1. Open population maximum likelihood spatial capture-recapture
- Author
-
Matthew Murchie, David L. Borchers, Bart J. Harmsen, Richard Glennie, Rebecca J. Foster, EPSRC, University of St Andrews. Statistics, University of St Andrews. School of Mathematics and Statistics, University of St Andrews. Scottish Oceans Institute, University of St Andrews. Centre for Research into Ecological & Environmental Modelling, and University of St Andrews. Marine Alliance for Science & Technology Scotland
- Subjects
Male ,Survival ,QH301 Biology ,Population Dynamics ,Inference ,01 natural sciences ,Population density ,Mark and recapture ,010104 statistics & probability ,Statistics ,Econometrics ,Quantitative Biology::Populations and Evolution ,Hidden Markov model ,QA ,0303 health sciences ,Agricultural and Biological Sciences(all) ,Applied Mathematics ,General Medicine ,Belize ,Markov Chains ,Survival Rate ,Population model ,Open population ,General Agricultural and Biological Sciences ,BDC ,Statistics and Probability ,Biometry ,Bayesian probability ,Animals, Wild ,Biology ,Models, Biological ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,QH301 ,Immunology and Microbiology(all) ,Animals ,Panthera ,QA Mathematics ,0101 mathematics ,030304 developmental biology ,Probability ,Population Density ,Panthera onca ,General Immunology and Microbiology ,Biochemistry, Genetics and Molecular Biology(all) ,Probability and statistics ,DAS ,Bayes Theorem ,Confidence interval ,Spatial capture-recapture - Abstract
Funding: Part-funded by UK EPSRC grant EP/K041061/1 (DB); Richard Glennie was funded by the Carnegie Trust. Open population capture‐recapture models are widely used to estimate population demographics and abundance over time. Bayesian methods exist to incorporate open population modeling with spatial capture‐recapture (SCR), allowing for estimation of the effective area sampled and population density. Here, open population SCR is formulated as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack‐Jolly‐Seber and Jolly‐Seber models, with and without activity center movement. The method is applied to a 12‐year survey of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centers are fixed over time, while including a model for activity center movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The HMM approach is compared with Bayesian data augmentation and closed population models for this application. The method is substantially more computationally efficient than the Bayesian approach and provides a lower root‐mean‐square error in predicting population density compared to closed population models. Postprint
- Published
- 2017