Can Trong Nguyen, Davina Vačkářová, Jan Weinzettel
{"title":"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。","authors":"Can Trong Nguyen, Davina Vačkářová, Jan Weinzettel","doi":"10.1038/s41597-025-05901-0","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1613"},"PeriodicalIF":6.9000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491505/pdf/","citationCount":"0","resultStr":"{\"title\":\"Consistent global dataset on biodiversity intactness footprint of agricultural production from 2000 to 2020.\",\"authors\":\"Can Trong Nguyen, Davina Vačkářová, Jan Weinzettel\",\"doi\":\"10.1038/s41597-025-05901-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"1613\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491505/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-05901-0\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05901-0","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
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.
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.