Fine-scale tracking with passive acoustic telemetry can yield great insights into the movement ecology of aquatic animals. To predict fine-scale positions of tagged animals in continuous space from spatially-discrete detection data, state-space modelling through the R package YAPS provides a promising alternative to frequently used positioning algorithms. However, YAPS cannot currently classify multiple kinds of movement that may be used as proxies for individual behaviours of study animals (behavioural states), an endeavour that is of increasing interest to movement ecologists.
We advance YAPS by incorporating the functionality to predict behavioural states by using an iterative maximization framework. Our model, which we call YAMS, occurs in continuous time and therefore we adapt current hidden Markov model (HMM) machinery to accommodate this while remaining within a likelihood framework that provides rapid fitting. We test our model using simulations and approximately 6 days’ worth of Northern pike data from Hald Lake, Denmark.
YAMS is shown to produce accurate parameter estimates and random effect predictions when model results were compared to simulated data, with behavioural state accuracies of 0.94 and 0.79 for two- and three-state models, respectively, and location state root mean squared errors of 1.8 m for both models. In addition, the behavioural states are shown to reflect varying speeds of the pike, yielding a highly interpretable classification.
This research has the potential to be broadly applicable to both ecologists interested in identifying fine-scale space use and behavioural states from acoustic telemetry data, as well as to statisticians who may wish to use standard HMM machinery to fit continuous-time HMMs to animal movement data.