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WEIGHTED DISTRIBUTIONS AND ESTIMATION OF RESOURCE SELECTION PROBABILITY FUNCTIONS
Author(s) -
Lele Subhash R.,
Keim Jonah L.
Publication year - 2006
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/0012-9658(2006)87[3021:wdaeor]2.0.co;2
Subject(s) - selection (genetic algorithm) , resource (disambiguation) , estimator , function (biology) , estimation , wildlife , wildlife management , computer science , ecology , statistics , geography , mathematics , biology , machine learning , engineering , computer network , systems engineering , evolutionary biology
Understanding how organisms selectively use resources is essential for designing wildlife management strategies. The probability that an individual uses a given resource, as characterized by environmental factors, can be quantified in terms of the resource selection probability function (RSPF). The present literature on the topic has claimed that, except when both used and unused sites are known, the RSPF is non‐estimable and that only a function proportional to RSPF, namely, the resource selection function (RSF) can be estimated. This paper describes a close connection between the estimation of the RSPF and the estimation of the weight function in the theory of weighted distributions. This connection can be used to obtain fully efficient, maximum likelihood estimators of the resource selection probability function under commonly used survey designs in wildlife management. The method is illustrated using GPS collar data for mountain goats ( Oreamnos americanus de Blainville 1816) in northwest British Columbia, Canada.