A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data

Meghan A Balk^{1

2}, John Deck^{3

4}, Kitty F Emery⁵, Ramona L Walls^{6

7}, Dana Reuter⁸, Raphael LaFrance⁵, Joaquín Arroyo-Cabrales⁹, Paul Barrett⁸, Jessica Blois¹⁰, Arianne Boileau¹¹, Laura Brenskelle¹², Nicole R Cannarozzi⁵, J Alberto Cruz⁹, Liliana M Dávalos¹³, Noé U de la Sancha^{14

15}, Prasiddhi Gyawali¹⁶, Maggie M Hantak⁵, Samantha Hopkins^{8

17}, Brooks Kohli¹⁸, Jessica N King⁵, Michelle S Koo¹⁹, A Michelle Lawing²⁰, Helena Machado⁸, Samantha M McCrane²¹, Bryan McLean²², Michèle E Morgan²³, Suzanne Pilaar Birch^{24

25}, Denne Reed²⁶, Elizabeth J Reitz²⁷, Neeka Sewnath¹², Nathan S Upham²⁸, Amelia Villaseñor²⁹, Laurel Yohe³⁰, Edward B Davis^{8

18}, Robert P Guralnick⁵

Affiliations

¹ National Ecology Observatory Network, Battelle, Boulder, CO 80301, USA.
² Smithsonian Institution, National Museum of Natural History, Washington, DC 20560, USA.
³ Berkeley Natural History Museums, University of California, Berkeley, Berkeley, CA 94720, USA.
⁴ Biocode LLC, Junction City, OR 97448, USA.
⁵ Florida Museum of Natural History, University of Florida, Gainesville, FL 32611, USA.
⁶ Critical Path Institute, Tucson, AZ 85718, USA.
⁷ BIO5 Institute, University of Arizona, Tucson, AZ 85719, USA.
⁸ Department of Earth Sciences, University of Oregon, Eugene, OR 97403, USA.
⁹ Archaeozoology Lab, Instituto Nacional de Antropologia e Historia, 06060 Mexico City, CdMx, Mexico.
¹⁰ Department of Life and Environmental Sciences, University of California, Merced, Merced, CA 95343, USA.
¹¹ Department of Archaeology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
¹² Department of Biology, University of Florida, Gainesville, FL 32611, USA.
¹³ Stony Brook University, Stony Brook, NY 11794, USA.
¹⁴ Department of Environmental Science and Studies, DePaul University, Chicago, IL 60614, USA.
¹⁵ Negaunee Integrative Research Center, The Field Museum of Natural History, Chicago, IL 60605, USA.
¹⁶ College of Science, University of Arizona, Tucson, AZ 85721, USA.
¹⁷ Museum of Natural and Cultural History, University of Oregon, Eugene, OR 97401, USA.
¹⁸ Department of Biology and Chemistry, Morehead State University, Morehead, KY 40351, USA.
¹⁹ Museum of Vertebrate Zoology, University of California, Berkeley, Berkeley, CA 94720, USA.
²⁰ Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX 77843, USA.
²¹ Department of Anthropology, University of Florida, Gainesville, FL 32611, USA.
²² Department of Biology, University of North Carolina, Greensboro, NC 27412, USA.
²³ Peabody Museum of Archaeology and Ethnology, Harvard University, Cambridge, MA 02138, USA.
²⁴ Department of Anthropology, University of Georgia, Athens, GA 30602, USA.
²⁵ Department of Geography, University of Georgia, Athens, GA 30602, USA.
²⁶ Department of Anthropology, University of Texas, Austin, Austin, TX 78712, USA.
²⁷ Georgia Museum of Natural History, University of Georgia, Athens, GA 30602, USA.
²⁸ School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA.
²⁹ Department of Anthropology, University of Arkansas, Fayetteville, AR 72701, USA.
³⁰ Department of Bioinformatics and Genomics, University of North Carolina Charlotte, Charlotte, NC 28223, USA.

PMID: 36212022
PMCID: PMC9535407
DOI: 10.1016/j.isci.2022.105101

A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data

Meghan A Balk et al. iScience. 2022.

. 2022 Sep 13;25(10):105101.

doi: 10.1016/j.isci.2022.105101. eCollection 2022 Oct 21.

Authors

Affiliations

¹ National Ecology Observatory Network, Battelle, Boulder, CO 80301, USA.
² Smithsonian Institution, National Museum of Natural History, Washington, DC 20560, USA.
³ Berkeley Natural History Museums, University of California, Berkeley, Berkeley, CA 94720, USA.
⁴ Biocode LLC, Junction City, OR 97448, USA.
⁵ Florida Museum of Natural History, University of Florida, Gainesville, FL 32611, USA.
⁶ Critical Path Institute, Tucson, AZ 85718, USA.
⁷ BIO5 Institute, University of Arizona, Tucson, AZ 85719, USA.
⁸ Department of Earth Sciences, University of Oregon, Eugene, OR 97403, USA.
⁹ Archaeozoology Lab, Instituto Nacional de Antropologia e Historia, 06060 Mexico City, CdMx, Mexico.
¹⁰ Department of Life and Environmental Sciences, University of California, Merced, Merced, CA 95343, USA.
¹¹ Department of Archaeology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
¹² Department of Biology, University of Florida, Gainesville, FL 32611, USA.
¹³ Stony Brook University, Stony Brook, NY 11794, USA.
¹⁴ Department of Environmental Science and Studies, DePaul University, Chicago, IL 60614, USA.
¹⁵ Negaunee Integrative Research Center, The Field Museum of Natural History, Chicago, IL 60605, USA.
¹⁶ College of Science, University of Arizona, Tucson, AZ 85721, USA.
¹⁷ Museum of Natural and Cultural History, University of Oregon, Eugene, OR 97401, USA.
¹⁸ Department of Biology and Chemistry, Morehead State University, Morehead, KY 40351, USA.
¹⁹ Museum of Vertebrate Zoology, University of California, Berkeley, Berkeley, CA 94720, USA.
²⁰ Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX 77843, USA.
²¹ Department of Anthropology, University of Florida, Gainesville, FL 32611, USA.
²² Department of Biology, University of North Carolina, Greensboro, NC 27412, USA.
²³ Peabody Museum of Archaeology and Ethnology, Harvard University, Cambridge, MA 02138, USA.
²⁴ Department of Anthropology, University of Georgia, Athens, GA 30602, USA.
²⁵ Department of Geography, University of Georgia, Athens, GA 30602, USA.
²⁶ Department of Anthropology, University of Texas, Austin, Austin, TX 78712, USA.
²⁷ Georgia Museum of Natural History, University of Georgia, Athens, GA 30602, USA.
²⁸ School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA.
²⁹ Department of Anthropology, University of Arkansas, Fayetteville, AR 72701, USA.
³⁰ Department of Bioinformatics and Genomics, University of North Carolina Charlotte, Charlotte, NC 28223, USA.

PMID: 36212022
PMCID: PMC9535407
DOI: 10.1016/j.isci.2022.105101

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.

Keywords: Animals; Biological database; Evolutionary history; Ornithology; Paleobiology; Phylogenetics; Systematics.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

**Figure 1**
FuTRES data workflow The FuTRES community collects data from a variety of sources: the field, the literature, online databases, or from museum collections. The users input data formatted to a template accessed through GEOME, which accommodates paleo-, zooarchaeo-, and neontological metadata types. FuTRES works with the user to preprocess the data, but is also building tools, such as an RShinyApp (https://github.com/futres/RShinyFuTRES), that will allow submitters to prepare their own data for GEOME. The trait terms are defined and standardized; if a term does not exist, the user can create an issue to request a term through https://github.com/futres/fovt. The data are then validated and stored in GEOME. The FuTRES workflow then converts the data into RDF triples and reasons over the ontology and terms, resulting in standardized, discoverable data. The FuTRES team provides a cleaning routine for the data, filtering data, simple metrics about data, mapping and visualization of data, and ultimately the download of data. The user then can access and discover trait data at the specimen level.

**Figure 2**
Data cleaning method with example (A–C). Here, we show *Otospermophilus beecheyi* as an example of the data cleaning process and success. Much data had unknown life stage (A), where purple colors denote known adults, yellow unknown life stage, and gray juveniles which we exclude from subsequent analyses. In this example, *Otospermophilus beecheyi* had 108 body mass records with no life stage reported. To remedy this, we created a distribution to test whether the unlabeled data were potentially adults. 1. Non-inferred, adult measurements were tested for outliers (results in B; gray bars below distributions are outliers). 2. From that set of data, we created +/−3σ upper and lower limits. 3. We tested the unlabeled, non-juvenile data against those limits (results in C; gray bars below distributions are outliers). Those within the limits we kept and labeled “possible adult; possibly good”, those outside of the limits were labeled “outliers” or “possible juvenile”.

**Figure 3**
Differences between dynamic and static body mass estimates The distribution of the number of standard errors (se) of the PanTHERIA mean body masses (indicated by the vertical hash marks along the x axis) is from FuTRES average body mass. Dotted line (dark gray) indicates the +/−3 se from FuTRES average body mass.

**Figure 4**
Body mass estimation for zooarchaeological deer astragali The relationship between modern deer astragali lateral length and body mass (black dots; black line) comes from data ingested to FuTRES from VertNet and K. Emery. Zooarchaeological data includes FuTRES data from K. Emery and additional data from Reitz et al. (2010). We predicted body mass (diamonds) from two sites (St. Catherines Island, 1565-1763 ACE in dark purple, and Fort Center, 200-800 ACE in light purple) and their associated +/− SE(vertical lines) from the relationship between modern deer astragali lateral length and body mass.

See this image and copyright information in PMC

References

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A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data

Affiliations

A solution to the challenges of interdisciplinary aggregation and use of specimen-level trait data

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

LinkOut - more resources

Full Text Sources