Incorporating bioclimatic and biogeographic data in the construction of species distribution models in order to prioritize searches for new populations of threatened flora

Abstract

The aim of this study was to analyse the usefulness of incorporating bioclimatic and biogeographic data into digital species prediction and modelling tools in order to identify potential habitats of rare or endangered flora taxa. Species distribution models (SDMs) were obtained using the Maximum entropy algorithm. Habitat suitability maps were based on sites of known occurrence of studied species. The study showed that highly reliable habitat prediction models can be obtained through the inclusion of bioclimatic and biogeographic maps when modelling these species. The resultant SDMs are able to fit the search area more closely to the characteristics of the species, excluding the percentage of highly suitable areas that are located far from the known distribution of the taxon, where the probability of finding the plant is low. Therefore, it is possible to overcome one of the most commonly encountered problems in the construction of rare or threatened flora taxa SDMs, derived from the low number of initial citations. The resulting SDMs and the vegetation map enable prioritization of the search for new populations and optimization of the economic and human resources used in the collection of field data.