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

Abstract

Ecological indicatorsBenthic macroinvertebrates Biological metricsStochastic-dynamic methodology a b s t r a c tAs 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][304][305][306][307][308][309][310][311][312][313][314][315][316][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.

Bioassessment of stream ecosystems enduring a decade of simulated degradation: lessons for the real world

“…Both reference and stream ecosystems exposed to potential stressors, including the biological communities within them, may vary substantially from year to year, as revealed by both empirical studies and modeling exercises (e.g., Gertseva et al 2004;Cabecinha et al 2007). We also saw some year-to-year variation in stream communities, as revealed by the main effects of year and its interactions with other sources of variation in our ANOVAs of the community descriptors, although we did not incorporate systematic temporal change in our simulation of reference community dynamics.…”

A predictive modelling tool for assessing climate, land use and hydrological change on reservoir physicochemical and biological properties

“…StDM has been tested, validated and applied in the development of management strategies for rivers (Cabecinha et al . ), reservoirs (Cabecinha et al . ), agro‐ecosystems (Cabral et al .…”

A Stochastic Dynamic Methodology (StDM) for reservoir's water quality management: Validation of a multi-scale approach in a south European basin (Douro, Portugal)

“…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. This methodology was successfully tested in several types of ecological systems, such as mountain running waters (Cabecinha et al, 2004(Cabecinha et al, , 2007, mediterranean agroecosystems (Santos and Cabral, 2003;Cabral et al, 2007), estuaries (Silva-Santos et al, 2006, 2008, and for simulating the impact of socioeconomic trends on threatened species .…”

“…One way to cope with the complexity of this problematic for sound environmental management of reservoirs is to apply mathematical models of different kinds (Even et al, 2007). Therefore, ecological integrity studies have been improved by creating dynamic models that simultaneously attempt to capture the structure and the composition in systems affected by long-term environmental disturbances (Jørgensen, 1994;Costanza and Voinov, 2003;Chaloupka, 2002;Cabecinha et al, 2004Cabecinha et al, , 2007Silva-Santos et al, 2006, 2008. The application of ecological models can synthesize the pieces of ecological knowledge, emphasizing the need for a holistic view of a certain environmental problem (Jørgensen, 2001;Cabecinha et al, 2004Cabecinha et al, , 2007Silva-Santos et al, 2006, 2008.…”

“…Therefore, ecological integrity studies have been improved by creating dynamic models that simultaneously attempt to capture the structure and the composition in systems affected by long-term environmental disturbances (Jørgensen, 1994;Costanza and Voinov, 2003;Chaloupka, 2002;Cabecinha et al, 2004Cabecinha et al, , 2007Silva-Santos et al, 2006, 2008. The application of ecological models can synthesize the pieces of ecological knowledge, emphasizing the need for a holistic view of a certain environmental problem (Jørgensen, 2001;Cabecinha et al, 2004Cabecinha et al, , 2007Silva-Santos et al, 2006, 2008. Any environmental assessment must begin with a conceptual model that includes the natural geographic and habitat setting, human activity that can potentially stress the ecosystem (e.g., agriculture), stressors resulting from that human activity (e.g., increased nutrients) and the effects of those stressors on the ecosystem (Stevenson et al, 2004).…”