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Title: A comparison of two modeling approaches for evaluating wildlife--habitat relationships

Author: Long, Ryan A.; Muir, Jonathan D.; Rachlow, Janet L.; Kie, John G.;

Date: 2009

Source: Journal of Wildlife Management

Publication Series: Scientific Journal (JRNL)

Description: Studies of resource selection form the basis for much of our understanding of wildlife habitat requirements, and resource selection functions (RSFs), which predict relative probability of use, have been proposed as a unifying concept for analysis and interpretation of wildlife habitat data. Logistic regression that contrasts used and available or unused resource units is one of the most common analyses for developing RSFs. Recently, resource utilization functions (RUFs) have been developed, which also predict probability of use. Unlike RSFs, however, RUFs are based on a continuous metric of space use summarized by a utilization distribution. Although both RSFs and RUFs predict space use, a direct comparison of these 2 modeling approaches is lacking. We compared performance of RSFs and RUFs by applying both approaches to location data for 75 Rocky Mountain elk (Cervus elaphus) and 39 mule deer (Odocoileus hemionus) collected at the Starkey Experimental Forest and Range in northeastern Oregon, USA. We evaluated differences in maps of predicted probability of use, relative ranking of habitat variables, and predictive power between the 2 models. Although space use by both species was predicted most accurately by the RSF based on cross-validation, differences in predictive power between models were more substantial for elk than mule deer. To maximize accuracy and utility of predictive wildlife-habitat models, managers must be aware of the relative strengths and weaknesses of different modeling techniques. We conclude that although RUFs represent a substantial advance in resource selection theory, techniques available for generating RUFs remain underdeveloped and, as a result, RUFs sometimes predict less accurately than models derived using more conventional techniques.

Keywords: elk, habitat modeling, logistic regression, mule deer, multiple regression, resource selection function, resource utilization function, spatial autocorrelation, utilization distribution

Publication Notes:

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  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

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Long, Ryan A.; Muir, Jonathan D.; Rachlow, Janet L.; Kie, John G. 2009. A comparison of two modeling approaches for evaluating wildlife-habitat relationships. Journal of Wildlife Management. 73(2): 294-302.

 


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