Applications of step-selection functions in ecology and conservation

Abstract

Recent progress in positioning technology facilitates the collection of massive amounts of sequential spatial data on animals. This has led to new opportunities and challenges when investigating animal movement behaviour and habitat selection. Tools like Step Selection Functions (SSFs) are relatively new powerful models for studying resource selection by animals moving through the landscape. SSFs compare environmental attributes of observed steps (the linear segment between two consecutive observations of position) with alternative random steps taken from the same starting point. SSFs have been used to study habitat selection, human-wildlife interactions, movement corridors, and dispersal behaviours in animals. SSFs also have the potential to depict resource selection at multiple spatial and temporal scales. There are several aspects of SSFs where consensus has not yet been reached such as how to analyse the data, when to consider habitat covariates along linear paths between observations rather than at their endpoints, how many random steps should be considered to measure availability, and how to account for individual variation. In this review we aim to address all these issues, as well as to highlight weak features of this modelling approach that should be developed by further research. Finally, we suggest that SSFs could be integrated with state-space models to classify behavioural states when estimating SSFs.

Wolves, people, and brown bears influence the expansion of the recolonizing wolf population in Scandinavia

“…Therefore, we used a longitudinal matched casecontrol design (Craiu et al 2004, Fortin et al 2005. We matched available, random sites at winter t to the actually selected area of newly formed pairs at winter t þ 1, with a 10:1 ratio for paired randomly selected sites (Thurfjell et al 2014, Zimmermann et al 2014). …”

“…We followed a step-selection function procedure with the observed location of a pair as the actual step and 10 random locations as a set of random steps (Fortin et al 2005, Thurfjell et al 2014. The 10 random locations and the observed pair location are a ''stratum'' (Fortin et al 2005).…”

Canopy structure drives orangutan habitat selection in disturbed Bornean forests

“…SSFs were used to identify canopy structural metrics that influenced orangutan movement (41,42). SSFs are a case-control resource selection function where the step (defined as the straight-line path between successive GPS locations) is the dependent variable.…”

“…We did not include interaction terms because we had no biological basis for doing so. SSFs were applied separately to each orangutan to account for individual variation, and to enable inferences about age-and sex-specific behaviors (41). Collinearity between predictor variables was assessed using generalized variance inflation factors (GVIFs), with all GVIF scores <3, and most <2, in all models.…”

“…1C). Available steps for each individual orangutan were generated by randomly drawing step lengths and turning angles from the movement distributions of all other observed orangutans, thereby avoiding issues of circularity (41). Each day of orangutan observation (nest-to-nest focal following) was processed separately to ensure that each sample represented an actual step (movement), and was not affected by the possibility that the GPS tracking of the orangutan began a few hours after it had already left the nest and started moving the next day.…”