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A General Framework for the Analysis of Animal Resource Selection from Telemetry Data
Author(s) -
Johnson Devin S.,
Thomas Dana L.,
Ver Hoef Jay M.,
Christ Aaron
Publication year - 2008
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00943.x
Subject(s) - telemetry , computer science , selection (genetic algorithm) , resource (disambiguation) , set (abstract data type) , autocorrelation , resource distribution , data set , data mining , model selection , operations research , machine learning , resource allocation , statistics , artificial intelligence , mathematics , telecommunications , computer network , programming language
Summary We propose a general framework for the analysis of animal telemetry data through the use of weighted distributions. It is shown that several interpretations of resource selection functions arise when constructed from the ratio of a use and availability distribution. Through the proposed general framework, several popular resource selection models are shown to be special cases of the general model by making assumptions about animal movement and behavior. The weighted distribution framework is shown to be easily extended to readily account for telemetry data that are highly autocorrelated; as is typical with use of new technology such as global positioning systems animal relocations. An analysis of simulated data using several models constructed within the proposed framework is also presented to illustrate the possible gains from the flexible modeling framework. The proposed model is applied to a brown bear data set from southeast Alaska.