Data from: Input matters matter: bioclimatic consistency to map more reliable species distribution models

1. Accuracy of global bioclimatic databases is essential to understand
biodiversity-environment relationships. Many studies have explored biases
and uncertainties related to species distribution models (SDMs) but the
effect of choosing a specific database among the different alternatives
has not been previously assessed. 2. The lack of bioclimatic congruence
(degree of agreement) between different databases is a main concern in
distribution modelling and it is critical in single-source models, for
which the database choice is decisive. In order to prevent unreliable
predictions derived from distorted input data, SDMs accuracy can be
assessed by mapping model predictions according to a bioclimatic
congruence measure derived from the comparison of multiple databases,
which can be achieved with the bioclimatic consistency maps that we
propose in this study. Here, i) we present the first global-scale
bioclimatic congruence map to analyse environmental mismatches between
recently updated bioclimatic databases. We also test the importance of
input matters on the reliability of distribution models of sixteen
mammals, by addressing ii) inconsistencies among species response curves
(temperature and precipitation), and iii) discrepancies among SDMs
predictions depending on the chosen bioclimatic database. Finally, iv) we
propose a strategy to assess bioclimatic consistency of model predictions,
showing its application to the specific case of Litocranius walleri. 3.
Our results confirm that the single-source modelling approach greatly
influences the estimation of species-environment relationship and
consequently, bias spatial predictions derived from SDMs. This is
especially true for studies conducted in polar and mountainous regions
which showed the smallest bioclimatic congruence. We show that by adding
bioclimatic congruence to SDMs projections, we can build a bioclimatic
consistency map that enables the detection of both risky and consistent
areas, as revealed for the case of L. walleri. 4. Assessing uncertainty in
bioclimatic input data is key to avoid erroneous conclusions in
macroecological and biogeographical studies. The spatial characterisation
of bioclimatic consistency provides an adequate empirical framework which
effectively illustrates bioclimatic data limitations. We strongly
recommend that this new strategy should be formally and systematically
incorporated into distribution modelling to build more reliable SDMs,
which are essential to develop successful biodiversity conservation
programmes.