IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v273y2014icp242-250.html
   My bibliography  Save this article

The Brownian bridge synoptic model of habitat selection and space use for animals using GPS telemetry data

Author

Listed:
  • Wells, Adam G.
  • Blair, Colby C.
  • Garton, Edward O.
  • Rice, Clifford G.
  • Horne, Jon S.
  • Rachlow, Janet L.
  • Wallin, David O.

Abstract

The growing application of GPS telemetry in wildlife studies created need for analytical methods to meet both practical and theoretical concerns when conducting analyses of habitat or resource selection. We devised a new analysis approach of individual-based movement models for estimation of resource selection based on probability of use. We merged the Brownian bridge model of space use with the synoptic model of habitat selection to describe and estimate patterns of habitat selection from GPS telemetry data. In doing so, our approach implicitly defines availability based on movement data when conducting analysis of GPS telemetry data. To do so, we employed a step-by-step approach, based on sequential triplets of observations of the animals’ movements. Availability was portrayed as a circular normal distribution at every middle GPS location, based on the existing Brownian bridge model of space use. This middle observation within the sequential triplet also reflected habitat selection, estimated by maximum likelihoods, based on the deviation from otherwise random movement between the first and third observations. This approach allowed each triplet across time to be treated as independent, identically distributed observations when estimating habitat selection. To demonstrate the utility of the model, we analyzed GPS location data collected from free-ranging mountain goats (Oreamnos americanus) in the Cascade Mountains of the western United States to evaluate patterns of habitat selection while foraging during late spring and early summer. Slope of the terrain was the primary factor influencing resource selection by mountain goats in our study, with females selecting steeper areas closer to escape terrain than males. Finally, we derived a resource selection function applicable over a broad geographic extent to evaluate sites for potential release of mountain goats to augment the population in Washington, which has declined over the last 50 years.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:273:y:2014:i:c:p:242-250
    DOI: 10.1016/j.ecolmodel.2013.11.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380013005449
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2013.11.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Horne, Jon S. & Garton, Edward O. & Rachlow, Janet L., 2008. "A synoptic model of animal space use: Simultaneous estimation of home range, habitat selection, and inter/intra-specific relationships," Ecological Modelling, Elsevier, vol. 214(2), pages 338-348.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wallentin, Gudrun, 2017. "Spatial simulation: A spatial perspective on individual-based ecology—a review," Ecological Modelling, Elsevier, vol. 350(C), pages 30-41.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:273:y:2014:i:c:p:242-250. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.