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Predictor weighting and geographical background delimitation: two synergetic sources of uncertainty when assessing species sensitivity to climate change

Acevedo, Pelayo, Jiménez-Valverde, Alberto, Lobo, Jorge M., Real, Raimundo
Climatic change 2017 v.145 no.1-2 pp. 131-143
analytical methods, biogeography, climate, climate change, issues and policy, mammals, models, prediction, uncertainty, Western European region
An accurate estimation of the expected consequences of climate change requires the proper quantification of the effect of climate on current species distributions. Several interrelated sources of uncertainty may affect the likelihood of species distribution models (SDMs) to determine the relative importance of climate. Our aim was to assess the relationship between the influence of geographical background (GB) delimitation and that of subtracting the non-climate effects from the weight of climatic predictors to estimate the combined influence of these two factors on predictions in climate change scenarios. The distribution of 40 endemic mammals in Western Europe have been modeled by (i) using the whole territory of Western Europe as the GB and also specifically delimiting the GB with a geographical criterion and (ii) considering climatic predictors in addition to other non-climatic variables in order to extract the pure effect of climate. The models were used to quantify species’ sensitivity to new climate scenarios. Results showed discrepancies among the analytical approaches. Changes in distribution obtained by considering the pure effect of climate were lower than those obtained by considering the apparent effect, and GB-delimited models yielded higher changes than those trained in Western Europe. We evidence that climate weighting and GB delimitation have dramatic influences on the projections of models when transferred to new scenarios. We emphasize that scientific studies and derived adaptation policies based on SDMs without an explicit consideration of the GB and the weighting of the climate-related variables may be misleading and in need of revision.