A data-driven geospatial workflow to improve mapping species distributions and assessing extinction risk under the IUCN Red List

biorxiv(2020)

引用 3|浏览7
暂无评分
摘要
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.
更多
查看译文
关键词
geospatial analysis,IUCN Red List,species range maps,Red List assessment
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要