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A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data
- Balk, Meghan A;
- Deck, John;
- Emery, Kitty F;
- Walls, Ramona L;
- Reuter, Dana;
- LaFrance, Raphael;
- Arroyo-Cabrales, Joaquín;
- Barrett, Paul;
- Blois, Jessica;
- Boileau, Arianne;
- Brenskelle, Laura;
- Cannarozzi, Nicole R;
- Cruz, J Alberto;
- Dávalos, Liliana M;
- de la Sancha, Noé U;
- Gyawali, Prasiddhi;
- Hantak, Maggie M;
- Hopkins, Samantha;
- Kohli, Brooks;
- King, Jessica N;
- Koo, Michelle S;
- Lawing, A Michelle;
- Machado, Helena;
- McCrane, Samantha M;
- McLean, Bryan;
- Morgan, Michèle E;
- Birch, Suzanne Pilaar;
- Reed, Denne;
- Reitz, Elizabeth J;
- Sewnath, Neeka;
- Upham, Nathan S;
- Villaseñor, Amelia;
- Yohe, Laurel;
- Davis, Edward B;
- Guralnick, Robert P
- et al.
Published Web Location
https://doi.org/10.1016/j.isci.2022.105101Abstract
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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