@article{fdi:010060479,
  title = {{F}orecasting fish distribution along stream networks : brown trout ({S}almo trutta) in {E}urope},
  author = {{F}ilipe, {A}. {F}. and {M}arkovic, {D}. and {P}letterbauer, {F}. and {T}isseuil, {C}l{\'e}ment and {D}e {W}ever, {A}. and {S}chmutz, {S}. and {B}onada, {N}. and {F}reyhof, {O}.},
  editor = {},
  language = {{ENG}},
  abstract = {{A}im {S}pecies inhabiting fresh waters are severely affected by climate change and other anthropogenic stressors. {E}ffective management and conservation plans require advances in the accuracy and reliability of species distribution forecasts. {H}ere, we forecast distribution shifts of {S}almo 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. {L}ocation {W}atercourses of {E}bro, {E}lbe and {D}anube river basins (c. 1,041,000 km(2); {M}editerranean and temperate climates, {E}urope). {M}ethods {T}he occurrence of {S}. trutta and variables of climate, land cover and stream topography were assigned to stream reaches. {D}ata obtained were used to build correlative species distribution models ({SDM}s) and forecasts for future decades (2020s, 2050s and 2080s) under the {A}1b emissions scenario, using four statistical techniques (generalised linear models, generalised additive models, random forest, and multivariate adaptive regression). {R}esults {T}he {SDM}s showed an excellent performance. {C}limate was a better predictor than stream topography, while land cover characteristics were not necessary to improve performance. {F}orecasts predict the distribution of {S}. trutta to become increasingly restricted over time. {T}he 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. {B}y 2080, 64% of the stream reaches sampled will be unsuitable habitats for {S}. trutta, with {E}lbe basin being the most affected, and virtually no new habitats will be gained in any basin. {M}ain conclusions {M}ore reliable predictions are obtained when the geographical data used for modelling approximate the environmental range where the species is present. {F}uture research incorporating both correlative and mechanistic approaches may increase robustness and accuracy of predictions.},
  keywords = {{C}limate change ; distribution modelling ; forecasts ; land cover ; stream ; fish ; topography ; {EUROPE}},
  booktitle = {},
  journal = {{D}iversity and {D}istributions},
  volume = {19},
  numero = {8},
  pages = {1059--1071},
  ISSN = {1366-9516},
  year = {2013},
  DOI = {10.1111/ddi.12086},
  URL = {https://www.documentation.ird.fr/hor/fdi:010060479},
}