Environmental and Ecological Statistics

, Volume 21, Issue 2, pp 313–328

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

Authors

  • Ian J. Fiske
    • Department of StatisticsNorth Carolina State University
  • J. Andrew Royle
    • USGS Patuxent Wildlife Research Center
    • Department of StatisticsNorth Carolina State University
Article

DOI: 10.1007/s10651-013-0256-1

Cite this article as:
Fiske, I.J., Royle, J.A. & Gross, K. Environ Ecol Stat (2014) 21: 313. doi:10.1007/s10651-013-0256-1

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.

Keywords

AmphibiansFinite-sample trajectoryHidden Markov modelOccupancyTrend estimationWildlife

Supplementary material

10651_2013_256_MOESM1_ESM.pdf (335 kb)
Supplementary material 1 (PDF 335 KB)

Copyright information

© Springer Science+Business Media New York 2013