Composite likelihood inference for analysis of individual animal identification data

Individual identification data collection is a common practice in animal behaviour, movement ecology, and conservation biology. While likelihood analysis is widely employed for ecological insights, the complexity of individual identification data, characterized by numerous interdependent individuals and identification times, makes direct likelihood calculation challenging. To address this, we introduce a composite likelihood inference framework. We establish the consistency and asymptotic normality of maximum composite likelihood estimators within this framework. Furthermore, we develop a composite likelihood-based information criterion for model selection, capable of handling complex individual identification data. Our approach is demonstrated through extensive simulations and applied to the northern bottlenose whale population in the Gully, Nova Scotia. This study provides a statistically rigorous framework for individual animal identification models, with potential applications extending beyond whale populations. • A novel composite likelihood-based inference framework is introduced for analysing complex individual identification data in animal ecology. • We propose a composite likelihood-based information criterion to address model selection challenges in analysing individual identification data. • The efficacy of the proposed approach is demonstrated through extensive simulations and application to a real northern bottlenose whale dataset.