Skip to main content
U.S. flag

An official website of the United States government

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

July 24, 2014

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.

Citation Information

Publication Year 2014
Title Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing
DOI 10.1007/s10651-013-0256-1
Authors Ian J. Fiske, J. Andrew Royle, Kevin Gross
Publication Type Article
Publication Subtype Journal Article
Series Title Environmental and Ecological Statistics
Series Number
Index ID 70117797
Record Source USGS Publications Warehouse
USGS Organization Patuxent Wildlife Research Center