Estimating Abundance from Multiple Sampling Capture-Recapture Data via a Multi-State Multi-Period Stopover Model

Worthington, H, McCrea, Rachel S., King, Ruth, Griffiths, Richard A. (2019) Estimating Abundance from Multiple Sampling Capture-Recapture Data via a Multi-State Multi-Period Stopover Model. Annals of Applied Statistics, . ISSN 1932-6157. (In press) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

PDF - Author's Accepted Manuscript
Restricted to Repository staff only
Contact us about this Publication Download (907kB)
[img]

Abstract

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 frame-work 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 exibility 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 signfi�cant 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: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Rachel McCrea
Date Deposited: 16 Nov 2017 20:46 UTC
Last Modified: 19 Jun 2019 14:31 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/64508 (The current URI for this page, for reference purposes)
McCrea, Rachel S.: https://orcid.org/0000-0002-3813-5328
Griffiths, Richard A.: https://orcid.org/0000-0002-5533-1013
  • Depositors only (login required):

Downloads

Downloads per month over past year