IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v209y2007i2p401-406.html
   My bibliography  Save this article

Geostatistics reveal the scale of habitat selection

Author

Listed:
  • Schaefer, James A.
  • Mayor, Stephen J.

Abstract

We developed a new model of habitat selection to detect the extent at which organisms respond to habitat. Based on the premise that selection of a resource entails a change in its variance, the model compares two variograms: sites used by animals versus the general environment. With an artificial landscape, we explored this model by varying the extent of perception by animals, which was revealed as the approximate point of maximum divergence in variance. Selection for greater resource abundance produced variograms with lower variance than the general environment, whereas selection for extremes of resource abundance generated the converse. We applied the model to winter resource selection of muskoxen. Variograms in forage abundance diverged sharply at 200m, consistent with a conventional analysis that demonstrated the strongest selection at that scale. The model appears widely applicable and capable of revealing the spatial scales at which organisms react to their environment.

Suggested Citation

  • Schaefer, James A. & Mayor, Stephen J., 2007. "Geostatistics reveal the scale of habitat selection," Ecological Modelling, Elsevier, vol. 209(2), pages 401-406.
    • Handle: RePEc:eee:ecomod:v:209:y:2007:i:2:p:401-406
    DOI: 10.1016/j.ecolmodel.2007.06.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380007003201
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2007.06.009?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. de Frutos, Ángel & Olea, Pedro P. & Vera, Rubén, 2007. "Analyzing and modelling spatial distribution of summering lesser kestrel: The role of spatial autocorrelation," Ecological Modelling, Elsevier, vol. 200(1), pages 33-44.
    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. Laforge, Michel P. & Vander Wal, Eric & Brook, Ryan K. & Bayne, Erin M. & McLoughlin, Philip D., 2015. "Process-focussed, multi-grain resource selection functions," Ecological Modelling, Elsevier, vol. 305(C), pages 10-21.

    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. Marmion, Mathieu & Luoto, Miska & Heikkinen, Risto K. & Thuiller, Wilfried, 2009. "The performance of state-of-the-art modelling techniques depends on geographical distribution of species," Ecological Modelling, Elsevier, vol. 220(24), pages 3512-3520.
    2. Jingyu Liu & Walter W. Piegorsch & A. Grant Schissler & Susan L. Cutter, 2018. "Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 803-823, June.
    3. Chao Xu & Didit O Pribadi & Dagmar Haase & Stephan Pauleit, 2020. "Incorporating spatial autocorrelation and settlement type segregation to improve the performance of an urban growth model," Environment and Planning B, , vol. 47(7), pages 1184-1200, September.
    4. Walsh, Andrew S. & Louis, Thomas A. & Glass, Gregory E., 2007. "Detecting multiple levels of effect during survey sampling using a Bayesian approach: Point prevalence estimates of a hantavirus in hispid cotton rats (Sigmodon hispidus)," Ecological Modelling, Elsevier, vol. 205(1), pages 29-38.
    5. Saeed Khorram & Mustafa Ergil, 2018. "Spatiotemporal patterns of sediment transport rate and beach–ocean profile for multi-hazard risk management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(1), pages 421-444, January.
    6. Wu, Daqian & Liu, Jian & Zhang, Gaosheng & Ding, Wenjuan & Wang, Wei & Wang, Renqing, 2009. "Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.)," Ecological Modelling, Elsevier, vol. 220(24), pages 3490-3498.

    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:209:y:2007:i:2:p:401-406. 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.