Modelling the factors that influence fish guilds composition using a back-propagation network: Assessment of metrics for indices of biotic integrity

Fish assemblages are reckoned as indicators of aquatic ecosystem health, which has become a key feature in water quality management. Under this context, guilds of fish are useful for both understanding aquatic community ecology and for giving sound advice to decision makers by means of metrics for indices of biotic integrity. Artificial neural networks have proved useful in modelling fish in rivers and lakes. Hence, this paper presents a back-propagation network (BPN) for modelling fish guilds composition, and to examine the contribution of five environmental descriptors in explaining this composition in the Garonne basin, south west France. We employed presence–absence data and five variables: altitude, distance from the river source, surface of catchment area, annual mean water temperature, and annual mean water flow. We found that BPN performed better for predicting species richness of guilds than multiple regression models. The standardised determination coefficient of observed values against estimated values was used to characterise model performance; it varied between 0.55 and 0.82. Some models showed high variability which was presumably due to spatial heterogeneity, temporal variability or sampling uncertainty. Surface of catchment area and annual mean water flow were the most important environmental descriptors of guilds composition. Both variables imply human influence (i.e. land-use and flow regulation) on certain species which are of interest to environmental managers. Thus, predicting guilds composition with a BPN from landscape variables may be a first step to assess metrics for water quality indices in the Garonne basin.