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Computational Ecology and Software, 2013, 3(4): 91-101
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Article

Bootstrap estimation of resource selection probability functions

Sandra V. Cardozo1, Bryan F. J. Manly2, Raydonal Ospina3 , Carlos T. S. Dias4
1Statistics Department, National University of Colombia, Bogota, Colombia
2Western EcoSystems Technology Inc. Cheyenne, Wyoming, USA
3Statistics Department, Federal University of Pernambuco, Recife/PE, Brazil
4Statistics Department, ESALQ, University of Sao Paulo, Piracicaba/SP, Brazil

Received 6 September 2013;Accepted 10 October 2013;Published online 1 December 2013
IAEES

Abstract
Resource selection functions (RSFs) are used for quantify how animals are selective in the use of the habitat period or food. A Resource Selection Probability Function (RSPF) can be estimated if N, the total number of units in the population, and n1 the total number of used units in the study period are both known and small. An approximation of the RSPF can then be estimated using any standard program for logistic regression but the variances of the estimates of the parameters are too small. Three methods of bootstrap sampling, parametric, nonparametric and a modified parametric method are proposed for the estimation of variances, with a discussion about the limitations of logistic regression for estimating RSPF. The method for estimating the RSPF described here has potential applications in medicine, ecology and other areas.

Keywords resource selection functions (RSFs);resource selection probability function (RSPF);bootstrap;logistic regression.


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