Data from: Continuous time resource selection analysis for moving animals

1. Resource selection analysis (RSA) seeks to understand how spatial
abundance covaries with environmental features. By combining RSA with
movement, step selection analysis (SSA) has helped uncover the mechanisms
behind animal relocations, thereby giving insight into the movement
decisions underlying spatial patterns. However, SSA typically assumes that
at each observed location, an animal makes a `selection' of the next
observed location. This conflates observation with behavioural mechanism
and does not account for decisions occurring at any other time along the
animal's path. 2. To address this, we introduce a continuous time
framework for resource selection. It is based on a switching
Ornstein-Uhlenbeck (OU) model, parameterised by Bayesian Monte Carlo
techniques. Such OU models have been used successfully to identify
switches in movement behaviour, but hitherto not combined with resource
selection. We test our inference procedure on simulated paths,
representing both migratory movement (where landscape quality varies
according to season) and foraging with depletion and renewal of resources
(where the variation is due to past locations of the animals). We apply
our framework to location data of migrating mule deer (Odocoileus
hemionus) to shed light on the drivers of migratory decisions. 3. In a
wide variety of simulated situations, our inference procedure returns
reliable estimations of the parameter values, including the extent to
which animals trade-off resource quality and travel distance (within 95%
posterior intervals for the vast majority of cases). When applied to the
mule deer data, our model reveals some individual variation in parameter
values. Nevertheless, the migratory decisions of most individuals are
well-described by a model that accounts for the cost of moving and the
difference between instantaneous change of vegetation quality at source
and target patches. 4.We have introduced a technique for inferring the
resource-driven decisions behind animal movement that accounts for the
fact that these decisions may take place at any point along a path, not
just when the animal's location is known. This removes an
oft-acknowledged but hitherto little-addressed shortcoming of stepwise
movement models. Our work is of key importance in understanding how
environmental features drive movement decisions and, as a consequence,
space use patterns.