Skip to main content
Kent Academic Repository

Does Your Species Have Memory? Analysing Capture-Recapture Data with Memory Models.

Cole, Diana J., Morgan, Byron J. T., McCrea, Rachel S., Pradel, Roger, Gimenez, Olivier, Choquet, Remi (2014) Does Your Species Have Memory? Analysing Capture-Recapture Data with Memory Models. Ecology and Evolution, 4 (11). pp. 2124-2133. ISSN 2045-7758. E-ISSN 2045-7758. (doi:10.1002/ece3.1037) (KAR id:38299)

PDF (Open Access Published Version of Paper) Publisher pdf
Language: English
Download this file
(PDF/149kB)
[thumbnail of Open Access Published Version of Paper]
Preview
Request a format suitable for use with assistive technology e.g. a screenreader
Official URL:
http://dx.doi.org/10.1002/ece3.1037

Abstract

1. We examine memory models for multi-site capture-recapture data. This is an important topic,as animals may exhibit behaviour that is more complex than simple first-order Markov movement between sites, when it is necessary to devise and fit appropriate models to data.

2. We consider the Arnason-Schwarz model for multi-site capture-recapture data, which incorporates just first-order Markov movement, and also two alternative models that allow for memory, the Brownie model and the Pradel model. We use simulation to compare two alternative tests which may be undertaken to determine whether models for multi-site capture-recapture data need to incorporate memory.

3. Increasing the complexity of models runs the risk of introducing parameters that cannot be estimated, irrespective of how much data are collected, a feature which is known as parameter redundancy. Rouan et al (JABES, 2009, pp 338-355) suggest a constraint that may be applied to overcome parameter redundancy when it is present in multi-site memory models. For this case, we apply symbolic methods to derive a simpler constraint, which allows more parameters to be estimated, and give general results not limited to a particular configuration. We also consider the effect sparse data can have on parameter redundancy, and recommend minimum sample sizes.

4. Memory models for multi-site capture-recapture data can be highly complex, and difficult to fit to data. We emphasise the importance of a structured approach to modelling such data, by considering a priori which parameters can be estimated, which constraints are needed in order for estimation to take place, and how much data need to be collected. We also give guidance on the amount of data needed to use two alternative families of tests for whether models for multi-site capture-recapture data need to incorporate memory.

Item Type: Article
DOI/Identification number: 10.1002/ece3.1037
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Diana Cole
Date Deposited: 14 Feb 2014 14:52 UTC
Last Modified: 16 Feb 2021 12:51 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/38299 (The current URI for this page, for reference purposes)

University of Kent Author Information

Cole, Diana J..

Creator's ORCID: https://orcid.org/0000-0002-8109-4832
CReDIT Contributor Roles:

Morgan, Byron J. T..

Creator's ORCID:
CReDIT Contributor Roles:

McCrea, Rachel S..

Creator's ORCID: https://orcid.org/0000-0002-3813-5328
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.