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Abstract. Habitat restoration is a core element for the recovery of many declining species. In western Canada, habitat restoration for the recovery of woodland caribou is focused on linear features (LFs) created by oil and gas exploration. At present, the only established criterion for LF restoration is when vegetation structure on LFs is similar to surrounding vegetation. Human-mediated habitat alteration impacts caribou population dynamics by increasing caribou predation rates in two ways: increasing alternate prey populations leading to higher predator numbers and increasing predator hunting efficiency. Linear features increase the movement rates-and may thus increase hunting efficiency-of wolves, a primary predator of caribou and a main hypothesized mechanism for population declines. One approach to determine LF recovery is to identify potential thresholds in the characteristics of regenerating LFs where efficiencies in wolf movement rates are no longer evident. We examined how vegetation affects wolf selection of, and movement on, LFs in northeastern Alberta using five-minute Global Positioning System locations from 20 wolves. Wolves selected LFs with shorter vegetation and traveled faster on LFs with shorter, sparser vegetation and increased vegetation variability. Travel speeds were reduced by 1.5-1.7 km/h when vegetation exceeded heights of 0.50 m, but at least 30% of a LF required vegetation exceeding 4.1 m to slow movement rates to those traveled while in forest. Policy implications: Most of the movement efficiency afforded to wolves by LFs is mediated when vegetation exceeds 0.50 m, and therefore, active restoration could be focused in areas that have not met this value. Rather than treating this value as a clear threshold equating to functional recovery, multiple metrics across trophic levels must also be evaluated to assess population recovery for caribou.

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One of the principal goals of wildlife research and management is to understand and predict relationships between habitat quality, health of individuals and their ability to survive. Infrequent sampling, non‐random loss of individuals due to mortality and variation in capture susceptibility create potential biases with conventional analysis methods. To account for such sampling biases, we used a multi‐state analytical approach to assess relationships between habitat, health and survival of grizzly bears Ursus arctos horribilis over a 10‐year period along the east slopes of the Canadian Rockies in Alberta, Canada. We defined bear health states by body condition estimated from the relationship between weight and body length. We used a sequential model building process to first account for potential sampling biases, and then explored changes in body condition relative to habitat use and survival. Bears that used regenerating forest habitats (mostly due to forest harvesting) containing a diversity of age classes were more likely to see gains in their body condition, whereas bears that used older forests were more likely to see reductions in body condition. Survival rate was reduced most by road densities which in turn were positively correlated with regenerating forest habitat. Human activities which promote young regenerating forests, such as forest harvesting, therefore promotes improved health (increased body condition) in bears, but are offset by reductions in survival rates. Multi‐state analyses represents a robust analytical tool when dealing with complex relationships and sampling biases that arise from dynamic environments.