Linking individual-based and statistical inferential models in movement ecology: A case study with black petrels (Procellaria parkinsoni)

Abstract

Individual-based models (IBMs) are increasingly used to explore ecological systems and, in particular, the emergent outcomes of individual-level processes. A major challenge in developing IBMs to investigate the movement ecology of animals is that such models must represent and parameterise unobserved behaviours occurring at multiple hierarchical levels. Approaches based on approximate Bayesian computation (ABC) methods have been used to support the parameterisation, calibration and evaluation of IBMs. However, a key component of the ABC approach is the use of multiple quantitative patterns derived from empirical data to exclude model structures and parameterisations that generate atypical or implausible patterns. We propose a modelling framework that integrates information derived from statistical inferential models, which are now widely used to describe the behaviour of moving animals, with ABC methodologies for the parameterisation and analysis of IBMs. To demonstrate its application, we apply this framework to high-resolution movement trajectories of the foraging trips of black petrels (Procellaria parkinsoni), an endangered seabird endemic to New Zealand. The outcomes of our study show that the use of inferential statistical models to summarise movement data can aid model selection and parameterisation procedures via ABC, and yield valuable insights into the modelling in movement ecology of animals.