Work

A data‐driven geospatial workflow to map species distributions for conservation assessments

Year: 2021

Type: article

Abstract: ABSTRACT Species distribution maps are essential for assessing extinction risk and guiding conservation efforts. Here, we developed a data-driven, reproducible geospatial workflow to map species distributions and evaluate their conservation status consistent with the guidelines and criteria of the IUCN Red List. Our workflow follows five automated steps to refine the distribution of a species starting from its Extent of Occurrence (EOO) to Area of Habitat (AOH) within the species range. The ranges are produced with an Inverse Distance Weighted (IDW) interpolation procedure, using presence and absence points derived from primary biodiversity data. As a case-study, we mapped the distribution of 2,273 bird species in the Americas, 55% of all terrestrial birds found in the region. We then compared our produced species ranges to the expert-drawn IUCN/BirdLife range maps and conducted a preliminary IUCN extinction risk assessment based on criterion B (Geographic Range). We found that our workflow generated ranges with fewer errors of omission, commission, and a better overall accuracy within each species EOO. The spatial overlap between both datasets was low (28%) and the expert-drawn range maps were consistently larger due to errors of commission. Their estimated Area of Habitat (AOH) was also larger for a subset of 741 forest-dependent birds. We found that incorporating geospatial data increased the number of threatened species by 52% in comparison to the 2019 IUCN Red List. Furthermore, 103 species could be placed in threatened categories (VU, EN, CR) pending further assessment. The implementation of our geospatial workflow provides a valuable alternative to increase the transparency and reliability of species risk assessments and improve mapping species distributions for conservation planning and decision-making.

Source: Diversity and Distributions

DOI: 10.1111/ddi.13424

Authors: Ruben Dario Palacio, Pablo Jose Negret, Jorge Velásquez-Tibatá, Andrew P. Jacobson