DOI: 10.5061/dryad.p352h
title: Data from: Comparison of approaches to combine species distribution models based on different sets of predictors

description: Distribution models should take into account the different limiting
 factors that simultaneously influence species ranges. Species distribution
 models built with different explanatory variables can be combined into
 more comprehensive ones, but the resulting models should maximize
 complementarity and avoid redundancy. Our aim was to compare the different
 methods available for combining species distribution models. We modelled
 19 threatened vertebrate species in mainland Spain, producing models
 according to three individual explanatory factors: spatial constraints,
 topography and climate, and human influence. We used five approaches for
 model combination: Bayesian inference, Akaike weight averaging, stepwise
 variable selection, updating, and fuzzy logic. We compared the performance
 of these approaches by assessing different aspects of their classification
 and discrimination capacity. We demonstrated that different approaches to
 model combination give rise to disparities in the model outputs. Bayesian
 integration was systematically affected by an error in the equations that
 are habitually used in distribution modelling. Akaike weights produced
 models that were driven by the best single factor and therefore failed at
 combining the models effectively. The updating and the stepwise approaches
 shared recalibration as the basic concept for model combination, were very
 similar in their performance, and showed the highest sensitivity and
 discrimination capacity. The fuzzy-logic approach yielded models with the
 highest classification capacity according to Cohen's kappa. In
 conclusion: i) Bayesian integration, employing the currently used
 equation, and the Akaike weight procedure should be avoided; ii) the
 updating and stepwise approaches can be considered minor variants of the
 same recalibrating approach; and iii) there is a trade-off between this
 recalibrating approach, which has the highest sensitivity, and fuzzy
 logic, which has the highest overall classification capacity.
 Recalibration is better if unfavourable conditions in one environmental
 factor may be counterbalanced with favourable conditions in a different
 factor, otherwise fuzzy logic is better.
description: Data_Description_ECOG-01477_Comparison of approaches to combine species distribution models based on different sets of predictorsHuman activities were described using different variables: water bodies (WR, LK, IMAR), population (HPd), distance to highways (Dhi) and land-cover. Land cover layers were processed so that the surface area of each class (PAST, SV, OAKM, CM, OAKW, CW, OLG, DHER, FT, IHER, LW, RW, HE, NM, VIN). Latitude and longitude were used as the spatial variables. Topography was represented by three variables: slope (S), degree of southward exposure (SE), and degree of westward exposure (WE), which were derived from digital elevation models. Climate variables (temperature and precipitation) were obtained from the datasets supplied by the Spanish Institute of Meteorology (Agencia Estatal de Meteorología).Data_Description_ECOG-01477.zip