O Papel De Mudanças Hidrológicas De Ordem Sazonal E Climática Na Estrutura Da Assembleia E História De Vida De Peixes Na Confluência Dos Rios Negro E Amazonas

INSTITUTO NACIONAL DE PESQUISAS DA AMAZÔNIA - INPA PROGRAMA DE PÓS-GRADUAÇÃO EM BIOLOGIA DE ÁGUA DOCE E PESCA INTERIOR

O papel de mudanças hidrológicas de ordem sazonal e climática na estrutura da assembleia e história de vida de peixes na confluência dos rios Negro e Amazonas

CRISTHIANA PAULA RÖPKE

Tese apresentada ao Programa Integrado de Pós-Graduação em Biologia Tropical e Recursos Naturais – INPA, como parte dos requisitos para obtenção do título de Doutora em CIÊNCIAS BIOLÓGICAS, área de concentração em Biologia de Água Doce e Pesca Interior.

Manaus, Amazonas Maio, 2016

INSTITUTO NACIONAL DE PESQUISAS DA AMAZÔNIA - INPA PROGRAMA DE PÓS-GRADUAÇÃO EM BIOLOGIA DE ÁGUA DOCE E PESCA INTERIOR

O papel de mudanças hidrológicas de ordem sazonal e climática na estrutura da assembleia e história de vida de peixes na confluência dos rios Negro e Amazonas

CRISTHIANA PAULA RÖPKE

Orientadora: Dra. Sidineia A. Amadio Instituto Nacional de Pesquisas da Amazônia – INPA

Co-orientadora: Dra. Claudia Pereira de Deus Instituto Nacional de Pesquisas da Amazônia – INPA

Co-orientador no exterior: Dr. Kirk O. Winemiller Texas A&M University

Manaus, Amazonas Maio, 2016

Ficha catalográfica

R784 Röpke, Cristhiana Paula O papel de mudanças hidrológicas de ordem sazonal e climática na estrutura da assembleia e história de vida de peixes na confluência dos rios Negro e Amazonas / Cristhiana Paula Röpke. --- Manaus: [s.n.], 2016. 145 f.: il.

Tese (Doutorado) --- INPA, Manaus, 2016. Orientadora: Sidinéia Aparecida Amadio Coorientadora: Claudia Pereira de Deus Área de concentração: Biologia de Água Doce e Pesca Interior

1. Mudanças climáticas. 2. Populações de peixes. 3. Lago de várzea. I. Título.

CDD 551.6

Sinopse

nio Estudou-se uma série temporal de 15 anos de dados biológicos para um lago de planície

de inundação localizado no encontro dos rios Negro e Solimões a fim de se verificar o

efeito de mudanças hidrológicos ocorridas nos últimos 15 anos, no qual ocorreram uma

série de eventos extremos, sobre a estrutura da assembleia de peixes e investimento em

reprodução para três espécies locais.

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Meus agradecimentos,

Ao INPA, CNPQ e FAPEAM pelos financiamentos das múltiplas propostas do Projeto Catalão ao longo dos últimos 15 anos pois assim permitiram a contínua alimentação do banco de dados que possibilitou o desenvolvimento deste estudo. Aos professores Dra. Sidinéia A. Amadio, Dra. Claudia Pereira de Deus, Dr. Jansen Zuanon e Dr. Efrem Ferreira por terem partilhado esses dados comigo. Ao CNPq pela bolsa de doutorado e à CAPES pela bolsa de doutorado sanduíche no exterior (PDSE).

Às professoras Sidinéia A. Amadio e Cláudia Pereira de Deus pela amigável orientação e aprendizado ao longo desses quatro anos. Ao professor Kirk O. Winemiller por ter me recebido, orientado e suportado minha permanência na Texas A&M por um ano, por ter se envolvido e contribuído profundamente com este estudo. Agradeço a vocês três por, além de terem sido orientadores no presente, terem se tornado parceiros para o futuro.

Aos professores Dra. Lucia H. Rapp Py-Daniel, Dr. Bruce Forsberg, Dr. Rosseval G. Leite, Dr. Carlos E. de C. Freitas e Dr. Igor Kaefer pela avaliação e sugestões que contribuíram para a melhora desta tese.

Ao meu querido Tiago Pires, por ter contribuído para o meu desenvolvimento profissional e pessoal ao longo dos últimos seis anos e vivido comigo este doutorado, nos últimos quatro anos. Muito obrigada pelas muitas discussões interessantes sobre ciência e correções dos meus textos em inglês.

Às Danis, por terem me ajudado com a interminável contagem de ovócitos (mais de 600 amostras!!), colaborando para existência do terceiro capítulo dessa tese 

Aos amigos de longa data Eurizângela Dary, Gislene Torrente-Vilara, Cleber Duarte, Fabiane Almeida e Rodrigo Neves dos Santos (na memória e no coração para sempre), Akemi Shibuya, André Galuch, Camila dos Anjos, Daniele Campos, Daniele Wolf de Fex, Thatyla Farago, Natália Wagner, Marina Carmona, Daniele Kaspper, Rafael Leitão, Sergio Santorelli, por fazerem a vida mais doce em Manaus e pelas discussões científicas que sempre rendem boas ideias.

Aos amigos que fiz em College Station, Carolina Arantes, Jaqueline de Bem, Fernanda Possato, Luis Espinola, David Seanz, Daniel Fitzgerald, Kelsie Neam e Leslie Kelso-Winemiller pelo recebimento acolhedor.

À minha família que mesmo distante está sempre presente e mesmo não compreendendo muito bem o que eu faço, ainda assim apoia minha escolha profissional. Minha motivação, minha força motriz!

Resumo O papel de mudanças hidrológicas de ordem sazonal e climática na estrutura da assembleia e história de vida de peixes na planície de inundação do rio Amazonas

As variações temporais de vazão dos grandes rios têm sido sugeridas a muito tempo como uma variável chave nas características ecológicas dos sistemas aquáticos. Recentemente, o efeito combinado de desflorestamento e mudanças climáticas globais tem resultado em mudanças no padrão de pluviosidade da bacia Amazônica e, consequentemente, no padrão sazonal de vazão na porção central do rio Amazonas. Isso tem mudado significativamente a magnitude e o calendário de começo das fases do regime anual de inundação. Os efeitos dessas mudanças sobre a ecologia e biologia dos organismos aquáticas ainda são pouco conhecidos. Neste estudo nós investigamos o efeito da dinâmica sazonal e interanual do regime de inundação sobre a estrutura da assembleia e o investimento reprodutivo de algumas espécies de peixes. A partir de análises de uma série temporal de dados hidrológicos e de peixes coletados em um lago de várzea próximo à confluência dos rios Negro e Amazonas nós podemos observar que: i) a conectividade sazonal do lago com esses dois rios tem um papel importante na taxa sazonal de turnover de espécies e recolonização das populações. A diversidade beta foi alta e a comunidade foi dominanda por espécies transitórias sazonalmente no lago. Um número relativamente alto de espécies que sabidamente se alimentam de recursos na planície de inundação foram capturadas quase exclusivamente durante o período da cheia. Durante a estação seca, a assembleia de peixes foi dominada por espécies de peixes com adaptações conhecidas para condições de baixa disponibilidade de oxigênio e tolerância à alta temperatura; ii) análises da série temporal completa de 13 anos de dados mostrou que houve uma significativa e concordante mudança no regime hidrológico e estrutura da assembleia de peixes. A mudança mais intensa coincidiu com a severa seca de 2005 a qual foi seguida de anos com pulsos de inundação de alta magnitude e um adiantamento do início da estação seca. Após 2005, a assembleia de peixes apresentou uma estrutura diferente e persistente em termos taxonômicos e funcionais; iii) as análises do efeito da condição hidrológica anual sobre a reprodução mostraram que eventos extremos e sua interação com a condição corpórea das fêmeas afetaram negativamente a fecundidade produzida por elas. Fêmeas de Acestrorhynchus falcirostris, uma espécie piscívora, reduziram a fecundidade quando a condição corpórea era ruim durante anos de cheias intensas. Secas fortes, com começo adiantado e de longa duração tiveram efeito negativo na fecundidade de Psectrogaster rutiloides, e interagiram com a condição corpórea das fêmeas na fecundidade de Triportheus angulatus. O diâmetro médio dos ovócitos das fêmeas foi mais afetado pela densidade das populações e condição hidrológica que pelo tamanho do corpo e condição corpórea das fêmeas em todas as espécies. A combinação desses resultados sugere que, de modo geral, eventos

vii extremos tem impacto negativo sobre a assembleia de peixes e sobre processos reprodutivos. Assim este estudo sugere que propostas direcionadas à conservação da biodiversidade e pesca em água doce na bacia Amazônica deveriam considerar o efeito das recentes mudanças hidrológicas que a bacia vem sofrendo, devido ao seu potencial de provocar fortes mudanças no funcionamento, estrutura e dinâmica das populações de organismos aquáticos.

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Abstract

Seasonal and climatic hydrological changes driving assemblage structure and fish reproduction in the central Amazon Flow varability in space and time is widely regarded as a master variable that shapes the ecological characteristics of rivers and streams. Recently combined effects of climate change and deforestation have changed precipitation patterns in the Amazon, which in turn have changed the magnitude and timing of annual flow cycle. Effects of these changes on freshwater ecology and biology are poorly known. Here we investigated the effect of seasonal and interannual hydrological dinamics on assemblage structure and fish reproductive investment, emphasising the role of extreme events. Analysis of long-term data for hydrology and fish surveys in a floodplain lake near the confluence of Amazon and Negro rivers reveals that: i) seasonal connectivity with two large rivers plays important role on seasonal species turnover and populational recolonization. Temporal 훽 diversity was high in the lake and the assemblage was dominated by seasonally transient species. Relatively large species known to feed on resources within the floodplain were captured almost exclusively during the flood period. During the dry season, the assemblage was dominated by fishes adapted to harsh conditions of high temperature and low dissolved oxygen concentrations; ii) analysis of long-term data set showed a significant and concordant changes in hydrology and assemblage structure. Major shifts coincided with an intense drought in 2005, followed by years with annual flood pulses of higher magnitude and earlier dry season onset. After 2005, the fish assemblage assumed alternative and persistent taxonomic and functional compositions indicative of a regime shift in response to an external driver; iii) the analysis of effects of annual hydrological condition on reproduction shows that extreme events and the interaction of extreme events and body condition affected female fecundity negatively. Females of Acestrorhynchus falcirostris, a piscivorous species, reduced the fecundity when had poor body condition in year of intense flood. Advanced, long and intense dry seasons had negative effect on Psetrogaster rutiloides fecundity and interacted with body condition on Triportheus angulatus fecundity. Mean oocyte size was more affected by environmental condition, as high population density and hydrological situation and its interaction with phenotype than only by individual’s phenotype (body size and body condition). Population density had a positive effect on the mean oocyte size in all studied species. The combination of these results suggests that biodiversity conservation and inland fisheries management in the Amazon will need to account for recent climatic and hydrologic changes that have the potential to induce ecological regime shifts and population dynamics.

Sumário

Introdução geral...... 1 Objetivos...... 9 Referências…...... 10

Capítulo 1. Seasonal dynamics of the fish assemblage in a floodplain lake at the confluence of the Negro and Amazon rivers...... 18 Abstract...... 19 Introduction...... 20 Methods...... 21 Results...... 25 Discussion...... 33 Acknowledgments...... 36 References...... 37 Supplementary information...... 45

Capítulo 2. Climatic related hydrological changes driving amazonian fish assemblage regime shift...... 50 Abstract...... 51 Introduction...... 52 Methods...... 54 Results...... 63 Discussion...... 72 Acknowledgments...... 75 References...... 75 Supplementary information...... 83

Capítulo 3. Effects of extreme seasonal events, phenotype and biotic interactions on reproductive investment in amazonian capital breeding fish…………...... 99

Abstract...... 100 Introduction...... 102 Methods...... 105 Results...... 109 Discussion...... 118 Acknowledgments...... 124 References...... 124 Supplementary information...... 133

Considerações Finais...... 142

Lista de figuras

Capitulo 1. Seasonal dynamics of the fish assemblage in a floodplain lake at the confluence of the negro and amazon rivers

Figure 1 Location of the Lago Catalão; rectangles highlight location of the study 22 area in relation to South America and the Amazon Basin (a) and Central Amazon (b). The ellipse (c) indicates the perennial portion of Lago Catalão, and the star indicates the sampling site selected to span a gradient between flooded forest and pelagic area within the lake. Figure 2 Temporal variation of discharge, water level, conductivity, temperature, 26 and dissolved oxygen in Lago Catalão from June 2010 to July 2011 and from April 2013 to October 2014. (a) Filled dots are for discharge, and open dots are for water level. Figure 3 Relationships of (a) fish abundance and species richness with discharge 28 of the Amazonas River and (b) the relationship of abundance with conductivity on ln scales. Figure 4 Ordination plot of Lago Catalão surveys (conducted from June 2010 to 30 July 2011 and April 2013 to October 2014) for the first two gradients (axes) derived from canonical correspondence analysis (CCA); vectors represent correlations of environmental variables with the two gradients; scores on the first two axes for species with highest scores appear in Table II. Figure 5 Histogram with number of species with various frequencies of 31 occurrence over time (a), and simple linear regression of species average abundance (ln- natural logarithm) against frequency of occurrence over time (b).

Capítulo 2. Climatic related hydrological changes driving amazonian fish assemblage regime shift

Figure 1. Study area at the confluence of the Negro River and Amazonas River in 56 two different moments (A) dry season and (B) flood season. A - satellite image showing the impact of dry season in 2009 on the floodplain lakes, even less extreme droughts can significantly reduce the extent of aquatic habitat; B - satellite image showing the largest flood registered in the last 110 years in the central Amazon; C and D - pictures showing the impact of the 2005 and 2010 droughts on study area. The channel that connected the lake to Negro River (C and D) and the lake was reduced to the deeper portion. Satellite images source: NASA; pictures: laboratory archives Figure 2. Time series of hydrological variables of the Negro River near its 58 confluence with the Amazon River. A- duration of each season; B- interannual variation in the timing of the start of each hydrological phases (dry, rising, flood, and receding); C- minimum, maximum and amplitude of the water level during each annual flood pulse during the study period.

Figure 3. Principal components analysis with species ordination according to 61 values for life history traits. Species life history classification: Equilibrium-large (solid squares); Equilibrium-small (squares); Intermediate-small (diamonds); Periodic-large (dots); Periodic-small (circles). Vectors were multiplied to improve visualization. Figure 4. PCA and PCoA ordination of survey years based on hydrology and 64 assemblage structure. (A) Hydrology, with positive scores on PCA1 associated with earlier onset of the dry season and minimum water level, and negative scores associated with higher flood-pulses amplitude and longer dry seasons. (B) Taxonomic structure. (C) Life history structure, depicted by fish outlines: ES- equilibrium small, EL equilibrium large, IS- intermediate strategy, PL- periodic large, and PS- periodic small. (D) Trophic structure, depicted by fish outlines numbers represents the group trophic level. The light blue dashed line connects sequential years’ trajectory. (Scores of the years were multiplied by a constant to improve plot visualization: taxonomic 4.9x; life history 5.4x; trophic 6.6x). Species abbreviations in (A) can be found in Table S2. Figure 5. Temporal tendency of fish assemblage diversity (measured by Shannon 65 index) and evenness of the Lago Catalão between 1999 and 2014. Figure 6. Hydrological and ecological temporal dynamics for the three major 68 assemblage structures. The best models to describe the shifts in the temporal series were estimated using nonlinear regression (Table S1). Solid line represents the predicted values by the model and the dashed lines represent the standard error around the mean estimates. (A) Segmented step-mean regression for the first PCA axis suggested a major change in hydrological characteristics at 2005, corroborated by STARS (p<0.001) (Table 1); (B) the best fit for the second axis of the PCA for hydrology suggests changes in 2008. (C, E, G) Segmented step- mean regression for the first axis indicated a regime shift in all assemblage structures from 2005 to 2007 corroborated by STARS test (Table 1); (D, F, H) null model was the best fitted model for all second axes. The variance explained by each axis can be found in Table 1. Figure 7. Life history groups abundance temporal trends identified by the 69 nonlinear regression. Best models describing the shifts in the temporal series. The shift-like change point moments were corroborated by STARS (Table 1) for equilibrium small (A), equilibrium large (B) and intermediate small (C); but not for periodic large (D), and periodic small (E). Solid line represents the predicted values by the model and the dashed lines represent the standard error around the mean estimates. Figure 8. Temporal trend of abundance by trophic level identified by the 70 nonlinear regression. Best models describing the shifts in the temporal series (Table S1), Consumer at: level 1 (A); level 1.5 (B); level 2 (C), and level 3 (D). The shift-like change point moments (in B and C) were corroborated by STARS for all groups (Table 1). Solid line represents the predicted values by the model and the dashed lines represent the standard error around the mean estimates. In A, the predicted values of the best fitted model “segmented stable-mean, linear” changed to a small linear relationship after including the term to account for error heteroscedasticity.

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Capítulo 3. Effects of extreme seasonal events, phenotype and biotic interactions on reproductive investment in amazonian capital breeding fish Figure 1. A visualization of the model presented in Table 2 showing females batch 112 fecundity for Acestrorhynchus falcirostris as a function of: a) standard length (log); b) mean oocyte size; c) interaction of days of flood with body condition (EW). Figure 2. A visualization of the model presented in Table 2 showing females mean 113 oocyte size for Acestrorhynchus falcirostris as a function of: a) standard length (log); b) residuals (SL ~batch fecundity (log); c) density of the population Figure 3. A visualization of the model presented in Table 2 showing females batch 114 fecundity for Triportheus angulatus as a function of: a) Standard length (log); b) days of flood; c) interactions of dry season onset with body condition (EW). Figure 4. A visualization of the model presented in Table 2 showing females mean 115 oocyte size for Triportheus angulatus as a function of: a) residuals (SL ~batch fecundity (log); b) standard length (log); c) body condition (EW); d) population density; e) predators density; f) minimum water level; g) interactions of days of flood with body condition (EW). Figure 5. A visualization of the model presented in Table 2 showing females batch 116 fecundity for Psectrogaster rutiloides as a function of: a) Standard length (log); b) mean oocyte size; c) maximum water level d) minimum water level. Figure 6. A visualization of the model presented in Table 2 showing females mean 117 oocyte size for Psectrogaster rutiloides as a function of: a) residuals (SL(log) ~ batch fecundity (log)); b) minimum water level; c) maximum water level; and d) interactions of predators density and population density (log).

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Lista de tabelas

Capitulo 1. Seasonal dynamics of the fish assemblage in a floodplain lake at the confluence of the Negro and Amazon Rivers

Table 1. Results of multiple regressions with reduced model with species 27 abundance and richness versus abiotic environmental variables. Data were natural logarithm transformed to reduce distribution skew and kurtosis. Table 2. Canonical Correspondence Analysis (CCA) results for Lago Catalão 29 fish species and abiotic variables. Table 3. Jaccard similarity index for pairwise comparisons of fish assemblage 32 samples among hydrological seasons and years. Comparisons between seasons in the same year and consecutive seasons are underlined. Comparisons between same season in different years are in parentheses.

Capítulo 2. Climatic related hydrological changes driving amazonian fish assemblage regime shift

Table 1. Significant change points suggested by STARS and variance explained 67 by multivariate statistics for annual hydrological regime in term of hydrological characteristics, assemblage taxonomic structure and life history structure, and univariate abundance by life history groups. Table 2. Summary of the dynamics of regime shifts identified for fish 71 assemblage in central Amazon

Capítulo 3. Effects of extreme seasonal events, phenotype and biotic interactions on reproductive investment in amazonian capital breeding fish Table 1. Estimates of the Generalized Linear Model (GLM) for the best model 110 relating batch fecundity and mean oocyte size to the independent varaibles for each species. AIC values of model selection can be found in appendix 7 and the quality of the best fitted model by residuals distribution in appendixes 8-10.

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INTRODUÇÃO GERAL

Mudanças climáticas de origem antrópica estão impactando a biodiversidade em escala global (Bellard et al., 2012), no entanto a magnitude desses impactos deve diferir de acordo com as regiões, ecossistemas e organismos ao redor do globo terrestre (Hansen & Cramer, 2015), e o conhecimento sobre as consequências das mudanças climáticas não são uniformemente conhecidas entre tais regiões (Hansen & Cramer, 2015). Embora o aumento em temperatura seja a principal consequência direta das mudanças climáticas recentes, um grande número de modelos prediz que nos trópicos os padrões de pluviosidade estarão sujeitos às mudanças em grande escala de forma mais intensa que a temperatura por si só (Giorgi, 2006; Diffenbaugh & Giorgi, 2012; Tremberth et al., 2013). Como consequência é esperado que eventos regionais de seca e cheia sejam mais intensos e frequentes (Diffenbaugh & Giorgi, 2012; Tremberth et al., 2013; Duffy et al., 2015). Os padrões de precipitação interanuais na Amazônia são controlados pelos fenômenos de Oscilação Sul no Pacífico (El Niño e La Niña), no entanto mais recentemente esse padrão também tem sido associado ao aumento na temperatura da superfície do mar Atlântico (aquecimento global) (Fu et al., 2013; Marengo et al., 2013). Além desses fenômenos, um número crescente de estudos tem apontado um efeito importante do desmatamento na porção sul da bacia amazônica, contribuindo para a redução regional de chuvas (Malhi et al., 2008; Bagley et al., 2014). Mundanças nos padrões de precipitação têm se tornado mais evidentes nas últimas três décadas, com uma pronunciada redução de chuvas na porção leste e sul da bacia Amazônica durante a estação seca e um pronunciado aumento da pluviosidade nas porções noroeste da bacia durante a estação chuvosa (Gloor et al., 2013). Essas mudanças em pluviosidade tem um efeito direto sobre o regime sazonal de inundação dos grandes rios na bacia Amazônica com efeitos especialmente potencializados na porção central da bacia. A hidrologia tem influencia sobre todos os processos ecológicos dos sistemas tropicais de rios- planícies de inundação (Junk & Piedade, 1997), e mudanças no regime hidrológico podem gerar respostas imediatas pelos organismos aquáticos, não só quando há ocorrência de eventos extremos, mas também pela mudança na pontualidade das estações, que leva a dissincronismo com processos biológicos fenologicamente sincronizados ao ambiente. Neste sentido a fauna aquática adaptada ao sistema de rio-planície de inundação poderia ser especialmente impactado. A Amazônia contém a fauna de peixes de água doce mais diversa do mundo, sendo que 50% dessa diversidade pode ser encontrada em áreas de planície de inundação (Junk et al., 2007), que compreendem cerca de 800.000 km2 (Melack & Hess, 2010). Entretanto, o efeito das mudanças hidrológicas interanuais sobre essa imensa diversidade ainda permanece desconhecido.

SAZONALIDADE CONTROLANDO PROCESSOS BIOLÓGICOS E ECOLÓGICOS EM PLANÍCIES DE

INUNDAÇÃO A maior parte dos grandes rios tropicais e subtropicais tem uma dinâmica hidrológica sazonal com períodos alternados de alta e baixa vazão de água, relacionada com a sazonalidade das chuvas. Essa flutuação sazonal é um dos fatores chave que regem a estrutura das comunidades aquáticas por meio de muitos mecanismos ecológicos que incluem limitações a fatores abióticas e interações bióticas (Lowe-McConnell ,1987; Junk et al., 1989; Bayley, 1991). Junk et al. (1989) realizaram o primeiro trabalho enfatizando a estreita relação entre sazonalidade e os componentes abiótico e biótico, quando propuseram o conceito de Pulso de Inundação. As inundações e secas permitem a troca periódica de água, nutrientes e organismos entre o rio e a planície de inundação, o que tem um efeito preponderante sobre a produtividade do sistema como um todo (Melack & Forsberg, 2001). Além disso, permitem processos de ocupação e desocupação periódicas das espécies nos ambientes aquáticos da planície de inundação (Rodríguez & Lewis, 1994, Fernandes, 1997; Saint-Paul et al., 2000, Fernandes et al., 2014). A sazonalidade da flutuação no nível hidrológico (com variações de 4 – 14 m na bacia Amazônica, cf. Goulding et al., 2003), gera disponibilidade de alimentos e abrigo contra predação distinto ao longo do gradiente sazonal de subida e descida da água para os peixes que utilizam diferentes recursos tróficos (Lowe-McConnell, 1987; Goulding et al., 1988). Durante o período de cheia, a floresta alagada provê maior quantidade de alimentos aos herbívoros, insetívoros e detritívoros, enquanto que durante o período de águas baixas a maior densidade de peixes aumenta a disponibilidade de alimento para os piscívoros (Goulding et al., 1988; Winemiller & Jepsen, 1998; Neves dos Santos et al., 2008; Correa & Winemiller, 2014). Durante os períodos de elevada disponibilidade de alimento, as espécies de peixes adquirem e acumulam energia suficiente para manter suas taxas metabólicas basais e promover o desenvolvimento dos gametas para o próximo período reprodutivo (Neves dos Santos et al., 2010). Os estudos desenvolvidos por Junk (1985), Arringthon et al. (2006) e Neves dos Santos et al. (2010) com peixes neotropicais que possuem reprodução e alocação energética sazonal sugerem que durante as fases de vazante e seca ocorre um a transferência da energia estocada como gordura presente na cavidade celomática e nos músculos para o desenvolvimento das gônadas. Para um grande número de espécies este processo conclui com a reprodução (desova) entre o final da seca e a cheia (Lowe-McConnell, 1987; Vazzoler et al., 1989a, b; Winemiller, 1989; Vazzoler & Menezes, 1992; Carvalho de Lima & Araujo-Lima, 2004; Leite et al., 2006). Esta sazonalidade no

2 estoque de gordura e esforço reprodutivo tem sido sugerida como uma adaptação à previsibilidade da flutuação unimodal periódica do nível da água e disponibilidade de alimento (Junk, 1985; Arrington et al., 2006). Outras adaptações parecem ter evoluído em resposta à pressão seletiva exercida pelas secas cíclicas, que talvez seja o período mais inóspito na maior parte dos ambientes aquáticos sujeitos ao pulso de inundação. No período de seca a temperatura da água torna-se mais elevada, ocorre a redução do oxigênio dissolvido e aumenta a concentração de peixes e predadores (Winemiller & Jepsen, 1998). Muitos peixes desenvolveram adaptações anatômicas, fisiológicas e comportamentais (Junk, 1985; Saint-Paul & Soares 1987, 1988, Soares et al., 2006; Winemiller, 1989; Lima-Filho et al., 2012) que lhes permite lidar com as condições abióticas severas. Todos esses fatores têm se mostrado importantes para a determinação do conjunto de espécies que ocorrem em certo local em certo período. Contudo, a importância relativa deles não é igual ao longo do pulso de inundação. A conectividade (entre rio e planície de inundação) parece ser o principal fator determinando tal composição durante o período de águas altas ao passo que fatores locais, tais como condições físico-químicas e interações bióticas, parecem ser os principais fatores durante o período de águas baixas (Rodrigues & Levis, 1994, 1997; Tejerina-Garro et al., 1998; Layman & Winemiller, 2005; Saint-Paul et al., 2000; Freitas et al., 2010; Silva et al., 2013; Freitas et al., 2014). A previsível sazonalidade na disponibilidade e qualidade dos ambientes provavelmente tem sido o principal fator de seleção natural sobre as atividades migratórias realizadas por um grande número de espécies de peixes (Lowe-McConnell, 1987). No entanto, enquanto muitas espécies deixam os ambientes da planície de inundação dos grandes rios no período de vazante e iniciam atividade migratória, outras espécies não o fazem ativamente. Estas espécies permanecem nos lagos da planície de inundação durante o período de seca “confiando” na brevidade do tempo de desconexão do lago com o rio. O acesso dos peixes aos ambientes da planície de inundação depende do momento e da duração em que a conexão (águas altas) e desconexão (águas baixas) do lago com o rio ocorre, o que pode variar interanualmente. Toda essa dinâmica tem forte influência sobre os processos no ciclo de vida dos peixes, especialmente na aquisição de energia, reprodução e sobrevivência da prole (Junk, 1985; Merona & Gascuel, 1993; Gomes & Agostinho, 1997, Bailly et al., 2008, Neves dos Santos et al., 2008; Neves dos Santos et al., 2010), que estão intimamente ligados à dinâmica populacional e e refletem nas comunidades de peixes no tempo e espaço.

SECAS, CHEIAS E VARIABILIDADE INTER E INTRAANUAL: EFEITO DE DISTÚRBIOS CAUSADOS

PELAS MUDANÇAS CLIMÁTICAS RECENTES SOBRE COMUNIDADE E POPULAÇÕES EM SISTEMAS

RIOS-PLANÍCIE DE INUNDAÇÃO A precipitação na bacia Amazônica tem mudado durante as últimas décadas em consequência do aumento no desmatamento e por efeito de mudanças climáticas globais de origem antrópica (Malhi et al., 2008; Marengo et al., 2011; Bagley et al., 2014). Isso tem resultado em mudanças na hidrologia e frequência de eventos hidrológicos extremos (cheias e secas mais fortes que o usual) na bacia Amazônica (Marengo & Espinoza, 2016, Satyamurty et al., 2013; Fu et al., 2013). Na porção central da bacia amazônica o número de cheias e secas severas durante os últimos 30 anos supera o de qualquer outro período desde que o monitoramento hidrológico começou em 1903 (Marengo et al., 2011; Langerwisch et al., 2012). Além disso, desde 1990 a duração da seca (medida pelo índice de precipitação) tem aumentado, se tornando mais longa e iniciando mais cedo (Satyamurty et al., 2013; Gloor et al., 2013). As mudanças na intensidade da seca e da cheia e amplitude de variação que o sistema sofre ao longo de um ciclo completo entre uma inundação e outra também aumentaram (Satyamurty et al., 2013). Na última década, dois dos maiores eventos de seca já registados para a Amazônia ocorreram num prazo de cinco anos (2005 e 2010). Estudos baseados em simulações sugerem que eventos de seca devem continuar a acontecer sob um cenário de aumento no desmatamento da floresta Amazônica e mudanças climáticas globais (Bagley et al., 2014). Embora os impactos provenientes de mudanças climáticas globais e atividades de desmatamento regionais tenham sido apontados como principais ameaças à fauna aquática, o conhecimento acerca do impacto dos eventos extremos e mudanças no regime anual de inundação na Amazônia pode ser considerado até o momento inexistente (veja Hansen & Cramer, 2015 para uma avaliação global). Os efeitos bióticos das mudanças em precipitação na Amazônia têm sido avaliados especialmente para o ecossistema terrestre de florestas, especificamente pelas consequências da seca sobre a mortalidade de árvores na floresta (Williamson et al., 2000; Malhi et al., 2008; Philips et al., 2009; Allen et al., 2010; Xu et al., 2011; Rowland et al., 2015). Poucos estudos avaliaram o efeito do impacto dos eventos climáticos extremos sobre ecossistemas aquáticos (Freitas et al., 2013; Correia et al., 2014), e nenhum deles avaliou o problema em longa escala de tempo. Apesar de ainda pouco conhecidos para a fauna Amazônica, os efeitos diretos mais nocivos à fauna de peixes certamente são os provenientes das secas extremas. Os efeitos mais diretos aconteceriam por meio do stress fisiológico devido ao aumento da temperatura da água, redução de oxigênio e quantidade excessiva de íons provenientes da excreção metabólica dos próprios

4 organismos e decomposição de matéria orgânica (Zuanon, 2008). Isso condiz com o observado nos eventos de seca em 1997, 2005 e 2010, em que condições de secas extremas resultaram em elevada mortalidade de peixes. Um grande número de evidências aponta que, embora todos os grupos taxonômicos e funcionais sofram durante eventos extremos, especialmente as secas, o tamanho do impacto não é randômico ou uniforme e alguns grupos funcionais parecem ser mais sensíveis que outros. É o que sugere, por exemplo, os resultados obtidos por Ledger et al. (2011 e 2012) a partir de secas experimentais, no qual espécies que estão no topo da cadeia trófica foram muito sensíveis e reduziram em abundância mais rapidamente que aquelas de outros níveis tróficos, e os resultados encontrados por Freitas et al. (2013), no qual, espécies sedentárias sofrem maior impacto que espécies migratórias. Além dos eventos extremos em si, o aumento na variabilidade intra e interanual do pulso de inundação (Satyamurty et al., 2013), aumentando a variabilidade do sistema, também pode agir como um fator remodelador da estrutura das comunidades de peixes (Milner et al., 2008; Ledger et al., 2011). As evidências do efeito da variabilidade ambiental sobre a diversidade são provenientes principalmente de estudos em escala espacial e sugerem uma redução de diversidade taxonômica e funcional (Poff & Allan, 1995; Hoeinghaus et al., 2007; Jardine et al., 2015). As assembleias de peixes na Amazônia têm uma alta diversidade taxonômica e funcional o que poderia lhes garantir resiliência aos distúrbios criados pelas mudanças climáticas recentes (Nash et al., 2015). No entanto, a capacidade de resposta resiliente também pode depender da frequência e intensidade dos distúrbios (Scheffer & Carpenter, 2003; Beisner et al., 2003) e da interação sinergética com outras fontes de impacto que podem fragilizar o ecossistema (Bozec & Mumby, 2015). Dada a preocupação para a perda de biodiversidade e serviços que o ecossistema oferece em respostas às ações antrópicas contra o ecossistema (Bellard et al., 2012) existe um apelo para a quantificação dos impactos sobre os ecossistemas ecológicos (Scheffer & Carpenter, 2003; Beisner et al., 2003). Os padrões observados ao nível de comunidade em um ambiente também são reflexo de processos que ocorrem ao nível populacional. Ao nível populacional os processos reprodutivos podem ser considerados os mais importantes. Recentemente um número crescente de estudos tem mostrado que as condições ambientais, e eventos climáticos extremos, tem importante papel na dinâmica populacional (Gilg et al., 2009; Post et al., 2009; Sandvick et al., 2012). Isso porque, além de elevarem a taxa de mortalidade drástica e abruptamente também afetam diretamente o investimento reprodutivo dos indivíduos da população (Lescroël et al., 2009; Bardsen & Tveraa 2012; Robert et al., 2012; Purchase & Moreau, 2012); uma vez que pode minimizar ou intensificar

5 as relações de trade-off entre sobreviver e reproduzir, se a condição ambiental comprometer a sobrevivência do indivíduo ou o futuro evento reprodutivo (Robert et al., 2015). Sob condições ambientais drásticas ou eventos de estresse prolongado o custo fisiológico para sobreviver após a reprodução pode fazer com que os indivíduos, especialmente as fêmeas, reduzam a fecundidade atual ou mesmo interrompam o evento reprodutivo do ano (Rijnsdorp, 1990; Clutton-Brock et al., 1996; Rideout et al., 2000). Alguns estudos têm evidenciado que condições ambientais adversas tem efeito fisiológico direto sobre a reprodução especialmente se acontecem no mesmo período em que ocorre a preparação gonadal para a reprodução (Miranda et al., 2013). Além do próprio stress causado pelas condições abióticas, indiretamente eventos de seca extremas ou cheias pouco intensas poderiam reduzir o recurso alimentar disponível (Reznick 1983) ou aumentar a densidade de predadores (Reznick & Endler, 1982, Schrader & Travis, 2012), assim como cheias intensas poderiam aumentar o recrutamento contribuindo para o aumento da densidade populacional (Albon et al., 1987; Schrader & Travis, 2012, Dantzer et al., 2013); fatores que têm sido encontrados afetando o investimento reprodutivo individual. Apesar do efeito direto isolado de cada um dos fatores citados, Houston & MacCnamara (1992) propuseram que ecossistemas variáveis no espaço e no tempo, como ambientes sazonais, poderiam selecionar organismos com capacidade de lidar com variações nas condições ambientais e fisiológicas individuais assim como a interação entre essas condições (state dependent decision), sendo capazes de responder de forma plástica para garantir sobrevivência e reprodução futura. Respostas plásticas com ajustes no investimento reprodutivo em função das condições ambientais tem sido observada em uma grande quantidade de organismos (Clutton-Brock et al., 1996, Rideout et al., 2000; Jorgensen & Fiksen, 2006; Rideout & Tomkiewicz, 2011; Pinot et al., 2014; Garnier et al., 2016). Sem, entretanto, referências a sistemas hídricos tropicais de água doce. A reprodução anual dos peixes em rios com planície de inundação tropical como o rio Amazonas é estreitamente ligada à aquisição sazonal de energia (Junk, 1985; Arrington & Winemiller, 2006; Neves dos Santos et al., 2010) e sobrevivência dos jovens (Carvalho de Lima & Araújo-Lima, 2004). Variações interanuais na intensidade da cheia e seca poderiam ter efeito direto sobre a aquisição e gasto energético aumentando o custo para a reprodução e mediando a fecundidade dos peixes. No entanto, devido a importância diferenciada desse momento considerando a estratégia trofica das espécies (veja Neves dos Santos et al., 2008, 2010), as consequências poderiam ser diferentes entre espécies. A compreensão desses processos também tem sido limitada pela obtenção sistemática de informações reprodutivas anuais por longos períodos e em um número considerável de indivíduos na população.

Figura 1. Fluxograma sobre possíveis impactos das mudanças climáticas e hidrológicas já existentes sobre processos biológicos e estrutura da assembleia de peixes nos sistemas hídricos de rios-planícies de inundação.

Questões abordadas

Com base em uma série de dados padronizados de 13 anos obtida entre 1999 e 2014 em um lago localizado no encontro dos rios Amazonas e Rio Negro, este estudo visou ampliar o conhecimento acerca das consequências biológicas e ecológicas de eventos drásticos de secas e cheias sobre o ecossistema aquático amazônico. Este estudo propôs em responder três questões específicas relacionadas aos impactos da variabilidade hidrológica recentes sobre a fauna de peixes da várzea amazônica: 1° - Qual o papel da sazonalidade da dinâmica hidrológica da conexão com os rios Negro e Amazonas sobre a dinâmica da assembleia de peixes do lago Catalão? Quão aberto é o sistema para recolonização sazonal por novos indivíduos e espécies? A hipótese deste capítulo foi de que a proximidade do Lago Catalão com esses dois grandes rios o tornaria altamente dinâmico com altos valores de beta diversidade entre as fases do ciclo hidrológico, sendo dominado por espécies que permanecem um curto tempo no sistema, o que indicaria uma alta possibilidade de

7 recolonização. As respostas para essas questões forneceram o conhecimento básico de funcionamento do sistema, necessário para subsidiar as próximas questões e fazem parte do primeiro capítulo. Este capítulo já se encontra publicado na Journal of Fish Biology (Röpke C. P., S. Amadio, K. O. Winemiller, J. Zuanon. 2015. Seasonal dynamics of the fish assemblage in a floodplain lake at the confluence of the Negro and Amazon Rivers. Jorunal of Fish Biology Doi: 10.1111/jfb.12791), mas o processo de análise gerou resultados adicionais ainda não publicados e que suportam decisões tomadas no processo de análise do segundo capítulo (anexos do segundo capítulo); 2° - Como a estrutura da assembleia de peixes do lago Catalão oscilou interanualmente ao longo dos últimos 13 anos (1999-2014), e qual foi o impacto dos eventos hidrológicos extremos sobre a estrutura da assembleia? A principal hipótese desta questão foi de que os eventos drásticos de seca e amplitude aumentada do pulso de inundação poderia ter resultado em mudança na estrutura da assembleia nos anos a partir de 2005; isso significaria que fatores extrínsecos seriam fundamentais na estruturação da assembleia. Essas questões foram investigadas avaliando-se características taxonômicas e funcionais (trófica e história de vida) da assembleia de peixes e encontram-se no segundo capítulo. Os principais resultados desse capítulo foram sintetizados na estruturação de um manuscrito submetidos à Proceedings of the National Academy of Science (PNAS); 3° - Como mudanças hidrológicas interanuais e eventos extremos alteram o custo da reprodução atual em peixes capital breeders, que evoluíram em um ambiente sazonalmente previsível? A principal hipótese deste capítulo foi de que eventos extremos aumentariam o custo da reprodução dessas espécies mesmo elas sendo adaptadas à sazonalidade, isso resultaria em redução da fecundidade, mas um possível aumento no tamanho do ovócito de modo a compensar a sobrevivência. Isso indicaria que os eventos hidrológicos têm um forte efeito na dinâmica populacional, pelo menos para espécies semelhantes às analisadas, e que eventos extremos poderiam ser especialmente impactantes sob o ponto de vista populacional. Essa questão foi investigada para as três espécies com maior número de fêmeas maduras capturadas ao longo da série temporal, a partir de informações sobre fecundidade e diâmetro dos ovócitos, e encontram- se no terceiro capítulo da tese. Os resultados deste estudo contribuem para a avaliação das consequências regionais de eventos hidrológicos extemos e incrementam o conhecimento mundial sobre os impactos das mudanças climáticas sobre a biodiversidade e segurança alimentar em ecossistemas tropicais de água doce.

OBJETIVOS

GERAL

Avaliar o efeito da dinâmica interanual e eventos hidrológicos extemos recentes sobre a ecologia e biologia da assembleia de peixes em um lago de planície de inundação (Lago Catalão) na Amazônia Central.

ESPECÍFICOS

Capítulo 1. Seasonal dynamics of the fish assemblage in a floodplain lake at the confluence of the Negro and Amazon rivers.

- Avaliar o efeito da sazonalidade da dinâmica hidrológica e limnológica da conexão do Lago Catalão com os rios Negro e Amazonas sobre a dinâmica da composição da assembleia de peixes; - Verificar quais espécies ocupam o sistema permanentemente e quais são espécies temporárias no sistema e sua relação com a densidade das populações locais;

Capítulo 2. Climatic related hydrological changes driving amazonian fish assemblage regime shift - Verificar as mudanças na estrutura taxonômica e funcional da assembleia de peixes do lago Catalão ao longo dos últimos 15 anos e sua relação com as mudanças hidrológicas recentes; - Avaliar as tendências de abundância por grupo funcional ao longo dos últimos 15 anos e sua relação com as mudanças hidrológicas recentes;

Capítulo 3. Effects of extreme seasonal events, phenotype and biotic interactions on reproductive investment in amazonian capital breeding fish - Estimar o efeito das condições hidrológicas sobre o investimento reprodutivo de peixes para três espécies com alocação energética do tipo capital breeding.

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CAPÍTULO 1

SEASONAL DYNAMICS OF THE FISH ASSEMBLAGE IN A FLOODPLAIN LAKE AT THE CONFLUENCE OF THE NEGRO AND AMAZON RIVERS

Cristhiana P. Röpke§*, Sidineia A. Amadio§§, Kirk O. Winemiller¶ and Jansen Zuanon§§

§Programa de Pós-Graduação em Biologia de Água Doce e Pesca Interior/Instituto Nacional de Pesquisas da Amazônia – INPA, Cx. Postal 2223, 69080-971, Manaus, Amazonas, Brasil. §§Coordenação de Biodiversidade – CBIO/Instituto Nacional de Pesquisas da Amazônia – INPA, Manaus, Amazonas, Brasil. ¶ Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, Texas 77843–2258, USA.

Running Title: Assemblage dynamics in the Central Amazon

Journal Style: Journal of Fish Biology (manuscript online published)

*Author to whom correspondence should be addressed. Tel.: +55 (92) 36433254; email: [email protected]

Abstract The temporal effect of discharge and limnology on fish composition and species diversity in a floodplain lake at the confluence of the Amazonas and Negro Rivers was evaluated. Species richness, abundance and assemblage composition were strongly influenced by seasonal discharge of the Amazon and Negro Rivers, which affects lateral connectivity and water conductivity and temperature. As a consequence, temporal 훽 diversity was high in the lake and the assemblage was dominated by seasonally transient species. Relatively large species known to feed on resources within the floodplain were captured almost exclusively during the flood period. During the dry season, the assemblage was dominated by fishes adapted to harsh conditions of high temperature and low dissolved oxygen concentrations. The seasonal flood pulse allows fishes to enter the lake and flooded marginal forest to exploit resources. Given its high temporal 훽 diversity and abundance of fishes, and relevance in local fisheries, Lago Catalão and other floodplain lakes in this region merit special attention for conservation.

Key-words: beta diversity; flood pulse; hydrology; Lago Catalão; multivariate ordination; Neotropics

INTRODUCTION

Spatial heterogeneity and dynamic hydrology of rivers and floodplains support high biodiversity and affect functional processes (Junk et al., 1989; Ward et al., 1999; Tockener & Stanford, 2002). Flood pulses cause lateral connectivity that influences water quality (Thomaz et al., 2007), nutrient dynamics (Melack & Forsberg, 2001), and life cycles of many organisms within floodplain aquatic habitats (Junk, 1985; Junk & Piedade, 1997; Arrington et al., 2006; Neves dos Santos et al., 2008). In tropical floodplain rivers, periodic flood pulses also have a strong influence on patterns of fish distribution and abundance (Junk et al., 1989; Arrington & Winemiller, 2004). During high-water periods, many fishes migrate from the river channel into lakes, forests and savannahs to exploit food resources, to spawn, or brood fry (Lowe-McConnell, 1987; Goulding et al., 1988). Abundance of juveniles of large- and medium-sized species also increase within floodplain habitats, especially those containing dense stands of aquatic macrophytes that harbour invertebrate prey and provide cover for predator avoidance (Gomes & Agostinho, 1997; Sanchez- Botero & Araujo-Lima, 2001; Scarabotti et al., 2011). During the low-water periods, most floodplain lakes are isolated from the active river channel, and abiotic and biotic features are strongly influenced by local dynamics (including predator-mediated processes), which leads to lower similarity among lakes (Rodríguez & Lewis, 1997; Tejerina-Garro et al., 1998; Saint-Paul et al., 2000). In response to shifting influences of lateral exchange of water and organisms versus internal ecosystem and community processes, community structure of floodplain lakes often undergoes large seasonal changes. In addition, seasonal flood pulses could cause high temporal β diversity in a local community, as have been shown for spatial β diversity (Bozelli et al., 2015). Floodplain habitats associated with large rivers in the Amazon encompass an area about 800000 km2 (Melack & Hess, 2010) and harbour about 50% of the Amazonian fish diversity, including almost all commercially important species (Junk et al., 2007). Despite their importance, Amazon floodplain landscapes are being altered at alarming rates as human activities expand into these areas (Junk, 2007; Renó et al., 2011; Junk, 2013; Castello et al., 2013). In addition to losses from direct anthropogenic actions, climate change appears to be affecting regional hydrology (Satyamurty et al., 2013; Gloor et al., 2013), which increases the urgency for increasing knowledge of the ecology of Amazonian floodplains (Barletta et al., 2010; Junk, 2013). In the central Amazon, the confluence of white waters (muddy water) of the Amazonas River (locally named Rio Solimões) and black and acidic waters of the Negro River creates one of the world’s most conspicuous ecotones. Each of these rivers supports numerous fishes adapted to its own physicochemical and related ecological conditions, but also fishes that apparently can live

20 within a broad spectrum of environmental conditions (Saint-Paul et al., 2000). Nonetheless, there has not been any research that examines fish assemblage structure in the region of the confluence of these rivers in the central Amazon so far. Heterogeneous and dynamic floodplain landscapes in this region may partially account for high species diversity. At their confluence, the two rivers share a common floodplain that contains several floodplain lakes and temporary channels that seasonally connect them to the rivers. The largest lake in this floodplain is Lago Catalão. Limnological studies have revealed that each river influences the lake’s water quality during particular phases of the annual flood pulse (Brito et al., 2014; Caraballo et al., 2014), which changes the water level about 10 meters on average (Bittencourt & Amadio, 2007). Floodplains adjacent to the meeting of the waters support high diversity of aquatic plants (Bleich et al., 2014) and also contain important habitats for reproduction by several migratory fishes of importance to commercial and subsistence fisheries (Carvalho de Lima & Araujo-Lima, 2004; Leite et al., 2006). This area, therefore, has been proposed as priority area for conservation (Ardura et al., 2013). Population genetics research on two commercially important fishes, Colossoma macropomum (Cuvier, 1818) and Prochilodus nigricans Agassiz, 1829, revealed that genetic diversity is lower in this area compared with other regions in Amazon Basin (Ardura et al., 2013). This finding is surprising given the proximity of two large rivers with very different water conditions and opportunities for dispersal, especially during the annual flood pulse when lateral connectivity is high. Indeed, other species showed greater genetic diversity in this region as a consequence of ecological diversifying selection according to water type (Cooke et al., 2012; 2014). The present study analysed fish assemblage composition within Lago Catalão with respect to seasonal hydrology. It was hypothesized that temporal β diversity would be high (large turnover rate among periods of the flood cycle) because occurrences of many species would be influenced by changes in water physicochemistry and accessibility of food resources in the floodplain.

MATERIAL AND METHODS

STUDY AREA This study was carried out in Lago Catalão, an Amazonian floodplain lake located near Manaus at the confluence of the Amazon and Negro rivers (3°08’– 3°14’ S and 59°53’–59°58’ W) (Fig. 1). The rainfall regime in the west, northwest and southwest portions of the Amazon Basin causes a cyclic water-level fluctuation that can be grouped into four seasons: rising season,

21 typically between January and April; flood season, typically between May and July; receding season, typically between August and September; and dry season, typically between October and December (Bittencourt & Amadio, 2007; Espinoza-Villar et al., 2009). This seasonal water-level fluctuation (flood pulse) influences the connectivity of the lake with both rivers. Lago Catalão is completely connected with both rivers during the flood season and may be totally isolated during the dry season (Brito et al., 2014). Limnological studies in this region show that lake water characteristics are mainly controlled by the flood pulse regime of the two rivers (Almeida & Melo, 2009; Brito et al., 2014; Caraballo et al., 2014). Although the lake receives a large input of water from both rivers, the Negro seems to have greatest influence on water quality during the early rising-water period, resulting in lower values of conductivity and pH, and the Amazon has dominant influence during most other periods (Brito et al., 2014; Caraballo et al., 2014). The seasonality of connectivity with both rivers and isolation during the dry season and correlated changes in water level also influence dissolved inorganic nutrient concentrations (Aprile & Darwich, 2013) and local phytoplankton productivity (Almeida & Melo, 2011).

Fig. 1. Location of the Lago Catalão; rectangles highlight location of the study area in relation to South America and the Amazon Basin (a) and Central Amazon (b). The ellipse (c) indicates the perennial portion of Lago Catalão, and the star indicates the sampling site selected to span a gradient between flooded forest and pelagic area within the lake.

SAMPLING AND DATA ANALYSIS Sampling was performed monthly from June 2010 to July 2011 and from April 2013 to October 2014 (33 months), which encompassed 2.5 flood pulse cycles. Fish were collected using ten gillnets with different mesh sizes (30, 40, 50, 60, 70, 80, 90, 100, 120 to140 mm between opposite knots), each measuring 10 m in length and from 1.5-3.5 m in height. Total monthly fishing effort was 257.26 m2 x 24 hours. Gillnets were set in the same area in a direction running from near shore toward deeper water offshore. During the flood season, gillnets were deployed along the flooded forest border, where the probability of fish capture generally is higher due to higher fish densities near the forest compared to open waters (personal observation). Nets were deployed for 24 hours, with fish removed every 6 hours. Captured fishes were euthanized in an ice bath and transported in boxes of ice to the laboratory where they were identified, measured for standard length (mm), and weighed (g). Fish surveys were authorized by IBAMA through license #101932, and procedures followed the INPA’s ethics committee rules (protocol number 33/2012). During each survey, dissolved oxygen (mg l-1), conductivity (µS cm-1) and water temperature (ºC) were recorded using a multiparameter meter. Although pH could be an important proxy for the relative influence of Negro versus Amazon waters on Lago Catalão, this information only was obtained during portions of the study period and therefore was not included in the analyses. However, Caraballo et al. (2014) showed that in years with strong Negro influence during rising-water season, conductivity within the lake declines markedly to values as low as 20 - 50 µS cm-1. Therefore, we used conductivity as a proxy for the influence of Negro River on of Lago Catalão. Water-column transparency was not measured; however, a previous study recorded highest values (~1 m) during the rising-water season, and during other periods transparency was ~0.6 m (Brito et al., 2014). Depth, aquatic macrophyte abundance and the amount of surrounding forest also can influence fish assemblage structure in floodplain lakes (Pouilly & Rodriguez, 2004; Correa et al., 2008, Scarabotti et al., 2011). At our survey site, aquatic and terrestrial vegetation within littoral habitats vary seasonally more than spatially, and therefore our temporal samples are assumed to be fairly representative for the lake as whole. Measures of water discharge (m3 s-1) were obtained from the Brazilian National Water Agency’s (Agência Nacional de Águas, ANA) measurements, which were taken at the narrowest portion of the Amazonas River about 2.5 km downstream from its confluence with the Negro River. Hydrological phases were classified according to Bittencourt & Amadio (2007), which are based on centennial data of water level recorded at the Manaus harbour (distant about 5 km from the study area). Phase thresholds were:

23 flood season water level > 26 m, receding season water level between 26 and 20 m, dry season water level < 20 meter, and rising season water level between 26 and 20 m. Relationships between abiotic variables and fish abundance (monthly total number of individuals collected) and species richness (monthly total number of species collected) were analysed using forward stepwise multiple linear regression (one for each dependent variable). All values were transformed as the natural logarithm. The F criterion for independent variables to enter was set at 1.00, and the F value to remove variables was set at 0.99, with tolerance set at 0.0001. Environmental variables were dissolved oxygen, conductivity, water temperature, and discharge; water level was not included due to its high correlation with discharge (r = 0.95). To assess how much of the variation in assemblage composition can be statistically explained by temporal changes in abiotic factors, canonical correspondence analysis (CCA) was performed using the same environmental and species abundance data (ln transformed). Statistical significance of correlations between biotic and abiotic components extracted from CCA was determined by a Monte Carlo test based on 999 permutations. For this analysis, rare species with low frequencies in monthly samples (frequency of occurrence < 10) and low abundance (< 16 specimens) were omitted because they can skew results (Gauch, 1982); these criteria resulted in a total of 54 species included in the data matrix and a total of 85 species excluded. Temporal β diversity, which implies gain and loss of species due to environmental filtering or biotic interactions (Legendre, 2014), was estimated using the Jaccard’s similarity index based on presence and absence of species in monthly surveys. Similarity values were tested against a null model using a simulation that assumed the probability of species occurrence is proportional to species frequency in a set of samples (method “r1” in oecosimu R). Temporal β diversity was calculated according to between-hydrologic season and between-year differences. Over the study period, we obtained two dry season samples, three rising season samples, and four samples each for flood and receding season, and therefore the possible number within-season, between-year comparisons differed among seasons. Student’s t test was used to compare the mean value for similarity between seasons within a year (dataset also included comparisons between consecutive seasons during consecutive years) with the mean value of similarity between different years (comparisons between the same season in different years). Species occurrence over time was analysed with a modification of the method of Collins (2000) and Ferreira (2007). Core species were defined as those that occurred in > 70% of the monthly surveys; occasional species were defined as those that occurred in > 20% and < 70% of surveys; and satellite species occurred in < 20% of the surveys. To test whether higher average abundant species were also more frequent over the time, linear regression was performed on ln-

24 transformed frequency and abundance data. For this analysis, all species captured during the study were included. All analyses were performed with R software (R Development Core Team, 2014). Multiple regressions were run using the function lm in the default stats library (Oksanen et al., 2014); CCA was run using the function cca in the vegan library; the null model and permutation test between observed and simulated Jaccard’s similarity values were performed using oecosimu in the vegan library.

RESULTS

SEASONAL VARIATION IN ASSEMBLAGE STRUCTURE AND RELATIONSHIPS WITH ABIOTIC FACTORS Surveys produced 6410 fish specimens representing 139 species. Abiotic environmental variables showed pronounced temporal variation associated with seasonal connections between the lake and the Amazonas and Negro rivers (Fig. 2 A-D). Conductivity and temperature were higher during the dry season. Conductivity dropped from 140 to about 20 µS cm-1 during the rising- water season when Negro River water entered the lake. Dissolved oxygen had a seasonal pattern in 2010-2011, and was more variable during 2013-2014. Fish abundance from monthly surveys varied from 31 to 741 specimens, and species richness ranged from 15 to 49. Abundance was negatively correlated with discharge (p < 0.001; Fig. 3A), and positively correlated with conductivity (p < 0.05; Fig. 3B); dissolved oxygen and temperature did not contribute significantly and were excluded from the final model (Table I). Species richness was significantly and negatively correlated only with discharge (p < 0.001; Fig. 3A). Dissolved oxygen and temperature contributed significantly to the model but did not have statistically significant correlations with species richness (Table I). The first two CCA axes modelled nearly 75% of variance in the relationship between the Lago Catalão fish assemblage (based on 54 non-rare species and 5994 specimens) and abiotic variables. Correlations between assemblage axis scores and physical and chemical variables were high and significant for the complete model (Table II). The gradient defined by the first canonical axis was strongly associated with seasons. Dry-season samples had negative scores, and flood- season samples had positive scores; rising and receding seasons had intermediate scores and high overlap. This gradient was strongly influenced by Amazonas River discharge (connecting and disconnecting the lake to the river seasonally) and conductivity increased in the receding and dry months (Fig. 4).

Fig. 2. Temporal variation of discharge, water level, conductivity, temperature, and dissolved oxygen in Lago Catalão from June 2010 to July 2011 and from April 2013 to October 2014. (a) Filled dots are for discharge, and open dots are for water level.

Species positioned at the extremes of the gradient defined by the first CCA axis (Table II) had relatively low abundance and were collected only during either the flood or dry season. Species with the most negative scores on CCA1 (dry season occurrence) were Auchenipterus britskii Ferraris & Vari, 1999, Hydrolycus scomberoides (Cuvier, 1816), Auchenipterichthys coracoideus (Eigenmann & Allen, 1942), Curimata knerii (Steindachner, 1876), Curimatella meyeri (Steindachner, 1882), and Psectrogaster amazonica Eigenmann & Eigenmann, 1889 (Table II). Species with largest positive scores on CCA1 (flood season occurrence) were Brycon amazonicus (Spix & Agassiz, 1829), Semaprochilodus taeniurus (Valenciennes, 1817), Ageneiosus dentatus Kner 1858, Ageneiosus ucayalensis Castelnau, 1855, Plagioscion squamosissimus (Heckel, 1840), Mesonauta festivus (Heckel, 1840), Rhaphiodon vulpinus Spix & Agassiz, 1829, Dekeyseria amazonica Rapp Py-Daniel, 1985, Pellona castelnaeana (Valenciennes, 1847), Hypophtalmus

26 marginatus Valenciennes, 1840, and Colossoma macropomum, (Table II). Samples from rising and receding water periods contained many cichlids (e.g. Cichla monoculus Spix & Agassiz, 1831, Heros spurius Heckel, 1840), but also a mix of sedentary and short and medium-distance migratory species [Triportheus albus Cope, 1872, Hypoptopoma gulare Cope, 1878, Serrasalmus elongatus Kner, 1859, Mylossoma aureum (Spix and Agassiz, 1829), Pellona falvipinnis (Valenciennes, 1837), Schizodon fasciatus (Valenciennes, 1850), Hemiodus immaculatus Kner, 1858] (Appendix S1).

Table I. Results of multiple regressions with reduced model with species abundance and richness versus abiotic environmental variables. Data were natural logarithm transformed to reduce distribution skew and kurtosis. Model Dependent Independent N Beta t p R2 F p adjusted Intercept 3.78 2.94 <0.01 Abundance Discharge 33 -0.65 -5.60 <0.01 0.62 27.07 <0.001 Conductivity 0.29 2.50 0.02 Intercept 10.72 2.24 0.03 Discharge -0.87 -3.63 <0.01 Richness 33 0.30 5.70 0.003 Dissolved oxygen 0.28 1.72 0.09 Temperature -0.33 -1.44 0.16

TEMPORAL BETA DIVERSITY Jaccard similarity between different seasons of the same year (mean = 0.43) was lower than the Jaccard similarity of the same season between years (mean = 0.47) (t-test = 2.22; df = 30; p < 0.05; Table III). The mean Jaccard similarity index for all pairwise seasonal comparisons was 0.41, indicating high temporal β diversity, and this value was higher than expected by a null model (mean = 0.36; permutation test, p < 0.001). Eighty-two species were classified as satellite species, 50 were occasional species, and only seven [Acestrorhynchus falcirostris (Cuvier, 1819), Hoplosternum littorale (Hancock, 1828), Pygocentrus nattereri Kner, 1858, Rhytiodus microlepis Kner, 1858, Schizodon fasciatus, Triportheus albus, Triportheus angulatus (Spix & Agassiz, 1829)] were core species that occurred in more than 70% of monthly samples (Fig. 5A; Appendix SI). More frequent species had higher average abundance (r = 0.69; p < 0.001) (Fig. 5B). Proportional contributions of core and occasional species to the total assemblage were highest during the dry season (core: rising = 7%, flood = 6%, receding = 8%, dry =10%; occasional: rising

= 49%; flood = 46%; receding = 50%; dry =53%); proportional contribution of satellite species was highest during the flood season (rising = 43%; flood = 46%; receding = 41%; dry =35%).

Fig. 3. Relationships of (a) fish abundance ( ; y=28.412−1⋅9919x; P<0.001) and species richness ( ; y=8.3143−0.4144x; P<0.01) with discharge of the Amazonas River and (b) the relationship of abundance with conductivity (y=−0.2171+1.1758x; P<0.05) on ln scales.

Table II. Canonical Correspondence Analysis (CCA) results for Lago Catalão fish species and abiotic variables.

CCA1 CCA2 CCA3 CCA4 Correlations of environmental variables with axes Dissolved oxygen -0.41 0.633 -0.207 0.293 Conductivity 0.672 0.003 -0.527 0.088 Temperature 0.482 0.328 -0.166 -0.583 Discharge -0.81 -0.245 -0.137 0.188 Eigenvalues 0.169 0.076 0.046 0.039 Species variance data % of explained variance 0.51 0.231 0.138 0.118 % cumulative 0.51 0.742 0.881 1 Monte Carlo permutation test for Eigenvalues Pearson correlation for species – environmental 0.892 0.818 0.812 0.827 variables P (permutation test full model) 0.001 Correlation values for species with strongest positive scores in axis 1 Brycon amazonicus 1.166 0.816 Semaprochilodus taeniurus 0.890 0.237 Ageneiosus ucayalensis 0.750 -0.107 Ageneiosus dentatus 0.746 0.803 Plagioscion squamosissimus 0.702 -0.337 Mesonauta festivus 0.694 0.101 Rhaphiodon vulpinus 0.653 -0.245 Dekeyseria amazonica 0.633 -0.076 Pellona castelnaeana 0.605 0.082 Colossoma macropomum 0.596 0.093 Hypophthalmus marginatus 0.559 0.023 Semaprochilodus insignis 0.525 0.090 Correlation values for the species with strongest negative scores in axis 1 Auchenipterus britskii -0.963 0.212 Hydrolycus scomberoides -0.787 -0.338 Auchenipterichthys coracoideus -0.769 0.228 Curimata knerii -0.720 -0.203 Curimatella meyeri -0.673 0.210 Psectrogaster amazonica -0.660 0.281 Anodus sp. -0.460 0.334 Potamorhina altamazonica -0.451 -0.059 Hoplias malabaricus -0.441 0.033 Plagioscion montei -0.392 -0.459 Auchenipterus nuchalis -0.391 0.313 Serrasalmus sp.n. -0.339 -0.089

Fig. 4. Ordination plot of Lago Catalão surveys (conducted from June 2010 to July 2011 and April 2013 to October 2014) for the first two gradients (axes) derived from canonical correspondence analysis (CCA); vectors represent correlations of environmental variables with the two gradients; scores on the first two axes for species with highest scores appear in Table II. Symbols designate hydrologic season: flood ( ); receding ( ); dry ( ); rising ( ).

Fig. 5. Histogram with number of species with various frequencies of occurrence over time (a), and simple linear regression of species average abundance (ln- natural logarithm) against frequency of occurrence over time (b).

Table III. Jaccard similarity index for pairwise comparisons of fish assemblage samples among hydrological seasons and years. Comparisons between seasons in the same year and consecutive seasons are underlined. Comparisons between same season in different years are in parentheses.

Flood Receding Dry Rising Flood Receding Rising Flood Receding Dry Rising Flood 2010 2010 2010 2011 2011 2011 2013 2013 2013 2013 2014 2014 Receding 2010 0.53 Dry 2010 0.35 0.50 Rising 2011 0.42 0.41 0.44 Flood 2011 (0.51) 0.35 0.37 0.37 Receding 2011 0.47 (0.41) 0.42 0.48 0.43 Rising 2013 0.42 0.43 0.43 0.53 0.38 0.40 Flood 2013 (0.50) 0.49 0.40 0.51 (0.52) 0.52 0.49 Receding 2013 0.37 (0.45) 0.43 0.41 0.38 (0.48) 0.43 0.39 Dry 2013 0.34 0.47 (0.53) 0.40 0.39 0.43 0.45 0.42 0.42 Rising 2014 0.51 0.47 0.41 (0.48) 0.35 0.41 (0.48) 0.45 0.43 0.40 Flood 2014 (0.49) 0.42 0.36 0.40 (0.43) 0.43 0.39 (0.49) 0.47 0.39 0.49 Receding 2014 0.39 (0.39) 0.31 0.38 0.37 (0.44) 0.40 0.38 (0.41) 0.39 0.37 0.36

DISCUSSION The fish assemblage of Lago Catalão undergoes major compositional changes during water level fluctuations associated with the annual flood pulse of the central Amazon region; consequently, temporal β diversity was high. Seasonal changes in water level result in changes in lateral connectivity and aspects of the lake’s abiotic environment, including water quality, degree of floodplain forest inundation, and aquatic macrophyte cover (Junk & Piedade, 1997). Core species (those occurring in > 70% of monthly samples) tended to be more abundant throughout the entire study period; however, the fish assemblage was dominated by occasional (occurrence > 20% and < 70%) and satellite (occurrence < 20%) species, and the proportional contribution of these groups to the assemblage varied seasonally. Similar seasonal variation in fish assemblage structure in floodplain lakes has been documented for river basins in many tropical (e.g. Rodríguez & Lewis, 1997; Tejerina-Garro et al., 1998; Galacatos et al., 2004; Jackson et al., 2013; Silva et al., 2013) and temperate (e.g. Winemiller et al., 2000; Zeug et al., 2005; Bleesey et al., 2012) regions. Although several studies have examined seasonal change in fish assemblages of floodplain lakes, studies reporting values for temporal β diversity are scarce. Freitas et al. (2010) found 47-60% assemblage turnover between flood and dry seasons in island lakes of the Amazonas River in the central Amazon. The mean Jaccard similarity for between-season comparisons is equivalent to about 60% species turnover. High seasonal turnover in fish assemblages observed in the present study and by Freitas et al. (2010) suggest a general phenomenon in floodplain lakes of the central Amazon. Comparison of observed Jaccard similarity values with values simulated by a null model indicated that seasonal species turnover was greater than expected by chance. It should be noted that datasets involving large species pools tend to produce null models with relatively low expected values (Chase et al., 2011). A question that arises from these results is whether high diversity and turnover rates found in this study are uniquely derived from the influence of two of the world’s largest rivers? Previous research showed that segments of the Amazonas River just downstream from confluences with major tributaries have higher species diversity of gymnotiform fishes than stretches located above confluences (Fernandes et al., 2004). Gascon and Smith (2004) suggested that when large rivers join, distinctive biotas of each river may persist over considerable distances downstream. They further proposed that species coexistence might be enhanced by greater food availability and habitat stability in these confluence zones. Opposing this hypothesis is the fact that we did not capture any species from Lago Catalão that are exclusively Negro River inhabitants and considered exclusively adapted to black waters. What is clear is that the lake’s proximity to two rivers and long periods of lateral connectivity result in a local community

33 dominated by temporary residents. Lateral migration during the flood pulse allows fishes to exploit habitats and resources (Lowe-McConnell 1987; Winemiller & Jepsen, 1998; Petry et al., 2003). Fish abundance and species richness in Lago Catalão were highest during the dry season, a finding consistent with those from studies of floodplain lakes in the Orinoco River and other parts of the Amazon (e.g. Rodrigues & Lewis, 1994; Fernandes, 1997; Saint-Paul et al., 2000; Galacatos et al., 2004; Lin & Charamaschi, 2005; Silva et al., 2013). During the flood season, fish abundance and species richness in survey samples generally are lower due to expansion of aquatic habitat that reduces per-unit-area densities of fishes (Saint-Paul et al., 2000). The large number of fishes captured from Lago Catalão during the dry season of 2010 probably was a consequence of a drought that was considered one of the strongest of the century (Marengo et al., 2011). Petry et al. (2003) also found higher alfa diversity in disconnected floodplain lakes of the Paraná River during dry years. These authors suggested that, despite having harsh environmental conditions during drought, disconnected lakes promote species coexistence because they are more productive than connected lakes. Extreme drought conditions sometimes result in massive fish mortality in central Amazon floodplain lakes (authors’ personal observations), which should contribute to reduce local species richness. Lateral connectivity during high-water periods apparently facilitated entry of migratory fishes into Lago Catalão; these species included Brycon amazonicus, Semaprochilodus taeniurus, Plagioscion squamosissimus, Rhaphiodon vulpinus, Pellona castelnaeana, Colossoma macropomum, Ageneiosus dentatus, and Ageneiosus ucayalensis. Granado-Lourencio et al. (2005) found that floodplain lakes closer to the active river channel had greater abundance of migratory fishes during the annual flood pulse compared to lakes located further from the river and that were less connected. In Mamoré River, the type of lateral connection (seasonal versus permanent) strongly influences fish assemblage composition in floodplain lakes (Pouilly & Rodriguez, 2004). Some of the species found in Lago Catalão only during the flood season, or captured in much greater numbers during the flood season, were detritivores/iliophage (e.g. S. taeniurus) and frugivores (e.g. B. amazonicus, C. macropomum), two trophic guilds with strong dependence on flooded forests (Goulding et al., 1988; Claro-Junior et al., 2004; Oliveira et al., 2006; Correa & Winemiller, 2014). Some studies suggest that availability of food resources in floodplains is directly proportional to the flooded area (Claro-Junior et al., 2004), which in turn is influenced by the magnitude of the flood pulse (Melack & Hess, 2010). In the present study, flood periods were sampled during years with flood pulses of relatively large magnitude and long duration, especially during 2013 and 2014. This could have contributed to greater frequencies of occurrence of migratory fishes in the lake, although most of specimens were immature (average standard length:

B. amazonicus- 178.5 mm, S. taeniurus- 109 mm, C. macropomum- 144 mm; Appendix SI). In the Paraná River, years with intense floods were found to be associated with greater juvenile survival and recruitment of the migratory detritivore Prochilodus lineatus (Valenciennes, 1837) (Gomes & Agostinho, 1997). Other fish species captured from Lago Catalão only during the flood season included carnivores and piscivores (e.g. P. squamosissimus, R. vulpinus, P. castelnaeana, A. dentatus, A. ucayalensis). This suggests that prey availability is greater in floodplain lakes compared with the river channel during flooding periods. When flooding recedes (August- September) and water drains from the lake into the main channel, these piscivores and many of their prey move with it (Fernandes, 1997). Migratory piscivores, frugivores, and detritivores were remarkbly absent or rare within our dry-season surveys. During the dry season, lateral connectivity declines until migration between the lake and river channel is no longer possible. The area of the lake also declines during this period, and assemblage structure should be strongly influenced by local processes, such as aquatic primary production, predation, and competition. Under these circumstances, changes in species relative abundance may be a function of differential mortality from predation and stressful abiotic (e.g. hypoxia) and biotic (e.g. parasitism) conditions (Rodriguez & Lewis, 1994). These factors can also influence distributional patterns of species among floodplain habitats (Rodríguez & Lewis, 1997; Tejerina-Garro et al., 1998; Layman & Winemiller, 2005). Water temperature and conductivity in Lago Catalão were high, and dissolved oxygen concentrations often were low during dry seasons. The fish assemblage during this time was dominated by A. britskii, A. coracoideus, C. knerii, C. meyeri, P. amazonica, Psectrogaster rutiloides (Kner, 1858), T. albus and T. angulatus, which are mostly small species that are either non-migratory or migrant over short-distances among local habitats. Some of the most abundant species captured during the dry season (five species of Curimatidae and two species of Triportheus) were fishes with special adaptations for coping with hypoxia (Winemiller, 1989; Jucá-Chagas, 2004; Soares et al., 2006). Fernandes (1997) suggested that conditions of Amazonian floodplain lakes during the dry season could be a strong filter, selecting for a subset of resident species able to cope with stressful abiotic conditions, and another subset of migratory species that exit the lakes before conditions become severely degraded. Inter- annual consistency of a distinct species composition dominated by core species during the dry season in Lago Catalão supports this hypothesis. Core species experienced a greater degree of environmental variation, (e.g. enduring harsh dry-season conditions), and most of these species are widely distributed in the Amazon (e.g. A. falcirostris, R. microlepis, T. albus, T. angulatus, H. littorale, P. nattereri; see Reis et al., 2003), a pattern that suggests tolerance to the range of environmental conditions that occur in floodplain aquatic habitats seasonally.

The confluence of the Amazon and Negro rivers just downstream from Lago Catalão is a unique geographic feature with circumstances that may contribute to high fish diversity and abundance. Although influenced by two large rivers with divergent water chemistry, Lago Catalão had a fish assemblage dominated by species restricted to white waters (Amazonas River) and those commonly encountered in both black (Negro River) and white waters, but none of the captured species would be considered black-water specialists (Saint-Paul et al., 2000; Appendix SI). Henderson and Crampton (1997) compared fish assemblages in floodplain lakes in central Amazon that had white versus black waters and found high similarity. They suggested that black-water fish assemblages might comprise a subset of white-water assemblage. They further proposed that dissolved oxygen concentration and biotic interactions are more important than differences in chemistry of black versus white water in determining species distributions, and findings from Lago Catalão seem consistent with this interpretation. However, Henderson and Crampton (1997), Saint-Paul et al. (2000) and our study were conducted in regions with conection of white and black water, endemic icthiofauna of the Negro River more likely would be found in remote regions of the basin, and if the sampling methods would be efficient to capture small characids which represent most of endemic black water species. Natural flood regimes in tropical floodplains sustain biodiversity and productivity of important fisheries (Lowe-McConnell, 1987; Ward et al., 1999; Winemiller, 2004). The central Amazon supports abundant migratory fishes with high market value, including tambaqui (C. macropomum) and curimbatá (P. nigricans). Recent population genetics research (Ardura et al., 2013) estimated that the central Amazon region receives fewer immigrant C. macropomum and P. nigricans. than the number of fish emigrating to other regions of the basin. Consistent with recommendations derived from recent assessments of Amazonian fisheries (Batista & Petrere, 2007; Arantes et al., 2013; Freitas et al., 2014), our study emphasize the need for protected areas with extensive floodplains and lateral connectivity to maintain fish diversity and productivity in the central Amazon.

Acknowledgements This study was founded by Amazonas Research Funding Agency (FAPEAM no. 062003342013) and National Institute for Amazonian Research (INPA). CPR received fellowship from Brazilian National Council for Scientific and Technological Development (CNPq) and Brazilian Government Agency for Support and Evaluation of Graduate Education (CAPES) (process 149428/2012-0 and BEX 3099/14-8, respectively). JZ received a productivity grant from CNPq (process 313183/2014-7); and KOW received support from NSF grant DEB 1257813. The authors are thankful to C. P. de Deus and T. B. Farago for providing limnological data, the two

36 anonymous reviewers for many valuable suggestions, and C. C. Arantes, L. Espinosa and T. H. S. Pires for their helpful comments to improve the manuscript.

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Appendix S1 supporting information: Seasonal abundance, frequency of occurrence over time (FOT) and range size of the species in Lago Catalão (confluence of the Amazonas and Negro rivers, central Amazon, Brazil), during the period of June 2010- July 2011 and April 2013- October 2014. Monthly data were grouped in hydrological seasons. Trophic category was inferred by stomach content analysis and published literature.

Abundance Range Trophic Species FOT Size Flood Rising Dry Receding (mm) Category Acarichthys heckelii (Müller & Troschel, 1849) 2 2 7 0.12 79-105 Herbivore Acaronia nassa (Heckel, 1840) 1 2 0.06 97-136 Carnivore Acestrorhynchus falcatus (Bloch, 1794) 1 2 0.06 144-162 Piscivore Acestrorhynchus falcirostris (Cuvier, 1819) 23 70 197 81 0.85 103-350 Piscivore Acestrorhynchus microlepis (Schomburgk, 1841) 2 5 5 2 0.27 125-265 Piscivore Ageneiosus aff. vittatus 1 0.03 97 Carnivore Ageneiosus dentatus Kner 1858 15 1 1 0.27 171-229 Piscivore Ageneiosus inermis (Linnaeus, 1766) 5 2 0.21 248-295 Piscivore Ageneiosus ucayalensis Castelnau, 1855 12 8 0.33 115-295 Piscivore

Agoniates anchovia Eigenmann, 1914 5 0.09 130-153 Piscivore Anadoras grypus (Cope, 1872) 4 7 0.21 105-130 Omnivore

Ancistrus dolichopterus Kner, 1854 4 0.06 92-105 Detritivore/Iliophage Anodus elongatus Agassiz, 1829 59 75 196 40 0.7 121-219 Zooplanctivore Anodus orinocencis (Steindachner, 1887) 5 1 3 0.18 110-208 Zooplanctivore Anodus sp. 6 4 24 12 0.42 136-229 Zooplanctivore

Astronotus crassipinnis (Heckel, 1840) 1 0.03 162 Carnivore Astronotus ocellatus (Agassiz, 1831) 2 1 0.06 152-168 Carnivore Auchenipterichthys coracoideus (Eigenmann & Allen, 1942) 3 16 100 33 0.3 76-105 Omnivore Auchenipterus britskii Ferraris & Vari, 1999 2 3 65 21 0.24 98-145 Invertivore Auchenipterus nuchalis (Spix & Agassiz, 1829) 12 7 41 26 0.48 105-182 Carnivore Boulengerella cuvieri (Agassiz, 1829) 1 0.03 190 Piscivore

Boulengerella maculata (Valenciennes, 1850) 1 0.03 290 Piscivore Brycon amazonicus (Spix & Agassiz, 1829) 16 0.18 125-220 Omnivore

Brycon melanopterus (Cope, 1872) 6 1 0.15 108-180 Omnivore Calophysus macropterus (Lichtenstein, 1819) 3 0.09 271-300 Piscivore Centromochlus heckelii (De Filippi, 1853) 1 0.03 80 Omnivore

Chaetobranchopsis orbicularis (Steindachner, 1875) 2 6 0.12 110-130 Zooplanctivore

Chaetobranchus flavescens Heckel, 1840 1 1 0.06 74-137 Omnivore

Chalceus erythrurus (Cope, 1870) 1 1 0.06 95-181 Insectivore Cichla monoculus Spix & Agassiz, 1831 10 3 2 5 0.36 86-249 Piscivore

Cichlasoma amazonarum Kullander, 1983 1 0.03 135 Omnivore Colossoma macropomum (Cuvier, 1818) 7 7 2 0.3 108-246 Herbivore

Crenicichla inpa Ploeg, 1991 1 0.03 118 Carnivore Curimata knerii (Steindachner, 1876) 4 14 3 0.18 71-131 Detritivore/Iliophage Curimata vittata (Kner, 1858) 8 1 0.12 125-155 Detritivore/Iliophage

Curimatella alburna (Müller & Troschel, 1844) 2 0.03 108-109 Detritivore/Iliophage Curimatella meyeri (Steindachner, 1882) 5 2 21 5 0.24 107-160 Detritivore/Iliophage

Cynodon gibbus Spix & Agassiz, 1829 1 7 0.06 195-240 Piscivore Cyphocharax plumbeus (Eigenmann & Eigenmann, 1889) 1 0.03 80 Detritivore/Iliophage Cyphocharax spiluropsis (Eigenmann & Eigenmann, 1889) 2 0.06 72-76 Detritivore/Iliophage Dekeyseria amazonica Rapp Py-Daniel, 1985 16 4 1 4 0.36 82-160 Detritivore/Iliophage

Eigenmannia limbata (Schreiner & Miranda Ribeiro, 1903) 1 0.03 342 Invertivore

Geophagus proximus (Castelnau, 1855) 1 3 0.06 115-150 Omnivore Hemiodus argenteus Pellegrin, 1908 2 12 28 0.18 105-195 Detritivore/Iliophage

Hemiodus atranalis (Fowler, 1940) 1 1 0.06 89-95 Detritivore/Iliophage

Hemiodus immaculatus Kner, 1858 4 12 0.21 113-187 Detritivore/Iliophage Hemiodus sp.n. 29 25 86 74 0.7 107-215 Omnivore Heros spurius Heckel, 1840 5 8 7 0.3 80-142 Herbivore

Hoplerythrinus unitaeniatus (Agassiz, 1829) 1 0.03 150 Piscivore Hoplias malabaricus (Bloch, 1794) 3 5 7 2 0.39 175-275 Piscivore Hoplosternum littorale (Hancock, 1828) 53 17 9 12 0.79 68-180 Zooplanctivore Hydrolycus scomberoides (Cuvier, 1816) 2 3 8 3 0.24 157-350 Piscivore Hypophthalmus edentatus Spix & Agassiz, 1829 5 4 5 5 0.42 186-305 Zooplanctivore Hypophthalmus fimbriatus Kner, 1858 5 2 4 0.15 217-264 Zooplanctivore

Hypophthalmus marginatus Valenciennes, 1840 23 5 2 6 0.42 192-313 Zooplanctivore Hypoptopoma gulare Cope, 1878 10 4 8 0.36 72-95 Detritivore/Iliophage

Hypostomus cf. plecostomus 1 2 0.09 157-215 Detritivore/Iliophage Hypselecara temporalis (Günther, 1862) 1 1 0.06 67-110 Carnivore Ilisha amazonica (Miranda Ribeiro, 1920) 7 7 0.09 130-188 Invertivore Jurengraulis juruensis (Boulenger, 1898) 2 3 0.06 139-170 Zooplanctivore Laemolyta proxima (Garman, 1890) 4 11 0.15 95-178 Herbivore Lepidosiren paradoxa Fitzinger, 1837 1 0.03 565 Piscivore Leporinus amazonicus Santos & Zuanon, 2008 7 1 0.15 125-234 Omnivore Leporinus fasciatus (Bloch, 1794) 1 2 2 0.15 105-219 Omnivore Leporinus friderici (Bloch, 1794) 1 2 1 1 0.15 110-200 Omnivore Leporinus trifasciatus Steindachner, 1876 3 2 2 8 0.3 110-271 Herbivore

Loricariichthys acutus (Valenciennes, 1840) 2 1 0.09 180-280 Detritivore/Iliophage Loricariichthys maculatus (Bloch, 1794) 1 0.03 217 Detritivore/Iliophage Loricariichthys nudirostris (Kner, 1853) 1 0.03 194 Detritivore/Iliophage Lycengraulis batesii (Günther, 1868) 2 4 1 0.15 110-152 Carnivore

Lycengraulis grossidens (Spix & Agassiz, 1829) 7 0.09 120-159 Carnivore Mesonauta festivus (Heckel, 1840) 13 7 4 0.36 50-80 Omnivore

Metynnis argenteus Ahl, 1923 4 0.09 68-125 Herbivore Metynnis hypsauchen (Müller & Troschel, 1844) 2 6 0.06 92-147 Herbivore Metynnis lippincottianus (Cope, 1870) 5 2 0.12 88-108 Herbivore Mylossoma aureum (Spix and Agassiz, 1829) 9 7 3 6 0.42 50-140 Herbivore Mylossoma duriventre (Cuvier, 1818) 11 26 31 62 0.61 60-180 Herbivore

Nemadoras elongatus (Boulenger, 1898) 3 1 0.12 62-112 Omnivore Nemadoras hemipeltis (Eigenmann, 1925) 3 11 1 0.09 92-125 Omnivore Nemadoras humeralis (Kner, 1855) 8 2 0.15 84-109 Zooplanctivore

Opsodoras boulengeri (Steindachner, 1915) 8 0.03 65-116 Invertivore Ossancora asterophysa Birindelli & Sabaj Pérez, 2011 5 5 0.15 75-90 Invertivore Ossancora punctata (Kner, 1853) 3 1 7 0.15 78-105 Invertivore Osteoglossum bicirrhosum (Cuvier, 1829) 4 1 5 4 0.18 270-561 Carnivore Oxydoras niger (Valenciennes, 1821) 1 0.03 165 Omnivore

Pellona castelnaeana (Valenciennes, 1847) 33 1 25 0.48 114-468 Piscivore Pellona flavipinnis (Valenciennes, 1837) 24 43 11 48 0.67 119-358 Piscivore Piaractus brachypomus (Cuvier, 1818) 1 0.03 175 Herbivore Pimelodus cf. blochii 16 23 2 12 0.48 93-180 Omnivore Pinirampus pirinampu (Spix & Agassiz, 1829) 8 1 1 0.21 171-390 Piscivore Plagioscion montei Soares & Casatti, 2000 2 4 5 9 0.24 170-275 Carnivore Plagioscion squamosissimus (Heckel, 1840) 16 2 1 6 0.42 155-315 Carnivore Potamorhina altamazonica (Cope, 1878) 10 4 30 11 0.36 109-222 Detritivore/Iliophage Potamorhina latior (Spix & Agassiz, 1829) 64 16 122 69 0.48 80-205 Detritivore/Iliophage Pristigaster cayana Cuvier, 1829 1 0.03 100 Invertivore Pristobrycon sp. 1 1 0.06 145-160 Piscivore Prochilodus nigricans Agassiz, 1829 4 3 2 7 0.33 67-290 Detritivore/Iliophage Psectrogaster amazonica Eigenmann & Eigenmann, 1889 6 2 31 3 0.36 60-126 Detritivore/Iliophage Psectrogaster rutiloides (Kner,1858) 157 131 564 18 0.64 64-161 Detritivore/Iliophage

Pseudanos trimaculatus (Kner, 1858) 1 0.03 90 Invertivore Pseudoplatystoma tigrinum (Valenciennes, 1840) 2 1 3 0.15 235-450 Piscivore Pterodoras granulosus (Valenciennes, 1821) 10 0.24 83-214 Herbivore Pterophyllum scalare (Schultze, 1823) 3 0.09 59-77 Omnivore Pterygoplichthys pardalis (Castelnau, 1855) 10 6 9 6 0.42 75-330 Detritivore/Iliophage Pygocentrus nattereri Kner, 1858 110 15 26 29 0.82 62-196 Piscivore Rhamphichthys marmoratus Castelnau, 1855 1 0.03 475 Carnivore Rhaphiodon vulpinus Spix & Agassiz, 1829 37 5 19 0.55 210-400 Piscivore

Rhytiodus argenteofuscus Kner, 1858 4 0.09 212-272 Herbivore Rhytiodus microlepis Kner, 1858 40 31 5 52 0.82 129-303 Herbivore Roeboides myersii Gill, 1870 1 1 3 0.12 76-127 Lepidophage

Satanoperca acuticeps (Heckel, 1840) 1 0.03 121 Omnivore Satanoperca jurupari (Heckel, 1840) 3 14 1 16 0.36 83-156 Omnivore

Satanoperca lilith Kullander & Ferreira, 1988 2 0.03 105-110 Omnivore Schizodon fasciatus (Valenciennes, 1850) 16 10 6 23 0.73 100-280 Herbivore Semaprochilodus insignis (Jardine & Schomburgk, 1841) 21 12 3 0.42 72-192 Detritivore/Iliophage Semaprochilodus taeniurus (Valenciennes, 1817) 31 14 5 0.36 68-222 Detritivore/Iliophage

Serrasalmus compressus Jégu, Leão & Santos, 1991 1 2 1 1 0.15 60-132 Piscivore

Serrasalmus eigenmanni Norman, 1929 1 0.03 110 Piscivore Serrasalmus elongatus Kner, 1859 18 5 20 2 0.58 63-190 Piscivore Serrasalmus maculatus Kner, 1858 9 1 40 21 0.42 50-152 Carnivore Serrasalmus rhombeus (Linnaeus, 1766) 1 1 0.06 140-172 Carnivore Serrasalmus sp.n. 5 7 21 15 0.61 83-147 Carnivore Sorubim elongatus Littmann, Burr, Schmidt & Isern, 2001 1 3 1 0.15 168-240 Carnivore Sorubim lima (Bloch & Schneider, 1801) 4 2 2 0.24 199-275 Carnivore Sorubim maniradii Littmann, Burr, Schmidt & Isern, 2001 1 2 2 0.15 160-245 Carnivore Steindachnerina bimaculata (Steindachner, 1876) 3 2 0.09 114-138 Detritivore/Iliophage Steindachnerina leucisca (Günther, 1868) 3 0.03 130-140 Detritivore/Iliophage Sternopygus macrurus (Bloch & Schneider, 1801) 1 0.03 270-330 Carnivore Stichonodon insignis (Steindachner, 1876) 1 0.03 75 Invertivore Tetragonopterus argenteus Cuvier, 1816 1 0.03 78 Invertivore Trachelyopterus galeatus (Linnaeus, 1766) 5 11 6 0.3 104-175 Omnivore Trachelyopterus porosus Eigenmann & Eigenmann, 1888 11 15 6 0.39 107-145 Invertivore Triportheus albus Cope, 1872 167 131 753 328 0.91 78-162 Omnivore Triportheus angulatus (Spix & Agassiz, 1829) 50 40 122 20 0.76 72-175 Omnivore Triportheus auritus (Valenciennes, in Cuvier & Valenciennes, 35 20 42 0.61 75-195 Omnivore 1850) Tympanopleura piperatus (Eigenmann, 1912) 1 0.03 75 Carnivore

Tympanopleura atronasus Eigenmann & Eigenmann, 1888 2 2 0.06 94-112 Omnivore

Tympanopleura rondoni (Miranda Ribeiro, 1914) 1 1 0.03 120-130 Omnivore Zungaro zungaro (Humboldt, 1821) 1 0.03 160 Piscivore

CAPÍTULO 2

CLIMATIC RELATED HYDROLOGICAL CHANGES DRIVING AMAZONIAN FISH ASSEMBLAGE REGIME SHIFT

Röpke, C.P.1*; Amadio2, S.; Deus, C.P.2; Zuanon, J.2; Ferreira, E.2; Pires, T.H. da S.1; Winemiller, K.O.3

1- Programa de Pós-Graduação em Biologia de Água Doce e Pesca Interior/Instituto Nacional de Pesquisas da Amazônia – INPA, Cx. Postal 2223, 69080-971, Manaus, Amazonas, Brasil. 2-Coordenação de Biodiversidade – CBIO/Instituto Nacional de Pesquisas da Amazônia – INPA, Manaus, Amazonas, Brasil. 3-Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, Texas 77843–2258, USA.

*Correspondence Author: Tel.: +55 (92) 36433254; email: [email protected]

Key-Words: Climate change, Amazon, regime shift, floodplain fish, long-term study

Journal: a summarized version of this chapter was submitted to Proceedings of the National Academy of Science (PNAS)

ABSTRACT

The precipitation pattern in the Amazon has been changing during the past few decades as a consequence of global climate change and deforestation, consequently changing the hydrological regime of the Amazonas River. It is widely accepted that the exposure of species to extreme conditions constrains biota promoting assemblage regime shifts. However, because of the lack of consistent multitaxon surveys, impacts on structure and diversity of riverine freshwater communities are still poorly known, especially in the tropics. Here we document that climatic related hydrological changes promoted abrupt fish assemblage regime shift in central Amazon, from 13 years of monthly surveys in a floodplain lake in the confluence of Amazonas and Rio rivers. Analysis based on nonlinear models for taxonomic, life history and trophic structures suggest a major regime shift after the intense 2005 drought, after what a period of increased hydrological amplitude of the flood pulse and dry season intensification started. We verified that taxonomic and functional structures changed remarkable after 2005. Diversity of species and function were higher before 2005. Most functional groups showed an abrupt reduction in relative abundance in 2005-2006, only first level consumers were less affected by the hydrological changes. Only nine years later (2014) the assemblage showed signs of functional recovery. These findings show the impact of recent broad scale environmental changes to the hydrological regime on the Amazonian floodplain fish assemblage.

INTRODUCTION

Growing concern about biodiversity loss underscores the need to quantify and understand temporal changes in natural ecosystems (Carpenter et al., 2011). There is increasing evidence that the recent global climate change is already showing measurable biological impacts. Understanding the effects of climate change on plant and animal populations, community structure and ecosystem functioning is one of the main challenges of modern ecology (Daufresne & Boët, 2007). Since the beginning of the 90’s many studies have attempted to understand and predict how this warming process influences population and ecosystems in a wide range of ecosystems and taxa type (Walther et al., 2002). However, most studies have focused on individuals or species populations rather than higher levels of organization as communities (see Woodward et al., 2010 for a review; Dossena et al., 2012; Mintenbeck et al., 2012). Until now, our understanding of climate change effects at the higher levels of organization is poorly understood. Considering only aquatic environments these studies have been concentrated in the northern hemisphere and tropical marine ecosystems focused on temperature effects (Whalter et al., 2002; Daufresne et al., 2003; Woodward et al., 2010) hindering the quest for general rules in the ecological impacts of global warming (Daufresne et al., 2003; Daufresne & Boët, 2007). The tropics are recognized as an important element in the dynamic process of global climate change (Dunham et al., 2010). In this region, climate change has more pronounced effects on precipitation patterns (spatial and temporal) than increase in temperature itself (Giorgi, 2006; Diffenbaugh & Giorgi, 2012). Among the predictions is expected an increase in the intensity of the extreme events as flood and drought, changes on the timing of these events and higher variability of the interannual flow (IPCC, 2007; Tremberth et al., 2013), which will cause disturbances in the aquatic ecosystem. Flow is widely regarded as a master variable that shapes the ecological characteristics of rivers and streams (Poff & Zimmerman, 2010). Numerous case studies provide the foundation of our scientific understanding that many types of flow alteration (e.g. magnitude, frequency, and timing) induce a variety of ecological responses (Bunn & Arthington, 2002; Bailly et al., 2008; Poff & Zimmerman, 2010). Some general pattern for instance is the sensitivity of trophic specialist and large body sized species to increasing in temporal flow variability (Poff & Ward 1989; Hoeinghaus et al., 2007); and population decline by equilibrium strategists due to the dependence on more hydrological stable environments (Winemiller 1989; Rose et al., 2001; Tedesco et al., 2008). Local extinction of large body sized predators seems to increase and the abundance of rare

52 species seems to decrease under lower flow and high temperature conditions (Legder et al., 2008; Ledger et al., 2012). Theoretical and empirical evidences pose that complex systems have limits of disturbance beyond which they will rapidly reorganize into an alternative regime (Selkoe et al., 2015). The strength of the environmental change determines how fast the community of organisms would be reorganized, from a gradual and linear change to an abrupt shift generating a new stable state (Beisner et al., 2003; Scheffer et al., 2001). The current ecological theory assumes that most of the time changes in population dynamics and community organization would be smooth and continuous (Doney & Sailley, 2013). A new reorganization characterized by a regime shift (Scheffer et al., 2001) would occur only with great pressure disturbance (Selkoe et al., 2015). In most occasions, if not always, regime shifts results in a less desirable ecosystem and typically lead to the loss of ecosystem services (Hastings & Whisham, 2010). Biodiverse ecosystems that have more functional equivalents could preserve functional resilience of the ecosystem, as observed in coral reefs (Nash et al., 2015), allowing rapid recover to the original state or more efficiently absorb disturbances and resist changing (Selkoe et al., 2015). Freshwater biodiversity in large rivers is strongly governed by the flood pulse phenomenon (Arringthon & Winemmiler, 2004; Junk, 1997; Jardine et al., 2015) which consist in high flow events that influence the river channel shape and allow access to otherwise disconnected floodplain habitats, and low flow events that limit overall habitat availability and quality (Junk et al., 1989). The Amazon ecosystem harbors one of the highest biodiversity of the world, and it has been considered as a hotspot that is threatened by both climate change (Malhi et al., 2008; Diffenbaugh & Giorgi, 2012) and deforestation (Malhi et al., 2008; Bagley et al., 2014). Simulation models and empirical evidence on amazon rainfall over the 21st century have demonstrated a trend in decrease of rainfall during dry season in the eastern portion and a simultaneous increase rainfall in the western portion during the wet season (Malhi et al., 2008; Bagley et al., 2014; Gloor et al., 2013). While the increase in rainfall on the west during wet months seems to be directly related to Atlantic sea surface temperatures (Yoon & Zeng, 2010; Gloor et al., 2013), studies also have identified an important effect of the deforestation on precipitation decrease across the amazon basin during driest months (Bagley et al., 2014). These events seem to be intensified under especial climatic conditions (Marengo et al., 2008) resulting in extreme events such as the severe droughts of 2005 and 2010. This seasonal precipitation imbalance has become linearly more pronounced since 1990 (Satyamurty et al., 2013), intensifying the seasonality of the hydrological regime especially in the middle portion of Amazonas River (Satyamurty et al., 2013; Fu et al., 2013). The hydrological

53 change became more pronounced after 2005, from which a sequence of historical records in droughts and floods has been registered (Gloor et al., 2013). The impact of these changes on aquatic organisms is yet not known. To date, few studies on aquatic ecosystem responses to changing precipitation and hydrology have been conducted in the Amazon region (Freitas et al., 2013; Correia et al., 2014) and none long term study was focused on the magnitude of this impact and ecological resilience, recovery capacity or resistence of the aquatic community to the disturbance. Methods based on time series analyses of monitoring studies have been emphasized as unique techniques to identify more surely warning changes and threshold moments between different stable states that could indicate a regime shift on the community (Carpenter et al., 2011; Beisner et al., 2003; Scheffer et al., 2001) as well as the role of extreme events as possible driven factors (Andersen et al., 2008; Williams et al., 2011). We investigate possible recent changes in fish assemblage structure in central Amazon and its relation with hydrological changes based on 13 years of standardized monthly sampling. From 1999 to 2014, fish were surveyed in Catalão Lake, a floodplain lake near the confluence of Negro and Amazon rivers. We asked four main questions: Does the fish assemblage structure changed over the past 15 years? If so, was it a smooth or a shift-like transition? Could the changes be driven by recent precipitation and hydrological changes? Is the impact similar across functional groups?

MATERIAL AND METHODS STUDY AREA

This study was carried out in the Catalão Lake, an Amazonian floodplain lake located near Manaus at the confluence of the Amazonas and Negro rivers (3°08’– 3°14’ S and 59°53’–59°58’ W) (Fig. 1). The rainfall regime in the west, northwest and southwest of the Amazonian basin promotes a cyclic water level fluctuation that can be grouped into four hydrometric periods in this region: rising water, typically between January and April; flood, typically between May and July; receding water, typically between August and September; and dry, typically between October and December (Bittencourt & Amadio, 2007; Espinoza-Villar et al., 2009). This seasonal water level fluctuation influences the connectivity of the lake with both rivers. Catalão Lake is completely connected with both rivers during the flood season and may be totally isolated during dry season (Brito et al., 2014). Limnological studies in this region show that the lake water characteristics are mainly controlled by the flood pulse regime of the rivers (Almeida & Melo, 2009; Brito et al.,

2014; Caraballo et al., 2014). The seasonality of the connectivity with Amazonas River and isolation during the dry season also influence dissolved inorganic nutrient concentrations (Aprile & Darwich, 2013) and local productivity (Almeida & Melo, 2011).

Fish surveys and access to biological information Using a standardized methodology, fishes have been monthly surveyed in Catalão Lake, since October 1999. For the present investigation, we analyzed data from October 1999 until October 2014. Our dataset has two gaps of one year each, due to logistic limitations that resulted in incomplete sampling during those periods: October 2001 to September 2002 and October 2005 to November 2006. Consequently, those two periods were not included in the analysis. Fish were sampled using a set of 10 gillnets of different mesh sizes (30, 40, 50, 60, 70, 80, 90, 100, 120 to140 mm between opposite knots), each of which was 10 m in length varying in 1.5 to 3 in height. Total monthly fishing effort was 257.26 m2 x 24 hours. Gillnets were deployed in the same area and each one was oriented from near shore towards deeper water offshore. During the flood season, gillnets were deployed near the flooded forest edge where the probability of fish capture is greater due to higher fish densities. Fish were removed from gillnets every 6 hours, and fish were euthanized in an ice bath and transported in boxes of ice to the laboratory at the National Institute for Amazonian Research – INPA, Manaus, Brazil. Fish surveys were authorized by IBAMA through license #101932, and procedures followed INPA’s ethics committee rules (protocol number 33/2012). In the laboratory each specimen was identified, measured for standard length (SL -mm), weighed (g), and sexed. Mature ovaries were immersed in Gilson solution and oocytes preserved in ethanol solution. Fecundity was estimated by counting all oocytes found in the most developed batch (batch fecundity) using the gravimetric method. Diameter oocyte was measured through stereomicroscope in millimeters (mm). Stomachs were removed from all specimens for dietary analysis that was based on items frequency of occurrence and relative volume (methodological details in Neves dos Santos et al., 2008, Röpke et al., 2014).

Figure 1: Study area at the confluence of the Negro River and Amazonas River in two different moments (A) satellite image showing the impact of dry season in 2009 on the floodplain lakes, even less extreme droughts can significantly reduce the extent of aquatic habitat; (B) satellite image showing the largest flood registered in the last 110 years in central Amazon in 2012; (C) and (D) - pictures showing the impact of the 2010 and 2005 droughts on the sampling area; (E) deepest portion of the lake during the drought of 2005. Satellite images source: NASA; pictures: laboratory archives.

Environmental data

Daily records of the water level in the Negro River were obtained from the website of Porto de Manaus Company (http://www.portodemanaus.com.br). Hydrological seasons were defined according to water level records of 100 years to distinguish four seasons (Bittencourt & Amadio, 2007). Seasons and their water level thresholds were: flood season (> 26 m); receding season (between 26 and 20 m); dry season (< 20 meter); and rising season (between 20 and 26 m). Annual hydrological cycles were defined by the beginning of a dry season of a given year until the end of the receding season during following year. Attributes that describe the flood pulse regime were estimated for each annual hydrological cycle: amplitude of the flood pulse (the difference between

56 the lowest and highest water level during one complete flood pulse cycle); annual minimum water level; annual maximum water level; timing of each season (the deviation in days between the onset of a given season during a given year in relation to the historical modal date based on a 100-year record of daily water level (rising– December 31st; flood– April 26th; receding– August 9th; dry– October 2nd); and length of each season (the number of days between the limits in water level that delimited the season) (Fig. 2). Principal components analysis (PCA) was used to ordinate hydrological seasons among years according to hydrological variables. Before analysis, data were standardized using decostand function in vegan library (Oksanen et al., 2014), PCA analysis was performed using stats library and prcomp function (Oksanen et al., 2014). All analyses were performed with R software (R Development Core Team, 2014).

Assemblage composition data Biotic information to describe taxonomic and life history and trophic structures of the assemblage followed the same flood pulse cycles defined for environmental data; beginning of a dry season of a given year until the end of the receding of the following year. The data was grouped based on a complete cycle once there was no change in this pattern over the analyzed time series (supplementary figure 1 and 2).

Taxonomic composition To ensure consistency of species identity in light of new taxonomic revisions, we retained the taxonomic nomenclature used at the beginning of the project (e.g., not splitting previously identified species into multiple species, which would bias the analysis by increasing taxonomic diversity over time). Rare species with low abundance (< 0.01 individual/survey unit) were omitted because they can bias results in multivariate statistical analysis (Gauch, 1982). In total, 97 common species out of 196 total species were included in the data matrix for analysis. To avoid bias due to inter-annual differences in sampling effort (e.g. differences in the total number of months among hydrological cycles) abundance data were transformed according to catch per unity effort (CPUE). Similarities in assemblage composition among annual hydrological cycles were investigated by principal coordinate analysis (PCoA) by considering sets of species captured between periods delimiting each annual cycle. Before analysis, data was square root transformed to reduce the influence of abundant species (Legendre and Gallagher, 2001) and converted in a Bray-Curtis distance matrix using vegdist function, in vegan library. PCoA analysis was performed using stats and BiodiversityR libraries using functions cmdscale and add.spec.scores (Kindt & Coe, 2005; Oksanen et al., 2014). All analyses were performed with R software.

Fig. 2. Time series of hydrological variables of the Negro River near its confluence with the Amazon River. A- duration of each season; B- interannual variation in the timing of the onset of each hydrological season (dry, rising, flood, and receding); C- minimum, maximum and amplitude of the water level during each annual flood pulse during the study period.

Functional structures Life history strategies- For all 97 species, we obtained information on life-history traits that were considered likely to respond to disturbance and influence population dynamic. Some examples are maximum size (as a proxy for longevity), maturation size, fecundity, oocyte diameter, and parental care (Winemiller & Rose, 1992; Tedesco et al., 2008). Most information was species-specific; whenever specific information was not available, we used available data for the genus or for the closest related genus if information was lacking at the genus level (this last case represents 6% of the total species). We assessed life history trait information from multiple sources, including primary data collected during our long-term research project at Catalão Lake, short term project in Uatumã River (Central Amazon), published scientific papers, gray literature (theses, symposium and workshops publications, and fishbase.org) available online. Information for species parental care came from our personal observations and aquarists’ reports (supplementary material). Life history traits were as follows: maximum size - maximum SL for the largest specimen captured in our surveys or the maximum size reported in the literature (when data from our project clearly underestimated the maximum size for the species); size at sexual maturation – SL of the smallest ripe female captured in our surveys or the L50 value when this information was available in the literature; fecundity - number of oocytes in the batch with greatest oocyte diameter within a mature ovary, representing the ripe cohort to be released in the next reproductive event; oocyte diameter – mean diameter based on the batch of largest oocytes in mature ovaries; parental care - was classified according to scores (ranging from 1 to 6) representing a summation of degrees of investment in individual offspring (Winemiller, 1989): special placement of zygotes (1); special placement of zygotes and brief period of parental care by one sex (2); special placement of zygotes and brief period of parental care by both sexes (3); special placement of zygotes and long period of parental care of larvae by one sex (4); special placement of zygotes and long period of parental care of larvae by both sex (6). We performed principal components analysis (PCA) using life-history traits data to ordinate species along a life history strategy continuum (Winemiller & Rose, 1992). Before analysis, data were standardized using decostand function in vegan library, PCA analysis was performed using stats library and prcomp function in R software. From the resulting correlations between species traits and PC axis scores, we identified five groups ordinated according to maturation size, maximum size and fecundity (PCA 1- 48%), and secondarily by oocyte diameter and parental care (PCA 2 - 30%) (Fig. 3):

- Equilibrium small (11 species) - small batch fecundity (<3,000); large oocytes diameter (1.5 – 12 mm); high parental care (score values >4); and maturation size <120 mm SL; and maximum size between 97 - 269 mm SL; - Equilibrium large (3 species) - small batch fecundity (<3,000); large oocytes diameter (1.5 – 12 mm); high parental care (score values >4); and maturation size >170 mm SL; and maximum size >400 mm SL; - Intermediate small (18 species) - batch fecundity between 1000 and 9000; larger oocytes diameter (1.4 – 2 mm) and presence of parental care development to a lesser degree when compared with equilibrium strategists but higher than periodic strategists; - Periodic large (28 species) - maturation size >164 mm SL; and maximum size >253 mm SL; batch fecundity between 1,000 and 202,960; small oocyte diameter (0.7 – 1.6 mm); no parental care; - Periodic small (35 species) - maturation size between 63 – 148 mm SL; and maximum size between 137 – 410 mm SL; batch fecundity between 6,762.3 and 74,227; small oocyte diameter (0.5 – 1.3 mm); no parental care.

Trophic strategies - Trophic classification for all 97 species was based on stomach contents data collected during some years of this long-term research project (1st, 4th and 9th years) and supplemented with information from the published literature. Analyses of stomach contents were based on two metrics: frequencies of occurrence and relative volumes of food categories. Each species was then assigned for one of four trophic levels: - Level 1- herbivorous and detritivorous species (26 species) that predominantly ingest plant material (seeds, fruits or leaves), filamentous algae and fine particulate organic matter that in the Amazon originates mostly from periphyton (Araujo-Lima et al., 1986, Forsberg et al., 1993); - Level 1.5- omnivorous species (21 species) that ingest combinations of animal, plant, and detritus; - Level 2- invertivorous and planktivorous species (26 species) that predominantly ingest insects (adults or immature forms of aquatic and terrestrial insects), microcurstaceans from the benthos or water column, spiders; shrimps, and/or molluscs; - Level 3- piscivorous species (24 species) that ingest adult, juvenile or larval fish, either whole or in pieces such as scales and fins.

Fig. 3. Principal components analysis with species ordination according to values for life history traits. Species life history classification: Equilibrium-large (solid squares); Equilibrium-small (squares); Intermediate-small (pyramids); Periodic-large (dots); Periodic-small (circles). Vectors were multiplied to improve visualization.

Data matrices containing CPUE values for each life history strategy or trophic level for each annual flood cycle. Temporal variation in life history and trophic assemblage structures was assessed using PCA, which was deemed suitable for multivariate analysis given the absence of zero values in the two data matrices, and low numbers of variables relative to the number of observations (Nicholls, 2011). Also, PCA was chosen to keep metric distances once the scores were used in linear regression analisys. Data were Hellinger transformed using the decostand function in the vegan library to reduce influence of more abundant groups before performing PCA using stats library and prcomp function in R software.

Statistical approaches We used a mix of statistical procedures proposed by Andersen et al. (2008), Nicholls (2011), Williams et al. (2011), and Seddon et al. (2014), which includes three steps: 1- regime shift detection, 2- test for response function and significance of the shift, and 3- detection of the

61 factors driving the regime shift (external x internal self-organizing processes). To evaluation temporal patterns, these steps were applied to hydrological data, assemblage taxonomic structure, assemblage functional group structure (life history and trophic), as well as univariate CPUE values for functional groups For regime shift detection, the Sequential T-test Analysis of Regime-Shifts algorithm (STARS) (Rodionov, 2004, 2006) was applied to the hydrological and assemblage structure ordination data (axis 1 and 2 of the PCA and PCoA), as well as functional group CPUE data for the long-term time series. Time series data were passed through a white-noise filter using the ordinary least-squares (OLS) method (Rodionov, 2006; Seddon et al., 2014). We set the p value at 0.05, and the window size to identify significant change points was set to 10. The analysis was performed with R software. Response functions and significance of shifts in hydrology, assemblage structure or abundance of functional groups were tested using generalized nonlinear least squares regression, a method that allows dealing with nonlinear relationships and non-normal error (Zuur et al., 2007). We tested the relationship between response and years by fitting six different models which could test multiple hypotheses to explain trajectories (Andersen et al., 2008; Seddon et al., 2014): - null (if no important change occurred over the time in hydrology or fish assemblage), linear (if hydrology or fish assemblage changed progressively over time, as a consequence of continuous dynamic); - segmented linear-linear (if the assemblage structures would be possible the same in different environmental condition tipically called histeresis or hydrology had changed continuously and returned as cyclic conditions); - segmented step mean (if a shift occurred in both assemblage structure and hydrology); - segmented linear – stable mean (if hydrology or assemblage had a continuous dynamic that stabilize in some moment over the time); - segmented stable mean – linear (if hydrology or assemblage had a stable structure thar start changing continuously over the time in some moment), and; - sigmoidal (if occurred a shift but the transition to the two different moments was smooth) relationships. The analyses were performed using the nlme package and gnls function (Pinheiro & Bates, 2015) in R, based on published equations (Seddon et al., 2014). In the segmented regressions, the regime shift moment detected by the STARS was used to indicate the change point in the time series. When any significant regime shift was identified by STARS, we tested the hypothesis that 2007 (our first year sampling after the strong drought of 2005) would be a possible change point

62 moment. The best model was identified using the Akaike’s Information Criterion adjusted for small samples (AICc), using AICc function available in AICcmodavg library (Mazerolle, 2015). Residuals of the best model were checked for temporal autocorrelation, and the trend was reanalyzed with the autocorrelation formulated as part of the covariance matrix of the observations. Autocorrelation and heterogeneity in variance were controlled by including an autoregressive correlation term and variance term for error in the model (Seddon et al., 2014; Zuur et al., 2007). A new Akaike’s Information Criterion for small samples test was applied to select the best model. The possible driving factors of assemblage dynamics (i.e. the relative importance of intrinsic processes, such as biotic interactions or population dynamic, and extrinsic factors, such as environmental perturbation) were inferred by comparing the type of response function between environmental and assemblage structure changes (Williams et al., 2011). Similar response functions (e.g., a segmented step mean-change for environmental data and segmented step mean- change in assemblage structure) are interpreted as extrinsically driven regime shifts in which environmental change induces shifts in the fish assemblage. In contrast, non-corresponding response functions (e.g., a segmented step mean-change for environmental data and a linear change in assemblage structure) were interpreted as a consequence of internal dynamics (higher importance of species interactions) or other unmeasured factors.

RESULTS

Hydrology and assemblage structure changes The first two ordination axes explained 60% of the temporal variation in hydrology (41 and 19% respectively), which corresponded to increasing hydrological amplitude, duration and advanced onset of the dry season in the first axis and increasing of maximum water level and duration of flood season in the second axis (Fig.4A, Fig. 2). Taxonomic, life history, and trophic structure had 50, 82 and 93% of the variance retained in the first two ordination axes, respectively (Fig.4B, C, D). Highest scores for taxonomic structure were associated with years which had higher proportional contribution of Psectrogaster rutiloides, Brycon melanopterus, Pinirampus pirinampus and Serrasalmus sp.n., and with a lower number of species; lowest scores were associated to years which had higher proportional abundance of Hemiodus sp. Curimatella alburna, Hoplosternum littorale, Ossancora punctata, Hemiodus argenteus, Trachelyopterus porosus and a higher number of species on the assemblages (Fig 4B). The years before 2005 had taxonomic Shannon diversity index and Evenness higher and less variable than the ones found

63 after 2005 (Fig. 5). Higher scores for life history and trophic structures were associated to the proportional higher abundance of periodic small (PS), and to first level consumers (respectively); whereas lower scores were associated with years which had proportional higher abundance of equilibrium small (ES), equilibrium large (EL) and intermediate small (IS), and 1.5 and 2 level consumers on the assemblage (life history and trophic structures respectively) (Fig. 4C, D).

Hydrology, assemblage and functional groups dynamics Results from STARS algorithm showed that the temporal changes observed in the first axis of the hydrological cycles and assemblage structures were significant. Over the past 15 years, two moments of major change were significant for hydrology and one moment of change in assemblage structure. Change points of hydrology occurred in 2005 (p = 0.001, Table 1) observed in the first axis of the PCA and in 2008 in the second axis. Taxonomic, life history and trophic assemblage structures had all significant change points in 2007 (all p< 0.01, Table 1). No change point was significant for the second axis of the assemblage structures. The change point of abundance (CPUE) for most groups of life history and trophic strategies was 2007 (Table 1); the exceptions were periodic with maturation at small size, periodic with maturation at large size and first level consumers which had no significant change point.

Fig. 4. PCA and PCoA ordination of survey years based on hydrology and assemblage structure. (A) Hydrology, with positive scores on PCA1 associated with earlier onset of the dry season and minimum water level, and negative scores associated with higher flood-pulses amplitude and longer dry seasons. (B) Taxonomic structure. (C) Life history structure, depicted by fish outlines: ES- equilibrium small, EL equilibrium large, IS- intermediate strategy, PL- periodic large, and PS- periodic small. (D) Trophic structure, depicted by fish outlines numbers represents the group trophic level. The light blue dashed line connects sequential years’ trajectory. (Scores of the years were multiplied by a constant to improve plot visualization: taxonomic 4.9x; life history 5.4x; trophic 6.6x). Species abbreviations in (B) can be found in Table S2.

Fig. 5: Temporal tendency of fish assemblage diversity (measured by Shannon index) and evenness in Catalão Lake between 1999 and 2014.

Regime shift response function and possible driver factors The year of 2005 was identified as a moment of significant change in hydrology. The following measured year (2007) was the moment when both assemblage structures and most functional groups abruptly changed in abundance (Table S1). Accordingly, segmented step-mean model was the best to describe changes in hydrology, and fish assemblage structure in both taxonomic and functional criteria. (Table S2, Fig. 6 A, C, E, G). A null model was the best fitted for assemblage structures summarized in the second ordination axis (Table S1, Fig. 6 D, F, H) suggesting that most of the temporal variation was retained by the first axis. Only the second ordination axis of hydrology was best explained by a different model, with a stable-mean until 2008 and linear positive relationship in the following years (Fig. 6B). In relation to each functional group, we found that segmented step-mean was also the optimal model describing temporal trends in abundance reduction for equilibrium small, equilibrium large, intermediate small strategists (Table S1, Fig 7A, B, C), and trophic levels 1.5 and 2 (Table S2, Fig. 8B, C). The response function and change moment were concordant with hydrology.

The congruence among the best-fitted model of the first axis for hydrology and assemblage structure (taxonomic, life history and trophic structures) suggests regime shifts environmentally driven (externally driven factor) (Table 2). The step-mean model with correspondent change-point about 2005/2007 suggests an extrinsic force driving the shifts in abundance also for most of the functional groups (equilibrium small species, equilibrium large, intermediate strategy, consumers at level 1.5 and 2 (Table 2). Periodic large, periodic small and consumers at level 3 had change- point in abundance around 2008 that could also be promoted by the environmental changes summarized in the second axis of the PCA that had a breaking point in 2008, or a delayed response of the first axis environmental changes (Table 1). The small negative linear regression in consumers at level 1 suggests some intrinsic processes or other factors not considered in our analysis.

Table 1: Significant change points suggested by STARS and variance explained by multivariate statistics for annual hydrological regime in term of hydrological characteristics, assemblage taxonomic structure and life history structure, and univariate abundance by life history groups.

STARS Axis DATA Change explanation (%) Significance Point Hydrological years PCA 1 43 2005 <0.001 characteristics PCA 2 16 … … PCoA 1 30 2007 <0.001 Taxonomic structure PCoA 2 14 … … PCA 1 61 2007 <0.001 Life history structure PCA 2 27 … … PCA 1 62 2007 <0.001 Trophic structure PCA 2 30 … … Equilibrium small 2007 <0.001 Equilibrium large 2007 <0.01 Intermediate small 2007 <0.001 Periodic large … … CPUE Periodic small … … Trophic level 1 ...... Trophic level 1.5 2007 0.01 Trophic level 2 2007 <0.01 Trophic level 3 2007 <0.05

Fig. 6. Hydrological and ecological temporal dynamics for the three major assemblage structures. The best models to describe the shifts in the temporal series were estimated using nonlinear regression (Table S1). Solid line represents the predicted values by the model and the dashed lines represent the standard error around the mean estimates. (A) Segmented step-mean regression for the first PCA axis suggested a major change in hydrological characteristics at 2005, corroborated by STARS (p<0.001) (Table 1); (B) the best fit for the second axis of the PCA for hydrology suggests changes in 2008. (C, E, G) Segmented step-mean regression for the first axis indicated a regime shift in all assemblage structures from 2005 to 2007 corroborated by STARS test (Table 1); (D, F, H) null model was the best-fitted model for all second axes. The variance explained by each axis can be found in Table 1.

Fig. 7. Life history groups abundance temporal trends identified by the nonlinear regression. Best models describing the shifts in the temporal series. The shift-like change point moments were corroborated by STARS (Table 1) for equilibrium small (A), equilibrium large (B) and intermediate small (C); but not for periodic large (D), and periodic small (E). Solid line represents the predicted values by the model and the dashed lines represent the standard error around the mean estimates.

Fig. 8. Temporal trend of abundance by trophic level identified by the nonlinear regression. Best models describing the shifts in the temporal series (Table S1), Consumer at: level 1 (A); level 1.5 (B); level 2 (C), and level 3 (D). The shift-like change point moments (in B and C) were corroborated by STARS for all groups (Table 1). Solid line represents the predicted values by the model and the dashed lines represent the standard error around the mean estimates. In A, the predicted values of the best- fitted model “segmented stable-mean, linear” changed to a small linear relationship after including the term to account for error heteroscedasticity.

Table 2: Summary of the dynamics of regime shifts identified for fish assemblage in central Amazon Driven factors Hydrological dynamics Ecological dynamics inferred PCoA 1 Taxonomic structure Segmented step-mean Extrinsic PCA 1 Life history structure Segmented step-mean Extrinsic PCA 1 Trophic structure Segmented step-mean Extrinsic Equilibrium small Segmented step-mean Extrinsic Equilibrium large Segmented step-mean Extrinsic Intermediate strategy Segmented step-mean Extrinsic PCA1 Segmented step-mean Periodic large Segmented linear, stable-mean Extrinsic Periodic small Segmented stable-mean, linear Extrinsic Trophic level 1 Small linear regression Intrinsic Trophic level 1.5 Segmented step-mean Extrinsic Trophic level 2 Segmented step-mean Extrinsic Trophic level 3 Segmented linear, stable-mean Extrinsic PCoA 2 Taxonomic Null +VAR

PCA2 Segmented stable-mean, linear PCA 2 Life history Null +VAR PCA 2 Trophic Null + AR

DISCUSSION

Our results support that fish assemblage of the Catalão Lake, central Amazon, has currently a new state likely as consequence of the basin scale change in precipitation resulting in changes on Amazonas River hydrological regime. This impact had taxonomic and functional consequences on assemblage structure and resulted in abundance reduction of most functional groups. Although our time series is short to infer the ecological consequences of the hydrological changes for a period longer than the past 15 years it was clear that climate modification occurring since 2005 shifted the assemblage structure in the studied floodplain region. Similarly, Freitas et al. (2013) found changes in assemblage structure and diversity reduction after the 2005 drought for all six lakes located along 400 km upstream from Catalão Lake, studying shorter time series (2004- 2007). Our results combined with Freitas et al. (2013) support that the hydrological changes occurred in a regional scale. The likelihood of regime shifts increases when large disturbances occur (Scheffer & Carpenter, 2003) such as the recent bioma/basin scale climatic changes in Amazon (Phillipe et al., 2009; Lewis et al., 2011; Gloor et al., 2013). The stable state condition since 2005 could be explained by the continuity of the disturbance. After 2005 extreme events of drought and flood occurred more frequently in the Amazon (Figs. 2 and 4) (Marengo et al., 2011; Marengo & Espinoza, 2015; Gloor et al., 2013). The last year of sampling (2014) of our time series showed hydrological characteristics and functional structure similar to that of the first years, suggesting some potential recovery and functional resilience, despite of unrecovered change in composition. The high fish biodiversity of the central Amazon (Albert & Reis, 2011) and the open characteristic of the floodplain lakes (Röpke et al., 2015) should contribute for resilience of the system (Selkoe et al., 2015). The significant increase on temporal variance of assemblage structure, lower number of abundant species associated to the last nine years (i.e. after 2005) and species diversity (Fig. 5) corroborates with the hypothesis that loss of biodiversity could imply in loss of stability (Hautier et al., 2015), and reduction of the capacity of recovering to the original state (Bozec & Momby, 2015). The long history of fisheries (Castello et al., 2013) and deforestation (Rennó et al., 2011) in central Amazon may be hampering resilience capacity for many fish populations, reinforcing the hydrological change impact. The low resilience to return to the original organization is likely a combined effect of loss of internal recover capacity and continuity of the hydrological disturbance. Using simulation technic Bozec & Mumbi (2015) that

72 a synergistic effect of chronic and acute external stressors simultaneously on an ecosystem increases the likelihood of regime shift and decreases resilience. Cyclic shifts related to decadal climatic oscillation also could have impact on fish fauna as observed at northern Pacific Ocean (Chavez et al., 2003). These decadal sea water temperature cycles, non-anthropogenic climatic change related, was found to be linked with changes on fish landing composition (Chavez et al., 2003). However, short cyclic events related to global climate change also have been reported negatively affecting fish landing based on contemporaneous measurements (Hera & Montua, 2000) and paleo estimatives (Finney et al., 2002). In the tropics, the main cyclic events are El Niño and La Niña that occur each 3 to 7 years, but the strong environmental shift was in a year of weak El Niño event (Marengo et al., 2008). Disentangling the role of hydrological changes due to other climatic processes of non-global change, global change related, the effect of regional fisheries and the identification of the recovery capacity of the assemblage will be possible only with continued monitoring. The strong influence of regional hydrology as an extrinsic driver is revealed by synchronous declines in abundances of species within functional groups. Several mechanisms probably account for this synchronicity among functional goups, including migration restriction and increased mortality due to harsh abiotic conditions, and predation (Legder et al., 2012) due to higher per-unit-area densities within smaller, less-connected aquatic habitats during the dry season (Röpke et al., 2015). For instance, during 2005 drought, total water surface storage was reduced in 70% (Frappart et al., 2012), as measured across all major basins of the Amazon. However, not all functional groups responded similarly, which suggests a significant role of intrinsic drivers. The stable abundance after 2007 in equilibrium large, equilibrium small, and intermediate strategies (mainly fishes of the families Cichlidae, Loricariidae and Serrasalmidae) could be explained by some demographic compensation via dispersal, keeping the population stable (Rose et al., 2001). However, the relatively modest resilience in unstable environments of those groups (Winemiller, 2005) would not allow elevate the intrinsic rate of population increase. Mean abundance of periodic large strategists declined gradually until 2008, after which abundance was stable and low (Fig. 7 D). This group includes mostly wide-ranging migratory species that typically spawn during the beginning of the ascending phase of the annual flood pulse (Brycon spp., Colossoma macropomum, Prochilodus nigricans, Semaprochilodus spp.) and therefore should be particularly sensitive to changes in flood-pulse timing as well as lateral connectivity (Merona &

Gascuel, 1993). The gradual decline in abundance of periodic small strategists after 2008 (Fig. 7 E) probably was influenced by reduced lateral connectivity during dry seasons. Several of these species migrate seasonally between floodplain lakes and the river channel for feeding and reproduction (e.g., Curimatella alburna, Triportheus angulatus, Ilisha amazonica), and would be especially affected by habitat connectivity during longer dry seasons. Temporal variation in the abundance of different trophic groups should be linked to the strong influence of hydrology on aquatic primary production, availability of terrestrial resources for fishes, and habitat availability during the dry season that influences fish density and predation rates (Ledger et al. 2012). The lack of response by primary consumers (herbivorous and detritivorous fishes; Fig. 8 A) probably can be explained by minimal changes in food availability for these species, most of which can shift from feeding on algae to detritus, and in some cases macrophytes (Mortillaro et al. 2015). Freitas et al. (2013) obtained a similar result as effect of the 2005 drought on fish assemblage in six lakes located upstream from Catalão Lake. Mean abundance of omnivores and secondary consumers declined after 2005, and these large and diverse groups could have been affected by changes in predation mortality as much as food resources. Most studies focusing on food web effects of extreme droughts and climate changes found a strong decline of third level consumer (predators) on the assemblage (Ledger et al. 2012; Woodward et al. 2012), however third level consumers group in Catalão Lake had a complex tendency (Fig 8 D) and suggest effect of multiple factors. The linear decrease tendency since the beginning of our surveys could be a fishery effect, as there is a historic fishing pressure on this group (Castello et al., 2013). However, fishing efforts and yields in the region did not show any pronounced increase from 2000 to 2007 (Freitas et al., 2013). Earlier onset of flood-pulse recession and longer periods of drought result in higher fish densities while reducing availability and access to predation refuges (Ledger et al. 2012), which could also account for the small increase in mean piscivore abundance after 2008. Our analysis of long-term dataset indicates that recent climate change is strongly associated with abrupt change in hydrology and aquatic ecology of the Amazonas River. Although other causal factors cannot be ruled out (e.g., fishing pressure), the similarity of response functions obtained for hydrological and assemblage datasets, together with concordance of their change points, strongly suggests that hydrology was the principal extrinsic driver. The stability of the modified assemblage structure after 2005 suggests limits for resilience of the Amazon fish assemblage structure to a

74 changing hydrological regime. Results from this study have direct importance for basin-wide management decisions for conservation and fisheries in the Amazon.

Acknowledgments This study was founded by Amazonas State Research Funding Agency (FAPEAM 062003342013; 062003422013), Brazilian National Council for Scientific and Technological Development (CNPq) ((575738/2008-1; 313183/2014-7), and National Institute for Amazonian Research (INPA). CPR and THSP received fellowship from Brazilian National Council for Scientific and Technological Development (CNPq) and Brazilian Government Agency for Support and Evaluation of Graduate Education (CAPES). JZ received a productivity grant from CNPq (313183/2014-7); and KOW received support from NSF grant (DEB 1257813). We thank all undergraduate and graduate students, volunteers, fishermen and Raimundo Sotero who helped in the field and laboratory work in the Projeto Catalão over the last 15 years. We thank C. Zhou for statistical help.

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Supplementary information

Supplementary figure 1: Non-Metric Multidimensal analysis of the seasonal assemblage composition of the Lago Catalão over 13 year of sampling

0.5 2010

0.4 2007 2013 2010 2003 0.3 2011 2004 2008 2011 2012 2009 0.2 2009 2014 2005 2013 2012 0,1

NMDS2 1999 2007 2008 0.0 2000 2002 2003 2005 -0.1 2001 2004 -0.2 Flood 2000 Drought -0.3 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 NMDS1 Supplementary figure 2: Non-Metric Multidimensal analysis of the seasonal assembling of the fish assemblage from dry to flood seasons over the 13 year of sampling

Table S2: Results for non-linear least squares regression fitted models; the best models were tested controlling the heteroscedasticity of standard error (+VAR) and temporal autocorrelation (+AR) components in the model. AICc - Akaike information criterion with a correction for finite sample sizes; LL - Log-likelihood; RSE - Residual standard error. The best fitted model is in bold.

Number of y x Model AICc LL RSE parameters Null 2 69.157 -32.078 2.125 Linear 3 66.998 -29.408 1.846 Segmented Linear- Linear 5 74.928 -29.130 1.970 Segmented Step- Mean 4 61.536 -28.383 1.724 PCA 1 Hydrological years Years Segmented Step- Mean + VAR 5 61.522 characteristics -26.678 0.034 Segmented Step- Mean + AR 5 65.354 -26.645 1.539 Segmented Linear, Stable Mean 4 67.782 -28.800 1.895 Segmented Stable- Mean, Linear 4 68.520 -30.169 1.942 Sigmoidal 5 DNC DNC DNC Null 2 57.720 -26.362 1.452 Linear 3 58.580 -25.202 1.394 Segmented Linear- Linear 5 64.920 -24.130 1.411 Segmented Step- Mean 4 58.250 -25.036 1.379 PCA 2 Hydrological years Years Segmented Linear, Stable Mean 4 60.210 characteristics -26.013 1.472 Segmented Stable- Mean, Linear 4 57.450 -24.634 1.342 Segmented Stable- Mean, Linear + VAR 5 58.563 -26.348 0.031 Segmented Stable- Mean, Linear + AR 5 57.712 -26.432 1.395 Sigmoidal 5 DNC DNC DNC Null 2 -16.931 11.0657 0.107 Linear 3 -31.094 19.880 0.056 PCoA 1 Taxonomic assemblage Years Segmented Linear- Linear 5 -24.373 21.472 0.055 structure Segmented Step- Mean 4 -36.954 22.810 0.045 Segmented Step- Mean + VAR 5 -35.250 21.968 0.024

Segmented Step- Mean + AR 5 -35.864 24.432 0.049 Segmented Linear, Stable Mean 4 -24.672 16.669 0.072 Segmented Stable- Mean, Linear 4 -22.973 15.820 0.077 Sigmoidal 5 -14.660 16.616 0.081 Null 2 -26.924 16.064 0.073 Null + VAR 3 -27.584 16.392 0.029 Null + AR 3 -23.501 16.084 0.073 Linear 3 -23.741 16.204 0.075 PCoA 2 Taxonomic assemblage Years Segmented Linear- Linear 5 DNC DNC DNC structure Segmented Step- Mean 4 -25.937 17.301 0.069 Segmented Linear, Stable Mean 4 -24.271 16.468 0.074 Segmented Stable- Mean, Linear 4 -23.531 16.099 0.076 Sigmoidal 5 -4.240 11.407 0.102 Null 2 -24.820 15.010 0.079 Linear 3 17.216 17.216 0.069 Segmented Linear- Linear 5 -23.106 20.838 0.058 Segmented Step- Mean 4 -30.097 19.382 0.059 PCA 1 Life history assemblage Years Segmented Step- Mean + VAR 5 -32.693 20.680 0.022 structure Segmented Step- Mean + AR 5 -28.397 20.698 0.059 Segmented Linear, Stable Mean 4 -23.070 15.868 0.077 Segmented Stable- Mean, Linear 4 -24.833 16.749 0.072 Sigmoidal 5 -12.232 15.402 0.088 Null 2 -37.093 21.146 0.049 Null + VAR 3 -41.012 23.106 0.017 Null + AR 3 -34.487 21.576 0.049 PCA 2 Life history assemblage Years Linear 3 -34.427 21.550 0.052 structure Segmented Linear- Linear 5 DNC DNC DNC Segmented Step- Mean 4 -35.429 22.050 0.049 Segmented Linear, Stable Mean 4 -33.871 21.270 0.053

Segmented Stable- Mean, Linear 4 -34.310 21.490 0.052 Sigmoidal 5 -11.382 14.980 0.094 Null 2 -23.119 14.159 0.084 Linear 3 -20.907 14.787 0.083 Segmented Linear- Linear 5 -20.065 19.318 0.065 Segmented Step- Mean 4 -26.339 17.501 0.068 PCA 1 Trophic assemblage Years Segmented Step- Mean + VAR 5 -31.354 20.010 0.023 structure Segmented Step- Mean + AR 5 -22.163 17.581 0.068 Segmented Linear, Stable Mean 4 -23.336 15.966 0.077 Segmented Stable- Mean, Linear 4 -19.668 14.167 0.088 Sigmoidal 5 -9.978 14.274 0.096 Null 2 -32.743 18.971 0.058 Null + VAR 3 -31.508 18.354 0.025 Null + AR 3 -33.284 20.975 0.058 Linear 3 -29.529 19.098 0.065 PCA 2 Trophic assemblage Years Segmented Linear- Linear 5 DNC DNC DNC structure Segmented Step- Mean 4 -29.743 19.204 0.060 Segmented Linear, Stable Mean 4 -29.502 19.084 0.060 Segmented Stable- Mean, Linear 4 -29.629 19.148 0.060 Sigmoidal 5 -11.047 14.809 0.093 Null 2 -56.472 30.840 0.023 Linear 3 -68.153 38.410 0.013 Segmented Linear- Linear 5 -60.717 39.640 0.013 Segmented Step- Mean 4 -71.109 39.890 0.012 CPUE – Equilibrium-small Years Segmented Step- Mean + VAR 5 -67.332 37.999 0.005 Segmented Step- Mean + AR 5 -67.112 40.076 0.012 Segmented Linear, Stable Mean 4 -60.205 34.440 0.018 Segmented Stable- Mean, Linear 4 -63.243 35.960 0.016 Sigmoidal 5 -61.301 39.650 0.013

Null 2 -78.079 41.630 0.102 Linear 3 -79.722 44.194 0.008 Segmented Linear- Linear 5 -70.254 44.128 0.009 Segmented Step- Mean 4 -86.709 47.688 0.006 CPUE – Equilibrium-large Years Segmented Step- Mean+ VAR 5 -72.443 40.554 0.004 Segmented Step- Mean + AR 5 -81.725 47.362 0.006 Segmented Linear, Stable Mean 4 76.313 42.490 0.010 Segmented Stable- Mean, Linear 4 -78.773 43.720 0.009 Sigmoidal 5 -62.420 40.495 0.012 Null 2 -23.794 14.479 0.082 Linear 3 -29.091 18.879 0.061 Segmented Linear- Linear 5 DNC DNC DNC Segmented Step- Mean 4 -34.516 21.591 0.049 CPUE – Intermediate-small Years Segmented Step- Mean + VAR 5 -33.094 20.450 0.022 Segmented Step- Mean + AR 5 -33.245 22.584 0.051 Segmented Linear, Stable Mean 4 -26.135 15.899 0.068 Segmented Stable- Mean, Linear 4 -25.671 17.401 0.070 Sigmoidal 5 -13.227 17.169 0.085 Null 2 -31.819 18.510 0.060 Linear 3 -43.772 26.220 0.035 Segmented Linear- Linear 5 DNC DNC DNC Segmented Step- Mean 4 -47.400 28.030 0.034 CPUE – Periodic-large Years Segmented Linear, Stable Mean 4 -52.881 30.770 0.024 Segmented Linear, Stable Mean + VAR 5 -42.685 27.842 0.213 Segmented Linear, Stable Mean + AR 5 -37.114 25.057 0.038 Segmented Stable- Mean, Linear 4 -40.601 27.920 0.039 Sigmoidal 5 -37.269 24.630 0.033 Null 2 0.254 2.470 0.208 CPUE – Periodic-small Years Linear 3 -0.929 4.800 0.181

Segmented Linear- Linear 5 DNC DNC DNC Segmented Step- Mean 4 -0.391 4.530 0.185 Segmented Linear, Step Mean 4 2.175 3.250 0.204 Segmented Step- Mean, Linear 4 -0.958 4.810 0.181 Segmented Stable- Mean, Linear + VAR 5 -0.294 4.480 0.078 Segmented Stable- Mean, Linear + AR 5 -1.012 7.006 0.185 Sigmoidal 5 DNC DNC DNC Null 2 -20.140 12.670 0.095 Linear 3 -18.380 13.530 0.092 Segmented Linear- Linear 5 -16.540 14.770 0.088 Segmented Step- Mean 4 -18.020 13.350 0.094 CPUE – Trophic-level 1 Years Segmented Linear, Step Mean 5 -16.730 12.700 0.099 Segmented Step- Mean, Linear 5 -20.370 14.520 0.086 Segmented Stable- Mean, Linear + VAR 4 -21.585 15.126 0.034 Segmented Stable- Mean, Linear + AR 4 -16.514 14.757 0.096 Sigmoidal 5 -6.640 12.610 0.110 Null 2 -7.310 6.260 0.155 Linear 3 -9.700 9.180 0.129 Segmented Linear- Linear 5 -5.370 9.180 0.136 Segmented Step- Mean 4 -11.500 10.090 0.121 CPUE – Trophic-level 1.5 Years Segmented Step- Mean+ VAR 5 -8.727 8.697 0.056 Segmented Step- Mean + AR 5 -11.325 12.162 0.122 Segmented Linear, Stable Mean 4 -7.170 7.920 0.143 Segmented Stable- Mean, Linear 4 -8.080 8.370 0.138 Sigmoidal 5 0.860 8.850 0.143 Null 2 -26.050 15.630 0.075 Linear 3 -33.250 20.960 0.052 CPUE – Trophic-level 2 Years Segmented Linear- Linear 5 -30.250 21.630 0.052 Segmented Step- Mean 4 -40.940 24.800 0.039

Segmented Step- Mean+ VAR 5 -39.851 26.425 0.065 Segmented Step- Mean + AR 5 37.671 25.335 0.039 Segmented Linear, Stable Mean 4 -31.910 20.290 0.055 Segmented Stable- Mean, Linear 4 -27.940 18.310 0.064 Sigmoidal 5 -19.940 19.260 0.066 Null 2 -27.000 16.100 0.072 Linear 3 -38.570 23.620 0.042 Segmented Linear- Linear 5 -33.630 26.100 0.039 Segmented Step- Mean 4 -42.830 25.750 0.036 CPUE – Trophic-level 3 Years Segmented Linear, Stable Mean 4 -42.910 25.790 0.036 Segmented Linear, Stable Mean + VAR 5 -38.721 25.860 0.036 Segmented Linear, Stable Mean + AR 5 -40.177 26.588 0.037 Segmented Stable- Mean, Linear 4 -36.940 22.800 0.045 Sigmoidal 5 -24.720 21.640 0.055

Table S2: Life history traits by specie, life history and trophic classifications and source of the information.

Maximum Maturation Oocyte size Parental Trophic Trophic Supplementary SP Fecundity Life history strategy Size (mm) size (mm) (mm) care strategy level bibliography

Cichla monoculus 400 188 3100 2.5 6 Equilibrium-large Piscivore 3 1, 2 Osteoglossum bicirrhosum 561 477 310 8 4 Equilibrium-large Invertivore 2 3 Pterygoplichthys pardalis 400 170 1157 3.7 4 Equilibrium-large Detritivore 1 4 Acarichthys heckelii 172 60 1340 1.7 6 Equilibrium-small Omnivore 1.5 5, 6 Ancistrus dolichopterus 200 92 100 2.9 4 Equilibrium-small Detritivore 1 Astronotus ocellatus 250 132 2300 2.4 6 Equilibrium-small Invertivore 2 7 Chaetobranchopsis orbicularis 265 91 1287 1.9 6 Equilibrium-small Planctivore 2 5 Cichlasoma amazonarum 135 65 1287 1.9 6 Equilibrium-small Omnivore 1.5 5 Dekeyseria amazonica 174 112 1019.2 2.5 4 Equilibrium-small Detritivore 1 Geophagus proximus 220 60 1645 2.7 6 Equilibrium-small Invertivore 2 Heros spurius 160 76 2003 1.5 6 Equilibrium-small Omnivore 1.5 8 Mesonauta festivus 120 48 1185 1.5 6 Equilibrium-small Omnivore 1.5 9, 10 Pterophyllum scalare 97 70 469 1.25 6 Equilibrium-small Invertivore 2 11, 12 Satanoperca acuticeps 151 85 1010 1.5 6 Equilibrium-small Invertivore 2 13 Satanoperca jurupari 269 104 1056 2 6 Equilibrium-small Invertivore 2 13 Trachelyopterus galeatus 237 110 2511.7 2 1 Intermediate-small Omnivore 1.5 63 Ageneiosus atronasus 150 140 1000 1.6 1 Intermediate-small Piscivore 3 13 Ageneiosus brevis 165 120 1000 1.6 1 Intermediate-small Piscivore 3 4, 14 Auchenipterichthys 200 89 983 1.5 1 Intermediate-small Omnivore 1.5 14 coracoideus Auchenipterus britskii 255 120 2466 1.7 1 Intermediate-small Invertivore 2 4 Auchenipterus nuchalis 225 128 2466 1.6 1 Intermediate-small Invertivore 2 4 Hoplias malabaricus 380 150 7459 1.8 2 Intermediate-small Piscivore 3 2, 4, 15 Hoplosternum littorale 300 107 6400 1.9 2 Intermediate-small Invertivore 2 16, 17 Hypoptopoma gulare 120 72 764 1.5 1 Intermediate-small Detritivore 1 10 Metynnis hypsauchen 200 100 4726.6 1.4 1 Intermediate-small Herbivore 1 2 Metynnis lippincottianus 200 95 4726.6 1.4 1 Intermediate-small Herbivore 1 2

Ossancora asterophysa 101 83 1990 1.6 1 Intermediate-small Invertivore 2 2 Ossancora punctata 113 83 1990 1.6 1 Intermediate-small Invertivore 2 2 Pygocentrus nattereri 300 115 8694.9 1.7 2 Intermediate-small Piscivore 3 2, 18 Serrasalmus elongatus 250 146 2935 1.7 2 Intermediate-small Piscivore 3 2, 19 Serrasalmus maculatus 260 95 4033 1.8 2 Intermediate-small Piscivore 3 2, 19 Serrasalmus sp.n. 290 118 2097.4 2 2 Intermediate-small Piscivore 3 2, 19 Trachelyopterus porosus 200 113 2511.7 2 1 Intermediate-small Invertivore 2 10, 14 Curimatella alburna 166 63 40864 1 0 Periodic-small Detritivore 1 20 Curimatella meyeri 205 76 64785 1 0 Periodic-small Detritivore 1 20 Triportheus angulatus 230 89 20634 1 0 Periodic-small Omnivore 1.5 2 Psectrogaster rutiloides 200 92 37407 1 0 Periodic-small Detritivore 1 2 Anadoras grypus 146 95 10961 1 0 Periodic-small Omnivore 1.5 Steindachnerina bimaculata 170 95 26815 1 0 Periodic-small Detritivore 2 20 Triportheus albus 227 95 9669 1 0 Periodic-small Omnivore 1.5 2 Acestrorhynchus microlepis 310 100 13370 0.9 0 Periodic-small Piscivore 3 2 Triportheus auritus 297 102 23178.9 1 0 Periodic-small Omnivore 1.5 2 Psectrogaster amazonica 200 104 39452 1 0 Periodic-small Detritivore 1 2 Laemolyta proxima 285 105 16486 1.1 0 Periodic-small Herbivore 1 21 Nemadoras humeralis 137 105 7452 0.9 0 Periodic-small Invertivore 2 Curimata knerii 200 110 20020 1 0 Periodic-small Detritivore 1 2 Lycengraulis batesii 181 110 11638 0.55 0 Periodic-small Invertivore 2 2, 14 Roeboides myersii 165 110 11601 0.9 0 Periodic-small Piscivore 3 2, 14 Mylossoma duriventre 220 114 25500 1.3 0 Periodic-small Herbivore 1 2, 22 Nemadoras hemipeltis 143 115 6762.3 0.9 0 Periodic-small Invertivore 1.5 Pellona flavipinnis 410 116 16112.2 0.9 0 Periodic-small Piscivore 3 23 Curimata vittata 193 117 20020 1 0 Periodic-small Detritivore 1 2 Mylossoma aureum 200 124 25500 1.3 0 Periodic-small Herbivore 1 2, 22 Chalceus epakros 243 125 16680.2 1.1 0 Periodic-small Invertivore 2 21 Lycengraulis grossidens 171 125 27276 0.7 0 Periodic-small Invertivore 2 2, 14 Hemiodus argenteus 222 126 42539 1.1 0 Periodic-small Omnivore 1.5 24 Potamorhina altamazonica 250 129 74227 1 0 Periodic-small Detritivore 1

Pimelodus aff. blochii 195 129 12290 0.6 0 Periodic-small Omnivore 1.5 2 Hemiodus sp.n. 249 135 31995 1.2 0 Periodic-small Omnivore 1.5 24 Hemiodus immaculatus 192 137 44600 1.2 0 Periodic-small Omnivore 1.5 24 Anodus elongatus 256 140 17300 1 0 Periodic-small Planctivore 2 2 Ilisha amazonica 220 140 13407 1 0 Periodic-small Invertivore 2 25 Leporinus amazonicus 253 140 20800 1 0 Periodic-small Omnivore 1.5 Schizodon fasciatus 319 141 70433 1 0 Periodic-small Herbivore 1 21 Leporinus friderici 330 141 28950 1 0 Periodic-small Omnivore 1.5 2, 21 Acestrorhynchus falcirostris 350 143 26515.8 1.1 0 Periodic-small Piscivore 3 2 Potamorhina latior 250 144 54667 1 0 Periodic-small Detritivore 1 2 Jurengraulis juruensis 170 148 36363 0.4 0 Periodic-small Planctivore 2 Semaprochilodus insignis 300 164 81070 1 0 Periodic-large Detritivore 1 26 Plagioscion montei 375 167 13411 0.8 0 Periodic-large Piscivore 3 27, 28 Prochilodus nigricans 350 177 69427.3 1 0 Periodic-large Detritivore 1 26, 29 Hydrolycus scomberoides 467 185 51132 1 0 Periodic-large Piscivore 3 14 Plagioscion squamosissimus 425 188 15991 0.8 0 Periodic-large Piscivore 3 2, 28 Pellona castelnaeana 572 190 88024.3 1 0 Periodic-large Piscivore 3 56, 80 Anodus sp. 253 190 21720 1 0 Periodic-large Planctivore 2 2 Rhytiodus microlepis 315 190 109800 1 0 Periodic-large Herbivore 1 28 Ageneiosus ucayalensis 350 200 24473 1.6 0 Periodic-large Piscivore 3 14 Semaprochilodus taeniurus 300 202 81070 1 0 Periodic-large Detritivore 1 26, 27 Leporinus fasciatus 305 210 54112.7 1.2 0 Periodic-large Omnivore 1.5 28 Anodus orinocencis 262 215 22400 1 0 Periodic-large Planctivore 2 Sorubim elongatus 322 225 19863.7 1.2 0 Periodic-large Piscivore 3 2 Rhytiodus argenteofuscus 328 231 110820 1 0 Periodic-large Herbivore 1 29 Hypophthalmus edentatus 450 240 64171 1 0 Periodic-large Planctivore 2 2 Brycon melanopterus 352 250 171545 1.5 0 Periodic-large Omnivore 1.5 30 Calophysus macropterus 400 250 47040 0.9 0 Periodic-large Piscivore 3 80, 84 Pterodoras granulosus 300 250 75117 0.9 0 Periodic-large Herbivore 1 27 Sorubim lima 380 255 26868 1.2 0 Periodic-large Piscivore 3 14, 27 Hypophthalmus fimbriatus 350 264 64171 1 0 Periodic-large Planctivore 2 27

Leporinus trifasciatus 320 264 32544 0.9 0 Periodic-large Omnivore 1.5 2, 29 Pinirampus pirinampu 1200 275 64444 0.8 0 Periodic-large Piscivore 3 31 Hypophthalmus marginatus 500 293 64171 1 0 Periodic-large Planctivore 2 Rhaphiodon vulpinus 558 293 98185 1 0 Periodic-large Piscivore 3 32 Rhamphicthys marmoratus 715 300 1000 1.3 0 Periodic-large Invertivore 2 Ageneiosus inermis 450 306 31400 1.6 0 Periodic-large Piscivore 3 14, 31 Brycon amazonicus 400 320 171545 1.5 0 Periodic-large Omnivore 1.5 30 Pseudoplatystoma tigrinum 1100 410 202960 1 0 Periodic-large Piscivore 3 27, 31 Colossoma macropomum 1200 600 1007364 1.2 0 Periodic-large Herbivore 1 27, 33

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CAPÍTULO 3

EFFECTS OF EXTREME SEASONAL EVENTS, PHENOTYPE AND BIOTIC INTERACTIONS ON REPRODUCTIVE INVESTMENT IN AMAZONIAN CAPITAL BREEDING FISHES

Cristhiana P Röpkea*; Daniele F Camposb; Daniele de F Wolf c; Claudia P Deusd; Sidinéia Amadiod

a- Programa de Pós-Graduação em Biologia de Água Doce e Pesca Interior/Instituto Nacional de Pesquisas da Amazônia – INPA, Cx Postal 2223, 69080-971, Manaus, Amazonas, Brasil b- Instituto Nacional de Pesquisas da Amazônia – INPA, Manaus, Amazonas, Brasil Laboratório de Dinâmica de Populações c- Instituto Nacional de Pesquisas da Amazônia – INPA, Manaus, Amazonas, Brasil Laboratório de Ecossistemas Aquáticos d- Coordenação de Biodiversidade – CBIO/Instituto Nacional de Pesquisas da Amazônia – INPA, Manaus, Amazonas, Brasil *Correspondence Author: krikaropke@gmailcom, Instituto Nacional de Pesquisas da Amazônia – INPA, Cx Postal 2223, 69080-971, Manaus, Amazonas, Brasil Tel: +55 (92) 36433254

Running headline: Individual and environmental effects on reproduction

Journal style: Journal of Animal Ecology

Summary

1. Costs and benefits of breeding vary according to individual's phenotype and environmental conditions. Environment may also interact with phenotypic variation increasing or reducing its effects. Under harsh environmental conditions, the costs of reproduction might be more pronounced for certain phenotypes than during good environmental condition. 2. We tested the relative importance of several biotic and abiotic measures on fecundity and oocyte size of three species of fish in the Amazon, over a time series of 13 years (1999-2014). Tested variables were body size, body condition, annual population density, annual predator density, hydrological condition of the dry and flood periods, and the interaction of individual’s phenotype and environmental condition. 3. Extreme events and the interaction of extreme events and body condition affected negatively female fecundity. Females of Acestrorhynchus falcirostris, a piscivorous species, reduced the fecundity under low body condition in years of intense flood. Long, intense and early dry seasons had negative effect on fecundity of the Psetrogaster rutiloides and Triportheus angulatus. Moreover, these factors interacted with body condition of females determining T. angulatus fecundity. Mean oocyte size was more affected under condition such as high population density and hydrological extremes and its interaction with individual’s phenotype as compared to individual’s phenotype alone. Population density had a positive effect on the mean oocyte size for all studied species. 4. The reduction of reproductive investment in fecundity under environmental condition such as, long floods for the piscivorous A. falcirostris and early, long and intense dry seasons for T. angulatus and P. rutiloides supports the prediction that the cost of reproduction increases under harsh environmental condition. The increase of oocyte size when population density was higher supports the hypothesis that females invest more in offspring fitness under higher chances of intraespecífic competition. 5. This study shows that females of the three freshwater fish species in the Amazon floodplain changed the reproductive characteristics according to individuals’ phenotype, population and predator’s densities, and hydrological condition. These results suggest that, under energetic costly environmental condition individual’s survival was apparently favored over offspring production; under higher population density offspring fitness (larger size) were favored.

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Key words: Extreme events, density-dependent effect, predators-density effect, fish reproduction, state dependent decision, trade-off, phenotypic plasticity

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Introduction

Most predictions from life history theory are rooted in the concept of trade-offs (Stearns 1989, Roff 2002). The reasoning is that the energy available to individuals is limited so any energy allocation to one trait reduces the allocation into other traits (Williams 1966; see Harshman & Zera 2007 for a review). Therefore, the energy allocated to current reproduction is expected to constrain the energy available for somatic maintenance, leading to decrease in future reproduction or survival (Stearns 1992). Thus, the reproductive investment should be balanced between survival and reproduction to maximize individual’s fitness. Costs and benefits of breeding commonly vary with individual's phenotype such as weight and physiological condition (Clutton-Brock, Guinness & Albon 1983, Stearns 1992). For example, it is expected that larger mothers in better condition to produce offspring in higher numbers and sizes (Rollinson & Rowe 2015). However, these reproductive parameters will likely vary under environmental factors that may increase the costs of breeding, such as high population density (Albon, Clutton-Brock & Guiness 1987, Clutton-Brock, Guinness & Albon 1983, Festa-Bianchet Gaillard & Jorgenson 1998), harsh environmental conditions (Tavecchia et al. 2005), and low food availability (Davis, Nager & Furness 2005). Environmental conditions may also interact with phenotypic variation increasing or reducing its effects (Albon, Clutton-Brock & Guiness 1987, Clutton-Brock et al. 1987), and it is under harsh environmental conditions that the costs of reproduction should be more apparent (Garnier et al. 2016, Robert et al. 2015). Life history theory predicts that under poor or harsh environmental conditions individuals should produce few but large offspring (Parker & Begon 1986, Sibly & Calow 1989, Roff 2002, Rollinson & Hutchings 2013). Moreover, optimal offspring size should decrease and fecundity increase with increasing environmental quality, including predation and population density pressures (Einum 2003, Reznick, Bryga & Endler 1990). Individuals that do not restrict reproduction in response to harsh environments are expected to experience an exacerbated cost of reproduction on subsequent survival (Clutton-Brock et al. 1996), reinforcing the trade-off responses in the population (Reznick 1985). MacNamara & Houston (1986) and Houston & MacNamara (1992) proposed that organism's decisions depend on its and environmental states, which might include the environment that the animal lives or various aspects of its physiology and morphology. According to this concept, variation in the costs or benefits of breeding will likely lead to changes in optimal reproductive strategies. Several studies suggest that animals modify their reproductive decisions in relation to their phenotypic or

102 environmental condition (e.g. Legget & Carscadden 1978, Fletcher, Hughes & Harvey 1994, Clutton- Brock et al. 1996, Rideout & Tomkiewicz 2011), which allows the existence of multiple reproductive tactics in the population. Year-to-year variation in environmental characteristics such as food supply, predation, or weather conditions which interfere in juveniles and adult survivorship can have immediate effects on reproductive traits such as fecundity or spawn frequency (Lack 1954, Cluttron-Brock et al. 1987, 1996, Lourdais et al. 2002, Oskarsson & Taggart 2006, Sundby & Nakken 2008). In seasonal environments, many ecological factors may influence parental fitness and affect reproduction. As such, reproduction will have different intensities across years (e.g. Johansson & Rowe 1999). In seasonal environments, most species have different seasons to reproduce and store energy, increasing feeding activity and energy reserves prior to reproduce (Boyd 2000). This overcomes the problem of energy allocation for reproduction (capital breeding strategy) in an environment where the food supply is not constant, however predictable (Boyd 2000). Because of that, for capital breeding species reproductive success has been suggested to depend mainly on body reserves rather than on environmental conditions (Stephens et al. 2009, Hamel, Coté & Festa-Bianchet 2010). Spawning is the most metabolically demanding activity in the life history for many species of ﬁsh, whether it occurs at a single spawning season or over multiple seasons (Glebe & Leggett 1981). Tropical floodplain rivers undergo large seasonal fluctuations in water level which impact directly the food resource availability (Lowe-McConnell 1987, Bayley 1988, Junk, Bayley & Sparks1989). During high water periods the flooded forest provides food resources for herbivorous and invertivorous fishes, while during low water periods aquatic organisms are concentrated in a smaller volume of water bodies, and consequently, prey availability for piscivores is higher (Lowe-McConnell 1987, Bayley 1988, Goulding, Carvalho & Ferreira 1988, Gomes & Agostinho 1997, Layman & Winemiller 2005). Previous research (Junk 1985, Gomes & Agostinho 1997, Arrington et al. 2006, Neves dos Santos, Amadio & Ferreira 2010) has documented the seasonal energy storage in fishes during the period of high resource availability (i.e. flood), and the subsequent consumption of these stored energy for reproduction and migration. The period of gonadal development for reproduction coincide with the lowest water level for most species at the floodplain lakes (Junk 1985, Neves dos Santos, Amadio & Ferreira 2010) and the stressful moment when either decreased resource availability (except for piscivores) and increase physiological cost for survival.

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Extreme hydrological events such as historically higher floods and droughts have become more frequent in the Amazon and are related to global climatic changes affecting precipitation differently (increasing or decreasing) in different parts of the basin (Marengo et al. 2008, Satyamurty et al. 2013, Fu et al. 2013, Gloor et al. 2013). Over the last years three severe droughts (1997, 2005, 2010, and in the current hydrological year of 2015-2016) and many frequent high flood (since 2009) occurred (Marengo et al. 2008, Satyamurty et al. 2013, Fu et al. 2013, Gloor et al. 2013). During drought events floodplain lakes in Amazon can lose over 70% of surface water storage or dry out completely (Frappart et al. 2012); as a consequence, the water temperature and levels of metabolic excreted substances (e.g. ammonia) increase considerably during dry season (Brito, Alves & Espirito- Santo 2014), leading to increased physiological costs for organisms and reducing drastically the energy stored by the individuals inhabiting these environments (Correia, Siqueira-Souza & Freitas 2014). In floodplain lakes, the impacts of extreme hydrological events and interannual variability in freshwater fish reproduction is largely unexplored; given the growing multitude and intensity of anthropogenic and environmental stressors, there is a higher urge in understanding interactions between ecological stressors and organims’s life history. Understanding patterns of species reproduction decision according to trophic strategy, life history and its underlying processes has profound implications for evolutionary demography (Cam et al 2013, Seather & Bakke 2000), population dynamics (Pianka 1970, Jeppsson & Forslund 2012) and extinction risk assessment (Jeppsson & Forslund 2012, 2014) for freshwater species. In this study we investigated the costs of reproduction as a function of the annual hydrological condition, population density, predator density, and individual’s phenotype of three Amazonian fish species of different trophic categories and, therefore, different seasonal food intake. The terminology individual’s phenotype in this study makes reference to individual size and body condition. Data was acquired along 13 years and includes a large variance of environmental condition.

Study species and predictions The study was conducted with three species: Acestrorhynchus falcirostris (Cuvier 1819), a piscivorous species, that can reach up to 35 cm in standard length (SL) (Ferreira, Zuanon & Santos 1998); Triportheus angulatus (Spix & Agassiz 1829) an omnivorous species (Claro-Junior et al. 2004; Yamamoto, Soares & Freitas 2004) which can reach up to 25 cm in SL; and Psectrogaster rutiloides (Kner 1858) a detritivorous species (Ferreira, Zuanon & Santos 1998) which can reach up to 20 cm in

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SL. These three species are among the 10 most abundant and frequent species in the study area (Röpke et al. 2015). Previous studies with these species or the genus show that these species are capital breeding, A. falcirsotris has higher energetic storage during the annual phases of receding and low water (Neves dos Santos, Amadio & Ferreira 2010), T. angulatus and P. rutiloides have energetic storage during high and receding water (Moreira-Hara, Zuanon & Amadio 2004). All species undergo vittelogenesis during low and onset of rising water and spawn during rising and high annual water phases (Fernandes 1997, Neves dos Santos, Amadio & Ferreira 2010, Garcia-Vazquez et al. 2015), none of the three undertake parental care. The annual reproduction for fish species in tropical floodplain have been linked to the seasonal energetic acquisition (Arrington et al. 2006, Neves dos Santos, Amadio & Ferreira 2010), and seasonal predictability for offspring survival (Vazzoler, Amadio & Caraciolo-Malta 1989a, b, Carvalho-Lima & Araújo-Lima 2004). The costs for reproduction (the number and quality of the offspring produced) can be a result of interannual variation in flood and dry seasons, as seasonal environmental conditions directly affect energy acquisition and energy expenditure. We predicted that A. falcirostris fecundity would decrease in years of long floods and late or non-intense dry season, as these conditions would reduce energy acquisition for a piscivorous fish, additionally we also considered the interaction of these environmental variables with body condition. For T. angulatus and P. rutiloides fecundity, we expected a negative effect of early and intense dry seasons due to metabolic cost of harsh drought conditions, also considering an interaction with body condition. For all species, we predicted that the fecundity would decrease in years of high population density, increase with individual’s size and body condition and predators’ density. Oocyte size was expected to be higher with the decrease in fecundity (trade-off relationship) and under increased predators’ density. Additionally, although individual’s phenotype, population density, predators’ density and hydrological condition are expected to be important, we expected that the importance of individual phenotype would be mediated by the hydrological condition prior to spawning.

Material and Methods Study area and sample collection Samples were collected in a floodplain lake at the confluence of the Amazonas and Negro rivers as detailed by Röpke et al. (2015) (and previous chapters of this thesis) from October 1999 until August 2014. Fish were captured monthly to include the complete reproductive period and

105 provide estimates of variation in sexual maturation stage, standard length (mm), fresh wet whole body weight (g), fresh eviscerated body weight (g), ovary weight (g). Sample size among collections varied with fish availability. Samplings were carried out using 10 gillnets of several mesh sizes (40 to140 mm between opposite knots) measuring 10 x 15- 35 m. Gillnets were set at the same site each month and left in the water for 24 h, with fish being removed every 6 h (details can be found in Röpke et al (2015) (and previous chapters of this thesis).

Fecundity and oocyte size Both mature ovaries were weighed, immersed in Gilson’s solution and periodically shaken to dissociate oocytes and conserved in ethanol solution (70 %) thereafter (Simpson 1951). Fecundity was estimated according to the gravimetric method (Vazzoler 1996). All oocytes of the subsample were measured (mm) and the frequency of occurrence by class size were plotted to distinguish the minimal diameter of mature oocytes and to estimate the batch fecundity. Batch fecundity was then estimated as BFec = p x N, where p is the proportion of mature oocytes estimated in the subsample; N is the total number of oocytes estimated for female. Mature oocytes were only considered to be those greater than or equal to the smaller size of last batch of the distribution. The maximum oocyte size was considered as the largest oocyte measured. Mean oocyte size was estimated by the arithmetic mean of the oocytes sizes of the last batch.

Body condition Body condition was estimated as the residuals of the relationship of log transformed standard length and total weight (body condition TW) and eviscerated weight (body condition EW) (Peig & Green 2010).

Hydrological condition Because most factors acting negatively on the reproductive investment are related to pre- reproductive period conditions, we estimated those hydrological variables that could be more ecologically relevant for fish reproduction at large rivers floodplain. Attributes that describe the hydrological condition and timing for receding and dry seasons previous to the spawning season, and flood season previous reproduction of each year (Menezes & Vazzoler 1992, Carvalho de Lima & Araújo-Lima 2004, Neves dos Santos, Ferreira & Amadio 2008), were measured as: maximum and

106 minimum annual water level; timing of receding and dry seasons (the deviation in days between the onset of a given season during a given year in relation to the historical date based on a 100-year record of daily water level (receding– August 9th; dry– October 2nd); and length of flood, receding, and dry seasons (number of days between the limits of the season). All measurements were made based on daily records of the water level in the Negro River which were obtained from the website of Porto de Manaus Company (http://wwwportodemanauscombr) and from the Brazilian National Water Agency (http://wwwanagovbr/widroweb); hydrological seasons were defined as Bittencourt & Amadio (2007) (details are presented in the chapter 2).

Population and predator densities Annual density of population of each study specie, and predators was measured as catch per unity effort (CPUE) based on the total number of specimens captured for each species along an annual flooding cycle. Annual flooding cycle was defined as the beginning of a receding season of a given year until the end of the flood season of the following year. Were considered potential predators all piscivorous and carnivorous species captured in the lake each year. Although no study addressing the predation on larvae and juveniles for Amazon floodplain fish fauna exists, it is common to find fish larvae in the stomach of piscivorous and carnivorous juvenile fish [e.g. Cichla monoculus, Acaronia nassa, Crenicichla wallaci, Serrasalmus elongatus (Röpke et al. 2014)].

Data analysis Statistical procedures We used linear regression method to model the influence of hydrological condition and biological factors (body condition, female standard length, density of predators and populations) on batch fecundity and mean oocyte size. Generalized linear model (GLM) was used to deal with different distribution function present in the data sets (Zurr, Ieno & Smith 2007). Were priorized the use of Gaussian distribution when normality was accepted with the raw or log transformed data. When normality was not accepted even after transformation other probability distribution was used. Linear models with Gaussian distribution were performed with log transformed data of batch fecundity for Acestrorhynchus falcirostris and Triportheus angulatus; a linear model with negative binomial distribution with raw data of batch fecundity for Psectrogaster rutiloides. Mean oocyte size of the Acestrorhynchus falcirostris was analyzed using linear model with Gamma distribution; mean oocyte

107 size of the Triportheus angulatus and Psectrogaster rutiloides was log transformed and analyzed using linear model with Gaussian distribution. Negative binomial model was performed with glmnb function from countreg package (Zeileis & Kleiber 2016), all other regressions were performed with glm function from MASS package, variables selection was done using function step MASS library. All analyses were done with R software (R Development Core Team 2014).

Variables selection Correlation analysis was performed for a primary selection of variables included in the model avoiding redundant variables (Appendix 1-4), regarding the threshold of 50% of correlation, which resulted in the exclusion of many hydrological variables. The initial model for all species included the variables: female standard length, body condition EW, mean oocyte size, annual density of the population (CPUE), density of predators (CPUE), days of flood previous to spawning or maximum water level value for the flood season before spawning, dry season onset or minimum annual water level or days of dry season. High correlated variables (i.e. maximum water level and days of flood) were tested by replacing each other in the initial model. Interaction of the body condition EW with hydrological variables and also density of population with density of predators were considered in the model for both, batch fecundity and oocyte size, once they are directly related to energy acquisition or physiological stress, with direct effect on the body condition. Because batch fecundity and standard length are close related individual’s traits, and both have effect on oocyte size, we used the residuals of the standard length and batch fecundity relationship (log transformed variables) to estimate the effect of fecundity on oocyte size. The initial set of variables was selected based on the best Akaike’s Information Criterion (AIC) providing a compromise between model deviance and the number of parameters used, preferring the one with fewest parameters. The final set of variables was made by AIC and the examination of standardized residuals. We also examined standardized residuals against all independent variables included or not included in the model, variables with pattern in the residuals were included in the final model when they increased the prediction power of the complete model (Zurr, Ieno & Smith 2007). Inspection of the standardized residuals was used to identify outliers and to verify the assumption of variance homogeneity, and quantile plots of the residuals and Shapiro-Wilk test was used to assess assumptions of normality. Gonads of Acestrorhynchus falcirostris taken in 2003 were not included in the analysis for oocyte mean size due to a low number of samples (3 in the total) and extreme values

108 of population density. Gonads of any species with too few oocytes in the last batch suggesting/implying a possible incomplete vittelogenesis were not included in the models as well.

Results Fecundity and oocyte size characteristics Frequency of distribution of the oocytes sizes for the three species were bimodal although trimodal distribution was frequently observed in Acestrorhynchus falcirostris (Appendix 5). The last mode with larger oocytes consisted of oocytes > 0.7 (range 0.7-1.3), > 0.5 (range 0.5-1.4) and > 0.9 (range 0.9-1.4) with annual average of the female’s maximum oocyte size ranging from 0.77-0.93, 0.85-1.09 and 0.65-0.80 for A. falcirostris, T. angulatus and P. rutiloides respectively (Appendix 5 and 6). The annual average female’s batch fecundity in the population ranged from 14,211 to 35,231, 6,244 to 26,593, and 13,753 to 60,610 oocytes for A. falcirostris, T. angulatus and P. rutiloides respectively (Appendix 6).

Optimal models results The best initial and final variables to predict female’s batch fecundity and mean oocyte size are presented in Appendix 7. Only standard length was equally important to predict batch fecundity for all three species with positive increase in reproductive investment with size, although hydrological condition, biotic interactions or body condition, had also high effect on species reproductive investment (Table 1). Batch fecundity (log) of A. falcirostris was positively related to standard length (log) (Fig. 1 a, main effect = 2.439), mean oocyte size (Fig. 1 b, main effect = 1.128), and the interaction of length of flood season with body condition (EW) (Fig. 1 c, main effect = 0.003). The positive increase of the fecundity with body condition (EW) was dependent on the length of flood season before spawning season. Females in poorer body condition had lower batch fecundity than females in better body condition when the reproductive season was preceded by long floods (about 180 days) than reproductive seasons preceded by short flood season (120 days or less). Mean oocyte size for A. falcirostris had negative effect with the standard length (log) (Fig. 2 a, main effect = - 0.106) and a positive effect with higher residual values of the relationship of standard length and

109 batch fecundity (Fig. 2 b, main effect = 0.024), and density of the population (log) (Fig. 2 c, main effect = 1.444). Batch fecundity (log) of T. angulatus had positive effect with standard length (log) (Fig. 3 a, main effect = 2.467), negative effect with length of flood season before spawning (Fig. 3 b, main effect = -0.006), and interaction of the body condition with dry season onset (Fig. 3 c, main effect = 0.007). In years with advanced dry season onset, females in poor body condition had lower batch fecundity than females in better body condition; in years with delayed dry season onset females in poor body condition had higher batch fecundity than females in better body condition. Mean oocyte size was positively related to residual of the standard length and batch fecundity relationship (Fig. 4 a, main effect = 0.052), standard length (Fig. 4 b, main effect = 0.210), and population density (Fig. 4 d, main effect = 1.240). It was negatively affected by body condition (EW) (Fig. 4 c, main effect = -0206), density of predators (Fig. 4 e, main effect = -0.546), minimum water level (Fig. 4 f, main effect = -0.019). Mean oocyte size was affected by the interaction of length of flood season and body condition (EW) (Fig. 4 g, main effect = 0.001). Females in poor body condition produced larger oocytes than females in better body condition (EW) in years preceded by short floods; the opposite was observed during years with long flood events. Batch fecundity (log) for P. rutiloides had positive effect with standard length (log) (Fig. 5 a, main effect 57653) and minimum water level (Fig. 5 d, main effect = 1860), negative effect with mean oocyte size (Fig. 5 b, main effect = -40291), and maximum water level (Fig. 5 c, main effect = -6579). Mean oocyte size was negatively affected by the residual of standard length and batch fecundity relationship (Fig. 6 a, main effect = -0.036), positively affected by minimum (Fig. 6 b, main effect = 0.015) and maximum water levels (Fig. 6 c, main effect = 0.031), and the interaction effect of density of predators and population (Fig. 6 d, main effect = 0.421). Oocyte size was higher under higher densities of P. rutiloides population than under lower densities, but the highest values occurred when density of predators was also high.

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Table 1: Estimates of the Generalized Linear Model (GLM) for the best model relating batch fecundity and mean oocyte size to the independent variables for each species. AIC values of model selection can be found in Appendix 7 and the quality of the best fitted model by residuals distribution in Appendices 8-10. Dependent Species Independent Variable Estimates Error t/z P df R2 variable Intercept -4.757 1.583 -3.005 0.003 Standard length (log) 2.439 0.266 9.171 0.000 Body condition (EW) -0.318 0.186 -1.705 0.090 Mean oocyte size (log) 1.128 0.491 2.297 0.023 Fecundity Predators density 0.565 0.286 1.973 0.050 Length of flood season 0.001 0.001 0.856 0.393 Length of flood season x 0.003 0.001 2.417 0.017 Body condition (EW) Acestrorhynchus Model 167 44% falcirostris Intercept 1.35 0.187 7.232 0.000 Standard length (log) -0.106 0.034 -3.137 0.002 Residuals (ln SL ~ ln 0.024 0.01 2.329 0.021 Mean oocyte Batch fecundity) size Body condition (EW) -0.009 0.005 -1.819 0.071 Population density 1.444 0.184 7.867 0.000 Predators density -0.073 0.043 -1.677 0.095 Model 167 31% Intercept -2.619 1.390 -1.884 0.062 Standard length (log) 2.467 0.290 8.497 0.000 Mean oocyte size (log) 0.759 0.465 1.631 0.106 Body condition (EW) 0.093 0.052 1.772 0.079 Predators density 0.784 0.406 1.934 0.056 Fecundity Dry season onset 0.002 0.005 0.512 0.610 Triportheus Length of flood season -0.006 0.002 -3.256 0.001 angulatus Dry season onset x body -0.007 0.003 -2.074 0.040 condition (EW) Model 120 53% Intercept -1.017 0.332 -3.063 0.003 Mean oocyte Residuals (ln SL ~ ln size 0.052 0.022 2.389 0.019 Batch fecundity)

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Standard length (log) 0.210 0.063 3.352 0.001 Body condition EW -0.206 0.057 -3.629 0.000 Population density 1.240 0.554 2.239 0.027 Minimum water level -0.019 0.009 -2.096 0.038 Length of flood 0.001 0.001 1.602 0.112 Predators density -0.546 0.075 -7.299 0.000 Body condition EWx 0.001 0 3.453 0.001 Length of flood Model 112 44% Intercept -94158 41940 -2.245 0.025 Standard length (log) 57653 6676 8.635 0.000 Population density -6411 10293 -0.623 0.533 Mean oocyte size (log) -40291 15464 -2.605 0.009 Fecundity Minimum water level 1860 590 3.151 0.002 Maximum water level -6579 786 -8.372 0.000 Population density x Mean

oocyte size (log) 140843 75122 1.875 0.061 Model 292 55% Psectrogaster Intercept -0.168 0.33 -0.51 0.611 rutiloides Residuals (ln SL ~ ln -0.036 0.013 -2.711 0.007 Batch fecundity) Body condition (EW) 0.012 0.007 1.751 0.081 Population density (log) -0.029 0.034 -0.866 0.387 Mean oocyte Minimum water level 0.015 0.005 3.251 0.001 size Predators density 0.686 0.467 1.47 0.143 Maximum water level 0.031 0.01 3.233 0.001 Population density (log) x 0.421 0.196 2.146 0.033 Predators density Model 292 23%

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Figure 1: A visualization of the model presented in Table 1 showing batch fecundity for Acestrorhynchus falcirostris females as a function of: A) standard length (log); B) mean oocyte size; C) interaction of days of flood with body condition (EW).

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Figure 2: A visualization of the model presented in Table 1 showing mean oocyte size for Acestrorhynchus falcirostris females as a function of: A) standard length (log); B) residuals (SL ~ batch fecundity (log)); C) density of population.

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Figure 3: A visualization of the model presented in Table 1 showing batch fecundity for Triportheus angulatus females as a function of: A) standard length (log); B) length of flood season; C) interactions of dry season onset with body condition (EW).

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Figure 4: A visualization of the model presented in Table 1 showing mean oocyte size for Triportheus angulatus females as a function of: A) residuals (SL ~batch fecundity (log)); B) standard length (log); C) body condition (EW); D) density of population; E) density of predators; F) minimum water level; G) interaction of days of flood with body condition (EW).

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Figure 5: A visualization of the model presented in Table 1 showing batch fecundity for Psectrogaster rutiloides females as a function of: A) standard length (log); B) mean oocyte size; C) maximum water level; D) minimum water level.

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Figure 6: A visualization of the model presented in Table 1 showing mean oocyte size for Psectrogaster rutiloides females as a function of: A) residuals (SL(log) ~ batch fecundity (log)); B) minimum water level; C) maximum water level; D) interactions of density of predators and density of population (log).

Discussion Optimal reproductive decisions for all three studied species clearly depended on all variables considered in this study, such as individuals’ phenotype (body condition and female SL), density of the populations and predators, and hydrologic condition. The relationships were complex and interactions of hydrology with individual’s phenotype were important determining females’ fecundity or oocyte size for all species. In seasonal environments, many ecological factors influencing parents’ fitness can affect in different intensities from one year to another (e.g., Johansson & Rowe 1999) and frequently are apparent only under harsh environmental condition (e.g., Decamps et al. 2009, Pinot,

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Gauffre & Bretagnolle 2014, Garnier et al. 2016). Because of that, the relative importance of different variables on the cost to reproduce can often be only apparent on long-term studies in natural environments (Pinot, Gauffre & Bretagnolle 2014, Robert et al. 2015), when more likely is registered the organismal response changes to different conditions.

The effect of the environmental condition and its interaction with other variables The effect of the environmental condition was evident in the changes of females’ fecundity to the onset of the dry season, strength of the dry and flood seasons, and its interactions with individual’s body condition (Figs. 1 C, 3 D, 5 D). Although the flood season is the worse period for the piscivorous A. falcirostris to prey, long flood events increased the among-individual differences in investment of fecundity (regarding energetic costs for reproduction) while a short flood (highly correlated with a weak flood, Appendix 1) reduced the among-individual differences in investment of fecundity (Fig 1 C). Earlier onsets of the dry season had more remarkable effect on the cost of reproduction for females of T. angulatus when in poor body condition than for those in better body condition (Fig 3 D). Drought events had negative effect on fecundity of all females of the P. rutiloides (Fig 5 D). These results suggest that the costs of reproduction on survival are more likely to occur in years with harsh environmental condition. Therefore, under energetic costly environmental condition females’ survival was apparently favored over offspring production as reported for many different organisms (Clutton- Brock et al. 1996, Lourdais et al. 2002, Lescoel et al. 2009, Garnier et al. 2016). During favorable years or when females were in good body condition, individuals were able to allocate more energy to reproduction, allowing them to survive and eventually reproduce again on the following year. Contrary to our prediction that long flood should increase energetic storage on females of this species, what could contribute to increase fecundity and survival of the females in the next breeding season, long floods had negative effect on fecundity of P. rutiloides and T. angulatus. One explanation could be that long floods have been associated to higher survival of fish fry and recruitment in floodplains (Merona & Gascuel 1993, Gomes & Agostinho 1997). The decreased fecundity by these species could be a potential response to increased juvenile survival probability in years of intense floods (Bailly, Agsotinho & Suzuki 2008). Under a high population density scenario, selection should favor the production of fewer and larger, competitively superior offspring (reviewed in Stearns 1976; see Dantzer et al. 2013 for recent findings about proximal causality), especially when the effects of density affect juveniles more than

119 adults (Sibley & Calow 1983). A number of studies have demonstrated a negative relationship between fish growth and density (e.g. Byström & García-Berthou, 1999; Romare, 2000). This would be the case for juveniles to occupy safe habitats in shallow areas or patchy distributed, such as sand banks and aquatic macrophytes stands in floodplains (Bottero-Sanches & Araújo-Lima 2001, Röpke et al. 2014). In our study population density showed positive relationships with oocyte size in all three studied species (Table 2, Figs 2 C, 4 C, 6 D) supporting this hypothesis and corroborating the findings of many previous authors (Schrader & Travis 2012, Leips & Travis 1999, Bardsen & Teveraa 2012, Dantzer et al. 2013), which found a positive relationship of density of population and size of the offspring produced. Additionally, long floods had negative effect on fecundity of P. rutiloides. This result suggests that large floods trigger the trade-off relationship among fecundity and oocyte size due to its link with density-dependent processes, this processes seems to be stronger in P. rutiloides than the other species. Females should invest more in reproduction under high predation pressure, as a consequence of the decreased survival probability of the adults (Reznick & Endler 1982), while reducing the offspring size. High density of predators had negative effect on oocyte size of T. angulatus and P. rutiloides although no significant effect on fecundity occurred for these species, and showed a marginal effect for A. falcirostris. The pattern found in oocyte size response agrees with the predictions of life-history theory (Law 1979) and findings by other experimental studies (Reznick & Endler 1982, Seger & Taborski 2012). High density of predators also had synergistic effect increasing oocyte size of the P. rutilodes when density of population also increased. Predators could influence the amount of resource available to their prey by interfering on foraging behavior of the prey and reducing resource availability once they restrict the distribution of prey (Reznick, Butler & Rodd 2001), which could be maximized under high population density. Theoretical predictions pose that females should increase the optimal offspring size when decreasing environmental quality (Einum & Feming 1999, Jonsson, Jonsson & Fleming 1996). However, some studies suggest that the expression of this maternal effects is context-dependent, and restricted maternal food availability has been shown to either decrease (Gagliano & McCormick 2007) or increase (Hutchings 1991; Allen Buckley & Marshall 2008) offspring size (Rollinson & Rowe 2015). Our results for T. angulatus and P. rutiloides are strongly supportive of this hypothesis. Although T. angulatus and P. rutiloides showed changes in oocyte size in response to hydrological condition, the type of response was different, highlighting the variability of responses among species.

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Triportheus angulatus fits the expectation based on theory, increasing oocyte size in years with intense dry season, especially by females in poor body condition when reproductive period was preceeded by short flood/low intensity flood, such environmental conditions that could indicate low survival probability for both adults and juveniles. Psectrogaster rutiloides, however, showed an opposite response, decreasing oocyte size in years preceded by short flood/low intensity flood season and in years with strong dry season. The increased investment in oocyte size by P. rutiloides when environmental condition indicates higher survival probability for both adults and juveniles, suggests that density-dependent processes would be more important determining investment in offspring size than the harsh environment itself for this species (similar to the observed in response to long and high intensity floods discussed above). Plaistow & Benton (2009) suggested that maternal effects under high competition pressure (that could result in low food availability) impacts more juveniles’ survival than under low competition populations (that could result in high food availability), where maternal effects has more impact on population growth rate. Psectrogaster rutiloides is a detritivore, which implies lower food resource limitation along the annual flood pulse than for T. angulatus, which is dependent on flooded forest (Claro-Junior et al. 2004, Yamamoto, Soares & Freitas 2004). Moreover, P. rutiloides has some other particular characteristics that could contribute for the evolution of a stronger density-dependent response, as compared to T. angulatus. It is one of the most abundant species in large rivers and floodplain lakes in the Amazon basin (Araújo-Lima et al. 1986, Goulding, Carvalho & Ferreira 1988, Fernandes 1997, Garcia-Vazquez et al. 2015), and it makes large schools than compared to T. angulatus (Goulding 1979). Mortality due to adverse environmental condition could have lower effect on population intrinsic growth rate in P. rutiloides, due to its higher batch fecundity, than in T. angulatus.

Body size effect on oocyte size and fecundity Body size (measured as standard length) affected batch fecundity for the three studied species. The increase of fecundity with size is one of the most commonly reported relationships for organisms with undetermined growth (Bagenal 1978, Wooton 1977). Small body size is a major constraint upon energy assimilation and metabolic rates, and hence upon the entire life of animals, including reproduction and survival (Calder 1985, Wikelski & Romero 2003). Adult female size would also positively influence the size of the offspring, as increasing energetic availability to invest in each

121 individual offspring (Rollinson & Rowe 2015). In our study, oocyte size was positively influenced by the size of the adult female only for T. angulatus, and showed a negative correlation with oocyte size for A. falcirostris, and no effect for P. rutiloides. The reduction of oocyte size with female’s size of A. falcirostris could be an effect of senescence if the larger females also were the oldest ones (Abdoli, Pont & Sagnes 2005). These authors, for example, found a positive relationship of fish size and oocyte size only for young females of Cottus gobio. Although is broadly conceived that the proportional unit contribution to fecundity investment declines as investment per offspring increases (Pianka 1976), the trade-off between fecundity and oocyte size occurred only in P. rutiloides and the correlation of this traits had a small significant effect but inverse relationship in A. falcirostris and T. angulatus, which most fecund females also produced larger oocytes. Similar positive relationship was reported to wild salmon (Kinnison et al. 2001) but this relationship was also dependent on migratory distance. Simultaneous investment on these two traits could have raised through independent selection forces (Caley, Schwarzkopf & Shine 2001) however, few empirical evidences exist. Simultaneous investment also could occur if no energetic limits existed to invest in number and size of the oocyte at the same time and if both would increase maternal fitness, which have been observed in salmon (Kinnison et al. 2001).

Extended summary and future prospects Our study included a broad range of interannual hydrological conditions experienced by fish populations in a floodplain lake in the Amazon. Life history responses by the studied species were complex and linked to both environment and individual’s phenotype. Some commonly reported relationships between traits, such as offspring number and size, showed inverse relationship, and body condition and fecundity was significant depending on hydrological condition. Overall, the costs of reproduction were evident under harsh hydrological condition for all three species. Interestingly, the changes in oocyte size was also more related to environmental condition and biotic interactions than to organism’s trade-off of fecundity and individual’s phenotype. Specifically, density of population and predators considered in the model had higher effects on the oocyte size than on female fecundity. Trade-off decisions manifested mainly in hash environmental condition when the cost of reproduction seems to be really important, while in regular years the reproduction apparently poses no important cost for females’ survival, corroborating with other studies (e.g. Robert et al. 2012, Garnier et al. 2016). Species life history and environmental disturbances have direct impact on species population

122 dynamics. Interannual environmental extremes are becoming more frequent and as such, it is expected that populations would oscillate in a more unstable ways and some decrease in numbers would occur, as observed for many species and functional groups in central Amazon (see chapter 2). In addition, our results suggest that there is an important individual variation among females, which contributes to population growth rate and dynamics. The studied species perform only short-ranged movements between river and floodplain lake however, floodplain-river ecosystem in tropical region harbor one of the most diverse freshwater fish fauna in the world. Future studies including species with other behaviour and life histories would help to elucidate the evolutionary processes that shaped life history in freshwater fish and the relative importance of intrinsic (individual’s phenotype) and extrinsic (environment and interactions) shaping the diversity of life histories. Our results and the advancement of knowledge on fish reproduction in natural freshwater ecosystem also would contribute to improve species management for fishery and species conservation.

Acknowledgements This study was founded by Amazonas State Research Funding Agency (FAPEAM 062003342013; 06200342201), Brazilian National Council for Scientific and Technological Development (CNPq) ((575738/2008-1; 313183/2014-7), and National Institute for Amazonian Research (INPA) CPR received fellowship from Brazilian National Council for Scientific and Technological Development (CNPq) and Brazilian Government Agency for Support and Evaluation of Graduate Education (CAPES), DC received fellowship from Amazonas State Research Funding Agency (FAPEAM 062003342013). We thank all undergraduate and graduate students, volunteers, fishermen and Raimundo Sotero who helped in the field and laboratory work in the Projeto Catalão over the last 15 years. We thank IBAMA through license #101932, and INPA’s ethics committee rules (protocol number 33/2012) for authorized the fish surveys.

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Supplementary information

Appendix 1: Pearson’s correlation coefficient between hydrological variables on the region of the confluence of Amazonas and Negro rivers preceding the reproductive period of the studied species.

Minimum Maximum Dry season Receding Length of Length of annual level annual level onset season onset dry season receding season Maximum annual level -0.09 Dry season onset 0.57 -0.05 Receding season onset 0.48 0.52 0.48 Length of dry season -0.86 0.12 -0.53 -0.52 Length of receding season 0.61 -0.46 0.44 -0.06 -0.66 Length of flood season 0.17 0.72 0.19 0.60 -0.26 -0.19

Appendix 2: Pearson’s correlation coefficient between biological variables for Acestrorhynchus falcirostris. Body condition TW= body condition estimated from total weight; Body condition EW= body condition estimated from eviscerated weight.

Standard Batch Maximum Mean Body condition Body condition Year length fecundity oocyte size oocyte size TW EW Standard length -0.24 Batch fecundity -0.28 0.52 Maximum oocyte size -0.17 -0.17 0.09 Mean oocyte size -0.15 -0.22 0.05 0.67 Body condition TW 0.11 -0.09 0.17 0.13 0.12 Body condition EW 0.09 -0.03 0.13 0.10 0.07 0.85 Population density -0.49 0.00 0.11 0.33 0.44 0.11 0.06

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Appendix 3: Pearson’s correlation coefficient between biological variables for Triportheus angulatus. TW= body condition estimated from total weight; EW= body condition estimated from eviscerated weight.

Standard Batch Maximum Mean Body condition Body condition Year length fecundity oocyte size oocyte size TW EW Standard length -0.39 Batch fecundity -0.26 0.56 Maximum oocyte size 0.30 0.16 0.10 Mean oocyte size 0.43 0.09 0.03 0.79 Body condition TW 0.30 -0.47 -0.07 0.05 0.00 Body condition EW 0.31 -0.49 -0.16 -0.05 -0.09 0.96 Population density -0.35 0.01 0.06 -0.10 -0.10 -0.01 -0.04

Appendix 4: Pearson’s correlation coefficient between biological variables for Psectrogaster rutiloides. TW= body condition estimated from total weight; EW= body condition estimated from eviscerated weight.

Standard Batch Maximum Mean Body condition Body condition Year length fecundity oocyte size oocyte size TW EW Standard length -0.41 Batch fecundity -0.45 0.60 Maximum oocyte size 0.04 -0.04 -0.16 Mean oocyte size 0.22 -0.13 -0.23 0.71 Body condition TW 0.34 -0.27 -0.03 0.06 0.18 Body condition EW 0.48 -0.31 -0.26 0.03 0.14 0.87 Population density -0.25 -0.15 -0.21 0.12 0.03 -0.44 -0.33

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Appendix 5: Frequency distribution of oocytes according to size for A. falcirostris, with three (A) and two (B) batches, T. angulatus (C), and P. rutiloides (D). 135

Appendix 6: Annual mean values of batch fecundity, mean oocyte size for each species and independent variables (biological: standard length and population density and predators density; hydrologicals: minimum water level, dry season onset, receding season onset, days of flood) for each species Standard deviation is presented in parenthesis; N= number of samples in each year.

Acestrorhynchus falcirostris Psectrogaster rutiloides Years Body Body Batch Mean oocyte Standard Population Batch Mean oocyte Population Standard condition N condition N fecundity (sd) size (sd) length (sd) density fecundity (sd) size (sd) density length (sd) (sd) (sd) 1999 2000 2001 32728 (11370) 0.84 (0.01) -0.846 (0.66) 268.3 (2.3) 0.14 3 33595 (15342) 0.85 (0.05) 0.646 (0.38) 0.09 145 (0.8) 3 2002 30037 (12210) 0.85 (0.04) -0.030 (0.79) 280.3 (35.9) 0.07 18 60610 (12208) 0.89 (0.08) 0.453 (0.49) 0.09 151.5 (6.3) 6 2003 29584 (6032) 0.86 (0.07) -0.524 (0.76) 252.8(28.4) 0.08 7 38033 (10472) 0.96 (0.05) 0.812 (0.65) 0.09 149.1 (5.5) 12 2004 35231 (14086) 0.93 (0.04) 0.622 (0.38) 254.5 (32.2) 0.10 6 52604 (21782) 0.98 (0.08) 1.346 (0.45) 0.14 147.4 (7.0) 15 2005 20226 (10520) 0.86 (0.03) 0.019 (0.96) 244.4 (25.4) 0.08 7 32659 (15875) 0.99 (0.09) 0.162 (0.65) 0.27 131.0 (13.5) 24 2006 14211 (8118) 0.83 (0.06) -1.113 (0.88) 264.8 (11.2) 0.04 5 29400 (18385) 0.10 (0.08) -0.352 (0.87) 0.26 134.7 (11.4) 25 2007 21067 (6969) 0.87 (0.05) -0.359 (0.59) 248.0 (23.0) 0.04 15 38999 (14164) 1.01 (0.13) -0.165 (0.64) 0.17 135.1 (10.0) 16 2008 20500 (13513) 0.91 (0.05) -0.120 (1.05) 256.2 (28.3) 0.09 22 32632 (13879) 1.08 (0.11) -0.047 (0.74) 0.23 137.3 (11.1) 20 2009 30839 (8287) 0.91 (0.05) -0.331 (1.03) 247.0 (24.8) 0.05 6 32663 (16110) 0.99 (0.12) 0.382 (0.71) 0.13 144.8 (10.3) 7 2010 19671 (10288) 0.90 (0.04) 0.504 (0.70) 238.8 (24.7) 0.08 33 18889 (7820) 0.96 (0.09) 0.306 (0.57) 0.20 131.6 (9.3) 13 2011 18618 (10609) 0.85 (0.06) -0.153 (0.86) 243.3 (29.0) 0.06 38 28599 (18148) 0.10 (0.08) 0.869 (0.81) 0.08 124 (15.1) 46 2012 21246 (12434) 0.81 (0.04) -0.228 (0.97) 253.9 (27.6) 0.03 16 13753 (7025) 1.09 (0.09) 0.834 (0.70) 0.23 124.6 (8.7) 77 2013 20588 (7330) 0.77 (0.03) 0.313 (0.70) 256.7 (30.7) 0.03 12 29170 (9725) 1.07 (0.12) 1.685 (0.66) 0.04 136.3 (5.8) 37

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Continue Appendix 6: Annual mean values of the dependent (batch fecundity, mean oocyte size) and independent variables (biological: standard length and population density by species; hydrologicals: minimum water level, dry season onset, receding season onset, days of flood) for each species Standard deviation is presented in parenthesis; N= number of samples in each year.

Triportheus angulatus Receding Predator Minimum Dry season Days of Years Batch fecundity Mean oocyte Body Population Standard season N density water level onset flood (sd) size (sd) condition (sd) density length (sd) onset 1999 16758 (7544) 0.65 (0.08) -0.704 (1.05) 0.06 154.6 (22.5) 11 0.627 16.95 11 33 183 2000 25643 (13651) 0.66 (0.08) 1.091 (0.55) 0.04 155.1 (8.2) 9 0.529 18.57 40 29 135

2001 0.442 18.00 4 26 134 2002 20723 (13955) 0.68 (0.07) 0.740 (0.91) 0.08 132.7 (19.3) 11 0.518 17.19 14 28 144 2003 18320 (5311) 0.78 (0.05) 0.507 (0.37) 0.08 143.8 (4.6) 5 0.282 19.01 13 23 121 2004 16170 (3725) 0.80 (0.08) -0.187 (0.60) 0.13 145.6 (4.3) 5 0.306 19.23 19 -8 85 2005 26593 (9487) 0.75 (0.08) 0.776 (0.63) 0.06 147.5 (8.8) 4 0.256 14.75 -25 -12 118

2006 0.158 16.89 -6 4 161 2007 8478 (8763) 0.76 (0.05) 0.244 (0.47) 0.04 125.9 (25.6) 10 0.150 17.74 -4 11 115 2008 6244 (3650) 0.70 (0.09) 0.636 (0.04) 0.02 110.6 (13.7) 3 0.186 18.43 -2 10 149 2009 18363 (6477) 0.83 (0.07) -0.178 (1.25) 0.03 147.8 (15.9) 9 0.186 15.86 9 31 197

2010 0.195 13.63 -19 -3 102 2011 13141 (8241) 0.71 (0.06) 0.962 (1.11) 0.06 113.5 (15.6) 9 0.165 16.76 -12 13 123 2012 13061 (7612) 0.80 (0.12) 1.277 (0.98) 0.06 123.2 (20.4) 38 0.179 15.96 -4 17 180 2013 14489 (11636) 0.80 (0.08) 0.603 (1.01) 0.02 136.1 (31.2) 14 0.103 19.35 -5 34 163

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Appendix 7: Model selection results for batch fecundity (BFEC) and mean oocyte diameter for each species

Species Model Selection AIC log(BFEC) ~ (log(SL)) + days of flood + minimum water level + body condition EW + population density + mean oocyte size + 170.8 days of flood : body condition EW + minimum water level : body condition EW log(BFEC) ~ (log(SL)) + days of flood + minimum water level + body condition EW + population density + mean oocyte size + 169.32 days of flood : body condition EW log(BFEC) ~ (log(SL)) + days of flood + body condition EW + 167.36 mean oocyte size + days of flood : body condition EW mean oocyte size ~ (residuals (log(BFEC)) ~ (log(comp))) + log(SL) + body condition EW + population density + days of -505.23 flood + minimum water level + days of flood:condition EW + minimum water level: body condition EW mean oocyte size ~ (residuals (log(BFEC)) ~ (log(comp))) + Acestrorhynchus log(SL) + body condition EW + population density + days of falcirostris -506.9 flood + minimum water level + minimum water level: body condition EW mean oocyte size ~ (residuals (log(BFEC)) ~ (log(comp))) + log(SL) + body condition EW + population density + days of -508.12 flood + minimum water level + minimum water level: body condition EW mean oocyte size ~ (residuals (log(BFEC)) ~ (log(comp))) + log(SL) + body condition EW + population density + minimum -509.71 water level + minimum water level: body condition EW mean oocyte size ~ (residuals (log(BFEC)) ~ (log(comp))) + log(SL) + body condition EW + population density + minimum -510.7 water level mean oocyte size ~ (residuals (log(BFEC)) ~ (log(comp))) + -511.68 log(SL) + body condition EW + population density

log(BFEC) ~ (log(SL)) + dry season onset + body condition EW + days of flood + population density + mean oocyte size + dry season onset : body condition EW + dry season onset : population -172.35 density + days of flood : body condition EW + log(SL):body condition EW log(BFEC) ~ (log(SL)) + dry season onset + body condition EW + Triportheus days of flood + population density + mean oocyte size + dry angulatus -173.96 season onset : body condition EW + dry season onset : population density + log(SL):body condition EW log(BFEC) ~ (log(SL)) + dry season onset + body condition EW + days of flood + population density + mean oocyte size + dry -174.9 season onset : body condition EW + dry season onset : population density

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log(BFEC) ~ (log(SL)) + dry season onset + body condition EW + days of flood + population density + dry season onset : body -175.59 condition EW + dry season onset : population density log(BFEC) ~ (log(SL)) + dry season onset + body condition EW + days of flood + population density + dry season onset : body -176.65 condition EW log(BFEC) ~ (log(SL)) + dry season onset + body condition EW + -178.61 days of flood + dry season onset : body condition EW log(mean oocyte size) ~ residuals(log(BFEC) ~ log(SL)) + log(SL) + body condition EW + population density + minimum -523.66 water level + days of flood + predation + minimum water level : body condition EW + days of flood : body condition EW log(mean oocyte size) ~ residuals(log(BFEC) ~ log(SL)) + log(SL) + body condition EW + population density + minimum -525.49 water level + days of flood + predation + days of flood : body condition EW

BFEC ~ (log(SL)) + body condition EW + minimum water level + log(mean oocyte size) + highest water level + population density + minimum water level: population density + highest water level : 6234.91 body condition EW + body condition EW: minimum water level + log(mean oocyte size) : population density BFEC ~ (log(SL)) + body condition EW + minimum water level + log(mean oocyte size) + highest water level + population density + highest water level : body condition EW + body condition EW: 6233.01 minimum water level + log(mean oocyte size) : population density BFEC ~ (log(SL)) + body condition EW + minimum water level + log(mean oocyte size) + highest water level + population density + 6231.48 highest water level : body condition EW + log(mean oocyte size) : population density BFEC ~ (log(SL)) + body condition EW + minimum water level + Psectrogaster log(mean oocyte size) + highest water level + population density + 6231.32 rutiloides log(mean oocyte size) : population density BFEC ~ (log(SL)) + minimum water level + log(mean oocyte size) + highest water level + population density + log(mean oocyte 6230.09 size) : population density log(mean oocyte size) ~ residuals (log(BFEC) ~ log(SL)) + log(SL) + body condition EW + log(population density) + predators density + log(population density) : predators density + -530.03 minimum water level + minimum water level:log(predators density) + maximum water level + minimum water level: body condition EW + highest water level : body condition EW log(mean oocyte size) ~ residuals (log(BFEC) ~ log(SL)) + log(SL) + body condition EW + log(population density) + predators density + log(population density) : predators density + -532.02 minimum water level + minimum water level:predators density + maximum water level + minimum water level: body condition EW

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log(mean oocyte size) ~ residuals (log(BFEC) ~ log(SL)) + log(SL) + body condition EW + log(population density) + predators density + log(population density) : predators density + -533.96 minimum water level + minimum water level:predators density + maximum water level log(mean oocyte size) ~ residuals (log(BFEC) ~ log(SL)) + log(SL) + body condition EW + log(population density) + -535.57 predators density + log(population density) : predators density + maximum water level + lowest water level log(mean oocyte size) ~ residuals (log(BFEC) ~ log(SL)) + body condition EW + log(population density) + predators density + -536.44 log(population density) : predators density + maximum water level + minimum water level

Appendix 8: Residual scatterplot of the best-fitted model for batch fecundity (log) and mean oocyte size of the Acestrorhynchus falcirostris. Residual against predicted values (A, C) and observed against predicted values (B, D).

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Appendix 9: Residual scatterplot of the best-fitted model for batch fecundity (log) and mean oocyte size of the Triportheus angulatus. Residual against predicted values (A, C) and observed against predicted values (B, D).

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Appendix 10: Residual scatterplot of the best-fitted model for batch fecundity and mean oocyte size (log) of the Psectrogaster rutiloides. Residual against predicted values (A, C) and observed against predicted values (B, D).

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Considerações Gerais e Algumas Perspectivas Futuras

Este estudo teve como principal foco o efeito da variabilidade interanual da condição ambiental sobre a fauna de peixes investigando padrões (capítulo 2) e processos (capítulo 3) em um ambiente aquático aberto (capítulo 1). A despeito do Lago Catalão ser um sistema aberto com condições para rápida recolonização e imigração de novos indivíduos para manter as populações, o evento de seca em 2005 teve um forte impacto sobre a comunidade de peixes. Os resultados obtidos sugerem que a sequência de eventos hidrológicos extremos que se seguiram à seca de 2005 dificultaram a recuperação, ou retorno, da estrutura da assembleia de peixes ao estado anterior a 2005. Além de provocar uma mudança marcante na composição de espécies, esses eventos geraram perda da diversidade de espécies e mudança na organização funcional da assembleia.

Embora o evento estocástico de mortalidade certamente teve um papel importante nos padrões observados, alguns resultados sugerem que certas características de história de vida são mais vantajosas que outras e que processos reprodutivos são diretamente afetados por eventos se secas e cheias fortes. Isso foi evidenciado pela estabilização na tendência de queda de alguns grupos funcionais nos últimos anos (peródicos de tamanho grande, e piscívoros) e a resposta retardada ao impacto da seca e outros eventos (periódicos de tamanho pequeno). Espécies sedentárias parecem responder mais imediatamente ao impacto e terem menor capacidade de aumentar a taxa de crescimento populacional frente às condições ambientais existentes após 2005. O investimento reprodutivo das três espécies estudadas ao nível populacional reduziu frente aos eventos hidrológicos extremos. Isso sugere que o processo reprodutivo, intimamente relacionado com mecanismos regulatórios do tamanho das populações, também é direta e negativamente afetado pela ocorrência de eventos hidrológicos extremos sendo cheias extremas negativas para espécies piscívoras e secas extremas negativas para espécies onívoras e detritívoras, mas possivelmente também negativa para outros grupos tróficos. Esses resultados e conclusões são evidências bastante importantes de que existe um impacto negativo já detectável das mudanças climáticas macroregionais sobre a fauna de peixes de várzea na bacia Amazônica.

Estudos com esta abordagem e avaliando dados empíricos, ainda são incipientes para esta região do globo; deste modo, este estudo traz uma relevante contribuição para a compreensão da magnitude do impacto climático na Amazônia, ainda que a escala do estudo seja bastante reduzida, e permite fazer modelos de projeções mais acurados se somado a outros impactos já conhecidos, como a pesca e desmatamento, sobre a fauna de peixes. 143

Algumas questões geradas desse estudo poderiam guiar futuros projetos e linhas de pesquisa, são elas:

- Quão representativo é o padrão observado para a assembleia de peixes do Lago Catalão em escala de bacia?

Uma das principais limitações deste estudo é a abrangência geográfica, bastante restrita, embora a série temporal seja razoável. É evidente a escassez de estudos de longa duração para ecossistemas aquáticos na Amazônia e se faz urgente. Isso restringe a extrapolação dos nossos resultados para outras áreas de várzea da bacia e a compreensão da abrangência do impacto dos eventos ambientais. Dados gerados a partir de monitoramento pesqueiro poderiam ser uma alternativa embora as incertezas no esforço de pesca representam uma limitação para comparações confiáveis. No entanto, mesmo esta informação não tem sido obtida sistematicamente na bacia hidrográfica.

A exemplo do que é adotado por outros programas que monitoram ecossistema terrestres, existe a necessidade do monitoramento de múltiplos trechos ao longo dos grandes rios da bacia Amazônica. O uso de métodos padronizados por grupos independentes, mas atuando em cooperação como uma rede de estudos de longa duração poderia ser uma forma mais factível de se monitorar regiões espacialmente distantes, visto as dificuldades logísticas da região. Isso permitira a comparação de séries temporais em uma ampla escala espacial a um custo menor. Além disso, há necessidade de desenvolvimento de estudos integrados que permitam a avaliação dos parâmetros físico-químicos dos ambientes aquáticos, mudanças na disponibilidade de recursos alimentares e a dinâmica populacional de outros componentes do ecossistema (e.g. zooplâncton & fitoplâncton) a fim de se compreender melhor os processos envolvidos.

- Quais processos estão moldando as tendências de abundância observadas entre os grupos funcionais? E qual é a importância relativa e papel da atividade pesqueira nestas tendências?

O acompanhamento da atividade pesqueira regional concomitantemente às amostragens científicas permitiria uma melhor compreensão do impacto da pesca nas tendências observadas de CPUE entre os grupos funcionais. A inclusão de informações referentes ao recrutamento anual de jovens na população, o que não foi possível neste estudo, e estudos experimentais de tolerância e

144 respostas às condições ambientais de acordo com a fase de desenvolvimento também contribuiriam para a compressão dos processos associados ao padrão detectado no segundo capítulo.

- As respostas no investimento reprodutivo observadas representam plasticidade fenotípica ou são consequência puramente genotípica sugerindo possível variabilidade na qualidade individual?

Eventos extremos de seca tiveram um forte impacto sobre a assembleia e populações de peixes ao mesmo tempo em que desencadearam respostas reprodutivas individuais (capítulo 3). Estas informações, associadas a ferramentas moleculares, poderiam elucidar os processos evolutivos e o efeito dos eventos climáticos como força seletiva. Especialmente se os eventos climáticos ascentuarem diferenças no sucesso reprodutivo de indivíduos decorrente de diferenças em “qualidade” nos indivíduos. Investigações genéticas ao nível populacional também poderiam elucidar o impacto desses eventos sobre a diversidade genética das populações. E estudos epigenéticos poderiam confirmar a capacidade plástica das espécies em lidar com a variabilidade ambiental.

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