Please use this identifier to cite or link to this item: https://repositorio.inpa.gov.br/handle/1/17097
Title: A multi-assemblage, multi-metric biological condition index for eastern Amazonia streams
Authors: Chen, Kai
Hughes, Robert Mason
Brito, Janaina G.
Leal, Cecília Gontijo
Leitão, Rafael Pereira
Oliveira-Júnior, José Max Barbosa de
Oliveira, Vívian Campos de
Dias-Silva, Karina
Ferraz, Silvio Frosini de Barros
Ferreira, Joice Nunes
Hamada, Neusa
Juen, Leandro
Nessimian, Jorge
dos Santos Pompeu, Paulo
Zuanon, Jansen
Keywords: Ecology
Anthropogenic Disturbance
Aquatic Insects
Biological Assessment
Biological Conditions
Ecological Indicators
Multi-metric Indices
Natural Variability
Physiological Sensitivity
Fish
Aquatic Organism
Environmental Assessment
Environmental Conditions
Environmental Gradient
Environmental Indicator
Environmental Stress
Fish
Human Activity
Physiological Response
Amazonia
Hexapoda
Issue Date: 2017
metadata.dc.publisher.journal: Ecological Indicators
metadata.dc.relation.ispartof: Volume 78, Pags. 48-61
Abstract: Multimetric indices (MMIs) are widely used for assessing ecosystem condition and they have been developed for a variety of biological assemblages. However, when multiple assemblages are assessed at sites, the assessment results may differ because of differing physiological sensitivities to particular stressor gradients, different organism size and guilds, and the effects of different scales of disturbances on the assemblages. Those differences create problems for managers seeking to avoid type-1 and type-2 statistical errors. To alleviate those problems, we used an anthropogenic disturbance index for selecting and weighting metrics, modeled metrics against natural variability to reduce the natural variability in metrics, and developed an MMI based on both fish and aquatic insect metrics. We evaluated eight different ways of calibrating and combining candidate metrics and found that MMIs with unweighted and modeled aquatic insect and fish metrics were the preferred MMI options. © 2017 Elsevier Ltd
metadata.dc.identifier.doi: 10.1016/j.ecolind.2017.03.003
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