Applying a stochastic-dynamic methodology (StDM) to facilitate ecological monitoring of running waters, using selected trophic and taxonomic metrics as state variables.

• Autor(a) principal: Cabecinha, Edna
• Data de Publicação: 2007
• Outros Autores: Silva-Santos, Pedro, Cortes, Rui, Cabral, João Alexandre
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10348/4319
• topic: Aquatic ecosystem monitoring; Ecological indicators; Benthic macroinvertebrates; Biological metrics; Stochastic-dynamic methodology

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.