UNIVERSIDADE FEDERAL DE GOIÁS

INSTITUTO DE CIÊNCIAS BIOLÓGICAS

PROGRAMA DE PÓS-GRADUAÇÃO EM

ECOLOGIA E EVOLUÇÃO

TESE DE DOUTORADO

DENIS SILVA NOGUEIRA

PADRÕES METACOMUNITÁRIOS DE INSETOS AQUÁTICOS DE RIACHOS

FLORESTADOS DA AMAZÔNIA

ORIENTADOR: PROF. DR. PAULO DE MARCO JUNIOR

GOIÂNIA, GO

ABRIL DE 2015 UNIVERSIDADE FEDERAL DE GOIÁS

INSTITUTO DE CIÊNCIAS BIOLÓGICAS

PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E EVOLUÇÃO

TESE DE DOUTORADO

DENIS SILVA NOGUEIRA

PADRÕES METACOMUNITÁRIOS DE INSETOS AQUÁTICOS DE RIACHOS

FLORESTADOS DA AMAZÔNIA

Tese apresentada à Universidade Federal de Goiás,

como parte das exigências do Programa de Pós-

Graduação em Ecologia e Evolução para obtenção

do título de doutor.

Orientador: Prof. Dr. Paulo De Marco Júnior

GOIÂNIA, GO

ABRIL DE 2015

DEDICATÓRIA

Aos meus pais, José Eugênio e Maria Aparecida,

minha irmã Denise, e minhas avós Almerita e

Zeneida, pelos incontáveis exemplos de dignidade e

generosidade.

A natureza que nos cerca esconde segredos só revelados àqueles

que questionam a si mesmo os “porquês?”. Todo cientista é uma

criança, que sedenta por entendimento, pergunta avidamente

esperando encontrar as respostas.

i AGRADECIMENTOS

Primeiramente agradeço a minha família, a base de tudo o que sou e do que pretendo ser. Meus maiores exemplos, conselheiros, incentivadores, financiadores, amigos. Quem diria dona Cida e seu Ceará, dado as condições iniciais chegar a esta altura do campeonato, não com um mais com dois filhos terminando doutorado, por méritos, em universidades públicas de qualidade, no Brasil! Inimaginável. Isso nem sequer passou pela cabeça de vocês nem das nossas, de minha irmã Denise ou da minha. Nem sempre foi fácil, e ainda é difícil (principalmente porque à distância é mais difícil pedir colo!), mas sei que sempre pude e sempre poderei contar com vocês. Tenho muita sorte de ter vocês como meus pais, e tenho orgulho de vocês, e isso se estende a minha irmã, avós! Amo vocês! Agradeço aos amigos queridos e verdadeiros, que me acompanharam, mesmo de longe, ou torceram por mim, ou reclamaram de minha ausência. Nem sempre fui compreendido, é verdade, mas meu caminho ainda está por ser trilhado, e vou levar com muitas saudades as lembranças de vocês aonde quer que eu vá. Não se fazem mais amigos assim, por isso vou citá-los aqui: Anderson, Iassanã, Robson, Paula, Juscemar, Jaqueline, Luiz Jr, Kim, Diana, Eric, Karen, Lucas, Aída, Denise, Leandro, Yulie, Dinei, Zenon, Andrezza. Andrezza Sayuri foi minha companheira nos momentos mais difíceis durante este processo. Você aturou meu estresse, mal humor, me ajudou em vários momentos de crises. Me ajudou efetivamente na identificação dos meus bichinhos. Sou eternamente grato. Não é isso que tenho a falar de você, mas sim da pessoa especial que você é. Por detrás dessa armadura você é generosa, delicada, meiga, amiga. Eu aprendo muito contigo todos os dias, me surpreendo com sua dedicação, responsabilidade, sua garra na busca por um sonho, pela realização de seus objetivos. Tenho certeza que isso vem de família, e que você vai conquistar todos os seus objetivos. Meus melhores dias nestes últimos 5 anos eu vivi com você. Obrigado por compartilharmos nossas famílias, nossos amigos, curiosidades sobre os insetos, principalmente, por ter topado vir para Goiânia para ficar comigo, cuidar de mim, no meio da graduação! Profa. Helena Soares Ramos Cabette, você é sem dúvida uma das grandes responsáveis por eu ter escolhido esta profissão, mais do que isso, por eu não ter desistido. Hoje tenho certeza de que quero ser professor, e fazer um pouco disso, transformar a vida das pessoas. É isso que você representa para mim Dra. Helena, um grande exemplo de pessoa, professora e pesquisadora. Sua paciência em ensinar, sua consciência da importância da educação, sua humildade, seu amor pela natureza, isso é contagiante, contaminante, inspirador, não tem preço, um valor incomensurável. ii Agradeço ao Dr. Leandro Juen, por ser um grande parceiro de trabalho, acima de tudo, um grande amigo, generoso e solicito sempre que precisei. É bom lembrar que você foi o responsável por eu ter prestado a prova aqui na UFG, por ter conseguido uma carta de aceite do Prof. Adriano. Você me recebeu em sua casa em um momento difícil da sua vida, e o fez como um irmão. Sou muito grato por todos os ensinamentos, conselhos, por continuar a me receber em sua casa em Belém, por financiar minha pesquisa, por continuar a me incentivar. Prof. Paulo De Marco Júnior. Talvez você seja a pessoa mais genial que eu tive a oportunidade de conviver. Como uma pessoas que faz isso tudo que você faz consegue ser tão generosa?! Admiro o professor, o cientista, o orientador, mas principalmente o idealista! Seu idealismo positivista é contagiante, nunca o ouvi dizer que algo não iria dar ceto a um aluno. Você mais do que ninguém sabe o poder do “SIM VOCÊ CONSEGUE!”, e você faz isso o tempo todo com seus alunos. Infelizmente não há muitos De Marco's por aí, e demorei a entender que a escassez “Demarquiana” aumenta a competição entre as pessoas. Denso dependência. Competição nesse mundo científico não é legal, ninguém ganha! Não tive muita maturidade para lidar com esse tipo de coisa a maior parte do tempo, mas isso não afetou minha admiração. Agradeço também a Profa. Flávia, grande exemplo de amor a profissão, dedicação e competência. Agradeço a Dra. Yulie Shimano, pela amizade, confiança, pelo compartilhamento de ideias e incentivo. Você é um grande exemplo de dedicação pra mim, te admiro muito, desde a graduação, sempre muito aplicada, inteligente e objetiva. Agradeço ao Dr. Luciano Montag pelo suporte financeiro a pesquisa desenvolvida na minha tese, pelas correções de textos e pelo incentivo. Agradeço aos colegas do Laboratório de Ecologia e Conservação da UFPA, especialmente à Lenize Calvão, Bruno Prudente, Thiago Begout, Márcio Cunha, que fizeram parte efetivamente deste trabalho, seja coletando, seja processando, ou corrigindo os artigos. Aos colegas do “TheMetalLand”, especialmente Daniel Paiva Silva, Mírian Almeida, Luciana Signorelli, Renata Frederico, Karina Dias-Silva, Fernanda Martins, Camila, Paulinho, vocês contribuíram em diferentes momentos com o meu desenvolvimento durante o doutorado. Obrigado pelas discussões e conversas agradáveis. Agradeço aos colegas do PPG Ecologia e Evolução, pelas discussões, ou pelas distrações, e principalmente pelo futebol sagrado de toda quinta-feira. Agradeço aos professores do PPG Ecologia e Evolução, especialmente aos Prof. Adriano, Mário, Bini. Com meus professores aprendi a ter uma admiração por muitos nomes clássicos, muitos nem li, confesso, mas são admiráveis pela trajetória e a iii influência nas ciências, no pensamento de uma dada época. E antes que alguém me pergunte o que é ciência, ou o que é hipótese, minha pergunta é quando alguém se torna um cientista? Quando é capaz de formular hipóteses embutidas em teorias mais amplas criadas por cientistas consagrados do passado? Quando publica um artigo, ou dez? Minha resposta seria que toda a criança já nasce um pouco cientista, e a evidência é que recorrentemente enchem seus pais com “porquês”, uma característica que é em suma inata do ser humano. Uma boa definição para “cientista” talvez seja “aquele que consegue manter na fase adulta indagações, por vezes infantis, sobre o mundo natural que o cerca, questionando eternamente as evidências com um certo grau de sofisticação, usando observação, abstração e criatividade. Acho que a obrigação de qualquer professor é incentivar os cientistazinhos das escolas e universidades. Agradeço a Prof. Ana Paula Caiado, por ter me ajudado com minhas limitações com a língua inglesa, corrigido várias vezes meus primeiros textos, pelo incentivo. Agradeço aos condôminos da “Vila do Seu João” (não tenho nome melhor agora), Bruno, Cristiele e seu filho Victor (nosso mascote), Raul, Marco, Margarida, Geizeane, Kieran Withey. Vocês mantiveram o clima leve e agradável, me fizeram enxergar com outros olhos meus problemas, compartilharam comigo suas experiências, amizades. Não há melhor lugar para se viver pra quem gosta de boa música, boa comida, boa cerveja (nem sempre), bons vinhos, e principalmente, de conversas agradáveis sobre qualquer que seja o tema. Agradeço também ao seu João e a dona Edma, pessoas maravilhosas que tive a grande sorte e o prazer de conviver. Agradeço a Tia Ísis, do “prédio da EcoEvol” no universitário, por ter cuidado de mim pelo tempo que fiquei por lá, quando cheguei em Goiânia. Por fim, cabe agradecer ao Governo Federal, pelos investimentos em educação pública de qualidade, espero que a tempestade passe logo e que possamos continuar avançando em direção a um país melhor, com menos desigualdades e melhores oportunidades para os jovens. Agradeço a Universidade Federal de Goiás, ao PPG Ecologia e Evolução, por oportunizar crescimento pessoal, formação profissional de altíssima qualidade.

iv RESUMO: Ecossistemas aquáticos lóticos são altamente complexos interagindo com fatores atuando em diferentes escalas espaciais e temporais. Especialmente pequenos riachos florestados recebem a influência direta da vegetação circundante por meio da entrada de matéria orgânica a qual representa a base da cadeia alimentar nestes ecossistemas. Estudei os efeitos do corte manejado de madeira sobre as comunidades de insetos aquáticos de riachos, testando a hipótese de que as comunidades seriam afetadas por estes impactos. Apesar das métricas utilizadas capturarem apropriadamente os impactos, não houveram efeitos negativos sobre nenhum aspecto das comunidades estudadas. Entretanto, os resultados deste estudo não podem ser generalizados considerando que estudamos apenas uma parcela da diversidade dos riachos, especificamente as ordens Trichoptera, Plecoptera e Coleoptera. Além disso, a ausência de efeitos do manejo de baixo impacto é dependente do regime de exploração adotado em cada empreendimento. Outra característica predominante em qualquer ecossistema lótico é seu fluxo direcional da correnteza, que por conseguinte, afeta a distribuição dos substratos, habitats, e de recursos alimentares dentro dos riachos. Estudei os efeitos da velocidade do fluxo tomando-o como o principal preditor do efeito seletivo dos hábitats sobre os atributos das espécies. Minha hipótese foi corroborada, mostrando que riachos de águas mais correntes apresentam faunas com atributos morfológicos e ecológicos de maior resistência a correnteza. Fatores ambientais, como a preferência por habitats, limitação dispersiva e interações bióticas estão entre os principais mecanismos responsáveis pela estruturação de comunidades, e tem sido amplamente enfatizados no contexto da teoria de metacomunidades. Uma metacomunidade é definida como o conjunto de comunidades numa região que são interligadas pelo fluxo de indivíduos, o que enfatiza a importância das dinâmicas espacias estruturando a diversidade e composição de espécies em cada comunidade local e para a diversidade regional. Testei os efeitos de fatores ambientais, filtros espaciais e interações bióticas em insetos aquáticos de riachos florestados da Amazônia, especificamente padrões de segregação

v intraguildas, como um sinal de competição, e da predação entre guildas, assumindo explicitamente que a abundância e distribuição de predadores devem seguir a disponibilidade das presas consumidas. Meus resultados suportam a existência de efeitos de predação sobre a organização da metacomunidade, como evidenciado pela fração exclusiva da matriz de predadores sobre a comunidade de presas de diferentes guildas, além de suportar efeitos ambientais e limitação dispersiva em diferentes guildas tróficas. Os estudo desenvolvidos na presente tese ajudam a compreender mais claramente como os efeitos dos impactos do corte seletivo de madeira podem afetar comunidades de insetos aquáticos, auxiliam na compreensão de quais são as características dos hábitats selecionando atributos ecológicos e morfológicos de insetos aquáticos na Amazônia, e avançam nossa compreensão dos efeitos que as interações bióticas podem causar sobre a estruturação das metacomunidades de riachos.

Palavras-chave: Amazônia, Ecologia de riachos, metacomunidades, insetos aquáticos, seleção de habitats, interações bióticas.

vi ABSTRACT: Lotic ecosystems are highly complex interacting with acting factors in different spatial and temporal scales. Small forested streams receive direct influence of the surrounding vegetation through the input of organic matter which is the base of the food chain in these ecosystems. I studied the effect of the reduced-impact selective logging on communities of aquatic insects streams to test the hypothesis that communities are affected by their impacts. Despite the metrics used appropriately capture the impact, there were no negative effects on insect communities. However, the results of this study may not be generalizable to other systems because we studied only a fraction of the diversity of

Amazonian streams, specifically the insect orders Trichoptera, Plecoptera and Coleoptera. In addition, the absence of the low impact management effects is dependent on the operating system adopted in each enterprise. Another predominant feature in any lotic ecosystem is its directional flow, which consequently affects the distribution of substrates, habitats, and food resources within the streams. I studied the effects of water flow taking it as the main predictor of the selective effect of habitat on the attributes of insect taxa. My hypothesis was confirmed by showing that most rapid flowing streams in

Amazon has faunas with morphological and ecological attributes of greater resistance to flow.

Environmental factors such as the preference for habitats, dispersive limitation and biotic interactions are among the main mechanisms responsible for structuring communities, and has been widely emphasized in the context of metacommunity theory. A metacommunity is defined as the set of communities in a region which are interconnected by the flow of individuals, which emphasizes the spatial dynamics in structuring the diversity and composition of species in each local community. I tested the effects of environmental factors, spatial filters and biotic interactions in aquatic insects of forested streams of the Amazon, specifically the intraguild segregation and interguild predation effect.

My results support the existence of predation effects on the organization of the metacommunity, as evidenced by the exclusive fraction of the predators matrix on prey assemblages of different guilds, and

vii still withstand environmental effects and dispersive limitation in different trophic guilds. The study developed in this thesis help us understand more clearly the effects of the selective logging impacts on aquatic insect communities, assist in understanding about what are the characteristics of the stream habitats filter ecological and morphological attributes of aquatic insects in the Amazon, and advance our understanding of the effects that biotic interactions may have on the structuring streams metacommunities.

Keywords: Amazon, Stream ecology, metacommunity, aquatic insects, habitat filters, biotic interactions.

viii SUMÁRIO APRESENTAÇÃO ...... 1 INTRODUÇÃO GERAL, ÁREA DE ESTUDO E METODOLOGIA GERAL ...... 3 INTRODUÇÃO GERAL ...... 4 ÁREA DE ESTUDO ...... 10 METODOLOGIA GERAL ...... 14 REFERÊNCIAS ...... 19 CAPÍTULO 1 – THE EFFECT OF REDUCED-IMPACT LOGGING ON INSECT COMMUNITIES OF FORESTED STREAMS FROM EASTERN AMAZON ...... 24 1. Introduction ...... 26 2. Material and Methods ...... 30 3. Results ...... 39 4. Discussion ...... 45 5. Conclusion ...... 52 6. Acknowledgements ...... 54 7. References ...... 55 8. Supplementary information ...... 65 CAPÍTULO 2 – HABITAT TEMPLET OF AQUATIC INSECTS IN FORESTED AMAZONIAN STREAMS: THE WATER FLOW AS A TRAIT FILTER ...... 71 Introduction ...... 73 Material and Methods ...... 77 Results ...... 82 Discussion ...... 94 Conclusion ...... 99 Acknowledges ...... 100 References ...... 101 Suplementary information ...... 107 CAPÍTULO 3 – BIOTIC INTERACTIONS AS A STRUCTURING MECHANISM IN INSECT METACOMMUNITIES: A TEST OF ENVIRONMENTAL CONTROL, DISPERSAL LIMITATION AND SPECIES INTERACTIONS IN STREAMS ...... 111 Introduction ...... 114 Material and Methods ...... 118 Results ...... 124 Discussion ...... 133 Conclusion ...... 141 Acknowledgements ...... 143 References ...... 144 CONSIDERAÇÕES FINAIS ...... 153 APÊNDICES ...... 157 Apêndice 1 – Lista e estatisticas descritivas dos 64 preditores ambientias selecionados, usados para compor o conjunto dos capítulos 2 à 3...... 157 Apêndice 2 – Siglas, significados e escalas de medida das 64 métricas obtidas por meio do Protocolo de Caracterização de hábitats (ver metodologia geral)...... 158 Apêndice 3 – Fichas de campo do protocolo de caracterização de hábitats utilizado para obtenção das métricas analisadas em todos os capítulos...... 159

ix APRESENTAÇÃO Esta tese está organizada em cinco capítulos, em que busco percolar as ideias testadas e os conceitos abordados a respeito dos padrões em metacomunidades de riachos. No capítulo 1, apresento a organização das ideias testadas nos três capítulos subsequentes, e os componentes comuns aos demais: a área estudada, metodologia geral. Após esta breve introdução sobre ecologia de ecossistemas lóticos e a integração com temas atuais em ecologia de metacomunidades, nos capítulos subsequentes testo diferentes hipóteses usando insetos aquáticos, compreendendo três ordens – Trichoptera, Plecoptera e

Coleoptera, como objetos de estudo, os quais serão oportunamente introduzidos dentro dos capítulos.

Os capítulos 2, 3 e 4 foram preparados e formatados na forma de manuscritos para publicação em revistas específicas (Forest Ecology and Management, Freshwater Biology e Oikos, respectivamente), mas ainda não foram submetidos. No capítulo 2 testo os efeitos do corte seletivo de madeira sobre as características estruturais e físicas dos riachos e da vegetação ripária, e os efeitos destas alterações sobre descritores das comunidades, como a riqueza, abundância e composição de espécies. Para medir as características ambientais utilizei um protocolo de avaliação ambiental desenvolvido nas últimas duas décadas nos Estados Unidos, e amplamente aplicado em ecologia de riachos ao redor do mundo. A seleção de preditores será discutida dentro do capítulo pertinente.

No capítulo 3 testo a associação das espécies pelos gradientes ambientais no contexto de habitats como filtros seletivos atuando sobre seus atributos e determinando a distribuição das espécies.

Neste, o principal pressuposto é de que o nicho fundamental das espécies compreende habitats para os quais as espécies em riachos foram seletivamente filtradas por seleção natural por meio da evolução de atributos adaptativos. O principal filtro de habitat hipotetizado por influenciar severamente a seleção de atributos das espécies é a velocidade da correnteza.

No capítulo 4 testo padrões metacomunitários com enfoque na distinção entre efeitos de nicho

1 e de limitação dispersiva, mas adicionando um componente de interações bióticas. Para tanto, decomponho a metacomunidade entre diferentes grupos tróficos, e testo predições sobre os efeitos de interações biótica dentro do mesmo nível trófico, assumindo que competição é observável pelo padrão de coocorrência entre as espécies (e.g., checkerboard units), e entre diferentes níveis tróficos assumindo a abundância de predadores como alternativa indireta para medir os efeitos da predação. O principal pressuposto para a atribuição de predação à abundância dos predadores é a expectativa de que sua distribuição espacial (assim como temporalmente) reflita a disponibilidade de presas, tendo ou não um efeito de consumo efetivo.

No capítulo final, resumo as principais implicações deste estudo e faço algumas considerações finais a respeito dos temas abordados nos capítulos 2 à 4.

2 INTRODUÇÃO GERAL, ÁREA DE ESTUDO E METODOLOGIA GERAL

3 INTRODUÇÃO GERAL Um dos maiores desafios da ecologia é compreender e gerenciar adequadamente a biodiversidade que se distribui ao redor do planeta, de forma a proporcionar a conservação do maior número possível de espécies, de suas histórias evolutivas e promover a manutenção do funcionamento dos serviços ecossistêmicos (Frazee et al., 2003; Rodrigues et al., 2004a; Bonn & Gaston, 2005).

Ecossistemas lóticos são um capítulo especial nesse desafio por serem extremamente sensíveis às modificações de uso do solo na bacia de drenagem, remoção da vegetação, regime de precipitação

(consequentemente, mudanças climáticas), poluição e invasão de espécies (Angeler, 2007; Heino et al.,

2009; Friberg et al., 2011; Heino, 2013a; Thorp, 2014). Apesar de sua importância para a manutenção do bem estar humano ser reconhecida a séculos, a vulnerabilidade de ecossistemas de água doce tem mais recentemente se tornado um tema ainda mais discutido, não apenas em termos da conservação da biodiversidade mas principalmente com relação à seguridade de água (Hoff, 2009; Vörösmarty et al.,

2010; Bogardi et al., 2012; Cook & Bakker, 2012).

Ecossistemas aquáticos lóticos são altamente complexos representando interações em quatro dimensões: a dimensão longitudinal – i.e., o gradiente espacial das cabeceiras para as planícies de inundação; a dimensão temporal – i.e., variando entre escalas geológicas a regimes de distúrbios frequentes (e.g., sazonalidade das chuvas); a dimensão lateral – i.e., interação com a paisagem adjacente; e a dimensão vertical – i.e., entrada de luz e matéria orgânica (Thorp et al., 2006; Humphries et al., 2014; Thorp, 2014). A complexidade destes ecossistemas refletem a interação com inúmeros fatores em diferentes escalas espaciais e temporais, incluindo a geomorfologia bacia de drenagem

(Ward et al., 2002), clima, seus efeitos sobre os solos e vegetação, interações com a descarga de sedimentos, nutrientes e matéria orgânica, regimes de precipitação (Humphries et al. 2014; Thorp

2014). Estes fatores interagem e variam ao longo da bacia de drenagem, variando em importância das

4 cabeceiras para as planícies de inundação. Uma ampla gama de modelos conceituais buscam explicar esta complexidade de forma mais preditiva, com destaque para o conceito do Rio Continuo (Vannote et al., 1980), o conceito do Pulso de Inundação (Junk et al., 1989), e mais recentemente a Síntese de

Ecossistemas Ribeirinhos (Thorp et al., 2006; Thorp, 2014a). Um modelo promissor proposto para integrar seus antecessores é o Conceito de Onda em Rios (River Wave Concept, Humphries et al. 2014).

Este modelo prediz que em diferentes posições ao longo do gradiente longitudinal a variação da magnitude da inundação temporalmente influencia a entrada de matéria orgânica e a contribuição autóctone, bem como seu transporte e estocagem no ecossistema aquático. A inovação deste modelo está na incorporação de predições dos demais num arcabouço unificado (Humphries et al. 2014). Isto posto, é evidente que a formação de rios, os processos ecossistêmicos e a biodiversidade são influenciados por fatores agindo em múltiplas escalas espaciais que vão da temporalidade geológica no macroecossistema até a escala de microhabitats, em que a distribuição de um único organismo é influenciado pela rugosidade dos substratos (Thorp 2014).

O conceito de metacomunidades incorpora a dinâmica espacial de ocupação de hábitats numa escala regional levando em consideração o efeito de fatores ambientais, da limitação à dispersão e de interações bióticas (Leibold et al., 2004; Holyoak et al., 2005). Uma metacomunidade é definida como um conjunto de comunidades locais que são ligadas por meio de dispersão de múltiplas espécies que potencialmente interagem (Leibold et al., 2004). Neste contexto, comunidades locais não apresentam necessariamente limites bem definidos e são condicionados simplesmente pelo escopo da amostragem das comunidades locais, o que operacionalmente equivale a assembléias de espécies que habitam um tipo específico de ambiente (e.g., comunidade de libélulas em poças), pertence a um dado nicho trófico

(e.g., insetos galhadores), grupo funcional (e.g., aves frugívoras), ou correspondem a agrupamentos filogenéticos (e.g., besouros rola-bosta da família Scarabaeidae). Neste estudo, assembleia e

5 comunidade serão utilizados intercambiavelmente para descrever o conjunto das espécies de insetos aquáticos habitando substratos bentônicos e a superfície limnética de riachos florestados da Amazônia

Oriental (ver Metodologia Geral). Os organismos estudados pertencem a ordens de insetos sem parentesco, mas que potencialmente co-ocorrem num mesmo riacho ou mesmo hábitat: Trichoptera,

Plecoptera e Coleoptera. Estes organismos correspondem a diferentes grupos funcionais que convergiram evolutivamente para viver nos ecossistemas aquáticos, apresentam diferentes estratégias de uso dos recursos e habitats em riachos, diferentes ciclos de vida e ocupam diferentes porções do nicho trófico.

Na presente tese o foco está na escala de trechos de pequenos riachos florestados dentro de uma mesma bacia hidrográfica, o qual foi utilizado para definir a unidade básica de amostragem das comunidades e dos fatores ambientais intervenientes (ver Metodologia Geral). Esta escala é apropriada porque engloba múltiplos tipos de substratos, e uma ampla variação de fluxos da correnteza e permite descrever, com certa representatividade, a diversidade de organismos e hábitats de um ecossistema de pequeno porte localmente, além de capturar a variabilidade de sua comunidade biológica (Wang et al.,

2006). Esta escala de amostragem também permite comparabilidade com outras regiões dado que é amplamente utilizada em programas de biomonitoramento ao redor do mundo (Lazorchak et al., 1998;

Peck et al., 2006; Hughes & Peck, 2008).

Preditores ambientais e as comunidades dos riachos florestados da Amazônia Oriental foram estudadas com diferentes enfoques em três capítulos preparados para publicação em revistas específicas. No capítulo 2 uso um protocolo extensivo para a caracterização dos riachos (ver

Metodologia) para avaliar os efeitos dos impactos do corte seletivo de madeira sobre as comunidades de insetos aquáticos. Assumindo que os efeitos dos impactos do corte seletivo são perceptíveis a partir das variáveis ambientais mensuradas e que causam sedimentação e homogeneização de hábitats dentro

6 dos riachos (Davies et al., 2005), testarei a hipótese de homogeneização biótica (Rahel, 2002) devido aos efeitos do corte seletivo de madeira. Minha predição é de que os efeitos do corte seletivo serão negativos para a diversidade, dissimilaridade de composição de espécies, e abundância de grupos mais relacionados com a vegetação ripária (e.g., fragmentadores de folhas).

Uma característica fundamental de todos os ecossistemas lóticos em qualquer lugar do planeta

é seu fluxo unidirecional constante. A força da correnteza erode e carrega sedimentos e matéria orgânica mais leve rio abaixo, formando sequências alternadas de áreas de corredeiras e de deposição de sedimento, seja em pequenos riachos de cabeceiras ou grandes e caudalosos rios (Allan & Castillo,

2007; Richard Hauer & Lamberti, 2007). A alternância do fluxo está diretamente associada com a variabilidade de substratos, com a formação de piscinas de depósitos aluviais e de sedimentos – que variam em granulosidade e distribuição dependendo do tamanho, grau de fixação e soterramento, geologia basal da bacia de drenagem (i.e., geomorfologia), e do tipo de vegetação ripária (Allan &

Castillo, 2007). A correnteza causa distúrbios mais ou menos frequentes dependendo da declividade e da estabilidade dos substratos, e tem consequências diretas sobre na distribuição dos organismos, na sua colonização de habitats, e estruturação e funcionamento das comunidades (Anderson & Wallace,

1984; Ribera, 2008). Por exemplo, numa porção de um riacho com fluxo mais lento da correnteza é suprida muito mais pelos recursos que se depositam no fundo (e.g., folhas, sedimento fino) do que pela matéria orgânia da coluna d'água, e isso afeta a composição de espécies localmente. Estes efeitos são de tal forma previsíveis que proporcionaram vários modelos conceituais para explicar a diversidade e distribuição das espécies em ecossistemas lóticos variando em escalas de trechos de riachos até a bacia hidrográfica inteira (e.g., Humphries et al., 2014; Vannote et al., 1980). A importância da unidirecionalidade do fluxo para a evolução de atributos das espécies tem sido menos discutida no contexto de comunidades principalmente por conta da carência de filogenias robustas (Anderson &

7 Wallace, 1984; Ribera, 2008; Statzner & Dolédec, 2011; Dijkstra et al., 2014). Várias considerações neste contexto foram feitas por Ribera (2008) e mais recentemente por Dijkstra et al. (2014), e serão retomadas no capítulo 3, onde testarei os efeitos do filtros ambientais sobre atributos funcionais de insetos aquáticos nos riachos estudados. Minha hipótese é de que o principal filtro ambiental atuando seletivamente sobre os atributos dos insetos aquáticos é a correnteza. Minha predição é de que em riachos com mais áreas de correnteza organismos dotados de atributos de resistência a correnteza serão mais dominantes, enquanto que em riachos com maiores porções de remanso organismos sem resistência a correnteza serão dominantes.

A proposição de novos conceitos geralmente é acompanhada por uma propulsão de estudos uma área de pesquisa, e isto tem sido verificado em ecologia de comunidades após a unificação do conceito de metacomunidades em um arcabouço teórico-preditivo (Leibold et al., 2004). A medida que o conceito de metacomunidades amadurece, novas maneiras de incorporar o espaço (Dray et al., 2006;

Griffith & Peres-Neto, 2006; F. Dormann et al., 2007; Peres-Neto & Legendre, 2010), juntamente com os fatores ambientais, e mais recentemente, as interações bióticas (Gray et al., 2012; Göthe et al., 2013) tem sido propostas. A decomposição da metacomunidade de acordo com os atributos das espécies, como potencial dispersivo e tamanho do corpo, tem sido frequentemente empregada para proporcionar melhores predições das idiossincrasias entre grupos de espécies co-ocorrentes (De Bie et al., 2012;

Heino, 2013b; Schmera et al., 2013, 2014; Heino & Grönroos, 2014). Interações bióticas são um aspecto negligenciado nos estudos de ecologia de metacomunidades, apesar de sua importância para a dinâmica temporal e espacial, tanto em escalas locais quanto na escala regional (Vellend 2010). Seu efeito para a coexistência regional já antecipada nos modelos dinâmicos (Leibold et al. 2004; Holyoak et al. 2005) tem sido difícil de estimar empiricamente em metacomunidades (Logue et al. 2011).

Enquanto quantificar a importância relativa das interações bióticas entre múltiplas espécies co-

8 ocorrentes em metacomunidades continua um desafio analítico e conceitual, novas abordagens têm buscado inferir o efeito das interações por meio de proxies funcionais, filogenéticos e biogeográficos a partir de redes de interações (Morales-Castilla et al., 2015).

A falta de conhecimento da importância dos serviços providos pelos invertebrados pelo público em geral refletem em inconsciências nas políticas públicas quanto a priorização de áreas para sua conservação (Cardoso et al., 2011). A carência de ciência básica resulta em deficiências no conhecimento taxonômico (deficit Linneano), da distribuição (deficit Wallaceano), da abundância

(deficit Prestoniano), e da importância da interações bióticas (deficit Eltoniano) e do nicho das espécies de invertebrados (deficit Hutchinsoniano) (Cardoso et al., 2011; Diniz-Filho et al., 2013; Morales-

Castilla et al., 2015). As soluções envolvem um conjunto de investimentos em pesquisa em áreas pouco estudadas, formação de taxonomistas, e integração entre diferentes áreas do conhecimento em biodiversidade (e.g., sistemática, evolução, ecologia) (Diniz-Filho et al., 2010; Cardoso et al. 2011).

Busco incorporar interações bióticas no capítulo 4.

Sem dúvida os avanços neste campo, como em qualquer outro, refletem um conjunto de fatores que incluem aspectos teóricos, testes empíricos, validações experimentais, e principalmente o uso de métodos mais sofisticados (Cottenie, 2005; Peres-Neto & Legendre, 2010; Logue et al., 2011). A reciclagem criativa dos métodos e a incorporação de ferramentas diagnosticas aliadas aos métodos existentes tem sido frequente (e.g., Gray et al., 2012; Göthe et al., 2013). Apesar de ser um aspecto crucial para o entendimento dos processos estruturando metacomunidades, o efeito das interações bióticas não tem sido largamente implementado, principalmente porque métodos analíticos poderosos como a partição da variância baseada em Análise de Redundância parcial (Peres-Neto & Legendre,

2010) não tem sido utilizado para esta finalidade. Por outro lado outras ferramentas interessantes que consideram interações bióticas (Leibold & Mikkelson, 2002) não levam em conta explicitamente

9 fatores ambientais, autocorrelação espacial na distribuição e abundância das espécies, limitação dispersiva. Tento desenvolver os argumentos que justificam a importância destes processos no quarto capítulo.

ÁREA DE ESTUDO Para os objetivos deste estudo, foram amostrados riachos de pequenos porte em áreas de corte seletivo de madeira, situados no município de Paragominas, na região sudeste do Pará. Paragominas apresenta um histórico que expressa muito bem a problemática do desmatamento da borda sul

Amazônica, sendo apontado como um dos municípios que mais sofreu desmatamentos no Pará até

2008, com quase 45% de sua área devastada concentrando atividades de exploração madeireira, pecuária, agricultura, reflorestamento e a mineração (Gerwing, 2002). Paragominas foi até recentemente um dos campeões de desmatamento no Brasil, sendo que os poucos remanescentes restantes ficaram localizados em áreas indígenas e na área de atuação do grupo madeireiro Cikel Brasil

Verde Madeiras Ltda., parceira deste estudo (ver agradecimentos). O complexo de fazendas estão localizadas a margem direita do Rio Capim (03°39’51,6”S e 48°33’46,3”W) pertencente ao grupo

Cikel (Figura 1).

Há mais de 40 anos a madeireira encontra-se instalada na região, e a cada ano explora uma determinada região de mata nativa que após o corte seletivo é destinada para a regeneração natural. No entanto, apenas depois de 1999 iniciou-se o plano de manejo florestal e o processo de auditoria e fiscalização das atividades da empresa, a qual recebeu um selo de retirada manejada de madeira em

2001 pela certificadora Forest Stewardship Council® (FSC). Dessa forma, as áreas de corte seletivo representam um gradiente de regeneração de aproximadamente 10 anos, compreendendo os períodos de exploração entre 2001 até 2011. A atividade de caça é totalmente coibida na área e a empresa conta com um sistema de segurança patrimonial que inclui a abertura para pesquisas científicas estratégicas,

10 recuperação de áreas degradadas, brigada de incêndio, as quais são constantemente fiscalizadas pelo

IBAMA (Instituto Nacional do Meio Ambiente) e pela Secretaria de Meio Ambiente do Estado do Pará.

O estudo foi realizado na maior fazenda do complexo Cikel, denominada Fazenda Rio Capim, que possui 141.000 ha (Figura 2). Destes 18.000 ha são constituídos por pastos, 11.000 ha constituídos de Floresta Ombrófila de Terra Firme preservada, considerada como Área de Preservação Permanente, sem nenhum tipo de exploração, 15.000 ha de áreas de Floresta Ombrófila de Terra Firme produtivas

(de exploração madeireira), mas que ainda não foram exploradas até o momento e 98.000 ha são áreas de Floresta Ombrófila de Terra Firme já exploradas para retirada manejada de madeira (Figura 3). A

área produtiva de madeira da Fazenda Rio Capim é denominada de Unidade de Manejo Florestal

(UMF) e está dividida em Unidades de Produção Anual (UPAs), que possuem cerca de 3.500 a 5.000 ha cada uma (Figura 3), as quais são exploradas em ciclos rotativos de 25 à 35 anos.

No Brasil a discussão em torno do corte seletivo de madeira tem tomando uma importância crucial na agenda ambiental do governo a partir do Programa Nacional de Florestas (Decreto 3.420, de abril de 2000), e da aprovação e regulamentação do Serviço Florestal Brasileiro (Lei 11.284, de 2 de março de 2006; Decreto 6.063, de 20 de março de 2007, respectivamente). Isto porque as propostas visam a implementação de um sistema de manejo florestal sustentável dentro das florestas públicas brasileiras. Como de costume, novas leis ambientais são tomadas bem antes de chegarmos a quaisquer conclusões sobre os efeitos dos impactos destas atividades sobre a biodiversidade e estabilidade dos ecossistemas florestais (Bauch et al., 2009), impossibilitando a regulamentação inequívoca das leis de proteção e a fiscalização pelos órgãos competentes do governo, bem como amenizando as restrições do mercado madeireiro internacional (Zimmerman & Kormos, 2012).

A retirada seletiva de madeira é uma das principais atividades econômicas desenvolvidas em

áreas de florestas tropicais (Ernst et al., 2006; Vieira et al., 2007). Historicamente, variações nas taxas

11 de desmatamento na Amazônia legal têm sido associadas a fatores macroeconômicos dirigidos predominantemente por grandes latifundiários e destina-se principalmente à criação de gado, produção de soja (Fearnside, 2001, 2005), retirada de madeira (Asner et al., 2005; Stickler et al., 2009) e atividade de mineradoras (Rodrigues et al., 2004b). Especialmente nas fronteiras sul e sudeste da

Amazônia, o avanço da ocupação humana associado às atividades madeireiras é intenso e representa muitas vezes o primeiro impacto direto sobre a biodiversidade (Uhl et al., 1989; Laurance et al., 2001).

Como resultado, em áreas de extração madeireira formam-se paisagens com mosaicos de florestas primárias e secundárias (Nepstad et al., 1991; Ferraz et al., 2005; Ray et al., 2005). Este representa uma oportunidade especial considerando o escopo deste estudo, permitindo avaliar os impactos potenciais sobre a fauna de riachos florestados da Amazônia (Flaspohler et al., 2002).

Estudos detalhados sobre a dinâmica temporal do desmatamento associado à exploração madeireira tem sido conduzidos em outras pesquisas na região Amazônica (Ferreira 2005; ver capítulo

2), e não serão tratados em profundidade ao longo da tese. Apenas um breve tratamento endereçado a ecossistemas aquáticos será realizado no capítulo 2. A despeito de sua importância para o bem estar humano e para a biodiversidade como um todo (Vörösmarty et al. 2010), em especial para as espécies obrigatoriamente aquáticas e semiaquáticas (e.g. peixes, insetos, anfíbios), ecossistemas aquáticos lóticos são pobremente conservados (Benstead et al., 2003; Benstead & Pringle, 2004; Herbert et al.,

2010). Dada a importância das áreas de vegetação ripária para a manutenção do funcionamento e para a biodiversidade de ecossistemas aquáticos lóticos, especialmente os pequenos riachos avaliar os efeitos dos impactos da retirada seletiva de madeira sobre as comunidades de insetos aquáticos de riachos florestados na Amazônia oriental constitui um primeiro passo em direção a avaliação de parâmetros quantificáveis associados aos impactos.

12 Figura 1 – Mapa da área de estudo mostrando a Bacia do Rio Capim, o complexo de fazendas da

CIKEL Brasil Verde LTDA., Os pontos de amostragem em áreas de corte manejado de madeira

(bolinhas pretas) e as áreas de referência (estrelas). As grades de diferentes cores representam as unidades produtivas anuais (UPAs) exploradas em diferentes anos.

13 METODOLOGIA GERAL Foram amostrados 36 riachos, sendo 27 em áreas de manejo florestal variando em termos do tempo de regeneração após o corte seletivo de 2001 até 2011, e nove riachos em áreas controle, sem manejo florestal ou qualquer tipo de exploração pelo menos desde o início do manejo florestal (~ 1999) da madeireira CIKEL. Informações sobre as áreas de estudo, detalhes do manejo florestal e localização de potenciais áreas de referência foram fornecidas pela madeireira, e informações adicionais foram obtidas por meio da análise de imagens de satélite LANDSAT 7 TM, e mapas georeferenciados providos pela CIKEL. Os trechos de riachos estudados foram selecionados de forma a ficarem a uma distância de pelo menos 200 metros acima de estradas, pontes, evitando-se áreas com evidência de acesso humano frequente. As coletas ocorreram entre agosto e setembro de 2012, no auge do período seco para a região, para minimizar os efeitos da variabilidade climática (e.g., chuvas torrenciais).

Em cada riacho foi definido um trecho de 150 metros dividido em 10 seções transversais de 15 metros cada, as quais representam as unidades amostrais para a coleta de características físicas e estruturais da vegetação e do canal (ver abaixo), bem como para a coleta de dados biológicos (Figura

4). Os dados biológicos foram obtidos usando uma rede de mão de cabo longo e malha 0,05 mm, e abertura de 18 cm de diâmetro (capacidade para aproximadamente 800 g de material). Cada amostra consistiu de três porções de substratos bentônicos as quais foram agrupadas compondo uma amostra a cada 7,5 metros, totalizando 20 subamostras por riacho. Em campo, o material foi parcialmente triado e conservado em álcool 85%, e parte das amostras foram acondicionadas em sacos com álcool 85% para triagem no laboratório. Foram estudados os imaturos das ordens Plecoptera e Trichoptera, e os adultos de Coleoptera aquáticos. A identificação dos exemplares se deu a partir de chave dicotômicas disponíveis na bibliografia especializada e em artigos (Pes et al., 2005; Segura & Valente-neto, 2011;

Segura et al., 2011; Hamada et al., 2014).

14 Descritores físico-químicos da água foram coletados em três pontos em cada riacho sempre antes das coletas biológicas e caracterização dos riachos. Foram medidas a temperatura da água (TºC), potencial hidrogeniônico (pH), condutividade elétrica (Ω-m¹), oxigênio dissolvido (mg-1L), sólidos dissolvidos totais (mg-1L), e turbidez (NTU). Estes descritores são geralmente utilizados para caracterização limnológica em ambientais aquáticos continentais. Os parâmetros limnológicos foram mensurados utilizando uma sonda multi parâmetros Horiba® modelo U-50.

Figura 2 - Localização do complexo de fazendas da Empresa Cikel Brasil Verde Madeira LTDA., no município de Paragominas, no estado do Pará. Áreas em verde representam remanescentes florestais,

áreas em cinza representam regiões desmatadas, e áreas com linhas diagonais são protegidas fora das propriedade da Fazenda CIKEL.

15 Figura 3 - Fazenda Rio Capim em vermelho e as Unidades de Produção Anual (UPA) exploradas.

16 Mapa fornecido pela CIKEL Brasil Verde LTDA.

Caracterização física dos riachos

A caracterização os riachos (igarapés na denominação local) foi realizada por meio do Protocolo de

Caracterização de Integridade de Habitats (PHI, na sigla em inglês), da Agência de Proteção Ambiental

Americana (EPA, na sigla em inglês), amplamente utilizada no Estados Unidos nos Programas de

Monitoramento e Avaliação Ambiental (EMAP, na sigla em inglês). Ambos os protocolos estão disponíveis no sítio da EPA (http://water.epa.gov/), e foram condensados em vários protocolos de avaliação ambiental de ambientes aquáticos, os usados aqui são o “Field Operations Manual for wadeable streams” (Peck et al., 2006) e o “Quantifying Physical Habitat in wadeable streams”

(Kaufmann et al., 1999), para a caracterização de pequenos riachos.

O protocolo avalia características dos riachos como o tamanho e dimensão, os gradientes longitudinais do canal, tipos e tamanho dos substratos, a complexidade e a cobertura dos habitats dentro e for a dos riachos, a estrutura e cobertura da vegetação ripária, alterações antrópicas, e métricas de interação entre o canal e a vegetação ripária. Adicionadas métricas obtidas de imagens de satélite

(média e desvio padrão de EVI) mais as resultantes do protocolo obtivemos 247 métricas descritivas para cada trecho de riacho amostrado. A seleção das métricas será descrita em cada capítulo subsequente, porque foram selecionadas para atender a diferentes objetivos da pesquisa. A lista dos preditores, suas respectivas médias e desvios, mínimos e máximos estão descrito no Apêndice 1.

17 A) B)

Figura 4 – Esquema da seção amostrada em cada riacho. A) Cada letra representa uma seção transversal (linha tracejada e pontilhada), o intervalo entre as letras representa uma seção longitudinal. As amostragens foram feitas sempre no sentido rio acima e as coletadas de dados biológicos foram realizadas em dois segmentos da de cada seção longitudinal. B) Em cada seção longitudinal foram definidas parcelas de 10 m2 de cada lado dos riachos para a caracterização da vegetação marginal.

18 REFERÊNCIAS Allan, J., & M. Castillo, 2007. Stream Ecology: Structure and function of running waters. Springer, Dordrecht, The Netherlands, 436 pp. Anderson, N. H., & J. B. Wallace, 1984. Habitat, life history, and behavioral adaptations of aquatic insects In Merrit, R. W., & K. W. C. P. Cummins (eds), An Introduction to the Aquatic Insects of North America. Kendall / Hunt Publishing Company VL -: 38–58. Angeler, D. G., 2007. Resurrection ecology and global climate change research in freshwater ecosystems. Journal of the North American Benthological Society 26: 12–22. Asner, G., D. Knapp, & E. Broadbent, 2005. Selective logging in the Brazilian Amazon. Science 310: 480–482. Bauch, S., E. Sills, & L. Rodriguez, 2009. Forest policy reform in Brazil. Journal of Forestry April/May: 132-138. Benstead, J. P., M. M. Douglas, & C. M. Pringle, 2003. Relationships of stream invertebrate communities to deforestation in eastern Madagascar. Ecological Applications 13: 1473–1490. Benstead, J. P., & C. M. Pringle, 2004. Deforestation alters the resource base and biomass of endemic stream insects in eastern Madagascar. Freshwater Biology 49: 490–501 Bogardi, J. J., D. Dudgeon, R. Lawford, E. Flinkerbusch, A. Meyn, C. Pahl-Wostl, K. Vielhauer, & C. Vörösmarty, 2012. Water security for a planet under pressure: Interconnected challenges of a changing world call for sustainable solutions. Current Opinion in Environmental Sustainability 4: 35–43. Bonn, A., & K. J. Gaston, 2005. Capturing biodiversity: selecting priority areas for conservation using different criteria. Biodiversity and Conservation 14: 1083–1100. Cardoso, P., T. L. Erwin, P. a. V. Borges, & T. R. New, 2011. The seven impediments in invertebrate conservation and how to overcome them. Biological Conservation Elsevier Ltd 144: 2647–2655. Cook, C., & K. Bakker, 2012. Water security: Debating an emerging paradigm. Global Environmental Change Elsevier Ltd 22: 94–102. Cottenie, K., 2005. Integrating environmental and spatial processes in ecological community dynamics. Ecology letters 8: 1175–1182. Davies, P., Cook, L., McIntosh, P., Munks, S., 2005. Changes in stream biota along a gradient of logging disturbance, 15 years after logging at Ben Nevis, Tasmania. For. Ecol. Manage. 219, 132–148.

De Bie, T., L. De Meester, L. Brendonck, K. Martens, B. Goddeeris, D. Ercken, H. Hampel, L. Denys, L. Vanhecke, K. Van der Gucht, J. Van Wichelen, W. Vyverman, & S. a J. Declerck, 2012. Body size and dispersal mode as key traits determining metacommunity structure of aquatic organisms. Ecology letters 15: 740–747. Dijkstra, K.-D. B., M. T. Monaghan, & S. U. Pauls, 2014. Freshwater biodiversity and aquatic insect diversification. Annual review of entomology 59: 143–163. Diniz-Filho, J. A. F., P. De Marco Jr, & B. a. Hawkins, 2010. Defying the curse of ignorance:

19 perspectives in insect macroecology and conservation biogeography. Insect Conservation and Diversity 3: 172–179. Diniz-Filho, J. A. F. et al. 2013. Darwinian shortfalls in biodiversity conservation. Trends Ecol. Evol. 28: 689–695. Dray, S., P. Legendre, & P. R. Peres-Neto, 2006. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modelling 196: 483–493. Ernst, R., Linsenmair, K. E., & M. Rödel, 2006. Diversity erosion beyond the species level: Dramatic loss of functional diversity after selective logging in two tropical amphibian communities. Biological Conservation 133: 143-155. Dormann, C. F. , J. M. McPherson, M. B. Araújo, R. Bivand, J. Bolliger, G. Carl, R. G. Davies, A. Hirzel, W. Jetz, W. Daniel Kissling, I. Kühn, R. Ohlemüller, P. R. Peres-Neto, B. Reineking, B. Schröder, F. M. Schurr, & R. Wilson, 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography 30: 609–628. Fearnside, P. M., 2001. Soybean cultivation as a threat to the environment in Brazil. Environmental Conservation 28(1): 23–38. Fearnside, P. M., 2005. Desmatamento na Amazônia brasileira : história , índices e conseqüências. Megadiversidade 1(1): 113-123. Ferreira, F.N. 2005. Análise da sustentabilidade do manejo florestal com base na avaliação de danos causados por Exploração de Impacto Reduzido (EIR) em floresta de terra firme no município de Paragominas-PA. Dissertação de Mestrado. Universidade Federal Rural da Amazôni, Belém, PA. 81 p. Ferraz, S. F. D., C. A. Vettorazzi, D. M. Theobald, & M. V. R. Ballester, 2005. Landscape dynamics of Amazonian deforestation between 1984 and 2002 in central Rondonia, Brazil: assessment and future scenarios. Forest Ecology and Management 204: 67–83. Flaspohler, D. J., C. J. F. Huckins, B. R. Bub, & P. J. Van Dusen, 2002. Temporal Patterns in Aquatic and Avian Communities Following Selective Logging in the Upper Great Lakes Region. 48: 339–349. Frazee, S. R., R. M. Cowling, R. L. Pressey, J. K. Turpie, & N. Lindenberg, 2003. Estimating the costs of conserving a biodiversity hotspot: a case-study of the Cape Floristic Region, South Africa. Biological Conservation 112: 275–290. Friberg, N., N. Bonada, D. C. Bradley, M. J. Dunbar, F. K. Edwards, J. Grey, R. B. Hayes, A. G. Hildrew, N. Lamouroux, M. Trimmer, & G. Woodward, 2011. Biomonitoring of human impacts in freshwater ecosystems: the good, the bad and the ugly In Woodward, G. (ed), Advances In Ecological Research. Academic Press: 1–68. Gerwing, J. J., 2002. Degradation of forests through logging and fire in the eastern Brazilian Amazon. Forest Ecology and Management 157: 131–141. Göthe, E., D. Angeler, & S. Gottschalk, 2013. The influence of environmental, biotic and spatial factors on diatom metacommunity structure in Swedish headwater streams. PloS one 8: e72237. Gray, D., S. Arnott, J. Shead, & A. Derry, 2012. The recovery of acid‐damaged zooplankton communities in Canadian Lakes: the relative importance of abiotic, biotic and spatial variables. Freshwater Biology 57: 741–758. 20 Griffith, D., & P. Peres-Neto, 2006. Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology 87: 2603–2613. Hamada, N., J. L. Nessimian, & R. B. Querino, 2014. Insetos Aquáticos na Amazônia brasileira: taxonomia, biologica e ecologia. Editora do INPA, Manaus, AM, Brasil, 724 pp. Heino, J., 2013a. The importance of metacommunity ecology for environmental assessment research in the freshwater realm. Biological reviews of the Cambridge Philosophical Society 88: 166–178. Heino, J., 2013b. Environmental heterogeneity, dispersal mode, and co-occurrence in stream macroinvertebrates. Ecology and evolution 3: 344–355. Heino, J., & M. Grönroos, 2014. Untangling the relationships among regional occupancy, species traits, and niche characteristics in stream invertebrates. Ecology and evolution 4: 1931–1942. Heino, J., R. Virkkala, & H. Toivonen, 2009. Climate change and freshwater biodiversity: detected patterns, future trends and adaptations in northern regions. Biological reviews of the Cambridge Philosophical Society 84: 39–54. Herbert, M. E., P. B. McIntyre, P. J. Doran, J. D. Allan, & R. Abell, 2010. Terrestrial reserve networks do not adequately represent aquatic ecosystems. Conservation biology : the journal of the Society for Conservation Biology 24: 1002–1011. Hoff, H., 2009. Global water resources and their management. Current Opinion in Environmental Sustainability 1: 141–147. Holyoak, M., M. A. Leibold, & R. D. Holt, 2005. Metacommunities: spatial dynamics and ecological communities. University of Chicago Press, Chicago and London. Hughes, R. M., & D. V Peck, 2008. Acquiring data for large aquatic resource surveys: the art of compromise among science, logistics, and reality. Journal of the North American Benthological Society 27: 837–859. Humphries, P., H. Keckeis, & B. Finlayson, 2014. The River Wave Concept: Integrating River Ecosystem Models. BioScience 64: 870–882. Kaufmann, P. P. R., P. Levine, E. G. Robison, C. Seeliger, D. D. V Peck, U. S. E. P. Agency, R. E. Branch, W. E. Division, N. Health, & E. Effects, 1999. Quantifying Physical Habitat in Wadeable Streams. US Environmental Protection Agency, Washington, DC EPA/620/R – 99/003. Laurance, W. F., P. M. Fearnside, M. A. Cochrane, S. D’Angelo, S. Bergen, & P. Delamonica, 2001. Development of the Brazilian Amazon - Response. Science 292: 1652–1654. Lazorchak, J. M., D. J. Klemm, & D. V Peck, 1998. Environmental monitoring and assessment program surface waters: field operations and methods for measuring the ecological condition of wadeable streams. US Environmental Protection Agency, Washington, DC, EPA/620/R – 94/004F. Leibold, M. A., M. Holyoak, N. Mouquet, P. Amarasekare, J. M. M. Chase, M. F. F. Hoopes, R. D. D. Holt, J. B. B. Shurin, R. Law, D. Tilman, M. Loreau, A. Gonzalez. The metacommunity concept: a framework for multi-scale community ecology. Ecology Letters 7: 601–613. Leibold, M. A., & G. M. Mikkelson, 2002. Coherence, species turnover, and boundary clumping: elements of meta-community structure. Oikos 2: 237–250.

21 Logue, J. B., N. Mouquet, H. Peter, & H. Hillebrand, 2011. Empirical approaches to metacommunities: a review and comparison with theory. Trends in ecology & evolution 26: 482–491. Morales-Castilla, I., Matias, M. G., Gravel, D., & Araújo, M. B. (2015). Inferring biotic interactions from proxies. Trends in Ecology & Evolution, in press. Nepstad, D. C., C. Uhl, & E. A. S. Serrão, 1991. Recuperation of a degraded Amazonian landscape: forest recovery and agricultural restoration. Ambio 20: 248–255. Orrock, J. L., J. H. Grabowski, J. H. Pantel, S. D. Peacor, B. L. Peckarsky, A. Sih, & E. E. Werner, 2008. Consumptive and nonconsumptive effects of predators on metacommunities of competing prey. Ecology 89: 2426–2435. Peck, D., A. Herlihy, B. Hill, R. Hughes, P. Kaufmann, D. Klemm, J. Lazorchak, F. McCormick, S. Peterson, P. Ringold, T. Magee, & M. Cappaert, 2006. Environmental Monitoring and Assessment Program-Surface Waters Western Pilot Study: Field Operations for Wadeable Streams. US Environmental Protection Agency, Office Research and Development, Washngton,DC, EPA/620/R – 06/003. Peckarsky, B. L., P. A. Abrams, D. I. Bolnick, L. M. Dill, J. H. Grabowski, B. Luttbeg, J. L. Orrock, S. D. Peacor, E. L. Preisser, O. J. Schmitz, & G. C. Trussell, 2008. Revisiting the classics: Considering nonconsumptive effects in textbook examples of predator prey Interactions. Ecology 89: 2416–2425. Peres-Neto, P. R., & P. Legendre, 2010. Estimating and controlling for spatial structure in the study of ecological communities. Global Ecology and Biogeography 19: 174–184. Pes, A., N. Hamada, & J. L. Nessimian, 2005. Chaves de identificação de larvas para famílias e gêneros de Trichoptera (Insecta) da Amazônia Central, Brasil. Revista Brasileira de Entomologia 181: 181–204. Rahel, F.J., 2002. Homogenization of Freshwater Faunas. Annu. Rev. Ecol. Syst. 33, 291–315.

Ray, D., D. Nepstad, & P. Moutinho, 2005. Micrometeorological and canopy controls of fire susceptibility in a forested Amazon landscape. Ecological Applications 15: 1664–1678. Ribera, I., 2008. Habitat constraints and the generation of diversity in freshwater macroinvertebrates. Aquatic insects: challenges to populations 289–311. Richard Hauer, F., & G. A. Lamberti, 2007. Methods in Stream Ecology Methods in Stream Ecology. Elsevier: 455–463. Rodrigues, A. S. L., S. J. Andelman, M. I. Bakarr, L. Boitani, T. M. Brooks, R. M. Cowling, L. D. C. Fishpool, G. A. B. Fonseca, K. J. Gaston, M. Hoffmann, J. S. Long, P. A. Marquet, J. D. Pilgrim, R. L. Pressey, J. Schipper, W. Sechrest, S. N. Stuart, L. G. Underhill, R. W. Waller, M. E. J. Watts, & X. Yan, 2004a. Effectiveness of the global protected area network in representing species diversity. Nature 428: 640–643. Rodrigues, R. R., S. V. Martins, & L. C. de Barros, 2004b. Tropical Rain Forest regeneration in an area degraded by mining in Mato Grosso State, Brazil. Forest Ecology and Management 190: 323–333. Schmera, D., T. Erős, & J. Heino, 2013. Habitat filtering determines spatial variation of macroinvertebrate community traits in northern headwater streams. Community Ecology 14: 77–88. Schmera, D., J. Podani, T. Eros, & J. Heino, 2014. Combining taxon-by-trait and taxon-by-site matrices 22 for analysing trait patterns of macroinvertebrate communities: A rejoinder to Monaghan & Soares (2014). Freshwater Biology 59: 1551–1557. Segura, M. O., & F. Valente-neto, 2011. Chave de famílias de Coleoptera aquáticos ( Insecta ) do Estado de São Paulo , Brasil. Biota Neotropica 11: 393–412. Segura, M. O., F. Valente-Neto, & A. A. Fonseca-Gessner, 2011. Elmidae (Coleoptera, Byrrhoidea) larvae in the state of São Paulo, Brazil: Identification key, new records and distribution. ZooKeys 151: 53-74. Southwood, T. R. E., 1977. Habitat, the templet for ecological strategies? Journal of Animal Ecology 46: 337–365. Statzner, B., & S. Dolédec, 2011. Phylogenetic, Spatial, and Species-Trait Patterns across Environmental Gradients: the Case of Hydropsyche (Trichoptera) along the Loire River. International Review of Hydrobiology 96: 121–140. Stickler, C., D. Nepstad, M. Coe, D. McGrath, H. Rodrigues, W. Walker, B. Soares-Filho, & E. Davidson, 2009. The potential ecological costs and cobenefits of REDD: a critical review and case study from the Amazon region. Global Change Biology 15: 2803–2824. Thorp, J. H., 2014a. Metamorphosis in river ecology: from reaches to macrosystems. Freshwater Biology 59: 200–210. Thorp, J. H., 2014b. Metamorphosis in river ecology: from reaches to macrosystems. Freshwater Biology 59: 200–210. Thorp, J. H., M. C. Thoms, & M. D. Delong, 2006. The riverine ecosystem synthesis: biocomplexity in river networks across space and time. River Research and Applications 22: 123–147. Uhl, C., D. Nepstad, R. Buschbacher, K. Clark, B. Kauffman, & S. Subler, 1989. Disturbance and regeneration in Amazonia: lessons for sustainable land-use. The Ecologist 19: 235–240. Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell, & C. E. Cushing, 1980. The River Continuum Concept. Canadian Journal of Fisheries and Aquatic Sciences 37: 130–137. Vellend, M., 2010. Conceptual synthesis in community ecology. The Quarterly review of biology 85: 183–206. Vieira, D. L. M., A. Scariot, & K. D. Holl, 2007. Effects of habitat, cattle grazing and selective logging on seedling survival and growth in dry forests of Central Brazil. Biotropica 39: 269–274. Vörösmarty, C., P. B. Mcintyre, M. O. Gessner, D. Dudgeon, A. Prusevich, P. Green, S. Glidden, S. E. Bunn, C. J. Vo, C. A. Sullivan, C. R. Liermann, & P. M. Davies, 2010. Global threats to human water security and river biodiversity. Nature 467: 555–561. Wang, L., P. Seelbach, & R. Hughes, 2006. Introduction to landscape influences on stream habitats and biological assemblages. American Fisheries Society Symposium 48: 1–23. Ward, J., K. Tockner, D. Arscott, & C. Claret, 2002. Riverine landscape diversity. Freshwater Biology 47: 517–539. Zimmerman, B., & C. Kormos, 2012. Prospects for Sustainable Logging in Tropical Forests. BioScience 62: 479–487. 23 CAPÍTULO 1 – THE EFFECT OF REDUCED-IMPACT LOGGING ON INSECT COMMUNITIES OF FORESTED STREAMS FROM EASTERN AMAZON Denis Silva Nogueira, Lenize Batista Calvão, Luciano Fogaça de Assis Montag, Leandro Juen, Paulo

De Marco Junior

Obs.: Formatado para submissão na revista Forest Ecology and Management

24 The effect of reduced-impact logging on insect communities of forested streams from eastern Amazon

Denis Silva Nogueira1a, Lenize Batista Calvão2, Luciano Fogaça de Assis Montag2, Leandro Juen2,

Paulo De Marco Junior3

1Programa de Pós-Graduação em Ecologia e Evolução, Universidade Federal de Goiás, Instituto

de Ciências Biológicas (Bloco ICB V), Campus II/UFG, Goiânia, Goiás, CEP: 74001-970. E-

mail: [email protected].

2Laboratório de Ecologia e Conservação, Universidade Federal do Pará, Instituto de Ciências

Biológicas, Rua Augusto Correia, Nº 1 Bairro Guamá, CEP 66.075-110, Belém, Pará, Brasil.

3Departamento de Ecologia, Universidade Federal de Goiás, Instituto de Ciências Biológicas

(Bloco ICB V), Campus II/UFG, Goiânia, Goiás, CEP: 74001-970.

acorrespondence author: [email protected]

Abstract: Selective logging have becoming a major source of threats to tropical forest, bringing challenges for both ecologists and managers to develop low impact forestry. Reduced-impact logging

(RIL) is a kind of selective logging accounting for such silvicultural practices to prevent strong forest disturbances. Our aims were evaluate the effects of RIL on insect communities of forested streams in the Eastern Amazon, testing the hypothesis of negative effects of habitat homogenization caused by

RIL on Trichoptera, Plecoptera and aquatic Coleoptera. Neither of the evaluated community metrics

25 were negatively affected by RIL. Functional groups of insects remained also unaffected by RIL impacts. Only small influences on Phylloicus abundance (Trichoptera) were perceived according to our treatments. We identified metrics of the Physical Habitat protocol, such as substrate heterogeneity, woody canopy cover, hill slope height, which varied among RIL and reference streams. Environmental metrics proposed to evaluate logging impacts showed reliable alternatives for monitoring RIL impacts on streams. We conclude that RIL impacts have not had detrimental impacts on community and allso on environmental conditions of the majority of the studied streams. Most of the studied could be used as references to other activities prevailing in the oriental Amazon region, specially oil palm plantations and conventional logging.

Keywords: Forestry impacts, stream communities, headwater diversity, Physical habitat integrity

1. Introduction Selective logging represent an low impact economic activity prevailing at major tropical forest outside reserves around the world (Putz et al., 2012). Reduced-impact logging (RIL) is a kind of selective logging management that take into account silvicultural practices to prevent strong forest disturbances (Azevedo-Ramos et al., 2006a; Feldpausch et al., 2005; Putz et al., 2008). RIL is thought to enable long term management in logging cycles spanning from two to four decades (Azevedo-

Ramos et al., 2006a; Feldpausch et al., 2005; Putz et al., 2008). Unlike conventional logging (CL), in which most of the marketable trees are cut off without management practices preventing forest degradation (Gerwing, 2006; Johns et al., 1996; West et al., 2014), in RIL a limited number of trees

(e.g., up to five trees per hectare) are cut off maintaining the remaining trees to preserve forest structure and enable regeneration for future exploitation cycles (Asner et al., 2005; Johns et al., 1996). Here we concerns about RIL activity, thought to be a sustainable alternative to preserve biodiversity while

26 getting economic value from the explored forest (Putz et al., 2012; Sist et al., 2012).

It is important to clarify what kind of impacts CL and RIL generate in the forest structure and on biodiversity. Firstly, Brazilian Forest Federal Law (Law 12.651, 25 May 2012) prohibited CL in

Permanent Preservation Areas or Legal Reserves, but this is still widely practiced in areas with low govern surveillance specially at Amazon (Zimmerman and Kormos, 2012). There have been demonstrated from Eastern Amazon that CL operations damage 16 trees more than RIL, an area equivalent to 100 m2 greater, with in average 7.4 damaged trees for each commercial tree cut against

4.5 in RIL (Feldpausch et al., 2005; Johns et al., 1996; Source et al., 2000). CL when accompanied of secondary exploitation leads to forest degradation (Asner et al., 2006, 2005; Broadbent et al., 2008;

Ferraz et al., 2005), increased hunting (Asner et al., 2009), favors exotic species invasion (Silvério et al., 2013; Veldman et al., 2009) and increase forests susceptibility to strong winds and fire (Broadbent et al., 2008; Holdsworth and Uhl, 1997; Nepstad et al., 1999). The management processes in RIL is less costly than CL considering the economic benefits of aggregating value from certification market (Putz et al., 2012). In poorly managed logging areas of CL, the edge effects surpass localized impacts through the years, generating overall results often equivalent to deforestation (Foley et al., 2005).

Studies concerning economic viability and social consequences of RIL at Eastern Amazon have been conducted since 1990s and have assisted their establishment as an alternative to CL, ranching and crops (Uhl et al., 1991; Verissimo et al., 1992). Notwithstanding, contrasting views in terms of economic and biodiversity sustainability prevails in the recent literature (Asner et al., 2006; E.

Broadbent et al., 2008; Putz et al., 2012; Zimmerman and Kormos, 2012). First of all, RIL is by far less destructive than any other agricultural activity superposed to tropical forest, inclusive CL (Gibson et al., 2011; Vieira et al., 2007), with a clear potential to be much less deleterious to biodiversity.

Nevertheless, RIL is frequently practiced in areas of moist old growth tropical forest, such as lowland and ombrophile forests, which holds great biodiversity, endemic and endangered species (Barlow et al.,

27 2007; Ernst et al., 2006; Webb and Peralta, 1998). Such forests have evolved during long time periods and have also grown over hundreds to thousands years (Fichtler et al., 2003) without strong influences of human disturbances (Laurance et al., 1998). Therefore, even if RIL are expected to cause lower environmental impacts than agriculture and crops, it still need the use of management practices to lower the threaten to biodiversity and guarantee long-term sustainability (Villegas et al., 2009;

Zimmerman and Kormos, 2012). Considering the increasing importance of secondary forest conservation to biodiversity (Gibson et al., 2011; Vieira et al., 2007), future direction regarding logging impacts effects should rely on empirical evidences provided by observational and experimental studies

(Bertault and Sist, 1997; Kreutzweiser et al., 2005a; Lewis, 2001). Nevertheless, we need more complete studies of the effect of RIL and post-RIL activities on biodiversity in order to learn how properly manage tropical forests in the Amazon, aiming to evaluate their effects on biodiversity through the RIL management time scales (Michalski and Peres, 2013).

Evidence suggest that RIL consistently affect some animal-mediated ecosystem processes (i.e., pollination, seed dispersal and army-ant raiding) (Schleuning et al., 2011), but had no effect on species richness or community composition of any these study groups specifically (Schleuning et al., 2011).

Additionally, there were no effect on community diversity, composition and species abundance of butterflies though to be sensitive to gap opening in forest areas (Koh, 2007; Lewis, 2001; Ribeiro and

Freitas, 2012) and diversity of dung beetles (Davis, 2000). Nevertheless, negative effects of RIL are reported for trees species richness (Clark and Covey, 2012) composition and functional diversity

(Baraloto et al., 2012), for ants, spiders, mammals and birds species richness and composition

(Azevedo-Ramos et al., 2006), and on amphibians composition (Adum et al., 2013). The negative effects of selective logging reflect unplanned management often without silvicultural practices to prevent impacts on non-commercial tree species as well as on forest structures related to biodiversity

(Clark and Covey, 2012; Lindenmayer, 1999).

28 Under the perspective of the stream biodiversity, remain unclear if RIL activity have direct and indirect effects on ecosystem functioning, mainly because energy suply are basically provided by the riparian vegetation (Davies et al., 2005a; Janisch et al., 2012). The width of the immediately adjacent riparian zone maintain the structure of small headwaters preventing sediment input and providing organic matter mainly by leaf breakdown (Paul et al., 2006). The warming of stream temperature due to canopy cover opening in areas of higher logging intensity are also expected if RIL reach close to riparian forests (Janisch et al., 2012). Even when riparian forests buffers are maintained, sediments and nutrients (especially fertilizers) could still reach the water bodies given the interaction of the riparian widths, geology and topography of the catchment (Gafner and Robinson, 2007). The interaction among biotic and abiotic features of riparian zones with streams channels needs consideration since they should affect stream communities greatly through sediment loads, warming, decrease of organic matter, or even changing the main energy source for organisms (Davies et al., 2005a; Gomi et al., 2006;

Haynes, 1999).

The few studies accounting for the effects of the managed selective logging in stream communities have controversial and often show inconclusive results. While some studies have not found effects on composition or species richness, changes in abundance were mostly pervasive for both fishes (Martin-Smith, 1998) and for macroinvertebrates communities (Kreutzweiser et al., 2005).

Negative effects of logging on streams communities have been reported for fish (Bojsen and Barriga,

2002) and aquatic insects (Benstead and Pringle, 2004; Haynes, 1999; Lorion and Kennedy, 2009), mainly for the component of the composition more closely related to leaf breakdown (e.g., shredders, detritivorous taxa). Small changes in abundance of some species have been reported in Amazonian fishes (Dias et al., 2010). Moreover, long-term studies of selective logging have found instead consistent impacts on biodiversity mainly occasioned by the input of sediments into the stream channels leading to habitat homogeneization (Davies et al., 2005b; Gomi et al., 2006). The question

29 remain opened if selective logging does or does not affect stream communities when practiced in low intensity, despite RIL have becoming a important activity in the Amazon forest.

Our aims were to evaluate the effects of the RIL on insect communities of forested streams in the Eastern Amazon. In order to achieve these objectives we account for the effects of selective logging by several quantitative and semi-quantitative measures of habitat characteristics caught at reference

(REF) and reduced-impact logging (RIL) streams. We test the effect of environmental predictors on community diversity and taxonomic composition of aquatic insects. We hypothesized a decrease in diversity of aquatic insects with increase of RIL impacts as captured by our environmental predictors.

We rely on the prediction that habitat alteration should lead to biotic homogenization in freshwaters

(Rahel, 2002) since higher input of sediment should decrease leaf resource and substrate heterogeneity

(e.g., Davies et al. 2005a, b). Even acknowledging that new niche opportunities created by gaps could increase diversity of some organisms in logged streams (e.g., heliotherms), we expect negative effect of

RIL on taxomic groups more related to organic matter. Given that, we expect changes in taxonomic composition of insects with RIL impacts, especially decrease shredders abundance due to decrease in leaf litter availability at RIL streams and increase of scrapers abundance due to increase of periphyton production since more canopy gaps are expected at RIL streams.

2. Material and Methods 2.1. Study area

Our study area is under the effects of the reduced-impact logging (RIL) at Paragominas municipality, southeastern of the State of Pará, Brazil (Figure 1). The area pertain to a private company that maintains 209.130,54 ha in a group of farms destined mainly to timber extraction in Paragominas county. Our study was conducted in the Rio Capim farm, which represent the larger area of the farm complex with 150,000 ha (the greater green area in Fig. 1), 18,000 ha composed of old pastures, 5,300

30 ha of areas of permanent protection which represented our control area (reference streams), and the remainder greater area which we take as our timber harvesting treatment (the RIL streams). Similar studies considering terrestrial organisms have been conducted in part of the studied areas (Azevedo-

Ramos et al., 2006).

The study area is on Belém Endemism Center, a high biodiversity region with Dense

Ombrophile Forest under pressures from extensive pasture and agriculture (mainly soya and oil palm)

(Gerwing, 2002). The State of Pará was the first in the rank of deforestation in Brazil in the last decade and accumulated losses of primary forest estimated in 22% since of the first monitoring in 1988 (INPE,

2010). In the short period between 2011 and 2012 around 1,835 km2 of natural areas were deforested, maintaining the first post in the rank of annual deforestation among the Brazilian states in the legal

Amazon (INPE, 2010). Such rates of forest conversion highlight the need for developing low impact activities in the region, as recently defended in the literature (Gerwing, 2002; Barreto et al., 1998).

2.2. Selective logging management

In Paragominas logging activities started in the 1970s, but only in 2000 the reduced-impact logging was established in order to agree with mandatory forest management plan of the prior Brazilian

Forest Code (Law 4.771/65). Timber harvesting activities passed to be developed following Forest

Stewardship Council® (FSC) certification standards under reduced-impact logging, monitored each two years since operation started (Azevedo-Ramos et al., 2006). In RIL, areas prone to exploitation are chosen after field inspection for economically viable tree species, which are then measured and mapped for future monitoring and exploitation. In addition, RIL management includes pre-exploitation botanic inventory, cutting vines, road and track opening, and oriented tree falling and drag with the aid of tractors with steel cables in order to minimize canopy openings (Azevedo-Ramos et al., 2006; Barreto et al., 1998; Veríssimo et al., 1992). Moreover, trees are cut off only when attain minimum diameter criteria for each species, logs are pulled with wire rope and pushed together in gaps where they could

31 be gathered with tractors and trucks. At sampled treatment areas just a limited number of old growth trees are cut off in order to maintain the forest structure in each management unit of about 100 ha, which represent an average of three tree per hectare (Azevedo-Ramos et al., 2006). The management applied in the studied area is thought to be sustainable in 35 years exploitation cycles, despite claims against any economic sustainability of selective logging in cycle lower than 60 years (Zimmerman and

Kormos, 2012).

2.3. Sampling design and environmental predictors

We have studied the aquatic biodiversity, physical and chemical water parameters, and physical characteristics of 36 streams, twenty seven of which in areas under RIL differing in time since last cut from 2000 to 2012. In addition, we sampled nine streams that had not been impacted by logging activities since middle 1990s, when the RIL activity started in the area (Information provided by

CIKEL, see Acknowledges), as our reference streams (reference sites, REF).

Effects of the reduced-impact logging was evaluated through a Physical Habitat Integrity protocol (PHI), which was based on a set of environmental characteristics measured in each studied stream reach (see section Physical integrity index). All the selected stream reaches were far at least 200 m upstream from closer roads in order to avoid possible confounding effects (e.g., bridges). Samples were taken between August and September of 2012, in the peak of dry period for the region minimizing climate variability effects and rain disturbance. The selection of sampling sites was based on satellite images and maps provided by the owner jointly with field inspections with support of local laborers about localization of headwater catchments (see Acknowledges). The basic sampling unit for this study was a 150 m stream reach, subdivided in 10 subsections of 15 m each (Supplementary Material, Figure

1S).

Measurement of physical and chemical water parameters were taken for each stream in three sections, always done before biological and habitat samplings. We measure water temperature (TºC),

32 potential Hydrogen-ionic (pH), electrical conductivity (Ω-m¹), dissolved oxygen (DO mg-1L), total dissolved solids (TDS mg-1L) and turbidity (NTU), mostly used to assess environmental conditions of freshwater ecosystems (Heino and Mykrä, 2008; Siqueira et al., 2012). Water parameters were taken using a multi parameter sensor Horiba® model U-50.

Figure 1 – Map study area with highlighted area indicating the localization sampled streams in RIL

(dots) and reference areas (stars) at Paragominas town. Colored grid represent Reduced-impact logging areas with colors differing among years of exploitation.

33 2.4. Physical habit integrity

For the purpose of the present paper, as it was essential the quantification of the subtle effects of

RIL impacts, we have adopted a detailed Physical Habitat Integrity protocol (details described in

Kaufmann et al., 1999), and we developed the PHI index accounting for quantifiable characteristics describing streams such as: channel morphology and dimension, riparian vegetation cover and structure, habitat complexity and cover, substrates characteristics, sediments, lateral channel-riparian interaction and disturbance due to human activity (Peck et al., 2006). The protocol is based on the Field

Operations Manual for Wadeable Streams of the United States Environmental Protection Agency (US-

EPA) (Ali and Abd el-Salam, 1999; Freemark, 1995; Lazorchak et al., 1998; Megan et al., 2007). The approach enable the physical habitat characterization to synthesize the magnitude of natural and RIL disturbances (Kaufmann et al., 1999; Lazorchak et al., 2000) wherewith we produced a multimetric environmental index which properly distinguished streams of reference from logged forests (details in

Table S1).

The protocol resulted in 247 metrics representing averages and standard deviations of physical habitat characteristics, limnological and physic predictors for each stream reach. The metrics were preselected according to three criteria (Barbour and Gerritsen, 1996; Kaufmann et al., 1999) including: non-zero variance, true discrimination among reference and non-reference streams (according to

Student t-test), and low colinearity among predictors. Collinearity among selected predictors were assessed using Spearman rank correlation, and we choose one of the pairs with correlation higher than

0.70. The final set off selected candidate physical characteristics contained 23 environmental variables

(Table S1), wherewith we calculated the PHI index.

The selected set of 23 environmental predictors were scored in three bounds based on the overlap of interquartile intervals (Barbour and Gerritsen, 1996), depending if the chosen predictor

34 decreased or increased with RIL impact. For instance, for the variable mean coverage of woody vegetation that ranged 29.89 to 91.59, with increasing values found in reference streams (mean 56.99 and 50.42 at REF and RIL stream, respectively), all sites with values inferior to the lower quartile of reference sites received score one; streams with values between 0% to 0.25% interquartile interval of reference sites received score three; and that ones with values above the 0.25% quartile received value five, independently if that sites were at reference or RIL. The procedure was the same for all the selected variables, but each variable received maximum score only if it increased or decreased at reference streams. These new scored predictors were averaged (summed and divided by the number of preselected predictors), and composed the quantitative Physical Habitat Integrity index (PHI) used as an environmental predictor of the reduced-impact logging impact, ranging from 1 to 5, from disturbed to less disturbed streams. The process ensure that RIL streams receive a higher PHI score only if the environmental characteristics analyzed is similar to that of a reference stream, while enable RIL streams to be scored close to to reference areas (Barbour and Gerritsen, 1996).

2.5. Biological sampling and identification

Substrate samples were taken in each 5 m, sorted before and after each subsection transect, totaling 20 subsamples by stream reach. Biological samples consisted of three portions of substrates taken with a circular hand net of 18 cm circumference (Dias-Silva et al., 2010; Juen et al., 2013;

Shimano et al., 2013). Samples were previously washed and separated in field and aquatic organisms were conserved in hydrated alcohol (Ethanol 92%). For this study we have chosen three aquatic insect orders – Coleoptera, Plecoptera and Trichoptera – because they represent broad ecological and functional aspects of aquatic insects communities in streams (Cummins and Merritt, 1984; Cummins et al., 2005). Coleoptera and Trichoptera (jointly with Diptera, not studied here) represent great part of the insect diversity of aquatic ecosystems as a whole (Balian et al., 2008). Especially, Trichoptera and

Plecoptera (jointly with Ephemeroptera, also not studied here) are considered sensitive to

35 environmental changes and are widely used in environmental assessment (Arimoro and Ikomi, 2009;

Bonada and Williams, 2002; Cummins and Merritt, 1984; Resh and Rosenberg, 1984; Van den Brink et al., 2013). Despite rarely be considered in bioassessment we include adult water beetles because they have distinctive ecological requirements (Jäch and Balke, 2008), mostly free swimming predators, with good dispersal ability, and are highly diversified ecologically in freshwater ecosystems (Elliott, 2008;

Sanchez-Fernandez et al., 2006). In addition, many water beetles are in the aquatic-terrestrial interface and should represent indicators of changes in riparian zones (Jäch and Balke, 2008). Each group were identified to the more accurate taxonomic level (Lower Taxonomic Unity, LTU) possible using specialized identification keys (Benetti et al., 2003; Hamada and Couceiro, 2003; Manzo, 2005; Pes et al., 2005; Segura et al., 2011). In most case, we identification was at genera level, but we keep family or subfamily level where identification of genera were not possible. After, all organisms were classified into five Feeding Functional Groups of macroinvertebrates as: Filtering Collectors, Gathering

Collectors, Shredders, Scrapers and Predators (K W Cummins et al., 2005). Species richness, abundance of each group and for the whole community, and abundance of the FFG were used as responses in further analysis.

2.6. Data Analyses

2.6.1. Environmental characteristics

All environmental predictors were evaluated according to reference and RIL treatments using

Student t-tests (see Table S1 for summary of statistics). The studied streams were ordered using a

Principal Component Analysis (PCA) according to the standardized set of environmental predictors jointly with PHI index. The number of axes retained to explain environmental variation were defined using Broken-stick criteria (Legendre and Legendre, 2012). It is expected that the effects of logging activity vary along with time since last cut off. In order to test such trends we regressed time since logging of RIL streams only against PHI index (our impact indicator).

36 2.6.2. Community patterns

All the studied taxa were evaluated according to references and RIL treatments using Student t- tests (see Table S2 for summary of statistics). We access the abundance-distribution patterns at proportion scale of the whole data set and for sites at REF and RIL streams by using rankabundance function of the package BiodiversityR (Kindt and Coe, 2005). Also, Specific abundance of all sampled taxa was ordered according to the PHI index directly in order to provide visual inspection of trends in abundance distribution. The approach consisted at providing scores for each species in each local community after ordering of total abundance of sites and species abundance, and centering by the value of the PHI index in that stream. The approach was done with R codes provided by Vitor L. Landeiro

(2008, not published).

Community composition were assessed using Bray-Curtis dissimilarity with Permutational

Analysis of Variance (Per-MANOVA) (Anderson, 2006), with reference and RIL streams as treatments.

We also conducted a Multivariate Regression of Distance Matrices (MDMR) with the same dissimilarity (Zapala and Schork, 2006). MDMR was conducted using the quantitative PHI index as a predictor. Both analysis were conducted using 'adonis' function of the vegan package (Oksanen et al.,

2013). Significance in Per-MANOVA and MDMR analysis were assessed with 999 permutations.

2.6.3. Diversity estimates

A recurring problem regarding biological data relates to sample bias or uneven effort due to inefficient sampling methods, collectors ability, climate condition (especially in the case of thermoregulatory behavior of many animals), population spatial distribution (aggregation, overdispersion), the weights given to species abundances (Colwell and Coddington, 1994). Such sample bias should be considered since lower richness estimates invalidate properly account for environmental impacts. We control sample bias by standardizing sampling efforts by local sample coverage (sample completeness), then analyzing estimated species richness with comparable sample

37 coverage (Chao and Jost, 2012; Chao et al., 2014; Colwell et al., 2012).

Sample coverage ranged from 81% at under-sampled stream to 99,6% in the best represented stream, only on 31 of the 36 streams had coverage higher than 90%. We compared the effects of considering sample bias in GAM regression models by performing the analysis with and without stream with sample coverage below 90% (see below).

We estimate the non-parametric first order jackknife for the whole data set and for each group separately as our response to RIL impact. In addition, we use total abundance of the whole data set and for each group (Trichoptera, Plecoptera and Coleoptera), as well as of the five FFG as response to model with our environmental predictors.

2.6.4. Regression Models

We evaluate environmental effects on response metrics (estimated richness, community abundance and FFG abundance) of the whole community and for each group with Generalized Linear

Models of PCA axes as environmental predictor. Assuming Poisson error distribution and using log link function, model checking showed heteroscedasticity, non-normality and increased residuals variance with the mean in some predictors. Since non-linearity patterns emerged we modeled the data using

Generalized Additive Models with smooth splines, which give a better fit and meet model assumptions.

GAM models were done using quasi-Poisson distribution and log link for counts of estimated species richness to handle with overdisperssion (Crawley, 2007; Zuur et al., 2009). We also repeated the analysis of estimated species richness eliminating sites with coverage value below 90%, to discount the effect of biased samples in regression models.

For abundance data we also use quasi-Poisson distribution and log link in GAM models, and

Gaussian distribution in one case (Filtering Gatherer abundance) because of overfiting. GAM models were done with functions of the mgcv package. We access the spatial correlation in residuals of GAM models using Mantel correlogram with functions available in the vegan and spdep packages (Bivand,

38 2014; Oksanen et al., 2013). In general, there was no spatial autocorrelation in residuals of the GAM regression models for all response variables at any distance classes as accounted for Mantel

Correlogram (Figure S2). All analysis were done using R software (R Development Core Team, 2013).

3. Results 3.1. Physical habitat characteristics

The PHI index ranged from 1.89 to 5 (Mean ± standard deviation: 3.45 ± 0.90), and it was normally distributed according to Shapiro-Wilk test (W = 0.95, p = 0.09). Reference streams had higher

PHI than RIL stream (4.5 ± 0.34 and 2.97 ± 0.63, respectively; t-value = 7.97, p < 0.01), and had homogeneous variances between treatments according to Levene’s test (p > 0.05). As PHI most of the environmental predictors included in PCA analysis also differed among treatments (Table S1). In addition, PHI did not varied along with time after logging considering RIL streams (R2 = 0.028, F =

0.715, P = 0.406), discarding temporal trends in RIL impacts.

The first three PCA axes higher than broken-stick model predictions explained 68.8% of environmental variation among streams. The first PCA axes distinguished the position on environmental gradients between reference and RIL streams (Figure 2A), which described a congruent correlation of canopy cover metrics negatively increasing along PC1 (38.8% explained). While the second axis described the increase of woody volume of falling logs and trees negatively (small, middle and big woodies, see Table 1), accounting for 20.1% of the environmental variation. Higher values of

PHI were also positively related to PC2, but there was no separation among reference and RIL streams.

The third PCA axis (9.9% explained) was negatively related to the width/depth ratio, incision height and active flooding width (a proxy for channel shape), while substrate heterogeneity and PHI were weekly but positively correlated to them (Figure 2B). Overal, RIL stream had more variability than

REF streams, associated to the second PCA axis, which was correleted to the amount of logs and trees

39 falling into and in the margins of RIL streams (Figure 2, Table 1).

Table 1 – Loadings of environmental predictors and summary statistics of three first Principal Components.

Environmental variables Acronym PC1 PC2 PC3 Substrate size (SD) Dgm_V -0.10 0.26 0.67 Filamentous algae (%) XFC_ALG 0.30 -0.20 0.19 Total canopy cover (Mean) XC -0.95 -0.08 -0.08 Total canopy cover (SD) SDCMG -0.65 -0.16 0.12 Riparian canopy cover (Mean) XCMG -0.93 -0.05 -0.11 Total woody cover (Mean) XCMGW -0.65 0.03 -0.25 Total woody cover (SD) SDCMGW -0.95 -0.12 -0.05 Woody understory canopy cover (Mean) XMW -0.82 -0.22 0.12 Canopy cover of big trees (Mean) XCL -0.79 0.11 -0.20 Canopy cover of big trees (SD) SDCL -0.72 -0.03 -0.06 Canopy cover of small trees (Mean) XCS -0.84 -0.24 0.05 Canopy cover of intermediate trees (Mean) XCM -0.95 -0.11 -0.08 Canopy cover of big plus intermediate trees (Mean) XCMW -0.97 -0.15 0.01 Canopy cover of small trees (SD) SDCS -0.53 -0.01 0.12 Intermediate canopy cover (Mean) XM -0.77 -0.12 -0.06 Woody volume of big falling trees in the channel (%) V1T_100 0.14 -0.92 0.12 Woody volume of big falling trees outside the channel (%) V1W_100 0.22 -0.94 0.07 Woody volume of middle falling trees in the channel (%) V2T_100 0.13 -0.93 0.10 Woody volume of middle falling trees outside the channel (%) V2W_100 0.21 -0.94 0.07 Woody volume of small falling trees outside the channel (%) V4W_100 0.16 -0.95 0.05 Active flooding width (Meters) XBKF_W 0.11 -0.07 -0.88 Incision height (SD) SDINC_H 0.33 -0.27 -0.62 Incision height (Mean) XINC_H 0.40 0.09 -0.19 Width depth ratio (Mean) XWD_RAT 0.17 -0.14 -0.70 Physical Habitat Integrity PHI index -0.76 0.36 0.34 Eigenvalues 9.31 4.82 2.38 Broken-stick 3.65 2.65 2.15 model Principal Component Analysis Statistics Explained 38.77 20.10 9.92 variation (%) Cumulative 38.77 58.87 68.79 explanation (%)

40 A)

B)

Figure 2 – Principal Component Analysis of environmental characteristics showing reference (black dot) and RIL (gray dot) streams ordered on the PCA1 and PCA2 (A) and PCA2 and PCA3 (B). The length of each arrow represent the strength of the correlation with each PCA axes. Acronyms for the environmental predictors are in table 1.

41 3.2. Community patterns

We have sampled 4,113 individuals in 36 streams, ranging from 16 in the less abundant to 268 on most abundant stream with in average 114 individuals at the studied area. Trichoptera represented most of the sampled insects with more than half of the total abundance (N individuals = 2.912, average individuals = 81 individuals), followed by Plecoptera (N individual = 670, average individuals 19) and

Coleoptera (N individuals 531, average individuals = 15). Coleoptera had the greater richness with 25 taxa, while Trichoptera was represented by 19 genera, and Plecoptera by three genera.

There were no difference in Bray-Curtis dissimilarity of the whole community and for each groups separately among reference and RIL streams as indicated by Per-MANOVA (Table 2). In addition, there were no linear trend in community dissimilarity related to PHI index as accounted for

Multivariate Regression based on Bray-Curtis dissimilarity (Table 2). Confirming those patterns, there were no clear trends in community constrained along with PHI index (Figure 3A).

The most abundant taxa were the Trichoptera Helicopsyche (n = 623), Macronema (n = 533),

Phylloicus (n = 413), Triplectides (n = 312), Glossosomatidae (n = 214), Leptonema (n = 182), and

Oecetis (n = 164), the Plecoptera Macrogynoplax (n = 473) and Anacroneuria (n = 174) and the

Coleoptera Gyretes (n = 293). Of those, only Phylloicus and Oecetis had abundances higher in reference streams than in RIL (Table S2). In general, the rank of abundance-distribution pattern remained proportionally the same in areas under RIL and reference streams (Figure 3B).

42 A)

B)

Figure 3 – Community patterns showing A) direct ordination weighted by taxa abundance per streams ordered by the PHI index ascendantly from left to right; and B) abundance-distribution rank among species plotted as proportion of total abundance (y-axis).

43 3.3. GAM models and residual spatial autocorrelation

The first PCA axis was negatively related to greater canopy cover and PHI index both higher in

REF streams, while the second PCA axis was negatively related to the amount of fallinf logs and trees whithin and outside streams. Therefore, there are evidence supporting negative effects of RIL activity on the structure of riparian vegetation on studied streams. However, those environmental impacts had no negative effects on community metrics as predicted. In general, all GAM analysis do not support the hypothesis of habitat homogeneization, with increase of habitat heterogeneity in RIL mainly related to logs and trees. No environmental effects as measured by the PCA axes were found for Plecoptera and

Trichoptera estimated richness (Table 3). However, there were weak positive effect of the first PCA axis on estimated richness of the whole community (Adj. R2 = 0.239, F = 3.144, P = 0.033; Figure 4 A and B) and Coleoptera (Adj. R2 = 0.196, F = 6.573, P = 0.008) in GAM models, contrary to our expectations (Table 3; Figure 4 A and B, respectively).

A) B)

Figure 4 – GAM model with Poisson error of estimated species richness modeled by the first Principal Component (PC1) for the whole community (A) and water beetles (B). Solid and Dotted lines in A and B represent smooth GAM prediction as a function of PC1 and their 95% confidence interval.

44 In addition, when we consider in GAM models only streams with sample coverage higher than

90%, the trends remained the same. Despite this, both whole community and Coleoptera data decreased considerably in explained variance for the first PCA axis, but they were still significant (Adj. R-square

= 0.186, F = 5.441, P = 0.027 and Adj. R-square = 0.115, F = 4.884, P = 0.036), indicating that at least part of the observed patterns in original data set is related to sample bias in Coleoptera sampling. There were no effects of our environmental predictors on the abundance of the whole community and of each group separately (Table 4). The abundances of any FFG were also unaffected by the PCA axes.

4. Discussion We have addressed the question whether RIL activity has affected aquatic insect communities in forested Amazonian headwater streams. As our results suggested, there were evidences for strong environmental effects related to RIL activity on the structure of the riparian vegetation, mainly related to gaps opening and falling of logs and trees whithin and outside the stream. Community estimated richness of studied communities were positively related to RIL impacts, as accounted for the first PCA axis, especially due to the increase in Coleoptera diversity. Trichoptera and Plecoptera, though to be sensitive environmental indicators (Kalaninová et al., 2014; Resh and Rosenberg, 1984; Ruiz-García et al., 2012; Van den Brink et al., 2013), remained unaffected by RIL. Overall these results were robust to sample bias, but positive when significant, contrary to our expectation.

There were no effects of our environmental predictors also on the abundance of any considered group as well as on the abundance of the FFG. That way we reject the hypothesis of biotic homogenization (Rahel, 2002) for the considered stream ecosystems. RIL impact on the studied stream communities were subtle, and the increase of water beetles in stream with less canopy cover (as described by the first PCA) could be explained by the increase of scrapers such as Elmids, despite scrapers as a whole were also unaffected by RIL impacts. In addition, no change in species composition

45 and proportion of abundance distribution were found between reference and RIL streams.

Headwater streams have been seen as an important biodiversity repository for the whole hydrographic ecosystem (Meyer et al., 2007) because it holds many endemic species closely related to energy transfer among the riparian input and the aquatic food web (Schoenly et al., 1991; Wallace and

Webster, 1996). Deforestation in the riparian vegetation can alter the resource base of stream ecosystems by affecting mainly species restricted to headwaters (Benstead and Pringle, 2004), but potentially also communities and water quality downstream (Alexander et al., 2007; Gomi et al., 2002;

Lorion and Kennedy, 2009; Wipfli et al., 2007). Despite be less impacting than other economic activities developed in Amazon forest, RIL have affected several animal mediated processes

(Schleuning et al., 2011) and abundance distribution of terrestrial and aquatic animals in short

(Azevedo-Ramos et al., 2006; Strehlow et al., 2002) and long term (Barlow et al., 2007; Davies et al.,

2005a; Haynes, 1999).

We have shown that PHI index and several of their environmental variables varied consistently among treatments. Despite properly distinguished among reference and RIL streams we found that environmental characteristics and also PHI was not sufficient to affect strongly the estimated richness, abundance or species composition of the studied communities. We attributed the absence of such effects to the considerable overall similarity in the streams conditions of the studied streams among the treatments. Nevertheless, by identifying important predictors that varied between treatments we have proved the reliability of PHI protocol in monitoring of subtle environmental impacts such as RIL. Such approach should be extremely useful in areas where the impacts are more pervasive, for instance in oil palm plantations. In RIL activities the main predictors to be monitored are canopy cover along the stratified vegetation layers, the amount of logs and trees falling into the stream and at riparian vegetation.

46 Table 2 – Permutation analysis of variance based on Bray-Curtis dissimilarity of log transformed community data modeled among categorical predictor (reference and RIL streams) and Multivariate regression analysis based on the same matrix modeled by PHI index.

Sums of Mean Response metric DF Squares Squares F Model R2 P- value Whole community Group 1 0.029 0.029 0.964 0.028 0.443 Residuals 34 1.017 0.03 0.972 Total 35 1.045 1 PHI index 1 0.018 0.018 0.586 0.017 0.775 Residuals 34 1.028 0.03 0.983 Total 35 1.045 1 Coleoptera Group 1 0.011 0.011 0.690 0.020 0.643 Residuals 34 0.518 0.015 0.980 Total 35 0.528 1 PHI index 1 0.004 0.004 0.263 0.008 0.912 Residuals 34 0.524 0.015 0.992 Total 35 0.528 1 Trichoptera Group 1 0.044 0.044 1.016 0.029 0.395 Residuals 34 1.472 0.043 0.971 Total 35 1.516 1 PHI index 1 0.026 0.026 0.602 0.017 0.712 Residuals 34 1.489 0.044 0.983 Total 35 1.516 1 Plecoptera Group 1 0.04 0.04 0.970 0.028 0.394 Residuals 34 1.393 0.041 0.972 Total 35 1.432 1 PHI index 1 0.043 0.043 1.057 0.03 0.353 Residuals 34 1.389 0.041 0.97 Total 35 1.432 1

47 Table 3 – Results of GAM models for estimated species richness of the whole community and for each group modeled by smooth terms of three first Principal Components as summary of environmental data. (S = Estimated Species Richness.).

Response GAM Adjusted Estimated Reference F P-value metric Model R-square DF DF All streams S ~ f (PC1) + 2.870 3.573 3.144 0.033 Community f (PC2) + 0.239 1.509 1.849 2.237 0.125 f (PC3) 1.000 1.000 0.017 0.897 S ~ f (PC1) + 1.264 1.485 6.573 0.008 Coleoptera f (PC2) + 0.196 1.000 1.000 2.253 0.143 f (PC3) 1.000 1.000 0.028 0.869 S ~ f (PC1) + 1.000 1.000 1.709 0.200 Plecoptera f (PC2) + 0.029 1.000 1.000 0.346 0.561 f (PC3) 1.000 1.000 2.089 0.158 S ~ f (PC1) + 3.461 4.265 2.491 0.062 Trichoptera f (PC2) + 0.251 1.992 2.477 1.017 0.380 f (PC3) 1.000 1.000 1.117 0.299 Stream with 90% sample coverage only S ~ f (PC1) + 1.000 1.000 5.441 0.027 Community f (PC2) + 0.186 1.455 1.778 3.705 0.043 f (PC3) 1.000 1.000 0.04 0.844 S ~ f (PC1) + 1.000 1.000 4.884 0.036 Coleoptera f (PC2) + 0.115 1.000 1.000 2.237 0.146 f (PC3) 1.000 1.000 0.196 0.661 S ~ f (PC1) + 1.000 1.000 1.170 0.289 Plecoptera f (PC2) + 0.029 1.000 1.000 0.82 0.373 f (PC3) 1.000 1.000 2.055 0.163 S ~ f (PC1) + 2.476 3.048 1.765 0.182 Trichoptera f (PC2) + 0.361 1.818 2.238 1.24 0.307 f (PC3) 3.397 4.064 1.577 0.214

48 Table 4 – Results of GAM models for total abundance of the whole community, of each group, and for the five Feeding Functional Group modeled by smooth terms of three first Principal Components as a summary of environmental data. (Abun = Abundance). Response GAM Adjusted Estimated Reference F P-value metric Model R-square DF DF Abun ~ f (PC1) + 3.236 4.020 2.444 0.070 Community f (PC2) + 0.244 1.000 1.000 0.819 0.373 Abundance f (PC3) 3.282 4.017 0.822 0.523 3.429 4.238 1.425 0.250 Abun ~ f (PC1) + Coleoptera f (PC2) + 0.212 1.984 2.427 1.796 0.176 Abundance f (PC3) 2.959 3.609 0.273 0.877 Abun ~ f (PC1) + 2.974 3.655 1.387 0.267 Plecoptera f (PC2) + 0.368 1.000 1.000 0.804 0.379 Abundance f (PC3) 7.974 8.680 1.552 0.190 3.014 3.759 2.126 0.106 Abun ~ f (PC1) + Trichoptera f (PC2) + 0.194 1.000 1.000 1.296 0.264 Abundance f (PC3) 2.602 3.179 0.519 0.683 Filtering Abun ~ f (PC1) + 1.000 1.000 0.281 0.600 Collectors f (PC2) + 0.283 4.148 5.138 1.814 0.139 Abundance f (PC3) 1.513 1.843 3.023 0.065 3.501 4.280 1.441 0.248 Filtering Abun ~ f (PC1) + Gatherers f (PC2) + 0.301 1.000 1.000 0.723 0.404 Abundance f (PC3) 6.582 7.534 1.261 0.308 Abun ~ f (PC1) + 3.245 4.051 1.593 0.201 Shredders f (PC2) + 0.170 1.000 1.000 1.803 0.189 Abundance f (PC3) 1.000 1.000 1.661 0.207 3.136 3.882 2.028 0.120 Abun ~ f (PC1) + Predators f (PC2) + 0.221 1.537 1.873 0.386 0.670 Abundance f (PC3) 3.649 4.449 1.245 0.313

Abun ~ f (PC1) + 3.083 3.858 2.037 0.116 Scrapers f (PC2) + 0.177 1.000 1.000 2.371 0.134 Abundance f (PC3) 1.000 1.000 2.046 0.163

49 It is important to note that PHI index and several environmental metrics properly indicates that

REF streams had attributes of high environmental quality such as high substrate heterogeneity, greater woody canopy cover, lower temperature. This difference highlights the needs for more quantifiable measures of riparian vegetation structure and stream habitats in order to establish the relationships between RIL impacts, as we have done here. If on the one hand, impacts were too subtle to markedly affect stream communities, on the other, by identifying the main predictors affected by RIL management we have demonstrated that environmental assessment could be improved and increase our ability to monitor selective logging impacts in Amazonian forested streams. For instance, lower substrate heterogeneity, enhanced sedimentation and water temperature have been attributed to be the most perceived impact of RIL in streams elsewhere (Davies et al., 2005b), and we have shown that those variables consistently represented the major differences among treatments here, jointly with canopy cover. Several canopy cover measures also differentiated reference from RIL streams. Since evidences of pervasive effects of those variables have been found for stream communities at a long term basis (Davies et al., 2005a), we should monitor those variables and their effects constantly in order to couple with biomonitoring of RIL activities.

We have not corroborated the hypothesis of habitat homogenization effects on species diversity of studied group in streams under RIL activity. Differences in physical environment were not sufficient to cause variance in species composition given that most of species were ubiquitously distributed in both treatments. Accordingly, no changes in community structure, composition and diversity were attributed to logging impacts at low intensity, but there were some trends when the impacts attained moderated intensity mainly associated to the increase of gatherer taxa which the authors attributed to input of fine sediments (e.g., fine particulate organic material) (Kreutzweiser et al., 2005b).

Given that we may consider that the RIL activity as developed at low intensity should not affect stream community. Nevertheless, abundances of some taxa were higher in reference streams, especially

50 the Trichoptera Phylloicus and Oecetis, indicating that some components of the fauna could already have been negatively affected by logging. While other groups should increase diversity in logged forests due possible thermoregulatory requirements (e.g., Odonata, gatherers), some sensitive larvae should decrease in abundance and diversity ( e.g., shredders). RIL is prone to cause negative effects on both habitat and community structure linked to riparian vegetation sources if practiced in moderate to high intensity (Kreutzweiser et al., 2005b). Some Trichoptera in tropical headwater streams are shredder larvae strongly related to riparian vegetation input, which have high consumption of enriched course organic matter and use leafs and other organic materials to build retreats (e.g., Phylloicus spp.,

Triplectides spp.). Phylloicus and Triplectides represent shredders/detritivorous larvae (Cummins et al.,

2005b), frequently found in organic mesohabitats in deposition reaches of streams (DSN personal observation). They were also among the most abundant taxa found in this study. These genera have been pointed out as bioindicators of pristine environments and could be strongly affected in severely impacted streams of Cerrado (Nogueira et al., 2011; Pereira et al., 2012) and Amazonian biomes

(Couceiro et al., 2009). Since shredders insects are frequents abundant and ubiquitously found in headwater their local abundance change could be a candidate to measure RIL impacts on stream communities.

Identifying factors influencing the abundance and distribution of shredders populations in forested tropical streams should be achieved in order to better monitor the effects of environmental impacts on riparian vegetation. In this study, the only representative shredder taxa with abundance significantly higher in reference sites was Phylloicus, which we recommend monitor in order to properly account for the subtle effects of RIL activities. Furthermore, developments are needed concerning the linkage between functional groups and traits that link directly to ecosystem functioning and stability in tropical freshwater ecosystems. A better comprehension of functional diversity of stream communities could improve our outcomes and provide insights about what we need really to

51 look for in order improve bioassessment in these ecosystems.

RIL impacts are often too subtle to affect the aquatic organisms needing long time comparisons and accurate evaluation of physical conditions to distinguish from natural disturbances within a region.

Secondary impacts such as roads, stream bridges and falling trees causes significant load of sediments into streams, changes in the available habitats and riparian forest structure, modification of stream morphology and warming of air and water temperature (Davies et al., 2005b). Those habitat characteristics should be properly accounted for to advice better management practices for RIL since they are probably the only perceptive impact on streams environment and associated communities

(Davies et al., 2005a, 2005b), as we also have demonstrated here.

Riparian vegetation around stream is an important corridor enabling connectivity for many terrestrial and aquatic animals, holding and protecting aquatic communities from land use impacts and providing resources by leaf breakdown (Benstead and Pringle, 2004). RIL activities should avoid those areas by respecting sufficient buffers of riparian vegetation protection, but the exact extent to which stream communities are affected in tropical forested streams remain uncertain. Manage planning for

RIL activity could be greatly improved if consider empirical evidences based on studies focusing at environmental impact assessment, in order to minimizing impacts, maintain biodiversity and, at the same time, get economic value from tropical forests (Zimmerman and Kormos, 2012). The best could be done in areas prone to logging activity (e.g., such as public reserves of Brazil), is to develop RIL standardized management system. The outcomes of these study should be considered in those areas where there are perspectives for oil palm plantations in the near future, especially in the oriental

Amazon.

5. Conclusion The main problem of assessing the effects of RIL activity in streams concern the difficulty to

52 measure properly and quantitatively their direct and indirect impacts caused by big trees cut off, small trees damage, trails, skids, roads and bridges to load logs. We have shown that the proposed metrics are reliable alternatives for monitoring RIL impacts on streams. By identifying the main predictors that have varied between RIL and reference streams we have contributed to biomonitoring aims of managed forest. On the other hand, only small influences on community abundance (especially the Phylloicus

Trichoptera) were perceived according to our treatments. Most of the studied streams are close to reference conditions in both environment and community structure and could be used as control areas to other activities prevailing in the oriental Amazon region, specially oil palm plantations and conventional logging. Considering that RIL activity impact stream riparian vegetation structure, our results provid new data and a protocol for the evaluation of RIL and conventional logging impacts in future studies.

53 6. Acknowledgements We thank to CIKEL LTDA by providing field support and accommodation during samplings, by detailed informations about the studied areas, especially by the maps for the selection of reference sites and experienced helpers which guided us to the field samplings. This research was supported by CNPq process 481015/2011-6, CIKEL LTDA and 33 Forest. The collection of biological material was authorized by permit 4997045 from IBAMA, the Brazilian Institute for the Environment and renewable

Natural Resources. DSN and LBC are supported by CAPES fellowship, PDM, LJ (process:

303252/2013-8) and LFAM (process: 301343/2012-8) are continuously funded by CNPq productivity grants.

54 7. References Adum, G.B., Eichhorn, M.P., Oduro, W., Ofori-Boateng, C., Rödel, M.-O., 2013. Two-stage recovery of amphibian assemblages following selective logging of tropical forests. Conserv. Biol. 27, 354–63.

Ali, G.H., Abd el-Salam, N.F., 1999. Factors controlling bioindicators for industrial pollution detection. Biomed. Environ. Sci. 12, 194–200.

Anderson, M.J., 2006. A new method for non-parametric multivariate analysis of variance. Austral Ecology 26, 32–46.

Arimoro, F.O., Ikomi, R.B., 2009. Ecological integrity of upper Warri River, Niger Delta using aquatic insects as bioindicators. Ecol. Indic. 9, 455–461.

Asner, G.P., Broadbent, E.N., Oliveira, P.J.C., Keller, M., Knapp, D.E., Silva, J.N.M., 2006. Condition and fate of logged forests in the Brazilian Amazon. PNAS 103, 12947–50.

Asner, G.P., Knapp, D.E., Broadbent, E.N., Oliveira, P.J.C., Keller, M., Silva, J.N., 2005. Selective Logging in the Brazilian Amazon. Science 310, 480–482.

Asner, G.P., Rudel, T.K., Aide, T.M., Defries, R., Emerson, R., 2009. A contemporary assessment of change in humid tropical forests. Conserv. Biol. 23, 1386–95.

Azevedo-Ramos, C., de Carvalho, O., do Amaral, B.D., 2006. Short-term effects of reduced-impact logging on eastern Amazon fauna. For. Ecol. Manage. 232, 26–35.

Balian, E. V, Segers, H., Leveque, C., Martens, K., 2008. The freshwater animal diversity assessment: An overview of the results. Hydrobiologia 595, 627–637.

Baraloto, C., Hérault, B., Paine, C.E.T., Massot, H., Blanc, L., Bonal, D., Molino, J.-F., Nicolini, E. a., Sabatier, D., 2012. Contrasting taxonomic and functional responses of a tropical tree community to selective logging. J. Appl. Ecol. 49, 861–870.

Barbour, M., Gerritsen, J., 1996. A framework for biological criteria for Florida streams using benthic macroinvertebrates. J. North Am. Benthol. Soc. 15, 185–211.

Barlow, J., Mestre, L. a. M., Gardner, T. a., Peres, C. a., 2007. The value of primary, secondary and plantation forests for Amazonian birds. Biol. Conserv. 136, 212–231.

Barreto, P., Amaral, P., Vidal, E., Uhl, C., 1998. Costs and benefits of forest management for timber

55 production in eastern Amazonia. For. Ecol. Manage. 108, 9–26.

Benetti, C.J., Cueto, J.A.R., Fiorentin, G.L., 2003. Gêneros de Hydradephaga (Coleoptera: Dytiscidae, Gyrinidae, Haliplidae, Noteridae) citados para o Brasil, com chaves para identificação. Biota Neotrop. 3, 1–20.

Benstead, J.P., Pringle, C.M., 2004. Deforestation alters the resource base and biomass of endemic stream insects in eastern Madagascar 490–501.

Bertault, J.-G., Sist, P., 1997. An experimental comparison of different harvesting intensities with reduced-impact and conventional logging in East Kalimantan, Indonesia. For. Ecol. Manage. 94, 209– 218.

Bivand, R., 2014. spdep: Spatial dependence: weighting schemes, statistics and models.

Bojsen, B., Barriga, R., 2002. Effects of deforestation on fish community structure in Ecuadorian Amazon streams. Freshw. Biol. 47, 2246–2260.

Bonada, N., Williams, D., 2002a. Exploration of the utility of fluctuating asymmetry as an indicator of river condition using larvae of the caddisfly Hydropsyche morosa (Trichoptera: Hydropsychidae). Hydrobiologia 147–156.

Bonada, N., Williams, D.D., 2002b. Exploration of the utility of fluctuating asymmetry as an indicator of river condition using larvae of the caddisfly Hydropsyche morosa (Trichoptera : Hydropsychidae). Hydrobiologia 481, 147–156.

Broadbent, E., Asner, G., Keller, M., Knapp, D., Oliveira, P., Silva, J., 2008. Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biol. Conserv. 141, 1745–1757.

Broadbent, E.N., Asner, G., Keller, M., 2008. Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biol. Conserv. 141, 1745–1757.

Chao, A., Gotelli, N.J., Hsieh, T., Sander, E., MA, K., Colwell, R.K., Ellison, A.M., 2014. Rarefaction and extrapolation with Hill numbers : a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67.

Chao, A., Jost, L., 2012. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547.

56 Clark, J. a., Covey, K.R., 2012. Tree species richness and the logging of natural forests: A meta- analysis. For. Ecol. Manage. 276, 146–153.

Colwell, R., Coddington, J., 1994. Estimating terrestrial biodiversity throught extrapolation. Philos. Trans. R. Soc. London 345, 101–118.

Colwell, R.K., Chao, a., Gotelli, N.J., Lin, S.-Y., Mao, C.X., Chazdon, R.L., Longino, J.T., 2012. Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. J. Plant Ecol. 5, 3–21.

Crawley, M.J., 2007. The R Book. John Wiley & Sons Ltd, England. 877p.

Cummins, K.W., Merrit, R.W., Andrade, P.C.M., 2005. The use of invertebrate functional groups to characterize ecosystem attributes in selected streams and rivers in southeast Brazil Stud. Neotrop. Fauna Environ. 40 , 71–90.

Cummins, K.W., Merritt, R.W., 1984. Ecology and distribution of aquatic insects, in: Merrit, R.W., Cummins, K.W.C.P. (Eds.), An Introduction to the Aquatic Insects of North America. Kendall / Hunt Publishing Company VL -, pp. 59–65.

Cummins, K.W., Merritt, R.W., Andrade, P.C., 2005. The use of invertebrate functional groups to characterize ecosystem attributes in selected streams and rivers in south Brazil. Stud. Neotrop. Fauna Environ. 40, 69–89.

Davies, P., Cook, L., McIntosh, P., Munks, S., 2005a. Changes in stream biota along a gradient of logging disturbance, 15 years after logging at Ben Nevis, Tasmania. For. Ecol. Manage. 219, 132–148.

Davies, P., McIntosh, P., Wapstra, M., Bunce, S., Cook, L., French, B., Munks, S., 2005b. Changes to headwater stream morphology, habitats and riparian vegetation recorded 15 years after pre-Forest Practices Code forest clearfelling in upland granite terrain, Tasmania, Australia. For. Ecol. Manage. 217, 331–350.

Davis, A., 2000. Does Reduced-Impact Logging Help Preserve Biodiversity in Tropical Rainforests? A Case Study from Borneo using Dung Beetles (Coleoptera: Scarabaeoidea. Environ. Entomol. 29, 467– 475.

Davis, A.J., 2000. Does Reduced-Impact Logging Help Preserve Biodiversity in Tropical Rainforests? A Case Study from Borneo using Dung Beetles (Coleoptera: Scarabaeoidea) as Indicators. Environ.

57 Entomol. 29, 467–475.

Dias, M.S., Magnusson, W.E., Zuanon, J., 2010. Effects of reduced-impact logging on fish assemblages in central Amazonia. Conserv. Biol. 24, 278–86.

Dias-Silva, K., Cabette, H.S.R., Juen, L., Jr, P.D.M., 2010. The influence of habitat integrity and physical-chemical water variables on the structure of aquatic and semi-aquatic Heteroptera. Zool. (Curitiba, Impresso) 27, 918–930.

Elliott, J.M., 2008. The ecology of riffle beetles (Coleoptera: Elmidae). Freshw. Rev. 1, 189–203.

Ernst, R., Linsenmair, K.E., Rödel, M., 2006. Diversity erosion beyond the species level: Dramatic loss of functional diversity after selective logging in two tropical amphibian communities. Biol. Conserv. 133, 143–155.

Feldpausch, T., Jirka, S., Passos, C., Jasper, F., Riha, S.J., 2005. When big trees fall: damage and carbon export by reduced impact logging in southern Amazonia. For. Ecol. Manage. 219, 199–215.

Ferraz, S.F.D., Vettorazzi, C.A., Theobald, D.M., Ballester, M.V.R., 2005. Landscape dynamics of Amazonian deforestation between 1984 and 2002 in central Rondonia, Brazil: assessment and future scenarios. For. Ecol. Manage. 204, 67–83.

Fichtler, E., Clark, D.A., Worbes, M., 2003. Age and Long-term Growth of Trees in an Old-growth Tropical Rain Forest, Based on Analyses of Tree Rings and 14C1. Biotropica 35, 306–317.

Foley, J.A., Defries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J. a, Prentice, I.C., Ramankutty, N., Snyder, P.K., 2005. Global consequences of land use. Science 309, 570–4.

Freemark, K., 1995. Assessing effects of agriculture on terrestrial wildlife: developing a hierarchical approach for the US EPA. Landsc. Urban Plan. 31, 99–115.

Gafner, K., Robinson, C.C.T., 2007. Nutrient enrichment influences the responses of stream macroinvertebrates to disturbance. J. North Am. Benthol. Soc. 26, 92–102.

Gerwing, J.J., 2002. Degradation of forests through logging and fire in the eastern Brazilian Amazon. For. Ecol. Manage. 157, 131–141.

58 Gerwing, J.J., 2006. The influence of reproductive traits on liana abundance 10 years after conventional and reduced-impacts logging in the eastern Brazilian Amazon. For. Ecol. Manage. 221, 83–90.

Gibson, L., Lee, T.M., Koh, L.P., Brook, B.W., Gardner, T. a, Barlow, J., Peres, C. a, Bradshaw, C.J. a, Laurance, W.F., Lovejoy, T.E., Sodhi, N.S., 2011. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–81.

Gomi, T., Sidle, R.C., Noguchi, S., Negishi, J.N., Nik, A.R., Sasaki, S., 2006. Sediment and wood accumulations in humid tropical headwater streams: Effects of logging and riparian buffers. For. Ecol. Manage. 224, 166–175.

Hamada, N., Couceiro, S., 2003. An illustrated key to nymphs of Perlidae (Insecta, Plecoptera) genera in Central Amazonia, Brazil. Rev. Bras. Entomol. 47, 477–480.

Haynes, A., 1999. The long term effect of forest logging on the macroinvertebrates in a Fijian stream. Hydrobiologia 405, 79–87.

Heino, J., Mykrä, H., 2008. Control of stream insect assemblages: roles of spatial configuration and local environmental factors. Ecol. Entomol. 33, 614–622.

Holdsworth, A.R., Uhl, C., 1997. Fire in Amazonian selectively logged rain forest and the potential for fire reduction. Ecol. Appl. 7, 713–725.

INPE, 2010. Monitoring of the Brazilian Amazon forest by satellite: Project PRODES. http://www.obt.inpe.br/prodes/index.html.

Jäch, M., Balke, M., 2008. Global diversity of water beetles (Coleoptera) in freshwater. Hydrobiologia 595, 419–442.

Janisch, J., Wondzell, S., Ehinger, W., 2012. Headwater stream temperature: Interpreting response after logging, with and without riparian buffers, Washington, USA. For. Ecol. Manage. 270, 302–313.

Johns, J.S., Barreto, P., Uhl, C., 1996. Logging damage during planned and unplanned logging operations in the eastern Amazon. For. Ecol. Manage. 89, 59–77.

Juen, L., Nogueira, D.S., Shimano, Y., Galli Vieira, L.C., Cabette, H.S.R., 2013. Concordance between Ephemeroptera and Trichoptera assemblage in streams from Cerrado – Amazonia transition. Ann. Limnol. - Int. J. Limnol. 49, 129–138. doi:10.1051/limn/2013048

59 Kalaninová, D., Bulánková, E., Šporka, F., 2014. Caddisflies (Trichoptera) as good indicators of environmental stress in mountain lotic ecosystems. Biologia (Bratisl). 69, 1030–1045.

Kasangaki, A., Chapman, L.J., Balirwa, J., 2008. Land use and the ecology of benthic macroinvertebrate assemblages of high-altitude rainforest streams in Uganda. Freshw. Biol. 53, 681– 697.

Kaufmann, P.P.R., Levine, P., Robison, E.G., Seeliger, C., Peck, D.D. V, Agency, U.S.E.P., Branch, R.E., Division, W.E., Health, N., Effects, E., 1999. Quantifying Physical Habitat in Wadeable Streams EPA/620/R–99/003.

Kindt, R., Coe, R., 2005. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre (ICRAF), Nairobi.

Koh, L.P., 2007. Impacts of land use change on South-east Asian forest butterflies: a review. J. Appl. Ecol. 44, 703–713.

Kreutzweiser, D., Capell, S., Good, K., 2005a. Effects of fine sediment inputs from a logging road on stream insect communities: a large-scale experimental approach in a Canadian headwater stream. Aquat. Ecol. 39, 55–66.

Kreutzweiser, D., Capell, S., Good, K., 2005b. Macroinvertebrate community responses to selection logging in riparian and upland areas of headwater catchments in a northern hardwood forest. J. North Am. Benthol. Soc. 24, 208–222.

Laurance, W.F., Ferreira, L. V, Rankin-de Merona, J.M., Laurance, S.G., 1998. Rain forest fragmentation and the dynamics of Amazonian tree communities. Ecology 79, 2032–2040.

Lazorchak, J., Hill, B., Averill, D., Peck, D., Klemm, D., 2000. Environmental Monitoring and Assessment Program -Surface Waters: Field Operations and Methods for Measuring the Ecological Condition of Non-Wadeable Rivers and Streams.

Lazorchak, J.M., Klemm, D.J., Peck, D. V, 1998. Environmental monitoring and assessment program surface waters: field operations and methods for measuring the ecological condition of wadeable streams.

Legendre, P., Legendre, L., 2012. Numerical Ecology, Third edit. ed. Elsevier, Amsterdam, The Netherlands.

60 Lewis, O.T., 2001. Effect of experimental selective logging on tropical butterflies. Conserv. Biol. 15 , 389–400.

Lindenmayer, D.B., 1999. Future directions for biodiversity conservation in managed forests: indicator species, impact studies and monitoring programs. For. Ecol. Manage. 115, 277–287.

Lorion, C.M., Kennedy, B.P., 2009. Relationships between deforestation, riparian forest buffers and benthic macroinvertebrates in neotropical headwater streams. Freshw. Biol. 54, 165–180.

Manzo, V., 2005. Key to the South America genera of Elmidae (Insecta: Coleoptera) with distributional data. Stud. Neotrop. Fauna Environ.

Maria, A., Pes, O., Hamada, N., Nessimian, J.J.L., Pes, A., 2005. Chaves de identificação de larvas para famílias e gêneros de Trichoptera (Insecta) da Amazônia Central, Brasil. Rev. Bras. Entomol. 181, 181– 204.

Martin-Smith, K., 1998. Effects of disturbance caused by selective timber extraction on fish communities in Sabah, Malaysia. Environ. Biol. Fishes 53, 155–167.

Martin-Smith, K.M., Laird, L.M., Bullough, L., Lewis, M.G., 1999. Mechanisms of maintenance of tropical freshwater fish communities in the face of disturbance. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 354, 1803–10.

Megan, M.H., Nash, M.S., Neale, A.C., Pitchford, A.M., 2007. Biological integrity in Mid-Atlantic Coastal Plains headwater streams. Environ. Monit. Assess. 124, 141–156.

Michalski, F., Peres, C.A., 2013. Biodiversity Depends on Logging Recovery Time. Science 339, 1521–1522.

Nepstad, D., Verssimo, A., Alencar, A., Nobre, C., 1999. Large-scale impoverishment of Amazonian forests by logging and fire. Nature 1405, 1997–2000.

Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, O., Minchin, P.R., O’Hara, R.B., Simpsin, G.L., Solymos, P., Stevens, M.H.H., Wagner, H., 2013. vegan: Community Ecology Package. R package version 2.0-9.

Paul, M.J., Meyer, J.L., Couch, C.A., 2006. Leaf breakdown in streams differing in catchment land use. Freshw. Biol. 51, 1684–1695.

61 Peck, D., Herlihy, A., Hill, B., Hughes, R., Kaufmann, P., Klemm, D., Lazorchak, J., McCormick, F., Peterson, S., Ringold, P., Magee, T., Cappaert, M., 2006. Environmental Monitoring and Assessment Program-Surface Waters Western Pilot Study: Field Operations for Wadeable Streams.

Putz, F.E., Sist, P., Fredericksen, T., Dykstra, D., 2008. Reduced-impact logging: Challenges and opportunities. For. Ecol. Manage. 256, 1427–1433.

Putz, F.E., Zuidema, P. a., Synnott, T., Peña-Claros, M., Pinard, M. a., Sheil, D., Vanclay, J.K., Sist, P., Gourlet-Fleury, S., Griscom, B., Palmer, J., Zagt, R., 2012. Sustaining conservation values in selectively logged tropical forests: the attained and the attainable. Conserv. Lett. 5, 296–303.

R Development Core Team, 2013. R: A language and environment for statistical computing.

Rahel, F.J., 2002. Homogenization of Freshwater Faunas. Annu. Rev. Ecol. Syst. 33, 291–315.

Resh, V.H., Rosenberg, D.M., 1984. The Ecology of Aquatic Insects. Praeger Publ., NY.

Ribeiro, D.B., Freitas, A.V.L., 2012. The effect of reduced-impact logging on fruit-feeding butterflies in Central Amazon, Brazil. J. Insect Conserv. 16, 733–744.

Ruiz-García, A., Márquez-Rodríguez, J., Ferreras-Romero, M., 2012. Implications of anthropogenic disturbance factors on the Trichoptera assemblage in a Mediterranean fluvial system: Are Trichoptera useful for identifying land-use alterations? Ecol. Indic. 14, 114–123.

Sanchez-Fernandez, D., Abellan, P., Mellado, A., Velasco, J., Millan, A., 2006. Are water beetles good indicators of biodiversity in Mediterranean aquatic ecosystems? The case of the segura river basin (SE spain). Biodivers. Conserv. 15, 4507–4520.

Schleuning, M., Farwig, N., Peters, M.K., Bergsdorf, T., Bleher, B., Brandl, R., Dalitz, H., Fischer, G., Freund, W., Gikungu, M.W., Hagen, M., Garcia, F.H., Kagezi, G.H., Kaib, M., Kraemer, M., Lung, T., Naumann, C.M., Schaab, G., Templin, M., Uster, D., Wägele, J.W., Böhning-Gaese, K., 2011. Forest fragmentation and selective logging have inconsistent effects on multiple animal-mediated ecosystem processes in a tropical forest. PLoS One 6, e27785.

Segura, M.O., Valente-neto, F., Fonseca-Gessner, A.A., 2011. Chave de famílias de Coleoptera aquáticos (Insecta) do Estado de São Paulo, Brasil. Biota Neotrop. 11, 393–412.

Shimano, Y., Juen, L., Salles, F.F., Nogueira, D.S., Cabette, H.S.R., 2013. Environmental and spatial processes determining Ephemeroptera (Insecta) structures in tropical streams. Ann. Limnol. - Int. J.

62 Limnol. 49, 31–41.

Silvério, D. V, Brando, P.M., Balch, J.K., Putz, F.E., Nepstad, D.C., Bustamante, M.C., 2013. Testing the Amazon savannization hypothesis: fire effects on invasion of a neotropical forest by native cerrado and exotic pasture grasses. Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 368, 1–8.

Siqueira, T., Bini, L.M., Roque, F.O., Couceiro, S.R., Trivinho-strixino, S., Cottenie, K., 2012. Common and rare species respond to similar niche processes in macroinvertebrate metacommunities. Ecography (Cop.). 35, 183–192.

Sist, P., Gourlet-fleury, S., Putz, F.E., Mooney, C.H.A., 2012. The Impacts of Selective Logging: Questionable Conclusions BioScience 62(9), 786-786.

Tomanova, S., Tedesco, P.A., Campero, M., Van Damme, P.A., Moya, N., Oberdorff, T., 2007. Longitudinal and altitudinal changes of macroinvertebrate functional feeding groups in neotropical streams: a test of the River Continuum Concept. Fundam. Appl. Limnol. 170, 233–241.

Uhl, C., Veríssimo, A., Mattos, M., 1991. Social, economic, and ecological consequences of selective logging in an Amazon frontier: the case of Tailândia. For. Ecol. Manage. 46, 243–273.

Van den Brink, F.W.B., Van der Velde, G., Wijnhoven, S., 2013. Diversity, occurrence and feeding traits of caddisfly larvae as indicators for ecological integrity of river-floodplain habitats along a connectivity gradient. Ecol. Indic. 25, 92–98.

Veldman, J.W., Mostacedo, B., Peña-Claros, M., Putz, F.E., 2009. Selective logging and fire as drivers of alien grass invasion in a Bolivian tropical dry forest. For. Ecol. Manage. 258, 1643–1649.

Verissimo, A., Barreto, P., Mattos, M., Tarifa, R., Uhl, C., 1992. Logging impacts and prospects for sustainable forest management in an old Amazonian frontier: The case of Paragominas. For. Ecol. Manage. 55, 169–199.

Vieira, D.L.M., Scariot, A., Holl, K.D., 2007. Effects of habitat, cattle grazing and selective logging on seedling survival and growth in dry forests of Central Brazil. Biotropica 39, 269–274.

Villegas, Z., Peña-Claros, M., Mostacedo, B., Alarcón, a., Leaño, C., Pariona, W., Choque, U., 2009. Silvicultural treatments enhance growth rates of future crop trees in a tropical dry forest. For. Ecol. Manage. 258, 971–977.

Webb, E.L., Peralta, R., 1998. Tree community diversity of lowland swamp forest in Northeast Costa

63 Rica, and changes associated with controlled selective logging. Biodivers. Conserv. 7, 565–583.

West, T.A.P., Vidal, E., Putz, F.E., 2014. Forest Ecology and Management Forest biomass recovery after conventional and reduced-impact logging in Amazonian Brazil. For. Ecol. Manage. 314, 59–63.

Zapala, M. a, Schork, N.J., 2006. Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proc. Natl. Acad. Sci. U. S. A. 103, 19430–5.

Zimmerman, B.L., Kormos, C.F., 2012. Prospects for Sustainable Logging in Tropical Forests. Bioscience 62, 479–487.

Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., Smith, G.M., Ebooks Corporation., 2009. Mixed Effects Models and Extensions in Ecology with R, Statistics for Biology and Health.

64 8. Supplementary information Details of Physical Habitat Integrity’s field surveys

The physical habitat characterization was made along the 150 m section taking measurements of the longitudinal and lateral dimensions of stream channels (Supplementary Material S1 and S2 – The

Protocol and Summary of metrics). The longitudinal dimension was evaluated within each 15 m subsection, from “A” to “B”, “B” to “C” until the “J” to “K” subsection (see Fig. S1), where we have taken 15 measures equidistant (one meter spaced) of several habitats characteristics such thalweg depth, substrate type and immersion (Peck et al., 2006). For each subsection the presence of woods, cobbles, lateral bars, pools and rifles type were classified, and the slope and sinuosity were taken. In addition, discharge (m3 -1s) and flow speed (m-1s) were taken three times and averaged for the stream section based on just one subsection.

The lateral dimension, measured in each transversal limit among sections, include channel morphology such width and height of seasonal wetted margin, channels secondary bars (small tributary), incision height of the channel embedment, inclination and fissures of margins, five depth measures transversally jointly with the type of substrate and substrate immersion. Information about vegetation density and structure are visually assessed in about 10 vs. 10 m around each transection from both stream margins, with percentage estimates of large trees (>5 m), understory trees (<0.5 to >5 m), undergrowth (<0.5 m) and exposed soil. Six measures of canopy cover were taken each transection in five positions covering stream channel.

Human influence was assessed in both margins through the visual inspection for human signals such wall, dike, artificial channels, dams, buildings, sidewalk or gravel road, highway or rail pipes for pickup or discharge, rubbish, garbage, parks, lawns, grain crops, grazing, forestry, logging, mining. In addition impacts are pondered by distance estimates from stream channel. Land use in the catchment

65 was established with satellite images jointly with field inspection.

The approach maximizes consistency across all sampling points and comparability of the results in the assessment of environmental condition. The physical habitat characteristics could be used in concert to produce a multimetric index or in specific sets of variables to represent desired property of the system (i.e., human impacts).

Figure S1 – Subsections along 150 m transects with transversal dashed lines indicating 15 m length between subsections where measures of physical characteristics and habitat structure variables were taken (below-left in the panel). In detail the distribution of samples for the studied communities which represented 2/3 of each subsection (above in the panel). Aquatic insect samples were taken in each 5 m marked by dotted lines before and after each dashed line (subsection limit).

66 Figure S2 – Mantel correlogram of GAM residuals modeled by the three first Principal Components with smooth splines. Significant spatial structure are shown as a filled square.

67 Table S1 – Summary of descriptive statistics of environmental predictors and Physical Habitat Integrity index (PHI), Spearman rank correlation of predictors with PHI, and Student t-test comparison among reference and RIL stream with 34 degree of freedom.

Spearman Among groups statistics correlation Variables names Reference RIL Mean Min Max PHI t-value p Mean±SD Mean±SD Physical Habitat Integrity 3.2 1.6 4.6 - 4.3±0.2 2.8±0.6 7.20 <0.01 Substrate size (SD) 1228.3 0.1 2800.2 0.50 1592.3±602.1 1088.3±753.7 1.89 0.07 Filamentous algae (%) 0.4 0.0 3.6 -0.31 0±0 0.5±1.0 -1.53 0.14 Total canopy cover (Mean) 139.7 78.3 216.6 0.56 171.9±22.9 127.3±23.1 5.20 <0.01 Total canopy cover (SD) 29.4 15.6 45.9 0.44 36.4±6.2 26.7±5.7 4.45 <0.01 Total woody cover (Mean) 27.5 16.4 39.7 0.42 33.1±4.4 25.4±5.6 3.89 <0.01 Total woody cover (SD) 111.3 59.8 179.5 0.60 143.5±19.6 98.9±22.7 5.46 <0.01 Intermediate canopy cover (Mean) 54.7 29.9 91.6 0.66 71.1±11.0 48.3±10.8 5.67 <0.01 Intermediate canopy cover (SD) 51.4 24.0 74.8 0.39 62.9±6.8 47.0±12.3 3.83 <0.01 Canopy cover of big trees (Mean) 18.5 5.4 35.8 0.66 27.2±7.0 15.2±6.4 4.90 <0.01 Canopy cover of big trees (SD) 14.6 5.9 23.3 0.56 19.2±3.4 12.9±4.2 4.24 <0.01 Canopy cover of small trees (Mean) 36.1 19.2 56.0 0.50 44.0±6.9 33.1±7.6 3.91 <0.01 Canopy cover of small trees (SD) 15.6 8.8 23.3 0.50 17.8±3.3 14.7±3.7 2.29 0.03 Canopy cover of big plus intermediate trees (Mean) 106.1 61.0 166.4 0.60 134.0±16.1 95.3±19.4 5.60 <0.01 Canopy cover of intermediate trees (Mean) 90.7 48.4 146.0 0.65 118.4±13.4 80.0±17.9 6.15 <0.01 Woody understory canopy cover (Mean) 36.0 11.4 54.4 0.53 47.3±5.7 31.7±9.8 4.72 <0.01 Incision height (SD) 2.3 0.3 4.8 -0.54 1.6±0.6 2.6±0.9 -3.14 <0.01 Incision height (Mean) 9.0 2.8 19.3 -0.51 6.7±2.6 9.9±3.5 -2.56 0.02 Woody volume in the channel (%) 5.1 0.3 23.1 -0.36 2.8±2.4 6.0±6.0 -1.65 0.11 Woody volume in the channel plus total canopy cover (%) 7.0 0.2 39.9 -0.31 4.0±3.1 8.2±9.1 -1.40 0.17 Active flooding width (Meters) 4.2 2.0 21.8 -0.59 2.7±0.6 4.7±4.2 -1.48 0.15 Width depth ratio (Mean) 10.9 7.2 23.6 -0.37 9.5±1.6 11.4±3.5 -1.71 0.10

68 Table S2 – Summary of taxa abundance and Student t-test comparison among reference and RIL stream with 34 degree of freedom. LTU: Lower functional units; FG: Functional groups.

Reference RIL Order LTU FG Sum Mean Min Max t-value P (Mean±SD) (Mean±SD) Derallus Pre 75 2.1 0 9 1.6±2.7 2.3±2.4 -0.72 0.48 Dryopidae Shr 10 0.3 0 2 0±0 0.4±0.6 -2.11 0.04 Hydrophilinae Shr 25 0.7 0 7 0±0 1.0±2.0 -1.47 0.15 Gyrelmis Pre 47 1.3 0 9 1.1±1.6 1.4±2.2 -0.37 0.71 Gyretes Pre 293 8.1 0 57 7.1±11.2 8.5±12.2 -0.32 0.75 Helochares Pre 6 0.2 0 1 0.2±0.4 0.1±0.4 0.32 0.75 Hydaticus Pre 3 0.1 0 1 0.1±0.3 0.1±0.3 0.22 0.83 Hydrocanthus Pre 7 0.2 0 1 0.1±0.3 0.2±0.4 -0.87 0.39 Hydrochus Gat 1 0.0 0 1 0±0 0.0±0.2 -0.61 0.54 Hydrodessus Pre 2 0.1 0 1 0±0 0.1±0.3 -0.89 0.38 Coleoptera Laccodytes Pre 6 0.2 0 3 0.2±0.6 0.1±0.6 0.20 0.84 Laccophilus Pre 3 0.1 0 2 0±0 0.1±0.4 -0.84 0.41 Larainae Scra 3 0.1 0 1 0±0 0.1±0.3 -1.11 0.27 Macrovatellus Pre 2 0.1 0 1 0.1±0.3 0.0±0.2 0.71 0.48 Microcylloepus Scra 1 0.0 0 1 0±0 0.0±0.2 -0.61 0.54 Phanocerus Scra 1 0.0 0 1 0±0 0.0±0.2 -0.61 0.54 Phaenostoma Shr 13 0.4 0 3 0.2±0.4 0.4±0.9 -0.75 0.46 Platynectes Pre 1 0.0 0 1 0±0 0.0±0.2 -0.61 0.54 Pronoterus Pre 1 0.0 0 1 0±0 0.0±0.2 -0.61 0.54 Tropisternus Pre 18 0.5 0 5 0.2±0.6 0.6±1.4 -0.92 0.36 Stegoelmis Scra 13 0.4 0 2 0.6±1.0 0.3±0.6 1.24 0.22 Trichoptera Amphoropsyche Scra 23 0.6 0 5 1.4±2.0 0.3±0.8 2.24 0.03 Austrotinodes Pre 5 0.1 0 2 0.3±0.7 0.1±0.3 1.43 0.16 Cernotina Fil 49 1.4 0 8 2±2.5 1.1±1.4 1.36 0.18 Chimarra Fil 57 1.2 0 16 1.4±2.7 1.6±3.7 -0.20 0.85 Cyrnellus Fil 3 0.1 0 1 0.1±0.3 0.1±0.3 0.22 0.83 Glossosomatidae Scra 214 5.9 0 66 11.2±21.9 3.9±9.4 1.41 0.17 Helicopsyche Scra 623 17.3 0 78 22.2±22.6 15.4±17.1 0.97 0.34 Hydroptilidae Scra 4 0.1 0 4 0.4±1.3 0±0 1.65 0.11 Leptonema Fil 182 5.1 0 25 3.2±3.5 5.8±7.4 -1.04 0.31 Macronema Fil 533 14.8 0 47 13.6±13.7 15.3±11.4 -0.37 0.71 Macrostemum Fil 93 2.6 0 20 2.4±6.3 2.6±4.4 -0.14 0.89 Marilia Pre 22 0.6 0 6 0.9±1.9 0.5±1.0 0.81 0.42 Nectopsyche Fil 17 0.5 0 4 0.3±0.7 0.5±0.9 -0.73 0.47 Phylloicus Shr 413 11.5 0 47 18.1±16.8 8.9±6.8 2.36 0.02 Polyplectropus Fil 73 2.0 0 11 2.4±3.3 1.9±2.1 0.55 0.58 Oecetis Pre 164 4.6 0 16 7.5±4.3 3.4±4.3 2.55 0.02 Smicridea Fil 71 2.0 0 14 2.2±2.7 1.9±3.3 0.27 0.79 Reference RIL Order LTU FG Sum Mean Min Max t-value P (Mean±SD) (Mean±SD) Triplectides Shr 312 8.7 0 63 9.1±16.8 8.5±13.1 0.11 0.91 Wormaldia Fil 54 1.5 0 24 1.4±1.8 1.5±4.7 -0.09 0.93 Anacroneuria Pre 174 4.8 0 40 2.3±3.4 5.8±9.6 -1.12 0.27

Plecoptera Enderleina Pre 23 0.6 0 5 0.6±1.1 0.6±1.3 -0.12 0.91

Macrogynoplax Pre 473 13.1 0 40 12±10.7 13.6±9.2 -0.44 0.66 CAPÍTULO 2 – HABITAT TEMPLET OF AQUATIC INSECTS IN FORESTED

AMAZONIAN STREAMS: THE WATER FLOW AS A TRAIT FILTER

Denis Silva Nogueira; Leandro Juen & Paulo De Marco Júnior

Obs.: Formatado de acordo com as normas da revista Freshwater Biology

71 Habitat templet of aquatic insects in forested Amazonian streams: the water flow as a trait filter

Denis Silva Nogueira1a; Leandro Juen2 & Paulo De Marco Junior3

1Programa de Pós-Graduação em Ecologia e Evolução, Universidade Federal de Goiás, Instituto

de Ciências Biológicas (Bloco ICB V), Campus II/UFG, Goiânia, Goiás, CEP: 74001-970. E-

mail: [email protected].

2Laboratório de Ecologia e Conservação, Universidade Federal do Pará, Instituto de Ciências

Biológicas, Rua Augusto Correia, Nº 1 Bairro Guamá, CEP 66.075-110, Belém, Pará, Brasil.

3Departamento de Ecologia, Universidade Federal de Goiás, Instituto de Ciências Biológicas

(Bloco ICB V), Campus II/UFG, Goiânia, Goiás, CEP: 74001-970.

acorrespondence author: [email protected]

Abstract: Stream metacommunities were studied to test the Habitat templet hypothesis, which predicts that distribution of species results from ecological filters acting on biological traits, which eliminates species at unsuitable environments. According to this hypothesis taxa present in a local assemblage are those passed by major local habitat filters along the course of evolution, which give importance to habitat characteristics to the assembly processes. We studied traits and environment relationships of insect metacommunities of forested Amazonian streams to test the habitat templet hypothesis taking the

72 water flow as a main templet. Our hypothesis of water flow filters on taxa traits was corroborated, and organisms having rheophilic traits were predominant in rapidly flowing streams. Our analysis suggest that habitat templet of forested streams are determined by the water flow posing selective restrictions to insect taxa. The ubiquity of life history and morphological trait evolution to colonize freshwater environments, especially lotic ecosystems, call attention for more studies about transferability of trait information across ecoregions, and open new possibilities for the use of traits from other region to less known region, especially for the Neotropical aquatic insects.

Keywords: Southwoodean shortfall, species sorting, traits, aquatic insects, lotic ecosystems.

Introduction Metacommunity theory have broadened the understanding of the interplay between spatial patterning in species distribution and the role of ecological filters specially in freshwater ecology

(Patrick and Swan 2011, Brown et al. 2011, Heino 2013, Bini et al. 2014). These developments have provided evidences towards the importance of environmental control in aquatic insects metacommunities (Heino and Mykrä 2008, Grönroos et al. 2013, Soininen 2014, Heino et al. 2014).

However, environmental control as predicted by metacommunity dynamics (e.g., sorting species, mass effect) often lack a direct mechanistic linkage between species traits and the environmental gradients in streams (Heino et al. 2013, Schmera et al. 2014, Heino and Peckarsky 2014). The association of species traits to environmental gradients in freshwater ecosystems have been properly considered by the river habitat templet hypothesis (Statzner et al. 1994; Resh et al. 1994, Townsend and Hildrew 1994). The habitat templet hypothesis proposed by Southwood (1977) predicts essentially that least adjusted biological traits are eliminated by action of natural selection in unsuitable environment, and that taxa present in a local assemblage of species are those passed by local habitat filters along the course of

73 evolution. Habitat templet prediction are in close accordance with the fundamental niche model

(Hutchinson 1957, Dolédec et al. 1996), and provide a mechanism by how communities are assembled taking into account habitat filters and evolutionary adjustments of species to the present (and past) environment (Southwood 1977, Statzner et al. 2001, Ribera 2008, Dijkstra et al. 2014).

Several studies have tested ideas and developing predictions based on traits syndromes of life cycle, morphology and ecology of species in the context of the habitat templet applied to river communities (Resh et al. 1994, Statzner et al. 1994, Townsend and Hildrew 1994; Díaz et al. 2008).

Many predictive frameworks have already considered traits of aquatic insects (and other organisms) to understand community structure and functioning in river and stream ecosystems (Vannote et al. 1980,

Thorp et al. 2006, Thorp 2014). Freshwater science has experienced important advances in the application of trait and functional approach both to applied and theoretic questions linked to the habitat templet concept at least since early 1980s (Vannote et al., 1980; reviewed by Statzner et al., 2001; and by Menezes et al., 2010). Habitat templets studies have also inspired methodological developments specifically devoted to these issues (Dolédec et al. 1996, Legendre et al. 1997, Dray and Legendre

2008, ter Braak et al. 2012, Dray et al. 2014). Coupled with natural history and trait data availability

(Statzner et al. 2004, 2005, Poff et al. 2006), ecologists are now with the tools to make better predictions of the environmental impacts on freshwater ecosystems (Menezes et al. 2010), which should improve greatly prediction made previously (Townsend & Hildrew, 1994; Poff, 1997; Townsend et al., 1997; Thorp et al., 2006).

Regarding aquatic insects, most of trait data were provided by the huge amount of natural history information produced for temperate regions (Merritt and Cummins 1984, Rosenberg and Resh

1993, Lancaster and Briers 2008). In fact, life history traits are available mostly from North America and European macroinvertebrates, but some advances has been made to incorporate similar traits to

Neotropical fauna (Tomanova and Usseglio-Polatera 2007, Tomanova et al. 2008), enabling the firsts

74 studies on traits and functional diversity in the region (e.g., Colzani et al. 2013). The lack of trait information for Neotropical freshwater organisms pose several limitations to the advance of the field both regarding theory and application, limiting particularly biomonitoring and assessment of environmental impacts based on successful trait approaches (Statzner et al. 2005, Beche and Statzner

2009, Menezes et al. 2010, Conti et al. 2014, Orlofske and Baird 2014). Similar traits of aquatic insects have evolved to colonize freshwater environments at major biogeographical regions (Menezes et al.

2010, Dijkstra et al. 2014). Accordingly, the use of trait information on aquatic insects of best known regions would be transferable to poorly studied regions (e.g., Neotropics), and provide better macroscale generalization of patterns across regions (Heino et al. 2013).

Evidences for the importance of habitat gradients are abundant considering a stream reach until the longitudinal gradient within a catchment scale (Vannote et al. 1980, Ribera 2008). Substrate heterogeneity and stability, water flow velocity, alternation between riffles and pools (specially its proportionality), input of leafs from riparian vegetation, decreasing size of loaded particles, water physico-chemistry, are among the environmental predictors related to habitat and resource gradients

(Anderson and Wallace 1984, Ribera 2008). Moreover, a remarkable characteristic of any lotic ecosystem is their permanent directional water flow that impose further pressures for microhabitat selectivity by the organism traits (Ribera 2008, Statzner and Dolédec 2011, Dijkstra et al. 2014).

Hydraulics constrain insect distribution in lotic ecosystems by pressing both morphological and behavioral adaptation to contend with the continuous dislodge of organisms and substrate downstream

(Anderson and Wallace 1984). In streams, adaptation pressures to streamlining, dorsoventral flattening

(e.g., mayflies, stoneflies, Psephenidae beetles) and anchoring structures (e.g., suckers Blephariceridae, silk anchoring lines in caddisflies and Simuliidae) have evolved across clades to minimize carrying effect of the water flow (Anderson and Wallace 1984, Ribera 2008). The fact that many insects linages independently have evolved strategies to colonize rapids and simultaneously losing dispersal ability (as

75 adults and in larval stage) may indicate important trade-offs along with water flow axes (Ribera 2008;

Resh et al. 1984). For instance, several Byrrhoidea families that colonized streams lose their ability to fly long distances, especially, many Elmidae taxa are able to fly only once in their entire life (Brown

1987; Elliott 2008). In a first glance such observations support the relative importance of adult fly and site oviposition behavior especially for rheophilic insect communities (Heino & Peckarsky 2014).

Habitat templet hypothesis predicts trade-offs between habitat stability and evolutive pressures towards higher reproduction and dispersal rates in more ephemeral habitats to enhance population persistence (Southwood 1977, Resh et al. 1994, Townsend and Hildrew 1994, Statzner et al. 2001). Life history traits along successional gradients are expected to vary towards greater developmental investments, contrasting expenditure in reproduction and colonization in the first succession stages

(Margalef 1963; see Statzner et al., 2001). Despite its crucial role for understanding ecological and evolutionary paths of habitat selection in freshwater insects (Ribera, 2008), comparative studies are scarce probably due to the lack of accurate phylogeographic information for most aquatic groups

(Dijkstra et al., 2014). Regarding the metacommunity concept, habitat templets could be considered as the major influential component of species sorting dynamics because congregate both ecological filters and natural selection mechanisms of species traits adjustments to support harsh environments.

Therefore, identifying habitat templets is crucial to make better predictions of metacommunity patterns and underlying processes.

In this paper, we seek to understand the relationships among traits and environment to test the habitat templet hypothesis of insect metacommunities of forested Amazonian stream. We caugth trait information from the literature and apply modern multivariate approaches to test the hypotheses of habitat filtering on community organization acting as a selective pressure on insects traits, consequently on taxa distribution. In line with prediction made by others (e.g., Townsend et al. 1997), our hypothesis are that i) taxa having traits related to the water flow (e.g., small body, rheophily, holdfasts) will be

76 dominant in average rapid flowing streams; ii) those taxa lacking such adaptations (e.g., large bodied forms, some case making caddisflies) will dominate slow flowing streams; iii) we also expect good swimmers (e.g., Gyrinidae) to be less sensitive to water flow gradients, but avoiding turbulent flows.

Additional predictions could be made considering feeding habits of insects, for instance, shredders will prefer pool where leafs enriched by microbes are more abundant (e.g., Vannote et al., 1980), but for the scale considered (only headwaters) habitat patterning among trophic groups is less prominent than physical constraints imposed by hydraulics.

Material and Methods We study 36 forested streams in areas of reduced-impact logging (RIL) at Paragominas municipality, southeastern of the State of Pará, Brazil. Aquatic biodiversity, physical and chemical water parameters, and physical characteristics of 36 streams, twenty-seven in areas under RIL differing in time since logging from 2000 to 2012, and nine streams in reference areas. Despite some environmental characteristics vary among reference and RIL streams, the taxonomic composition, richness and abundance of most studied taxa remained unaltered by the environmental impacts

(Chapter 2), and we assume this effects not be so severe at least to the organisms considered. The samples of biological and environmental data followed standardized protocol described elsewhere (see

Chapter 1 and 2), but that have also been applied in other studies .

Environmental predictors were collected in each stream following the EMAP (Acronym for

Environmental Monitoring and Assessment Program) sampling protocol for habitat characterization detailed elsewhere (Chapter 2; Peck et al. 2006). The calculations of all metrics of the protocol are detailed in Kaufmann et al. (1999). Most of the 237 environmental predictors of the protocol have not varied consistently among reference and RIL areas and have only a small variance among streams

(Appendice 1). In order to select a subset environmental characteristics of the protocol we calculate the

77 variance and pairwise Pearson correlation among predictors within five categories: channel morphology, substrate, riparian vegetation cover and structure, cover by large woody debris (including fish cover), stream hydraulics (i.e., flow type, turbulence) and human disturbance. In addition, we have sampled seven water physic-chemical parameters measured by a multi parameter sensor Horiba® model U-50, air humidity and temperature. We also included two habitat quality descriptors, the

Habitat Integrity Index derived from Petersen (1992) (see Nessimian et al., 2008), and the Physical

Habitat Index derived from pre-selected predictors of the EMAP protocol (see Chapter 1 methods for a detailed description of the procedure). Enhanced Vegetation Index (EVI) were obtained from satellite images LandSat 7TM using circular buffers around each stream reach, using the Jiang et al.

(2008) formula [EVI = G*((N-R)/(N+C1*R-C2*B+L)], where N, R and B are corrected Landsat 7TM near infrared, red and blue bands, respectively, and G, C1 and C2 are constants. As the environmental predictors of the protocol resulted in highly collinear information within and among categories, we select a subset of predictors according to the following criteria: 1) predictors with higher variance were chosen; 2) predictors with lower mean correlation (< 0.5) to all others in that class were chosen; 3) predictors with lower general correlation in relation to all classes were chosen. This processes removed three-quarter of all metrics and resulted in 64 environmental predictors (Appendices 1), which were further selected by interactive processes (i.e., forward selection plus variance inflation factor analysis, see analysis).

Functional traits and community database

All taxa were identified to the lowest taxonomic level possible (i.e., families or genera) using specialized keys (see Chapter 1). We have identified 41 aquatic insects taxa belonging to the orders

Coleoptera (23 taxa), Plecoptera (three taxa) and Trichoptera (19 taxa). Two Coleoptera taxa

(Hydrophilidae) were not identified and were not considered in further analysis due to dubious biological information. For each taxa we search the literature for traits syndromes previously defined in

78 best known temperate regions (Merritt and Cummins 1996, Hugueny et al. 2004, Statzner et al. 2005,

Poff et al. 2006, Dolédec and Statzner 2008), but we also used trait information from Neotropical studies (Tomanova et al. 2006, 2008, Tomanova and Usseglio-Polatera 2007, Colzani et al. 2013).

These functional traits represent categorical information about life history, mobility, ecology, morphology and behavior of aquatic insects as defined by Poff et al. (2006). The trait syndrome and trait state considered in the analysis represent the information gathered from the bibliography and was confirmed by specialist on Neotropical fauna (see Acknowledges; Table 1). Especially, we classified all studied taxa according to twelve traits for which were given different weights according to whether the trait state confer better adjustments to that taxa. Trait scoring given to the organisms were strongly based on Poff et al. (2006), Tomanova and Usseglio-Polatera (2007), Tomanova et al. (2008) and

Colzani et al. (2013). Categorical traits, with the respective literature from which we got specific information for each taxa are available in Table S1, and summary of the information to weight traits are provided in Table 2.

We evaluate the taxa traits – environment relationships under the assumption of the habitat templet model of Southwood (1977). Habitat templet model predicts that traits composition are filtered by environmental conditions and determine ultimately the species occurence at local sites. Similar traits are expected to be filtered under similar conditions even across biogeographic barriers (Poff et al.

2006), potentially leading to macroecological patterns in traits-environment adjustments (Heino et al.,

2013). Furthermore, most of the traits we used are phylogenetically labile, i.e., not conserved within clades of aquatic insects, indicating convergence in species traits to track environmental constraints

(Poff et al., 2006). This does not means they are independent within the considered groups, neither they are not collinear, and we will consider this drawbacks in the analysis (e.g., trait data processing) and discussion section. These traits represent three orthogonal dimensions of labile traits – food resource

(trophic habit), development time as measured by thermal and body size and traits related to habitat

79 stability (e.g., rheophilic traits). In addition, Poff (1997) have made some predictions of the association of landscape filters acting on traits, in a search of mechanistic understanding of such relationships. We modified his classification to accommodate our environmental predictors and further traits from the

Amazonian stream insects (Table 2). As all the studied stream pertain to the same hydrographic basin, which are inserted in a climatically region, with relatively similar geomorphology and almost constant thermal regime, have not passed by strong anthropogenic modifications. Thus, we concentrate on the prediction related to stream channel morphology at reach scale, channel characteristics in a stream section, microhabitats and vegetation. Fortunately, as our approach to measure habitat characteristics have been extensive, comprising initially ~240 predictors after reduced to 64 predictors, consideration of predictions made by Poff (1997) were also possible (see Table 2).

Data analysis

The analysis of taxa traits – environment relationships was done using the RLQ analysis

(Dolédec et al. 1996) and fourth corner analysis (Legendre et al. 1997), both consisting of relating trait matrix (Q) to the environmental matrix (R) by means of matrix multiplication using the taxonomic abundance (L) as the central linkage matrix. While the RLQ analysis aims in relating traits to environmental gradients by means of co-inertia ordination weighted by the abundance matrix and test their overall significance, fourth corner analysis test for the significance the relation of each trait to the environmental gradients (ter Braak et al. 2012, Dray et al. 2014). RLQ enable finding the simultaneous ordination among the three eigenvectors matrices that maximize the scores covariance among Q and R matrices through co-inertia analysis using any desirable ordination method (Dolédec et al. 1996; Dray et al., 2014). In our study, taxa abundance were ordered by Correspondence Analysis (CA), ordinal traits and quantitative environmental descriptors were ordered by Principal Component Analysis

(PCA). Prior to environmental PCA the set of 64 predictors were standardized and screened using forward selection procedure and only significant predictors (at alpha = 0.05) were retained to further

80 RLQ analysis (Blanchet et al. 2008). In RLQ and Fourth Corner analyses we removed swimming capacity due to high collinearity with attachment, body shape, habit and respiration (> 0.70). Moreover, we admit that those predictors are efficient surrogates for swimming performance or lack thereof.

Fourth corner analysis was done using the function fourth corner of the ade4 package.

Significance testing for the individual and overall association of taxa traits – environment relationships were done by 9999 Monte Carlo permutations with model type 6, which permute rows and columns of

L matrix in two separate steps (ter Braak et al. 2012, Dray et al. 2014). The joint permutation has been demonstrated to be the only model able to control type I error in fourth corner analysis (see ter Braak et al. 2012). In addition, the combined outputs of the RLQ and fourth corner analyses were tested for significance according to Dray et al. (2014). The significance for the total inertia in RLQ analysis was initially tested using randtest function, with model type 6; after, we use the multivariate statistic

(termed SRLQ) proposed by Legendre & Dray (2008) to measure the overall association between R and

Q inflated matrices (both weighted by taxa abundance matrix L) by applying the function fourthcorner2. In order to test individually taxa traits in relation to the environment, we used the modification of the fourth corner analysis (Legendre et al., 1997; Dray & Legendre, 2008), proposed by

Dray et al. (2014), using the function fourthcorner.rlq. RLQ and fourth corner analysis were done using ade4 package (Dray and Dufour 2008) and we rely our analysis based on R scripts provided by Kleyer et al. (2012) and Dray et al. (2014).

In RLQ analysis one seek to understand the relationship among traits and environmental descriptors (Dolédec et al. 1996). However, by searching for congruent relationships among several trait syndromes, the procedure enable to find functional groups that best describe the taxonomic composition in a given metacommunity. We search for congruent functional groups using Euclidean distance of RLQ scores in Ward hierarchical clustering analysis (Borcard et al. 2011, Everitt et al.

2011). The number of functional groups in cluster analysis was determined via Calinski-Harabasz

81 criteria (Kleyer et al. 2012), which yielded five congruent functional groups. All analysis were done using R software (R Development Core Team 2013).

Table 1 – Trait syndrome, type, state and scoring considered for the analysis of taxa trait – environment relationships. Scoring was done in the exact order as shown in Poff et al. (2006).

Trait syndrome Trait type Trait states Trait Scoring Life history Life span very short, short, Long 1, 2, 3 Mobility Swimming ability None, weak, good 1, 2, 3 Attachment Attached, some, free 1, 2, 3 Armoring None, weak, good 1, 2, 3 Morphology Shape Cylindrical, hydrodynamic 1, 2 Respiration Tegument, gills, Plastron 1, 2, 3 Body size Small, medium, large 1, 2, 3 Rheophily Depositional, both, erosional 1, 2, 3

Crawler/Climber, sprawler, Ecology Habit (preferential) 1, 2, 3 swimmer

Trophic group Filterer, scraper, predator, shredder 1, 2, 3, 4, 5

Results Environmental characteristics

Out of the total 64 environmental predictors originally present in our data set, only seven were used in PCA after forward selection. The first two PCA axes explained 51.90 % of the total variation in the environmental descriptors. The first PCA axis explained 34.91 % of the data and was related to total ground cover, percentage of exposed soil, percentage of riffles, mean residual pools depth positively and to average leaves banks substrates negatively, indicating increasing power of water flow to deliver substrate downstream (Figure 1). The second PCA axis was related to oxide reduction potential and mean bakfull width and mean residual pool depth positively, and to mean residual pools depth negatively, which we interpret as a negative relation with the magnitude of seasonal floods.

82 Table 2 – Predictive relationships among stream habitat characteristics measured by the Physical Habitat Characterization protocol expected to act as filters on insects traits at local scale (150 m) of stream (Modified from Poff 1997). Constraint or filter Spatial scale Stream features Stream attributes Insects traits syndromes operationalization Rheophily, attachment, habitat preferences, body size and Slope Flow velocity Downstream dislodge shape Bankfull confinement Flood stream power Flood intensity Flood resistance % of fine sediment on Sediment preference Lithology Sediment size variation habitats Resistance to abrasion Stream reach Rheophily, attachment, habitat preference, body size and Stream size Width depth ratio Width / depth shape Organic matter input Trophic groups, habitat preference, Trophic group Land use Riparian conditions Organic matter cover Trophic groups, habitat preference Channel morphology Bankfull geometry Flood intensity Flood resistance Channel Hydraulics Water velocity (Turbulence) Rheophily, attachment, body size and shape Morphometry Pool depth Habitat volume Habitat preference Substrate size Substrate types Substrate requirement Body size, habitat preference, Trophic group Stream section Substrate sedimentation, Fine sediments Embeddedness Morphological, behavioral, habitat preference lack of Interstitial space Maximum sediment size, wood Flood refuge Substrate stability Attachment, body size, rheophily, habitat preference, habit Water depth Nearbed hydraulics Attachment, body size, rheophily, habit, swimming ability Hydraulic stress resistance Flow velocity (turbulence) Attachment, body size, rheophily, habitat preference, habit microhabitat Particle size (excluding wood) Substrate types Substrate requirement Habitat preference Morphological, behavioral, habitat preference, swimming Large stones Flood refugia Substrate stability hability, trophic group Food resources Organic matter input Trophic groups, thermal preferences Riparian Canopy cover Shading Autotrophic production Trophic groups vegetation Wood instream cover Habitat stability Habitat preference

83 Community structure and trait pattern

Canonical Analysis (CA) explained in the two first axes 19.07% and 12.40% of the total variation in the community structure, which results from low beta diversity and many abundant taxa shared among most of streams (Figure 2). As our main interest was in the relation among traits and environment both weighted by the taxa scores provided by CA (which maximize the covariance in taxonomic matrix L), the poor power to discriminate composition and structure does not invalidate further analysis, despite decreasing RLQ overall fit.

Figure 1 – Principal Component Analysis (PCA) biplot ordination of streams by environmental descriptors. The first two PCA axes (λ1 = 2.44 and λ2 =1.19) explained 34.91 % and 16.99 % of the total environmental variations, respectively. Acronyms: PCT_RI: Percentage of Riffles; XFC_LEB: Average Leaves Banks Substrates; XG: Total Ground Cover; XGB: Pecentage of exposed soil; RP100: Mean residual pool depth; ORP: Oxide Reduction Potential; XBKF_W: Mean bankfull width.

84 Figure 2 – Canonical Analysis (CA) biplot ordination of streams (dots) based on taxa abundances. The percentage of explained variance in the first CA axes (λ1 = 0.31 and λ2 =0.20) were 19.09 % and 12.40

%, respectively.

The first two axes of the PCA analysis explained 64.80% of total trait variation (Figure 3). The first PCA axis explained 40.14% and was positively related to attachment and retreat building capacity and negatively related to body size, body shape and swimming capacity. The second PCA was positively related to rheophily and negatively related to trophic group, armoring, life span and respiration.

85 Figure 3 – Principal Component Analysis (PCA) biplot ordination of taxa ordered by traits. The first two PCA axes (λ1 = 4.42 and λ2 =2.71) explained 40.12 % and 24.66 %, respectively.

RLQ and Fourth Corner analysis

Our results corroborate habitat templet hypothesis, i.e. of selective pressures acting on traits to determine taxa distribution at local scales of streams. Permutation test were significant both models (P

< 0.001 and P = 0.031), indicating greater association between traits and environmental gradients than expected. RLQ analysis explained a greater fraction of the projected trait - environment as represented by the inertia in matrix R with (39.06 % in the first, 10.05% in the second RLQ axis). The first axis of the RLQ summarized 24.29 % of the total inertia in trait matrix Q, and only 4.55 % in the second

86 (28.84 % in total). Correlation of taxa scores and sites in CA analysis was equal to that of RLQ (Table

3), indicating no loss of resolution in the analysis of the trait and environmental constraints in RLQ co- inertia solution.

Table 3 – RLQ summary statistics for the projection of each individual analysis on the first RLQ axes and for the joint co-inertia analysis of the triplet table.

RLQ Summary statistics Axis 1 Axis 2 Taxa abundance scores (CA) Correlation 0.316 0.142 Maximum inertia 0.554 0.447 Ratio 0.571 0.319 Traits weighted scores (PCA) Total inertia 2.87 5.60 Maximum inertia 3.57 5.94 Ratio 0.80 0.94 Environment weighted scores (PCA) Total inertia 1.87 3.05 Maximum inertia 2.44 3.63 Ratio 0.80 0.94 RQL analysis Eigenvalues 0.54 0.07 Covariance 0.73 0.26 sdR 1.37 1.09 sdQ 1.69 1.65 Correlation 0.32 0.14 % explained by traits (Q / RLQ)* 24.29 % 4.55 % % explained by the environment (R / RLQ)* 39.06 % 10.05% * Calculated using Q or R Inertia in the numerator and RLQ covariance in the denominator for the first and second axes, respectively (see Dolédec et al. 1996 for details).

87 Figure 4 – RLQ biplot ordination of the covariance of environmental descriptors and taxa traits weighted by their abundance. The first two axes (λ1 = 0.56 and λ2 = 0.09) represented a correlation of

0.31 and 0.15 among traits and environment, respectively. Acronyms: PCT_RI: Percentage of Riffles;

XFC_LEB: Average Leaves Banks Substrates; XG: Total Ground Cover; XGB: Pecentage of exposed soil; RP100: Mean residual pool depth; ORP: Oxid Reduction Potential; XBKF_W: Mean bankfull width. Codes for traits are in table 1.

88 Overall, our hypothesis was supported by both RLQ and fourth corner analysis in combination

(SRLQ = 0.627 p-value = 0.031), indicating significant association among traits and environmental gradients. Specially, our hypothesis of positive association of rheophilic insects with water flow and negative association of larger retreat building caddisflies was supported (Figure 4 and 5; Table S2).

Body size and rheophilic traits were positively related to the first RLQ axis, but only rheophily was identified as significant in the cross correlation among traits and environment (Figure 5 and 6). Neither trait collinear to swimming capacity were constrained by the water flow, which corroborate the expected lack of flow constraints on swimmers (i.e., diving and whirligig beetles). An unexpected negative relation of low water flow and armoring was found, and we attribute to the dense populations of shredders Triplectides and Phylloicus in streams pools that have the capacity to make mobile retreats. Moreover, we find an important positive relation between exposed soil and rheophily and negative relation to retreat building, which we interpret acknowledging the correlation channel shape and slop effects on erosional processes in the riparian zone of streams. In both cases, our results were supported by both RLQ and fourth corner analysis (Figure 5 and 6). Finally, no traits – environment association was relevant in the second RLQ axis that had smaller gradient length (Figure 6, Table S2).

89 Figure 5 – Association of taxa traits and environment according to RLQ axis. Red squares represent positive, blue squares negative, and gray squares are non-significant associations. Significance was assessed with 9999 Monte Carlo permutation (model type 6; see methods). Acronyms: PCT_RI:

Percentage of Riffles; XFC_LEB: Average Leaves Banks Substrates; XG: Total Ground Cover; XGB:

Pecentage of exposed soil; RP100: Mean residual pool depth; ORP: Oxid Reduction Potential;

XBKF_W: Mean bankfull width.

90 Figure 6 – Association of traits and environment according to Fourth Corner crossed analysis. Red squares represent positive, blue squares negative, and gray squares are non-significant associations.

Significance was assessed with 9999 Monte Carlo permutation (model type 6; see methods).

Acronyms: PCT_RI: Percentage of Riffles; XFC_LEB: Average Leaves Banks Substrates; XG: Total

Ground Cover; XGB: Pecentage of exposed soil; RP100: Mean residual pool depth; ORP: Oxid

Reduction Potential; XBKF_W: Mean bankfull width.

91 Functional groups

Constrained hierarchical cluster of RLQ axes resulted in five congruent functional groups

(Figure 7 and 8), which segregate case building caddisflies (functional group A) negatively in the left below quadrant, rheophilic stoneflies (functional group B) positively with the first RLQ xis, filter- feeding caddisflies (functional group C) in the right below quadrant (positively in the first and negative in the second RLQ axis), predators with increasingly greater body size positively in the second axis

(functional group D), and smaller herbivorous beetles less centered in the biplot. The variability in the group formed along with RLQ axis could be interpreted as a measure of generality of traits related to habitat preference, behavior, morphologic and behavior. Caddisflies (functional groups A and C) were in general more labile regarding those traits than the other groups which is associated with habitats, armoring, respiration and trophic strategy (see the directions of the vectors). Predators water beetles

(functional group D) were also variable but along with the body size and shape only, likely indicating swimming ability (not included in the analysis but collinear with these traits). Herbivorous beetles

(functional group E) were less variable, and stoneflies (functional group B) were still more constricted, indicating highly specialized traits.

92 Figure 7 – Functional groups established by Ward's hierarchical cluster of the tow first RLQ analysis

(see methods), represented on taxonomic composition ordination. The position of taxon relative to the group centroid represent the degree of marginality in trait space. The size of the circle represent the degree of variation in traits of each group.

93 Figure 8 – Functional groups established by Ward's hierarchical cluster of the tow first RLQ analysis

(see methods), represented on traits ordination. The size and direction of the circles represent the degree of variation in taxa traits and correlation with traits, respectively.

Discussion

Our analysis supported the hypothesis of distribution of traits adjusted to the water flow with insects having rheophilic traits associated to areas of higher water flow velocity. Taxa lacking rheophilic traits were distributed against the water flow axis as expected and were mainly shredders insects. Swimmer insects were not constrained by the water flow speed as also expected. Our analysis

94 corroborate habitat templet hypothesis for the studied forested streams, and we were able to show that water flow is an important predictor of trait distribution at the stream scale by posing selective restrictions to insect taxa. In addition, our approach enabled to disentangle true effects from spurious association among traits and environment. Body size and rheophily were traits positively related to the first RLQ axis, but only rheophily was identified as significant by fourth corner analysis.

Most caddisflies are generalists with broad range of adaptation to streams conditions mainly associated to the retreat building behavior conferred by the production of silk by larvae (Wiggins and

Mackay 1978, Mackay and Wiggins 1979, Wiggins 1996), and our results also support this view. Water beetles are recognized to have broad morphological and trophic preferences (Dijkstra et al. 2014).

Elmids are mostly herbivorous feeding on algae attached to the substrates in rapid flows (Elliott 2008,

Jach and Balke 2008), Hydrophilids are highly labile inhabiting a variety of aquatic and terrestrial habitats, and having diversified feeding habits (Bloom et al. 2014). Such food and habitat preference linkages to stream gradients (and other freshwaters) have been overlooked in the context of evolution and diversification of water beetles and other insects and deserve attention in future studies (Dijkstra et al. 2014). Importantly, such aspects cover the evolutionary bound of the habitat templet concept still poorly tested under light of a rigorous evolutionary background and phylogenetic tools for freshwater organisms (Dijkstra et al. 2014).

We show that Amazonian streams data are concordant to the resistance - resilience conceptual gradients in traits distribution along with disturbance gradients as proxy for temporal and spatial filters

(e.g., turbulence of water flow, see also Table 1) proposed by Townsend et al. (1997). Nevertheless, the lack of some trait information (e.g., mobility of adult, voltinism, thermal preference, number of stages outside stream) and our different approach to weight traits (based on Poff et al. 2006) prevent further comparisons. We acknowledge these results are not new, but the fact we have evidenced association of traits to water flow of Amazonian stream have implication to metacommunity structure and for broad

95 scale predictions for trait - environment associations (see below). Moreover, our approach using trait syndromes which were weighted according to previous expectation of adaptability conferred to taxa related to three main conceptual gradients: 1) food resources (i.e., trophic traits), 2) development time

(i.e., body size) and 3) habitat stability – which are closest to the fundamental niche ideas (Southwood

1977, Poff et al. 2006). Consequently these trait syndromes represent better description of the functional attributes of studied insects, while our habitat characterization protocol enable better description of stream habitats (see methods), both favoring habitat templet hypothesis testing.

However, instead of the relation between traits and bed stability verified by Townsend et al. (1997), our main predictor of traits – disturbance gradients was the percentage of rapid water flows.

All studied stream are part of the Capim River hydrography which represent old sedimentary areas with plate topography (< 1000) and highly sinuous curse, opened and shallow valleys, but with recent restarting of erosional processes (Santos et al. 2012). Also, due to the old geology, most of the tributaries load fewer sediments quantities which makes the water clearer, and the lower variation in altitude and high sinuosity make the water flow velocity slower at the reach scale. Such features prevent strong flooding to sweep and loading of sediments, especially where riparian vegetation are maintained (Thorp et al. 2006, Thorp 2014). In consequence of the lower loading capacity sediments are deposited even in small streams. This implies that for Amazonian stream under similar geomorphological, hydrological and topographical conditions (Tocantins-Araguaia complex, Xingu), as most hydrographic basins of the south Amazonian Brazilian shield (Hoorn and Wesselingh

2011) riparian contribution of woody materials should have disproportional contribution to habitat heterogeneity and flow variability, as predicted by Poff (1997). Despite heavily conjectural, such explanation seemed straightforward because rapids where not a common feature in most of the studied streams, and most of the structural complexity was found in the presence of logs and wood material

(personal observation).

96 The most general characteristics of stream, i.e., their continuous directionality of flow, seemed the best predictors of traits – environmental relationships, and represent important correlations with relation to several other habitats templet, such as substrate distribution and size variation (i.e., linked to habitat availability and heterogeneity), loading of suspended organic matter (i.e., affecting food resource to filterers), microhabitat features (e.g., water depth, velocity, particle size) affecting morphological and behavioral features of insects (Poff et al., 2006; specific predictions of Poff 1997).

Despite importantly considered in coldwater regions (Dijkstra et al. 2014), thermal gradients may be less influential on species distribution since it is almost constant in closed forested tropical streams, but needs consideration in open areas (i.e., great rivers, Savannah streams) and deforested streams (Ward &

Standford 1982; Haidekker & Hering 2008). In addition, there are some important restrictions to insect respiration and mobility imposed by the water flow which may constraint the aquatic insect distribution. For instance, rapidly flowing water is suitable for taxon having plastrons, spiracular gills, cutaneous respiration, or tracheal gills, but it limits the occurrence of organism that breath atmospheric air directly because rapid flow constrain vertical movement in the water column by dislodging organism downstream implying further energy losses to keep in rapids (Resh et al. 1984).

In the context of impact evaluation Tomanova et al. (2008) have been able to assign macroinvertebrate traits to integrity of neotropical stream, especially the association of shredders, scrapers and collector-filterers to pristine streams, opposing to collector-gatherers, predators to impacted streams. In addition, they found organism breathing through gills or plastron associated to pristine streams and those breathing by tegument associated to impacted ones. As we does not have such as severe gradient of anthropogenic impacts, we aimed to describe traits – environmental gradients in close to pristine environmental gradients. In our case, flow velocity and habitat features related to flow turbulence should be considered as the main features pressing adjustments in the taxa distribution in the Amazonian forested streams. We found that traits linked to rheophily such as

97 streamlined and flattened body forms, and small size were positively related to rapid water flow velocity (on the first RLQ axis). On the other hand, retreat building and armoring were negatively related to flow, which are due to the dominance of Phylloicus (Calamoceratidae) and Triplectides

(Leptoceridae) in streams with larger pools. These caddisflies have evolved behavioral habits to build strong retreats from leafs and small twigs, and have similar sister genera distributed worldwide, which could indicate selective pressures towards the use of coarse organic material inputed from riparian vegetation anywhere (Boyero et al. 2006, 2011).

Several challenges remains to advance comprehension of the importance of the niche and neutral theory in metacommunity ecology, especially the lack natural history information. Darwinian shortfalls – the lack of relevant phylogenetic information for most of organism (Diniz-Filho et al 2013), added to Wallacean and Linean shortfalls (Bini et al. 2006), limits predictions about the importance of habitat selectivity on evolutive aspects of poorly studied aquatic insect taxa (Dijkstra et al. 2014), especially in the Neotropics (see above and Diniz-Filho et al., 2010). In line with the perspective of diversification studies (De Moor and Ivanov 2008, Likens and Huryn 2009, Dijkstra et al. 2014), an important step starts by understanding the relation among traits and environments, first in remaining natural ecosystems and after incorporating anthropogenic impacts (Tomanova et al. 2008, Verberk et al.

2008, Menezes et al. 2010), which we face on the light of Southwood's habitat templet hypothesis as did others (e.g., Townsend et al., 1997; Díaz et al., 2008; Tomanova et al., 2008; Picazo et al., 2012).

Perhaps we fail to describe all possible templets for the considered insects, but our results indicate and give support to other studies (Poff, 1997; Townsed et al., 1997) that also put water flow as an important habitat feature in lotic-forested ecosystems. Several authors have discussed the main drawbacks to advance invertebrate studies directed to conservation priorization (Diniz-Filho et al., 2010; Cardoso et al., 2011), but a “Southwoodean shortfall” – i.e., lack of knowledge of species habitats maybe the better description to this problem at least for Neotropical aquatic insects. There is no simple way to

98 advance the field since this depends on joint workforce of several researches areas around natural history (i.e., taxonomy, ecology) which is by itself time consuming, given that Wallacean and Linnean shortfalls (biogeographic and taxonomic deficits, respectively) simpler to solve is prevailing in the region (Bini et al. 2006, Diniz-Filho et al. 2010).

Conclusion

We were able to confirm the validity of the habitat templet concept about the traits – environmental gradient (i.e., water flow) from forested Amazonian stream. The ubiquity of life history and morphological trait evolution to colonize freshwater environments, especially lotic ecosystems, call attention for more studies about transferability of trait information across ecoregions, and open new possibilities for the use of traits from other region to less known region, especially for the Neotropical aquatic insects. Such endeavor have already started from different points of view (Tomanova and

Usseglio-Polatera 2007, Tomanova et al. 2008, Colzani et al. 2013), but could easily converge to better predictive approach to Neotropical freshwater ecology. We emphasize four main challenges to improve the field specially regarding the habitat templet concept (in any order): i) using available data from best known regions (e.g., temperate) to drawn scenarios and foment new trait sampling in poorly studied ones (trait information transferability across regions); ii) building better models or at least more informed use of available models in freshwater science to establish the proper linkage among geomorphological and hydromorphological features of streams at multiple scale to species traits (e.g.,

Poff 1997; Thorp 2014); iii) rely heavily on theory (ecological and evolutionary) and replenish theory and methods available with more research oriented on getting better trait data; and iv) validating trait information experimentally for as broader as possible spread of taxonomic groups occurring in

Neotropical freshwaters.

99 Acknowledges

We are grateful to field sampling and logistic support by 33 Forest and CIKEL LTDA. We thank to

Msc. Sara Almeida by the help with RLQ analysis. We thank to the Brazilian National Research

Council (CNPq) by funding the project “Tempo de resiliência das comunidades aquáticas após o corte seletivo de madeira na Amazônia Oriental” (n 481015/2011-6), by the productivity fellowship of LJ

(process 303252/2013-8), and for continuous grant to PDM research, and CAPES for the fellowship by

DSN. We thank to Dr. Jorge Luiz Nessimian and Dr. Nelson Ferreira-Junior by checking trait informations.

100 References

Anderson, N. H. and Wallace, J. B. 1984. Habitat, life history, and behavioral adaptations of aquatic insects. - In: Merrit, R. W. and Cummins, K. W. C. P. (eds), An Introduction to the Aquatic Insects of North America. Kendall / Hunt Publishing Company VL -, pp. 38–58. Beche, L. A. and Statzner, B. 2009. Richness gradients of stream invertebrates across the USA: taxonomy- and trait-based approaches. - Biodivers. Conserv. 18: 3909–3930. Bini, L. M. et al. 2006. Challenging Wallacean and Linnean shortfalls : knowledge gradients and conservation planning in a biodiversity hotspot. Diversity and Distributions 12: 475–482. Bini, L. M. et al. 2014. Nutrient enrichment is related to two facets of beta diversity for stream invertebrates across the United States. - Ecology 95: 1569–78. Blanchet, F. G. et al. 2008. Forward selection of explanatory variables. - Ecology 89: 2623–32. Bloom, D. D. et al. 2014. Clade Age and Diversification Rate Variation Explain Disparity in Species Richness among Water Scavenger Beetle (Hydrophilidae) Lineages. - PLoS One in press. Borcard, D. et al. 2011. Numerical Ecology with R. - Springer. Boyero, L. et al. 2006. How biological diversity influences ecosystem function: a test with a tropical stream detritivore guild. - Ecol. Res. 22: 551–558. Boyero, L. et al. 2011. Global distribution of a key trophic guild contrasts with common latitudinal diversity patterns. - Ecology 92: 1839–1848. Brown, H., 1987. Biology Of Riffle Beetles. Annual Review of Entomology 32: 253–273. Brown, B. L. et al. 2011. Metacommunity theory as a multispecies, multiscale framework for studying the influence of river network structure on riverine communities and ecosystems. - J. North Am. Benthol. Soc. 30: 310–327. Cardoso, P., T. L. Erwin, P. a. V. Borges, & T. R. New, 2011. The seven impediments in invertebrate conservation and how to overcome them. Biological Conservation 144: 2647–2655. Colzani, E. et al. 2013. Responses of Aquatic Insect Functional Diversity to Landscape Changes in Atlantic Forest. - Biotropica 0: 1–8. Conti, L. et al. 2014. A trait-based approach to assess the vulnerability of European aquatic insects to climate change. - Hydrobiologia 721: 297–315. De Moor, F. C. and Ivanov, V. D. 2008. Global diversity of caddisflies (Trichoptera: Insecta) in freshwater. - Hydrobiologia 595: 393–407. Díaz, A. M. et al. 2008. Biological traits of stream macroinvertebrates from a semi-arid catchment: Patterns along complex environmental gradients. - Freshw. Biol. 53: 1–21. Dijkstra, K.-D. B. et al. 2014. Freshwater biodiversity and aquatic insect diversification. - Annu. Rev. Entomol. 59: 143–63. Diniz-Filho, J. A. F. et al. 2010. Defying the curse of ignorance: perspectives in insect macroecology and conservation biogeography. - Insect Conserv. Divers. 3: 172–179.

101 Diniz-Filho, J. A. F. et al. 2013. Darwinian shortfalls in biodiversity conservation. - Trends Ecol. Evol. 28: 689–695. Doledec, S. and Statzner, B. 2008. Invertebrate traits for the biomonitoring of large European rivers: an assessment of specific types of human impact. - Freshw. Biol. 53: 617–634. Doledec, S. et al. 1999. Species traits for future biomonitoring across ecoregions: patterns along a human-impacted river. - Freshw. Biol. 42: 737–758. Dolédec, S. et al. 1996. Matching species traits to environmental variables: a new three-table ordination method. - Environ. Ecol. Stat. 3: 143–166. Dray, S. and Legendre, P. 2008. Testing the species traits-environmental relationships: the fourth-corner problem revisited. - Ecology 10: 1633–1647. Dray, S. and Dufour, A.-B. 2008. The ade4 Package: Implementing the duality diagram for ecologists. - J. Stat. Softw. 27: 1–5. Dray, S. et al. 2014. Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation. - Ecology 95: 14–21. Elliott, J. M. 2008. The ecology of riffle beetles (Coleoptera: Elmidae). - Freshw. Rev. 1: 189–203. Everitt, B. S. et al. 2011. Cluster Analysis. Grönroos, M. et al. 2013. Metacommunity structuring in stream networks: roles of dispersal mode, distance type, and regional environmental context. - Ecol. Evol. 3: 4473–87. Haidekker, A., & D. Hering, 2008. Relationship between benthic insects (Ephemeroptera, Plecoptera, Coleoptera, Trichoptera) and temperature in small and medium-sized streams in Germany: A multivariate study. Aquatic Ecology 42: 463–481. Heino, J. 2013. The importance of metacommunity ecology for environmental assessment research in the freshwater realm. - Biol. Rev. Camb. Philos. Soc. 88: 166–78. Heino, J. and Mykrä, H. 2008. Control of stream insect assemblages: roles of spatial configuration and local environmental factors. - Ecol. Entomol. 33: 614–622. Heino, J. and Peckarsky, B. L. 2014. Integrating behavioral, population and large-scale approaches for understanding stream insect communities. - Curr. Opin. Insect Sci. 2: 1–7. Heino, J. et al. 2013. A macroecological perspective of trait patterns in stream communities. - Freshw. Biol. 58: 1539–1555. Heino, J. et al. 2014. Metacommunity organisation, spatial extent and dispersal in aquatic systems: patterns, processes and prospects. - Freshw. Biol.: n/a–n/a. Hoorn, C. and Wesselingh, F. P. 2011. Amazonia: Landscape and Species Evolution: A look into the past. Hughes, R. M. and Peck, D. V 2008. Acquiring data for large aquatic resource surveys: the art of compromise among science, logistics, and reality. - J. North Am. Benthol. Soc. 27: 837–859. Hugueny, B. et al. 2004. Biological trait composition of European stream invertebrate communities: assessing the effects of various trait filter types. in press.

102 Hutchinson, G. 1957. Concluding remarks. - Cold Spring Harb. Symp: 415–427. Jach, M. A. and Balke, M. 2008. Global diversity of water beetles (Coleoptera) in freshwater. - Hydrobiologia 595: 419–442. Jiang, Z. et al. 2008. Development of a two-band enhanced vegetation index without a blue band. - Remote Sens. Environ. 112: 3833–3845. Kaufmann, P. P. R. et al. 1999. Quantifying Physical Habitat in Wadeable Streams.: EPA/620/R– 99/003. Kleyer, M. et al. 2012. Assessing species and community functional responses to environmental gradients: Which multivariate methods? - J. Veg. Sci. 23: 805–821. Lancaster, J. and Briers, R. 2008. Aquatic Insects: Challenges to Populations. Legendre, P. et al. 1997. Relating behavior to habitat: Solutions to the fourth-corner problem. - Ecology 78: 547–562. Legendre, P. et al. 2005. Analyzing beta diversity: partitioning the spatial variation of community composition data. - Ecol. Monogr. in press. Likens, G. E. and Huryn, A. D. 2009. Aquatic Insects – Ecology, Feeding, and Life History. - In: Encyclopedia of Inland Waters. pp. 132–143. Mackay, R. and Wiggins, G. 1979. Ecological diversity in Trichoptera. - Annu. Rev. Entomol. 24: 185– 208. Margalef, R. 1963. On certain unifying principles in ecology. - Am. Nat. 97: 357–374. McGill, B. J. et al. 2006. Rebuilding community ecology from functional traits. - Trends Ecol. Evol. 21: 178–85. Menezes, S. et al. 2010. Beyond taxonomy: a review of macroinvertebrate trait-based community descriptors as tools for freshwater biomonitoring. - J. Appl. Ecol. 47: 711–719. Merrit, R. W. and Cummins, K. 1996. An Introduction to the Aquatic Insects of North America. - Kendall/Hunt Publishing Company. Merritt, R. W. and Cummins, K. W. 1984. An Introduction to the Aquatic Insects of North America. - Kendall / Hunt Publishing Company. Nessimian, J. L. et al. 2008. Land use, habitat integrity, and aquatic insect assemblages in Central Amazonian streams. - Hydrobiologia 614: 117–131. Orlofske, J. M. and Baird, D. J. 2014. Incorporating continuous trait variation into biomonitoring assessments by measuring and assigning trait values to individuals or taxa. - Freshw. Biol. 59: 477–490. Patrick, C. J. and Swan, C. M. 2011. Reconstructing the assembly of a stream-insect metacommunity. - J. North Am. Benthol. Soc. 30: 259–272. Pavoine, S. and Bonsall, M. B. 2011. Measuring biodiversity to explain community assembly: a unified approach. - Biol. Rev. Camb. Philos. Soc. 86: 792–812. Peck, D. et al. 2006. Environmental Monitoring and Assessment Program-Surface Waters Western Pilot

103 Study: Field Operations for Wadeable Streams.: EPA/620/R–06/003. Petchey, O. L. et al. 2009. A functional guide to functional diversity measures. - In: Biodiversity, Ecosystem functioning, and Humam Wellbeing. pp. 49–59. Petersen, R. C. 1992. The RCE: a Riparian, Channel, and Environmental Inventory for small streams in the agricultural landscape. - Freshw. Biol. 27: 295–306. Picazo, F. et al. 2012. Are patterns in the taxonomic, biological and ecological traits of water beetles congruent in Mediterranean ecosystems? - Freshw. Biol. 57: 2192–2210. Pillar, V. D. and Duarte, L. D. S. 2010. A framework for metacommunity analysis of phylogenetic structure. - Ecol. Lett. 13: 587–96. Poff, N. L. 1997. Landscape filters and species traits: towards mechanistic understanding and prediction in stream ecology. - J. north Am. Benthol. Soc. in press. Poff, N. L. et al. 2006. Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships. - J. North Am. Benthol. Soc. 25: 730–755. R Development Core Team 2013. R: A language and environment for statistical computing. in press. Resh, V. H. et al. 1984. Aquatic insect respiration. - In: Merrit, R. W. and Cummins, K. W. C. P. (eds), An Introduction to the Aquatic Insects of North America. Kendall / Hunt Publishing Company VL -, pp. 27–37. Resh, V. H. et al. 1994. Theoretical Habitat Templets, Species Traits, and Species Richness - A Synthesis of Long-Term Ecological Research on the Upper Rhone River in the Context of Concurrently Developed Ecological Theory. - Freshw. Biol. 31: 539–554. Revell, L. J. et al. 2008. Phylogenetic signal, evolutionary process, and rate. - Syst. Biol. 57: 591–601. Ribera, I. 2008. Habitat constraints and the generation of diversity in freshwater macroinvertebrates. - Aquat. insects challenges to Popul.: 289–311. Rosenberg, D. and Resh, V. H. 1993. Freshwater Biomonitoring and Benthic Macroinvertebrates. - Chapman & Hall. Santos, E. Dos et al. 2012. Characterization of kaolin from the Capim River region - Brazil. - Appl. Clay Sci. 55: 164–167. Schmera, D. et al. 2014. Combining taxon-by-trait and taxon-by-site matrices for analysing trait patterns of macroinvertebrate communities: a rejoinder to Monaghan & Soares (). - Freshw. Biol. 59: 1551–1557. Soininen, J. 2014. A quantitative analysis of species sorting across organisms and ecosystems. - Ecology 95: 3284–3292. Southwood, T. R. E. 1977. Habitat, the templet for ecological strategies? - J. Anim. Ecol. 46: 337–365. Statzner, B. and Dolédec, S. 2011. Phylogenetic, Spatial, and Species-Trait Patterns across Environmental Gradients: the Case of Hydropsyche (Trichoptera) along the Loire River. - Int. Rev. Hydrobiol. 96: 121–140. Statzner, B. et al. 1994. The Synthesis of Long-Term Ecological Research in the Context of Concurrently Developed Ecological Theory - Design of A Research Strategy for the Upper Rhone

104 River and Its Floodplain. - Freshw. Biol. 31: 253–263. Statzner, B. et al. 2001. Species traits and environmental constraints: entomological research and the history of ecological theory. - Annu. Rev. Entomol. 46: 291–316. Statzner, B. et al. 2004. Biological trait composition of European stream invertebrate communities: assessing the effects of various trait filter types. - Ecography (Cop.). 27: 470–488. Statzner, B. et al. 2005. Invertebrate traits for the biomonitoring of large European rivers: an initial assessment of trait patterns in least impacted river reaches. - Freshw. Biol. 50: 2136–2161. Stegen, J. C. and Swenson, N. G. 2009. Functional trait assembly through ecological and evolutionary time. - Theor. Ecol. 2: 239–250. Stoddard, J. L. et al. 2008. A process for creating multimetric indices for large-scale aquatic surveys. 27: 878–891. Ter Braak, C. J. F. et al. 2012. Improved testing of species traits-environment relationships in the fourth-corner problem. - Ecology 93: 1525–1526. Thorp, J. H. 2014. Metamorphosis in river ecology: from reaches to macrosystems. - Freshw. Biol. 59: 200–210. Thorp, J. H. et al. 2006. The riverine ecosystem synthesis: biocomplexity in river networks across space and time. - River Res. Appl. 22: 123–147. Tomanova, S. and Usseglio-Polatera, P. 2007. Patterns of benthic community traits in neotropical streams: relationship to mesoscale spatial variability. - Fundam. Appl. Limnol. / Arch. für Hydrobiol. 170: 243–255. Tomanova, S. et al. 2006. Trophic levels and functional feeding groups of macroinvertebrates in neotropical streams. - Hydrobiologia 556: 251–264. Tomanova, S. et al. 2008. Using macroinvertebrate biological traits for assessing biotic integrity of neotropical streams. - River Res. Appl. 24: 1230–1239. Townsend, C. R. and Hildrew, A. G. 1994. Species traits in relation to a habitat templet for river systems. - Freshw. Biol. 31: 265–275. Townsend, C. R. et al. 1997. Species traits in relation to temporal and spatial heterogeneity in streams: a test of habitat templet theory. - Freshw. Biol. 37: 367–387. Tuomisto, H. et al. 2003. Dispersal, environment, and floristic variation of western Amazonian forests. - Science 299: 241–4. Vannote, R. L. et al. 1980. The River Continuum Concept. - Can. J. Fish. Aquat. Sci. 37: 130–137. Verberk, W. C. et al. 2008. Life-history strategies in freshwater macroinvertebrates. - Freshw. Biol. 53: 1722–1738. Villéger, S. et al. 2008a. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. - Ecology 89: 2290–2301. Villéger, S. et al. 2008b. Stable trophic structure across coastal nekton assemblages despite high species turnover. - Mar. Ecol. Prog. Ser. 364: 135–146.

105 Ward, J. V, & J. A. Stanford, 1982. Thermal responses in the evolutionary ecology of aquatic insects. Annual Review of Entomology 27: 97–117. Weiher, E. 2011. A primer of trait and functional diversity. - In: Magurran, A. E. and Brian, J. M. (eds), Biological Diversity: Frontiers in Measurement and Assessment. Oxford University Press, pp. 175–193. Whittier, T. R. et al. 2007. A Structured Approach for Developing Indices of Biotic Integrity: Three Examples from Streams and Rivers in the Western USA. - Trans. Am. Fish. Soc. 136: 718–735. Wiggins, G. B. 1996. Larvae of the North American Caddisfly Genera (Trichoptera). Wiggins, G. B. and Mackay, R. J. 1978. Some relationships between systematics and trophic ecology in Nearctic aquatic insects, with special reference to Trichoptera. - Ecology 59: 1211–1220.

106 Suplementary information

Table S1 – Classification, lower taxonomic unit (LTU) and species traits categories used in the analysis following with the respective consulted bibliography. All traits were recoded to follow ordinal scores proposed by Poff et al. (2006, see methods).

Trophi Order Family LTU Swim Attach Armor Size Habits Shape Resp Rheo Life Retreat Bibliography c Hamada et al (2014); Domınguez & Coleoptera Dryopidae Dryopidae None Attached Good Medium Shred Crawler Cylindrical Gills Depositional Long No Fernández (2009) Epler (2010); Merritt & Cummins Coleoptera Dytiscidae Hydaticus Good Free Good Large Pred Swimmer Hydrodynamic Plastron Both Long No (2007); Domınguez & Fernández (2009) Spangler (1985); Hamada et al. Coleoptera Dytiscidae Hydrodessus Good Free Good Small Pred Swimmer Hydrodynamic Plastron Both Long No (2014); Domınguez & Fernández (2009) Epler (2010); Hamada et al (2014); Coleoptera Dytiscidae Laccodytes Good Free Good Small Pred Swimmer Hydrodynamic Plastron Both Long No Merritt & Cummins (2007); Domınguez & Fernández (2009) Epler (2010); Merritt e Cummins Coleoptera Dytiscidae Laccophilus Good Free Good Small Pred Swimmer Hydrodynamic Plastron Both Long No (2007); Domınguez & Fernández (2009) Merritt & Cummins (2007); Coleoptera Dytiscidae Macrovatellus Good Free Good Medium Pred Swimmer Hydrodynamic Plastron Both Long No Domınguez & Fernández (2009) Hamada et al (2014); Domınguez & Coleoptera Dytiscidae Platynectes Good Free Good Medium Pred Swimmer Hydrodynamic Plastron Both Long No Fernández (2009) Epler (2010); Hamada et al (2014); Coleoptera Elmidae Gyrelmis None Attached Good Small Scra Crawler Cylindrical Plastron Erosional Long No Domınguez & Fernández (2009) Epler (2010); Hamada et al (2014); Coleoptera Elmidae Microcylloepus None Attached Good Small Scra Crawler Cylindrical Plastron Erosional Long No Domınguez & Fernández (2009) Epler (2010); Hamada et al (2014); Coleoptera Elmidae Phanocerus None Attached Good Small Scra Crawler Cylindrical Plastron Erosional Long No Domınguez & Fernández (2009) Epler (2010); Hamada et al (2014); Coleoptera Elmidae Stegoelmis None Attached Good Small Scra Crawler Cylindrical Plastron Erosional Long No Domınguez & Fernández (2009) Epler (2010); Hamada et al (2014); Coleoptera Gyrinidae Gyretes Good Free Good Medium Pred Swimmer Hydrodynamic Plastron Both Long No Domınguez & Fernández (2009) Epler (2010); Hamada et al (2014); Coleoptera Hydrochidae Hydrochus None Some Good Medium Gath Sprawler Cylindrical Plastron Depositional Long No Domınguez & Fernández (2009) Coleoptera Hydrophilidae Derallus Good Free Good Small Pred Swimmer Hydrodynamic Plastron Both Long No Epler (2010); Short & Torres (2006); Domınguez & Fernández

107 Trophi Order Family LTU Swim Attach Armor Size Habits Shape Resp Rheo Life Retreat Bibliography c (2009) Epler (2010); Hamada et al (2014); Coleoptera Hydrophilidae Helochares Weak Free Good Medium Pred Sprawler Cylindrical Plastron Depositional Long No Domınguez & Fernández (2009) Epler (2010); Domınguez & Coleoptera Hydrophilidae Hydrophilini Weak Free Good Small Shred Sprawler Cylindrical Plastron Depositional Long No Fernández (2009) Epler (2010); Domınguez & Coleoptera Hydrophilidae Paracymus Weak Free Good Small Shred Sprawler Cylindrical Plastron Depositional Long No Fernández (2009) Domınguez & Fernández (2009); Coleoptera Hydrophilidae Quadriops Weak Free Good Small Shred Sprawler Cylindrical Plastron Depositional Long No Domınguez & Fernández (2009) Coleoptera Hydrophilidae Tropisternus Good Free Good Large Pred Swimmer Hydrodynamic Plastron Both Long No Epler (2010); Epler (2010); Hamada et al (2014); Coleoptera Noteridae Hydrocanthus Good Free Good Small Pred Swimmer Hydrodynamic Plastron Both Long No Domınguez & Fernández (2009) Epler (2010); Hamada et al (2014); Coleoptera Noteridae Pronoterus Good Free Good Small Pred Swimmer Hydrodynamic Plastron Both Long No Domınguez & Fernández (2009) Hamada et al (2014); Merritt & Very Plecoptera Perlidae Anacroneuria Weak Free Weak Medio Pred Sprawler Hydrodynamic Gills Erosional No Cummins (2007); Domınguez & Short Fernández (2009) Hamada et al (2014); Merritt & Very Plecoptera Perlidae Enderleina Weak Free Weak Medio Pred Sprawler Hydrodynamic Gills Erosional No Cummins (2007); Domınguez & Short Fernández (2009) Hamada et al (2014); Merritt & Very Plecoptera Perlidae Macrogynoplax Weak Free Weak Medio Pred Sprawler Hydrodynamic Gills Erosional No Cummins (2007); Domınguez & Short Fernández (2009) Hamada et al (2014); Merritt & Leaf Trichoptera Calamoceratidae Phylloicus None Some Good Medium Shred Sprawler Cylindrical Gills Depositional Short Cummins (2007); Domınguez & Wood Fernández (2009) Very Hamada et al (2014); Domınguez & Trichoptera Ecnomidae Austrotinodes None Some None Small Pred Sprawler Cylindrical Tegument Erosional No Short Fernández (2009) Hamada et al (2014); Merritt & Very Trichoptera Glossosomatidae Glossosomatidae None Attached Good Small Scra Crawler Cylindrical Tegument Both Sand Cummins (2007); Domınguez & Short Fernández (2009) Hamada et al (2014); Merritt & Very Trichoptera Helicopsychidae Helicopsyche None Attached Good Small Scra Crawler Cylindrical Gills Both Sand Cummins (2007); Domınguez & Short Fernández (2009) Hamada et al (2014); Merritt & Fixed Trichoptera Hydropsychidae Leptonema None Some Weak Medium Filter Sprawler Cylindrical Gills Erosional Short Cummins (2007); Domınguez & Retreat Fernández (2009) Hamada et al (2014); Merritt & Fixed Trichoptera Hydropsychidae Macronema None Attached None Medium Shred Sprawler Cylindrical Gills Both Short Cummins (2007); Domınguez & Retreat Fernández (2009) Hamada et al (2014); Merritt & Trichopter Mediu Fixed Hydropsychidae Macrostemum None Attached Weak Filter Spraw Cylindrical Gills Erosional Short Cummins (2007); Domınguez & a m Retreat Fernández (2009)

108 Trophi Order Family LTU Swim Attach Armor Size Habits Shape Resp Rheo Life Retreat Bibliography c Hamada et al (2014); Merritt & Very Fixed Trichoptera Hydropsychidae Smicridea None Some None Small Filter Spraw Cylindrical Gills Erosional Cummins (2007); Domınguez & Short Retreat Fernández (2009) Hamada et al (2014); Merritt & Very Trichoptera Hydroptilidae Hydroptilidae None Attached None Small Scra Craw Cylindrical Tegument Both Sand Cummins (2007); Domınguez & Short Fernández (2009) Holzenthal (1986); Holzenthal & Very Trichoptera Leptoceridae Amphoropsyche None Attached Weak Small Scra Spraw Cylindrical Gills Both Sand Rázuri-Gonzales (2011); Calor & Short Froehlich (2008) Hamada et al (2014); Merritt & Very Leaf Trichoptera Leptoceridae Nectopsyche None Attached Weak Small Filter Spraw Cylindrical Gills Both Cummins (2007); Domınguez & Short Wood Fernández (2009) Hamada et al (2014); Merritt & Very Trichoptera Leptoceridae Oecetis None Some Weak Small Pred Spraw Cylindrical Gills Both Sand Cummins (2007); Domınguez & Short Fernández (2009) Very Leaf Hamada et al (2014); Domınguez & Trichoptera Leptoceridae Triplectides None Some Good Medium Shred Spraw Cylindrical Gills Depositional Short Wood Fernández (2009) Hamada et al (2014); Merritt & Trichoptera Odontoceridae Marilia None Some Good Medium Pred Spraw Cylindrical Gills Both Short Sand Cummins (2007); Domınguez & Fernández (2009) Hamada et al (2014); Merritt & Very Fixed Trichoptera Philopotamidae Chimarra None Attached None Small Filter Sprawler Cylindrical Tegument Erosional Cummins (2007); Domınguez & Short Retreat Fernández (2009) Hamada et al (2014); Merritt & Very Fixed Trichoptera Philopotamidae Wormaldia None Attached None Small Filter Sprawler Cylindrical Tegument Erosional Cummins (2007); Domınguez & Short Retreat Fernández (2009) Very Hamada et al (2014); Domınguez & Trichoptera Polycentropodidae Cernotina None Attached None Small Filter Sprawler Cylindrical Tegument Erosional No Short Fernández (2009) Very Hamada et al (2014); Domınguez & Trichoptera Polycentropodidae Cyrnellus None Attached None Small Filter Sprawler Cylindrical Tegument Erosional No Short Fernández (2009) Hamada et al (2014); Merritt & Trichopter Very Polycentropodidae Polyplectropus None Attached None Small Filter Sprawler Cylindrical Tegument Erosional No Cummins (2007); Domınguez & a Short Fernández (2009)

109 Tabel S2 – Correlation of species traits weighted scores and environmental predictors weighted scores, both ordinated by PCA, and the two first RLQ axes. Highlighted

(bold) descriptors were those with stronger association with the RLQ axes around 0.7 as measured by the Pearson correlation.

RLQ (loadings) Descriptors Acronyms Axis 1 Axis 2 Traits weighted scores (PCA) Attachment Attach -0.57 -0.76 Armoring Armo -0.70 0.36 Body Shape Shape 0.51 0.88 Respiration Resp -0.11 0.74 Body Size Size 0.48 0.51 Rheophily Rheo 0.93 0.11 Habit Habit -0.27 0.57 Trophic group Trophic -0.35 0.27 Retreat building Retrait -0.84 -0.55 Environment weighted scores (PCA) Riffles extent (%) PCT_RI 0.90 0.49 Average Leaves Banks Substrates (m2) XFC_LEB -0.35 -0.69 Total Ground Cover (%) XG 0.51 -0.17 Exposed soil (%) XGB 0.83 0.35 Mean residual pools depth (m) RP100 0.38 0.72 Oxid Reduction Potential (+H mV) ORP 0.04 -0.19 Mean bankfull width (m) XBKF_W 0.39 -0.29 CAPÍTULO 3 – BIOTIC INTERACTIONS AS A STRUCTURING MECHANISM IN INSECT

METACOMMUNITIES: A TEST OF ENVIRONMENTAL CONTROL, DISPERSAL

LIMITATION AND SPECIES INTERACTIONS IN STREAMS

Denis Silva Nogueira; Yulie Shimano, Leandro Juen & Paulo De Marco Junior

Obs.: formatado de acordo com as normas da revista Oikos

111 Biotic interactions as a structuring mechanism in insect metacommunities: a test of environmental control, dispersal limitation and species interactions in streams

Denis Silva Nogueira1a; Yulie Shimano2, Leandro Juen3 & Paulo De Marco Junior4

1Programa de Pós-Graduação em Ecologia e Evolução, Universidade Federal de Goiás, Instituto de Ciências Biológicas (Bloco ICB V), Campus II/UFG, Goiânia, Goiás, CEP: 74001-970. 2Programa de Pós-Graduação em Zoologia, Universidade Federal do Pará e Museu Paraense Emílio Goeldi. Rua Augusto Correia, Nº 1 Bairro Guamá, CEP 66.075-110, Belém, Pará, Brasil. 3Laboratório de Ecologia e Conservação, Universidade Federal do Pará, Instituto de Ciências Biológicas, Rua Augusto Correia, Nº 1 Bairro Guamá, CEP 66.075-110, Belém, Pará, Brasil. 4Departamento de Ecologia, Universidade Federal de Goiás, Instituto de Ciências Biológicas (Bloco ICB V), Campus II/UFG, Goiânia, Goiás, CEP: 74001-970. acorrespondence author: [email protected]

112 Abstract: Metacommunity theory have broadened the conceptual framework about mechanisms governing community patterns local and regionally. Despite the growing evidence towards the prevalence of environmental over spatial control, there has been a missing component related to biotic interactions as a structuring mechanism in metacommunities. In order to consider intra and interguild interactions, i.e., competition and predation noise in metacommunity structuration, we investigated 36

Amazon stream insect communities taking into account checkerboard units and predator matrix, respectively. Our assumption for incorporation of predators abundances as proxy for predation pressures was that there should be spatial signals in predators distributions according to spatial distribution of prey availability, even if predation was not observed directly (non-consumptive effect).

We did not found support for intraguild interactions (i.e., competition noise), nevertheless we found support for interguild interaction, i.e., predation noise effect on studied metacommunity. Our results suggest that shredders, scrapers and filterers guilds are not structured in the same way. We confirmed our hypothesis of an important biotic control by predators on filterers, scrapers and shredders, but no prey control over predators in our interguild analysis. Moreover, we have found greater contribution of the biotic component over environmental control and dispersal limitation in some analysis. Predation noise added up to the effect of pure environmental and spatial signals in metacommunity structuration, which if not considered should retain important variance in the unexplained fraction.

Keywords: Eltonian noise hypothesis; nestedness; mass effect; species sorting; predation; aquatic insects; Amazonian streams.

113 Introduction

Metacommunity theory has emerged as a conceptual framework which seeks for elucidating mechanisms governing community patterns broader scales (Leibold et al. 2004, Holyoak et al. 2005).

Operationally, a metacommunity is defined as a set of local communities within a region, potentially linked by species movements, while each community represent a collection of samples of species incidence or abundance in a loosely definable area (Holyoak et al. 2005). In a metacommunity, species are expected to interact with each other locally, leading to changes in species demographic rates

(Holyoak et al. 2005). In turn, demographic dynamics potentially alters regional patterns of species co- occurrence due to migration, extinction and speciation (Hubbell 2001). Hence, accounting for process acting at multiple spatial scale has been considered an advanced of metacommunity theory (Leibold et al. 2004).

Under this approach observable patterns are generated by trade-offs among three ecological processes, namely, environmental control, biotic interactions and dispersal limitation (Holyoak et al.

2005). These processes are contra balanced to form different theoretical models with increasing importance of dispersal limitation and decreasing environmental and biotic control from large to small spatial scales (Leibold et al. 2004, Holyoak et al. 2005). Four dynamic models have been proposed by

Leibold et al. (2004): 1) Patch dynamics, expected to occur in homogeneous or constant environments, where coexisting species show trade-offs in their dispersal and competition abilities to colonize newly vacant habitats in a region. 2) species sorting, in which species distribution are adjusted along with heterogeneous environment and there is no limitation to species reach suitable sites. Best competitors overcome other species in their most suitable habitat.3) Mass effect, where more productive sites are also the richer ones and provide propagules that colonize poorer or sink sites within a heterogeneous region, emphasizing the importance of spatial dynamics on local population demography. Finally, in the

114 opposite extreme of the conceptual gradient, 4) neutral models assume dispersal limitation to be the strongest factor structuring metacommunities and determining regional diversity by demographic processes in continuous ecological drift, migration, extinction and speciation. Only negligible importance are given to environmental heterogeneity since species are expected to be functionally equivalent (Hubbell 2001; Leibold et al 2004).

Evidences provided by a meta-analysis of variance partitioning suggest that species sorting is the prevailing mechanism by which metacommunities are structured in several environments, even considering multiples organisms ranging from passive to highly mobile ones (Cottenie 2005). Species sorting is also an important structuring mechanism across ecosystems, trophic groups as autotrophs and omnivores, and several types of dispersal modes ranging to mobile and passive organisms (Soininen

2014). The main evidence for species sorting in those analysis is a significant pure environmental control on metacommunity structuration. However, elucidating metacommunity dynamics remain a difficult task for empiric validation, partially because powerful analytical tools (such as variance partitioning) are usually applied without incorporating biotic interaction or functional traits explicitly or lack a direct measure of species dispersal (Jacobson and Peres-Neto 2009, De Bie et al. 2012).

An integrative framework have been developed to test for alternative patterns, nominally, the

Elements of Metacommunity Structure (hereafter EMS, Leibold & Mikkelson 2002). EMS provided the framework to test metacommunity patterns in co-occurrence matrix taking metrics of congruence in species distribution ranges, turnover and boundary clumping (Leibold and Mikkelson, 2002; Presley et al., 2010). Contrasted to null models, EMS enable to test for randomness, checkerboard, turnover or nestedness, Clementsian gradients (communities as definable entities), Gleasonian gradients

(individualistic species distribution), and evenly spaced gradients (Leibold and Mikkelson, 2002). Such approaches do not test interactions directly but by assuming indirect co-occurrence patterns as the outcomes of exclusive competition (Leibold and Mikkelson 2002). Predation is not explicitly

115 considered in EMS approach, despite be equally important form of species interactions structuring metacommunities (but see Shevtsov et al. 2013). With this respect variance partitioning approach have gained relative merits in metacommunity analysis, mainly to quantifying the relative roles of dispersal limitation, environmental control (Peres-Neto and Legendre 2010). A new avenue of possibilities opens to consider incorporating biological information (e.g., biotic interactions and traits) as further predictors (jointly with spatial and environmental ones) of metacommunity structure in the variance partitioning analysis and analogue multivariate methods (Göthe et al. .

In stream metacommunities dispersal limitation seems not constraints insects dispersal at the catchment scale and also among catchments since overland and hydrographic network dispersal are equally likely (Landeiro et al. 2011, 2012, Cañedo-Argüelles et al. 2015). Besides, there is a growing amount of evidence leading to the importance of environmental control as the main structuring mechanism in these systems, supporting niche over neutral processes. However, effects of biotic interactions (e.g., competition, predation, facilitation), an important conceptual component of niche theory (Hutchinson 1957; Sobéron & Nakamura 2009; Leibold et al. 2004), have not been accounted for in most of these studies (but see Gray et al 2012; Göthe et al 2013a). It is reasonable to infer that different guilds of aquatic insects should congruently adjusted along with environmental gradients of streams, independently of their taxonomic relatedness, since co-occurring species have passed by similar ecological filters (Grönroos and Heino 2012). Hence, functional groups and either trophic guilds of aquatic insects are expected to respond differently and have contrasting co-occurrence patterns to the environmental conditions in streams (Heino 2009), leading to intertrophic divergence in occupation and structure patterns. Otherwise, within a given trophic guild, species should respond similarly to the environmental gradients and often compete for resources, enhancing the importance of intratrophic interactions, while between trophic guilds the importance of competition tend to be weaker than predation and resource availability in determining community structuration (Shevtsov et al. 2013).

116 Therefore, confronting co-occurrence patterns by means of EMS allied with variance partitioning confirm our expectations about the importance of intra and interguild interactions on the general metacommunity patterns (Shevtsov et al. 2013). Rejecting the Eltonian noise hypothesis, which predicts lack of biotic interactions effect at broader spatial scales (Soberón & Nakamura 2009), imply that biotic interactions present individual signals in metacommunity structuration. Additionally, incorporating biotic interactions in variance partitioning enable to test if patterns related to species sorting, the prevailing mechanisms for stream insects metacommunities (Cottenie 2005, Soininen

2014), are solely determined by environmental control or added up to the importance of niche theory relative to neutral dynamic (Gray et al. 2012, Göthe et al. 2013a).

In this paper, we evaluate metacommunity structure of stream insects taking into account effects of environmental control, dispersal limitation and biotic interactions, including intra (i.e., competition) and intertrophic (i.e., predation or prey effects) interactions using two approaches: EMS and variance partitioning, respectively. EMS was used to specifically test co-occurrence patterns, including competitive exclusion signals (i.e., checkerboard units) at the stream scale as a mean to inform the magnitude of intraguild interaction. In order to test if environmental control, dispersal limitation and interguild interactions determine insects metacommunity in streams, we used variance partitioning. We have made specific predictions for the whole metacommunity and for the trophic guilds of aquatic insects: 1) we expect the metacommunity as a whole to be structured specifically by a

Clementsian gradient and environmental control, as an indicative of species sorting. For the trophic guilds (see guilds definition in methods) we expect 2) filterers, scrapers and shredders to be structured by a Clementsian gradient in the intraguild analysis, and strong environmental control and by predation effect (interguild interaction). 3) For predators we expect metacommunity be structured at random in

EMS analysis because we do not expect predator to compete among themselves and neither disjunction or clumping in their regional composition. We expect predator be structured by the avaibility of prey in

117 the intertrophic analysis, which we measure by assuming that all the other groups are potential prey to predators taxa. The main assumption for incorporation of predators abundances as proxy for predation pressures is that there should be spatial signals in predators distributions according to prey availability spatial paterning, as in temporal dynamics of predator-prey interactions (Gotelli, 2007). Such patterns could be due to effective predation effect or by non-comsumptive effects of predation avoidance

(Orrock et al. 2008; Peckarsky et al. 2008). Conversaly prey abundance should present the same effect on predators, which if not incorporated in variance partitioning models should retain unexplained variance in model residuals.

Material and Methods

Study area

We studied 36 forested streams in Paragominas municipality, southeastern of the State of Pará,

Brazil. Twenty-seven streams were located in areas under reduced-impact logging (RIL) and nine reference areas without exploration registers since 1970s. The study area is the major remaining continuous forest area outside public reserves in the Belém Endemism Center, and is embedded on a matrix of extensive pasture and agriculture (mainly soybean and oil palm). High biodiversity have been reported mainly in areas of dense Ombrophile Forest of the region (Gerwing 2002).

We have demonstrated that some environmental variability does exist among reference and

RIL streams, but such environmental variability have not influenced the community composition and species diversity (Nogueira et al. unpublished; see Chapter 2). We considered that all studied streams holds biodiversity and taxonomic composition equivalents to that of natural areas in the region, and they will be analyzed as such. Aquatic insects samples, physico and chemical water parameters, and structural characteristics of stream channel and riparian vegetation were collected in a reach of 150 metters of each streams (see Chapter 1 and Chapter 2). The survey were performed between August and

118 September of 2012, in the peak of dry period for the region (Gerwing 2002), which reduce climate variability and rain disturbance effects on stream insects and environmental paramenters.

Biological data and Trophic Guild definition

In each stream, a 150 m reach divided in 20 segments of 7.5 meters, was defined as the basic sampling unit. Aquatic insects of the orders Coleoptera, Plecoptera and Trichoptera were sampled using a circular handnet of 18 cm of circunference. These groups are highly biodiversity and represent broad ecological and functional aspects of aquatic insects in streams (Merritt and Cummins 1984). For instance, many water beetles have adapted to live on the land-water interface along their life cycle (e.g.,

Dryopidae, Hydrophilidae), some are mostly free swimming and voracious predators with good dispersal ability (e.g., Dytiscidae) (Jäch and Balke 2008). Each group were identified to the Lower

Taxonomic Unity (LTU) using specialized identification keys (Hamada et al. 2014). In most case, we reach to genera level, but we keep family or subfamily level where identification of genera were not possible.

All organisms were classified into one of the five Functional Feeding Groups (FFG) representing the trophic guilds of macroinvertebrates: Filtering Collectors (hereafter, just Filterers),

Gathering Collectors, Shredders, Scrapers and Predators. The FFG classification reflects the strategy of food acquisition and type of the consumed item (Cummins and Klug 1979). In order to avoid misleading assignment of trophic guilds we only attributed an organism to one FFG if it was mature larva or an adult (for water beetles) and checked for consistency with biological information on the

Amazon aquatic insects (Hamada et al. 2014). Filterers represent all the organisms which produce silk nets (e.g., most Hydropsychidae and Philopotamidae in our study) or have specialized hairs in their legs

(e.g., some Leptoceridae) or buccal apparatus (e.g., Macrostemum) to obtain fine particulate organic matter from the water column. Filterers are also taken as omnivorous because they often eat any palatable material that fall into their nets including animal parts and excretions (Cummins and Klug

119 1979). Scrapers are those that feed on algae and periphyton attached to the surface underwater substrates (e.g., Hidroptilidae, Glossosomatidae, Elmidae). Shredders feed on coarse organic matter

(leaf enriched by microorganism biofilms). They are often associated to leaf banks in stream pools.

Predators eats parts or the whole preys. Gathering collectors were represented by only one individuals and were not considered in the trophic guild analyses. In our data set water beetles represented most of the predators insects, jointly with some stoneflies, while caddisflies contained most of all the remaining trophic guilds (Table S1). For one Hydrophilidae taxa we were not able to classify among the trophic guild and they were considered only in the analysis of the whole metacommunity.

Environmental predictors

Environmental data encompass 64 predictors grouped into differents sets, i.e. vegetation cover, substrate heterogeneity and stability, woody in the channel, topography and declivity of the stream reach, stream channel morphology, human impacts predictors, a Physical Habitat Integrity index composed of several predictors of impact (see chapter 1 and chapter 2), multiple scale Enhanced

Vegetation Index (Appendix 1). The selection criteria to compose the predictors data set was based on maximum total variance within each set of metrics above, and reducing colinearity among them (see chapter 3). Some environmental predictors were higly colinear among the different set of predictors

(mainly vegetation cover) and were removed from using iterative analysis after forward selection and also variance inflation factor inspection (see data analysis).

Spatial predictors

In order to model spatial structures accordingly in canonical ordinations by means of partial

RDA (see below) it is desirable transfor the geographical coordinates to spatial filters representing all the multiple spatial scales encompassing the study region (Borcard and Legendre 2002). We use the truncated Euclidean Distance derived from spatial coordinates of each stream. The minimum spanning tree linking two stream was used with a threshold of 0.178 km, as a criteria for truncation of the

120 overland distance matrix. No differential weights were given to the network of stream connections given that insect adults are equally capable to disperse by overland and following hydrographic networks (Landeiro et al. 2011; Grönroos et al. 2013). The spatial distance matrix was than analysed using Principal Coordinates of Neighbourhood Matrix (PCNM) to obtain eigenfunctions describing spatial configuration among studied streams. Forward selection of spatial eigenvectors were applied to avoid inflaction of adjusted R2 in variation partitioning, by using the forward.sel function of the packfor package. We considering double criteria (alpha equal 0.05, and full model adjusted R2) as a stope rules in the spatial filters selection (Blanchet et al. 2008).

Data Analysis

In order to test our hypothesis we evaluate metacommunity patterns of stream insects tanking into account the effects of environment control, dispersal limitation and biotic interactions (intra and interguilds). EMS was used to test co-occurrence patterns, which includes the test for competitive exclusion signals at the stream scale as a mean to inform the magnitude of intraguild interactions. The

EMS patterns were tested for the whole metacommunity and the FFG studied (Scrapers, Shredders,

Filterers and Predators). EMS enable to test for randomness, checkerboard, Clementsian, Gleasonian and Evenly spaced gradients (Leibold and Mikkelson, 2002; Presley et al., 2010). However, as for the studied metacommunity we have sampled environmental predictors within a small range of mostly preserved headwaters, which differ only in some environmental characteristics of substrates and canopy cover (Appendix 1), we also interpret Quasi-structure patterns as sugested by Presley et al. (2010).

Quasi-structures are expected for coherent metacommunities which shown non-significant turnover or nestedness and are related to the truncation of the empirical environmental gradient or due to sampling designs decisions (omission of larger streams or impacted streams in our case). Once we expect a

Clementsian gradient for the whole metacommunity and for some FFG assemblages, so specific expectations regarding species distribution in co-occurrence matrix are: i) significant positive

121 coherence, ii) positive turnover (significant or not), and iii) significant positive boundary clumping

(Leibold and Mikkelson, 2002; Presley et al., 2010). EMS significance was assessed by contrasts of observed patterns with null models (except boundary clumpling analyzed against chi-squared distribution). EMS analysis were done using the metacom R package (Dallas 2014), with 9999 Monte

Carlo permutations under the fixed-probability null model (method “r1”).

Especially for EMS sampling bias and diferential habitat quality could be a problem in the detection of metacommunity patterns (Presley et al. 2010). We employed an evaluation of sample coverage based on the number of individuals needed to reach to a cut off percentage of the local richness estimates. We considered the effect of sampling bias, by performing the EMS analysis with and without considering assemblages with sample coverage rarefaction (Chao & Jost 2012), standardized to 90%. Therefore, community structure along with environmental gradients was evaluated considering possible bias in species estimates each local assemblages. Of the total, 31 local assemblages had reliable estimates of species richness (higher than 90%). The analysis was conducted using the function rarefaction.individual of the rareNMtests package to drawn individual based rarefaction curves (Cayuela and Gotelli 2014). Rarefaction based on sample coverage and individual numbers are presented for all streams in figure S2.

Variance partitioning analysis was used to test if environmental control, dispersal limitation and intertrophic interactions (i.e., predation) determine aquatic insects metacommunity structure.

Variance partitioning based on partial constrained Redundance Analysis (pRDA) (Legendre and

Legendre 2012) was used to evaluate our three candidate metacommunity processes: environmental control, dispersal limitaion and interguild interation. For the whole data set, variance partitioning was analyzed using environmental and spatial predictors. As the spatial filters (see above), the environmental predictors were submitted to forward selection and only the selected predictors were

122 analyzed in the subsequent variance partitioning of the metacommunity structure. Collinearity among environmental predictors were checked using variance inflaction factor (VIF) with the vegan function vif.cca and only predictors below 10 VIF were considered. Before the analyses all the environmental predictors were standardized to give mean zero and unit variance. The analyses were conducted using varpart function of the vegan package (Oksanen et al. 2012) with Hellinger transformation of the metacommunity abundance data (Legendre and Gallagher 2001).

Metacommunity data was partitioned into four components: purely environmental [E], purely spatial [S], shared fraction [E∩S] and unexplained fraction [Residual]. Variance partitioning consist of applying partial Redundance Analysis to the metacommunity matrix considering each preditor matrix individially and controlling for the other (Y ~ [E] | [S], alternatively Y ~ [S] | [E], where Y is a metacommunity). The total explained fraction (Y ~ [E] + [S] + [E∩S]) was also obtained in order to decompose the total variation in the individual and shared fractions by subtraction of the total (see

Legendre and Legendre 2012). The total explanation are reported as the adjusted R squared (Peres-Neto et al. 2006). Significant [E] is indicative of species sorting, significant [S] is indicative of neutral model/patch dynamic (which meens no environmental control), and significant both [E] and [S] are indicative of species sorting with dispersal limitation (or mass effect dynamics) (Cottenie, 2005). The structuring of each feeding functional groups was done using the same approach as above. For these analysis, the individual fractions of the environment [E], spatial [S] and biotic [B] predictors were tested for significance. The amount of exclusive and shared fractions were represented with Venn diagrams. The abundance of biotic predictor matrix were standardized using the Hellinger transformation to scale to the metacommunity data, attending to linearity and normality required for partial RDA (Legendre and Gallagher 2001).

In addition, to detect if significant pure spatial fraction [S] are related to dispersal limitation as predicted by the neutral model, or are related to the absense of environmental predictors in or dataset,

123 we employed the procedure recently described by Diniz-Filho et al. (2012). The approach consists of computing a Mantel Correlation between the pairwise correlation matrix (RC) of species abundances predicted by the pure spatial model and the Manhattan dissimilarity (MC) calculated for the Moran's I correlograms generated for each species abundance in the pure spatial model. We calculated Moran's I correlograms between constrained species abundances for five distance lags given our small study spatial extent. Significance in the Mantel test was obtained after 9999 Monte Carlo permutations. The expectation according to a neutral processes is no correlation between matrices RC and MC. We followed their notation for the matrices (see schematic representation in Figure 1 of (Diniz-Filho et al.

2012)). This procedure was run only in the cases that significant pure spatial component were identified in the variance partitioning analysis for two and three matrices of predictors as recommended by the authors. All the analysis were done using the R software (R Development Core Team 2013) .

Results

Aquatic insect metacommunity was composed of a matrix of 36 streams by 41 species, in which 31 streams have sample coverage estimates of richness higher than 90% (minimum = 81%, maximum = 99.6%). For the whole metacommunity and for each FFG the size and fill of the matrix varied little after controlling for biased sample coverage, while minimum and maximum values of total abundance and richness varied consistently for the whole data set, but remained unaltered in FFG

(Table 1).

The metacommunity did not exhibited coherence for the whole data set, but coherent patterns emerged when the best sampled subset of streams were considered in the EMS analysis (Table 2;

Figure 1). The patterns of the studied insect metacommunity after removing sampling artifacts were congruent with quasi-Clementsian structure with positive significant coherence, positive non- significant turnover and positive significant boundary clumping (Table 2). The analysis of functional

124 group showed that shredders metacommunities were consistent with a Clementsian or quasi-

Clementsian pattern, depending on the control of sampling bias in species richness estimation (Table

2). We observed that biased estimates introduce breaks in species distribution along with the manin gradient, increasing the number of embedder absences in the matrices (decreasing coherence).

Differently, was scrapers presented significant coherence when analyzing all streams. EMS patterns for scrapers approximate to a quasi-Clementsian gradients, since turnover were non-significant but positive. There was no correlation between EMS statistics of coherence and turnover and the matrix size (Spearman correlation (rho) = 0.127, P = 0.734 and rho = 0.066, P = 0.865, respectively) and neither matrix fill (rho = 0.103, P = 0.785 and rho = 0.079, P = 0.838, respectively). However, the

Boundary clumping statistic was weakly but marginaly related to matrix size (rho = 0.624, P = 0.060).

There was negative correlations between coherence null model significance and matrix size and matrix fill (rho = -0.774, P = 0.008 and rho = 0.805, P = 0.005, respectively). Turnover was only marginaly correlated with matrix size (rho = -0.587, P = 0.080), but was significaly related to matrix fill (rho =

0.830, P= 0.006). All this diagnistic analysis indica that EMS analysis should be interpreted with care.

Accordingly, variance partitioning have confirmed our expectation of purely environmental effects on the whole metacommunity matrix for the scale considered (adjs. R2 = 0.319, F = 3.369, P <

0.001; Table 3). Seven environmental predictors were related to the variation in the metacommunity structure, with different sets of species related to distinct environmental gradients (Table 3;

Suplementary Information Figure S1). Percentage of riffles, average leafs litter substrates, total ground cover, percentage of exposed soil, average depth of residual pools, oxid reduction potential and the mean of the bakfull width were the predictors selected by iterative processes (i.e., forward selection).

Only one middle scale spatial filters was selected in RDA model, but explained only a small non- significant fraction of the whole metacommunity strucute (Figure 2).

125 Table 1 – Descritive statistic of the analyzed matrices in the EMS tests showing the matrice size (number of rows times number of colunms), matrice fill (percent of presenses), abundance and species richness the the whole metacommunity and each trophc ghuild.

Number Number Matrix size Matrix Abundance Richness Data set of of rows (rows vs. Colunms fill (%) (min / max) (min / max) Colunms Whole 36 41 1476 36.52 16 / 268 5 / 24 metacommunity Unbiased 31 10 1240 39.19 41 / 268 9 / 24 streams Filterers 36 10 360 41.11 2 / 47 1 / 7 Unbiased 31 10 310 42.26 2 / 47 1 / 7 streams Scrapers 32 8 256 30.9 1 / 117 1 / 6 Unbiased 29 7 203 40.89 1 / 117 1 / 6 streams Shredders 35 5 175 64.57 1 / 93 1 / 5 Unbiased 31 5 155 65.81 5 / 93 2 / 5 streams Predators 36 16 576 27.95 10 / 68 2 / 8 Unbiased 31 16 496 29.23 10 / 68 2 / 8 streams

A) B)

Figure 1 – Ordination of aquatic insects species occurences by sites (streams) based on the first axis of reciprocal averaging for A) all studied stream and B) streams with better sample coverage representation of local diversity only. Black filled areas are species presence.

126 Table 2 – Elements of Metacommunity Structure of the whole data and for each FFG, with and without streams with unbiased richness estimates. EMS statistics are the number of embedded absences (Coherence), number of replacements (Turnover), and Morisita index (Boundary Clumping). Significance was assessed with fixed rows – probability columns null model under 9999 Monte Carlo permutations.

Data set EMS Observed Z Mean ± SD Simulated P-value Pattern All streams Coherence 562.00 1.47 621.71 ± 40.54 0.141 Turnover 9760.00 -0.73 7803.44 ± 2683.38 0.466 Random Whole Boundary clumping 2.31 – – < 0.001 metacommunity Coherence 431.00 2.14 495.04 ± 29.90 0.032 Turnover 4897.00 0.35 5525.55 ± 1782.01 0.724 Quasi-Nested Unbiased streams Boundary clumping 1.82 – – < 0.001 Coherence 100.00 0.66 108.02 ± 12.11 0.508 All streams Turnover 989.00 0.10 1022.47 ± 340.68 0.922 Random Boundary clumping 1.17 – – 0.011 Filterers Coherence 84.00 0.49 89.02 ± 10.26 0.625 Turnover 809.00 -0.01 807.65 ± 255.24 0.996 Random Unbiased streams Boundary clumping 1.18 – – 0.017 Coherence 35.00 2.16 58.39 ± 10.80 0.030 All streams Turnover 371.00 1.07 645.47 ± 256.29 0.284 Quasi-Nested Boundary clumping 1.87 – – < 0.001 Scrapers Coherence 34.00 1.13 43.90 ± 8.76 0.259 Turnover 317.00 0.45 390.65 ± 163.17 0.652 Random Unbiased streams Boundary clumping 1.61 – – < 0.001

All streams Coherence 10.00 3.06 23.83 ± 4.52 0.002 Turnover 51.00 1.96 228.25 ± 90.55 0.050 Nested Boundary clumping 1.60 – – < 0.001

Shredder Coherence 7.00 3.39 20.00 ± 3.83 0.001 Turnover 45.00 1.72 174.23 ± 75.07 0.085 Quasi-Nested Unbiased streams Boundary clumping 1.54 – – 0.000 Coherence 163.00 1.23 192.80 ± 24.29 0.220 All streams Turnover 5116.00 -1.36 3569.47 ± 1136.81 0.174 Random Boundary clumping 1.67 – – < 0.001 Predator Coherence 133.00 1.26 158.99 ± 20.59 0.207 Unbiased streams Turnover 2651.00 0.02 2671.02 ± 877.11 0.982 Random Boundary clumping 1.62 – – < 0.001

127 For all trophic guilds the biotic component almost invariably explained at least a part of the variation in the metacommunity structure, while the environmental and spatial fraction varied considerably among the guilds (Table 3; Figure 2). For filterers the biotic matrix explained 8.1% of the total variation (adjs. R2 = 0.081, F = 2.081, P = 0.014), while the spatial component was 8.8% (adjs. R2

= 0.088, F = 2.081, P = 0.014), while only small (3.8%) non-significant fraction were attributed to the environment. For scrapers the amount of explained variation for biotic fraction explained 19.2% (adjs.

R2 = 0.192, F = 4.922, P < 0.001), while spatial fraction explained significative 9.1% (adjs. R 2 = 0.091,

F = 2.853, P = 0.008), and 9.5% were retained for the environmental fraction (adjs. R 2 = 0.095, F =

2.933, P = 0.002). Shredders were explained by biotic with 10.9% and environmental predictors with

9% (adjs. R2 = 0.109, F = 2.230, P = 0.007 and adjs. R2 = 0.090, F = 1.901, P = 0.017, respectively), and only low non-significant were related spatial fraction. These result confirm our hypothesis of a strong biotic control by predators structuring filterers, scrapers and shredders, but with distinct importance spatial and environmental control among the groups (Table 3). Finally, predators were also structured mainly by the the environmental farction with 13.4% ( adjs. R2 = 0.134, F = 1.736, P =

0.005), with no important biotic or spatial fraction, refuting our hypotheesis that predators are structured by potential resource availability. Compared to the amount of unexplained fraction in the model for the whole metacommunity (lacking biotic component), we have obtained better results for almost all the trophic groups when considering the biotic component (with exception of scrapers).

For each trophic group distinct preditors were selected within each set, i.e., environmental, spatial and biotic (Table 3; Figure S1). Filteres were constrained by course, middle and fine scaled spatial filters, and by the abundance of Gyretes, Oecetis and Macrogynoplax. Scrappers was constrained by exposed soil in the riparian zone, leaf litter banks in the channel and by averaged number of living trees shelters in the stream section, middle scaled spatial filters, and by Enderleina,

Anacroneuria and Gyretes predators. Shredders abundances were constrained by the percentage of

128 riffles, average and standard deviation of exposed soil in the riparian zone, leaf litter banks in the stream channel, and standard deviation of substrate embeddedness in the mid channel. Shredders were also related to Gyretes, Laccophilus, Tropisternus, Oecetis and Marilia predators. Finely, predators assemblage were constrained by leaf litter banks, percentage of riffles, oxid reduction potential, mean residual pools in a stream reach, mean woody understory and mean riparian woody cover, declivity, undergrowth cover and proportion of herbacious vegetations cover.

Filterers and Scrapers were the only group for which the predicted by pure spatial fraction had important spatial autocorrelation as measured by Moran's I, but very differently. Especially, filterers have shown strong positive spatial structure and high colinearity in the species abundance distribution pattern in the first distance classes, as measured by the pure spatial RDA component (Figure 3A). There were no correlation in the community component (RC) and the Manhattan dissimilarity (MC) as predicted by the pure spatial RDA component (Mantel R = -0.218. P = 0.684), also indicating non neutrality. In contrast, scrapers had negative spatial autocorrelation and low colinearity among species in the first distance classes. In addition, there was no correlation between the pairwise matrix of species abundance (RC) and Manhattan dissimilarity (MC) predicted by pure spatial factors (Mantel R = -0.416,

P = 0.976). Scrapper taxa also shown idiosyncratic spatial processes in the firsts distance classes

(Figure 3B), indicating distinctive dispersal ability (limitation) and strong agregation patterns among species.

Finally, the most representative predators present in the biotic matrix were the water beetle

Gyretes (N= 239), the caddisfly Oecetis (N= 164), and the stoneflies Anacroneuria and Macrogynoplax

(N= 174 and 473, respectively; Table S1), while the most representative potential prey were the caddisflies Smicridea (N= 71), Leptonema (N = 182), Polyplectropus (N = 73), Glossosomatidae (N =

214), Phylloicus (N = 413), Triplectides (N = 312) and Helicopsyche (N = 623) (Table S1).

129 Figure 2 – Venn diagram showing the results of variance partitioning analysis for the whole metacommunity and for each Feeding Functional Groups as predicted by two or three predictor matrices, respectively: Environmental, Spatial and Biotic.

130 Table 3 – Variance partitioning showing explained fractions for the whole metacommunity and for each FFG. Fractions are as follow: [E] = Environment; [S] = Space; [Shared] = Environment plus Space; and [B] = Biotic (for predadors [B] means prey abundance, for all the others [B] is predators abundance. Acronyms of environmental predicotrs: PCT_RI: Percentage of riffles; XFC_LEB: Average litter banks; XG: Average shrub vegetation cover; XGB: Average exposed soil; RP100: Average Residual Pools; ORP: Oxid Reduction Potential; XBKF_W: Average Bankfull width; LRBS: Relative Bed Stability; SDGB: Standar Deviation of exposed soil; XFC_ROT: Average Number of Living Trees Shelter; VCEMBED: Standard deviation of embeddedness in the Mid-channel (%); XMH: Riparian mid-layer (0.5 to 5 m high) herbaceous cover; XCMGW: Riparian woody cover; Declivity: channel slope; SDG: Standard deviation of Shrud Cover; XGH: Riparian ground-layer (< 0.5 m high) herbaceous cover; SDCM: Standard deviation of Dossel plus Intermidiate Canopy Cover; SDG: undergrowth cover.

Explained Predictors Df Adj. R2 F P-value fractions Community data PCT_RI, XFC_LEB, XG, XGB, RP100, ORP, [E] 7 0.319 3.369 <0.001 XBKF_W [Shared] 0 0.018 [S] PCNM9 1 0.006 1.249 0.181 [Residuals] 0.656 Filterers [E] LRBS, XFC_LEB 2 0.038 1.731 0.059 PCNM1, PCNM14, PCNM3, PCNM16, [S] 6 0.088 1.652 0.010 PCNM4, PCNM8 [B] Gyretes, Oecetis, Macrogynoplax 3 0.081 2.081 0.014 [Residuals] 0.678 Scrapers [E] XGB, XFC_LEB, XFC_ROT 3 0.095 2.933 0.002 [S] PCNM17, PCNM13, PCNM12 3 0.091 2.853 0.008 [B] Enderleina, Anacroneuria, Gyretes 3 0.192 4.922 <0.001 [Residuals] 0.474 Shredders [E] PCT_RI, SDGB, XFC_LEB, XGB, VCEMBED 5 0.09 1.901 0.017 [S] PCNM15, PCNM2 2 0.032 1.799 0.086 [B] Gyretes, Laccophilus, Tropisternus, Oecetis, Marilia 5 0.109 2.23 0.007 [Residuals] 0.497 Predators XFC_LEB, PCT_RI, ORP, RP100, XMW, XCMGW, [E] 10 0.134 1.736 0.005 Declivity, SDG, XGH, SDCM [S] PCNM14, PCNM1, PCNM3, PCNM11 3 -0.017 0.78 0.739 Smicridea, Leptonema, Gyrelmis, Polyplectropus, [B] 7 0.067 1.462 0.063 Glossosomatidae, Stegoelmis, Dryopidae [Residuals] 0.453

131 A) B)

C)

Figure 3 – Spatial correlograms for abundances of A) filterers, B) scrappers and C) shredders species predicted by the pure spatial [S] component of partial RDA in the studied streams. The black dots are averaged spatial autocorrelation among species as obtained by the Moran's I spatial correlogram and the vertical bars represent the variability (SD) in the correlation of species spatial patterns.

132 Discussion

Metacommunity concept has enhanced the understanding of the importance of niche and neutral processes at local and regional scales in terrestrial and aquatic ecosystems (Soininen 2014).

However, most of the research conducted in streams ecosystems has considered only the environmental portion of the niche as a structuring force acting upon species occurrence and community structure

(e.g., Grönroos et al. 2013), often without considering the effect of biotic interactions (e.g., Göthe et al.

2013a). We face this problem evaluating metacommunity patterns of stream insects with two complementary approach: EMS and variance partitioning. The first enabled us to disentangle the effects of species co-occurence patterns, including competitive interaction (i.e., checkerboard units), aggregation (Clementsian), individualistic (Gleasonian), segregation (evenly spaced), from random patterns in species distribution (Leibold and Mikkelson 2002, Presley et al. 2010). However, as already suspected (Presley et al. 2010) EMS approach was sensitive to biased sample coverage becoming quasi-nested for the whole data set, non-significant for scrappers, and changing from quasi-nested to nested gradients for shredders when analysis were conducted with the streams corrected for sample coverage. EMS results should be interpreted with care given that null models significance was dependent upon the matrix size and fill. Such fail in correctly detect significance should be a problem also in other studies specially in small data sets (Presley et al. 2010), but also at large spatial scale because of biogeographic and historic segregation in species distribution patterns (Heino & Alahuhta

2014). Further constraints to advance empirical validation of EMS rely on limitations of data type (only incidence data), sampling bias in species detectability and consequently diversity estimates (Presley et al 2010), fail to include spatial autocorrelation as a potential mechanisms generating species co- occurrence (Legendre 1993) and disregard dispersal limitation effect. EMS provided empirical tests for long standing patterns in ecological theory (Leibold and Mikkelson, 2002) but still lack the proper

133 linkage to the metacommunity processes (Leibold et al., 2004), and this field seems fruitful for investigation.

Despite this, there was no signal of competitive interaction among species for the metacommunity and trophic guilds since the number of embedded absences was ever positive (for competitive interactions these metric would result in negative values of embedded absences due to discontinuity in species occurrences in checkerboard matrices). Overall, these results refute our hypothesis of intraguild interactions in streams and of a Clementsian gradients, and support partially

(contrary to our expectation) nestedness gradients in distribution patterns of stream insects. This effect should be associated to the fact that we have employd our analysis at genera level (as often done in similars studies in streams), increasing unrealistically the distribution of the considered taxa.

The second approach was used to evaluate the effect of environmental, spatial and biotic interactions on metacommunity structure of stream insects. We have shown that incorporating the biotic component into variance partitioning greatly improved the explanation of metacommunity patterns of stream insects. Moreover, we have found unexpectedly greater contribution of the biotic component over environmental control and dispersal limitation in some analysis conducted with the trophic guilds separately. Biotic interactions effect have been considered in terms of Eltonian noise hypothesis in species distribution models, which predict that the interactions are not importat for the scale considered in those analysis – i.e., macroscale (Soberón & Nakamura 2009; De Araújo et al.

2014), nevertheless Fraterrigo et al. (2014) have shown that biotic interactions changed in regional scale according to climate for invasive grass. The question about what are the relevant scale for ecological interactions remains unsoulved. Our results have implications for metacommunity scale, because it represent intermediate spatial scale between macroscale and local communities.

Metacommunity theory predicts that biotic interactions should be an important structuring process at small spatial scales, where species are able to track unfavorable environmental conditions and disperse

134 but should be limited by competitors or predators (Leibold et al. 2004; Holyoak et al. 2005). We highlight that environmental control remained the most important fraction for the whole insect metacommunity, and just a little non-significant fraction of explanation was related to dispersal limitation (0.6%). These result are in concordance with the growing evidence that environmental control as predicted by sorting species dynamics is the prevailing mechanism in stream metacommunities (see Soininen 2014, for a review). However, the total amount of explanation when considering only two explanatory data sets (space and environment) was lower than if we consider the biotic component within the trophic guild analysis.

Considering each trophic guild a general pattern and some peculiarities regarding the three predictor matrices arise. In summary filterers, scrapers and shredders are mainly determined by predation effects, filteres and scrapers surfer some dispersal limitation effects, and shredders and scrapers are also constrained by environmental control. For predators, enviromental control emerged as the most possible mechanism. The interguild interactions effect as suggested by our analysis of insect metacommunities was unanticipated by previous studies in streams insect metacommunities. Our approach are in consonance with the recent claim that small scale populational processes, such as predation, and organism behavioral strategies, allied to species sorting dynamics, are an important (sub- explorated) feature structuring stream metacommunities at sub-basin scale (Heino and Peckarsky

2014).

Despite filterers have shown some biotic control and dispersal limitation both significant, we acknowledge that some pivotal environmental predictors could explain the lack of environmental fit and the so many and multi scaled spatial filters in RDA (see Table 3). For instance, flow, particles suspended in the water column, hydraulics and substrate stability (as also showed in or analysis) are often associated with their micro and mesoscale distribution within a stream reach (Lancaster 2008). It is probable that further small scale missing predictors of meso and microhabitats sufficiently explain

135 filterers structuration in streams (i.e., slop, flow speed, cobbles, and suspended organic matter). On one hand, Hydropsychidae caddisflies, which are the main filterers in our data set (Table S1), have relative good dispersal ability in the adult stage – some species could disperse up to ~2 km overland (Kovats et al. 1996). The fact that Hydropsychidae species be among the most widespread distribution in the

Neotropic freshwaters and among the greatest abundance in streams invariable along the year (Flint Jr. et al. 1999), also suggest effective multi-generation dispersal phenomena (Lancaster 2008). On other hand, Philopotamidae and Polycentropodidae caddisfly are less likely to disperse long distances by behavioral and morphological constraints – they are small sized, likely poor dispersers when adults, and in the larval stage they expend most of the time attached close to the substrate of female oviposition (Wiggins 1996), suggesting low larval drift. We don't have such level of resolution of life history information for all freshwater insects in tropical streams, but some evidences from temperate regions suggest that small scale populational processes could explain at least part of regional occupation and dispersal limitation in stream metacommunities (Heino & Peckarsky 2014). Filterers had strong positive spatial structure and high colinearity in the species abundance distribution pattern in the Moran's I autocorrelogram, which could be explained because of the lack in our analysis of similar environmental constraints to all of them, since we demostrate no evidence for neutrality in species structuration, despite significant spatial signatures.

The fact that scrapers were controlled by biotic interaction, environmental constrol and dispersal limitation in our analysis needs further investigations and carefull interpretation given that negative spatial autocorrelation among species were detected by isolatin-by-distance disgnostic analysis (Diniz-Filho et al. 2012). According to Borcard et al. (2011, pp. 278), “the significant and negatively correlated variables are either related to very local, almost 'accidental' data structures, or they belong to the pure spatial fraction of variantion in partitioning, i.e. they correspond to biotic interactions.” Both hypothesis seems straighforward for our data, because our streams have closed

136 canopy cover which limits sun light entrance also limiting autotrophic production eaten by scrapers.

Negative spatial autocorrelation of the pure spatial componet, and significant environmental control and biotic effect should also be an indicative of Mass effects dynamic for scrapers given that 1) forested headwaters should limit resources required by this organisms and 2) establishment of populations in this streams should be related to ecological drift. Scrapers distribution are constrained by availability of resources attached to the substrate in streams (e.g., algae, periphyton density) (Kohler & McPeek

1989). Dispersal limitation seems possible for some scrappers taxa, given the variability of spatial autocorrelation of scrapers populations along with the firsts distance classes in Moran's I Correlogram.

All studied streams pertains to the same hydrography (All are Capim River tributaries), preventing broader geographic barrier effects, but reinforcing dispersal limitation in scrapers. Scrapers generally have small size and low mobility preferring areas of stream with higher flow speed and substrate stability, and are often assumed to be poorer dispersers (Resh et al., 1984). Scrapers probably are more susceptible to predation by other aquatic invertebrates, but should not be so attractive for fishes due to their small size and cryptic behaviour (Kohler and McPeek 1989, Wellborn et al. 1996). In addition, the small Glossosomatidae was among the most abundant scraper in our study making than a potentially abundant resource for predators insects. The confirmation of the predation hypothesos needs investigation of the gut content of rheophylic predators (e.g., Megaloptera and Plecoptera). Maybe we fail to detect greater effect of environment as we expect previously because scrapers feed on periphyton, which occurs in streams of mid-order where there is higher entrance of sun light (Vannote et al. 1980, Allan and Castillo 2007). Göthe et al. (2013a) have shown that higher density of

Ephemeroptera and Trichoptera grazers has an important structuring effect on diatoms metacommunities, jointly with environmental and spatial constraints. A control by the interactions between resource availability and environment determining scrapers abundance structuration could not be ruled out, since they present higher density and diversity at middle sized reach of stream networks

137 where environmental conditions enable higher availability of primary producers attached to the substrates (e.g., Vannote et al. 1980). Such drawback in our sampling design focused in small headwaters could explain at least part of the lack of clear EMS patterns and low explained pure environmental fraction for scrapers.

Shredders are tightly dependent on allochthonous organic matter that fall into the stream from the riparian vegetation (Cummins et al. 1989), which are mainly deposited in pools of slow water flow.

In addition, shredders are expected to be the most abundant guild in headwaters (Vannote et al. 1980).

As our streams have a great coverage of pristine forest and are narrow (about 1 m mean width), the input of organic matter may favor shredders abundance and distribution. Only shredders corroborate our hypothesis of Clementsian distribution in EMS analysis, independently of sample coverage, and they were satisfactorily explained by environmental control and dispersal limitation. However, even for them the predator effects the most prominent. Such predator control on shredders probably reflects invertebrates predation of greater body size in headwaters, despite they be considerably inconspicuous due to the camouflage provided by the use of organic materials in shelters and association to organic substrate (Cummins et al. 1989). Morover, the predation effects likely could represent congruence in the distribution patterns in both shredders and predators than any effect of predation itself, but this is an open question.

Predators were significantly related to the availability prey in potential of the biotic matrix and to environmental constraints. We associate this patterns to the fact that more abundant predators in or analysis are or water beetles, which are good swimmers and have stronger dispersal capacity, either rheophylic stoneflies and caddisflies. Also, in small headwater streams, the density of insectivores fishes are predicted to be limited to small bodied fishes (e.g., Vannote et al. 1980). At least in fishless lentic environments, invertebrate predation seems to be more important than in environments where there is dominance of fishes, posing fitness trade-offs among habitat selection and strategies by preys to

138 avoid predation (Wellborn et al. 1996). Such trade-offs may explain partially the patterns found in stream metacommunities, as in lentic habitats, since insect predators in headwater streams are often more abundant than insectivores fishes (e.g., Vannote et al. 1980). Despite seemed a straightforward hypothesis such transition of predators dominance between fish and invertebrates along with environmental gradients of streams needs more empirical studies (see Creed 2006). The fact that we have sucessfully incorporated a predation matrix into variance partitioning have several advantages towards empirical consideration of biotic interactions in stream metacommunities and elsewhere. As biotic interactions vary with abiotic conditions and may also be masked by dispersal (e.g., Mass effect), as highlighted above (but see Poff 1997), deal with such effect in the context of variance partitioning enable one to consider together the effects of biotic interactions (i.e., predation) along with environmental gradients and spatial filters (e.g., barriers, isolated refuges). Further research are needed to test the validation of such outcomes, and to incorporate biotic interactions into variance partitioning, but we suggest the way is to extend variance partitioning coulpled with co-occurence analysis traditionaly used to test assembly rules.

According to theoretical model of consumptive and nonconsumptive predator-prey interaction, predators affect prey metacommunities by i) controlling the abundance of prey directly, and ii) by imposing behavioral constraints in habitat selection and migration (Orrock et al. 2008). Such consumptive and nonconsumptive effect have been greatly overlooked in streams metacommunities

(Peckarsky et al. 2008). We have found by correlational evidences that predation imposed by invertebrates could be an important biotic mechanism structuring headwaters metacommunity, despite we are unable to disentangle consumptive from nonconsumptive effects with the data we have on hands. Effect of predation (direct and indirect) at a metacommunity scale considering potentially interacting species along environmental gradients have not been studied in many ecosystems

(Peckarsky et al. 2008), specially in headwaters, but if confirmed could improve greatly our

139 understanding of the importance of biotic interactions in stream metacommunities.

Metacommunity theory predicts that biotic interactions should be an important mechanism of community structure especially at small spatial scales (Holyoak et al. 2005, Heino and Peckarsky

2014). Dispersal limitation in turn could become aparent at broader spatial scales because geographic barrier prevents species to disperse, particularly for weak dispersers (Heino and Peckarsky 2014,

Cañedo-Argüelles et al. 2015). Despite some studies have incorporated biological traits related to dispersal ability – body size and dispersal mode (De Bie et al. 2012, Grönroos et al. 2013), traits of susceptibility to predators were not considered in stream. The consideration of morphological, demographic and behavioral traits have not been fully explored in insect metacommunities (Heino and

Peckarsky 2014), which we aknowledge be the major impediment for advance metacommunity concept in streams and elsewhere. The degree to which insects in streams evolved structures and behaviors to avoid predation, if it depends of environmental conditions (Wellborn et al. 1996), and whether dispersal are be displayed by predation in streams organism needs further investigation. Variance partitioning based on partial RDA have been proved the most powerful methods for hypothesis testing in metacommunities (Peres-Neto & Legendre 2010), and with incorporation of more diagnostic tecnics based on theoretical expectation of isolation-by-distance processes (Diniz-Filho et al. 2012), based on species traits (De Bie et al. 2012) or in the diferential habitat quality (Heino & Alahuhta 2014) should improve greatly prediction made to date on metacommunity strucutration. The EMS approuch despite be the only method to test for multiple patterns in metacommunities once (Leibold & Mikkelson 2002;

Presley et al. 2010), should be interpreted with care, and preferentially used as an diagnostic tools instead of a hypothesis testing approach in isolation. The reason to justify such critcisms are 1) fail to detect short gradients in species ordination, 2) the fail to handle with sample bies in species richness estimates, and 3) the poor linkage among patterns and processes already stabilished in the dynamic models of metacommunity theory (Leibold et al. 2004; Holyoak et al. 2005), which are the strength of

140 the variance partitioning (Peres-Neto & Legendre 2010; Diniz-Filho et al. 2012). The outlined approach open the possibility for scrutiny of predation hypothesis in more complete stream metacommunity data sets by the incorporation of both availability of resources for insects consumers

(e.g., microorganims) and their potential predators (e.g., invertebrates predators, insectivorous fishes).

An important question to address in the future studies is whether and how much the strength of biotic interaction (i.e., predation) change along environmental gradients and which traits determine susceptibility to predation, dispersal limitaion and habitat association in stream metacommunities.

Conclusion

Our results showed that shredders, scrapers and filterers are not structured in the same way, depending of the guild, distinct mechanisms prevails in streams insect metacommunities. Still, our results confirm our hypothesis of a strong biotic or at least considerable control by predators on filterers, scrapers and shredders, but not of a prey control over predators. Our hypothesis of

Clementsian gradients was rejected, and we believe that the type of stream (i.e., small headwaters) and the use of genera instead of species leval may explain at least part of these results. Our results point out against the claim that biotic interactions, especially predation, do not strongly affect the community structure of invertebrate taxa in streams (for a review Vison & Hawkins 1998), however, we found no competition effect within the trophic guilds accordingly to previous views (Vison & Hawkins 1998).

More biological information is needed to advance our understanding about the importance of biotic interactions in stream metacommunities. We demonstrated successfully that incorporating biotic interactions greatly improved the amount of explained fraction in variance partitioning. The fact that ours analyses ware restricted to some taxa prevent generalizations to the whole trophic functioning of stream metacommunities. Meanwhile, the inclusion of other groups of abundant and voracious invertebrate predators (e.g., Dragonflies and danzenflies, Dobsonflies and water bugs), insectivorous

141 fishes, and other preys (Mayflies, midges), coupled with more information on habitat characteristics and resources (Vinson & Hawkins 1998), and species traits should increase the understanding of the importance of predation in stream metacommunities. This call attention for the need for a multiple interacting guilds approach to understand the patterns, processes and functioning of stream metacommunities.

142 Acknowledgements

We are graetfull to field sampling and logistic support confered by 33 Forest e CIKEL LTDA.

We thanks to Lenize Calvão, Bruno Prudente, Marcio Cunha and Thiago Begout. We would like to thank Dr. Jani Heino for helpfull coments in a first draft of this papers and for advise about EMS interpretation. MSc Fernanda Martins highlighted important questions and corrected the english writing. We thank to the Brazilian Nacional Research Concil (CNPq) by the project funding entitled

“Tempo de resiliência das comunidades aquáticas após o corte seletivo de madeira na Amazônia

Oriental” process 481015/2011-6, by the productivity fellowship of LJ (process 303252/2013-8), and for continously fomenting PDM research, and to CAPES for the fellowship to DSN.

143 References

Allan, J. and Castillo, M. 2007. Stream Ecology: Structure and function of running waters. - Springer.

Blanchet, F. G. et al. 2008. Forward selection of explanatory variables. - Ecology 89: 2623–32.

Borcard, D. and Legendre, P. 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. - Ecol. Modell. 153: 51–68.

Cañedo-Argüelles, M. et al. 2015. Dispersal strength determines meta-com- munity structure in a dendritic riverine network. - J. Biogeogr.: 1–13.

Cayuela, L. and Gotelli, N. J. 2014. rareNMtests: Ecological and biogeographical null model tests for comparing rarefaction curves.

Chao, A. and Jost, L. 2012. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. - Ecology 93: 2533–2547.

Cottenie, K. 2005. Integrating environmental and spatial processes in ecological community dynamics. - Ecol. Lett. 8: 1175–82.

Creed, R.P., 2006. Predator transitions in stream communities: a model and evidence from field studies. J. North Am. Benthol. Soc. 25: 533–544.

Cummins, K. and Klug, M. 1979. Feeding ecology of stream invertebrates. - Annu. Rev. Ecol. Syst. 10: 147–172.

Cummins, K. et al. 1989. Shredders and riparian vegetation. - Bioscience 1: 24–30.

Dallas, T. 2014. metacom: an R package for the analysis of metacommunity structure. - Ecography (Cop.). 37: 402–405.

De Araújo, C. B., L. O. Marcondes-Machado, & G. C. Costa, 2014. The importance of biotic interactions in species distribution models: A test of the Eltonian noise hypothesis using parrots. - Journal of Biogeography 41: 513–523.

De Bie, T. et al. 2012. Body size and dispersal mode as key traits determining metacommunity structure of aquatic organisms. - Ecol. Lett. 15: 740–7.

Diniz-Filho, J. A. F. et al. 2012. Spatial autocorrelation analysis allows disentangling the balance

144 between neutral and niche processes in metacommunities. - Oikos 121: 201–210.

Flint Jr., O. S. et al. 1999. Catalog of the Neotropical Caddisflies (Insecta: Trichoptera). - Ohio Biological Survey.

Fraterrigo, J., S. Wagner, & R. Warren, 2014. Local-scale biotic interactions embedded in macroscale climate drivers suggest Eltonian noise hypothesis distribution patterns for an invasive grass. - Ecology Letters 17: 1447–1454.

Gerwing, J. J. 2002. Degradation of forests through logging and fire in the eastern Brazilian Amazon. - For. Ecol. Manage. 157: 131–141.

Gotelli, N. J. 2007. Ecologia. Editora Planta, Londrina – Paraná, 260p.

Göthe, E. et al. 2013a. The influence of environmental, biotic and spatial factors on diatom metacommunity structure in Swedish headwater streams. - PLoS One 8: e72237.

Göthe, E. et al. 2013b. Metacommunity structure in a small boreal stream network. - J. Anim. Ecol. 82: 449–458.

Gray, D. et al. 2012. The recovery of acid‐damaged zooplankton communities in Canadian Lakes: the relative importance of abiotic, biotic and spatial variables. - Freshw. Biol. 57: 741–758.

Grönroos, M. and Heino, J. 2012. Species richness at the guild level: effects of species pool and local environmental conditions on stream macroinvertebrate communities. - J. Anim. Ecol. 81: 679–91.

Grönroos, M. et al. 2013. Metacommunity structuring in stream networks: roles of dispersal mode, distance type, and regional environmental context. - Ecol. Evol. 3: 4473–87.

Hamada, N. et al. 2014. Insetos Aquáticos na Amazônia brasileira: taxonomia, biologica e ecologia (N Hamada, JL Nessimian, and RB Querino, Eds.). - Editora do INPA.

Heino, J. 2009. Species co-occurrence, nestedness and guild-environment relationships in stream macroinvertebrates. - Freshw. Biol. 54: 1947–1959.

Heino, J., Alahuhta, J., 2014. Elements of regional beetle faunas: faunal variation and compositional breakpoints along climate, land cover and geographical gradients. J. Anim. Ecol. Doi:10.1111/1365-2656.12287.

Heino, J., Melo, A.S., Bini, L.M., 2014. Reconceptualising the beta diversity-environmental

145 heterogeneity relationship in running water systems. Freshw. Biol. doi:10.1111/fwb.12502.

Heino, J. and Peckarsky, B. L. 2014. Integrating behavioral, population and large-scale approaches for understanding stream insect communities. - Curr. Opin. Insect Sci. 2: 1–7.

Holyoak, M. et al. 2005. Metacommunities: spatial dynamics and ecological communities. - University of Chicago Press.

Hubbell, S. P. 2001. The Unified Neutral Theory of Biodiversity and Biogeography. - Princeton University Press.

Hutchinson, G. 1957. Concluding remarks. - Cold Spring Harb. Symp: 415–427.

Jäch, M. and Balke, M. 2008. Global diversity of water beetles (Coleoptera) in freshwater. - Hydrobiologia 595: 419–442.

Jacobson, B. and Peres-Neto, P. R. 2009. Quantifying and disentangling dispersal in metacommunities: how close have we come? How far is there to go? - Landsc. Ecol. 25: 495–507.

Kohler, S. L. and McPeek, M. A. 1989. Predation risk and the foranging behavior of competing stream insects. - Ecology 70: 1811–1825.

Kovats, Z. et al. 1996. Inland dispersal of adult aquatic insects. - Freshw. Biol. 36: 265–276.

Lancaster, J. 2008. Movement and dispersion of insects in stream channels: what role does flow play? - Aquat. insects challenges to Popul.: 139–157.

Landeiro, V. L. et al. 2011. Spatial eigenfunction analyses in stream networks: do watercourse and overland distances produce different results? - Freshw. Biol. 56: 1184–1192.

Landeiro, V. L. et al. 2012. The roles of dispersal limitation and environmental conditions in controlling caddisfly (Trichoptera) assemblages. - Freshw. Biol. 57: 1554–1564.

Legendre, P. 1993. Spatial autocorrelation: trouble or new paradigm? - Ecology 74: 1659–1673.

Legendre, P. and Gallagher, E. 2001. Ecologically meaningful transformations for ordination of species data. - Oecologia 129: 271–280.

Legendre, P. and Legendre, L. 2012. Numerical Ecology. - Elsevier.

Leibold, M. A. and Mikkelson, G. M. 2002. Coherence, species turnover, and boundary clumping:

146 elements of meta-community structure. - Oikos 2: 237–250.

Leibold, M. A. et al. 2004. The metacommunity concept: a framework for multi-scale community ecology. - Ecol. Lett. 7: 601–613.

Merritt, R. W. and Cummins, K. W. 1984. An Introduction to the Aquatic Insects of North America. - Kendall / Hunt Publishing Company.

Oksanen, J. et al. 2012. vegan: Community Ecology Package (R package version 1.17-6).

Orrock, J. L. et al. 2008. Consumptive and nonconsumptive effects of predators on metacommunities of competing prey. - Ecology 89: 2426–2435.

Peckarsky, B. L. et al. 2008. Revisiting the classics: Considering nonconsumptive effects in textbook examples of predator prey Interactions. - Ecology 89: 2416–2425.

Peres-Neto, P. R. and Legendre, P. 2010. Estimating and controlling for spatial structure in the study of ecological communities. - Glob. Ecol. Biogeogr. 19: 174–184.

Peres-Neto, P. R. et al. 2006. Variation partitioning of species data matrices: estimation and comparison of fractions. - Ecology 87: 2614–25.

Poff, N. L. 1997. Stream Ecology Landscape filters and species traits : towards mechanistic understanding and prediction in stream ecology. - J. North Am. Benthol. Soc. 16: 391–409.

Presley, S. J. et al. 2010. A comprehensive framework for the evaluation of metacommunity structure. - Oikos 119: 908–917.

R Development Core Team 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing. in press.

Resh, V. H., S. S. Balling, G. A. Lamberti, & C. H. Eriksen, 1984. Aquatic insect respiration In Merrit, R. W., & K. W. C. P. Cummins (eds), An Introduction to the Aquatic Insects of North America. Kendall / Hunt Publishing Company VL -: 27–37.

Shevtsov, J. et al. 2013. Evaluating the role of biotic interactions in structuring communities using a gradient analysis of multiple interacting guilds. - Oikos 122: 1594–1605.

Soberón, J., M. Nakamura, J. Soberon, M. Nakamura, J. Soberón, & M. Nakamura, 2009. Niches and distributional areas: concepts, methods, and assumptions. Proceedings of the National Academy of

147 Sciences of the United States of America 106: 19644–19650.

Soininen, J. 2014. A quantitative analysis of species sorting across organisms and ecosystems. - Ecology 95: 3284–3292.

Vannote, R. L. et al. 1980. The River Continuum Concept. - Can. J. Fish. Aquat. Sci. 37: 130–137.

Wellborn, G. A. et al. 1996. Mechanisms creating community structure across a freshwater habitat gradient. - Annu. Rev. Ecol. Syst. 27: 337–363.

Wiggins, G. B. 1996. Larvae of the North American Caddisfly Genera (Trichoptera).

148 Table S1 – Lower taxonomic units (LTU), Feeding Functional Group (FFG) classification, abundance and occurrence of the three studied aquatic insect orders. Fcol: Filtering Collector; Fgat: Filtering gatherer; Scr: Scraper; Shr: Shredder; Pre: Predator. Order LTU FGG Abundance Occurence Coleoptera Derallus Pre 75 22 Coleoptera Dryopidae Shr 10 9 Coleoptera Gyrelmis Scr 47 18 Coleoptera Gyretes Pre 293 27 Coleoptera Helochares Shr 6 6 Coleoptera Hydaticus Pre 3 3 Coleoptera Hydrocanthus Pre 7 7 Coleoptera Hydrochus Fgat* 1 1 Coleoptera Hydrodessus Pre 2 2 Coleoptera Hydrophilini - 13 8 Coleoptera Laccodytes Pre 6 3 Coleoptera Laccophilus Pre 3 2 Coleoptera Macrovatellus Pre 2 2 Coleoptera Microcylloepus Scr 1 1 Coleoptera Phanocerus Scr 1 1 Coleoptera Platynectes Pre 1 1 Coleoptera Pronoterus Pre 1 1 Coleoptera Stegoelmis Scr 13 8 Coleoptera Tropisternus Pre 18 7 Plecoptera Anacroneuria Pre 174 19 Plecoptera Enderleina Pre 23 11 Plecoptera Macrogynoplax Pre 473 35 Trichoptera Amphoropsyche Scr 23 11 Trichoptera Austrotinodes Pre 5 4 Trichoptera Cernotina Fcol 49 21 Trichoptera Chimarra Fcol 57 12 Trichoptera Cyrnellus Fcol 3 3 Trichoptera Glossosomatidae Scr 214 19 Trichoptera Helicopsyche Scr 623 30 Trichoptera Hydroptilidae Scr 4 1 Trichoptera Leptonema Fcol 182 28 Trichoptera Macronema Shr 533 34 Trichoptera Macrostemum Fcol 93 15 Trichoptera Marilia Pre 22 10 Trichoptera Nectopsyche Fcol 17 11 Trichoptera Oecetis Pre 164 27 Trichoptera Phylloicus Shr 413 34 Trichoptera Polyplectropus Fcol 73 22 Trichoptera Smicridea Fcol 71 17 Trichoptera Triplectides Shr 312 28 Trichoptera Wormaldia Fcol 54 18

149 Order LTU FGG Abundance Occurence Total 41 5 4085 - Coleoptera 19 4 503 - Plecoptera 3 1 670 - Trichoptera 19 4 2912 -

A) Metacommunity B) Filterers - Space

C) Filterers – Biotic D) Scrapers - Environment

150 E) Scrapers - Space F) Scrapers - Biotic

G) Shredders – Environment H) Shredders - Biotic

151 I) Predators – Environment

Figure S1 – Biplot ordination for all the pure significant RDA partial models constrained by environmental, spatial and biotic predictors. Response matrix are: A. metacommunity; B – C. filterers; D – F. Scrapers; G-H. Shredders; I) Predators.

Figure S2 – Sample coverage on x-axis ploted against rarefaction estimates based on exponential

Shannon index (Hill order q=1) on y-axis for all studied streams.

152 CONSIDERAÇÕES FINAIS CONCLUSÕES GERAIS

Ecossistemas aquáticos lóticos estão entre os mais complexos ecossistemas do planeta Terra, que envolve processos atuando em múltiplas escalas espaciais e temporais. Apesar disso, os estudos sobre a origem e a diversificação das biodiversidade destes ecossistemas, pelo menos quanto aos insetos quáticos ainda são extremamente escassos. Entretanto, sabe-se que estes ecossitemas foram invadidos inúmeras vezes por diferentes grupos não relacionados de insetos, o que sugere que este ecossistema apresenta oportunidades únicas de novos nichos ainda não explorados, escape a predadores e competidores. Por outro lado, os ecossistemas lóticos impuseram aos insetos inúmeros desafios para vencer limitações ecofisiológicas, movimentação e dispersão, para se fixarem nos substratos, ou para manterem suas posições sem gastos adicionais de energia.

Os resultados da presente tese tem implicações para as pesquisas sobre a avaliação dos efeitos do corte seletivo de madeira sobre a biodiversidade de riachos na Amazônia, enfatiza do uso de atributos funcionais para a compreensão dos mecanismos ecológicos e evolutivos de filtragem de espécies em comunidades, e a importância da integração de interações bióticas nas análises de metacomunidades. A principal conclusão do capítulo 2 se concentra na ausência de efeitos negativos sobre a diversidade, composição taxonômica e abundância das espécies. Meu argumento é que estes resultados não podem ser generalizados e devem ser interpretados como um primeiro diagnóstico na avaliação destes impactos, servindo de bases para futuros biomonitoramentos ou reavaliações. Devo realçar a efetividade do protocolo de caracterização física de hábitats (ver metodologia geral; Apêndice

3) usado em todos os artigos. O protocolo inteiro é muito difícil e demorado para ser aplicado em escalas amplas para a avaliação ambiental, necessita de treinamento em processos e fatores hidrogeomorficos tanto para aplicação em campo quanto para interpretação conceitual (Capítulo 1 e 2).

Entretanto, o protocolo é muito útil pois permite medir com muitos detalhes as variações em um trecho de riacho, é extremamente informativo e sensível a impactos, mesmo os sutís (como o corte seletivo).

154 Apesar de nem todos estados do Brasil terem políticas de biomonitoramento da qualidade da água dos rios, esta ferramenta já está sendo utilizada em Minas Gerais, por pesquisadores da UFMG, e os resultados têm sido muito positivo, permitindo critérios mais acurados sobre o que é uma condição impactada e o que recuperar para restabelecer a qualidade do ecossitema aquático (Dr. M. Callisto, informação pessoal). O protocolo permite também uma maior capacidade de predição e congruência nas comparações entre regiões.

Os resultados principais do Capítulo 3 sugeram que o uso de atributos ecológicos e morfológicos das espécies são essenciais para melhores predições em metacomunidades de insetos de riachos. Os resultados sugerem similaridades nas predições realizadas em outros estudos e os padrões de associação entre atributos morfológicos e ecológicos ligados a reofilia e a seletividade gerada pela correnteza. Em riachos, insetos com corpos achatados dorsoventralmente, fusiformes ou hidrodinâmicos, que apresentam estruturas de fixação (linhas de âncoragem) apresentam preferência por áreas de correnteza, e são dominantes em em riachos com fluxo mais rápido em média. Insetos que constroem grandes abrigos preferem áreas de piscinas onde a correnteza é menos turbulenta. A similaridade com resultados de outros estudos sugerem uma generalidade destes padrões entre regiões

(ver Capítulo 3). Discuto brevemente outras carências em entomologia aquática de riachos neotropicais não discutidas nos demais capítulos, e que tem recebido pouca atenção, como a carência nas informações dos hábitats dos insetos aquáticos da região Neotropical.

Finalmente, discuto algumas ideias a respeito das implicações da (e falta de) inclusão de interações bióticas nos estudos de metacomunidades (Capítulo 4). Os resultados deste estudo sugerem que interações bióticas, inferidas a partir da matriz de predadores, agregam informações únicas não compartilhadas pelos fatores ambientais e espaciais, e as quais atributo aos efeitos da predação. Discuto a respeito da integração entre modelos conceituais de metacomunidades, métodos analíticos sofisticados, e a necessidade de mais informações de história natural orientadas para o teste de

155 hipóteses para guiar novos avanços nesta área. Por exemplo, algumas perguntas que poderão render novos estudos experimentais e observacionais: 1) quais atributos determinam a susceptibilidade à predação? 2) qual é a escala espacial relevante para diagnosticar os efeitos da predação e de outras interações em riachos? 3) como a relação entre a densidade de predadore e presas varia em riachos? 4) quais são os principais predadores? Muitas destas perguntas talvez já tenham respostas para regiões mais estudadas, mas colocá-las no contexto da teoria de metacomunidades abrem novas possibilidades pouco exploradas na literatura.

156 APÊNDICES

Apêndice 1 – Lista e estatisticas descritivas dos 64 preditores ambientias selecionados, usados para compor o conjunto dos capítulos 2 à 3.

Environmental Environmental

Descriptors * Mean SD Min Max Descriptors * Mean SD Min Max PHI 3.21 0.85 4.6 4.6 SDCM 26.57 5.75 41.59 41.59 Temp_ar 28.14 1.25 30.7 30.7 SDCMW 25.23 5.54 38.45 38.45 Umid_ar 87 6.59 99 99 SDCMG 29.38 7.28 45.91 45.91 ORP 352.11 27.47 404.33 404.33 XCMGW 27.55 6.32 39.71 39.71 OD 6.52 1.24 9.19 9.19 XCMGW.1 27.55 6.32 39.71 39.71 Turb 3.31 2.86 12.63 12.63 XCDENMID 91.81 4.07 98.13 98.13 XFC_ALG 0.37 0.91 3.64 3.64 VCDENMID 5.2 3.06 15.18 15.18 XFC_LWD 12.65 10.26 51.14 51.14 XCDENBK 95.98 2.82 98.93 98.93 XFC_OHV 18.22 10.22 44.55 44.55 VCDENBANK 4.79 3.1 14.11 14.11 XFC_ROT 16.18 9.99 40.68 40.68 Declivi 0.47 0.41 1.84 1.84 XFC_LEB 41.6 17.78 82.05 82.05 RP100 0.62 0.48 2.01 2.01 XFC_BIG 22.86 15.45 74.32 74.32 LRBS -3.51 0.36 -2.65 -2.65 XFC_RCK 0.32 0.69 3.64 3.64 W1H_HALL 0.14 0.28 1.46 1.46 XCL 18.52 8.46 35.8 35.8 W1_HNOAG 0.11 0.26 1.46 1.46 SDCL 14.63 4.85 23.33 23.33 C1W_100 26.3 24.26 150.67 150.67 XCS 36.15 8.85 56.02 56.02 C1D_100 8.76 4.71 22.67 22.67 SDCS 15.6 3.81 23.35 23.35 C2D_100 3.57 2.97 11.33 11.33 XMW 36.02 11.27 54.43 54.43 C1T_100 35.06 26.86 173.33 173.33 SDMW 14.42 4.14 26.2 26.2 XBKF_W 4.18 3.69 21.81 21.81 XMH 15.39 8.92 43.41 43.41 SDBKF_H 0.57 2.17 11.98 11.98 SDMH 11.36 6.15 27.67 27.67 XINC_H 9 3.59 19.27 19.27 XGW 20.62 8.37 38.98 38.98 SDINC_H 2.34 0.97 4.79 4.79 SDGW 11.16 3.77 22.06 22.06 SDUN 0.25 0.84 5.11 5.11 XGH 12.95 5.79 32.05 32.05 Sinuosi 1.14 0.09 1.34 1.34 SDGH 8.02 3.6 16.37 16.37 XEMBED 55.05 15.56 85.82 85.82 XGB 0.75 1.26 5.57 5.57 VEMBED 38.2 6.94 44.95 44.95 SDGB 1.11 2.31 13.2 13.2 XCEMBED 56.18 18.14 87.73 87.73 SDC 21.14 5.59 34.21 34.21 VCEMBED 36.1 7.32 44.12 44.12 XM 51.42 13.12 74.77 74.77 Dgm_X 446.41 549.08 2978.7 2978.7 SDM 18.98 5.95 40.3 40.3 Dgm_V 1228.27 742.6 2800.17 2800.17 XG 33.58 10.07 50.23 50.23 PCT_RI 0.2 0.2 0.81 0.81 SDG 14.5 4.42 25.58 25.58 PCT_GL 0.69 0.21 0.99 0.99 * Significado das siglas no Apêndice 2.

157 Apêndice 2 – Siglas, significados e escalas de medida das 64 métricas obtidas por meio do Protocolo de Caracterização de hábitats (ver metodologia geral).

PHI: Índice de integridade física de habitats; Temp_ar: Temperatura do ar (°C); Umid_ar: Umidade do ar (%); ORP: Potencial de óxido-redução (H+); OD: Oxigênio Dissolvido (mg-1L); Turb (NTU): Turbidez; XFC_ALG: Algas filamentosas (%); XFC_LWD: Número de madeiras grandes (Média); XC: Cobertura do dossel (Média de todos os estratos); Dgm_V: Tamanho do substrato (DP); XCMG: Cobertura do dossel (Soma de todos os estratos); SDCMG: Cobertura do dossel (DP); XCMGW: Cobertura lenhosa (%); SDCMGW: Cobertura lenhosa (DP); XMW: Subbosque lenhoso (Média); XCL: Cobertura do dossel de árvores grandes (Média); SDCL: Cobertura do dossel de árvores grandes (DP); XCS: Cobertura do dossel de árvores pequenas (Média); XCM: Cobertura do dossel total mais estrato intermediário (Média); XCMW: Cobertura do dossel total mais estrato intermediário lenhoso (Média); SDCS: Cobertura do dossel de árvores pequenas (DP); XM: Cobertura do dossel intermediário (Média); V1T_100: Volume de madeiras grandes caídas dentro do canal vezes o dossel de árvores grandes (Estimativa); V1W_100: Volume de madeiras grandes caídas dentro do canal (Estimativa); V2T_100: Colume de madeiras médias caídas dentro do canal mais cobertura do dossel de árvores grandes (Estimativa); V2W_100: Volume de madeiras médias caídas dentro do leito; V4W_100: Volume de madeiras pequenas caídas for a do leito; XBKF_W: Largura do leito sazonal ativo (Media em metros); SDINC_H: Altura da incisão do topo do vale ao canal (DP da estimativa); XINC_H: Altura da incisão do topo do vale ao canal (Média da estimativa). XWD_RAT: razão entre a largura e a profundidade (em 150 metros); XFC_OHV: Vegetação pendurada sobre o canal (média); XFC_ROT: Árvores vivas sobre o canal (Média); XFC_LEB: Banco de folhas (Média); XFC_BIG: Grande abrigos para peixes (Estimativa média de grandes árvores caídas); XFC_RCK: Matacão (Média); SDMW: Subbosque lenhoso (DP); XMH: Subbosque de herbáceas (Média); SDMH: Subboque de herbáceas (DP); XGW: Cobertura rasteira lenhosa (Média); SDGW: Cobertura rasteira lenhosa (DP); XGH: Cobertura rasteira de herbáceas (Média); SDGH: Cobertura rasteira de herbáceas (DP); XGB: Solo exposto (Média); SDGB: Solo exposto (DP); SDC: Cobertura total do dossel (DP); SDM: Cobertura do dossel intermediário (DP); XG: Cobertura rasteira (Média); SDG: Cobertura rasteira (DP); SDCM: Cobertura do dossel total mais dossel intermediário (DP); SDCMW: Cobertura do dossel total mais dossel intermediário lenhoso (DP); XCMGW.1: Cobertura lenhosa (%); XCDENMID: Cobertura do dossel sobre o canal (% Média); VCDENMID: Cobertura do dossel sobre o canal (% DP); XCDENBK: Cobertura do dossel nas margens (% Média); VCDENBANK: Cobertura do dossel nas margens (% DP); Declivi: Declividade (% do canal); RP100: Piscinas resíduais (metros quadrados por 150 m); LRBS: Estabilidade relativa do leito (Estimativa); W1H_HALL: Proximida de impacto humano total (Estimativa); W1_HNOAG: Proximida de impacto humano não agrícola (Estimativa); C1W_100: número de madeiras grandes no leito (Soma); C1D_100: Número de madeiras no leito total (Soma); C2D_100: Número de madeiras médias no leito (Soma); C1T_100: Número de madeiras grandes no leito mais cobertura do dossel superior (Estimativa); SDBKF_H: Altura do leito sazonal (DP); SDUN: Distância entre margens escavadas (metros); Sinuosi: Sinuosidade; XEMBED: Imersão por sedimento fino no meio do canal mais margens do leito (% Média); VEMBED: Imersão por sedimento fino no canal mais margens do leito (% SD); XCEMBED: Imersão por sedimento fino no canal principal (% Média); VCEMBED: Imersão por sedimento fino no canal principal (% DP); Dgm_X: Tamanho do substrato (Média); PCT_RI: Corredeiras (% da área do canal); PCT_GL: Fluxo lento ou suave (% da área do canal). 158 Apêndice 3 – Fichas de campo do protocolo de caracterização de hábitats utilizado para obtenção das métricas analisadas em todos os capítulos.

159 160 161 162 163 164 165