Estimating abundance from multiple sampling capture-recapture data via a multi-state multi-period stopover model

Worthington, Hannah and McCrea, Rachel and King, Ruth and Griffiths, Richard (2019) Estimating abundance from multiple sampling capture-recapture data via a multi-state multi-period stopover model. Annals of Applied Statistics, 13 (4). pp. 2043-2064. ISSN 1932-6157

Full text not available from this repository.


Capture-recapture studies often involve collecting data on numerous capture occasions over a relatively short period of time. For many study species this process is repeated, for example, annually, resulting in capture information spanning multiple sampling periods. To account for the different temporal scales, the robust design class of models have traditionally been applied providing a framework in which to analyse all of the available capture data in a single likelihood expression. However, these models typically require strong constraints, either the assumption of closure within a sampling period (the closed robust design) or conditioning on the number of individuals captured within a sampling period (the open robust design). For real datasets these assumptions may not be appropriate. We develop a general modelling structure that requires neither assumption by explicitly modelling the movement of individuals into the population both within and between the sampling periods, which in turn permits the estimation of abundance within a single consistent framework. The flexibility of the novel model structure is further demonstrated by including the computationally challenging case of multi-state data where there is individual time-varying discrete covariate information. We derive an efficient likelihood expression for the new multi-state multi-period stopover model using the hidden Markov model framework. We demonstrate the significant improvement in parameter estimation using our new modelling approach in terms of both the multi-period and multi-state components through both a simulation study and a real dataset relating to the protected species of great crested newts, Triturus cristatus.

Item Type:
Journal Article
Journal or Publication Title:
Annals of Applied Statistics
Uncontrolled Keywords:
?? great crested newtshidden markov modelindividual time-varying discrete covariatemulti-state datastatistics and probabilitymodelling and simulationstatistics, probability and uncertainty ??
ID Code:
Deposited By:
Deposited On:
12 Oct 2022 11:45
Last Modified:
15 Jul 2024 23:04