Factors affecting the transferability of bioindicators based on stream fish assemblages

The development of multimetric indices (MMIs) to measure the biotic condition of aquatic habitats is based on metrics derived from biological assemblages. Considering fish assemblages, the inconsistencies in metrics responses outside of the places where they were developed limit MMI transferability and applicability to other locations, requiring local calibration. The factors behind the low transferability of these MMIs are still poorly understood. We investigated how environmental dissimilarity and spatial distance influence the transferability of metrics generated from local stream fish assemblages to other regions. We also tested whether functional and taxonomic metrics respond differently to the spatial distance. We used data from 239 fish assemblages from streams distributed across a Brazilian, the upper Parana basin and characterized each site according to the level of anthropogenic disturbance at the landscape scale using an Anthropogenic Pressure Index (API). We divided the upper Parana basin into sub-basins and used two of them to create template response models of the metrics in relation to the API. We used these response models to predict the responses outside the template sub-basins. Our response variable representing a metric of transferability was the absolute difference between metric's predicted and observed value for each site (prediction error). We thus modeled the prediction error in relation to the predictor variables that were i) the environmental dissimilarity between each site with the average of the sites from template sub-basins (climatic, topographic and soil type variables) and ii) the spatial distance (overland and watercourse distance) between each site and the center of the template sub-basin. We found that errors in metric predictions were associated with both environmental dissimilarity and spatial distance. Furthermore, functional and taxonomic metrics responded equally to spatial distance. These results indicate the need for local calibration of metrics when developing MMIs, especially if the protocols already available come from distant and environmentally dissimilar places.