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Understanding the causes and consequences of animal movements is of fundamental biological interest because any alteration in movement can have direct and indirect effects on ecosystem structure and function. It is also crucial for assisting spatial wildlife management under variable environmental change scenarios. Recent research has highlighted the need of quantifying individual variability in movement behavior and how it is generated by interactions between individual requirements and environmental conditions, to understand the emergence of population-level patterns. Using a multi-annual movement data set of 213 individual moose (Alces alces) across a latitudinal gradient (from 56 degrees to 67 degrees N) that spans over 1100 km of varying environmental conditions, we analyze the differences in individual and population-level movements. We tested the effect of climate, risk, and human presence in the landscape on moose movements. The variation in these factors explained the existence of multiple movements (migration, nomadism, dispersal, sedentary) among individuals and seven populations. Population differences were primarily related to latitudinal variation in snow depth and road density. Individuals showed both fixed and flexible behaviors across years, and were less likely to migrate with age in interaction with snow and roads. For the predominant movement strategy, migration, the distance, timing, and duration at all latitudes varied between years. Males traveled longer distances and began migrating later in spring than females. Our study provides strong quantitative evidence for the dynamics of animal movements in response to changes in environmental conditions along with varying risk from human influence across the landscape. For moose, given its wide distributional range, changes in the distribution and migratory behavior are expected under future warming scenarios.

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.)

Habitat models may provide viable tools for co-management of large ungulates and forest resources, yet their applicability has not been comprehensively evaluated in managed forest. We examined 2 inherently different approaches to model the relative winter habitat suitability for moose (Alces alces) in the coastal area of northern Sweden. An empirical approach based on GPS positions of 15 female moose was used to scrutinize the assumptions and functional mechanisms of a process-oriented, conceptual approach, based on published material on the species' preferences for habitat components related to food and cover. For both model approaches habitat was described using estimates of forest-stand characteristics based on satellite imagery. The empirical model also included variables relating to topographic properties of the landscape as well as distances to landscape features. The output from both models was a habitat suitability index (HSI) score, enabling the models to be compared with each other. The models showed different results, highlighting the need to include the spatially explicit distribution of environmental variables in future conceptual, process-oriented models.