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Summary1. Animal movements are the primary behavioural adaptation to spatiotemporal heterogeneityin resource availability. Depending on their spatiotemporal scale, movements have been catego-rized into distinct functional groups (e.g. foraging movements, dispersal, migration), and havebeen studied using different methodologies. We suggest striving towards the development of acoherent framework based on the ultimate function of all movement types, which is to increaseindividual fitness through an optimal exploitation of resources varying in space and time.2. We developed a novel approach to simultaneously study movements at different spatiotem-poral scales based on the following proposed theory: the length and frequency of animalmovements are determined by the interaction between temporal autocorrelation in resourceavailability and spatial autocorrelation in changes in resource availability. We hypothesizedthat for each time interval the spatiotemporal scales of moose Alces alces movements corre-spond to the spatiotemporal scales of variation in the gains derived from resource exploita-tion when taking into account the costs of movements (represented by their proxies, forageavailability NDVI and snow depth respectively). The scales of change in NDVI and snowwere quantified using wave theory, and were related to the scale of moose movement usinglinear mixed models.3. In support of the proposed theory we found that frequent, smaller scale movements weretriggered by fast, small-scale ripples of changes, whereas infrequent, larger scale movementsmatched slow, large-scale waves of change in resource availability. Similarly, moose inhabit-ing ranges characterized by larger scale waves of change in the onset of spring migratedlonger distances.4. We showed that the scales of movements are driven by the scales of changes in the netprofitability of trophic resources. Our approach can be extended to include drivers of move-ments 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 oncommunity dynamics and conservation.Key-words: Alces alces, foraging, Fourier transform, GPS, migration, NDVI, phenology,resource, spatiotemporal scales, snowIntroduction

Habitat alteration by humans may change the supply of food and cover for wild ungulates, but few studies have examined how these resources are utilised over time by individuals of different sex and reproductive status. We examined circadian and seasonal variation in habitat utilisation within a moose Alces alces population in central Norway. Our study area covers forests and open habitats, both influenced by human alterations (e.g. forestry and agriculture). We expected moose to select habitats with good forage and cover in all seasons, but to select open foraging habitats mainly during night-time. Moose selected good foraging habitats, such as young forest stands and cultivated land during night, whereas the utilisation of older forest stands providing cover increased during daytime. This circadian pattern changed throughout the year, seemingly related to variation in hours of daylight and provision of forage. Young forest stands provided higher density of preferred food plants compared to older stands and were highly selected from spring until autumn. Relative to young forest, the selection for older forest stands increased towards winter, likely due to provision of higher plant quality late in the growing season, and to reduced accumulation of movement-impeding snow during winter. Selection of cultivated land varied among seasons, being highest when crop biomass was high. We also found some indications of state-dependent habitat selection as reproducing females avoided open, food rich areas in the first months after their calves were born, whereas males and females without young selected these areas in spring and summer. Our results clearly show that moose exploit the variations in cover and food caused by forestry and agriculture. This is particularly relevant for moose in Norway as current changes in forestry practice lead to a reduction in young, food-rich forest stands, possibly aggravating the already declining body conditions and recruitment rates of moose. Copyright © 2011 The Authors. Published under a CC-BY license.

Ungulate‐vehicle collisions (UVC) constitute a widespread and increasing problem in large parts of the world. This has generated an intensive search for mitigating measures, but often based on a weak understanding of the underlying spatiotemporal factors. We examined the effects of harvest density (a proxy for moose density), traffic‐related variables and climate on the spatiotemporal variation in number of moose‐vehicle collisions (MVC) in 14 Norwegian counties based on 31 year of data. Moose density was the most important factor explaining the variation in MVC, both within and between counties. In addition, the spatiotemporal variation in MVC was positively related to traffic volume (private car mileage) and snow depth, and negatively related to winter temperature. The relationship between traffic volume and temporal variation in MVC was stronger in counties with general low traffic volume, possibly because high traffic volume can act as a barrier to moose road crossings. Likewise, the temporal effect of snow depth was mainly present in counties with on average deep snow, i.e., where it constitutes a constraint on moose movement and space use during winter. Our study highlights the different importance between areas of the factors underlying the spatiotemporal variation in MVC. A notable exception was the variation in moose density, which follows an isometric scaling to the variation in MVC in all counties. Thus, a given percentage decrease in moose density is likely to return a similar percentage decrease in MVC. A significant population reduction may therefore be an efficient mitigating measure to reduce the number of MVC in Norway. From a harvesting and conservation point of view, other possible preventive measures to reduce MVC should also be considered. However, because of the strong temporal effects of moose density and snow depth, evaluations of other mitigating actions should always seek to control for temporal variation in these variables. © 2011 Rolandsen et al. This is an open access article under the terms of the Creative Commons Attribution License

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.

The field of habitat ecology has been muddled by imprecise terminology regarding what constitutes habitat, and how importance is measured through use, selection, avoidance and other bio-statistical terminology. Added to the confusion is the idea that habitat is scale-specific. Despite these conceptual difficulties, ecologists have made advances in understanding ‘how habitats are important to animals’, and data from animal-borne global positioning system (GPS) units have the potential to help this clarification. Here, we propose a new conceptual framework to connect habitats with measures of animal performance itself—towards assessing habitat–performance relationship (HPR). Long-term studies will be needed to estimate consequences of habitat selection for animal performance. GPS data from wildlife can provide new approaches for studying useful correlates of performance that we review. Recent examples include merging traditional resource selection studies with information about resources used at different critical life-history events (e.g. nesting, calving, migration), uncovering habitats that facilitate movement or foraging and, ultimately, comparing resources used through different life-history strategies with those resulting in death. By integrating data from GPS receivers with other animal-borne technologies and combining those data with additional life-history information, we believe understanding the drivers of HPRs will inform animal ecology and improve conservation.

In human-dominated landscapes, species with large spatial requirements, such as large carnivores, have to deal with human infrastructure and activities within their home ranges. This is the case for the brown bear ( Ursus arctos L., 1758) in Scandinavia, which is colonizing more human-dominated landscapes, leading inevitably to an overlap between their home ranges and anthropogenic structures. In this study, we investigated fine-scale habitat selection by brown bears to examine how they deal with this potential disturbance. Using Global Positioning System (GPS) data, we studied (i) habitat selection of female brown bears within their home range and (ii) the influence of diurnal variation in human disturbance on fine-scale habitat use. As expected, females selected habitats within their home range that provided abundant food resources and minimized human-caused disturbance. In addition, our temporal analysis of habitat selection revealed an avoidance of disturbed areas and a selection of slopes by bears during periods of highest human activities, i.e., during daylight hours. We clearly demonstrate the importance of considering the fluctuations in human activity when studying habitat selection, especially at fine spatial scales. Failing to do so may considerably reduce the power to detect important fine-scale habitat-selection behaviors.

Despite its central place in animal ecology no general mechanistic movement model with an emergent home-range pattern has yet been proposed. Random walk models, which are commonly used to model animal movement, show diffusion instead of a bounded home range and therefore require special modifications. Current approaches for mechanistic modeling of home ranges apply only to a limited set of taxa, namely territorial animals and/or central place foragers. In this paper we present a more general mechanistic movement model based on a biased correlated random walk, which shows the potential for home-range behavior. The model is based on an animal tracking a dynamic resource landscape, using a biologically plausible two-part memory system, i.e. a reference- and a working-memory. Our results show that by adding these memory processes the random walker produces home-range behavior as it gains experience, which also leads to more efficient resource use. Interestingly, home-range patterns, which we assessed based on home-range overlap and increase in area covered with time, require the combined action of both memory components to emerge. Our model has the potential to predict home-range size and can be used for comparative analysis of the mechanisms shaping home-range patterns.

We examined the relationship between survival of roe deer (Capreolus capreolus) fawns at Trois Fontaines, Champagne-Ardennes, France, and factors related to bed-site selection (predator avoidance and thermoregulation) and maternal food resources (forage availability in the maternal home range). Previous studies have demonstrated that at small scales, the young of large herbivores select bed sites independently from their mothers, although this selection takes place within the limits of their mother’s home range. Fawn survival was influenced largely by the availability of good bed sites within the maternal home range, not by the fawn’s selection of bed sites; however, selection for thermal cover when selecting bed sites positively influenced survival of young fawns. Typical features of a good home range included close proximity to habitat edges, which is related to forage accessibility for roe deer. The availability of bed sites changed as fawns aged, probably due to an increased mobility of the fawn or a different use of the home range by the mother; sites offering high concealment and thermal protection became less available in favor of areas with higher forage accessibility. Despite the minor influence of bed-site selection on survival, roe deer fawns strongly selected their bed sites according to several environmental factors linked to predator avoidance and thermoregulation. Fawns selected for sites providing concealment, light penetration, and avoided signs of wild boar (Sus scrofa) activity. Avoidance of sites with high light penetration by young fawns positively affected their survival, confirming a negative effect on thermoregulation due to reduced thermal cover. Selection for light penetration by older fawns was less clear. We discuss these results in the context of cross-generational effects in habitat selection across multiple scales, and the potential influence of the ‘ghost of predation past’.

The workshop ‘ Spatial models in animal ecology, management and conservation ’ held at Silwood Park (UK), 9–11 March 2010, aimed to synthesize recent progress in modelling the spatial dynamics of individuals, populations and species ranges and to provide directions for research. It brought together marine and terrestrial researchers working on spatial models at different levels of organization, using empirical as well as theory-driven approaches. Different approaches, temporal and spatial scales, and practical constraints predominate at different levels of organization and in different environments. However, there are theoretical concepts and specific methods that can fruitfully be transferred across levels and systems, including: habitat suitability characterization, movement rules, and ways of estimating uncertainty.