Conditional modelling of ring-recovery data

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.