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A General Framework for the Analysis of Animal Resource Selection from Telemetry Data

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  • Devin S. Johnson
  • Dana L. Thomas
  • Jay M. Ver Hoef
  • Aaron Christ

Abstract

Summary We propose a general framework for the analysis of animal telemetry data through the use of weighted distributions. It is shown that several interpretations of resource selection functions arise when constructed from the ratio of a use and availability distribution. Through the proposed general framework, several popular resource selection models are shown to be special cases of the general model by making assumptions about animal movement and behavior. The weighted distribution framework is shown to be easily extended to readily account for telemetry data that are highly autocorrelated; as is typical with use of new technology such as global positioning systems animal relocations. An analysis of simulated data using several models constructed within the proposed framework is also presented to illustrate the possible gains from the flexible modeling framework. The proposed model is applied to a brown bear data set from southeast Alaska.

Suggested Citation

  • Devin S. Johnson & Dana L. Thomas & Jay M. Ver Hoef & Aaron Christ, 2008. "A General Framework for the Analysis of Animal Resource Selection from Telemetry Data," Biometrics, The International Biometric Society, vol. 64(3), pages 968-976, September.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:968-976
    DOI: 10.1111/j.1541-0420.2007.00943.x
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    References listed on IDEAS

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    1. Fred L. Ramsey & Dale Usner, 2003. "Persistence and Heterogeneity in Habitat Selection Studies Using Radio Telemetry," Biometrics, The International Biometric Society, vol. 59(2), pages 332-340, June.
    2. P. G. Blackwell, 2003. "Bayesian inference for Markov processes with diffusion and discrete components," Biometrika, Biometrika Trust, vol. 90(3), pages 613-627, September.
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    Cited by:

    1. Chloe Bracis & Eliezer Gurarie & Bram Van Moorter & R Andrew Goodwin, 2015. "Memory Effects on Movement Behavior in Animal Foraging," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
    2. Ephraim M. Hanks & Devin S. Johnson & Mevin B. Hooten, 2017. "Reflected Stochastic Differential Equation Models for Constrained Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 353-372, September.
    3. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    4. James C. Russell & Ephraim M. Hanks & Murali Haran, 2016. "Dynamic Models of Animal Movement with Spatial Point Process Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 22-40, March.
    5. Simon Benhamou, 2011. "Dynamic Approach to Space and Habitat Use Based on Biased Random Bridges," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    6. Lorenzo Fattorini & Caterina Pisani & Francesco Riga & Marco Zaccaroni, 2011. "A Permutation-based Combination of Sign Tests for Assessing Habitat Selection," Department of Economics University of Siena 622, Department of Economics, University of Siena.
    7. Dhanushi A. Wijeyakulasuriya & Ephraim M. Hanks & Benjamin A. Shaby & Paul C. Cross, 2019. "Extreme Value-Based Methods for Modeling Elk Yearly Movements," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(1), pages 73-91, March.
    8. Dhanushi A Wijeyakulasuriya & Elizabeth W Eisenhauer & Benjamin A Shaby & Ephraim M Hanks, 2020. "Machine learning for modeling animal movement," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-30, July.
    9. Thomas M Newsome & Guy-Anthony Ballard & Christopher R Dickman & Peter J S Fleming & Chris Howden, 2013. "Anthropogenic Resource Subsidies Determine Space Use by Australian Arid Zone Dingoes: An Improved Resource Selection Modelling Approach," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    10. Wells, Adam G. & Blair, Colby C. & Garton, Edward O. & Rice, Clifford G. & Horne, Jon S. & Rachlow, Janet L. & Wallin, David O., 2014. "The Brownian bridge synoptic model of habitat selection and space use for animals using GPS telemetry data," Ecological Modelling, Elsevier, vol. 273(C), pages 242-250.

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