( PDF ) Defesas contra herbivoria e descritores da vegetação : relações com variáveis edáficas em uma área de cerrado

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UNIVERSIDADE FEDERAL DE SÃO CARLOS

CENTRO DE CIÊNCIAS BIOLÓGICAS E DA SAÚDE

PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E RECURSOS

NATURAIS

VINÍCIUS DE LIMA DANTAS

DEFESAS CONTRA HERBIVORIA E DESCRITORES DA

VEGETAÇÃO: RELAÇÕES COM VARIÁVEIS EDÁFICAS EM UMA

ÁREA DE CERRADO

SÃO CARLOS

2010

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UNIVERSIDADE FEDERAL DE SÃO CARLOS

CENTRO DE CIÊNCIAS BIOLÓGICAS E DA SAÚDE

PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E RECURSOS

NATURAIS

VINÍCIUS DE LIMA DANTAS

DEFESAS CONTRA HERBIVORIA E DESCRITORES DA

VEGETAÇÃO: RELAÇÕES COM VARIÁVEIS EDÁFICAS EM UMA

ÁREA DE CERRADO.

Dissertação apresentada ao Programa de

Pós-Graduação em Ecologia e Recursos

Naturais da Universidade Federal de São

Carlos, como parte dos requisitos para

obtenção do título de mestre em Ecologia e

Recursos Naturais.

Orientador: Prof.Dr. Marco Antônio Batalha

SÃO CARLOS

2010

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Ficha catalográfica elaborada pelo DePT da

Biblioteca Comunitária da UFSCar

D192dc

Dantas, Vinícius de Lima.

Defesas contra herbivoria e descritores da vegetação :

relações com variáveis edáficas em uma área de cerrado /

Vinícius de Lima Dantas. -- São Carlos : UFSCar, 2010.

64 f.

Dissertação (Mestrado) -- Universidade Federal de São

Carlos, 2010.

1. Ecologia de comunidades. 2. Herbivoria. 3.

Autocorrelação espacial. 4. Solos. 5. Matéria orgânica. I.

Título.

CDD: 574.5247 (20

)

Vinicius

Lima

Dantas

DEFESAS CONTRA HERBIVORIA E DESCRITORES DA

VEGETACAO:

RELAC~ES

COM

VARI~VEIS

EDAFICAS

UMA

AREA

DE CERRADO

Dissertqgo apresentada

Universidade Federal de Sgo Carlos, como parte dos

requisitos para obten~go do titulo .de Mestre em Ecologia e Recursos Naturais.

BANCA EXAMINADORA

Presiden

~ro$&?&arc&$nt6nio P.

Batalha

Examinador

"L-G

profa. Dra. Ana Teresa Lombardi

PPGERNKJFSCar

Examinado

Dedico este trabalho aos meus pais

Robinson José de Santana e minha mãe

Sônia Regina de Lima, por que sempre

acreditaram em mim e me apoiaram

com muito amor.

Agradecimentos

Agradeço as instituições abaixo pelo apoio financeiro, sem o qual

seria inviável a realização deste trabalho, e às pessoas abaixo as quais foram

fundamentais na minha formação profissional e pessoal, e sem as quais eu não

teria conseguido chegar até aqui:

- Ao meu orientador, o Prof. Marco Antônio Batalha, pela excelente

orientação e pela paciência;

- A Priscilla de Paula Loiolla, pela companhia no campo, por suas

ideias geniais, por todas as vezes que me fez chorar de rir, e principalmente pela

amizade inestimável;

- A Danilo Muniz da Silva, pela companhia no campo, por sua

eterna disposição em ajudar, pelas ideias compartilhadas, pelas piadas

infindáveis, tão importantes no trabalho de campo, e pela amizade;

- A Gustavo Henrique de Carvalho, pela paciência e preciosa

assistência no aprendizado do R, e pela amizade;

- A Marcus Vinicius Cianciaruso e Igor Aurélio da Silva pelas

instigantes discussões que me ajudaram a entender melhor a ciência, a ecologia,

e a vida, e pela amizade;

- A toda equipe do laboratório pela ajuda no campo e bons

momentos no laboratório;

- À minha namorada Mariana Luciano Afonso, pela ajuda no

campo, mas principalmente pelo amor e paciência durante os períodos difíceis, e

por me fazer querer ser sempre uma pessoa melhor;

- À professora Dalva M. dos Santos, porque foi fundamental para

que eu decidisse mudar de caminho no mestrado e pudesse descobrir a minha

verdadeira vocação na biologia;

- À professora Maria Inês e técnica Maristela, pela ajuda com as

análises químicas;

- Ao meu irmão João Vitor, que eu me orgulho tanto e amo muito;

- Ao programa de Pós-graduação em Ecologia e Recursos Naturais,

pelo suporte;

- A Fundação de Amparo a Pesquisa do Estado de São Paulo

(Fapesp) pelo apoio financeiro no projeto;

- Ao Conselho Nacional de Desenvolvimento Científico e

Tecnológico (CNPq), pela bolsa concedida a mim.

Resumo

O solo, juntamente com o fogo e as variações climáticas, é considerado um dos principais

determinantes do cerrado brasileiro, a savana mais rica do mundo. O solo pode influenciar as

espécies de planta selecionando aquelas adaptadas a explorar e competir por recursos, mas

também pode influenciar os padrões de alocações em defesas contra herbivoria. Embora

muitos estudos tenham se voltado a entender as relações entre solo e vegetação no cerrado,

poucos se focaram efeitos em escala local. De forma geral, nossa expectativa é de que

descritores da comunidade, como composição florística, riqueza, equabilidade, diversidade e

abundância total estejam relacionados com o solo, mesmo em escala local, dentro de uma

determinada fisionomia, e que plantas em solos pobres em nutrientes invistam mais em

defesas contra herbivoria devido ao alto custo em repor as folhas perdidas. Em uma área de

cerrado, lançamos 100 parcelas contíguas de 25 m

cada, identificamos todos os indivíduos

em nível de espécies, coletamos amostras de solo e medimos os seguintes traços foliares de

defesa contra herbivoria: área foliar específica, razão C:N, quantidade de água, dureza,

densidade de tricomas, quantidade de látex, e presença de alcaloides, terpenoides e taninos.

Para testar a relação entre a composição florística e as variáveis do solo, usamos uma análise

de redundância parcial, controlando a autocorrelação espacial. Para testar a relação entre as

variáveis do solo e (1) a abundância das cinco espécies mais abundantes, (2) a abundância

total, (3) a riqueza de espécies, (4) a equabilidade e (5) a diversidade de espécies; para prever

a distribuição dos traços de defesa contra herbivoria por meio das variáveis do solo,

utilizamos regressões múltiplas ou modelos autorregressivos, na presença de autocorrelação

espacial. Encontramos uma baixa relação entre o solo e a composição florística,

provavelmente devido à presença de espécies funcionalmente redundantes e espécies com

dispersão limitada. O conteúdo de matéria orgânica esteve positivamente relacionado à

abundância de Myrsine umbellata, a espécie mais abundante na área, e à abundância total, e

negativamente relacionada à equabilidade, o que sugere que um mecanismo de

retroalimentação positiva pode ser a causa da dominância de Myrsine umbellata. Também

encontramos uma relação positiva entre soma de bases e a riqueza de espécies provavelmente

refletindo um gradiente de fertilidade. Contrariamente às nossas expectativas, não

encontramos relação entre o investimento total em defesas e a fertilidade do solo,

provavelmente refletindo uma baixa variação nas variáveis do solo em escala local ou

variações fenotípicas entre indivíduos da mesma espécie ou ambas. Entretanto, a presença de

taninos esteve relacionada positivamente com o conteúdo de matéria orgânica, o que pode

refletir menor tolerância à herbivoria em solos mais pobres ou uma alta acumulação de

matéria orgânica no solo devido à lenta taxa de decomposição de folhas com tanino. De forma

geral, sugerimos que o solo é um importante fator estruturando a comunidade, mesmo em

escala local, e que a dominância de espécies de cerrado pode estar relacionada a mecanismos

de retroalimentação positiva.

Palavras-chave: autocorrelação espacial, matéria orgânica, modelos autorregressivos,

Myrsine umbellata, riqueza, soma de bases, resistência contra herbivoria

Abstract

Together with fire and climate changes, soil is considered a major determinant in the Brazilian

cerrado, the richest savanna in the world. Soil can influence plants by filtering species capable

of acquiring resources and compete for them, but can also influence plant patterns of

allocation in defense against herbivory. Although many studies focused on plant soil

relationship in cerrado, few focused on the influence of soil at fine scale. We expected

community descriptors, such as floristic composition, richness, evenness, diversity, and total

abundance to be related to soil features at fine scale within a physiognomy. We also expected

plants on nutrient-poor soils to present higher anti-herbivory defenses. In a cerrado site, we

placed 100 contiguous 25 m

plots, in which we identified all woody individuals, measured

soil variables and the following leaf traits: specific leaf area, C:N ratio, water content,

toughness, trichomes, latex, and presence of tannins, alkaloids, and terpenoids. We did a

partial redundancy analysis to test for relationship between soil features and floristic

composition, controlled for spatial dependence. We also did multiple regression or spatial

autoregressive models to test for relationships between soil features and: (1) the abundance of

the five commonest species, (2) total abundance, (3) richness, (4) evenness, and (5) diversity

and to predict defense traits based on soil features. We found no relationship between soil and

floristic composition, probably due to functional redundancy or limited dispersal. Organic

matter was positively related to Myrsine umbellata, the most abundant species, and total

abundance, and negatively related to evenness, what suggests positive feedbacks to cause the

dominance by Myrsine umbellata. We also found a positive relationship between sum of basis

and species richness, probably reflecting a fertility gradient. Contrary to our expectations, we

found no relationship between total defenses and total soil fertility or soil variables, what

could result from low variability in soil fertility at fine scale or of high phenotypic variability.

Presence of tannins was positively related to organic matter, possibly reflecting a strategy

towards lower tolerance due to low reserve allocation or interactions with other resources.

However, since tannins decrease leaf decomposition rates, organic matter could be

accumulating in soil. Overall, we suggest that soil is an important factor structuring cerrado

community even at fine scales and that the dominance of cerrado species could be related to

positive plant-soil feedbacks.

Keywords: autoregressive models, anti-herbivory resistance, Myrsine umbellata, organic

matter, spatial autocorrelation, species richness, sum of basis.

Sumário

INTRODUÇÃO GERAL 13

REFERÊNCIAS BIBLIOGRÁFICAS 16

CAPITULO 1: ESTRUTURA DA VEGETAÇÃO: RELAÇÕES EM ESCALA LOCAL

COM VARIÁVEIS EDÁFICAS NUMA ÁREA DE CERRADO 19

Vegetation structure: fine scale relationships with soil in a cerrado site 20

Abstract 21

Introduction 22

Methods 23

Results 25

Discussion 26

Literature cited 28

CAPITULO 2: DEFESAS CONTRA HERBIVORIAS E VARIÁVEIS EDÁFICAS EM

ESCALA LOCAL NUMA SAVANA BRASILEIRA 36

Fine scale relationship between anti-herbivory defense traits and soil features in a

Brazilian savanna 37

Abstract 38

Introduction 49

Material and Methods 41

Results 46

Discussion 47

Acknowledgments 50

References 51

CONCLUSÃO GERAL 63

INTRODUÇÃO GERAL

Introdução Geral

O cerrado é um dos maiores domínios vegetacionais do país ocupando

originalmente cerca de 23% do território brasileiro (Rizzini 1997), e foi avaliado como sendo

a savana mais rica em espécies do mundo (Kier et al. 2005, Mendonça et al. 2008, Ratter et al.

2003). Por abrigar tantas espécies, estudos em escala local costumam encontrar uma alta

variação na composição de espécies em áreas de cerrado (Carvalho and Martins 2009,

Carvalho et al. 2008). No entanto, pouco se conhece sobre os fatores que determinam essa

enorme variabilidade de formas e seu padrão de distribuição no espaço (Simon et al. 2009).

Um estudo recente usando abordagens filogenéticas, sugeriu que o fogo foi um

fator chave para o surgimento do cerrado e sua separação das florestas há milhares de anos

atrás (Simon et al. 2009). Também tem sido sugerido que variações climáticas têm um papel

importante na distribuição do cerrado (Coutinho 1990, Oliveira-Filho & Ratter 2002) e

existem evidências de que as matas de galeria vêm se expandido sobre áreas de cerrado em

resposta a variações climáticas (Silva et al. 2008).

O solo também é um fator determinante na distribuição das espécies e na

estruturação do cerrado, sendo um dos principais determinantes das variações fisionômicas

encontradas no cerrado (Alvin & Araujo 1952, Coutinho 1990, Goodland & Pollard 1973,

Haridasan 2000, Oliveira-Filho & Ratter 2002) e na diferenciação entre cerrado e outros tipos

vegetacionais (Amorin & Batalha 2007, Ruggiero et al. 2002, Silva et al. 2008).

Outro fator que pode estar relacionado com os padrões de distribuição espacial

das espécies de planta no cerrado é a herbivoria. No entanto, apesar de existir evidência para

uma forte pressão dos herbívoros sobre as plantas do cerrado (Costa et al. 2008) a influência

deste fator tem sido pouco estudada. A herbivoria exerce uma forte pressão seletiva sobre

comunidade e populações de planta, aumentando a mortalidade de plantas, removendo

biomassa que poderia ser alocada para crescimento e reprodução (Coley et al. 1985), e

reduzindo a habilidade competitiva das plantas (Coley & Barone 1996). Desta forma a

herbivoria aumenta a pressão sobre espécies mais palatáveis, favorecendo espécies que sejam

mais resistentes ou tolerantes a herbivoria (Schädler et al. 2003, Mauricio 2000).

O padrão de alocação das planta para resistência ou tolerância à herbivoria

depende amplamente do ambiente em que a planta está inserida (Nuñez-Farfán et al. 2007) e

estudos empíricos mostraram que este padrão de alocação é influenciado pela disponibilidade

de recursos no solo (Fine et al. 2006). De forma geral, espécies em solos mais pobres tendem

a investir mais recursos em resistência contra herbivoria, já que a baixa disponibilidade de

recursos torna a tolerância mais custosa (Fine et al. 2006). Taxas maiores de herbivoria são

encontradas em florestas estacionais do que em cerrado (Neves et al. 2010), o que foi

atribuído à maior esclerofilia nas folhas de cerrado relacionada à menor disponibilidade de

nutrientes. Assim, o solo parece exercer um papel ainda mais importante no cerrado, já que

influencia o padrão de distribuição das defesas contra herbivoria, e assim, altera indiretamente

o padrão de seleção de plantas pelos herbívoros.

Apesar de existirem muitos estudos pesquisando possíveis fatores que

determinam a distribuição da vegetação do cerrado, a maioria deles tem se focado em

variações em escala regional, principalmente em fatores relacionados com a separação entre

as diferentes fisionomias do cerrado (Amorin & Batalha 2007, Carvalho & Martins 2009,

Goodland & Pollard 1973, Marimon Junior & Haridasan 2005, Ruggiero et al. 2002) ou

mesmo a separação entre o cerrado e outros tipos vegetacionais (Amorin & Batalha 2006,

Ruggiero et al. 2002, , Silva et al. 2008, Simon et al. 2009). Devido à alta variação a curtas

distâncias na composição de espécies, estudos em escala local poderiam ajudar a conhecer

melhor os processos influenciando comunidades de cerrado. Variações no solo ocorrem a

distâncias tão curtas quanto 1 m (Downes & Beckwith 1951, Souza & Martins 2004), assim, o

solo é um forte candidato a influenciar a distribuição das espécies e de seus atributos

funcionais em escala local. Este trabalho teve como objetivo estudar possíveis relações entre

as variáveis do solo e as características da comunidade, como a distribuição espacial das

espécies de plantas e de suas defesas contra herbivoria em escala local.

Referência bibliográficas

Alvin, P. T. & Araujo, W. (1952) El suelo como factor ecológico en el desarollo de

vegetación en al centro-oeste del Brasil. Turrialba 2:153-160.

Amorim, P. K. & Batalha, M. A. (2006) Soil characteristics of a hyperseasonal cerrado

compared to a seasonal cerrado and a floodplain grassland: implications for plant

community structure. Brazilian Journal of Biology 66:661-667.

Amorim, P. K.& Batalha, M. A. (2007) Soil-vegetation relationships in hyperseasonal

cerrado, seasonal cerrado, and wet grassland in Emas National Park (central Brazil). Acta

oecologica 32:319-327.

Carvalho, D. A. & Martins, F. M. (2009) Shrub and tree species composition in the cerrado

of southwest Minas Gerais. Cerne 2:142-154.

Carvalho, F.A.; Rodrigues, V. H. P.; Kilca, R. V.; Siqueira, A. S.; Araújo, G. M. & Schiavini,

I. (2008) Composição florística, riqueza e diversidade de um cerrado sensu stricto no

sudeste do estado de goiás. Bioscience Journal 4:64-72.

Coley, P. D.; Bryant, J. P. & Chapin III, F. S. (1985) Resource availability and plant

antiherbivore defense. Science 230:895-899.

Coley, P. D. & Barone, J. A. (1996) Herbivory and plant defenses in tropical forests. Annual

Review Ecology, Evolution and Systematics 27:305-335.

Costa, A. N.; Vasconcelos, H. L.; Vieira-Neto, E. H. M. & Bruna, E. M. (2008) Do herbivores

exert top-down effects in Neotropical savannas? Estimates of biomass consumption by

leaf-cutter ants. Journal of Vegetation Science 19:849-854.

Coutinho, L. M. (1990) Fire in the ecology of the Brazilian Cerrado. Ecol Stud 84:82-105

Downes, R. G. & Beckwith, R. S. (1951) Studies in the variation of soil reaction. I. Field

variation at Barooga, N.S.W. Australian Journal of Agricultural Research 2:60-72.

Fine, P. V. A.; Miller, Z. J.; Mesones, I.; Irazuzta, S.; Appel, H. M.; Stevens, M. H. H.;

Sããksjãrvi, I.; Shultz, J. C. & Coley, P. D. (2006) The growth-defense trade-off and habitat

specialization by plants in amazonian forests. Ecology 87:150-162

Goodland, R. & Pollard, R. (1973) The Brazilian cerrado vegetation: a fertility gradient.

Journal of Ecology 61:219-224.

Haridasan, M. (2000) Nutrição mineral de plantas nativas do cerrado. Revista Brasileira de

Fisiologia Vegetal 12:54-64.

Kier, G.; Dinerstein, E.; Ricketts, T. H.; Wolfgang, K.; Kreft, H. & Barthlott, W. (2005)

Global patterns of plant diversity and floristic knowledge. Journal of Biogeography

32:1107-1116.

Marimon Junior, B. H. & Haridasan, M. (2005) Comparação da vegetação arbórea e

características edáficas de um cerradão e um cerrado sensu stricto em áreas adjacentes

sobre solos distróficos no leste do Mato Grosso, Brasil. Acta Botanica Brasilica 19:913-

926.

Mauricio, R. (2000) Natural selection and the joint evolution of tolerance and resistance as

plant defenses. Evolutionary Ecology 14:491-507.

Mendonça, R. C. et al. (2008) Vascular flora of the Cerrado biome: Checklist with 12,356

species. In: Sano, S. M.; Almeida, S. P., Ribeiro, J. F. (eds) Cerrado: Ecology and Flora.

Embrapa Cerrados/Embrapa Informação Tecnológica, Brasília, pp 421–1279.

Neves, F. S.; Araújo, L. S.; Espírito-Santo, M. M.; Fagundes, M.; Fernandes, G. W.; Sanchez-

Azofeifa, G. A. & Quesada, M. (2010) Canopy herbivory and insect herbivore diversity in

a dry forest–savanna transition in Brazil. Biotropica 42:112-118

Núñez-Farfán, J.; Fornoni, J. & Valverde, P. L. (2007) The evolution of resistance and

tolerance to herbivores. Annual Review of Ecology, Evolution and Systematics 38:541-

566.

Oliveira-filho, A. T. & Ratter, J. A. (2002) Vegetation phisiognomies and woody flora of the

cerrado of the cerrado biome. In: Oliveira PS, Marquis RJ (eds) The cerrados of Brazil:

ecology and natural history of neotropical savannas, Columbia University Press, New

York, pp 13-32

Ratter, J. A.; Bridgewater, S. & Ribeiro, J. F. (2003) Analysis of the floristic composition of

the Brazilian cerrado vegetation III: comparison of the woody vegetation of 376 areas.

Edinburgh Journal of Botany 60:57–109.

Rizzini, C. T. (1997) Tratado de Fitogeografia do Brasil. Âmbito Cultural, Rio de Janeiro.

Ruggiero, P. G. C.; Batalha, M. A.; Pivello, V. R. & Meirelles, S. T. (2002) Soil-vegetation

relationships in cerrado (Brazilian savana) and semideciduous Forest, Southeastern Brazil.

Plant Ecology 160:1-16

Schädler, M.; Jung, G.; Auge, H. & Brandl R (2003) Palatability, decomposition and insect

herbivory: patterns in a successional old-field plant community. Oikos 103:121-132

Silva, L. C. R.; Sternberg, L.; Haridasan, M.; Hoffmann, W. A.; Miralles-Wilhelm, F. &

Franco, A.C. (2008) Expansion of gallery forest into central Brazilian savannas. Global

Change Biology 14:2108–2118

Simon, M. F.; Gretherc, R.; Queiroz, L. P.; Skemae, C.; Penningtone, R. T. & Hughes, C. E.

(2009) Recent assembly of the Cerrado, a neotropical plant diversity hotspot, by in situ

evolution of adaptations to fire. Proceedings of the National Academy of Sciences 48:

20359–20364.

Souza, A.F. & Martins, F. R. (2004) Microsite espaecialization and spatial distribution of

Geonoma brevispata, a clonal palm in south-eastern Brazil. Ecological Research 19:521-

532

CAPITULO 1: ESTRUTURA DA VEGETAÇÃO:

RELAÇÕES EM ESCALA LOCAL COM VARIÁVEIS

EDÁFICAS NUMA ÁREA DE CERRADO

Trabalho submetido à revista Biotropica com o título “Vegetation structure: fine scale

relationships with soil in a cerrado site”. Aqui o artigo se encontra dividido em sessões para

facilitar a leitura, mas a versão original foi submetida sem as sessões seguindo as normas para

artigos submetidos para a categoria “Insights”.

Vegetation Structure: Fine Scale Relationships with Soil in a Cerrado Site

Vinícius de Lima Dantas

and Marco Antônio Batalha

Federal University of São Carlos, Departament of Botany, PO Box 676, 13565-905, São

Carlos, SP, Brazil

– Corresponding author; e-mail: [email protected].

ABSTRACT – Soil is a major determinant in the Brazilian cerrado, considered at larger

scales a fertility gradient. We provide evidence that soil may be filtering the occurrence of

cerrado species even at fine scale and that positive plant-soil feedbacks can be responsible for

the abundance of the dominant species.

Keywords: autoregressive models, organic matter, savanna, soil-vegetation relationships,

spatial autocorrelation, species richness, sum of basis.

INTRODUCTION

THE BRAZILIAN CERRADO IS THE RICHEST SAVANNA IN THE WORLD, with about 7000 plant

species, of which about 1500 are shrubs or trees (Castro et al. 1999). Due to its high richness,

high degree of endemism, and present conservation status, the cerrado is one of the

biodiversity hotspots in the world. (Myers et al. 2000). Together with fire and seasonality, soil

is a main determinant of changes in plant species and vegetation structure in the Brazilian

cerrado, whose physiognomic variation is considered by some authors to be a fertility

gradient (for example, Goodland & Pollard 1973).

The cerrado tends to occur on well-drained, acid, and nutrient-poor soils, with high levels

of exchangeable aluminum, and, at increased water availability or soil fertility, it tends to be

replaced by forest (Goodland & Ferri 1979, Oliveira-Filho & Ratter 2002). The cerrado

productivity gradient is related to higher availability of bases in the soil (Goodland & Ferri

1979), whereas the sclerophyllous features of the cerrado vegetation are attributed to direct

and indirect effects of high aluminum contents and low nutrient availability (Arens 1958,

1963; Goodland & Ferri 1979, Sarmiento 1984).

Although many studies about the cerrado focused on soil-vegetation relationship, few of

them were carried out at fine scale (Ferreira et al. 2009). Since the cerrado flora is extremely

rich, being possible to find high species turnover even at small distances, studying the

processes that influence this turnover could help us to understand what drives cerrado

responses to the environment. As long as changes in soil features can be found at distances as

small as 1 m (Souza & Martins 2004), soil is an important candidate to exert fine scale effects

upon the cerrado vegetation, because at fine scale, other factors, such as climate and fire

frequency, are more homogeneous.

We looked for relationships between soil and vegetation at fine scale in a cerrado site,

trying to answer the following questions:(1) is floristic composition related to soil

features?;(2) are total abundance, richness, evenness, and diversity related to soil features?

We assumed soil to be an important environmental filter at fine scale in cerrado plant

communities. Thus, we expected floristic composition to change with fertility reflecting, to

some extent, success in exploring resources and competing with other species (Magurran

2004). Moreover, species not only respond to soil, but also influence it as well. Additionally,

we expected that, on nutrient-rich soils, where resources are available and competition may be

lower (MacArthur 1972, Bertness & Callaway 1994) total abundance, richness, evenness and

diversity would be higher.

METHODS

We studied a cerrado woodland site at Federal University of São Carlos, southeastern

Brazil (approximately, 21°58‟05.3”S, 47°52„10.1”W). The area is on dystrophic Oxisols, 850

m asl, under mesothermic, subtropical climate, with wet summers and dry winters (Cwa;

Köppen 1931). Mean annual temperature and precipitation lies around 21.3C and 1315.18

mm, respectively.

We placed a grid of 100 25-m

contiguous plots and sampled all individuals of the woody

component (stem diameter at soil level ≥ 3 cm; SMA 1997). We identified them to species

level using identification keys based on vegetative characters (Mantovani et al. 1985, Batalha

& Mantovani 1999). We used Plantminer (Carvalho et al. 2010) to correct species names, to

find author names of all species, and to include them into families according to the latest

phylogenetic classification.

We collected soil samples from soil surface (0-5 cm deep), the most correlated with

cerrado vegetation structure (Ruggiero et al. 2002, Amorim & Batalha 2006). In each plot,

we collected a composite sample, mixing five sub-samples, four in the corners of each plot

and one in the center, analysed at the University of São Paulo, according to Embrapa (1997),

Silva (1999) and Raij et al. (2001). We determined pH, organic matter, available phosphorus,

total nitrogen, exchangeable K

, Ca

, Mg

, and Al

. We calculated sum of bases, cation

exchange capacity, base saturation, and aluminum saturation. We also determined sand, silt,

and clay proportions (for a complete description see Silva & Batalha 2008).

When necessary, we transformed variables to achieve normality. We calculated

correlograms using Moran index (Moran 1950) as spatial autocorrelation index. Subsequently,

we used only those soil variables that had the range of autocorrelation at distances smaller

than 25 m, since, in this case, soil features would have enough variability to influence plant

species distribution. Although the greatest distance between pairs of plots was higher than 50

m, beyond 25 m the results for spatial dependence were not reliable due to the small number

of pairs of plots to compare.

To avoid collinearity, we tested for correlations among soil variables and, when the

coefficient of correlation was higher than 0.7, we excluded one of the soil features in the

subsequent analysis. We also preferred more synthetic variables. We standardised soil

variables to zero mean and unit variance. To answer the first question, we did a partial

redundancy analysis (Jongman et al. 1995), using the vegetation and soil matrices, and using

the spatial coordinates to control for spatial autocorrelation. We also selected the five

commonest species and did stepwise multiple regressions with soil features as explanatory

variables. When we found significant relationships in the multiple regressions, we tested the

residuals for spatial autocorrelation with Moran‟s index, since spatial dependence could cause

type I error (Dormann et al. 2007). Since residuals were normally distributed, we used

autoregressive model to account for spatial autocorrelation (Dormann et al. 2007). To answer

the second question, we counted the numbers of individuals (abundance) and species

(richness) per plot and calculated evenness (Pielou 1975) and Shannon‟s diversity (Shannon

and Weaver 1949), using them as response variables. We selected the best stepwise model

using the Akaike Information Criteria (AIC). We did all analyses in R (R Development Core

Team 2009), using the vegan (Oksanen et al. 2009) and spdep (Bivand et al. 2009) packages.

RESULTS

We sampled 2062 individuals, belonging to 61 species and 27 families. The five

commonest species were Myrsine umbellata Mart. (567 individuals), Vochysia tucanorum

Mart. (168 individuals), Myrcia guianensis (Aubl.) DC. (131 individuals), Miconia albicans

(Sw.) Triana (125 individuals), and Piptocarpha rotundifolia (Less.) Baker (103 individuals).

The richest families were Fabaceae (with eight species), Myrtaceae (with six species),

Malpighiaceae and Melastomataceae (with four species each), and Annonaceae,

Erythroxylaceae e Rubiaceae (with three species each), summing 50 percent of all species

sampled.

Soil was dystrophic (P < 0.5 cmol

/kg, K

< 0.1 cmol

/kg, Mg

< 0.2 cmol

/kg, Ca

< 0.4

cmol

/kg), acidic (pH < 4), and with high concentration of Al

(> 1.7 cmol

/kg). The only

soil features that showed a complete range of variation at the scale of our study were organic

matter, calcium, aluminum, sum of basis, aluminum saturation, cation exchange capacity, and

H+Al. Many of these variables were correlated with each other (Table 1) and, thus, for

statistical analyses, we used only organic matter, sum of basis, and aluminum saturation,

which were related to many others, but not correlated with each other.

The first axis of the redundancy analysis explained 12.86% (p= 0.005) of the variation in

floristic composition and was more related to organic matter and exchangeable aluminum.

The commonest species, Myrsine umbellata, showed a significant relationship with soil, but

only with organic matter (z=4.36; p<0.001; table 2). The other four commonest species

showed no relationship with soil features. Total abundance was positively related with organic

matter (t= 4.43;p<0.001), species richness was positively related with sum of basis (t = 3.19; p

= 0.002), and evenness was negatively related with organic matter (z=-3.3192, p<0.001). We

found no relationship between diversity and soil features (Table 2).

DISCUSSION

Albeit significant, we found low explanatory power for floristic composition in relation to

soil features at fine scale, as found by other studies that could not distinguish floristic

composition among different cerrado physiognomies based on soil (Ruggiero et al. 2002,

Marimon Junior & Haridasan 2005, Amorin & Batalha 2007). Floristic composition seems to

be more related to soil features between different neighbor vegetation types, such as wet

grassland, seasonal forest and cerrado (Amorin & Batalha 2007, Ruggiero et al. 2002, Silva

et al. 2008). Within cerrado physiognomies, there seems to be functionally redundant species,

corroborating the traditional idea that savannas are more stable in functional than floristic

terms (Sarmiento 1996). Another possibility that remains to be tested is whether neutral

processes prevail at fine scale, which is likely to occur in species-rich communities (Hubbel

2005), such as the cerrado. Nevertheless, since we found relationships between soil features

and the abundance of the commonest species, total abundance, richness, and evenness in the

regression analyses, there seems to be a deterministic relationship, at least to some extent,

between resource availability and vegetation structure within cerrado physiognomies.

The soil features related to vegetation structure were organic matter and sum of basis.

Organic matter is related to high soil fertility, since the negatively charged surfaces of organic

matter retain nutrients and some organic molecules chelate micronutrient, making them

available for plant roots (Salisbury & Ross 1991). In cerrado organic matter is the main

source of nitrogen and sulfur, both at critic low levels in cerrado (Goodland & Ferri 1979).

Moreover, organic matter provides clay aggregation stability, which allows water and air to

move through the soil and permits roots to penetrate with little resistance (Motta et al. 2002).

Organic matter may be even more important than clay in providing nutrients for plants in

cerrado communities (Goodland &Ferri 1979). Sum of basis is calculated as the sum of total

calcium, potassium, and magnesium, which are related to hydration regulation and, thus, to

water availability. Calcium and potassium are related to enzymatic activation; calcium, to

plant growth; and magnesium, to basal metabolism (Larcher 2000).

All the relationships we found with organic matter may be associated with the abundance

of Myrsine umbellata. Since Myrsine umbellata is by far the commonest species in the

community, the negative relationship between organic matter and evenness and its positive

relationship with total abundance, probably reflects the abundance of this species. We

expected higher evenness to be related to higher nutrient availability because of the lack of

competition exclusion of inferior competitors, but we did not corroborate this idea. Given that

changes in organic matter in surface soil are likely to be caused by the vegetation itself

(Sparovek & Camargo 1997, Ruggiero et al. 2002, Silva et al. 2008), a possible explanation

for the positive relationship between organic matter and the dominance of Myrsine umbellata

could be that this species increases soil organic matter, which could, in turn, favor this

species, in a mechanism of positive feedback (Kulmatisky et al.2008).

Richness showed a positive relationship with sum of basis, which could be related to

environmental filtering in nutrient-poor soils and more resources in nutrient-rich soils

allowing coexistence of more species. We did not find any relationship between richness and

exchangeable aluminum, contrary to Carvalho and Martins (2009), who compared different

cerrado physiognomies. Maybe, at fine scale, variations in aluminum could not be wide

enough to influence plants, especially because cerrado species are adapted to deal with it

(Sarmiento 1984). Richness and evenness were related with different non-correlated soil

features, and these two community descriptors are the components of diversity. Thus, the lack

of relationship between diversity and soil features could reflect different spatial patterns of

distribution of sum of basis (positively related with richness) and organic matter (positively

related with evenness) that would cancel out each other.

In conclusion, although it was not possible to infer cause-and-effect relationships, our

study indicated that soil is an important factor structuring cerrado community, even at fine

scales. Soil was not related to floristic composition, suggesting functional redundancy among

plants, but some soil features were related to community descriptors, such as abundance,

richness, and evenness. There can be a positive feedback between Myrsine umbellata, by far

the most abundant species, and organic matter, which remains to be tested. Moreover, fine

scale studies should be carried out among different cerrado physiognomies to partition soil-

vegetation relationships within and among habitats.

LITERATURE CITED

AMORIM, P. K., AND M. A. BATALHA. 2006. Soil characteristics of a hyperseasonal cerrado

compared to a seasonal cerrado and a floodplain grassland: implications for plant

community structure. Braz. J. Biol. 66: 661-667.

AMORIM, P. K., AND M. A. BATALHA. 2007. Soil-vegetation relationships in hyperseasonal

cerrado, seasonal cerrado, and wet grassland in Emas National Park (central Brazil). Acta

Oecol. 32: 319-327.

ARENS, K. 1958. O cerrado como vegetação oligotrófica, pp 59-57. Bol Fac Fil Ciênc Ltr

Universidade de São Paulo, São Paulo, BR.

ARENS, K. 1963. As plantas lenhosas dos campos cerrados como vegetação adaptada às

deficiências minerais do solo. In: Ferri MG (ed) III Simpósio sobre o cerrado, pp 13-115.

Edgard Blucher /Edusp, São Paulo, BR.

BATALHA, M. A., AND W. MANTOVANI. 1999. Chaves de identificação das espécies vegetais

vasculares baseadas em caracteres vegetativos para a ARIE Cerrado Pé-de-Gigante (Santa

Rita do Passa Quatro, SP). Rev. Inst. Florest.11: 137-158.

BERTNESS, M. D., AND R. CALLAWAY.1994. Positive interactions in communities. Trends

Ecol. Evol. 5: 191-193.

BIVAND, R., L. ANSELIN, R. ASSUNÇÃO, O. BERKE, A. BERNAT, M. CARVALHO, Y. CHUN, C.

DORMANN, S. DRAY, R. HALBERSMA, E. KRAINSKI, N. LEWIN-KOH, H. LI, J. MA, G. MILLO,

W. MUELLER, H. ONO, P. PERES-NETO, M. REDER, M. TIEFELSDORF, ANDD. YU. 2009.

spdep: Spatial dependence: weighting schemes, statistics and models. R package version

0.4-34. http://CRAN.R-project.org/package=spdep.

CARVALHO, D. A., AND F. M. MARTINS. 2009. Shrub and tree species composition in the

cerrado of southwest Minas Gerais. Cerne 15: 142-154.

CARVALHO, G. H., M. V. CIANCIARUSO, AND M. A. BATALHA. 2010. Plantminer: a web tool

for checking and gathering plant species taxonomic information. Environmental Modelling

and Software: in press.

CASTRO, A. A. J. F., F. R. MARTINS, J. Y. TAMASHIRO, AND G. J. SHEPHERD. 1999. How rich is

the flora of Brazilian cerrados. Ann. Missouri Bot. Gard. 86: 192-224.

DORMANN, C. F., J. M. MCPHERSON, M. B. ARAÚJO, R. BIVAND, J. BOLLIGER, G. CARL, R.G.

DAVIES, A. HIRZEL, W. JETZ, W. D. KISSLING, I. KÜHN, R. OHLEMÜLLER, P. R. PERES-

NETO, B. REINEKING, B. SCHRÖDER, F. K. SCHURR, AND R. WILSON. 2007. Methods to

account for spatial autocorrelation in the analysis of species distributional data: a review.

Ecography 30: 609-628.

Embrapa. 1997. Manual de métodos de análise do solo. Embrapa, Rio de Janeiro, BR.

FERREIRA, J. N., M. M. DA C. BUSTAMANTE, AND E. A. DAVIDSON. 2009. Linking woody

species diversity with plant available water at a landscape scale at a Brazilian savanna. J.

Veg. Sci. 20: 826-835.

GOODLAND, R., AND M. G. FERRI.1979. Ecologia do Cerrado. Universidade de São Paulo, São

Paulo, BR.

GOODLAND, R.,AND R. POLLARD. 1973. The Brazilian cerrado vegetation: a fertility gradient.

J. Ecol. 61:219-224.

HUBBEL, S. P. 2005. Neutral theory in community ecology and the hypothesis of functional

equivalence. Funct. Ecol. 19: 166-172.

JONGMAN, R..H..G., C. J. F. TER BRAAK, AND O. F. R. VAN TONGEREN. 1995. Data analysis in

community and landscape ecology. Cambridge University, Cambridge, UK.

KÖPPEN, W. 1931. Grundriss der Klimakunde. Berlin, Gruyter.

KULMATISKY, A., K. H. BEARD, J. R. STEVENS, AND S. M. COBBOLD. 2008. Plant-soil

feedbacks: a meta-analytical review. Ecol. Letters11: 1-13.

LARCHER, W. 2000. Ecofisiologia Vegetal. Rima, São Carlos, BR.

LOPES, A. S. 1984. Solos sob “cerrado”. Características, propriedades e manejo. Associação

Brasileira para Pesquisa da Potassa e do Fosfato, Piracicaba, BR.

MACARTHUR, R. H. 1972. Geographical Ecology. Harper & Row, New York, U.S.A.

MAGURRAN, A. E. 2004. Measuring biological diversity. Blackwell Science, Oxford, UK.

MANTOVANI, W., H. F. LEITÃO FILHO, AND F. R. MARTINS. 1985. Chave baseada em

caracteres vegetativos para identificação de espécies lenhosas da Reserva Biológica de

Moji Guaçu, SP. Hoehnea 12: 35–66.

MARIMON JUNIOR, B. H., AND M. HARIDASAN. 2005. Comparação da vegetação arbórea e

características edáficas de um cerradão e um cerrado sensu stricto em áreas adjacentes

sobre solos distróficos no leste do Mato Grosso, Brasil. Acta Bot. Bras. 19: 913-926.

MORRAN, P. A. P. 1950. Notes on continuous stochastic phenomena. Biometrika 37: 17-23

MOTTA, P. E. F., N. CURI, AND D. P. FRANZMEIER. 2002. Relation of soil and geomorphic

surfaces in the Brazilian cerrado. In: Oliveira PS, Marquis RJ (eds) The cerrados of

Brazil: ecology and natural history of neotropical savannas, pp 13-32. Columbia University

Press, New York, U.S.A.

MYERS, N., R. A. MITTERMEIER, C. G. MITTERMEIER, G. A. B. FONSECA, AND J. KENT. 2000.

Biodiversity hotspots for conservation priorities. Nature 403: 853–858.

OKSANEN, J., R. KINDT, P. LEGENDRE, B. O'HARA, G. L. SIMPSON, P. SOLYMOS, M. HENRY, H.

STEVENS,ANDH. WAGNER. 2009. vegan: Community Ecology Package. R package version

1.15-2. http://CRAN.R-project.org/package=vegan

OLIVEIRA-FILHO, A.T., AND J. A. RATTER.2002. Vegetation phisiognomies and woody flora of

the cerrado biome. In: Oliveira PS, Marquis RJ (eds) The cerrados of Brazil: ecology and

natural history of neotropical savannas, pp 13-32. Columbia University, New York, U.S.A.

Pielou, E.C. 1975. Ecological diversity. Wiley, New York, U.S.A.

R DEVELOPMENT CORE TEAM. 2009. R: A language and environment for statistical

computing. Wien, R Foundation for Statistical Computing. Avalable via DIALOG.

http://www.r-project.org. Accessed 21 Oct 2009.

RAIJ, B. VAN, J. A. QUAGGIO, H. CANTARELLA, M. E. FERREIRA, A. S. LOPES, AND O. C.

BATAGLIA. 1987. Análise química do solo para fins de fertilidade. Fundação Cargill,

Campinas, BR.

RUGGIERO, P. G. C., M. A. BATALHA, V. P. PIVELLO, AND S. T. MEIRELLES. 2002. Soil-

vegetation relationships in cerrado (Brazilian savana) and semideciduous Forest,

Southeastern Brazil. Plant Ecol. 160:1-16.

SALISBURY, F. B., AND C. W. ROSS.1991. Plant physiology. Belmont: Wadsworth Publishing

Company, California, U.S.A.

SARMIENTO G. 1984. The ecology of neotropical savannas. Harvard University Press,

Massachusetts, U.S.A.

SARMIENTO G.1996. Biodiversity and water relations in tropical savannas. In: Solbrig O. T.,

E. Medina, and J. F. Silva (Eds) Biodiversity and Savanna Ecosystem Processes, pp 61-75,

Springer, Berlin, DE.

SHANNON, C. E.,AND W. WEAVER. 1949. The mathematical theory of communication. The

University of Illinois Press, Urbana, U.S.A.

SILVA, F. C. 1999. Manual de análises químicas de solos, plantas e fertilizantes. Embrapa,

Brasília, BR.

SILVA, D. M., AND M. A. BATALHA. 2008. Soil–vegetation relationships in cerrados under

different fire frequencies. Plant Soil 311: 87-96

SILVA, L. C. R., L. STERNBERG, M. HARIDASAN, W. A. HOFFMANN, F. MIRALLES-WILHELM,

AND A. C. FRANCO. 2008. Expansion of gallery forest into central Brazilian savannas.

Glob. Change Biol. 14: 2108–2118.

SMA SECRETARIA DE ESTADO DO MEIO AMBIENTE. 1997.Cerrado: bases para conservação e

uso sustentável das áreas de cerrado do estado de São Paulo. SMA, São Paulo, BR.

SOUZA, A. F., AND F. R. MARTINS. 2004. Microsite specialization and spatial distribution of

Geonoma brevispata, a clonal palm in south-eastern Brazil. Ecol. Res. 19: 521-532.

SPAROVEK, G., AND O. A. CAMARGO. 1997. Sampling strategies for tropical forest nutrient

cycling studies: a case study in São Paulo, Brazil. Ver. Bras. Ci. Solo 21: 635-642.

Table 1. Pearson‟s correlation for soil variables varying within the scale of our study. OM

(organic matter; g/kg); Ca

(mg/kg), Al

(mmolc/kg); SB (sum of basis; mmolc/kg); m

(aluminum saturation; %); CEC (cation exchange capacity; mmolc/kg); H+Al (mg/kg).

Data were collected at a cerrado site at São Carlos, Brazil (approximately, 21º58‟05.3”S,

47º52„10.1”W)

CEC

H+Al

1.00

0.30

1.00

0.66

-0.16

1.00

0.42

0.94

-0.09

1.00

0.03

-0.82

0.61

-0.83

1.00

CEC

0.82

0.12

0.74

0.23

1.00

H+Al

0.78

0.01

0.77

0.12

0.34

0.99

1.00

Table 2. Regression analyses using the abundance of the five commonest species, total abundance, richness, evenness, and diversity as response

variables and soil features as explanatory variables. Best model selected according to the Akaike Information Criterion. Soil features were: OM

(Organic matter; g/kg); Al

(mmolc/kg); SB (Sum of basis; mmolc/kg). Data collected at a cerrado site at São Carlos, Brazil (approximately,

21º58‟05.3”S, 47º52„10.1”W).

Stepwise model

Moran's ssd

Rho

Moran's ssd

3+e

Myrsine umbellata

OM, SB

0.22***

5.54***

0.63***

-0.16

4.4***

-1.18

Vochysia tucanorum

OM, Al

,SB

0.01

0.23

-0.23

0.46

Myrcia guianensis

OM, Al

,SB

0.03

0.81

0.07

-1.19

Miconia albicans

OM, Al

,SB

0.01

0.19

-0.26

0.28

Piptocarpha rotundifolia

0.01

0.23

Total abundance

0.16***

1.57

4.4***

Richness

, SB

0.11**

-0.17

1.71

3.16**

Pielou's evenness

OM, SB

0.13***

5.12***

0.59***

0.19

-3.22***

0.50

Diversity

OM, SB

0.06*

3.26***

0.42*

-0.11

0.22

0.14

: coefficient of multiple regression (linear regression when there is one variable in the model);

: Moran‟s statistic standard deviate before

correcting for spatial dependence;

: Rho statistic for autoregressive models;

: Moran‟s statistic standard deviate after correcting for spatial

dependence;

: t statistic for each variable include in the model;

: not significant; *: p<0.05; **: p<0.01; ***: p<0.00.

CAPITULO 2: DEFESAS CONTRA HERBIVORIAS E

VARIÁVEIS EDÁFICAS EM ESCALA LOCAL NUMA

SAVANA BRASILEIRA

Trabalho submetido à revista Plant and Soil com o título “Fine scale relationship between

anti-herbivory defense traits and soil features in a Brazilian savanna”.

Fine scale relationship between anti-herbivory defense traits and soil features in a

Brazilian savanna

Vinícius de Lima Dantas

1,2

and Marco Antônio Batalha

– Federal University of São Carlos, Department of Botany, Brazil. PO Box 676, 13565-506,

São Carlos, SP, Brazil

– e-mail: [email protected]. Phone: +55 16 3351 8307. Fax: +55 16 33518308

Abstract – Herbivory selective pressure causes evolution of chemical, mechanical, and

phenological leaf defenses. Plant allocation to defense depends on environmental resources.

Soil is a major environmental filter in the Brazilian savanna known as “cerrado”, and so we

expected plants on nutrient-poor soils to present higher anti-herbivory defenses. In a cerrado

site, we measured soil variables and the following leaf traits: specific leaf area, C:N ratio,

water content, toughness, trichomes, latex, and presence of tannins, alkaloids, and terpenoids.

We did multiple regressions or spatial autoregressive models, to predict defense traits based

on soil features. Contrary to our expectations, we found no relationship between total defenses

and total soil fertility or soil variables, what could result from low variability in soil fertility at

fine scale or of not taking phenotypic plasticity into account. Presence of tannins was

positively related to organic matter, possibly reflecting a strategy towards lower tolerance due

to low reserve allocation or interactions with other resources. Since tannins decrease leaf

decomposition rates, organic matter could be accumulating in soil. We did not find other

relationships between defense traits and soil features, but we found variability in soil features

at local scale, which could be affecting plant community processes.

Keywords: anti-herbivory strategy, resistance, defense traits, organic matter, soil fertility,

spatial autocorrelation.

Introduction

Herbivory is an ecological and evolutionary agent, exerting a strong selective influence at

population and community levels, by increasing plant mortality, removing biomass that might

be allocated to growth or reproduction (Coley et al. 1985), and reducing plant competitive

ability (Coley and Barone 1996). Thus, herbivory restricts plants potential distribution to

regions that they might tolerate (Fine et al. 2006). This pressure results in the evolution of

chemical, mechanical, phenological, and physiological defenses in plants (Coley and Barone

1996; Strauss and Agrawal 1999), such as differences in leaf water, leaf nitrogen, leaf

toughness (Coley 1983), timing of leaf flush (Aide 1992), production of secondary

compounds (Howe and Jander 2008), and increased capacity to tolerate herbivory (Strauss

and Agrawal 1999).

The evolution and maintenance of each defensive strategy is affected by the relative fitness

costs and benefits that each strategy involves (Núñez-Farfán et al. 2007). Thus, a mixed

pattern of defense allocation on tolerance to herbivory (recovery after herbivory) and

resistance to herbivory (traits that decrease the amount of attack by herbivores) may be

evolutionarily unstable when they are limited by factors other than herbivory, such as the

environment (Núñez-Farfán et al. 2007). Low resource environments favor the establishment

of species with low growth rate and highly defended long-lived leaves, since plant costs of

recovering leaf loss would be high (Coley et al. 1985). In other words, higher investments in

resistance would be favored when tolerance is too expensive. Fine et al. (2006) corroborated

this idea, suggesting the existence of a universal growth vs. defense trade-off on high vs. low

resource environments.

Different vegetation types are limited by different resources; hence, it could be expected

that the type of resource should also influence plant tolerance to herbivory. It was suggested

that the effect of resources on plant tolerance to herbivory may depend not only on low vs.

high fertility gradients, but also on: (1) what resource is primarily limiting the plant; (2)

whether there is an alternative resource affecting plant growth; and (3) whether the acquisition

of one of these resources by the plant is affected by herbivory (Wise and Abrahamson 2005).

So, this “Limiting Resource Model” can result in higher, lower, or equal tolerance to

herbivory, depending on the combination of these factors (Wise and Abrahamson 2005).

Soil chemical and physical features are main limiting resources in tropical regions and,

thus, strongly influence plant community (Sollings 1998). Chemical and physical soil features

that affect most plant distribution are phosphorous, aluminum toxicity, depth of water table,

amount and arrangement of pores of different sizes, availability of base-metal cations,

micronutrients, and nitrogen (Sollings 1998). Soil, together with fire and climate, is the main

determinant of vegetation in the cerrado, a Brazilian savanna (Coutinho 1990; Haridasan

2000). Cerrado soils are usually poor, acidic, well-drained, and with high levels of

exchangeable aluminum (Lopes 1984; Queiroz-Neto 1982; Reatto et al. 1998). The pseudo-

xeromorphic features of the cerrado vegetation may be related to aluminum toxicity (Arens

1958) or oligotrophism (Arens 1963).

Although the cerrado flora is extremely rich, being possible to find high species turnover

even at small scale, few studies have focused on environmental factors influencing vegetation

characteristics of cerrado at local scales, within cerrado physiognomies. Given the importance

of soil on limiting the cerrado vegetation and since changes in soil features can be found at

distances as small as 1 m (Downes and Beckwith 1951, Souza and Martins 2004), soil is

likely to influence changes at such a scale. Thus, it is possible that the influence of soil on

plant the trade-off between growth and resistance (hereafter called “defense”) found by Fine

at al. (2006) could remain even at fine scale, and tolerance to herbivory in relation to soil

could be responding to soil and, thus, be a determinant of vegetation features at more

homogeneous conditions, such as those found at local scale. So, we asked the following

questions:

(1) Is soil fertility related to total resistance against herbivory in a cerrado site? Overall,

we expected the investment in defense traits against herbivory to increase from nutrient-rich

to nutrient-poor soils (Fine et al. 2006);

(2) If not, is total resistance related to different soil features when considered separated?

We expected investments in defense to increase with the decrease of mineral nutrients or soil

features related to nutrient availability and with the increase of soil features related to toxicity

or oligotrophism, such as aluminum and aluminum saturation.

(3) Are different defense traits related to different soil features? We expected the increase

in a given defense trait to be related to lower levels of mineral nutrients or soil features related

to fertility, and with higher level of features related with aluminum. We also expect structural

defenses to respond better, since it has been demonstrated to tropical forests that mature

leaves invest more in physical than in chemical defenses (Coley and Barone 1996).

Materials and Methods

Vegetation sampling

We studied a cerrado woodland site at Federal University of São Carlos, southeastern

Brazil (approximately, 21°58‟05.3”S, 47°52„10.1”W). The area is on dystrophic Oxisol, 850

m above sea level, under mesothermic, subtropical climate, with wet summers and dry winters

(Cwa; Köppen 1931). Mean annual temperature and precipitation lies around 21.3C and

1315.18 mm, respectively. In this site, we placed 100 5 m x 5 m contiguous plots, in which

we sampled all individuals of the woody component, that is, individuals with stem diameter at

soil level equal to or higher than 3 cm (SMA 1997). We identified them to species level using

identification keys based on vegetative characters (Batalha and Mantovani 1999; Mantovani

et al. 1985) and comparing the collected material to vouchers lodged at the Federal University

of São Carlos and State University of Campinas herbaria. We used Plantminer (Carvalho et al.

2010) to check species names, to include all species into families according to the latest

phylogenetic classification, and to find author names of all species.

For each species in the sample, we picked 10 individuals at random. When, for a given

species, there were less than 10 individuals in the sample, we made an additional effort to

look for more individuals nearby the plots. Thus, sample size was 10 individuals per species

(Cornelissen et al. 2003). From September 2008 to April 2009, for each individual in the

sample, we collected fully expanded and hardened leaves, without obvious symptoms of

pathogen or herbivore attack, and measured the following leaf defense traits: nutritional

quality, specific leaf area, water content, latex content, number of trichomes, toughness, and

presence of alkaloids, terpenoids, and tannins (Agrawal and Fishbein 2006).

We measured total carbon (C) and nitrogen (N) concentration to calculate the C:N ratio, as

an indicator of leaf nutritional quality. For each species, carbon and nitrogen concentration

were determined in five replicates at University of São Paulo. Nutritional quality is an

important constitutive plant defense, since high C:N ratios difficult nitrogen acquisition by

herbivores (Agrawal and Fishbein 2006).

Low values of specific leaf area tend to correspond to relatively high investments in leaf

defenses, particularly structural ones (Cornelisen et al. 2003). Specific leaf area also indicates

rapid growth and high leaf palatability (Agrawal and Fishbein 2006). Similarly, water leaf

content is related to increased leaf palatability and, so, low levels of leaf water should help

avoiding herbivory (Agrawal and Fishbein 2006), since it is related to low palatability

(Schädler et al. 2003). To measure specific leaf area and leaf water content, we collected two

leaves from each individual, kept them in a cooler, and weighted them still fresh. We scanned

the leaves to determine leaf area with the Image J 1.33 software (Rasband 2004) and oven-

dried them at 80°C for 72 h to obtain leaf dry mass. We obtained specific leaf area by

dividing leaf area by leaf dry mass (Cornelisen et al. 2003). We calculated water content by

the difference between fresh mass and dry mass, divided by leaf area (Agrawal and Fishbein

2006).

Trichomes are also important physical defenses against herbivores. Using five replicates

for each species, we counted the number of trichomes, on both leaf surfaces, in 28 mm² discs,

with a dissecting microscope. We also measured leaf toughness, which is related to nutritional

and defense constituents, probably influencing herbivore activity (Agrawal and Fishbein

2006). We used a force gauge penetrometer (Chatillon DFE 010) with a cone tip, drilling the

leaf at both sides of the mid-rib.

Latex is an important chemical strategy against herbivory (Agrawal and Fishbein 2006).

We measured latex by cutting the tip of an intact leaf in the field and collecting the exuding

latex onto a filter paper disc. When latex ceased to flow, this disc were placed on another dry

filter paper disc, oven-dried at 60°C for 72 h, and weighted (Agrawal and Fishbein 2006). We

analysed compounds frequently present in Brazilian plants that could act as chemical defenses

against herbivores (Lima 2000): alkaloids, terpenoids, and tannins. We carried out the tests

following Falkenberg et al. (2003): a series of three assays, Mayer, Dragendorff, and Wagner

reactions, to determine alkaloid presence; Liebermann-Burchard and Salkowisk reactions to

test terpenoid presence; and ferric chloride reaction to determine tannin presence.

Soil sampling

We collected soil samples from soil surface (0-5 cm deep), the one most correlated with

cerrado vegetation structure (Amorim and Batalha 2006; Ruggiero et al. 2002). In each plot,

we collected a composite sample, mixing five sub-samples, four in the corners of each plot

and one in the center. They were analysed at the University of São Paulo, according to

Embrapa (1997), Silva (1999), and Raij et al. (2001). We determined pH, organic matter,

available phosphorus, total nitrogen, exchangeable K+, Ca+2, Mg+2, and Al+3. We

calculated sum of bases, cation exchange capacity, base saturation, and aluminum saturation.

We also determined sand, silt, and clay proportions.

Soil pH was determined in CaCl

solution, using 10 ml of soil in 25 ml of solution. CaCl

was used to avoid salt and oxides influences. Organic matter was determined by organic

carbon oxidation with potassium dichromate and subsequent potassium dichromate titration

with ammonic ferrous sulfate, using 0.5 g of soil and 10 ml of potassium dichromate solution.

A correction factor (1.33) was used to compensate partial carbon oxidation. Available

phosphorus was determined by spectrophotometry after anion exchange resin extraction,

using 2.5 cm

of soil. Total nitrogen was determined by digestion with H

, followed by

distillation with NaOH, using from 0.5 to 1 g of soil, 1 g of H

, and 15 ml of NaOH.

Cations K+, Ca+2, Mg+2, and Al+3 were extracted with 1 M KCl, using 10 cm

of soil and

100 ml of solution. Then, potassium, calcium, and magnesium were determined by an EDTA

complexometry. Aluminum was determined by NaOH titration. Sum of bases was calculated

as the sum of potassium, calcium, and magnesium. Cation exchange capacity was calculated

as sum of bases plus H+ and Al+3 concentrations. Base saturation was calculated as a

percentage of total cation exchange capacity. Aluminum saturation was calculated as a

percentage of sum of bases and Al+3.

We quantified soil sand, silt, and clay proportions using the Boyoucus method: first, soil

particles were settled using a dispersant, suspension was separated from the sediment, and

clay content was calculated by suspension density using a densimeter; then, the sediment was

sieved to separate the sand, which was weighted. Silt proportion was calculated by the

difference.

Statistical analyses

We plotted all soil variables in a correlograms to test which soil variables had full range of

variability at the scale of our study. We did not consider for statistical analysis spatially

autocorrelated variables that had a range between positive and negative values of

autocorrelation smaller than 25 m, since their scale of variability were greater than the

sampling scale.

We also calculated Pearson‟s correlation between all pairs of variable selected above, to

choose variables not highly correlated with each other, thus, avoiding multicolinearity effects

in the analysis (Zar 1999). Since many of the variables were correlated, we decide to pick

only the less correlated and redundant ones (r < 0.70).

To answer the first question, we did a linear regression between defense and fertility

indices per plot. To obtain defense index per plot, we followed Fine et al. (2006), but we

weighted total defense by species abundance per plot; thus, we followed these steps: (1) for

each species and each quantitative defense trait, we calculated trait mean value; (2) we

standardised all defense traits, both quantitative and qualitative, to zero mean and unit

variance; (3) we summed all values of defense traits, obtaining a single value of total defense

per species; (4) we weighted the matrix of species abundance per plot with these values; and

(5) we summed all weighted values obtaining total defense per plot. For, specific leaf area and

water content, we used their inverse, since higher values of these traits represent low defense.

To calculate fertility index, we used a similar procedure: (1) we first log- or square root-

transformed soil variables to achieve normality; (2) we standardised them; and (3) we

summed the values per plot of soil variables related to soil fertility and the inverse of soil

variables related with low fertility (for example, exchangeable aluminum). This approach has

the problem of considering traits (and here also soil features) to have equal weights, but it is

preferable than to assign subjectively different weights (Fine et al. 2006). To answer the

second question, we did stepwise multiple linear regressions with soil features as explanatory

variables and the defense index as response variable.

To answer the third question, we did stepwise multiple regressions with each defense trait

as response variable and the soil features selected above as explanatory variables. We

obtained trait value per plot by weighting the species abundance matrix by species mean trait

value, and then summing all values of each plot. Soil and trait values per plot were log- or

square root-transformed to achieve normality, and soil variables were standardised to zero

mean and unit variance. When regression residuals were spatially autocorrelated, we used

tests that accounted for spatial autocorrelation, instead of multiple regressions, to avoid type I

error. Since all residuals that were autocorrelated were normally distributed, we used

simultaneous autoregressive models to correct spatial dependence (Dormann et al. 2007).

We selected the best stepwise regression models with the Akaike Information Criterion.

We tested the residuals of the multiple regressions for spatial autocorrelation among plots

using Moran‟s I index (Moran 1950), assuming data to be stationary and anisotropic. We did

all analyses with the vegan (Oksanen et al. 2009) and spdep (Bivand et al. 2009) packages for

R (R Development Core Team 2009).

Results

We sampled 2,062 individuals, belonging to 61 species and 27 families, for which we

measured defense traits (Table 1). The richest families were Fabaceae (eight species),

Myrtaceae (seven species), Malpighiaceae and Melastomataceae (four species each), and

Annonaceae, Erythroxylaceae, and Rubiaceae (three species each), accounting for half of the

species sampled. Only two species presented latex Kielmeyera grandiflora and Kielmeyera

coriacea. Tocoyena formosa was the only species to present alkaloids, whereas 26 species

presented terpenoids and 57 species presented tannins (Table 1). Soil was dystrophic (P < 0.5

cmol

-1

, K+ < 0.1 cmol

-1

, Mg+2 < 0.2 cmol

-1

, Ca+2 < 0.4 cmol

-1

), acidic (pH <

4), and with high concentration of Al+3 (> 1.7 cmol

-1

All soil variables presented spatial

autocorrelation, but the only soil variables that showed a complete range of variation (from

positive to negative autocorrelation) at the scale of our study were organic matter, calcium,

aluminum, sum of basis, aluminum saturation, cation exchange capacity, and hydrogen ions,

showing that the others have greater scales of variation. Organic matter, Al+3, cation

exchange capacity and H+ showed a range of variability of 15 m, whereas Ca+2, sum of basis

and aluminum saturation showed a range of variability of 25 m (Figure 1). After selecting less

correlated soil variables, only organic matter, sum of basis, and Al+3 remained for statistical

analysis.

Concerning total defense index, we found no relationship with soil variables, either when

we used total fertility index or when we used soil features separately (Table 2). We found

positive relationship between the presence of tannins and organic matter (t = 4.37, p < 0.001),

but no other defense trait distribution was related to any of the soil features (Table 2).

Discussion

Contrary to our expectation, total investment in defense traits against herbivory did not

increase from nutrient-rich to nutrient-poor soils. Fine et al. (2006) found a relationship

between total defenses and total soil fertility at large scale, comparing different vegetation

types, but, at fine scale, within a plant community, this relationship does not seem to hold,

maybe because the variation in soil features is much larger in regional scale. Moreover, our

study focused in the effects of soil variables in the establishment of well versus poorly

defended species in space, but did not account for phenotypic plasticity in plant traits in

response to nutrient availability, which could be assessed by measuring trait values for every

individual at the community.

When taking into account each defense trait separately, we found positive significant

relationships between the presence of tannins and organic matter. This could be resulting from

higher investments in this chemical defense in response to high soil nutrient availability, since

negatively charged surfaces of organic matter retains nutrients and some organic molecules

chelate micronutrient, making them available for plant roots (Salisbury and Ross 1991).

Organic matter also provides clay aggregation stability, which allows water and air to move

through the soil readily and permits roots to penetrate with little resistance (Motta et al. 2002).

It was also suggested that organic compounds could favor plant growth, although there is not

much support to this idea (Salisbury and Ross 1991). Thus, our result goes in the opposite

direction of what was expected by the trade-off suggested by Fine et al. (2006), since we

found a greater abundance of well chemically defended species in richer soils, where species

should be less defended since they are more tolerant, and they had found high defenses in

poor soils due to low tolerance.

The discussion of whether a species have higher or lower investments in tolerance in high

nutrient availability is still a poorly resolved matter (Wise and Abrahamson 2007). Whereas

some studies have suggested higher tolerance to herbivory in poor soils, others have

suggested the opposite (Hawkes and Sullivan 2001). Although the second idea seems more

counterintuitive, some authors have suggested mechanisms to explain lower tolerance in

richer soils. Tolerance to herbivory could be negatively correlated to nutrient availability,

when nutrient levels are high and root reserve allocation is reduced (Strauss and Agrawal

1999). Since root reserves are known to be important to tolerance, species growing on rich

soils could have low tolerance to herbivory (Strauss and Agrawal 1999) and, hence, high

investment in defenses.

Another explanation is possible according to the Limiting Resource Model (Wise and

Abrahamson 2005), which intents to explain why many studies have found strong support to

both greater and lower tolerance to herbivory in low resources environments (Hawkes and

Sullivan 2001). The model provides a series of predictions depending on which are the first

and the second plant limiting resources and whether herbivory is affecting or not resource

acquisition by plant. Thus, depending on the combination of these factors, it is possible to find

higher, lower, or equal tolerance to herbivory, as empirical data has demonstrated (Wise and

Abrahamson 2007; Wise and Abrahamson 2008). According to the predictions of the model,

low tolerance on rich soils should happen when there is an alternate resource whose

acquisition are being affected by herbivory, as, for example, carbon from the atmosphere.

Since a species that grows on rich soils is growing at the maximum it could grow without

limitation by soil resources, when herbivory affects carbon acquisition, this species would

have a much higher decrease in fitness than when herbivory occurs on plants growing on poor

soils.

However, the fact that organic matter is related to increasing mineral availability

(Salisbury and Ross 1991) should lead us to find the abundance of individuals with tannins to

be also related to nutrient content, for example, sum of basis, what we did not find. This may

be related to the fact that, at small scale, what matters is not total nutrient in soil, but whether

it is availability to the plant, which is increased when organic matter level is high.

Conversely, the lack of relation with sum of basis could be an indication that higher

abundance of species with tannins is not a consequence of higher organic matter in soil, but

high organic matter in soil is a consequence of higher abundance of species with tannins.

Studies in other vegetation types have shown that plant defensive strategies against herbivory

are affected by belowground biota and decomposition rates, since lower leaf nutritional

quality or higher leaf toxicity could affect not only herbivores, but also the decomposition

system (Bardgett et al. 1998, Loranger et al. 2002). As long as leaf decomposition rate is

negatively correlated with leaf tannins (Loranger et al. 2002), it could lead to the

accumulation of organic matter.

Tannins were the only defense trait related to soil features. Since we sampled only mature

leaves, the fact that many species presented tannins (57 from 61 species) is an indication that

mature leaves uses chemical as well as structural defenses. The results found for tropical

forests – that mature leaves invest in mechanical defensive traits and young leaves in

chemical traits – may not stand for cerrado. Nevertheless, we found high variability in many

soil features at local scale, suggesting that although many of them do not seem to influence

the establishment of well or poorly defended species, community processes at fine scale must

be related to soil distribution and this may influence plant community at some level.

Acknowledgements: We are grateful to “Conselho Nacional de Desenvolvimento

Científico e Tecnológico”, for the scholarships granted to both authors; to “Fundação de

Amparo à Pesquisa do Estado de São Paulo”, for financial support; to ML Afonso, JR Freitas,

P Loiola, DM Silva, IA Silva, and JF Silva, for valuable help in field; to DM Silva and GH

Carvalho, for suggestions on the manuscript and help in data analysis; and to IA Silva, for

assistance in species identification.

References

Agrawal AA, Fishbein M (2006) Plant defense syndromes. Ecology 87: 132-149

Aide TM (1992) Dry season leaf production and escape from herbivory. Biotropica 24:532-

537

Amorim PK, Batalha MA (2007) Soil-vegetation relationships in hyperseasonal, seasonal

cerrado, and wet grassland in Emas National Park (central Brazil). Acta Oecol 32:319-327

Arens K (1958) O cerrado como vegetação oligotrófica. Bol Fac Fil Ciênc Ltr, Universidade

de São Paulo, pp 59-57

Arens K (1963) As plantas lenhosas dos campos cerrados como vegetação adaptada às

deficiências minerais do solo. In: Ferri MG (ed.) III Simpósio sobre o cerrado, Edgard

Blucher /EDUSP, São Paulo, pp 13-115

Bardgett RD, Wardle DA, Yates GW (1998) Linking above-ground and below-ground

interactions: how plant responses to foliar herbivory influences soil organisms. Soil Biol.

Biochem. 14:1867-1878

Batalha MA, Mantovani W (1999) Chaves de identificação das espécies vegetais vasculares

baseadas em caracteres vegetativos para a ARIE Cerrado Pé-de-Gigante (Santa Rita do

Passa Quatro, SP). Rev Inst Florest 11:137-158

Bivand R, Anselin L, Assunção R, Berke O, Bernat A, Carvalho M, Chun Y, Dormann C,

Dray S, Halbersma R, Krainski E, Lewin-Koh N, Li H, Ma J, Millo G, Mueller W, Ono H,

Peres-Neto P, Reder M, Tiefelsdorf M, Yu D (2009) spdep: Spatial dependence: weighting

schemes, statistics and models. R package version 0.4-34. http://CRAN.R-

project.org/package=spdep

Carvalho GH, Cianciaruso MV, Batalha MA (2010) Plantminer: a web tool for checking and

gathering plant species taxonomic information. Environmental Modelling and Software: in

press.

Coley PD (1983) Herbivory and defensive characteristics of tree species in lowland tropical

forest. Ecol Monogr 53:209-233

Coley PD, Bryant JP, Chapin III FS (1985) Resource availability and plant antiherbivore

defense. Science 230:895-899

Coley PD, Barone JA (1996) Herbivory and plant defenses in tropical forests. Annu Rev Ecol

Syst 27:305-335

Coutinho LM (1990) Fire in the ecology of the Brazilian Cerrado. Ecol Stud 84:82-105

Cornelissen JHC, Lavorel S, Garniel E, Díaz S, Buchmann N, Gurvich DE, Reich PB, Steege

H ter, Morgan HD, Heijden MGA van der, Pausas JG and Poorter H (2003) A handbook of

protocols for standardized and easy measurement of plant functional traits worldwide. Aust

J Bot 51:335-380

Dormann CF, McPherson JM, Araújo MB, Bivand R, Bolliger J, Carl G, Davies RG, Hirzel

A, Jetz W, WD Kissling, Kühn I, Ohlemüller R, Peres-Neto PR, Reineking B, Schröder B,

Schurr FK, Wilson R (2007) Methods to account for spatial autocorrelation in the analysis

of species distributional data: a review. Ecography 30:609-628

Downes RG, Beckwith RS (1951) Studies in the variation of soil reaction. I. Field variation at

Barooga, N.S.W. Aust J Agr Res 2:60-72

Embrapa (1997) Manual de métodos de análise do solo. Embrapa, Rio de Janeiro

Falkenberg MB, Santos RI, Simões CMO (2003) Introdução à análise fitoquímica. In Simões

CMO, Schenkel G, Gomann G, Mello JCP, Mentz L, Petrovick P (ed) Farmacognosia: da

planta ao medicamento. Universidade Federal do Rio Grande do Sul, Porto Alegre

Fine PVA, Miller ZJ, Mesones I, Irazuzta S, Appel HM, Stevens MHH, Sããksjãrvi I, Shultz

JC, Coley PD (2006) The growth-defense trade-off and habitat specialization by plants in

amazonian forests. Ecology 87:150-162

Goodland R, Ferri MG (1979) Ecologia do Cerrado. Universidade de São Paulo, São Paulo

Goodland R, Pollard R (1973) The Brazilian cerrado vegetation: a fertility gradient. J Ecol

61:219-224

Haridasan M (2000) Nutrição mineral de plantas nativas do cerrado. Rev Bras Fisiol Veg

12:54-64

Hawkes CV, Sullivan JJ (2001) The impact of herbivory on plants in different resource

conditions: a meta-analysis. Ecology 82:2045–2058

Howe GA, Jander G (2008) Plant Immunity to insect herbivores. Annu Rev Plant Biol 59:41-

Köppen W (1931) Grundriss der Klimakunde. Berlin, Gruyter.

Lima MIS (2000) Substâncias do metabolismo secundário de algumas espécies nativas e

introduzidas no Brasil. In: Larcher W (ed) Ecofisiologia Vegetal, Rima, São Carlos, pp 33-

Lopes AS (1984) Solos sob “cerrado”. Características, propriedades e manejo. Associação

Brasileira para Pesquisa da Potassa e do Fosfato, Piracicaba

Loranger G, Ponge j-F, Imbert D, Lavelle P (2002) Leaf decomposition in two semi-

evergreen tropical forests: influence of litter quality. Biol Fertil Soils 35:247-252.

Mantovani W, Leitão Filho HF, Martins FR (1985) Chave baseada em caracteres vegetativos

para identificação de espécies lenhosas da Reserva Biológica de Moji Guaçu, SP. Hoehnea

12:35–66

Morran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17-23

Motta PEF, Curi N, Franzmeier DP (2002) Relation of soil and geomorphic surfaces in the

Brazilian cerrado. In: Oliveira PS, Marquis RJ (eds) The cerrados of Brazil: ecology and

natural history of neotropical savannas, Columbia University Press, New York, pp 13-32

Núñez-Farfán J, Fornoni J, Valverde PL (2007) The evolution of resistance and tolerance to

herbivores. Annu Rev Ecol Evol Syst 38:541-566

Oksanen J, Kindt R, Legendre P, O'Hara B, Simpson GL, Solymos P, Henry M, Stevens H,

Wagner H (2009) vegan: Community Ecology Package. R package version 1.15-2.

http://CRAN.R-project.org/package=vegan

Queiroz-Neto JP (1982) Solos da região do cerrado e suas interpretações (revisão de

literatura). Rev Bras Cien Solo 6:1-12

R Development Core Team. 2009. R: A language and environment for statistical computing.

Wien, R Foundation for Statistical Computing. Avalable via DIALOG. http://www.r-

project.org. Accessed 21 Oct 2009

Raij B van, Quaggio JA, Cantarella H, Ferreira ME, Lopes AS, Bataglia OC (1987) Análise

química do solo para fins de fertilidade. Fundação Cargill, Campinas

Rasband W (2004) ImageJ: Image process and analysis in Java. National Institutes of Health,

Bethesda

Reatto A, Correia JR, Spera ST (1998) Solos do bioma cerrado: aspectos pedológicos. In:

Sano SM, Almeida SP. Cerrado: ambiente e flora. Embrapa, Planaltina, pp 47-86

Ruggiero PGC, Batalha MA, Pivello VR, Meirelles ST (2002) Soil-vegetation relationships in

cerrado (Brazilian savana) and semideciduous Forest, Southeastern Brazil. Plant Ecol

160:1-16

Salisbury FB, Ross CW (1991) Plant physiology. Belmont: Wadsworth Publishing Company,

California

Schädler M, Jung G, Auge H, Brandl R (2003) Palatability, decomposition and insect

herbivory: patterns in a successional old-field plant community. Oikos 103:121-132

Silva FC (1999) Manual de análises químicas de solos, plantas e fertilizantes. Embrapa,

Brasília

SMA Secretaria de Estado do Meio Ambiente (1997) Cerrado: bases para conservação e uso

sustentável das áreas de cerrado do estado de São Paulo. SMA, São Paulo

Sollings P (1998) Factors influencing species composition in tropical lowland rain Forest:

does soil matter? Ecology 79:23-30

Souza AF and Martins FR (2004) Microsite espaecialization and spatial distribution of

Geonoma brevispata, a clonal palm in south-eastern Brazil. Ecol Res 19:521-532

Strauss SY, Agrawal AA (1999) The ecology and evolution of plant tolerance to herbivory.

Tree 5:179-185

Wise MJ, Abrahamson WG (2005) Beyond the compensatory continuum: environmental

resource levels and plant tolerance of herbivory. Oikos 109:417-428

Wise MJ, Abrahamson WG (2007) Effects of resource availability on tolerance of herbivory:

a review and assessment of three opposing models. Am Naturalist 169:443-454

Wise MJ, Abrahamson WG (2008) Applying the limiting resource model to plant tolerance of

apical meristem damage. Am Naturalist 172:635–647

Zar JH (1999) Biostatistical analysis. Prentice-Hall, New Jersey

Table 1: Leaf defense traits (mean ± standard deviations) from woody species collected in a cerrado site at Federal University of São Carlos,

Brazil (approximately, 21º58‟05.3”S, 47º52„10.1”W), in the rainy season of 2008. Water leaf content (mg.cm

-2

); specific leaf area (cm

-1

); leaf

toughness (N); leaf trichomes density per area (cm

-2

); latex content (mg); C:N ratio; presence (1) or absence (0) of chemical defenses: alkaloids

(A), terpenoids (Te), and tannins (Ta).

Species

Family

Water

Specific leaf area

Toughness

Latex

Trichomes

Nutritional

quality

Acosmium dasycarpum (Vogel)

Yakovlev

Fabaceae

0.02±0.005

78.626±8.710

1.227±0.240

0.000±0.000

1040.200±334.030

16.784±2.733

Acosmium subelegans (Mohlenbr.)

Yakovlev

Fabaceae

0.017±0.005

82.041±14.153

1.018±0.216

0.000±0.000

55.600±123.210

16.630±3.164

Aegiphila lhotskiana Cham.

Lamiaceae

0.027±0.005

103.966±15.022

0.712±0.228

0.000±0.000

841.200±192.836

14.721±2.374

Annona coriacea Mart.

Annonaceae

0.027±0.007

86.254±17.198

2.084±0.498

0.000±0.000

203.800±71.852

28.189±4.422

Annona crassiflora Mart.

Annonaceae

0.020±0.000

117.139±25.048

0.701±0.080

0.000±0.000

379.600±81.929

24.607±4.933

Banisteriopsis megaphylla (A.Juss.)

B.Gates

Malpighiaceae

0.018±0.006

127.473±28.145

0.422±0.198

0.000±0.000

617.400±330.540

14.820±3.313

Bauhinia rufa Steud.

Fabaceae

0.016±0.005

67.283±5.410

1.132±0.239

0.000±0.000

954.400±227.058

19.447±1.707

Byrsonima coccolobifolia Kunth

Malpighiaceae

0.020±0.000

97.4±16.128

0.776±0.097

0.000±0.000

13.800±12.357

22.468±2.805

Byrsonima verbascifolia Rich. ex

Malpighiaceae

0.025±0.007

74.42±6.576

0.680±0.113

0.000±0.000

2056.500±792.667

40.168±6.681

Juss.

Campomanesia adamantium

(Cambess.) O.Berg

Myrtaceae

0.013±0.005

81.004±14.263

1.073±0.203

0.000±0.000

603.800±401.108

27.663±2.903

Casearia sylvestris Sw.

Salicaceae

0.010±0.000

123.956±19.570

0.667±0.223

0.000±0.000

143.200±114.154

15.955±2.912

Connarus suberosus Planch.

Connaraceae

0.02±0.000

65.119±5.599

1.444±0.232

0.000±0.000

1403.000±561.190

29.951±5.808

Dalbergia miscolobium Benth.

Fabaceae

0.02±0.000

76.002±10.460

0.760±0.256

0.000±0.000

11.400±9.762

17.965±2.736

Daphnopsis sp Mart.

Thymelaeaceae

0.017±0.005

132.102±43.950

1.231±0.222

0.000±0.000

0.600±0.894

23.764±5.592

Davilla elliptica A.St.-Hil.

Dilleniaceae

0.016±0.005

129.334±42.730

0.852±0.257

0.000±0.000

215.600±158.371

28.791±2.862

Davilla rugosa Poir.

Dilleniaceae

0.011±0.003

188.293±39.525

0.824±0.252

0.000±0.000

227.800±133.607

28.312±3.456

Dimorphandra mollis Benth.

Fabaceae

0.012±0.004

102.249±14.677

0.285±0.115

0.000±0.000

623.400±173.259

12.824±1.495

Diospyros hispida A.DC.

Ebenaceae

0.023±0.005

62.249±6.733

1.040±0.320

0.000±0.000

708.000±278.318

33.142±5.446

Erythroxylum cuneifolium O.E.Schulz

Erythroxylaceae

0.011±0.003

160.394±42.361

0.399±0.154

0.000±0.000

1.000±2.236

18.156±1.648

Erythroxylum suberosum A.St.-Hil.

Erythroxylaceae

0.019±0.003

90.410±12.658

1.338±0.409

0.000±0.000

0.400±0.894

18.381±3.089

Erythroxylum tortuosum Mart.

Erythroxylaceae

0.02±0.000

90.593±13.504

0.942±0.362

0.000±0.000

1.600±1.342

20.375±1.497

Fagara rhoifolia Engl. in Engl. &

Prantl

Rutaceae

0.013±0.005

105.943±14.723

0.452±0.155

0.000±0.000

247.000±100.814

16.721±3.276

Gochnatia pulchra Cabrera

Asteraceae

0.010±0.000

103.086±803.475

0.76±0.589

0.000±0.000

2699.6±803.475

21.030±5.893

Guapira noxia (Netto) Lundell

Nyctaginaceae

0.031±0.007

104.277±23.772

0.878±0.240

0.000±0.000

17.200±24.773

10.427±0.679

Guapira opposita (Vell.) Reitz

Nyctaginaceae

0.019±0.007

120.074±27.549

0.795±0.185

0.000±0.000

37.600±55.162

11.283±0.918

Heteropterys umbellata A.Juss.

Malpighiaceae

0.011±0.003

117.969±21.672

0.379±0.144

0.000±0.000

27.000±44.688

17.185±4.415

Ilex cerasifolia Loes.

Aquifoliaceae

0.01±0.000

156.835±42.937

0.630±0.169

0.000±0.000

519.000±85.758

27.948±7.895

Kielmeyera coriacea Mart. & Zucc.

Clusiaceae

0.031±0.003

77.096±13.265

1.103±0.213

0.005±0.005

0.800±1.304

27.690±5.027

Kielmeyera grandiflora A.St.-Hil.

Clusiaceae

0.041±0.009

76.627±12.924

1.880±0.396

0.009±0.003

0.000±0.000

33.816±9.240

Leandra lacunosa Cogn.

Melastomataceae

0.022±0.004

126.108±25.624

0.846±0.321

0.000±0.000

256.800±35.231

28.718±3.329

Lippia velutina Schauer

Verbenaceae

0.014±0.005

164.427±59.599

0.452±0.109

0.000±0.000

925.800±130.239

17.302±2.669

Machaerium acutifolium Mart. ex

Benth.

Fabaceae

0.014±0.005

91.939±10.788

0.881±0.211

0.000±0.000

214.200±123.401

10.702±2.371

Miconia albicans Steud.

Melastomataceae

0.017±0.005

90.741±17.495

0.727±0.249

0.000±0.000

65100.000

30.919±6.920

Miconia ligustroides Naudin

Melastomataceae

0.019±0.003

96.689±21.997

0.568±0.100

0.000±0.000

5.400±6.504

26.619±2.556

Miconia rubiginosa DC.

Melastomataceae

0.02±0.000

64.441±7.438

0.712±0.100

0.000±0.000

281.000±52.192

38.415±4.261

Myrcia bella Cambess.

Myrtaceae

0.017±0.005

88.552±11.776

1.164±0.218

0.000±0.000

1020.000±307.864

32.868±5.892

Myrcia guianensis (Aubl.) DC.

Myrtaceae

0.02±0.000

64.426±8.164

1.269±0.312

0.000±0.000

660.800±369.921

28.653±3.666

Myrcia sp DC. ex Guill.

Myrtaceae

0.0125±0.005

77.878±7.813

0.860±0.153

0.000±0.000

1094.750±503.692

28.172±1.701

Myrcia splendens (Sw.) DC.

Myrtaceae

0.010±0.000

104.432±23.877

0.848±0.098

0.000±0.000

1067.000±384.667

38.978±4.806

Myrcia tomentosa (Aubl.) DC.

Myrtaceae

0.016±0.005

87.259±13.447

1.083±0.253

0.000±0.000

418.600±137.671

25.046±2.835

Myrsine coriacea Nadeaud

Myrsinaceae

0.012±0.004

125.938±21.262

0.517±0.075

0.000±0.000

307.800±93.106

19.588±0.689

Myrsine umbellata G.Don

Myrsinaceae

0.020±0.000

84.201±19.922

1.091±0.321

0.000±0.000

0.200±0.447

30.977±3.458

Ocotea pulchella Mart.

Lauraceae

0.014±0.005

69.967±12.467

1.356±0.208

0.000±0.000

1445.200±710.185

30.018±6.148

Ouratea spectabilis Engl.

Ochnaceae

0.0225±0.005

62.808±11.806

2.325±0.474

0.000±0.000

32.138±6.722

Palicourea coriacea (Cham.)

K.Schum.

Rubiaceae

0.029±0.005

111.645±20.856

0.883±0.380

0.000±0.000

2.400±4.336

17.939±5.496

Pera glabrata (Schott) Poepp. ex

Baill.

Peraceae

0.020±0.000

73.580±9.122

0.931±0.197

0.000±0.000

31.028±1.942

Phyllanthus acuminatus Vahl

Phyllanthaceae

0.010±0.000

207.404±40.957

0.290±0.060

0.000±0.000

27.659±1.464

Piptocarpha rotundifolia Baker

Asteraceae

0.020±0.000

95.997±21.772

1.123±0.344

0.000±0.000

1304.400±418.845

28.332±7.607

Plenckia populnea Reissek

Celastraceae

0.012±0.004

106.168±12.255

0.654±0.204

0.000±0.000

21.158±5.185

Psidium laurotteanum Cambess. in

A.St.-Hil.

Myrtaceae

0.018±0.004

73.770±14.123

1.259±0.139

0.000±0.000

1583.200±133.412

38.277±5.824

Rudgea viburnoides (Cham.) Benth.

Rubiaceae

0.033±0.005

80.721±14.276

1.226±0.248

0.000±0.000

687.000±257.630

25.226±3.647

Schefflera macrocarpa (Cham. &

Schltdl.) Frodin

Araliaceae

0.036±0.011

52.195±16.763

1.044±0.330

0.000±0.000

2392.200±450.335

31.360±5.455

Schefflera vinosa (Cham. & Schltdl.)

Frodin & Fiaschi

Araliaceae

0.028±0.006

61.864±5.268

0.910±0.231

0.000±0.000

1571.600±1038.238

27.194±4.838

Stryphnodendron adstringens (Mart.)

Coville

Fabaceae

0.022±0.004

81.427±14.873

0.727±0.125

0.000±0.000

23.600±48.387

18.030±1.465

Stryphnodendron obovatum Benth.

Fabaceae

0.017±0.007

121.016±30.463

0.434±0.143

0.000±0.000

4.600±2.302

18.448±2.972

Styrax ferrugineus Nees & Mart.

Styracaceae

0.020±0.005

69.605±19.303

1.462±0.335

0.000±0.000

437.400±12.341

36.008±3.902

Tabebuia ochracea Standl.

Bignoniaceae

0.021±0.003

77.181±13.026

1.284±0.358

0.000±0.000

620.200±148.288

19.923±2.948

Tapirira guianensis Aubl.

Anacardiaceae

0.020±0.000

74.561±10.860

0.730±0.141

0.000±0.000

18.000±31.369

33.766±3.630

Tocoyena formosa (Cham. &

Schltdl.) K.Schum.

Rubiaceae

0.020±0.005

86.199±10.765

0.790±0.300

0.000±0.000

1626.600±959.065

27.202±5.748

Vochysia tucanorum Mart.

Vochysiaceae

0.024±0.005

106.929±16.193

1.192±0.182

0.000±0.000

9.000±14.213

21.216±2.549

Xylopia frutescens Aubl.

Annonaceae

0.010±0.000

197.119±33.671

0.412±0.122

0.000±0.000

140.400±130.856

21.432±1.796

counted on one leaf, with a eletronic microscope, due to an extremely high number of trichomes

Table 2: Results and models of the regression analysis of defense traits against herbivory as a function of soil variables. Defense traits used were:

Total defenses index; SLA: specific leaf area (cm

-1

); C:N ratio; Water leaf content (mg.cm

-2

); leaf toughness (N); Leaf latex content (mg); leaf

trichomes density per area (cm

-2

); presence of chemical defenses: alkaloids, terpenoids and tannins. The stepwise model is the model that best

explained variation in defense traits according to the Akaike Information Criterion. Soil features were: OM (Organic matter; gkg-1); Al3+

(Exchangeable aluminum; mmolc kg-1); SB (Sum of basis; mmolc kg-1). Data were collected at a cerrado site at Federal University of São

Carlos, Brazil (approximately, 21º58‟05.3”S, 47º52„10.1”W).

Stepwise model

Moran's ssd

Rho

Moran's ssd

Total defenses index

Total fertility index

0.002

Total defenses index

0.002

SLA

0.15***

2.0*

0.24

0.18

C:N ratio

0.19***

1.68*

0.19

0.35

Water leaf content

0.17***

1.75*

0.20

0.13

Leaf toughness

0.16***

2.24*

0.25

0.11

Leaf latex content

OM, SB, Al+3

0.01

Leaf trichomes

density

OM, SB, Al+3

0.02

Alkaloids

OM, SB, Al+3

0.04

Terpenoids

OM, SB, Al+3

0.02

Tannins

0.16***

0.58

: coefficient of multiple regression (linear regression when there is one variable in the model)

: Moran‟s statistic standard deviate before correcting for spatial dependence

: Rho statistic for autoregressive models

: Moran‟s statistic standard deviate after correcting for spatial dependence

: not significant; *: p<0.05; **: p<0.01; ***: p<0.001

Figure 1: Correlograms of soil variables using Moran‟s index as response variable and lag distances as

explanatory variables. Each lag unit represents a distance of 5 m. “*” represents significant

autocorrelation. Significant points over the line represents positive autocorrelation and above the line

negative autocorrelation. Soil features are: OM (Organic matter; gkg-1); Ca2+ (exchangeable

calcium; mmolc kg-1); Al3+ (exchangeable aluminum; mmolc kg-1); SB (sum of basis;

mmolc kg-1); m (aluminum saturation; %); CEC (cation exchange capacity; mmolc kg-1); H

(exchangeable hydrogen; mmolc kg-1). *: p<0.05; **: p<0.01; ***: p<0.001. Data were collected

at a cerrado site at Federal University of São Carlos, Brazil (approximately, 21º58‟05.3”S,

47º52„10.1”W).

CONCLUSÃO GERAL

Conclusões gerais

Este trabalho nos permitiu chegar às seguintes conclusões:

- Embora não seja possível inferir relações de causa e efeito, nossos resultados

indicaram que o solo é um fator importante estruturando comunidades de cerrado mesmo em

escala local;

- O solo não se relacionou com a composição florística, o que parece indicar

redundância funcional de algumas espécies;

- O solo se relacionou com descritores da comunidade como abundância,

riqueza e equabilidade, o que sugere um papel determinístico para o solo, contrariando

previsões da teoria neutra;

- A alta dominância de Myrsine umbelata parece estar relacionada com

mecanismos de retroalimentação positiva, que favorecem uma maior distribuição dessa

espécie no cerrado em questão;

- Os investimentos totais em defesas contra herbivoria não foram influenciados

pela fertilidade total do solo em escala local, o que provavelmente resulta da baixa variação

da fertilidade nessa escala. Assim, essa relação parece ser verdadeira apenas quando

comparamos diferentes tipos vegetacionais;

- Dos nove traços de defesa contra herbivoria medidos, o único que se

relacionou com os nutrientes do solo foi a presença de taninos. Tendo em vista que esse traço

se relacionou positivamente com o conteúdo de matéria orgânica do solo, o que significa

maior investimento em defesas em solos mais férteis, sugerimos três possibilidades: (1) um

maior investimento em defesas em solos mais ricos, para compensar a baixa alocação em

reservas, importantes para tolerar a herbivoria; (2) a limitação por um recurso alternativo, o

que de acordo com o modelo dos recursos limitantes levaria a uma diminuição da tolerância à

herbivoria em solos mais ricos, e consequentemente, a necessidade de compensar com

investimentos em defesa; ou (3) defesas contra herbivoria alteram as condições do solo, pois

folhas com taninos se decompõem mais lentamente levando à acumulação de matéria

orgânica no solo próximo às plantas com taninos;

- Mais trabalhos deveriam estudar o cerrado em escala fina para particionar a

relação entre solo e vegetação entre associação dentro de hábitat e entre diferentes hábitats;

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