Home > A hidden Markov open population model for spatial capture-recapture surveys
A hidden Markov open population model for spatial capture-recapture surveys
David Borchers, Centre for Research into Ecological and Environmental Modelling, University of St Andrews will present the Department of Statistical Science seminar with a talk entitled, "A hidden Markov open population model for spatial capture-recapture surveys".
Abstract: Open population capture-recapture models are widely used to estimate population demographics and abundance over time The only published open population methods for spatial capture-recapture surveys use Markov chain Monte Carlo methods for inference, and have relatively high computational cost. We formulate open population spatial capture-recapture surveys as a hidden Markov model (HMM), allowing inference by maximum likelihood for both Cormack-Jolly-Seber and Jolly-Seber models with and without animal activity centre movement. The method is applied to a twelve-year survey of male jaguars (Panthera onca) in the Cockscomb Wildlife Sanctuary Basin, Belize, to estimate survival probability and population abundance over time. For this application, inference is shown to be biased when assuming activity centres are fixed over time, while including a model for activity centre movement provides negligible bias and nominal confidence interval coverage, as demonstrated by a simulation study. The hidden Markov model approach is compared with a Bayesian MCMC method and a series of closed population models applied to the same data. The method is much more efficient than the Bayesian approach and provides a lower root-mean-square error in predicting population density compared to a series of closed population models. The HMM formulation provides a framework that is easily extendable.
Mon, 04 Feb 2019 - 13:00
PD Hahn Lecture Theatre 3, Upper Campus, UCT
Science Faculty Level 6, PD Hahn Building
University of Cape Town Contact us