Jacinto González-Dacosta

The Global Biodiversity Information Facility (GBIF) is the largest databank on primary biodiversity data. We examined the completeness and geographical biases for all insect data on GBIF to determine its representativeness. Our results demonstrate that GBIF is far from providing a reliable representation about the global distribution of insects. Despite the growing number of records during the last years, few spatial units are well‐surveyed. At coarse resolutions, 34% of the world terrestrial cells lack data and barely 0.5% have completeness values above 90%. Insects are crucial in many ecological functions, and their alarming decline makes it more pressing to have a representative sample to improve our predictive capacity. However, the dynamic nature of species distributions and the strength of anthropogenic forces call for immediate conservation decisions that cannot wait for the empirical data on the identity and distribution of insects.

Understanding the factors shaping species' distributions is a key longstanding topic in ecology with unresolved issues. The aims were to test whether the relative contribution of abiotic factors that set the geographical range of freshwater fish species may vary spatially and/or may depend on the geographical extent that is being considered. The relative contribution of factors, to discriminate between the conditions prevailing in the area where the species is present and those existing in the considered extent, was estimated with the instability index included in the R package SPEDInstabR. We used 3 different extent sizes: 1) each river basin where the species is present (local); 2) all river basins where the species is present (regional); and 3) the whole Earth (global). We used a data set of 16,543 freshwater fish species with a total of 845,764 geographical records, together with bioclimatic and topographic variables. Factors associated with temperature and altitude show the highest relative contribution to explain the distribution of freshwater fishes at the smaller considered extent. Altitude and a mix of factors associated with temperature and precipitation were more important when using the regional extent. Factors associated with precipitation show the highest contribution when using the global extent. There was also spatial variability in the importance of factors, both between species and within species and from region to region. Factors associated with precipitation show a clear latitudinal trend of decreasing in importance toward the equator.

We present and discuss VARSEDIG, an algorithm which identifies the morphometric features that significantly discriminate two taxa and validates the morphological distinctness between them via a Monte-Carlo test. VARSEDIG is freely available as a function of the RWizard application PlotsR (http://www.ipez.es/RWizard) and as R package on CRAN. The variables selected by VARSEDIG with the overlap method were very similar to those selected by logistic regression and discriminant analysis, but overcomes some shortcomings of these methods. VARSEDIG is, therefore, a good alternative by comparison to current classical classification methods for identifying morphometric features that significantly discriminate a taxon and for validating its morphological distinctness from other taxa. As a demonstration of the potential of VARSEDIG for this purpose, we analyze morphological discrimination among some species of the Neotropical freshwater family Characidae.

We herein present FactorsR, an RWizard application which provides tools for the identification of the most likely causal factors significantly correlated with species richness, and for depicting on a map the species richness predicted by a Support Vector Machine (SVM) model. As a demonstration of FactorsR, we used an assessment using a database incorporating all species of terrestrial carnivores, a total of 249 species, distributed across 12 families. The model performed with SVM explained 91.9% of the variance observed in the species richness of terrestrial carnivores. Species richness was higher in areas with both higher vegetation index and patch index, i.e., containing higher numbers of species whose range distribution is less fragmented. Lower species richness than expected was observed in Chile, Madagascar, Sumatra, Taiwan, and Sulawesi.