Hierarchical Modelling Reveals Local Environmental Metrics as Key Predictors of Fish Stream Assemblage Structure

ABSTRACT Environmental factors act at multiple spatial scales, shaping the fish assemblage structure in streams. Understanding catchment‐stream relationships through hierarchical classification can aid in the conservation and management of freshwater ecosystems. The main goal of our work was to use the Hierarchical Modelling of Species Communities framework to answer the following questions: Which environmental variables are more important in structuring stream assemblages? Do ecomorphological traits influence species' responses to environmental conditions? What is the pattern of species co‐occurrences along the streams? We fitted models with two alternative response datasets: a community matrix (site × species) with presence/absence data (PA) and abundance data (ABU). Species ecomorphological traits were also included in the models. We tested models considering the environmental variables at each scale separately (local and catchment) and considering all variables together. Variables at the local scale explained 57.1% (PA) and 53.3% (ABU) of the overall variance portion partitioning, while catchment variables explained only 36.6% and 23.3%, respectively. Traits explained only 47.9% (PA) and 18.93% (ABU) of the variation. No significant correlation between species co‐occurrences was found. Our results suggest that local‐scale metrics better predict the structure of fish assemblages in headwater streams from the Chapada Diamantina region of Caatinga.

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Publication Details

Journal
Ecology Of Freshwater Fish
Published
2025-04-29
DOI
https://doi.org/10.1111/eff.70006
Citations
1
Primary Topic
Fish Ecology and Management Studies
Type
article
Field-Weighted Citation Impact
2.31

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article

Hierarchical Modelling Reveals Local Environmental Metrics as Key Predictors of Fish Stream Assemblage Structure

André Teixeira da Silva, Alexandre Clístenes de Alcântara Santos, Fabiane Barreto Souza, Nuño Caiola
1 citations
Ecology Of Freshwater Fish
Fish Ecology and Management Studies
2.31
article

Hierarchical Modelling Reveals Local Environmental Metrics as Key Predictors of Fish Stream Assemblage Structure

André Teixeira da Silva, Alexandre Clístenes de Alcântara Santos, Fabiane Barreto Souza, Nuño Caiola
article en
1 citations

Abstract

ABSTRACT Environmental factors act at multiple spatial scales, shaping the fish assemblage structure in streams. Understanding catchment‐stream relationships through hierarchical classification can aid in the conservation and management of freshwater ecosystems. The main goal of our work was to use the Hierarchical Modelling of Species Communities framework to answer the following questions: Which environmental variables are more important in structuring stream assemblages? Do ecomorphological traits influence species' responses to environmental conditions? What is the pattern of species co‐occurrences along the streams? We fitted models with two alternative response datasets: a community matrix (site × species) with presence/absence data (PA) and abundance data (ABU). Species ecomorphological traits were also included in the models. We tested models considering the environmental variables at each scale separately (local and catchment) and considering all variables together. Variables at the local scale explained 57.1% (PA) and 53.3% (ABU) of the overall variance portion partitioning, while catchment variables explained only 36.6% and 23.3%, respectively. Traits explained only 47.9% (PA) and 18.93% (ABU) of the variation. No significant correlation between species co‐occurrences was found. Our results suggest that local‐scale metrics better predict the structure of fish assemblages in headwater streams from the Chapada Diamantina region of Caatinga.

Ecology Of Freshwater FishVol. 34(3)
Universidade Federal da Bahia (BR), Universidade Estadual de Feira de Santana (BR), Instituto Federal de Educação, Ciência e Tecnologia Baiano (BR)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado da Bahia, Brazilian Biodivesity Fund
Openalex Percentile: Top 15%
Fish Ecology and Management Studies
2.31
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