A Finite-State Continuous-Time Approach for Inferring Regional Migration and Mortality Rates from Archival Tagging and Conventional Tag-Recovery Experiments
Article first published online: 5 MAR 2008
© 2008, The International Biometric Society
Volume 64, Issue 4, pages 1196–1206, December 2008
How to Cite
Miller, T. J. and Andersen, P. K. (2008), A Finite-State Continuous-Time Approach for Inferring Regional Migration and Mortality Rates from Archival Tagging and Conventional Tag-Recovery Experiments. Biometrics, 64: 1196–1206. doi: 10.1111/j.1541-0420.2008.00996.x
- Issue published online: 24 NOV 2008
- Article first published online: 5 MAR 2008
- Received April 2007. Revised November 2007. Accepted November 2007.
- Finite-state continuous-time process;
- Implanted archival tag;
- Markov process;
- Pop-up satellite archival tag;
- Spatially structured population;
Summary Spatially structured population dynamics models are important management tools for harvested, highly mobile species and although conventional tag recovery experiments remain useful for estimation of various demographic parameters of these models, archival tagging experiments are becoming an important data source for analyzing migratory behavior of mobile marine species. We provide a likelihood-based approach for estimating the regional migration and mortality rate parameters intrinsic to these models that may use information obtained from conventional tag recovery and archival tagging experiments. Specifically, we assume that the regional location and survival of animals through time is a finite-state continuous-time stochastic process. The stochastic process is the basis of probability models for observations provided by the different types of tags. Results from application to simulated tagging experiments for western Atlantic bluefin tuna show that maximum likelihood estimators based on archival tagging observations and corresponding confidence intervals perform similar to conventional tagging observations for a given number of tag releases and releasing tags in each region can improve the behavior of maximum likelihood estimators regardless of tag type. We provide an example application with Atlantic bluefin tuna released with conventional tags in 1990–1992.