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Animal space use has been studied by focusing either on geographic (e.g. home ranges, species' distribution) or on environmental (e.g. habitat use and selection) space. However, all patterns of space use emerge from individual movements, which are the primary means by which animals change their environment. Individuals increase their use of a given area by adjusting two key movement components: the duration of their visit and/or the frequency of revisits. Thus, in spatially heterogeneous environments, animals exploit known, high-quality resource areas by increasing their residence time (RT) in and/or decreasing their time to return (TtoR) to these areas. We expected that spatial variation in these two movement properties should lead to observed patterns of space use in both geographic and environmental spaces. We derived a set of nine predictions linking spatial distribution of movement properties to emerging space-use patterns. We predicted that, at a given scale, high variation in RT and TtoR among habitats leads to strong habitat selection and that long RT and short TtoR result in a small home range size. We tested these predictions using moose (Alces alces) GPS tracking data. We first modelled the relationship between landscape characteristics and movement properties. Then, we investigated how the spatial distribution of predicted movement properties (i.e. spatial autocorrelation, mean, and variance of RT and TtoR) influences home range size and hierarchical habitat selection. In landscapes with high spatial autocorrelation of RT and TtoR, a high variation in both RT and TtoR occurred in home ranges. As expected, home range location was highly selective in such landscapes (i.e. second-order habitat selection); RT was higher and TtoR lower within the selected home range than outside, and moose home ranges were small. Within home ranges, a higher variation in both RT and TtoR was associated with higher selectivity among habitat types (i.e. third-order habitat selection). Our findings show how patterns of geographic and environmental space use correspond to the two sides of a coin, linked by movement responses of individuals to environmental heterogeneity. By demonstrating the potential to assess the consequences of altering RT or TtoR (e.g. through human disturbance or climatic changes) on home range size and habitat selection, our work sets the basis for new theoretical and methodological advances in movement ecology., (© 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.)

Animal movements are the primary behavioural adaptation to spatiotemporal heterogeneity in resource availability. Depending on their spatiotemporal scale, movements have been categorized into distinct functional groups (e.g. foraging movements, dispersal, migration), and have been studied using different methodologies. We suggest striving towards the development of a coherent framework based on the ultimate function of all movement types, which is to increase individual fitness through an optimal exploitation of resources varying in space and time. We developed a novel approach to simultaneously study movements at different spatiotemporal scales based on the following proposed theory: the length and frequency of animal movements are determined by the interaction between temporal autocorrelation in resource availability and spatial autocorrelation in changes in resource availability. We hypothesized that for each time interval the spatiotemporal scales of moose Alces alces movements correspond to the spatiotemporal scales of variation in the gains derived from resource exploitation when taking into account the costs of movements (represented by their proxies, forage availability NDVI and snow depth respectively). The scales of change in NDVI and snow were quantified using wave theory, and were related to the scale of moose movement using linear mixed models. In support of the proposed theory we found that frequent, smaller scale movements were triggered by fast, small-scale ripples of changes, whereas infrequent, larger scale movements matched slow, large-scale waves of change in resource availability. Similarly, moose inhabiting ranges characterized by larger scale waves of change in the onset of spring migrated longer distances. We showed that the scales of movements are driven by the scales of changes in the net profitability of trophic resources. Our approach can be extended to include drivers of movements other than trophic resources (e.g. population density, density of related individuals, predation risk) and may facilitate the assessment of the impact of environmental changes on community dynamics and conservation., (© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.)

1. Animal migration has long intrigued scientists and wildlife managers alike, yet migratory species face increasing challenges because of habitat fragmentation, climate change and over-exploitation. Central to the understanding migratory species is the objective discrimination between migratory and nonmigratory individuals in a given population, quantifying the timing, duration and distance of migration and the ability to predict migratory movements. 2. Here, we propose a uniform statistical framework to (i) separate migration from other movement behaviours, (ii) quantify migration parameters without the need for arbitrary cut-off criteria and (iii) test predictability across individuals, time and space. 3. We first validated our novel approach by simulating data based on established theoretical movement patterns. We then formulated the expected shapes of squared displacement patterns as nonlinear models for a suite of movement behaviours to test the ability of our method to distinguish between migratory movement and other movement types. 4. We then tested our approached empirically using 108 wild Global Positioning System (GPS)-collared moose Alces alces in Scandinavia as a study system because they exhibit a wide range of movement behaviours, including resident, migrating and dispersing individuals, within the same population. Applying our approach showed that 87% and 67% of our Swedish and Norwegian subpopulations, respectively, can be classified as migratory. 5. Using nonlinear mixed effects models for all migratory individuals we showed that the distance, timing and duration of migration differed between the sexes and between years, with additional individual differences accounting for a large part of the variation in the distance of migration but not in the timing or duration. Overall, the model explained most of the variation (92%) and also had high predictive power for the same individuals over time (69%) as well as between study populations (74%). 6. The high predictive ability of the approach suggests that it can help increase our understanding of the drivers of migration and could provide key quantitative information for understanding and managing a broad range of migratory species., (© 2010 The Authors. Journal compilation © 2010 British Ecological Society.)