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Extreme Value-Based Methods for Modeling Elk Yearly Movements

  • Dhanushi A. WijeyakulasuriyaEmail author
  • Ephraim M. Hanks
  • Benjamin A. Shaby
  • Paul C. Cross
Article
  • 75 Downloads

Abstract

Species range shifts and the spread of diseases are both likely to be driven by extreme movements, but are difficult to statistically model due to their rarity. We propose a statistical approach for characterizing movement kernels that incorporate landscape covariates as well as the potential for heavy-tailed distributions. We used a spliced distribution for distance travelled paired with a resource selection function to model movements biased toward preferred habitats. As an example, we used data from 704 annual elk movements around the Greater Yellowstone Ecosystem from 2001 to 2015. Yearly elk movements were both heavy-tailed and biased away from high elevations during the winter months. We then used a simulation to illustrate how these habitat effects may alter the rate of disease spread using our estimated movement kernel relative to a more traditional approach that does not include landscape covariates. Supplementary materials accompanying this paper appear online.

Keywords

Animal movement Disease spread Resource selection Heavy-tailed MCMC 

Notes

Acknowledgements

We gratefully acknowledge financial support from DOE DE-AC02-05CH11231, USGS G16AC00055, NSF EEID 1414296, NIH GM116927-01, NSF MRI-1626251, NSF DMS-1752280 and NSF DEB-1245373. Computations for this research were performed on the Pennsylvania State University’s Institute for CyberScience Advanced CyberInfrastructure (ICS-ACI). This content is solely the responsibility of the authors and does not necessarily represent the views of the Institute for CyberScience. We thank Montana, Fish, Wildlife and Parks, Idaho Department of Fish and Game, Wyoming Game and Fish Department, Yellowstone and Grand Teton National Parks, US Fish and Wildlife Service, and Wildlife Conservation Society for providing elk location data. Any mention of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the US Government.

Supplementary material

13253_2018_342_MOESM1_ESM.pdf (200 kb)
Supplementary material 1 (pdf 200 KB)

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Copyright information

© International Biometric Society 2018

Authors and Affiliations

  1. 1.Department of StatisticsPennsylvania State UniversityUniversity ParkUSA
  2. 2.Institute for CyberSciencePennsylvania State UniversityUniversity ParkUSA
  3. 3.U.S. Geological SurveyNorthern Rocky Mountain Science CenterBozemanUSA

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