreasing particle size, the greater the energy required to

access the associated organic matter and the greater protection afforded (Sarkhot et al., 2007).

More work in determining the relative stability of the carbon present in cabrucas and secondary

forests is necessary.

Conclusion

The predominance of secondary forests warrants deeper understanding of their nutrient

dynamics and species composition to ensure wise use and management for future generations

(Ewel, 1979). In the specific case of the Atlantic Forest which also supports the cacao producing

region of southern Bahia, the regeneration of primary forest may be beyond reach, but a

functioning and profitable “mosaic of cabruca and secondary forest” (Rolim & Chiarello, 2004)

is presently attainable. Realizing this goal requires the acknowledgment of and reconciliation of

potentially competing conservation goals.

The results of this study show that despite slightly lowered tree density and tree diversity

compared to secondary forest, traditional cacao production presents an agroforestry system

which closely resembles secondary forest in terms of biomass production, carbon inputs and

nutrient delivery. Because these results show that lowered floristic diversity of cabrucas can

maintain or increase stocks of soil carbon as secondary forest, it is crucial to distinguish the

importance between the potentially conflicting ecosystem services of enhancing floristic

diversity (and by extension wildlife diversity) and carbon sequestration—the two additional

commodities often promoted in environmentally-conscious cacao products.

Future research efforts should include landscape-level models on the effect of different

degrees of forest fragmentation interspersed with cabrucas and agricultural activities to establish

critical limits beyond which various ecosystem services such as wildlife habitat, carbon

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sequestration, water are (Cassano et al., 2008). Understanding the specific thresholds to each

particular ecosystem service will enable a more informed discussion of the trade-offs between

increasing productivity and economic ambitions and biodiversity goals (Franzen & Mulder,

2007).

Threats to secondary forests and fragments of varying stages of regeneration include

selective extraction, fragmentation, and biological invasion (Phillips, 1997). Evidence of all

three activities was apparent in the fragments investigated in this study. Connectivity with

healthy forest is essential to maintain secondary forests. Proximity to native forest stands is

crucial as decay, degradation, and in reverse, recovery, is dependent on dispersal factors

(Cardoso da Silva & Tabarelli, 2000). However, recovery, as measured by forest structure and

tree species diversity can take on the order of 300 years (Liebsch et al., 2008). Addressing the

problems instigated by edge effects and forest separation is crucial to landscape regeneration and

the maintenance of forest landscape (Laurance et al., 2002; Santos et al., 2008). The severe

fragmentation observed in this study with many forest fragments < 0.5 ha may be a common

occurrence in the cacao growing region of southern Bahia, and may accelerate with the

settlement of hundreds of families in the movement of land reform. It is therefore critical to

consider the management practices or partitioning of land within land settlements as part of a

broader landscape management plan.

It is important to note that three significant reserves lie within 50 km of the study sites –

the Una Biological Reserve, Serra do Conduru State Park and the Serra Grande National Park. If

at their best cabrucas reflect the historical diversity of the region and are recipients or

benefactors of local diversity sources, the disappearance or fragmentation of these refuges will

precipitate the decline in the diversity of cabruca systems as well.

107

108

Programs that promote environmental certification in a similar vein as organic certification

have been proposed to encourage the adoption of practices such as reduced weeding in cabrucas

to allow the recruitment of native forest species (Bedê et al., 2007). While such measures could

prove beneficial, the local farming communities and environmental policy makers should first

articulate priorities of ecosystem services for solutions to forest degradation and economic

stagnation to be effective.

Table 4-1. Soil chemical properties and particle size distribution of cacao and secondary forest study sites in the Assentamento Frei Vantuy, Ilhéus, Bahia, Brazil.

Site pH Coarse sand

Fine sand

Silt Clay Base saturation

1:2 (H2O)

1:2 (KCl) % Dry weight %

Cacao JC 5.1 4.3 21.2 15.1 25.3 38.4 39.9 EVC 5.1 4.5 13.5 11.6 23.1 51.8 47.3 AC 5.3 4.9 22.2 12.9 31.5 33.4 70.2 VIC 5.1 4.5 48.7 19.2 16.9 15.2 57.1 Secondary forest JR 4.7 4.0 22.0 19.6 25.2 33.2 16.4 BR 4.9 4.6 32.4 16.9 23.0 27.7 62.6 BBR 5.0 4.8 27.9 16.3 24.4 31.4 67.3 VR 4.4 3.9 64.5 20.7 6.1 8.7 22.9 Site names were formed by the name of the proprietor (e.g. J= Josadabes; EV= João Evangelista; A= Aldienor; B= Brito; V=VI= Virgínia) and land-use type (C= Cacao/ Cabruca; R= Forest Reserve).

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Table 4-2. Summary of the floristic diversity of four cabruca sites (0.25 ha) located in the Frei Vantuy Settlement, Ilhéus, Bahia, Brazil. Values in parentheses represent diversity parameters excluding cacao.

JC EVC AC VIC No. of individuals 172 (41) 182 (37) 201 (59) 178 (40) No. of species 18 (17) 21 (20) 24 (23) 16(15) Margalef’s Diversity Index 3.31 (4.58) 3.85 (5.54) 4.34 (5.40) 2.89 (3.80) Shannon Information Index (H′) 1.14 (2.59) 1.05 (2.92) 1.42 (2.77) 1.05 (2.29) Pielou’s Evenness Index (J′) 0.39 (0.90) 0.35 (0.96) 0.45 (0.88) 0.38 (0.85) Jaccard’s Similarity Coefficient

JC EVC

AC VIC

--

0.26 0.19 0.21

0.26

-- 0.19 0.21

0.19 0.19

-- 0.22

0.21 0.21 0.22

-- Site names were formed by the name of the proprietor (e.g. J = Josadabes; EVC = João Evangelista; A = Aldienor; VI=Virgínia) and land-use type (C = Cacao/Cabruca). Stem densities include unidentified species. Diversity parameters do not include unidentified species.

110

111

Table 4-3. Summary of the floristic diversity of four secondary forest sites (0.25 ha) located in the Frei Vantuy Settlement, Ilhéus, Brazil.

JR BR BBR VR No. of individuals 308 301 260 239 No. of species 46 44 46 57 Margalef’s Diversity Index 7.88 7.55 8.13 10.26 Shannon Information Index (H′) 2.85 2.49 2.87 2.76 Pielou’s Evenness Index (J′) 0.79 0.66 0.75 0.67 Jaccard’s Similarity Coefficient

JR BR

BBR VR

--

0.22 0.18 0.17

0.22

-- 0.38 0.20

0.18 0.38

-- 0.23

0.17 0.20 0.23

-- Site names were formed by the name of the proprietor (e.g. J = Josadabes; B =BB= Brito; V = Virgínia) and land-use type (R = Forest Reserve). Stem densities include unidentified species. Diversity parameters do not include unidentified species.

Table 4-4. Phytosociological parameters (excluding cacao) of cabrucas in southern Bahia observed in other studies.

Author Location System Area (ha) ≥ DBH (cm) †Stems Species H′

Sambuichi, 2002

Fazenda Novo Horizonte, Ilhéus-BA, 14° 37′ S, 39° 16′ W

Cabruca 2.6 5 138 41 3.35

Hummel, 1995

Fazenda Serra Grande, Ilhéus-BA, 14° 45′ S, 39° 14′ W

Cabruca 2.6 5 145 40 NA

Sambuichi & Haridasan 2007

Ilhéus-BA, 14° 41′-14° 44′ S, 39° 09′-39° 12′ W O1 O2 O3 N1 N2

Cabruca

“Old” “Old” “Old” “New” “New”

3 10

47 158 175 102 355

46 85 113 82 180

3.31 3.34 3.99 3.54 4.22

Lobão, 2007 Fazenda Pau Brasil, Ibirapitanga-BA, 14° 10′ S, 39° 24′ W

Fazenda Bom Retiro, Piraí do Norte-BA, 13° 48′ S, 39° 24′ W

Fazendo Vapor, Ubatã, BA 14° 13′S, 39° 3′ W

Cabruca 1‡ 15 152

120

180

43

36

52

3.29

3.24

3.97

NS= Not specified †Stem densities in cabruca systems do not include cacao ‡Results reported on a per hectare basis; point-centered quarter method

112

113

Table 4-5. Phytosociological parameters of forest fragments in southern Bahia, observed in other studies.

Author Location System Area (ha)

≥ DBH (cm) †Stems Species H′

Lobão, 2007 Fazenda Nova Esperança, Itapé-BA, 14° 10′ S, 39° 24′ W

Fazenda Marinêda, Jussari-BA, 15° 11′ S, 39° 30′ W

2º Forest “Medium”

“Advanced”

1‡ 15 80

249

35

72

3.16

3.77

Elias Jr., 1998 Veracruz Florestal Ltd., Eunapolis-BA, 16° 22′ S, 39° 34′ W

2º Forest

1 ‡ 5 800 106 3.96

Martini et al., 2007

Serra do Conduru State Park, Bahia, Brazil Old growth forest (OGF)

Old logged forest (OLF)

Recently logged forest (RLF)

1º Forest

0.1 4.8

254

263

253

144

137

134

NS

NS= Not specified; NA= Not available †Stem densities in cabruca systems do not include cacao ‡Results reported on a per hectare basis; point-centered quarter sampling method

Table 4-6. Tree species (≥ 5 cm DBH) identified in the selected study areas of cabruca and secondary forest in the Assentamento Frei Vantuy, Ilhéus, Bahia, Brazil.

Occurrence Family/ Scientific name Common name 2º Forest Cabruca

ANACARDACEAE Anacardium occidentale L. Cajueiro vermelho 1 Tapirira sp. Pau-pombo 14 Astronium sp. Aroeira 1 Spondias sp.1 Cajarana 2 Spondias sp. 2 Cajazeira 6 15

ANNONACEAE Duguetia magnolioidea Maas. Bacumuxá 6 2 Guatteria sp. Pindaiba preta 7 Xylopia sp. 1 Pindaiba 5 Xylopia sp. 2 Pindaiba vermelho 2

APOCYNACEAE Aspidosperma sp. Pitiá 2 Himatanthus sp. Janauba 5 Macoubea guianensis Aublet Pau de leite 1

ARALIACEAE Didymopanax morototoni Decne & Planch.

Matatauba 59 17

ARECACEAE Bactris sp. Tucum mirim 8 Elaeis guineensis N.J. Jacquin Dendê 98 Euterpe edulis (Mart.) Jussara 163 Polyandrococos caudescens (Mart.) Burí 1 Syagrus oleracea Patí 11

BOMBACEAE Eriotheca macrophyllum Imbiruçu 5 1 Hydrogaster sp. Bomba de água 1

BORAGINACEAE Cordia sp. 1 Baba de boi 1 Cordia sp. 2 Claraiba 6 2

BURSERACEAE Protium sp. Amescla 12

CARYOCARACEAE Caryocar sp. Pequi doce 1

CHRYSOBALANACEAE Couepia sp. Oití 1

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Table 4-6. Continued. Occurrence Family/ Scientific name Common name 2º Forest Cabruca

CLUSIACEAE Garcinia sp. Olandí 2 Symphonia sp. Landirana 1 Tovomita sp. Mangue 15

COMBRETACEAE Terminalia brasilensis Araçá da água 2

ELAEOCARPACEAE Sloanea obtusifolia Gindiba 5 1

ERYTHROXILACEAE Erythroxylum sp. Peroba-rosa 10

EUPHORBIACEAE Cnidoscolus marcgravii Pohl. Penão 1 Hyeronima alchorneoides Cajueiro 2 1 Mabea sp. 1 Cambreaçu 7 Mabea sp. 2 Cambreaçu mirim 2 Tetrorchidium rubruvenium Coarana brava 3 1

FABACEAE (CAESALPINOIDEAE) Apuleia sp. Jitaí-peba 1 Arapatiella sp. Arapatí Caesalpinia ferrea leiostachya Pau ferro 1 1 Cassia multijuga Cobí 6 16 Copaifera sp. Pau óleo 1 Tachigalia multijuga Ingauçu 6 1 Zollernia sp. Mocaítaba 1

FABACEAE (MIMOSOIDEAE)

Inga affinis Ingá cipo 2 2 Inga sp. 1 Ingá 4 Inga sp. 2 Ingá vermelho 1 Pithecolobium polycephalum Monzê 1

FABACEAE (PAPILIONOIDEAE)

Andira sp. Angelim 1 Erythrina sp. Eritrina 2 Lonchocarpus sp. Cabelouro 1 Machaerium angustfolium Vog. Sete cascas 15 2 Pterocarpus sp. Pau sangue 3 2

FLACOURTIACEAE Banara sp. 1 Carpotroche brasiliensis Sapucainha 1

115


LAURACEAE Cryptocarya sp. Louro cravo 33 5 Nectandra sp. 1 Louro canela 1 Nectandra sp. 2 Louro graveto 3 Nectandra sp. 3 Louro sabão 11 7 Nectandra sp. 4 Louro verdadeiro 6 Nectandra sp. 5 Louro bosta 1

LECYTHIDACEAE Cariniana sp. Jequitibá 1 Eschweilera ovata (Cambess.) Miers Biriba 47 Lecythis lurida (Miers.) Mori Inhaiba 2 Lecythis pisonis Cambess. Sapucaia 1 1

MALVACEAE Sterculia chicha (St. Hill) Samuma 35 1 Theobroma cacao Cacaueira 6 556

MELASTOMATACEAE Miconia sp. 1 Mundururu 13 1 Miconia sp. 2 Mundururu branco 3 Miconia sp. 3 Mundururu ferro 3 3 Miconia sp. 4 Mundururu folha de lixa 5 Miconia sp. 5 Mundururu tresquinha 2 Tibouchina sp. Mundururu vermelho 2

MELIACEAE Cedrela sp. Cedro-rosa 4 Guarea sp. 1 Carrapeta 5 Guarea sp. 2 Cedro cabacinha 2 1

MONIMIACEAE Mollinedia sp. Café preto 28

MORACEAE Artocarpus sp. Jaqueira 195 30 Brosimum sp. Conduru 13 Ficus sp. Gameleira 1 Ficus insipida Willd. Gameleira-branca 2 2 Ficus gomelleira Kunthe et Bouché Gameleira-preta 5 3 Helicostylus sp. Amora 2 Sorocea sp. Amora branca 1

MYRISTICACEAE Virola gardnerii Bicuíba 1

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MYRTACEAE Eugenia sp. 1 Batinga 4 Eugenia sp. 2 Murta 5 1 Eugenia sp. 3 Murta branca 11 5 Eugenia sp. 4 Murta preta 4 1 Psidium sp. Araçá branco 3 Syzygium aromaticum Cravinho 1

NYCTAGINACEAE Guapira sp. Farinha-seca 15

OLACACEAE Olacaceae sp. 1

POLYGONACEAE Coccoloba sp. Pê de bolo 1 2

RUBIACEAE Genipa americana Jenipapo 5 2 Rubiaceae sp. Pau cravo 2

RUTACEAE Citrus sp. Tangerina 1 Zanthoxylum sp. Paparaiba de espinho 3 8

SAPINDACEAE Cupania sp. Camboatã 9

SAPOTACEAE Manilkara sp. Parajuzinho 1 Manilkara salzmanii Maçaranduba 1 Pouteria sp. 1 Abiu da mata 5 Pouteria sp. 2 Bapeba 3 1

SIMAROUBACEAE Simarouba sp. Paparaiba 30 10

ULMACEAE Trema micrantha Curindiba 1

URTICACEAE Cecropia sp. Embauba 1 14 Pouroma guianensis Aubl. Tararanga 1

UNIDENTIFIED Unidentified sp. 1-3 6 Unidentified sp. 4-9 48 Total 1108 733

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Table 4-7. Mean total nutrient concentrations of surface soil (0-10cm) and litter and total annual input of nutrients through litterfall in traditional cacao systems and adjacent secondary forest sites in the Assentamento Frei Vantuy, Ilhéus, Bahia, Brazil.

N P K Ca Mg

Cabruca Soil (ug g-1) 336.6*

(5.1) 102.0 (5.1)

621.8* (27.5)

521.8* (25.4)

Litter (% dry weight) 0.064* (0.002)

0.43* (0.05)

1.46 (0.09)

0.37* (0.03)

Litter (kg ha-1 yr-1) 6.24* (0.45)

42.04 (5.74)

141.33 (9.16)

35.77 (2.00)

Secondary Forest Soil (ug g-1) 248.2*

(11.2) 92.9 (4.3)

439. * (26.7)

284.6* (12.1)

Litter (% dry weight) 0.046* (0.003)

0.31* (0.03)

1.46 (0.19)

0.28* (0.05)

Litter (kg ha-1 yr-1) 4.42* (0.20)

30.02 (3.17)

141.74 (22.40)

26.23 (4.06)

Error reported in parentheses is ± SE. Values marked with an asterisk represent significant differences between land uses at P< 0.05.

118

Maps courtesy of Faria Filho & Araujo, 2003

Figure 4-1. Location of study area among land use types in the municipality of Ilhéus, Bahia, Brazil.

119

1 km

6 5

4

3 1 2

1 Josadabes’ Cabruca (JC); Josadabes’ Forest Reserve (JR) 2 Brito’s Forest Reserve (BR); Brito’s Second Forest Reserve (BBR) 3 Aldienor’s Cabruca (AC) 4 Joao Evangelista’s Cabruca (EVC) 5 Virginia’s Cabruca (VIC); Virginia’s Forest Reserve (VR) 6 Low inundated clearing

LEGEND

N

Figure 4-2. Approximate locations of observed traditional cacao and secondary forest study sites in the Assentamento Frei Vantuy, Ilhéus, Bahia, Brazil.

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C

B A

Figure 4-3. Example of forest structure of secondary forest fragments. A) Josadabes forest reserve (JR); B) Looking up at the canopy of Brito’s forest reserve II (BBR); C) Understory of Josadabes reserve (JR).

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C

B A

Figure 4-4. Example of forest structure of cabruca systems. A) The shade and cacao layers of Virginia’s cabruca (VIC); B) Josadabes cabruca (JC); C) Floor of Virginia’s cabruca (VIC).

122

0

25

50

75

100

125

150

175

200

0-10

10-2

0

20-3

0

30-4

0

40-5

0

50-6

0

60-7

0

70-8

0

80-9

0

90-1

00

> 10

0

DBH (cm)

No.

Indi

vidu

als

0.1 ha, 199 ind.

A

0

25

50

75

100

125

150

175

200

0-10

10-2

0

20-3

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40-5

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0

80-9

0

90-1

00

> 10

0

DBH (cm)

No.

Indi

vidu

als

0.25 ha, 289 ind.

B

Figure 4-5. Diameter class distribution of stems in two secondary forest fragments. A) Josadabes’ Reserve (JR); B) Virginia’s Reserve (VR).

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0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

Mg/

ha

CabrucaForest

Figure 4-6. Monthly litterfall in cacao and secondary forest collected from the period September 2005 to August 2006. Error bars represent ± SE.

124

Figure 4-7. Standing biomass on the forest floor of forested plots in May and November, 2005. A) All eight forest systems. B) Cabruca versus secondary forest systems. Error bars represent ± SE.

CACAO FOREST

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

JC EVC AC VIC JR BR BBR VR

Mg/

haMayNovember

A

0.0

2.0

4.0

6.0

8.0

10.0

MAY NOVEMBER

Mg/

ha

CacaoForest

B

125

126

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0


Plot

mg

OM

/ g s

oil

< 53um150um - 53um250um - 150um2 mm - 250um

A CACAO FOREST

0%

20%

40%

60%

80%

100%


Plot

% T

otal

OM

< 53um150um - 53um250um - 150um2 mm - 250um

B CACAO FOREST

Figure 4-8. Total organic matter content and organic matter distribution among particle size fractions of cacao and secondary forest sites. A) Total organic matter content per gram of oven dry soil; B) Organic matter distribution by soil particle size class. Error bars represent ± SE.

CHAPTER 5 OVERVIEW AND SYNTHESIS

The role of species diversity in maintaining critical ecosystem functions ranks among the

most contentious scientific debates of our time. How we respond to the rapid changes in the

redistribution of species across the planet in this age of accelerated globalization rests as much

upon our understanding of how species behave and interact within different environments as

upon our priorities between biodiversity preservation and resource management.

Today, carbon is arguably the most talked about chemical element, thanks to the

mainstreaming of discussions surrounding global warming. With popularity come fads and

scams. Throughout the developing world, “carbon sequestration”, along with “biodiversity” and

“organic” are terms pitched to small farming communities as an environmental cash cow.

Because of the high expectations of the carbon market to offset past and future CO2 production,

scrutiny of the conditions under which long-term carbon storage is possible is necessary as are

multidisciplinary efforts to quantify realistic carbon sequestration rates under well-defined

environmental and human-use conditions. Real scientific data are necessary to form the basis of

the carbon market for both producers and consumers alike.

The importance of vegetation diversity on ecosystem functions such as carbon dynamics,

including sequestration is yet undetermined. The inability to predict the long-term effect of plant

diversity or even of individual species on the terrestrial carbon cycle is a liability in a growing

carbon credit market. Because of its intersection with the global economy, the question of

diversity and ecosystem function is not strictly an academic exercise. The models used to

estimate soil carbon storage potentials should be the best available and suited to the particular

environment in which a carbon management plan is to be executed.

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The aim of this work was to establish a firm relationship between plant litter diversity, and

the soil ecosystem function of decomposition, whose principal objectives were i) to compare the

capacity of small-scale traditional agroforestry system (low tree species diversity) and secondary

forest (high tree species diversity) to provide selected ecosystem services and ii) to predict the

decomposition behavior of litter mixtures under controlled conditions. Chapter 2 developed

analytical and conceptual tools used in the litter-mix prediction efforts described in Chapter 3. In

Chapter 4, two land-uses provided tropical forest systems of differing tree diversity in which to

contrast the direct and indirect effects of species composition and diversity on soil functions.

The setting was staged in an area dependent on dwindling forest resources for income and

where the term “biodiversity” was perhaps of greater economic interest than ecological for those

concerned. In such an environment, the conflation of science and philosophy can be detrimental

to both the producer who may not receive the crop (or monetary) yields promised or to the

consumer who may pay to support a false product. Selling biodiversity as a scientifically

grounded management practice to growers, especially vulnerable small-scale growers demands

universally validated and repeatable results.

In the Atlantic Rain Forest region of southern Bahia, a land formerly dominated by cacao

production, reforestation projects flourish in the effort to reclaim lands degraded by the

proliferation of pasture following the devastation of the cacao production due to witch’s broom

in the 1990s. In particular, the resurgence of cabruca, the traditional, method of cacao

production has become favored by institutions formerly predisposed toward high-input, full-sun,

large-scale plantation production.

This work has shown that selected functions related to nutrient delivery such as litterfall

production, carbon storage, and soil nutrient status were supplied equally well by managed,

128

production-oriented forest systems as by “natural” stands. However, the functional parity is

misleading. This is due in part to the great variation in what is designated secondary forest and

also to the selected functions observed. Hence the choice of ecosystem function will determine

the level of importance of diversity in maintaining it. If the function desired can be

accomplished by a few productive species as by multiple species, priorities between potentially

conflicting goals of conservation and service must be drawn. For this region, such results

underscore the importance of dialog between the conservation and cacao-producing communities

on the priorities of cabruca systems and the need to investigate the effect of size of land-holding

on species diversity, forest structure and soil properties.

The prediction of soil ecosystem services as a function of land cover and land use is the

cornerstone of soil organic carbon models from Century to NuCM. Yet these models ignore the

predominance of mixed substrates and use simplified estimates based on homogenous soil inputs

which has been shown by many studies to be inadequate to describe rates of carbon

mineralization and nutrient release. In tropical forest systems, the shortcomings of such models,

even if conservatively estimated at only a few percent in prediction accuracy, when integrated

over the area covered by tropical forests, could represent a miscalculation on the order of

gigatons of carbon either sequestered in the soil or released to the atmosphere.

Few studies have sought to predict litter mixture behavior, particularly the magnitude and

direction of litter interactions that occur during mixed litter decomposition. Fewer have

attempted to devise an alternative measure of litter mixture diversity based on multiple chemical

traits. The principal contribution of this work was the successful modeling of the carbon

mineralization of leaf mixtures on the basis of chemical identity and a novel construct, and

mixture chemical diversity. These two concepts supersede the former taxonomic descriptions of

129

mixtures by species identity and species richness. By adopting litter chemistry as relevant

functional traits to decomposition processes I have demonstrated the possibility to predict not

only aggregate mixture behavior but also the strength and magnitude of litter interactions—a feat

not yet noted in the published literature. While limited in scope because of its controlled

laboratory setting, the use of ground litter tissue and the limitation to two species mixtures, the

results presented here, in conjunction with continuous evolution models of decomposition

incorporating IR, provide a mechanistic footing on which to advance SOM models.

As a summary of chemical heterogeneity the chemical diversity index (CDQ) offers a

snapshot of resource heterogeneity that can also be extended to live-vegetation systems. An

unanticipated finding was uniqueness of the relationship between foliar chemical diversity and

species richness for a vegetation community. Because foliar chemistry is the outcome of

interspecies and environmental interactions, an index that summarizes chemistries of many

species can serve as a monitoring tool in conjunction with other measures. Furthermore, the

index is dynamic because even while the species composition of a vegetation community may

remain constant, its chemical profile will not.

Future Work

The importance of vegetation diversity on ecosystem functions such as wildlife

preservation and aesthetics are well established. Its relevance to soil-based functions, in

particular carbon dynamics, including sequestration is yet undecided. The inability to predict the

effect of plant diversity or even of individual species on the terrestrial carbon cycle over the long

term is a liability in a growing carbon credit market. Because of its intersection with global

economy, the question of diversity and ecosystem function is not strictly an academic exercise.

The models used to estimate soil carbon storage potentials should be the best available and suited

to the particular environment in which a carbon management plan is to be executed.

130

131

Further development of this work should include the following:

• Documentation of the spatial and temporal variability in the chemical heterogeneity of litter mixtures under field conditions

• Expansion to mixtures comprised of more than two species;

• Robustness to changes in relative abundance (unequal species distribution);

• Field-level studies which include the effect of litter particle size and the importance of litter chemistry on macro and mesofauna and;

• Investigation of the environmental mediation (mineralogy, moisture, soil nutrients, pH, CO2 enrichment) of microbial community and activity on the utilization of mixed substrates.

• Field testing of reforestation using chemical heterogeneity as a tool in defining site biodiversity, while limiting the number of species required for reforestation.

Changes in climate, such as shifts in precipitation patterns, elevated CO2 and increased

temperature, stand to influence biogeochemical cycles via their affect on the world’s vegetation.

This may entail alterations in the species composition of plant communities, enhanced above-

and belowground productivity or modifications in plant chemistry and morphology. In a CO2-

rich world, the impact of relatively swift changes in vegetation chemistry and seasonality on the

millennia of give and take between insects and plants carries implications for pest incidence and

pollination. The use of litter chemistry as the starting point of decomposition pathways enhances

our ability to foresee the changes that climate change can impart on soil ecosystem functions.

APPENDIX A USING INFRARED SPECTROSCOPY TO DETERMINE THE RELATIVE

CONTRIBUTION OF ORGANIC INPUTS TO SURFACE SOIL ORGANIC MATTER

Introduction

Soil boasts the largest reservoir of organic carbon in terrestrial ecosystems, (Post et

al. 1982, Schimel 1995) whose stores are principally subsidized by the death and

transformation of plant biomass – litterfall, root turnover and root exudates. The relative

contribution of aboveground and belowground inputs to soil carbon (quantity and quality)

is an important element in determining management strategies for the maintenance and

sequestration of soil carbon in forested systems. Past studies using carbon isotope

methods and/or litter- and root-exclusion experiments in temperate and tropical forest

systems have reported root respiration and root decomposition as greater contributors to

soil CO2 efflux than that deriving from the decomposition of aboveground litter

deposition (Bowden et al., 1993; Li et al., 2004, Sulzman et al., 2005). Root biomass has

been also shown to be the dominant source of new and stable carbon in surface soil

horizons (Balesdent & Balabane, 1996; Gale & Cambardella, 2000; Hobbie et al., 2004;

Rasse et al., 2005). The majority of studies monitoring the dynamics of soil carbon

employ field designs based on root and litter exclusion experiments followed by carbon

isotopic labeling or nuclear magnetic resonance (NMR) spectroscopy, both expensive and

time-consuming methods. Diffuse reflectance infrared spectroscopy (DRIFTS) is a

technique swiftly gaining popularity in the environmental science community because of

the facility, economy and versatility with which it can determine an array of properties of

organic and inorganic materials.

This study explored the use of DRIFTS to assess the relative contribution of leaf-

and root-derived inputs to soil carbon. Using replicated suites of foliage, litter, soil and

132

roots of mono-specific plots of 10 tropical forest species, I asked: How does the spectral

fingerprint of soil organic matter under a single species compare to those of foliage, litter

and fine roots of the same species and how does this relationship change with depth? The

inquiry was extended to natural and managed multi-species forested systems to see

whether the trends observed under neat, mono-specific conditions were maintained in

polycultural conditions.


Study Site and Sampling

Single-species stands

Samples were drawn from a 35-year-old arboretum established in the Pau Brasil

Ecological Station, in Porto Seguro, Brazil in the state of Bahia (16°23' S, 39° 11'W)

whose mean annual temperature and precipitation classify the climate as Af in the

Koppen system. Soils were classified as Oxisols (Montagnini et al., 1995) with sandy

surface textures. Live foliage, litter and soil were sampled in June 2004, from 19 plots

representing ten tropical tree species native to the Brazilian Atlantic Rain Forest of

southern Bahia (Table A-1). Plots measuring approximately 2 m x 2 m contained three to

five mature trees of one species. Understory growth was supressed due to frequent

cutting. Species were selected based on the facility to collect sufficient foliage and litter

material for subsequent analysis, with preference for those with replicate plots. Freshly

senesced litter was collected from the soil surface by hand and selected on the basis of

color and apparent lack of fungal colonization. Foliage was collected from the lower

canopy using a tree pruner. Leaves showing signs of extreme insect damage were

discarded. Soil was collected from the 0-5 cm and 5-10 cm depth with a shovel from

three random locations within the plot after the removal of the litter layer. From this soil

133

material, coarse and fine roots of undetermined viability were extracted by sieving and by

hand. Fine roots (< 2mm) were retained for later analysis.

Mixed-species stands

Material from mixed-species stands were gathered from forest fragments of the

Atlantic Rain Forest in southern Bahia, located in the heart of the cacao-producing region

between Itabuna and Ilhéus (14°39' S, 39° 10'W) within the same climate zone as the

research station. Sixteen 10 m x 10 m plots in high clay soils (54%) under secondary

forest and nine 10 m x 10 m plots on sandy soils (70%) from secondary forest and

cabruca, a traditional cacao production system were sampled (Chapter 4). Traditional

cacao production is characterized by the thinning of the forest cover and planting cacao in

the understory, thereby preserving some of the inherent tree species diversity (CIT). By

the accounts of local inhabitants, the areas under cabruca had been under production for

at least 50 years. The presence of the palm jussara, Euterpe edulis, and the absence of

common plantation trees, such as jackfruit (Artocarpus heterophylla) marked the forest

stand on clay soil as mature. The fragment of forest on sandy soils had been uncut for at

least 50 years but showed signs of disturbance on its perimeter. Mean stem density of the

secondary forest and cacao plots was 12 trees per 10 x 10 m plot, with 2-5 species per

100 m2 subplot in the instance of the cacao-production area, and 4-13 species per 100 m2

in secondary forest (Chapter 4). In a departure from the mono-specific stands, in lieu of

freshly senesced litter, forest floor was collected from a 50 cm x 50 cm frame randomly

tossed three times within each subplot and pooled to form a composite sample. Soil was

collected from the 0-10 cm depth using a shovel, again from three random locations

within each subplot. Roots were extracted from a 20 cm x 20 cm x 10 cm block carved

from the soil near the center of each subplot.

134

Sample Preparation

Soils were air dried, sieved to < 2mm and ball-milled after the removal of large bits

of organic matter. Roots, foliage and forest floor were dried at 60°C for 48 hours and

ball-milled prior to spectral analysis. Sub-samples of ground soil and tissue materials

were combusted at 400°C for 16h (Nelson & Sommers, 1996) and 500°C for five hours

(CIT), respectively, to generate “ashed” samples.

Spectral Analysis and Isotopic Carbon Analysis

Combusted and non-combusted soil and tissue samples were chemically

characterized using diffuse reflectance infrared Fourier transform spectroscopy

(DRIFTS). Spectra were scanned on a Digilab7000 Series infrared spectrometer without

KBr dilution in the mid-infrared region (4000 cm-1 to 400 cm-1) at a resolution of 4 cm-1.

A total of 64 scans, normalized against an IR-grade KBr standard, were averaged to

generate a final spectrum for each sample. Spectra were transformed using the standard

normal variate method to correct for baseline drift (Barnes et al., 1989).

Prepared samples of soil (0-5cm), litter and roots derived from the single-species

plots were analyzed for natural 13C isotopic ratios using an inductively-coupled plasma

mass-spectrometer (Elan 9000, Perkin Elmer, Waltham, MA).

Statistical Analysis

The sum of squared differences (SSD) between the soil spectra of 18 single species

plots and the spectra of their respective inputs was used as a simple graphical comparison

to gauge the similarity between soil organic matter and organic inputs. The SSD was

computed by summing the difference between the absorbance values of spectra of

organic inputs (foliage, litter and roots) from soil spectra over all wavepoints, W or

135

( 2

1iORGiSOM

W

iabsabsSSD −= ∑

=

) . (3-1)

Ash-subtraction is a mathematical technique that amplifies the signal of the organic

component by reducing the spectral contribution of the inorganic fraction (Westerhaus et

al., 2004). The SSD between soil and organic inputs was determined for ash-subtracted

and non-ash subtracted spectra. Values of the SSD (both whole and ash-subtracted)

calculated from roots (y) were plotted against those of litter and foliage (x). The

clustering of points on the y=x line indicates equal contribution of the two inputs, while a

deviation from the line indicates dominance of one of the inputs. Points clustered below

the 1:1 line exhibit a smaller SSD of the input represented along the vertical axis relative

to the input represented along the horizontal axis. The regression line best representing

the points was used to test the alternative hypothesis “HA≠1”.

Significant differences in isotopic ratios between soil and litter and soil and roots

were determined by the two-tailed Student t-test for dependent samples, performed at

95% confidence level. Analysis was conducted in R version 2.5.1 (R Development Core

Team, 2006).


Relative Importance of Above- and Belowground Inputs

In the monoculture systems, comparison of the single-species SSD relative to soil

organic matter of three inputs–foliage, litter, and roots–showed greater similarity between

the soil and root spectra than between soil and foliage, and soil and litter spectra (Fig. A-

1). As means of comparison, the relationship between SSDlitter and SSDfoliage was plotted

and found not to differ significantly from unity (Fig. A-1A). The equivalence of litter

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and foliage relative to soil organic matter (SOM) was conserved with soil depth,

confirming the interpretability of the graphical method.

The slopes of SSDroot versus SSDfoliage and SSDroot versus SSDlitter differed

significantly from 0 and 1 within 95% confidence interval (Fig. A-1 B,C). This

relationship was exaggerated at the 5-10 cm depth; the spectra of roots were closer to

those of the soil than were foliar or litter spectra, as suggested by the decrease in slope.

However, while a trend of increasing similarity of SOM to root matter with depth was

observed, the effect was not significant.

The use of ash-subtracted spectra enhanced the organic matter signal, particularly

in soil spectra. While the slopes were greater in the analysis of ash-subtracted soil, litter

and root matter, the associated confidence intervals were much narrower and significant

difference from unity was maintained (Fig. A-1D).

Interestingly, the computed SSD of root, litter and foliage were not similar between

replicates of single-species plots. Such discrepancies could arise from site differences that

can lead to subtle changes in the quantity and quality of carbon supplied to the soil by

roots and foliage, including but not limited to, light availability, soil moisture content and

surrounding tree density. The most distal points in Fig. A-1 were replicates of species

clustered below the1:1 line but occurred on poorly drained soils in a low-lying section of

the arboretum.

Delta 13C data of soil, root and litter inputs of the single-species plots also indicated

greater similarity between soil and roots (Fig. A-2). The isotopic signature of 13C

differed significantly for all three substrates (P<0.001), and the difference between soil

137

and root signatures were significantly lower than that of soil and litter signatures

(P<0.001).

In the multi-species systems, evidence to suggest the dominance of root-derived

carbon in the SOM was ambivalent. Following the same analysis, the spectral signature

of the top 10 cm of the clayey soil under secondary forest bore greater similarity to the

non-combusted spectral profile of roots than to that of surface-deposited litter (Fig. A-3).

The relationship was strengthened when employing ash-subtracted spectra. In contrast,

neither roots nor litter dominated in the secondary forest and cabruca systems situated on

the sandy soil. A possible explanation for this is the relative instability of these systems.

Because of the high activity expressed through the extraction of species in the cabruca

system and the influence of new, introduced species in the forest fragment, sufficient

time may not have passed for an equilibrium between aboveground vegetation and

belowground soil carbon to occur, thereby blurring the parentage soil carbon signature.

Interpretation

This study centered on the application of infrared spectroscopy to discern the

relative similarity in chemical composition of surface and subsurface inputs to that of soil

organic matter. Soil organic matter spectra were found to resemble root matter more

closely than litter in mono-specific plots and in one of the multi-species plots. The direct

comparison of initial inputs and SOM signatures assumes that the decomposition

trajectories taken by root and litter are sufficiently unique that their end-products can be

directly associated with them. This includes the possibility in which recalcitrant

compounds of inputs are conserved despite transformations brought on by the action of

the decomposer community and, therefore, able to serve as biomarkers. In the case of

138

litter-derived material, such identifiers may be secondary metabolites rich in phenolics,

and, in the case of roots, polymers such as suberin (Rasse et al., 2005).

Natural carbon isotope abundance has been used to track above- and belowground

carbon allocation in grassland and forested systems (Peterson & Fry, 1987; Staddon,

2004) and to monitor soil carbon dynamics (Balesdent et al., 1987) arising from shifts in

vegetation communities such as cropping systems (Vitorello et al., 1989), C3 versus C4

plants (Dzurec et al., 1985) or legumes and non-leguminous species (Rao, 1994).

Because the isotopic signature of organic matter is strongly influenced by its consumers,

δ13C is also a helpful tool in charting food webs (Rothe & Gleixner, 2000; Ruess et al.,

2005). Preferential incorporation of lighter 12C by soil fauna leads to the enrichment of

residual materials in the heavier 13C isotope.

The enrichment in natural 13C from litter to roots (Fig. A-2) arises from multiple

processes by which photosynthetic C is fractionated and partitioned amongst plant organs

(Badeck et al., 2005). The observed proximity of the δ13C soil and roots has several

viable interpretations. The first is that, assuming the same degree of decomposition and

decomposition trajectory undergone by root and litter, root carbon is a significantly

greater contributor to SOM than plant carbon. However, if litter were preferentially

consumed by decomposers over root matter, litter-derived end products could constitute

the majority of SOM through the greater exposure of litter to microbial activity, even

while its original isotopic signature is more distant to SOM. In the absence of information

about decomposition pathways, interpretation of δ13C values is inconclusive.

Consumers of litter detritus exhibit preference for carbon forms and leaf litter has

been commonly to be the preferred substrate over root matter (Kramer & Gleixner,

139

2008). However, a recent study conducted in a temperate forest system demonstrated a

stark contrast in the degrading communities of root- and leaf-derived materials, with the

majority of soil invertebrates obtaining their carbon from roots (Pollierer et al., 2007).

Under such a scenario, the proximity of the carbon isotopic signature of roots and SOM

supports the interpretation that root-derived OM constitutes a greater proportion of SOM

than litter-derived OM and that the spectral analysis is corroborated.

Already used successfully to calibrate and predict NMR-determined alkyl C and O-

alkyl C contents (Leifeld, 2006), which are key functional groups used for studying the

structure and turnover of SOM, DRIFTS presents a potential tool in the quantifying the

relative contribution of various inputs to soil carbon. Its utility in calibration and

prediction can permit greater sample throughput which can facilitate the expansion of

spatial coverage for landscape-level assessments as well as finer scaled sampling over

soil particle size fractions. An advantage over isotopic analysis is that no assumptions of

decomposition trajectory or decomposition community have to be made. If successful,

this approach can help to identify the dominant source of soil organic matter and its final

destination may be useful in evaluating soil carbon management strategies in forested

systems.

Conclusion

Using DRIFTS as the basis for chemical characterization, greater similarity

between soil organic matter and fine-root inputs than between SOM and leaf litter was

observed. Many studies have used root- and litter-exclusion experiments or isotopic

ratios for determining the relative contribution of organic inputs to soil carbon. This

method potentially offers a faster, cost-effective technique for a qualitative assessment

with no field-level experimental manipulation and minimal sample preparation. Once

140

141

validated by traditional methods, DRIFTS analysis presents a valuable monitoring tool

for land management practices in which carbon sequestration is a primary objective.

Table A-1. Ten tropical forest species used to analyze foliage, litter, root inputs to soil organic matter.

Common name Scientific name Duplicated?

Amescla mirim Protium sp. Y Angico branco Anadenathera peregrina N Arapaçu Sclerolobium chrysophyllum Y Arapati Arapatiella psilophylla Y Gindiba Sloanea obtusifolia Y Imbiruçu Bombax macrophyllum Y Jatobá Hymenaea sp. Y Pau marfim Balfourodendron riedelianum Y Sapucaia Lecythis pisonis Y Vinhático Plathymenia foliolosa Y

142

D C

B A

Fig. A-1. Sum of squared differences from of organic inputs plotted against each other at the0-5

cm and 5-10 cm depths. A) Foliage vs. Litter. B) Roots vs. Foliage. C) Roots vs. Litter. D) Roots vs. Litter for ash-subtracted soil.

143

Fig. A-2. Isotopic carbon signatures of 18 single-species plots.

144

B

A

Fig. A-3. Sum of squared differences between forest floor litter and root litter in a multi-species

forested system atop a clayey soil, based on non-combusted and combusted (cleaned) spectra A) Non-combusted; B) Ash-subtracted spectra.

145

146

Fig. A-4. Sum of squared differences between forest floor litter and root litter in a multi-species

forested system atop a sandy soil, based on non-combusted and combusted (cleaned) spectra A) Non-combusted; B) Ash-subtracted spectra.

B

A

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BIOGRAPHICAL SKETCH

The author was born and raised in Jamaica, New York in an auspicious year for New

Yorkers (Subway Series, Woodstock, alleged men on Moon). At the age of five, after hearing

that stars were round, self-luminous, gaseous spheres instead of the silver things that adorned her

kindergarten drawings, she decided on a future in astrophysics. Epps was given a good start at

Eastwood Elementary (aka P.S. 95Q), the Immaculate Conception School, and the Bronx High

School of Science. She remained faithful to her original plan and entered Harvey Mudd College

in Claremont, California, from which she graduated with a degree in physics with an emphasis in

astrophysics. Immediately thereafter, the author strayed from the heavenly spheres and headed

south, literally, to New Orleans, Louisiana and attempted to establish a career in photography but

was instead waylaid by music after spotting a ‘cello for sale in the window of Werlein’s. Some

years of lessons and music production followed, to varying degrees of success, and her return to

the West Coast marked an exciting and eclectic period during which she served the community

as a waitress, fisherman, cook, store clerk, and SAT tutor. The inescapable necessity of reliable

income led the author to the San Francisco School where she began as a part-time algebra

teacher and left four years later as the eighth grade teacher. After graduating a second time from

the eighth grade, Ms. Epps entered the United States Peace Corps as a volunteer in the

Agroforestry Program in Cameroon, Central Africa, where she was introduced to soil in all of its

glory and, became fascinated with phosphorus.

Returning early from Peace Corps service, Ms. Epps entered graduate school at the

University of California Davis in the Program in International Agricultural Development with an

emphasis in soil science. She later applied to the Department of Soil Science from which she

earned her master’s degree. A visit to the University of Florida to resolve laboratory trouble

with a method of sequential phosphorus fractionation of wetland sediments led her to converse

with Dr. Nicholas B. Comerford to whom she is grateful for all that followed.

linking species richness, litter chemical diversity...

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