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dc.contributor.authorBalk, M.A.
dc.contributor.authorDeck, J.
dc.contributor.authorEmery, K.F.
dc.contributor.authorWalls, R.L.
dc.contributor.authorReuter, D.
dc.contributor.authorLaFrance, R.
dc.contributor.authorArroyo-Cabrales, J.
dc.contributor.authorBarrett, P.
dc.contributor.authorBlois, J.
dc.contributor.authorBoileau, A.
dc.contributor.authorBrenskelle, L.
dc.contributor.authorCannarozzi, N.R.
dc.contributor.authorCruz, J.A.
dc.contributor.authorDávalos, L.M.
dc.contributor.authorde la Sancha, N.U.
dc.contributor.authorGyawali, P.
dc.contributor.authorHantak, M.M.
dc.contributor.authorHopkins, S.
dc.contributor.authorKohli, B.
dc.contributor.authorKing, J.N.
dc.contributor.authorKoo, M.S.
dc.contributor.authorLawing, A.M.
dc.contributor.authorMachado, H.
dc.contributor.authorMcCrane, S.M.
dc.contributor.authorMcLean, B.
dc.contributor.authorMorgan, M.E.
dc.contributor.authorPilaar Birch, S.
dc.contributor.authorReed, D.
dc.contributor.authorReitz, E.J.
dc.contributor.authorSewnath, N.
dc.contributor.authorUpham, N.S.
dc.contributor.authorVillaseñor, A.
dc.contributor.authorYohe, L.
dc.contributor.authorDavis, E.B.
dc.contributor.authorGuralnick, R.P.
dc.date.accessioned2022-12-01T21:16:27Z
dc.date.available2022-12-01T21:16:27Z
dc.date.issued2022
dc.identifier.citationBalk, M. A., Deck, J., Emery, K. F., Walls, R. L., Reuter, D., LaFrance, R., Arroyo-Cabrales, J., Barrett, P., Blois, J., Boileau, A., Brenskelle, L., Cannarozzi, N. R., Cruz, J. A., Dávalos, L. M., de la Sancha, N. U., Gyawali, P., Hantak, M. M., Hopkins, S., Kohli, B., … Guralnick, R. P. (2022). A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data. IScience, 25(10).
dc.identifier.issn2589-0042
dc.identifier.doi10.1016/j.isci.2022.105101
dc.identifier.urihttp://hdl.handle.net/10150/667106
dc.description.abstractUnderstanding 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. © 2022 The Authors
dc.language.isoen
dc.publisherElsevier Inc.
dc.rightsCopyright © 2022 The Authors. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAnimals
dc.subjectBiological database
dc.subjectEvolutionary history
dc.subjectOrnithology
dc.subjectPaleobiology
dc.subjectPhylogenetics
dc.subjectSystematics
dc.titleA solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data
dc.typeArticle
dc.typetext
dc.contributor.departmentBIO5 Institute, University of Arizona
dc.contributor.departmentCollege of Science, University of Arizona
dc.identifier.journaliScience
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleiScience
refterms.dateFOA2022-12-01T21:16:27Z


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Copyright © 2022 The Authors. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as Copyright © 2022 The Authors. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).