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A Solution to the Challenges of Interdisciplinary Aggregation and Use of Specimen-Level Trait Data

Meghan A. Balk,John Deck,Kitty F. Emery,Ramona L. Walls,Dana Reuter,Raphael LaFrance,Joaquin Arroyo-Cabrales,Paul Barrett,Jessica Blois,Arianne Boileau,Laura Brenskelle,Nicole R. Cannarozzi,J. Alberto Cruz,Liliana M. Davalos,Noe U. de la Sancha, Prasiddhi Gyawali,Maggie M. Hantak,Samantha Hopkins,Brooks Kohli,Jessica N. King, Michelle S. Koo, A. Michelle Lawing, Helena Machado, Samantha M. McCrane, Bryan McLean, Michele E. Morgan, Suzanne Pilaar Birch, Denne Reed, Elizabeth J. Reitz, Neeka Sewnath, Nathan S. Upham, Amelia Villasenor, Laurel Yohe, Edward B. Davis, Robert P. Guralnick

iScience(2022)

引用 4|浏览30
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摘要
Understanding variation of traits within and among species through time and across space is central to many questions in biology. Many resources assemble species-level trait data, but the data and metadata underlying those trait measurements are often not reported. Here, we introduce FuTRES (Functional Trait Resource for Environmental Studies; pronounced few-tress), an online datastore and community resource for individual-level trait reporting that utilizes a semantic framework. FuTRES already stores millions of trait measurements for paleobiological, zooarchaeological, and modern specimens, with a current focus on mammals. We compare dynamically derived extant mammal species' body size measurements in FuTRES with summary values from other compilations, highlighting potential issues with simply reporting a single mean estimate. We then show that individual-level data improve estimates of body mass—including uncertainty—for zooarchaeological specimens. FuTRES facilitates trait data integration and discoverability, accelerating new research agendas, especially scaling from intra- to interspecific trait variability.
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关键词
Ornithology,Animals,Systematics,Evolutionary history,Phylogenetics,Biological database,Paleobiology
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