SREL Reprint #3554

Quantifying drivers of wild pig movement across multiple spatial and temporal scales

Shannon L. Kay1, Justin W. Fischer1, Andrew J. Monaghan2, James C. Beasley3,4, Raoul Boughton5,
Tyler A. Campbell6, Susan M. Cooper7, Stephen S. Ditchkoff8, Steve B. Hartley9, John C. Kilgo10,
Samantha M. Wisely11, A. Christy Wyckoff12,13, Kurt C. VerCauteren1, and Kim M. Pepin1

1United States Department of Agriculture, Animal Plant Health Inspection Service, Wildlife Services,
National Wildlife Research Center, 4101 LaPorte Avenue, Fort Collins, CO 80521-2154, USA
2Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80305, USA
3Savannah River Ecology Laboratory, Aiken, SC 29802, USA
4Warnell School of Forestry and Natural Resources, Athens, GA 30602, USA
5Range Cattle Research and Education Center, 3401 Experiment Station, Ona, FL 33865, USA
6East Foundation, 200 Concord Plaza Drive, Suite 410, San Antonio, TX 78216, USA
7Texas AgriLife Research, Texas A&M University System, 1619 Garner Field Road, Uvalde, TX 78801, USA
8School of Forestry and Wildlife Sciences, Auburn University, 3301 Forestry and Wildlife Sciences Building,
Auburn, AL 36849, USA
9United States Geological Survey, Wetland and Aquatic Research Center, 700 Cajundome Blvd,
Lafayette, LA 70506, USA
10United State Department of Agriculture, Forest Service, Southern Research Station,
P.O. Box 700, New Ellenton, SC 29809, USA
11Department of Wildlife Ecology and Conservation, University of Florida,
Gainesville, FL 32611-0430, USA
12Caesar Kleberg Wildlife Research Institute, Texas A&M University–Kingsville, Kingsville, TX 78363, USA
13Santa Lucia Conservancy, 26700 Rancho San Carlos Rd, Carmel, CA 93923, USA

Abstract:
Background: The movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management.
Methods: We obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season.
Results: We found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales.
Conclusions: The analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.

Keywords: Animal movement, Reaction norm, Feral swine, GPS, Home range, Wild pig, Sus scrofa, MCP, AKDE

SREL Reprint #3554

Kay, S. L., J. W. Fischer, A. J. Monoghan, J. C. Beasley, R. Boughton, T. A. Campbell, S. M. Cooper, S. S. Ditchkoff, S. B. Hartley, J. C. Kilgo, S. M. Wisely, A. C. Wyckoff, K. C. VerCauteren, and K. M. Pepin. 2017. Quantifying drivers of wild pig movement across multiple spatial and temporal scales. Movement Ecology 5(14): 1-15.

This information was provided by the University of Georgia's Savannah River Ecology Laboratory (srel.uga.edu).