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Keywords:

  • Climate change;
  • distribution modelling;
  • forecasts;
  • land cover;
  • stream fish;
  • topography

Abstract

Aim

Species inhabiting fresh waters are severely affected by climate change and other anthropogenic stressors. Effective management and conservation plans require advances in the accuracy and reliability of species distribution forecasts. Here, we forecast distribution shifts of Salmo trutta based on environmental predictors and examine the effect of using different statistical techniques and varying geographical extents on the performance and extrapolation of the models obtained.

Location

Watercourses of Ebro, Elbe and Danube river basins (c. 1,041,000 km2; Mediterranean and temperate climates, Europe).

Methods

The occurrence of S. trutta and variables of climate, land cover and stream topography were assigned to stream reaches. Data obtained were used to build correlative species distribution models (SDMs) and forecasts for future decades (2020s, 2050s and 2080s) under the A1b emissions scenario, using four statistical techniques (generalised linear models, generalised additive models, random forest, and multivariate adaptive regression).

Results

The SDMs showed an excellent performance. Climate was a better predictor than stream topography, while land cover characteristics were not necessary to improve performance. Forecasts predict the distribution of S. trutta to become increasingly restricted over time. The geographical extent of data had a weak impact on model performance and gain/loss values, but better species response curves were generated using data from all three basins collectively. By 2080, 64% of the stream reaches sampled will be unsuitable habitats for S. trutta, with Elbe basin being the most affected, and virtually no new habitats will be gained in any basin.

Main conclusions

More reliable predictions are obtained when the geographical data used for modelling approximate the environmental range where the species is present. Future research incorporating both correlative and mechanistic approaches may increase robustness and accuracy of predictions.