Ask any wildlife manager: their first burning question is `how many are there?’, and their second is `are they trending upwards or downwards?’ Capture-recapture is one of the most popular methods for estimating population size and trends. As the name suggests, it relies on being able to identify the same animal upon multiple capture occasions. The pattern of captures and recaptures among identified animals is used to estimate the number of animals never captured.
Physically capturing and tagging animals can be a dangerous and stressful experience for both the animals and their human investigators – or if it transpires that the animals actually enjoy it, biased inference may result. Consequently, researchers increasingly favour non-invasive sampling using natural tags that allow animals to be identified by features such as coat markings, dropped DNA samples, acoustic profiles, or spatial locations. These innovations greatly broaden the scope of capture-recapture estimation and the number of capture samples achievable. However, they are imperfect measures of identity, effectively sacrificing sample quality for quantity and accessibility. As a result, capture-recapture samples no longer generate capture histories in which the matching of repeated samples to a single identity is certain. Instead, they generate data that are informative – but not definitive – about animal identity.
I will describe a new framework for drawing inference from capture-recapture studies when there is uncertainty in animal identity. In the cluster capture-recapture framework, we assume that repeated samples from the same animal will be similar, but not necessarily identical, to each other. Overlap is also possible between clusters of samples generated by different animals. We treat the sample data as a clustered point process, and derive the necessary probabilistic properties of the process to estimate abundance and other parameters using a Palm likelihood approach.
Because it avoids any attempts at explicit sample-matching, the cluster capture-recapture method can be very fast, taking much the same time to analyse millions of sample-comparisons as it does to analyse hundreds. I will describe a preliminary framework for abundance estimation from acoustic monitoring. Cluster capture-recapture can also be used for behavioural studies, and I will show an example using camera-trap data from a partially-marked population of forest ship rats.