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