Applying a stochastic-dynamic methodology (StDM) to facilitate ecological monitoring of running waters, using selected trophic and taxonomic metrics as state variables.
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2007
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As an improvement of a previous work [Cabecinha, E., Cortes, R., Cabral, J.A., 2004. Performance
of a stochastic-dynamic modelling methodology for running waters ecological
assessment. Ecol. Modell. 175, 303–317], the present paper examined the applicability of
a holistic stochastic-dynamic methodology (StDM) in predicting the tendencies of benthic
macroinvertebrate metrics from mountain streams facing expected scenarios either: (1) of
pollution increase due to the agricultural intensification; or (2) of farming activity abandonment
becoming less pollutant as a non-point source. The StDM is a sequential modelling
process developed in order to predict the ecological status of changed ecosystems, from
which management strategies can be designed. These procedures focus on the interactions
between conceptually isolated key-components, such as some relevant trophic and taxonomic
metrics and changes in local environmental conditions. The dataset recorded from
the field included true gradients of environmental changes. The samples of aquatic macroinvertebrate,
environmental and physical–chemical data were collected from four watersheds
of mountain rivers in Northeast Portugal, between 1983 and 1985. The dynamic model
developed was preceded by a conventional multivariate statistical procedure performed
to discriminate the significant relationships between the selected components of the studied
watersheds. The model validation was based on independent data from a watershed
not included in the model construction. Overall, the simulation results are encouraging
since they seem to demonstrate the StDM reliability in capturing the stochastic environmental
dynamics of the studied aquatic ecosystems facing agricultural scenarios that will
characterize the region, namely by predicting credible behavioural patterns for the selected
metrics.
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Aquatic ecosystem monitoring , Ecological indicators , Benthic macroinvertebrates , Biological metrics , Stochastic-dynamic methodology
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Cabecinha E. et al, 2007