Considering spatial constraints to identify areas for new species sampling: A species-specific prioritization approach

Abstract

Currently, many regions of the world are under-sampled, which means our knowledge about biodiversity has gaps. Many studies proposed methods to identify areas for new specimen sampling; however, none considered the application of spatial constraints for this prioritization, which does not consider logistical issues. Here, we propose an approach to identify priority areas for new specimen sampling, considering distributional aspects based on species distribution models and kernel density estimation for species occurrences. We also used different species weights (e.g., endemicity degree) and spatial constraints (e.g., proximity to roads or land cover). Our method applies to one or several species since our approach separately considers the interaction between the distribution and density of occurrences of each species. We used the Gran Chaco biome as the study area and Leguminosae as a target species group to exemplify the use of our approach. We verified that Gran Chaco presents low sample coverage. Paraguay and Bolivia should be prioritized for new samples, especially considering the endemicity degree of the species. The Gran Chaco is one of the most extensive formations of dry forests on the planet and one of the regions with the highest levels of deforestation, exposing the urgency of field research in the region. Our approach identifies conserved priority areas for new specimen sampling accessible through roads, with a low density of occurrences and high suitability for different species. Our method will help scientists in the field work, reducing costs and facilitating the planning to prioritize regions for new specimen sampling.