2000 - 2020年全球农业生产生物多样性完整性足迹一致性数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Can Trong Nguyen, Davina Vačkářová, Jan Weinzettel
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引用次数: 0

摘要

全球生物多样性正在迅速下降,主要原因是国内和跨界消费驱动的农业生产。本文基于生物多样性完整性指数(BII)建立了生物多样性损失足迹时间序列,解决了生物多样性时空数据不一致带来的挑战。整合了大量土地利用、土地覆盖和辅助数据集,生成了一致的高分辨率协调土地利用(HHLU)地图时间序列。这些地图利用线性混合效应模型来量化空间BII。生物多样性完整性损失(BII足迹)随后被归因于特定的作物和牲畜商品。本研究提供了全面的全球数据集,包括HHLU和BII地图,以及2000年至2020年跨越14个生物群落、193个国家和地区、154种作物和9种牲畜的综合BII足迹。这些数据集有助于进行时空分析,以确定全球生物多样性完整性和生物多样性足迹的趋势和模式,从而阐明国际贸易中隐含的生态权衡。这些见解可以鼓励采取适当的干预措施,改变消费模式和供应链,从而有效地保护全球生物多样性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Consistent global dataset on biodiversity intactness footprint of agricultural production from 2000 to 2020.

Global biodiversity is rapidly declining, primarily due to agricultural production driven by both domestic and transboundary consumption. This study addresses the challenges posed by inconsistent spatiotemporal biodiversity data by developing a time series of biodiversity loss footprints based on Biodiversity Intactness Index (BII). Numerous land use, land cover, and auxiliary datasets were integrated to produce a consistent time series of high-resolution harmonized land use (HHLU) maps. These maps were utilized to quantify spatial BII using linear-mixed effect models. Biodiversity intactness loss (BII footprint) was subsequently attributed to specific crops and livestock commodities. This study provides comprehensive global datasets, including HHLU and BII maps, and synthesized BII footprints across 14 biomes, 193 countries and territories, 154 crop items, and 9 livestock categories from 2000 to 2020. These datasets facilitate spatiotemporal analyses to identify trends and patterns in global biodiversity integrity and biodiversity footprints, thereby elucidating the ecological trade-offs embedded in international trade. These insights can encourage appropriate interventions to transform consumption patterns and supply chains toward the effective conservation of global biodiversity.

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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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