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Site occupancy models Open Access


Other title
site occupancy, zero inflated binomial, detection error
Type of item
Degree grantor
University of Alberta
Author or creator
Moreno-Prieto, Monica Rocio
Supervisor and department
Lele, Subhash (Mathematical and Statistical Sciences)
Examining committee member and department
He, Fangliang (Renewable Resources)
Prasad, NGN (Mathematical and Statistical Sciences Department)
Bayne, Erin ( Biological Sciences)
Department of Mathematical and Statistical Sciences

Date accepted
Graduation date
Doctor of Philosophy
Degree level
The probability of a species to be present on a certain site is a quantity of interest for monitoring programs. Data for the occupancy of a species is recorded along with habitat covariates that are suspected to relate with its status (presence/absence). The objective of the analysis is to estimate the proportion of sites in which the species is present and the effects of the habitat covariates on the status of the species. Nevertheless, it is possible to have some errors in the observations. The most popular approach to estimate occupancy while accounting for the detection error is that of multiple surveys, for which every site is visited several times. The effectiveness of this approach relies on two main assumptions: 1) during the time of the study the population is closed and 2) the replicate visits at every site are independent from each other. In my thesis I evaluated the multiple surveys approach under two perspectives: the statistical properties and the sensibility of its assumptions. For the former, I found that the estimates are unstable when the number of visited sites and the number of surveys are small. To overcome these flaws in the estimation procedure, I developed an alternative estimation method, based on penalized likelihood, that provides better estimates for small number of sites and surveys. The analysis of the sensibility of the assumptions revealed that the violation of the assumptions could lead to biased estimates. The single survey model does not require the closed population assumption, but the popular belief for the non-identifiability of the parameters sanctioned its use. I tested the reliability of the estimates for the probability of occupancy and detection from information collected from a single survey and found that, contrary to popular belief, the parameters are identifiable under certain conditions. Finally, I developed a model (the cluster sampling model) to include the dependence between sites. This model allows estimation of the site occupancy using information collected on a single survey from sites that are correlated.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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