Modeling resource selection using polytomous logistic regression and kernel density estimates

Chadwick D. Rittenhouse, Joshua J. Millspaugh, Andrew B. Cooper, Michael W. Hubbard, Steven L. Sheriff, Robert A. Gitzen

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Wildlife resource selection studies typically compare used to available resources; selection or avoidance occurs when use is disproportionately greater or less than availability. Comparing used to available resources is problematic because results are often greatly influenced by what is considered available to the animal. Moreover, placing relocation points within resource units is often difficult due to radiotelemetry and mapping errors. Given these problems, we suggest that an animal's resource use be summarized at the scale of the home range (i.e., the spatial distribution of all point locations of an animal) rather than by individual points that are considered used or available. To account for differences in use-intensity throughout an animal's home range, we model resource selection using kernel density estimates and polytomous logistic regression. We present a case study of elk (Cervus elaphus) resource selection in South Dakota to illustrate the procedure. There are several advantages of our proposed approach. First, resource availability goes undefined by the investigator, which is a difficult and often arbitrary decision. Instead, the technique compares the intensity of animal use throughout the home range. This technique also avoids problems with classifying locations rigidly as used or unused. Second, location coordinates do not need to be placed within mapped resource units, which is problematic given mapping and telemetry error. Finally, resource use is considered at an appropriate scale for management because most wildlife resource decisions are made at the level of the patch. Despite the advantages of this use-intensity procedure, future research should address spatial autocorrelation and develop spatial models for ordered categorical variables.

    Original languageEnglish
    Pages (from-to)39-47
    Number of pages9
    JournalEnvironmental and Ecological Statistics
    Volume15
    Issue number1
    DOIs
    StatePublished - Mar 2008

    Keywords

    • Fixed kernel
    • Habitatuse
    • Polytomous logistic regression

    Fingerprint

    Dive into the research topics of 'Modeling resource selection using polytomous logistic regression and kernel density estimates'. Together they form a unique fingerprint.

    Cite this