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A general framework for modelling population abundance data

Besbeas, Panagiotis, Morgan, Byron J. T. (2020) A general framework for modelling population abundance data. Biometrics, 76 (1). pp. 281-292. ISSN 0006-341X. E-ISSN 1541-0420. (doi:10.1111/biom.13120) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:82320)

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Time‐series data resulting from surveying wild animals are often described using state‐space population dynamics models, in particular with Gompertz, Beverton‐Holt, or Moran‐Ricker latent processes. We show how hidden Markov model methodology provides a flexible framework for fitting a wide range of models to such data. This general approach makes it possible to model

abundance on the natural or log scale, include multiple observations at each sampling occasion and compare alternative models using information criteria. It also easily accommodates unequal sampling time intervals, should that possibility occur, and allows testing for density dependence using the bootstrap. The paper is illustrated by replicated time series of red kangaroo abundances, and a univariate time series of ibex counts which are an order of magnitude larger. In the analyses carried out, we fit different latent process and observation models using the hidden Markov framework. Results are robust with regard to the necessary discretization of the state variable. We find no effective difference between the three latent models of the paper in terms of maximized likelihood value for the two applications presented, and also others analyzed. Simulations suggest that ecological time series are not sufficiently informative to distinguish between alternative latent processes for modeling population survey data when data do not indicate strong density dependence.

Item Type: Article
DOI/Identification number: 10.1111/biom.13120
Uncontrolled keywords: Beverton‐Holt, Gompertz, hidden Markov models, Moran‐Ricker, state‐space models, Viterbi
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QH Natural history > QH541 Ecology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Byron Morgan
Date Deposited: 31 Jul 2020 15:12 UTC
Last Modified: 04 Mar 2024 19:07 UTC
Resource URI: (The current URI for this page, for reference purposes)

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