Persistence and heterogeneity in habitat selection studies Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/5q47rr24x

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  • Recently the independent multinomial selections model (IMS) with the multinomial logit link has been suggested as an analysis tool for radio-telemetry habitat selection data. This model assumes independence between animals, independence between sightings within an animal, and identical multinomial habitat selection probabilities for all animals. We propose two generalizations to the IMS model. The first generalization is to allow a Markov chain dependence between consecutive sightings of the same animal. This generalization allows for both positive correlation (individuals persisting in the same habitat class in which they were previously sighted) and negative correlation (individual vacating the habitat class in which they were previously sighted). The second generalization is to allow for heterogeneity. Here, a hierarchical Dirichlet-multinomial distribution is used to allow for variability in selection probabilities between animals. This generalization accounts for over-dispersion of selection probabilities and allows for inference to the population of animals, assuming that the animals studied constitute a random sample from that population.. Both generalizations are one parameter extensions to the multinomial logit model and allow for testing the assumptions of identical multinomial selection probabilities and independence. These tests are performed using the score, Wald, and asymptotic likelihood ratio statistics. Estimates of model parameters are obtained using maximum likelihood techniques, and habitat characteristics are tested using drop-in-deviance statistics. Using example data, we show that persistence and heterogeneity exist in habitat selection data and illustrate the difference in analysis results between the IMS model and the persistence and heterogeneity models. Through simulation, we show that analyzing persistence data assuming independence between sightings within an animal gives liberal tests of significance for habitat characteristics when the data are generated with positive correlation and conservative tests of significance when the data are generated with negative correlation. Similarly, we show that analyzing heterogeneous data, assuming identical multinomial selection probabilities, gives liberal tests of significance for habitat characteristics.
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