Shortcuts for biomonitoring programs of stream ecosystems: Evaluating the taxonomic, numeric, and cross-taxa congruence in phytoplankton, periphyton, zooplankton, and fish assemblages.

Autor: Faquim RCP; Câmpus Anápolis de Ciências Exatas e Tecnológicas-Henrique Santillo, Universidade Estadual de Goiás, Anápolis, Goiás, Brazil., Machado KB; Departamento de Ecologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, Goiás, Brazil., Teresa FB; Câmpus Anápolis de Ciências Exatas e Tecnológicas-Henrique Santillo, Universidade Estadual de Goiás, Anápolis, Goiás, Brazil., Oliveira PHF; Departamento de Ecologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, Goiás, Brazil., Granjeiro GF; Faculdade de Planaltina, Universidade de Brasília, Planaltina, Distrito Federal, Brazil., Galli Vieira LC; Faculdade de Planaltina, Universidade de Brasília, Planaltina, Distrito Federal, Brazil., Nabout JC; Câmpus Anápolis de Ciências Exatas e Tecnológicas-Henrique Santillo, Universidade Estadual de Goiás, Anápolis, Goiás, Brazil.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2021 Oct 14; Vol. 16 (10), pp. e0258342. Date of Electronic Publication: 2021 Oct 14 (Print Publication: 2021).
DOI: 10.1371/journal.pone.0258342
Abstrakt: Different biological groups can be used for monitoring aquatic ecosystems because they can respond to variations in the environment. However, the evaluation of different bioindicators may demand multiple financial resources and time, especially when abundance quantification and species-level identification are required. In this study, we evaluated whether taxonomic, numerical resolution and cross-taxa can be used to optimize costs and time for stream biomonitoring in Central Brazil (Cerrado biome). For this, we sampled different biological groups (fish, zooplankton, phytoplankton, and periphyton) in stream stretches distributed in a gradient of land conversion dominated by agriculture and livestock. We used the Mantel and Procrustes analyses to test the association among different taxonomic levels (species to class), the association between incidence and abundance data (numerical resolution), and biological groups. We also assessed the relative effect of local environmental and spatial predictors on different groups. The taxonomic levels and numerical resolutions were strongly correlated in all taxonomic groups (r > 0.70). We found no correlations among biological groups. Different sets of environmental variables were the most important to explain the variability in species composition of distinct biological groups. Thus, we conclude that monitoring the streams in this region using bioindicators is more informative through higher taxonomic levels with occurrence data than abundance. However, different biological groups provide complementary information, reinforcing the need for a multi-taxa approach in biomonitoring.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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