Taxonomic and non-taxonomic responses of benthic macroinvertebrates to metal toxicity in tropical reservoirs: The case of cantareira complex, São Paulo, Brazil
Benthic macroinvertebrates are organisms that are recognized as water quality bio-indicators. A wide variety of indices and metrics have been shown to respond to a variety of anthropogenic impacts, usually under a general condition of environmental impairment. The absence of a clear distinction in t...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/149545 |
| Acceso en línea: | http://hdl.handle.net/11336/149545 |
| Access Level: | acceso abierto |
| Palabra clave: | BIO-INDICATOR BIOMONITORING CHIRONOMID METAL OLIGOCHAETES TOXICITY https://purl.org/becyt/ford/1.5 https://purl.org/becyt/ford/1 https://purl.org/becyt/ford/1.6 |
| Sumario: | Benthic macroinvertebrates are organisms that are recognized as water quality bio-indicators. A wide variety of indices and metrics have been shown to respond to a variety of anthropogenic impacts, usually under a general condition of environmental impairment. The absence of a clear distinction in the relations between specific pollutants and biotic variables is very common and can lead to biased interpretation of biomonitoring. The aims of this research were to test taxonomic and non-taxonomic responses to specific environmental conditions instead to general conditions. For this purpose, we estimated the theoretical toxicity by comparing toxicity values published by EPA with metal concentrations in water and sediments. Then we tested the responses of biological variables to toxicity and other environmental conditions using the linear mixed effects models approach. We generated 32 models considering 24 different biological metrics and indices that were grouped in five levels. Taxonomic and abundance metrics were best predictor than functional or tolerance-based indexes. The strongest model was that which considered subfamily taxonomic resolution responding to Al_w and Cr_s. |
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