Hidden Markov Model for Dependent Mark Loss and Survival Estimation

Jeffrey L. Laake, Devin S. Johnson, Duane R. Diefenbach, Mark A. Ternent

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. We describe a hidden Markov model (HMM) framework that integrates a mark loss model with a Cormack–Jolly–Seber model for survival estimation. Mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. Double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. We use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. The HMM approach is demonstrated with a dataset of black bears (Ursus americanus) with two ear tags and a subset of which were permanently marked with tattoos. The data were analyzed with and without the tattoo. Dropping the tattoos resulted in estimates of survival that were reduced by 0.005–0.035 due to tag loss dependence that could not be modeled. We also analyzed the data with and without the tattoo using a single tag. By not using.

Supplementary materials accompanying this paper appear on-line.

Original languageEnglish (US)
Pages (from-to)524-540
Number of pages17
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number4
StatePublished - Dec 2014

All Science Journal Classification (ASJC) codes

  • General Environmental Science
  • Applied Mathematics
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Hidden Markov Model for Dependent Mark Loss and Survival Estimation'. Together they form a unique fingerprint.

Cite this