Grizzly bear response to spatio‐temporal variability in human recreational activity

Abstract

1. 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.2. 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.3. We found that models including data on recreational activity outperformed trailproximity models when interactions with movement covariates were included.Responses were highly variable among individuals and across classes: males, females, and females with cubs. 4. 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 unifiedmodelling 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. 376 | Journal of Applied Ecology LADLE Et AL. SUPPORTING INFORMATIONAdditional supporting information may be found online in the Supporting Information section at the end of the article. How to cite this article: Ladle A, Avgar T, Wheatley M, Stenhouse GB, Nielsen SE, Boyce MS. Grizzly bear response to spatio-temporal variability in human recreational activity. J

Temperature shapes movement and habitat selection by a heat-sensitive ungulate

“…We used conditional logistic regression (via the 'clogit' function in the 'survival' package; Therneau 2019) to fit the SSF containing our variables of interest (extracted at the end of each step; listed in Table 1) and interactions between each variable and ambient temperature. We included step length (i.e., distance between consecutive fixes) both to reduce bias in selection estimates (Forester et al 2009) and to explicitly model its interaction with another variable of interest (Avgar et al 2016;Prokopenko et al 2017;Ladle et al 2019). Interaction coefficients detail how temperature influences step length and selection of cover types at differing temperatures.…”

“…Because SSFs estimate selection conditionally at each GPS location, each location or step can be connected with a distinct time and spatial location, enabling inference on how animals change movement and habitat selection in space and time in response to specific stimuli. SSFs have been used to characterize animal movements in relation to landscape features, such as grizzly bear (Ursus arctos) response to human activity (Ladle et al 2019) and North American elk, African wild dog (Lycaon pictus), and wolverine (Gulo gulo) response to roads (Abrahms et al 2016;Prokopenko et al 2017;Scrafford et al 2018). Likewise, SSFs that incorporate interactions between temperature and other variables of interest can characterize changes in movement behavior and habitat use in response to differences in temperature.…”

Across scales, pronghorn select sagebrush, avoid fences, and show negative responses to anthropogenic features in winter

“…We repeated this 5000 times for each coefficient to generate median and confidence interval estimates (2.5th and 97.5th quantiles; Ladle et al. ). The RSF took the following form: where w ( x ) was proportional to the probability of pronghorn selection, and representative of the relative probability of selection for covariates ( h n ), at location x in environmental space, and B n 's were coefficients estimated for each covariate.…”

“…We used generalized linear models to maximize the use-availability likelihoods with an exponential link function for each ASY (McDonald 2013). Because individual pronghorn were represented in more than one ASY, we took a random sample of model coefficients from ASYs for each pronghorn, to avoid issues of lack of independence of ASYs with the same individual for population-level inference (sensu Ladle et al 2018). We bootstrapped coefficients by sampling coefficients for each individual, not ASY, at a rate of two times the number of total ASYs to generate mean coefficient values.…”

“…We bootstrapped coefficients by sampling coefficients for each individual, not ASY, at a rate of two times the number of total ASYs to generate mean coefficient values. We repeated this 5000 times for each coefficient to generate median and confidence interval estimates (2.5th and 97.5th quantiles; Ladle et al 2018). The RSF took the following form:…”