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Efficient Sequential Monte Carlo Algorithms for Integrated Population Models

Finke, A; King, R; Beskos, A; Dellaportas, P; (2019) Efficient Sequential Monte Carlo Algorithms for Integrated Population Models. Journal of Agricultural, Biological and Environmental Statistics , 24 (2) pp. 204-224. 10.1007/s13253-018-00349-9. Green open access

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Abstract

In statistical ecology, state-space models are commonly used to represent the biological mechanisms by which population counts—often subdivided according to characteristics such as age group, gender or breeding status—evolve over time. As the counts are only noisily or partially observed, they are typically not sufficiently informative about demographic parameters of interest and must be combined with additional ecological observations within an integrated data analysis. Fitting integrated models can be challenging, especially if the constituent state-space model is nonlinear/non-Gaussian. We first propose an efficient particle Markov chain Monte Carlo algorithm to estimate demographic parameters without a need for linear or Gaussian approximations. We then incorporate this algorithm into a sequential Monte Carlo sampler to perform model comparison. We also exploit the integrated model structure to enhance the efficiency of both algorithms. The methods are demonstrated on two real data sets: little owls and grey herons. For the owls, we find that the data do not support an ecological hypothesis found in the literature. For the herons, our methodology highlights the limitations of existing models which we address through a novel regime-switching model. Supplementary materials accompanying this paper appear online.

Type: Article
Title: Efficient Sequential Monte Carlo Algorithms for Integrated Population Models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s13253-018-00349-9
Publisher version: https://doi.org/10.1007/s13253-018-00349-9
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bayesian inference, Capture–recapture, Integrated population models, Model comparison, Sequential Monte Carlo, State-space models
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/1570960
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