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Outdoor recreation on trail networks is a growing form of disturbance for wildlife. However, few studies have examined behavioural responses by large carnivores to motorised and non‐motorised recreational activity — a knowledge gap that has implications for the success of human access management aimed at improving habitat quality for wildlife.We used an integrated step selection analysis of grizzly bear (Ursus arctos) radiotelemetry data and a spatio‐temporal model of motorised and non‐motorised human recreational activity to examine how human recreational activity on trails affects both habitat selection and movement behaviour of individual bears. Grizzly bears were captured and radiocollared in the west‐central Alberta Rocky Mountains and Foothills, and trail cameras were deployed on trails to obtain data on human recreational activity.We found that models including data on recreational activity outperformed trail‐proximity models when interactions with movement covariates were included. Responses were highly variable among individuals and across classes: males, females, and females with cubs.Male and solitary female grizzly bears increased avoidance of trails with a high probability of motorised activity as well as displaying increased movement rates in response to motorised recreation. Females with cubs did not increase avoidance, however they had the largest response in terms of higher movement rates. In contrast, for all classes, selection for proximity to trail increased when probability of non‐motorised activity was high, and the effect on movement was dampened relative to the motorised response.Synthesis and applications. By combining selection and movement into a unified modelling framework, we show that bears alter selection and movement behaviour in response to trails and recreation, and that such responses are determined by the type of recreational activity. Reduced selection and increased movement in proximity to motorised trails could affect bears' ability to exploit foraging opportunities in these areas. Future access management actions for grizzly bear recovery should consider frequency and type of linear feature use by humans rather than solely relying on thresholds relating to feature densities. By combining selection and movement into a unified modelling framework, we show that bears alter selection and movement behaviour in response to trails and recreation, and that such responses are determined by the type of recreational activity. Reduced selection and increased movement in proximity to motorised trails could affect bears' ability to exploit foraging opportunities in these areas. Future access management actions for grizzly bear recovery should consider frequency and type of linear feature use by humans rather than solely relying on thresholds relating to feature densities. [ABSTRACT FROM AUTHOR]

Ecological patterns and processes often take place within linear-feature networks, and this has implications when analysing the spatial configuration of such patterns or processes across a landscape., One such pattern is the use of landscapes by human recreationists: an important variable in animal habitat selection and behaviour. Due to the difficulty in obtaining data, ecologists tend to use coarse metrics such as linear-feature density, while the extent and timing of human activity are often ignored. Remote detector equipment and its increasing use in ecological studies allow for large volumes of data on human activity to be collected. However, the analysis of these data still can be challenging., Using a combination of generalised linear mixed-effects models and network-based ordinary kriging, we developed a method for estimating spatial and temporal variations in motorised and non-motorised activities across a complex linear-feature network. Trail cameras were set up between 2012 and 2014 and monitored motorised and non-motorised activities at 238 different trail sites across a 2824 km2 region of the eastern slopes and foothills of central Alberta's Rocky Mountains., We evaluate the predictive capacity of this approach, demonstrate its application and discuss its merits and limitations. This method offers a straightforward analysis that can be applied to remotely acquired data to give a useful metric for assessing wildlife responses to human activity, and has potential application beyond the highlighted example. [ABSTRACT FROM AUTHOR]