Environmental predictors of fish assemblages in insular streams of the eastern Amazon

Abstract Understanding fish species distributions remains a key challenge in ecology, particularly in complex environments such as small Amazonian streams. These data are essential for developing and supporting effective management and conservation strategies, especially in highly diverse regions like the Amazon Basin, where substantial gaps in biodiversity knowledge persist. We assessed the structure and composition of fish assemblages in 22 intra-insular streams in the Marajó Archipelago, eastern Brazilian Amazon, testing their responses to local environmental conditions (i.e., environmental variables), land use and land cover, and fluvial distances between streams. Sampling occurred during the dry season, in October 2022 and June 2023. Relationships between assemblage structure and predictors were analyzed using Linear Models and Redundancy Analysis (RDA). We collected 7813 individuals representing 54 species, 26 families, and nine orders. The linear model explained 79% of the variation in species richness, with canopy cover, temperature, pH, and forest cover as key predictors. RDA indicated local environmental conditions as the main drivers of species composition. These findings highlight the need to account for multiple environmental gradients in Amazonian fish conservation.

Authors

Institutions

Publication Details

Journal
Aquatic Ecology
Published
2026-05-18
DOI
https://doi.org/10.1007/s10452-026-10308-z
Primary Topic
Fish biology, ecology, and behavior
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Environmental predictors of fish assemblages in insular streams of the eastern Amazon

Bruno Prudente, Matheus S. Almeida, Ronaldo Santos da Silva, Jean C. G. Ortega et al.
Aquatic Ecology
Fish biology, ecology, and behavior
article

Environmental predictors of fish assemblages in insular streams of the eastern Amazon

Bruno Prudente, Matheus S. Almeida, Ronaldo Santos da Silva, Jean C. G. Ortega, Luciano F. A. Montag, Thaisa S. Michelan, Leandro Juen, Dennys G. Amador, Márcio J. da Silva, Marina B. Mendonça
article en

Abstract

Abstract Understanding fish species distributions remains a key challenge in ecology, particularly in complex environments such as small Amazonian streams. These data are essential for developing and supporting effective management and conservation strategies, especially in highly diverse regions like the Amazon Basin, where substantial gaps in biodiversity knowledge persist. We assessed the structure and composition of fish assemblages in 22 intra-insular streams in the Marajó Archipelago, eastern Brazilian Amazon, testing their responses to local environmental conditions (i.e., environmental variables), land use and land cover, and fluvial distances between streams. Sampling occurred during the dry season, in October 2022 and June 2023. Relationships between assemblage structure and predictors were analyzed using Linear Models and Redundancy Analysis (RDA). We collected 7813 individuals representing 54 species, 26 families, and nine orders. The linear model explained 79% of the variation in species richness, with canopy cover, temperature, pH, and forest cover as key predictors. RDA indicated local environmental conditions as the main drivers of species composition. These findings highlight the need to account for multiple environmental gradients in Amazonian fish conservation.

Aquatic EcologyVol. 60(2)
Teagasc - The Irish Agriculture and Food Development Authority (IE), Universidade Federal do Sul e Sudeste do Pará (BR), Instituto Sócioambiental (BR), Universidade Federal do Pará (BR)
Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação Amazônia Paraense de Amparo à Pesquisa, Universidade Federal do Pará, Ministério da Ciência, Tecnologia, Inovações e Comunicações, Instituto Chico Mendes de Conservação da Biodiversidade
Life in Land
Openalex Percentile: Top 4%
Fish biology, ecology, and behavior
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.