Kimberly J Komatsu, Meghan L Avolio, Josep Padullés Cubino, Franziska Schrodt, Harald Auge, Jeannine Cavender-Bares, Adam T Clark, Habacuc Flores-Moreno, Emily Grman, W Stanley Harpole, Jens Kattge, Kaitlin Kimmel, Sally E Koerner, Lotte Korell, J Adam Langley, Tamara Münkemüller, Timothy Ohlert, Renske E Onstein, Christiane Roscher, Nadejda A Soudzilovskaia, Benton N Taylor, Leho Tedersoo, Rosalie S Terry, Kevin Wilcox
{"title":"CoRRE Trait Data: A dataset of 17 categorical and continuous traits for 4079 grassland species worldwide.","authors":"Kimberly J Komatsu, Meghan L Avolio, Josep Padullés Cubino, Franziska Schrodt, Harald Auge, Jeannine Cavender-Bares, Adam T Clark, Habacuc Flores-Moreno, Emily Grman, W Stanley Harpole, Jens Kattge, Kaitlin Kimmel, Sally E Koerner, Lotte Korell, J Adam Langley, Tamara Münkemüller, Timothy Ohlert, Renske E Onstein, Christiane Roscher, Nadejda A Soudzilovskaia, Benton N Taylor, Leho Tedersoo, Rosalie S Terry, Kevin Wilcox","doi":"10.1038/s41597-024-03637-x","DOIUrl":null,"url":null,"abstract":"<p><p>In our changing world, understanding plant community responses to global change drivers is critical for predicting future ecosystem composition and function. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands; however, their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Data, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species' responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The dataset contains complete categorical trait records for all 4,079 plant species obtained from a comprehensive literature search, as well as nearly complete coverage (99.97%) of imputed continuous trait values for a subset of 2,927 plant species. These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"795"},"PeriodicalIF":5.8000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11258227/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-03637-x","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
In our changing world, understanding plant community responses to global change drivers is critical for predicting future ecosystem composition and function. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands; however, their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Data, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species' responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The dataset contains complete categorical trait records for all 4,079 plant species obtained from a comprehensive literature search, as well as nearly complete coverage (99.97%) of imputed continuous trait values for a subset of 2,927 plant species. These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide.
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.