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.

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Rapidly increasing populations of wild boar in Sweden and Europe cause much damage to crops, and there is a critical need for more knowledge about their habitat utilization, especially of agricultural fields. In our study, we first assess the spatial pattern of damage in relation to the edges of agricultural fields. Next, with the aid of global positioning system collars, we studied the pattern of movement of wild boar on agricultural fields. Finally, in order to understand the role of agricultural fields, we studied how habitat selection may vary throughout the year. We found edge effects on damage patterns in agricultural fields. During winter and spring, we found wild boar not only to follow edges, but also to move along narrow landscape elements within agricultural fields. In our habitat analysis, we found strong avoidance of exposed agricultural fields throughout the year, but significantly less when crops are ripe.

Predation risk may affect space use and foraging patterns of prey animals, with strong down-stream effects on diet composition and ecological interactions. Wild boar Sus scrofa is a notorious crop raider but also a popular game species, yet little is known about how risk perception of human hunting affects wild boar space use. We studied the effects of human hunting on the movement of GPS-collared female wild boar. We found that the hunting method affected whether the wild boar fled or hid. After fleeing into refuge ranges, wild boar moved less and preferred habitats that provided cover and forage such as mast or crops. This suggests that the wild boar tried to reduce the risk of being detected, and possibly also that they avoided competition with resident wild boar in the refuge by using forage that could not be monopolised. The type of hunting thus strongly affected the type of avoidance behaviour displayed by wild boar, with implications for their movement and space use. This suggests that adjusting hunting method to season could be an important management tool for minimising crop losses.

In animal behaviour, there is a dichotomy between innate behaviours (e.g., temperament or personality traits) versus those behaviours shaped by learning. Innate personality traits are supposedly less evident in animals when confounded by learning acquired with experience through time. Learning might play a key role in the development and adoption of successful anti-predator strategies, and the related adaptation has the potential to make animals that are more experienced less vulnerable to predation. We carried out a study in a system involving a large herbivorous mammal, female elk, Cervus elaphus, and their primary predator, i.e., human hunters. Using fine-scale satellite telemetry relocations, we tested whether differences in behaviour depending on age were due solely to selection pressure imposed by human hunters, meaning that females that were more cautious were more likely to survive and become older. Or whether learning also was involved, meaning that females adjusted their behaviour as they aged. Our results indicated that both human selection and learning contributed to the adoption of more cautious behavioural strategies in older females. Whereas human selection of behavioural traits has been shown in our previous research, we here provide evidence of additive learning processes being responsible for shaping the behaviour of individuals in this population. Female elk are indeed almost invulnerable to human hunters when older than 9–10 y.o., confirming that experience contributes to their survival. Female elk monitored in our study showed individually changing behaviours and clear adaptation as they aged, such as reduced movement rates (decreased likelihood of encountering human hunters), and increased use of secure areas (forest and steeper terrain), especially when close to roads. We also found that elk adjusted behaviours depending on the type of threat (bow and arrow vs. rifle hunters). This fine-tuning by elk to avoid hunters, rather than just becoming more cautious during the hunting season, highlights the behavioural plasticity of this species. Selection on behavioural traits and/or behavioural shifts via learning are an important but often-ignored consequence of human exploitation of wild animals. Such information is a critical component of the effects of human exploitation of wildlife populations with implications for improving their management and conservation.