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.