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Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing

Environmental and Ecological Statistics

By:
, ,
DOI: 10.1007/s10651-013-0256-1

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Abstract

Ecologists and wildlife biologists increasingly use latent variable models to study patterns of species occurrence when detection is imperfect. These models have recently been generalized to accommodate both a more expansive description of state than simple presence or absence, and Markovian dynamics in the latent state over successive sampling seasons. In this paper, we write these multi-season, multi-state models as hidden Markov models to find both maximum likelihood estimates of model parameters and finite-sample estimators of the trajectory of the latent state over time. These estimators are especially useful for characterizing population trends in species of conservation concern. We also develop parametric bootstrap procedures that allow formal inference about latent trend. We examine model behavior through simulation, and we apply the model to data from the North American Amphibian Monitoring Program.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing
Series title:
Environmental and Ecological Statistics
DOI:
10.1007/s10651-013-0256-1
Volume
21
Issue:
2
Year Published:
2014
Language:
English
Publisher:
Springer
Contributing office(s):
Patuxent Wildlife Research Center
Description:
16 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
313
Last page:
328
Number of Pages:
16