Tese - Maisa Carvalho Vieira (2)

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UNIVERSIDADE FEDERAL DE GOIÁS (UFG) 
INSTITUTO DE CIÊNCIAS BIOLÓGICAS (ICB) 
PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E EVOLUÇÃO 
 
 
 
 
 
 
 
MAISA CARVALHO VIEIRA 
 
 
 
 
 
Estruturação da comunidade zooplanctônica em reservatórios 
tropicais 
 
 
 
 
 
 
 
 
 
GOIÂNIA 
2022
 
 
 
 
 
 
MAISA CARVALHO VIEIRA 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Estruturação da comunidade zooplanctônica em reservatórios 
tropicais 
 
 
 
 
 
 
 
Tese apresentada ao Programa de Pós-
Graduação em Ecologia e Evolução, da 
Universidade Federal de Goiás (UFG), 
como requisito para obtenção do título de 
Doutora em Ecologia e Evolução. 
Área de concentração: Ecologia e 
Evolução 
 
 
Orientador: Prof. Dr. Luis Mauricio Bini 
Co-orientador: Prof. Dr. Ludgero Cardoso 
Galli Vieira 
 
 
 
 
 
 
Goiânia – GO 
2022 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Por acaso você chegou, fez morada 
em meu coração e na minha alma. O 
maior motivo da minha existência. 
Dedico à Isis.
 
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AGRADECIMENTOS 
Fazer um doutorado não é fácil, fazer um doutorado com uma gravidez, puerpério e toda 
mudança que isso gera na vida de uma mulher é ainda mais difícil e, junto a isso tudo 
uma pandemia. Digamos que meu doutorado foi nível ‘hard” e se hoje estou aqui 
concluindo é graças a muitas pessoas que estiveram nos bastidores. Primeiramente, eu 
agradeço a Deus, por ter sido meu refúgio nos momentos de angústia e dor, por ter sido 
minha fortaleza nos momentos que me senti perdida e por ter me guiado à felicidade e 
manutenção da saúde mental. Agradeço minha mãe por ter sido minha principal rede de 
apoio fora de casa. Agradeço ao meu companheiro, Hugo, que além de ser meu amigo, 
namorado, marido, pai da minha filha, ainda é revisor dos meus textos, conselheiro, 
“campoman”, entre várias outras qualidades que poderia citar aqui em várias e várias 
linhas. Sem vocês, com certeza, eu não teria conseguido sozinha. Agradeço também ao 
meu pai que acreditou em mim, me ensinou enfrentar os desafios e aprender com os 
erros. 
Agradeço a Carla, que se prontificou em identificar minhas amostras quando eu 
descobri que não poderia por conta da gravidez. Eu nem sei como agradecer você 
Carlinha, você é uma amiga incrível! Agradeço também a minha amiga Rafa G., que 
sempre que precisei estendeu sua mão para me ajudar, sempre me ouviu, me aconselhou 
e me acolheu. Foi um pouco de psicóloga, “campogirl”/fotógrafa, várias risadas e 
fofocas. Também agradeço ao Jean, que sempre tirou minhas dúvidas em relação a 
estatística, você é muito bom cara! Agradeço também ao restante dos meus amigos do 
laboratório Lea“t”, Matheus, Fagner, Letícia, Sara, Aline, Flávio, Gisele, pelas boas 
conversas, risadas e cafés. Agradeço também aos amigos do Nepal que me ajudaram na 
amostragem de campo, Leo B., Hasley, Gustavo G., Leo G., Carol, João Paulo, Thalia, 
Gleicon e Pedro (motorista). Não é fácil subir e descer barco em 40 represas, ficar 
 
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remando o tempo todo porque não tínhamos motor, empurrar a van quando atolou (por 
diversas vezes) e ainda espirrar lama na cara, levar mordida de cachorro em fazenda, 
correr de bode, colocar a mão em água muito duvidosa, entre outras coisas que 
aconteceram no campo (as fotos embaixo resumem bem meu agradecimento a vocês 
rsrs). 
 
Fonte: Acervo pessoal, PAD-DF (2018). 
 
Agradeço ao professor Júnior, por ter cedido o laboratório AquaRiparia (UnB) para 
algumas análises físico-químicas da água, e a Daphne na Embrapa Cerrados, por ter me 
auxiliado nas análises de nitrogênio e fósforo total. 
Agradeço ao professor Fabrício, que foi minha inspiração para entrar na academia, um 
excelente professor, e se hoje estou terminando um doutorado é porque lá na minha 
graduação me incentivou a tal. Agradeço ao prof. Luiz Felipe Velho, que me forneceu 
os dados do terceiro capítulo da minha tese. Também agradeço aos professores da UnB 
que tiveram papeis fundamentais na minha formação, entre eles o prof. Antônio Felipe 
(in memoriam), com seu empolgamento pelo conhecimento científico e que faz muita 
falta entre nós, e o prof. Ludgero, que é uma das grandes referências para mim hoje, 
excelente profissional e acessível. Também agradeço aos professores da UFG pelos 
ensinamentos e exemplos, em especial as prof. Priscila e Jascieli, duas mulheres fortes 
nas quais me espelho. 
 
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Agradeço ao meu orientador Luis Mauricio Bini, por ter me dado a oportunidade de 
aprender com ele. Com certeza, foi uma das pessoas que mais me surpreendeu no 
doutorado, e com muita sabedoria soube conduzir diversas situações. Você com certeza 
faz toda diferença com seus orientandos, é compreensível, é um excelente professor, e 
tudo que se propõe a fazer, faz com eficiência e maestria. E agora, eu entendo a 
admiração do Ludgero por você. 
Agradeço aos apoios financeiros, à Coordenação de Aperfeiçoamento de Pessoal de 
Nível Superior (CAPES) pela bolsa de estudo, à Fundação de Amparo à Pesquisa do 
Distrito Federal (FAPDF) pelo financiamento do projeto que resultou em dois capítulos 
dessa tese. 
Agradeço ao professor Adriano Melo e Fernando Lansac-Tôha por avaliarem meus 
relatórios de acompanhamento durante o período do doutorado. 
Também agradeço aos membros da banca por aceitarem o convite e contribuírem para a 
melhoria desse trabalho. 
Agradeço aos meus amigos de fora da academia, principalmente minha amiga Juliana 
(Xuliana), que sempre ouviu meus desabafos do dia a dia. Sua amizade é muito 
importante para mim (mesmo que você nem vá ver isso daqui). 
E não poderia me esquecer também das pessoas que me ajudaram com a Isis (sem ser 
minha mãe e o Hugo). À Rosana, que foi a primeira pessoa que confiei em cuidar da 
Isis. Foi em um momento muito delicado da pandemia, onde eu precisava me esconder 
no escritório para conseguir estudar, enquanto ela ficava com a Isis para mim, fingindo 
que eu não estava em casa (para que as coisas fluíssem melhor). À Maria Clara, que 
ama a Isis mais do que tudo, e sempre que precisei deixar a Isis aos seus cuidados nunca 
me negou ajuda, mesmo não recebendo nada por isso (você tem minha eterna gratidão). 
 
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À Vanessa e ao Gustavo, meus vizinhos/primos, que sempre me socorreram em 
reuniões ou em imprevistos de última hora, e se prontificaram em me ajudar com ela. 
Meu muito obrigada a todos vocês! 
 
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SUMÁRIO 
Resumo .......................................................................................................................... 14 
Abstract ......................................................................................................................... 15 
Introdução Geral .......................................................................................................... 16 
Capítulo 1: Correlates of zooplankton metacommunity structure in small 
reservoirs ....................................................................................................................... 26 
Abstract ........................................................................................................................... 27 
Introduction .................................................................................................................... 28 
Methods .......................................................................................................................... 32 
Results ............................................................................................................................ 36 
Discussion ....................................................................................................................... 39 
References ...................................................................................................................... 45 
Supplementary Material .................................................................................................56 
Capítulo 2: Diversidade beta temporal de comunidades locais zooplanctônicas em 
pequenos reservatórios ................................................................................................. 71 
Resumo ........................................................................................................................... 72 
Abstract ........................................................................................................................... 73 
Introdução ....................................................................................................................... 74 
Metodologia .................................................................................................................... 77 
Resultados ....................................................................................................................... 82 
Discussão ........................................................................................................................ 94 
Referências ..................................................................................................................... 98 
Material Suplementar ................................................................................................... 110 
Capítulo 3: Evidence that dams promote biotic differentiation of zooplankton 
communities in two Brazilian reservoirs .................................................................. 128 
Abstract ......................................................................................................................... 130 
Introduction .................................................................................................................. 131 
Material and methods ................................................................................................... 134 
Results .......................................................................................................................... 139 
Discussion ..................................................................................................................... 146 
Conclusions and caveats ............................................................................................... 150 
References .................................................................................................................... 151 
Supplementary Material ............................................................................................... 159 
Considerações Finais .................................................................................................. 174 
 
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Resumo 
Nesta tese, investigamos os padrões e processos das metacomunidades zooplanctônicas 
e suas variações espaciais e temporais utilizando dados de 39 pequenos reservatórios 
(Capítulo 1 e 2) e dois reservatórios hidrelétricos (Capítulo 3). No primeiro capítulo, 
quantificamos a importância de diferentes variáveis preditoras (variáveis ambientais 
locais, de uso e ocupação do solo, espaciais e comunidades biológicas passadas) na 
estruturação de metacomunidades zooplanctônicas. Encontramos que a comunidade 
passada foi importante preditor e que a importância dos mecanismos variaram ao longo 
do tempo, sugerindo necessidade de incluir dados temporais nas análises de 
metacomunidades. No segundo capítulo, avaliamos a diversidade beta temporal e sua 
relação com a variabilidade ambiental, a área do reservatório, a vegetação 
remanescente, a densidade média do fitoplâncton e a quantidade de pequenos 
reservatórios próximos ao estudado. Nós não encontramos relações significativas com 
os preditores, mas encontramos altos valores de diversidade beta temporal, e de perdas 
de abundância de táxons entre períodos de seca e chuva e ganhos de abundância de 
táxons entre períodos de chuva e seca. Esses resultados sugerem a importância da 
variação sazonal e hidrológica sobre a diversidade beta temporal, e indicam que nosso 
conhecimento sobre os determinantes da dinâmica temporal dessas comunidades ainda é 
limitado. No terceiro capítulo avaliamos se a diversidade beta da comunidade 
zooplanctônica aumentaria após o barramento de dois reservatórios hidrelétricos. 
Nossos resultados foram consistentes com a hipótese de diferenciação biótica e 
indicaram a importância de manter amostragens temporais. 
 
Palavras-Chave: Metacomunidade; Diversidade beta; Composição de espécies; 
Pequenos reservatórios; Reservatório hidrelétrico; Zooplâncton; Temporal
 
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Abstract 
In this thesis, we investigated the patterns and processes of zooplanktonic 
metacommunities and their spatial and temporal variations using data from 39 small 
reservoirs (Chapters 1 and 2) and two hydroelectric reservoirs (Chapter 3). In the first 
chapter, we quantify the importance of different predictor variables (local 
environmental variables, land use and occupation, spatial variables, and past biological 
communities) in the zooplanktonic metacommunities structuring. We found that past 
community was an important predictor and that the importance of mechanisms varied 
over time, suggesting the need to include temporal data in metacommunity analyses. In 
the second chapter, we evaluated the temporal beta diversity and its relationship with 
environmental variability, the reservoir area, the remaining vegetation, the average 
phytoplankton density, and the small reservoirs number close to the one studied. We did 
not find significant relationships with the predictors, but we did find high values of 
temporal beta diversity, and taxon abundance losses between rain and dry periods and 
taxon abundance gains between dry and rain periods. These results suggest the 
importance of seasonal and hydrological variation on temporal beta diversity and 
indicate that our knowledge about the temporal dynamics determinants of these 
communities is still limited. In the third chapter, we evaluated whether the beta diversity 
of the zooplankton community would increase after the damming of two hydroelectric 
reservoirs. Our results were consistent with the biotic differentiation hypothesis and 
indicated the importance of maintaining temporal sampling. 
 
Keywords: Metacommunity; Beta diversity; Species composition; Small reservoirs; 
Hydroelectric reservoir; Zooplankton; Temporal
 
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Introdução Geral 
 
Um dos principais objetivos da ecologia é compreender a estruturação das 
comunidades biológicas (ou seja, riqueza, composição e distribuição da abundância 
entre espécies). Para isso, a teoria de metacomunidades prevê que a estruturação das 
comunidades locais é determinada por interações bióticas, filtragem ambiental e 
dispersão (Leibold et al., 2004, 2010; Vellend, 2010; Leibold & Chase, 2017). As 
variações na abundância e na composição de espécies entre os locais (diversidade beta 
espacial) e períodos de tempos (diversidade beta temporal) de uma mesma região 
(Anderson et al., 2006, 2011; Legendre & Gauthier, 2014), também são fundamentais 
para o arcabouço da teoria de metacomunidades (Tuomisto & Ruokolainen, 2006; 
Heino et al., 2015). 
Diferentes mecanismos podem atuar para determinar a estrutura das 
comunidades, entre eles, os mais estudados são baseados em processos ambientais e 
espaciais (e.g., Soininen et al., 2011; Zhao et al., 2017; Rocha et al., 2020; Sinclair et 
al., 2021). Os processos ambientais estão relacionados ao nicho ecológico (Hutchinson, 
1957) e as espécies são “filtradas” pelas condições locais segundo seus requerimentos 
ecológicos (Mittelbach & Schemske, 2015). Em estudos empíricos, as variáveis 
ambientais locais (e.g., limnológicas para ambientes aquáticos) são utilizadas 
frequentemente para representar o processo de filtragem ambiental, a heterogeneidade 
ambiental e a variabilidade ambiental (e.g., Declerck et al., 2011; Hatosy et al., 2013; 
Padial et al., 2014; Zorzal-Almeida et al., 2017). Frequentemente,os processos 
espaciais são relacionados a dispersão dos organismos (por exemplo, Cottenie, 2005). 
Entretanto, sua inferência é complexa, pois os processos espaciais também podem 
depender de fatores como a extensão do gradiente ambiental (Heino et al., 2015), 
 
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extensão espacial da região em estudo (Cottenie 2005), tamanho do propágulo e do 
organismo (De Bie et al. 2012; Padial et al. 2014; Martin et al. 2021), ou outras 
variáveis espacialmente autocorrelacionadas que não foram mensuradas no estudo 
(Peres-Neto e Legendre 2010; Diniz-Filho et al. . 2012). 
Contudo, considerando a complexidade biológica e dos ambientes, os 
mecanismos relacionados aos processos ambientais e espaciais supracitados na 
estruturação das comunidades podem ser particionados, entre eles, em variáveis 
ambientais locais (e.g. variáveis limnológicas), em variáveis na escala de paisagem (e.g. 
uso e ocupação do solo), tamanho da área, em relação a produtividade primária e a 
quantidade de locais “fontes” de populações próximos ao estudado. Recentemente, uma 
outra dimensão é relacionada à fatores históricos como possíveis mecanismos da 
estruturação das comunidades locais (Andersson et al., 2014; Castillo-Escrivà et al., 
2017; Oliveira et al., 2020; Ortega et al., 2021), no qual a comunidade do presente pode 
ser fortemente influenciada pela comunidade do passado. A força dessa relação pode 
depender de algumas características, como o tempo de geração de espécies, o intervalo 
de tempo entre as amostragens e variabilidade ambiental temporal. 
Ambientes aquáticos de água doce são ótimos modelos para o estudo dos 
padrões e processos das comunidades biológicas, pois além da sua delimitação física 
mais facilmente determinada (do que ambientes terrestres, por exemplo), também há 
uma grande variabilidade ambiental e biológica ao longo do espaço e do tempo 
(Reynolds, 1998; Tundisi, 2003; Leibold et al., 2004; Fernandes et al., 2013). Além 
disso, os ecossistemas aquáticos de água doce estão entre os ambientes naturais mais 
ameaçados do mundo, por exemplo, por causa dos represamentos de rios (Dudgeon et 
al., 2006; Reid et al., 2019). O barramento promove diferentes alterações hidrológicas 
(Ward, 1989) e essas alterações podem levar a grandes mudanças na biodiversidade, 
 
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dependendo do tamanho, disposição da barragem e modo de operação (Pereira et al., 
2020; Picapedra et al., 2020). Muitos estudos já relataram que a construção de barragens 
altera a biodiversidade de peixes, bentos, plâncton e microrganismos aquáticos 
(Agostinho et al., 2008; Pelicice et al., 2015; Simões et al., 2015; Rechisky et al., 2013; 
Braghin et al., 2018; Schmidt et al., 2020). 
Dessa forma, a diversidade beta é uma ferramenta importante para avaliar 
tendências negativas (homogeneização biótica) e positivas (diferenciação biótica) do 
impacto do represamento nas comunidades biológicas. Considerando comunidades 
zooplanctônicas, o efeito da barragem ocorre principalmente pelas mudanças na 
velocidade do fluxo, sedimentação, estratificação térmica, disponibilidade de alimentos, 
qualidade da água, entre outros parâmetros (ver mais em Braghin et al., 2018; Schmidt 
et al., 2020). Essas comunidades são muito diversas e possuem diferentes funções nos 
ecossistemas, por exemplo, participação no fluxo de energia nos ecossistemas aquáticos 
e ciclagem de nutrientes (Allan, 1976; Ferdous & Muktadir, 2009). Os principais 
organismos que fazem parte dessa comunidade são os rotíferos, microcrustáceos 
(cladóceros e copépodes) e amebas testáceas. 
Desse modo, o objetivo geral dessa tese foi investigar os padrões e os processos 
da estruturação das comunidades zooplanctônicas (e de seus respectivos grupos, 
microcrustáceos, rotíferos e amebas testáceas) em reservatórios tropicais. Essa tese está 
dividida em três capítulos. No primeiro capítulo, “Correlates of zooplankton 
metacommunity structure in small reservoirs”, quantificamos a importância relativa 
de diferentes variáveis preditoras (variáveis ambientais locais, de uso e ocupação do 
solo, espaciais e efeito histórico biológico) na estruturação de metacomunidades 
zooplanctônicas (e de seus grupos) de pequenos reservatórios no Distrito Federal e em 
Goiás. No segundo capítulo, “Diversidade beta temporal de comunidades locais 
 
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zooplanctônicas em pequenos reservatórios”, calculamos a diversidade beta temporal 
do zooplâncton (e de seus grupos) e suas partições de perdas e ganhos de abundâncias 
de espécies em pequenos reservatórios no Distrito Federal e em Goiás, e avaliamos a 
relação com a variabilidade ambiental, a área do reservatório, a vegetação 
remanescente, a densidade média do fitoplâncton e a quantidade de pequenos 
reservatórios próximos ao estudado. No terceiro capítulo, “Evidence that dams 
promote biotic differentiation of zooplankton communities in two Brazilian 
reservoirs”, testamos a hipótese de que a diversidade beta da comunidade 
zooplanctônica aumentaria (diferenciação biótica) após o represamento em dois 
reservatórios hidrelétricos (reservatório Santo Antônio do Jari, na divisa do Amapá com 
o Pará, e reservatório Serra do Facão, em Goiás). 
No primeiro capítulo, os resultados indicaram que o efeito histórico foi 
importante para explicar a metacomunidade zooplanctônica, e em menor grau, as 
variáveis ambientais locais e espaciais. No segundo capítulo, encontramos altos valores 
de diversidade beta temporal, com características marcantes de perdas de abundância de 
táxons entre períodos de seca e chuva, e ganhos de abundância de táxons entre períodos 
de chuva e seca. Nossas variáveis preditoras não explicaram a variação da comunidade 
ao longo do tempo. No terceiro capítulo, encontramos que houve uma tendência à 
diferenciação biótica da comunidade zooplanctônica após o represamento, sendo mais 
evidente no reservatório de armazenamento (Serra do Facão). 
 
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26 
 
 
 
 
 
 
 
CAPÍTULO 1: 
Correlates of zooplankton metacommunity structure in small reservoirs27 
 
Abstract 
 
Different mechanisms act to structure metacommunities, among them, the most studied 
are those related to environmental and spatial drivers. Also, most studies on 
metacommunities are based on data obtained in snapshot surveys, hindering the test of 
other potential mechanisms (e.g., historical effects). Here, we quantified the relative 
importance of different sets of variables that could potentially structure zooplankton 
communities in 39 small reservoirs in the Brazil Midwest. In addition to local 
environmental, spatial and land use variables, we tested the effect of earlier zooplankton 
composition on (later) zooplankton metacommunity structure. The relative importance 
of these sets of explanatory variables varied over time. Unexpectedly, land use variables 
were not correlated to zooplankton community. The local environmental variables that 
best correlated with zooplankton metacommunity structure were those related to 
clear/turbid water states. Earlier zooplankton community data were significant 
predictors (specially for microcrustaceans), indicating the role of historical effects on 
later zooplankton metacommunity structure. Spatial variables were also often 
significant. Our results highlight the importance of considering multiple surveys in 
studies aiming to unveil the determinants of zooplankton metacommunity structure. 
 
Keywords: Artificial shallow lakes; Species composition; Historical effects; Variance 
partitioning 
 
28 
 
Introduction 
 
Local communities encompassing a metacommunity are structured by environmental 
factors, biotic interactions, and dispersal (Leibold et al. 2004, 2010; Vellend 2010; 
Leibold and Chase 2017). This basic idea assumes that the species of a given regional 
pool have specific environmental requirements and, therefore, those occurring in local 
communities have passed through multiple environmental filters (Mittelbach and 
Schemske 2015). Dispersal, in turn, depending on its intensity, can increase or decrease 
the relationship between species abundances and environmental gradients (Heino et al., 
2015; Thompson et al. 2020). Dispersal rates should be sufficient for species to colonize 
and develop viable populations in local communities that have suitable environmental 
characteristics (considering their ecological requirements). On the other hand, when 
dispersal rates are too high (e.g., through mass effects), the strengths of the relationships 
between species abundances and environmental gradients tend to be reduced because 
many species may occur in environmentally suboptimal local communities (Heino et al. 
2015). 
Despite the conceptual advance promoted by metacommunity theory (e.g., 
Ricklefs 2008), empirical results show that our power to predict species distributions is 
limited (e.g., Soininen 2014, 2016). Although the low predictive power may be related 
to stochastic processes, this inference - based on large residual variation - assumes that 
all relevant environmental variables have been measured and that biotic interactions can 
be overlooked (Vellend et al. 2014). Thus, much of the unexplained variation may also 
result from the absence of relevant explanatory variables (i.e., a problem of model 
misspecification). Furthermore, most studies on metacommunities in aquatic 
environments are, in general, based on snapshot surveys (but see Eros et al. 2012; 
 
29 
 
Wojciechowski et al. 2017; Sinclair et al. 2021, for some exceptions) and, therefore, 
they do not consider how previous ecological conditions have influenced current or 
more recent local communities (Vass and Langenheder 2017; Langenheder and 
Lindström 2019). 
Recently, however, some metacommunity studies have considered earlier 
environmental (Andersson et al. 2014) and biological data (Mergeay et al. 2011; 
Castillo-Escrivà et al. 2017; Oliveira et al. 2020; Ortega et al. 2021) as potential 
explanatory factors of the variation in later local communities. The strength of the 
relationship between earlier and later communities may depend on several factors, such 
as species generation time, time interval between samplings and temporal 
environmental variability. For example, Jacquemin and Pyron (2011) found large 
differences between stream fish communities sampled in 1940 and those sampled later 
(1996 and 2007) and attributed these differences to anthropogenic impacts (e.g., 
changes in water quality and species introductions) that occurred between the years. On 
the other hand, aquatic metacommunities may be temporally autocorrelated (i.e., local 
communities at time t = 1 are correlated to those at time t + 1) even when large 
environmental changes (e.g., hydrological variations) occur between sampling times 
(Oliveira et al. 2020). Similarly, even when long periods are considered, correlations 
between more recent and past metacommunities can be detected (Mergeay et al. 2011; 
Castillo-Escrivà et al. 2017). 
Correlations between the same metacommunity studied over time can emerge 
from priority effects (Fukami 2015). These effects occur when the first species that 
colonize a local community develop higher abundances and, therefore, preempt 
resources and restrict the establishment of late-arriving species (Mergeay et al. 2011). 
Even after severe environmental disturbances that can reduce the abundances of early 
 
30 
 
colonists and that, consequently, would open windows of opportunity for other species, 
the priority effects can be maintained when the species of the initial communities 
develop dormant propagule banks (e.g., resting egg banks; Hairston 1996; De Meester 
et al. 2002; Mergeay et al. 2011; Castillo-Escrivà et al. 2017). Thus, early-arriving 
species would have advantages as they would quickly reestablish their populations from 
the propagule banks. Alternatively, local communities assessed successively over time 
may be similar because strong structuring environmental gradients are maintained or re-
established between samplings. For example, strong environmental gradients across a 
watershed (from small headwater to higher order streams) that influence fish 
communities tend to be maintained over time (Oliveira et al. 2020). 
In zooplankton communities, assuming temporal changes in environmental 
gradients, short generation times (Wetzel 1975; Allan 1976; Gillooly 2000) and rapid 
responses to environmental changes (Jeppesen et al. 2011), low correlations between 
data from local communities obtained in successive samplings can be expected. 
Empirically, these changes can be verified, for example, with an ordination analysis that 
would show a low juxtaposition of the scores of the (same) sampling units during two or 
more sampling events, considering both environmental and zooplankton data (e.g., 
Retting et al. 2006; Li et al. 2019; Picapedra et al. 2020). On the other hand, many 
zooplankton species can form a resting egg bank (Hairston 1996; Gyllström and 
Hansson 2004; Havel and Shurin 2004) and their communities can be structured by 
priority effects despite changes in environmental gradients. In these cases, the results 
from ordination analyses would show high congruence. 
Zooplankton communities can also be influenced by variables obtained at the 
landscape scale, in addition to those obtained locally. With the intensification of land 
use (i.e., agriculture, pasture, urban areas), natural and artificial aquatic environments 
 
31 
 
tend to receive high nutrient loads, sediments, and pesticides (Dodson et al. 2005; Van 
Egeren et al. 2011). Despite their socioeconomic importance and high biodiversity 
(Czerniawski and Domagała 2014; Czerniawski and Kowalska-Góralska 2018), small 
reservoirs (i.e., reservoirs used in agriculture) are especially impacted by activities 
carried out in adjacent terrestrial ecosystems (Joniak and Kuczyńska-Kippen 2010; 
Chen et al. 2019). The small size, depth, and thepossibility to receive nutrient-rich 
effluents, make small reservoirs prone to eutrophication, which favors the dominance of 
a few tolerant zooplankton species (e.g., Le Quesne et al. 2020). 
Here, we quantified the relative importance of different groups of variables 
(local scale environment, land use, spatial variables, and earlier zooplankton community 
data) that could structure zooplankton communities (microcrustaceans, rotifers and 
testate amoebae) in small reservoirs. Zooplankton groups are considered excellent 
ecological indicators (Jeppesen et al. 2011). Therefore, we predicted that local 
environmental factors would be significantly correlated with zooplankton community 
structure. However, because land use variables may be strongly correlated with water 
quality, we expected they would also be important in structuring local zooplankton 
communities (Van Egeren et al. 2011; Santos et al. 2021). Finally, we expected a 
relationship between data of local communities that were obtained in two consecutive 
times (i.e., the metacommunity structure at time t would be similar to that observed at 
time t+1). This expectation can be justified considering, among others, the maintenance 
of spatial environmental gradients between two sampling periods and the role of priority 
effects. For example, microcrustaceans and rotifers can develop large resting egg banks 
that can quickly, after disturbances (e.g., alternating dry and rainy periods), determine 
the composition of local communities (De Meester et al. 2002; Allen et al. 2011). 
However, experimental studies have shown that the effect of resistance egg bank on 
 
32 
 
local community structures over time may be greater for microcrustaceans than for 
rotifers (Lopes et al. 2016). Thus, considering the different zooplanktonic groups, we 
expected local communities at an earlier time to be correlated with local community 
structures at a later time especially for microcrustaceans and, to a lesser degree, for 
rotifers. 
 
Methods 
 
Study area 
 
This study was carried out in small reservoirs (i.e., artificial shallow lakes) 
located in the Rio Preto watershed (Federal District and Goiás State; Brazil; Figure 1). 
The Alto Rio Preto Microbasin (MARP) belongs to the São Francisco River Basin and 
has an area of 3,470 km², with 2,344.045 km of drainage (Schrage and Uagoda 2017). 
The Rio Preto watershed is characterized by flat terrains, which facilitates the intensive 
use of the landscape (Figure S1). The small reservoirs are mainly used for irrigation. 
The climatic characteristics of the studied area are typical of the Brazilian Midwest, 
with two well-defined seasons: a rainy one (from October to April) and a dry one (from 
May to September; Bustamante et al. 2012). The sampling campaigns were carried out 
during dry and rainy seasons. The number of ponds analyzed varied because some were 
dry or inaccessible in some months. Thus, 23 small reservoirs were sampled in 05/2017 
(dry), 39 in 02/2018 (rainy), 38 in 09/2018 (dry), and 38 in 02/2019 (rainy). 
 
33 
 
 
Figure 1. Small reservoirs sampled in the Rio Preto watershed (MARP), Distrito 
Federal and Goiás (GO), Brazil. 
 
Zooplankton 
 
At each reservoir, we collected zooplankton samples by filtering 1,000 L of 
water through a plankton net with a 68 µm mesh size. Subsequently, the samples were 
fixed in a 4% formaldehyde solution and buffered with sodium tetraborate. For 
quantitative and qualitative analyses, zooplankton samples were concentrated to a 
known volume (minimum 50 mL and maximum depending on the number of organisms 
and amount of sediment in the sample). We used a Hensen-Stempel pipette to remove 
10 % of the total volume of each sample and, using Sedgwick-Rafter chambers, we 
counted each aliquot under an optical microscope (Olympus CX31 - 400x); however, 
samples with low densities were fully counted (Bottrell et al. 1976). Qualitative analysis 
 
34 
 
was performed by counting species that were not registered in the quantitative analysis. 
The microcrustaceans (copepods and cladocerans), rotifers and testate amoebae were 
identified to the lowest possible taxonomic level (usually species) using specialized 
taxonomic keys (Koste 1978; Vucetich 1978; Ogden and Hedley 1980; Reid 1985; 
Matsumura-Tundisi 1986; Paggi 1995; Velho and Lansac-Tôha 1996; El Moor-Loureiro 
1997; Gomes-Sousa 2008). Densities were expressed as individuals/m3. 
 
Local environmental variables 
 
The following local environmental variables were obtained using a Horiba® 
water quality meter: temperature, turbidity, pH, dissolved oxygen, and conductivity. 
Water transparency and depth were measured with a Secchi disk. We recorded 
presence-absence data of aquatic macrophytes (emergent/floating leaves, submerged, 
and free floating) in each small reservoir. Water samples were analyzed for 
orthophosphate, total phosphorus, inorganic nitrogen, and total nitrogen using standard 
methods (APHA 2005; ASTM 2016). However, not all variables were measured in all 
sampling campaigns (Table S1). 
The dimensions of the reservoirs (perimeter (km) and area (km²)) were obtained 
using data from Google Earth®. We also calculated the shoreline development index 
(Häkanson 2004). 
 
Land use variables 
 
We delimited the watershed area upstream of each small reservoir using the 
Digital Elevation Model (DEM) of the study area (Advanced Land Observing Satellite, 
 
35 
 
Phased Array L-band Synthetic Radar, https://asf.alaska.edu/). After, we used data from 
the MapBiomas Project (https://mapbiomas.org) in ArcMap® to obtain the following 
variables: watershed area (km2), remaining vegetation (%), pasture (%), agriculture (%), 
agriculture/pasture mosaic (%), and urban occupation (%). 
 
Data analysis 
 
All analyzes were performed using the R software (R Core Team 2021). The 
relationships between community abundance data at time t and our set of explanatory 
matrices (local environment, land use, space, and community data at time t-1; see 
below) were tested using partial Redundancy Analysis (pRDA) and variance 
partitioning (as described in Peres-Neto et al. 2006). For these analyses, we used the 
functions “rda” and “varpart” of the vegan package (Oksanen et al. 2020), respectively. 
The explanatory variables of each set were selected, before variation partitioning, 
following the method described in Blanchet et al. (2008) and Bauman et al. (2018a). To 
do so, we used the functions “mem.sel” and “forward.sel” from the adespatial package 
(Dray et al. 2021). Community abundance data were log-transformed, and Hellinger 
standardized prior to analyses. 
As mentioned above, the abundance community data at time t-1were used as an 
explanatory matrix of the zooplankton community data at time t. Thus, the community 
data gathered, for example, in 05/2017, were used as explanatory variables of the 
community data observed in 02/2018 and so on, successively, for the other pairs of 
sampling dates. However, owing to the high dimensionality of the community data, we 
first summarized the data obtained at time t-1 using a Bray-Curtis based Principal 
Coordinate Analysis (PCoA). Then, the PCoA axes scores, calculated with the 
https://asf.alaska.edu/
https://mapbiomas.org/
 
36 
 
“cmdscale” function of the “stats” package (R Core Team 2021), were used as candidate 
variables (i.e., as potential explanatory variables of the zooplankton community 
obtained in the most recent campaign). 
We used the geographic coordinates of the small reservoirs to generate Moran’s 
Eigenvector Maps (MEM) (Bauman et al. 2018b), those with positive correlations were 
then used as spatial predictors. Different connectivity and weighting matrices were 
tested (Table S2). This procedure was used to model the spatial component in the best 
possible way. For that, we used the functions “listw.candidates”, “listw.select”from the 
“adespatial” package (Dray et al. 2021). All significance tests were based on 9999 
permutations. 
 
Results 
 
We found a total of 285 zooplankton taxa during the four sampling campaigns 
(95 rotifers, 112 testate amoebae, and 78 microcrustaceans, including the larval and 
juvenile forms of copepods) (Table S3). Among rotifers, Lecane Bulla (63.31%), 
Keratella americana (51.08%), L. leontina (43.17%), Polyarthra vulgaris (43.17%) and 
L. signifera (39.57%) were the most frequent. As for microcrustaceans, immature 
copepods (nauplii and juveniles of Cyclopidae and Diaptomidae) were highly frequent 
(57.55% up to 84.89%), as well as Ilyocryptus spinifer (46.05%). Finally, Arcella 
vulgaris (74.1%), A. costata (63.31%), Lesquereusia spiralis (61.15%), Centropyxis 
spinosa (51.8%), and Netzelia corona (51 .8%) were the most frequent taxa among 
testate amoebae (Table S3). The numbers of taxa occurring in only one sampling 
campaign were 33 (34.73%), 19 (24 .35%) and 33 (29.46%) for rotifers, 
microcrustaceans and testate amoebae, respectively. 
 
37 
 
Turbidity, pH, depth, water transparency, and macrophytes (submerged and 
emergent) were often selected by the forward procedure. Among land use, the most 
often selected variables were watershed area and the percentages of agricultural and 
urbanized areas in the landscape. The first PCoAs axis based on past communities was 
selected most of the time. Spatial variables (MEMs) were not selected for zooplankton 
communities in 05/2017, rotifers in 09/2018, testate amoebae in 05/2017, and 
microcrustaceans in 05/2017. In general, MEMs representing large spatial scales were 
the most often selected (Table S4, S5, S6, and S7). 
Variation in zooplankton community structure (i.e., considering the whole 
community) in 05/2017 was significantly correlated with the local environmental 
variables only (adjusted R² = 8.7%; P = 0.001). In 02/2018, the variation in the 
zooplankton community was explained by spatial variables (adjusted R² = 4.5%; P = 
0.018) and by the zooplankton community observed in 05/2017 (i.e., PCoA axes 
obtained with the community data in 05/2017; adjusted R² = 4.3%; P = 0.024). For the 
zooplankton community in 09/2018, the spatial and environmental fraction were 
significant (adjusted R² = 8.4%; P = 0.001 and R² = 0,026; P = 0.001, respectively). The 
spatial (adjusted R² = 3.1; P = 0.034) and past community variables (adjusted R² = 
4.9%; P = 0.003) were significantly correlated with the zooplankton community data 
recorded in 02/2019 (Figure 2). 
 
38 
 
 
Figure 2. Relative contributions (% of variance accounted) of spatial (S), local 
environmental (LE), early community (EC) and land use (LU) variables in the 
structuring the zooplankton metacommunity of the Rio Preto watershed. Values in bold 
indicate that the components were significant (< 0.05%). We did not use a set of 
explanatory variables in variation partitioning when their variables were not previously 
selected. Components of Venn diagrams without values (empty) indicate values close to 
or <0. 
 
Variation in rotifer community structure in 05/2017 was explained by spatial 
(adjusted R² = 8.1%; P = 0.001) and local environmental variables (adjusted R² = 
12.3%; P = 0.001). Spatial variables (adjusted R² = 13.2 %; P = 0.001) were 
 
39 
 
significantly correlated with the rotifer community recorded in 02/2018. No variables 
were significantly correlated with the variation in rotifer community structure in 
09/2018. In 02/2019, only the local environmental fraction was significant (adjusted R² 
= 3.1%; P = 0.038; Figure 2). 
Testate amoebae community data in 05/2017 and 02/2018 were significantly 
correlated with local environmental (adjusted R² = 16.9%; P = 0.001) and spatial 
variables (adjusted R² = 12.3%; P = 0.001), respectively. In 09/2018, the spatial 
(adjusted R² = 5.8%; P = 0.001) and local environmental (adjusted R² = 4.1%; P = 
0.001) fractions were significant. The spatial (adjusted R² = 4.4%; P = 0.005) and local 
environmental (adjusted R² = 7.3 %; P = 0.002) fractions were also significant for the 
testate amoebae data recorded in 02/2019 (Figure 2). 
The variation in the microcrustacean community structure in 05/2017 were 
significantly correlated with the local environmental variables (adjusted R² = 3.1 %; P = 
0.042). In 02/2018, the microcrustacean data were correlated with the spatial variables 
(adjusted R² = 9.1%; P = 0.001) and with the PCoA axes extracted from the 
communities recorded in the previous sampling campaign (adjusted R² = 8.5%; P = 
0.001). In 09/2018, the variation in the microcrustacean community was significantly 
explained only by the community recorded in 02/2018 (adjusted R² = 6.8%, P = 0.009). 
In 02/2019, the spatial variables (adjusted R² = 4.2%, P = 0.03) and the PCoA axes from 
the microcrustacean community data recorded in 09/2018 (adjusted R² = 9.8%; P = 
0.001) explained significant portions of the variation among local microcrustacean 
communities (Figure 2). 
 
Discussion 
 
 
40 
 
We found that the relative importance of the different sets of explanatory 
variables varied among the sampling campaigns. In general, only land use variables 
were not significantly correlated with zooplankton community structure in any sampling 
campaign. On the other hand, spatial, local environmental variables, and the community 
data obtained at an earlier time were more often correlated with the community 
structure. Other studies on aquatic metacommunities also found that variation 
partitioning fractions varied through time (Eros et al. 2012; Erős et al. 2014; Ortega et 
al. 2021; Li et al. 2021). The temporal variation in the relative importance of 
explanatory variables suggests that mechanisms underlying zooplankton community 
structure change over time and that analyses based on snapshot surveys may overlook 
temporally variable mechanisms (Sinclair et al. 2021; but see Cottenie et al. 2003). 
 In general, pH, turbidity, transparency, depth, and aquatic macrophytes were the 
main variables that contributed to the pure local environmental fraction. Several studies 
have demonstrated the influence of these variables in structuring zooplankton 
communities in different aquatic ecosystems (Jeppesen et al. 1997; Cottenie et al. 2003; 
Dejen et al. 2004; Shurin et al. 2010; Zhao et al. 2017; Sinclair et al. 2021). The 
importance of pH in structuring zooplankton communities, for example, has been 
emphasized for different types of aquatic ecosystems (Shurin et al. 2010; Gray and 
Arnott 2011), including water bodies used in agriculture (Le Quesne et al. 2020). 
However, despite the predominance of low pH (< 6.5 on average) in our study area – a 
condition generally associated with low zooplanktonic species richness – the total 
species richness was high. These results demonstrate the importance of the small 
reservoirs for the maintenance of zooplankton biodiversity (see also Le Quesne et al. 
2020). 
 
41 
 
The local environmental variables that were important in structuring the 
zooplankton communities (i.e., turbidity, transparency, depth, and aquatic macrophytes) 
are generally related to feedback mechanisms that determine the alternative stable states 
in shallow aquatic environments (Scheffer et al. 1993). Thus, similarly to what was 
observed in previous studies (Cottenie et al. 2001, 2003), spatial variations in turbidity 
and in other variables that characterize the states of clear (with submerged aquatic 
macrophytes) and turbid (without submerged aquatic macrophytes) waters may have 
determined the spatial variations in zooplankton community structure. 
The frequent response of testate amoebae local communities to environmental 
gradients was, to some extent, an unexpected result. In general, the analysis of testate 
amoebae in zooplankton studies is comparatively rare. Therefore, it isnot yet possible 
to assess whether this group can be considered an ecological indicator as reliable as the 
traditionally evaluated zooplankton groups (e.g., microcrustaceans and rotifers). Results 
obtained in other studies, however, indicate that testate amoebae can be used to infer 
environmental changes (Velho et al. 2003; Mitchell et al. 2008; Arrieira et al. 2015; Qin 
et al. 2016; Schwind et al. 2017; Nasser et al. 2019; Wang et al. 2020). 
The evidence regarding the effects of land-use variables on aquatic communities, 
as well as their relative predictive power in comparison with local-scale variables (e.g., 
pH and water transparency), is ambivalent (e.g., compare results from Dodson et al. 
2005; Van Egeren et al. 2011; Zorzal-Almeida et al. 2017; Firmiano et al. 2020 with 
those from Mantyka-Pringle et al. 2014; Machado et al. 2016; Barbosa et al. 2019; 
Rocha et al. 2020; Santos et al. 2021). Land-use variables were not significantly 
correlated with zooplankton community structure in our study. This result was 
unexpected considering the large variation in land use (Figure S1). Thus, at least in our 
 
42 
 
study, the evidence points to the primacy of local-scale variables as predictors of 
zooplankton structure as compared to land-use variables. 
The spatial fraction was significant in most of the analyzes performed in this 
study. Similar results were observed in other studies (Beisner et al. 2006; De Bie et al. 
2012; Gomes et al. 2020; Rocha et al. 2020). However, the mechanisms associated with 
a significant spatial fraction cannot be easily inferred (e.g., Vellend et al. 2014; Brown 
et al. 2017). For example, in addition to processes related to dispersal (e.g., Cottenie 
2005), the effects of spatially autocorrelated, but unmeasured, environmental variables 
on the structuring of metacommunities cannot be ruled out (Peres-Neto and Legendre 
2010; Diniz-Filho et al. 2012). However, studies have shown that the average size of 
propagules can predict the relative importance of environmental and spatial variables. 
Particularly, the dispersal limitation related spatial processes would be less important 
for organisms with smaller propagule sizes (De Bie et al. 2012; Padial et al. 2014; 
Martin et al. 2021). However, we did not find support for this prediction since the 
effects of spatial variables were similar for microcrustaceans and rotifers. Considering 
the small spatial distances between the small reservoirs (longest distance equal to 60 
km), in general, it is unlikely that the significant spatial fraction detected in this study 
indicates that dispersal limitation is a major factor in structuring the zooplankton 
metacommunity (e.g., Shurin 2000; Havel and Shurin 2004). Even acknowledging scale 
effects, the importance of dispersal as a structuring process of zooplankton communities 
is still a controversial issue considering the available literature (e.g., Gray and Arnott 
2011). 
Metacommunity studies are predominantly based on data obtained at a single 
time. This widely used sampling design, however, has been criticized. For example, the 
mechanisms that structure metacommunities may vary over time (Brown et al. 2017; 
 
43 
 
Zhao et al. 2017; Sinclair et al. 2021) or local communities observed at time t may 
reflect past environmental conditions (Fischer et al. 2001; Andersson et al. 2014; Vass 
and Langenheder 2017) or be a result of priority effects (Fukami 2015). Consistent with 
these considerations, the zooplankton dataset obtained at time t-1 was an important 
predictor of the metacommunity at time t (especially for microcrustaceans). Previous 
studies have proposed that priority effects could be investigated by quantifying the 
effects of past environmental conditions on local communities (Andersson et al. 2014; 
Vass and Langenheder 2017). However, using data from local communities themselves 
obtained at an earlier time, as done in this study, seems to be a more direct alternative 
(see also Mergeay et al. 2011; Castillo-Escrivà et al. 2017; Oliveira et al. 2020; Ortega 
et al. 2021). 
Assuming strong environmental effects on local communities and temporal 
invariability of environmental gradients, the effect of earlier community data would 
emerge simply through the action of niche-based mechanisms (which act similarly in 
the two analyzed times). However, if the spatial fraction detected in this study is not 
related to unmeasured environmental variables, this explanation seems unlikely since 
the local environmental fraction was not preponderant. The priority effects, where the 
first colonizers dominate the composition of local communities (Fukami 2015), could 
also explain the fraction associated with the earlier community data. In turn, priority 
effects can be maximized by a storage effect, for example, by a large propagule bank 
(Mergeay et al. 2011). 
For rotifers and testate amoebae, the composition of previous communities did 
not influence the succeeding metacommunities. These results suggest a high turnover in 
composition. Although we confirmed our hypothesis that stronger correlations between 
local communities at earlier and later times would be observed for microcrustaceans in 
 
44 
 
comparison to rotifers (considering, for example, the experimental results of Lopes et al. 
2016), the fact that we were unable to detect a relationship for the latter group was 
surprising. This is so because several studies indicate that propagule bank sizes of 
rotifers are much higher than those of microcrustaceans (e.g., Frisch et al. 2009 and 
references in Lopes et al. 2016). We speculate that the benthic-pelagic decoupling for 
rotifers (as inferred by the absence of relationship between community structure during 
consecutive sampling campaigns) may be caused by the stronger effects of aerial 
dispersal on local community structure (which indeed tend to be stronger for rotifers 
than for microcrustaceans). 
In conclusion, our main results indicate the importance of including past 
community as potential predictors of zooplankton community structure in small 
reservoirs. Including the earlier community data as additional predictors in multivariate 
canonical analyses (Mergeay et al. 2011; Castillo-Escrivà et al. 2017; Oliveira et al. 
2020; Ortega et al. 2021) is a straightforward way to measure the potential impacts of 
historical effects. However, for this to be possible, we echo previous calls (Langenheder 
et al. 2012; Eros et al. 2012) for the need of considering the temporal dimension in 
studies aiming to evaluate the determinants metacommunity structure. 
 
Acknowledgements 
 
This study was partially financed by the Fundação de Apoio a Pesquisa do Distrito 
Federal (FAPDF) (proc. 6067/2013-3), Coordenação de Aperfeiçoamento de Pessoal de 
Nível Superior (CAPES; scholarships to MCV and JCGO; Finance Code 001) and the 
Brazilian Council of Research (CNPq; grants to LMB and LCGV). This work has been 
developed in the context of Instituto Nacional de Ciência e Tecnologia (INCT) in 
 
45 
 
Ecology, Evolution and Biodiversity Conservation, supported by MCTIC/CNpq (proc. 
465610/2014-5) and Fundação de Amparo a Pesquisa de Goiás (FAPEG). We thanks to 
Hasley R. Pereira, Leonardo B. Silva, Rafaela V. Granzotti, Gustavo F. Granjeiro, João 
Paulo A. Motta and Thallia S. Silva for helping with sampling. We declare that our 
manuscript complies with the current Brazilian laws. 
 
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