Sarah B Richardson, Lauralee An, Sarah E Pollack, Hemalatha Velappan, Ruth Nogueron, Jessica Richter, Shelley L Gardner, Karen L Williams, John C Hermanson, Elizabeth Dow Goldman, Suzanne M Peyer
{"title":"Global planted forest data for timber species.","authors":"Sarah B Richardson, Lauralee An, Sarah E Pollack, Hemalatha Velappan, Ruth Nogueron, Jessica Richter, Shelley L Gardner, Karen L Williams, John C Hermanson, Elizabeth Dow Goldman, Suzanne M Peyer","doi":"10.1038/s41597-024-04125-y","DOIUrl":null,"url":null,"abstract":"<p><p>Discerning whether certain timber species were harvested from natural forests versus often less restricted planted forests can help ascertain the legality of wood products that enter the global market. However, readily available global planted forest data to the species level have been scarce. We confronted the need for such data by developing a two-pronged dataset, consisting of 'polygon' and 'non-polygon' location-based data, collectively, Planted Forest Timber Data. We obtained the polygon data from the World Resources Institute's Spatial Database of Planted Trees v2.0, extracting data specific to traded timber species. We derived the non-polygon data from peer-reviewed literature and government documents. The polygon dataset encompasses 27 countries and 253 species and the non-polygon dataset spans 91 countries and 447 species. We envision that the more these two living datasets grow, the more they will mutually benefit from one another for data cross-validation. This assembled information is meant to equip global leaders in forest governance, policy, enforcement, and research with vetted data for promoting legal timber trade and protecting biodiversity.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1269"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584855/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04125-y","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}
引用次数: 0
Abstract
Discerning whether certain timber species were harvested from natural forests versus often less restricted planted forests can help ascertain the legality of wood products that enter the global market. However, readily available global planted forest data to the species level have been scarce. We confronted the need for such data by developing a two-pronged dataset, consisting of 'polygon' and 'non-polygon' location-based data, collectively, Planted Forest Timber Data. We obtained the polygon data from the World Resources Institute's Spatial Database of Planted Trees v2.0, extracting data specific to traded timber species. We derived the non-polygon data from peer-reviewed literature and government documents. The polygon dataset encompasses 27 countries and 253 species and the non-polygon dataset spans 91 countries and 447 species. We envision that the more these two living datasets grow, the more they will mutually benefit from one another for data cross-validation. This assembled information is meant to equip global leaders in forest governance, policy, enforcement, and research with vetted data for promoting legal timber trade and protecting 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.