GIATAR: a Spatio-temporal Dataset of Global Invasive and Alien Species and their Traits.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-09-11 DOI:10.1038/s41597-024-03824-w

Ariel Saffer, Thom Worm, Yu Takeuchi, Ross Meentemeyer

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Abstract

Monitoring and managing the global spread of invasive and alien species requires accurate spatiotemporal records of species presence and information about the biological characteristics of species of interest including life cycle information, biotic and abiotic constraints and pathways of spread. The Global Invasive and Alien Traits And Records (GIATAR) dataset provides consolidated dated records of invasive and alien presence at the country-scale combined with a suite of biological information about pests of interest in a standardized, machine-readable format. We provide dated presence records for 46,666 alien taxa in 249 countries constituting 827,300 country-taxon pairs in locations where the taxon's invasive status is either alien, invasive, or unknown, joined with additional biological information for thousands of taxa. GIATAR is designed to be quickly updateable with future data and easy to integrate into ongoing research on global patterns of alien species movement using scripts provided to query and analyze data. GIATAR provides crucial data needed for researchers and policymakers to compare global invasion trends across a wide range of taxa.

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GIATAR：全球入侵物种和外来物种及其特征的时空数据集。

要监测和管理入侵物种和外来物种在全球的传播，就必须对物种的存在情况进行准确的时空记录，并提供相关物种的生物特征信息，包括生命周期信息、生物和非生物限制因素以及传播途径。全球入侵和外来物种特征与记录（GIATAR）数据集以标准化、机器可读的格式，提供了国家尺度上入侵和外来物种存在的综合日期记录，以及相关害虫的一系列生物信息。我们提供了 249 个国家的 46,666 个外来分类群的存在日期记录，这些国家构成了 827,300 个国家-分类群对，其中分类群的入侵状态要么是外来的，要么是入侵的，要么是未知的。GIATAR 可根据未来数据进行快速更新，并可利用提供的脚本查询和分析数据，方便地与正在进行的全球外来物种移动模式研究相结合。GIATAR 为研究人员和政策制定者提供了所需的关键数据，以比较各种分类群的全球入侵趋势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.