Imanol Miqueleiz, Robin Abell, Carmen Revenga, Paulo Petry, Michele Thieme, Shawn Weis, Etienne Fluet-Chouinard, Andrew M Durso, Timothy M Boucher, Bradley B Bogues, Aruni Chenxi, Danielle Levanti, Riley J Saxton, Peter B McIntyre
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引用次数: 0
摘要
大规模的生物多样性保护需要了解物种在世界各地的分布情况。第一次全球淡水生态区划分(Abell et al. 2008)综合了水文边界和鱼类生物地理格局,识别出426个不同的区域。在这里,我们更新并扩展了生态区域方法,以涵盖所有已描述的淡水依赖脊椎动物物种:23,130种鱼类,鸟类,两栖动物,爬行动物和哺乳动物。基于生物地理数据库和广泛的文献检索,我们建立了生态区域尺度淡水脊椎动物数据库(freshvert v1.0),共包含96,129个生态区域种群记录(平均每个物种4.15条记录,SD = 11.25)。所有淡水脊椎动物在生态区域的全球归属为分析全球生物多样性、比较脊椎动物类群之间的生物地理格局以及在各种空间尺度上优先保护水生物种提供了资源。
Ecoregional distributions of the world's freshwater vertebrate species.
Large-scale conservation of biodiversity requires understanding how species are distributed around the world. The first global delineation of freshwater ecoregions (Abell et al. 2008) integrated hydrological boundaries and fish biogeographic patterns to recognize 426 distinctive zones. Here, we update and expand that ecoregional approach to encompass every described freshwater-dependent vertebrate species: 23,130 species of fishes, birds, amphibians, reptiles, and mammals. Our database of freshwater vertebrates at the ecoregional scale (FreshVerts v1.0) includes a total of 96,129 ecoregional population records (average of 4.15 records per species, SD = 11.25) based on biogeographic databases and extensive bibliographic searches. This global attribution of all freshwater vertebrates to ecoregions provides a resource for analyzing global biodiversity, comparing biogeographic patterns among vertebrate taxa, and prioritizing efforts to conserve aquatic species across a variety of spatial scales.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.