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.
Highlights
Step selection functions, Step-Selection Function (SSF) – statistical models of landscape effects on movement probability Quantifying movement using Step Selection Functions (SSFs) Recent progress in positioning technology has facilitated the collection of large amounts of spatial data on animals
SSFs have a distinct advantage over regular Resource selection function (RSF) because they include the serial nature of animal relocations and can associate parameters of movement rules with landscape features, and they can model the choices presented to the animal as it moves through the landscape [15]
As strong as the tool might be, there are several pitfalls that must be avoided in order to accurately capture behaviours and ecological processes
Summary
SSFs – statistical models of landscape effects on movement probability Quantifying movement using SSFs Recent progress in positioning technology has facilitated the collection of large amounts of spatial data on animals. Later researchers using SSFs (Table 1) limited the distributions of observed length and turning angles in an attempt to select random steps matching used steps depending on season [16,24,25,26], time of day [17,22,27], or behaviour [16,23,24]. Selection of length and turning angle for random steps is likely the most critical aspect of SSFs that needs to be further developed by future research (discussed in “Choosing the appropriate scale & Calculating available steps”). Roads GPS location (t1, t2, ..., tn) Step lengths (15-min fix rate) Step lengths (30-min fix rate) Step lengths (45-min fix rate) Step lengths (60-min fix rate) Steps crossing a road
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have