Statistical inference to identify learning in movement processes
Analytical and computational tools may also be used to infer learning processes from data. For example, the step-selection function (SSF, (Fortinet al. 2005) is of particular utility when it is connected to regular samples of location data and allows for inference of movement parameters that depend on different habitat types. Computationally efficient approaches such as integrated step selection analysis (iSSA) (Avgaret al. 2016), provide practitioners a straightforward way to evaluate movement decisions against actual observations. A generalized form of the SSF, termed the coupled SSF (Potts et al. 2014), allows for the inclusion of memory and past social interactions. Here memory and past interactions can be included into the model, as one or more spatio-temporal maps, sometimes referred to as cognitive maps. Although superficially similar to a changing habitat layer, the contents of the cognitive maps are particular to each individual as they are populated by information gleaned from the individual’s past experiences (Fagan et al. 2013). With this structure in place in a SSF model, one can test how the individual’s movement behavior is governed by maps whose contents arise from different types of memories or social interactions. Coupled SSFs have been used to test for evidence of memory (Schlägelet al. 2017) and learning (Merkleet al. 2014) in animal movement patterns.
Analysis via SSF assumes that animals’ location data are known without error. If error is significant, as it can be for marine systems, a different class of model, known as state space models, are needed. State space models are hierarchical and feature separate models for the movement process and the measurement error process. These models can be modified to include a hidden Markov process, whose latent state is determined by physiological status (e.g., searching or travelling) or by learning (Avgaret al. 2016). Such models, while flexible, may suffer from parameter estimability issues (Auger-Méthéet al. 2016) and must be implemented with care.