Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing
Environmental and Ecological Statistics
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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.
Suggested Citation
Fiske, I.J., Royle, J., and Gross, K., 2014, Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing: Environmental and Ecological Statistics, v. 21, no. 2, p. 313-328, https://doi.org/10.1007/s10651-013-0256-1.
| 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 |
| Publication Date | June 25, 2013 |
| 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 |
| Larger Work Title | Environmental and Ecological Statistics |
| First page | 313 |
| Last page | 328 |