Abstract
The impact of climate change on the habitat suitability for large brown trout (Salmo trutta L.) was studied in a segment of the Cabriel River (Iberian Peninsula). The future flow and water temperature patterns were simulated at a daily time step with M5 models' trees (NSE of 0.78 and 0.97 respectively) for two short-term scenarios (2011–2040) under the representative concentration pathways (RCP 4.5 and 8.5). An ensemble of five strongly regularized machine learning techniques (generalized additive models, multilayer perceptron ensembles, random forests, support vector machines and fuzzy rule base systems) was used to model the microhabitat suitability (depth, velocity and substrate) during summertime and to evaluate several flows simulated with River2D©. The simulated flow rate and water temperature were combined with the microhabitat assessment to infer bivariate habitat duration curves (BHDCs) under historical conditions and climate change scenarios using either the weighted usable area (WUA) or the Boolean-based suitable area (SA). The forecasts for both scenarios jointly predicted a significant reduction in the flow rate and an increase in water temperature (mean rate of change of ca. −25% and +4% respectively). The five techniques converged on the modelled suitability and habitat preferences; large brown trout selected relatively high flow velocity, large depth and coarse substrate. However, the model developed with support vector machines presented a significantly trimmed output range (max.: 0.38), and thus its predictions were banned from the WUA-based analyses. The BHDCs based on the WUA and the SA broadly matched, indicating an increase in the number of days with less suitable habitat available (WUA and SA) and/or with higher water temperature (trout will endure impoverished environmental conditions ca. 82% of the days). Finally, our results suggested the potential extirpation of the species from the study site during short time spans.
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