Performance of a stochastic-dynamic modelling methodology for running waters ecological assessment.
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2004
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Resumo
An holistic stochastic-dynamic modelling methodology has been developed in order to predict the ecological status of lotic
systems in Northeast Portugal. These procedures focus on the interactions between conceptually isolated key-components, such
as some relevant benthic macroinvertebrate metrics and changes in local habitat conditions. The proposed model was preceded
by a conventional multivariate statistical treatment performed to discriminate the significant relationships between prevailing
biological and environmental variables. Since this statistical analysis is static, the dataset recorded from the field included true
gradients of habitat changes. In this way, the factors time and space are implicit in the respective treatment. Such a procedure
gives credibility to the parameters included in the dynamic model construction. In order to enhance the importance of monitoring
in aquatic systems based on ecological integrity indicators, different biotic metrics were selected from the studied benthic
macroinvertebrate communities. The samples of aquatic macroinvertebrate, environmental and physical-chemical data were
collected from three watersheds of mountain rivers in Northeast Portugal, between 1983 and 1985. The model validation was
based on independent data from another watershed not included in the model construction. Thereafter, the model behaviour was
tested facing a “new” scenario, namely ongoing organic pollution disturbances in the region. The results are encouraging since,
after the model validation, they seem to demonstrate the reliability of the model (1) to assess the ecological status of running
waters from the studied watersheds and (2) to predict the behaviour of key macroinvertebrate metrics, along an ecological
gradient from relatively pristine conditions to serious human impacts.
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Running waters , Ecological indicators , Aquatic macroinvertebrates , Aquatic ecosystem integrity , Stochastic-dynamic modelling , Holistic approaches
Citação
Cabecinha E. et al, 2004