Conditional modelling of ring-recovery data

McCrea, Rachel S., Morgan, Byron J. T., Brown, Daniel I., Robinson, Rob A. (2012) Conditional modelling of ring-recovery data. Methods in Ecology and Evolution, 3 (5). pp. 823-831. ISSN 2041-210X. (doi:10.1111/j.2041-210X.2012.00226.x) (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)

Abstract

1. Ring-recovery data can be used to obtain estimates of survival probability which is a key demo- graphic parameter of interest for wild animal populations. Conditional modelling of ring-recovery data is needed when cohort numbers are unavailable or unreliable. It is often necessary to include in such analysis a recovery probability that is declining as a function of time, and failure to do this can result in biased estimates of annual survival. 2. Corresponding estimates of survival probability need to be reliable in order for correct conclu- sions to be drawn regarding the effects of climate change. 3. We show that standard logistic modelling of a decline in recovery probability is unsatisfactory, and propose and investigate a range of alternative procedures. 4. Methods are illustrated by application to a recovery data set on grey herons. The model selected is a scaled-logistic model, and it is shown to provide a unifying analysis of several data sets collected on different common bird species. The model makes specific predictions, providing potential new insights and avenues for ecological research. The wider performance of this model is evaluated through simulation. 5. In this study, we propose a new scaled-logistic model for the analysis of ring-recovery data with- out cohort numbers, which incorporates a reporting probability that declines over time. The model is shown to perform well in simulation studies and for both a single real data set and several real data sets in combination. Its use has the potential to reduce bias in estimates of wild animal survival that currently do not incorporate such reporting probabilities. Alternative models are shown to possess undesirable features.

Item Type:	Article
DOI/Identification number:	10.1111/j.2041-210X.2012.00226.x
Uncontrolled keywords:	blackbird, declining recovery probability, grey heron, logistic models, song thrush, time segmentation, wren
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics Q Science > QH Natural history > QH541 Ecology
Divisions:	Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User:	Byron Morgan
Date Deposited:	09 Jan 2013 16:26 UTC
Last Modified:	29 May 2019 09:54 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/32846 (The current URI for this page, for reference purposes)