The Agroforestry Species Switchboard, a global resource to explore information for 107,269 plant species.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Roeland Kindt, Ilyas Siddique, Ian Dawson, Innocent John, Fabio Pedercini, Jens-Peter Lillesø, Lars Graudal
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Abstract

The Agroforestry Species Switchboard is a comprehensive vascular plant database that guides users to information for a particular taxon from a global but fragmented set of resources. Via standardized species names, a user can rapidly determine which among the 59 contributing databases contain information for a species of interest, and understand how this information can be accessed. By providing taxonomic identifiers to World Flora Online, it is straightforward to check for changes in taxonomy. Among the 59 databases referenced, ten covered over 10,000 species, 20 between 1,000 and 10,000 species, and 22 between 100 and 1,000 species. The top ten plant families for species richness across covered databases were the Fabaceae (9,537 species), Asteraceae (6,041), Rubiaceae (4,812), Poaceae (3,947), Myrtaceae (3,544), Euphorbiaceae (2,689), Malvaceae (2,478), Rosaceae (2,374), Lauraceae (2,334) and Lamiaceae (2,107). Information included in the Switchboard distinguishes 54,812 tree-like species, covering most known tree species globally. Among its applications, the Switchboard can assist species selection for ecological restoration projects to synergize biodiversity and human well-being objectives.

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农林业物种总机,一个探索107,269种植物物种信息的全球资源。
农林业物种总机是一个综合性维管植物数据库,指导用户从全球但分散的资源集中获取特定分类单元的信息。通过标准化的物种名称,用户可以快速确定59个贡献数据库中哪些包含感兴趣的物种的信息,并了解如何访问这些信息。通过向World Flora Online提供分类标识符,可以直接检查分类法中的变化。在59个数据库中,超过10000种的数据库有10个,1000 ~ 10000种的有20个,100 ~ 1000种的有22个。物种丰富度排名前10位的植物科依次为:蚕豆科(9537种)、菊科(6041种)、茜草科(4812种)、豆科(3947种)、紫金桃科(3544种)、大戟科(2689种)、锦葵科(2478种)、蔷薇科(2374种)、樟科(2334种)和Lamiaceae(2107种)。总机中包含的信息区分了54,812种树状物种,涵盖了全球已知的大多数树种。在其应用中,交换机可以协助生态恢复项目的物种选择,以协同生物多样性和人类福祉目标。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
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.
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