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Differences in botanical diet compositions among a large number of moose faecal samples collected during winter correlated with the nutritional differences identified in the same samples (Mantel‐r = 0.89, p = 0.001), but the nutritional differences were significantly smaller (p < 0.001).Nutritional geometry revealed that moose mixed Scots pine Pinus sylvestris and Vaccinium spp. as nutritionally complementary foods to reach a nutritional target resembling Salix spp. twigs, and selected for Salix spp. browse (Jacob's D > 0).Available protein (AP) and total non‐structural carbohydrates (TNC) were significantly correlated in observed diets but not in hypothetical diets based on food availability.The level of Acetoacetate in moose serum (i.e. 'starvation') was weakly negatively associated with digestibility of diets (p = 0.08) and unrelated to increasing AP:TNC and AP:NDF ratios in diets (p > 0.1).Our study is the first to demonstrate complementary feeding in free‐ranging moose to attain a nutritional target that has previously been suggested in a feeding trial with captive moose. Our results add support to the hypothesis of nutritional balancing as a driver in the nutritional strategy of moose with implications for both the management of moose and food resources. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Abstract: Infrastructure is increasingly part of wildlife habitats. However, it is not clear how infrastructure affects habitat quality for wildlife adapted to natural disturbances. While potentially providing suitable habitat such as early-successional forest, infrastructure also enables human access, which may modify animal’ movements, especially where hunted species are concerned. To investigated the effect of infrastructure for moose (Alces alces, n =138), a heavily harvested species, we modelled circadian distances and movement rates over the year as a function of moose’ distance to the nearest road, house and power line in different human-modified landscapes in Sweden (latitude 57–67). Distances between moose and roads followed a circadian pattern. Animals were more likely to be closer to roads between 18:00 in the evening and 6:00 in the morning (i.e., during times when traffic volumes are generally lower). Moose moved relatively faster when 125m or closer to a road, or alternatively, were closer to roads when more active. We did not find these relationships between moose and houses or power lines. With respect to roads, our results suggest that moose may make a temporal adjustment. During hours when humans are less active, road-near habitats may be sought out. We suggest considering different resolutions to study the impact of different infrastructure types. We recommend future research to investigate animal movement and behaviour in relation to infrastructure to understand the utilization of human-modified habitats over time, and thus providing key information for wildlife management and conservation, particularly for species that are adapted to disturbed landscapes. [Copyright &y& Elsevier]

Abstract: Human–wildlife conflicts like wildlife–vehicle collisions pose major challenges for the management and conservation of mobile wildlife in human-dominated landscapes, particularly when large species are involved. Mitigation measures to reduce risk of collisions may be based on information given by wildlife movement and collision data. To test whether movement and collision data indicate different spatiotemporal risk zones, we predicted year-around probabilities of road-crossings of GPS-marked female moose (Alces alces) (n =102), and compared them with spatiotemporal patterns of police recorded moose-vehicle collisions (n =1158). Probability of moose road-crossings peaked in May, June, and between mid November and the beginning of January, i.e. during moose migration. Moose-vehicle collisions were more likely during autumn and winter. Comparing environmental attributes of crossing and collision sites showed significant differences. The likelihood of collisions increased with the abundance of human-modified areas and higher allowed speed, and was lower on forest roads. We found that animal movement data alone are insufficient to predict collision risk zones, while analyses of collision data alone overestimate the collision risk in certain habitats. Our findings suggest that higher collision risk is largely due to low light and poor road surface conditions rather than to more animal road-crossings. This suggests that efforts to reduce wildlife collisions should focus on driver attitudes and road conditions rather than animal movement, and any efforts to model the collision risk will require actual collision data, and not just movement data. [Copyright &y& Elsevier]