Snow depth influences harvest of a boreal ungulate more than socio-economic factors: Implications for food security in a changing climate

Climate change may affect the ability of hunters to harvest wildlife and, hence, threaten food security of local people. However, few studies have investigated the relative influence of environmental conditions on wildlife harvest rates. We harnessed a 24-year dataset of harvest dates for a boreal ungulate in a region where climate change is having pronounced impacts on snow depth, precipitation, and temperatures to investigate the effect of weather on harvest rates. We used generalized linear models and a model selection framework to examine the influence of weather covariates (snow depth, mean daily temperature, precipitation) and socio-economic factors (gasoline and red meat prices, employment rates, and moose [Alces americanus] harvest) on harvest rates of bison (Bison bison) in Yukon, Canada, at two temporal scales: annual and daily. At an annual scale, snow depth was the only covariate that was important in explaining bison harvest. No socioeconomic variables improved our model beyond the null. At the daily scale, snow depth and mean daily temperature influenced bison harvest rates, with a 1 SD increase resulting in a 14 % and 9 % increase in daily harvest rates, respectively. Increased snow depth facilitates ease of travel in remote, roadless areas by snowmobile to locate bison and truncates movements of bison, resulting in increased harvest rates. Decreased snow depth due to climate change will impact hunter access to boreal ungulates and food security for northern people. More broadly, our data suggests that in some socioecological systems, environmental covariates have a greater influence on wildlife harvest rates than socioeconomic factors and need to be considered in future studies to better understand and predict harvest rates.

Dynamic winter weather moderates movement and resource selection of wild turkeys at high-latitude range limits

For wide-ranging species in temperate environments, populations at high-latitude range limits are subject to more extreme conditions, colder temperatures, and greater snow accumulation compared with their core range. As climate change progresses, these bounding pressures may become more moderate on average, while extreme weather occurs more frequently. Individuals can mitigate temporarily extreme conditions by changing daily activity budgets and exhibiting plasticity in resource selection, both of which facilitate existence at and expansion of high-latitude range boundaries. However, relatively little work has explored how animals moderate movement and vary resource selection with changing weather, and a general framework for such investigations is lacking. We applied hidden Markov models and step selection functions to GPS data from wintering wild turkeys (Meleagris gallopavo) near their northern range limit to identify how weather influenced transition among discrete movement states, as well as state-specific resource selection. We found that turkeys were more likely to spend time in a stationary state as wind chill temperatures decreased and snow depth increased. Both stationary and roosting turkeys selected conifer forests and avoided land covers associated with foraging, such as agriculture and residential areas, while shifting their strength of selection for these features during poor weather. In contrast, mobile turkeys showed relatively weak resource selection, with less response in selection coefficients during poor weather. Our findings illustrate that behavioral plasticity in response to weather was context dependent, but movement behaviors most associated with poor weather were also those in which resource selection was most plastic. Given our results, the potential for wild turkey range expansion will partly be determined by the availability of habitat that allows them to withstand periodic inclement weather. Combining hidden Markov models with step selection functions is broadly applicable for evaluating plasticity in animal behavior and dynamic resource selection in response to changing weather. We studied turkeys at northern range limits, but this approach is applicable for any system expected to experience significant changes in the coming decade, and may be particularly relevant to populations existing at range peripheries.

Buffalo on the Edge: Factors Affecting Historical Distribution and Restoration of Bison bison in the Western Cordillera, North America

The historic western edge of the bison (Bison bison) range and the ecological processes that caused its formation are frequently debated with important implications for bison restoration across North America. We test the hypothesis that a combination of bottom-up habitat suitability and top-down harvest pressure from Indigenous peoples were important processes in forming the western edge of bison distribution. Using 9384 historical journal observations from 1691–1928, we employ MaxEnt ecological niche modelling to identify suitable bison habitat across the Western Cordillera from bottom-up climatic, land cover, and topographic factors. We then use mixed-effect logistic regression to test if bison occurrence in journal records can be in part explained by the abundance of humans, wolves, or grizzly bears, in addition to MaxEnt-derived habitat suitability. We find support for our hypothesis because of the limited suitable habitat in the Rocky Mountains that likely prevented westward bison dispersal from their core habitat, and there was a negative relationship between bison occurrence and human harvest pressure. On this basis, we propose that intensive human harvest from large populations in the Western Cordillera, subsidized by other wildlife, salmon, and vegetation resources, is an underappreciated socioecological process that needs to be restored alongside bison populations. Co-managing bison with Indigenous peoples will also mitigate the adverse effects of overabundant bison populations and maximize the ecological and cultural benefits of bison restoration.

Seasonal resource selection of an arboreal habitat specialist in a human-dominated landscape: a case study using red panda

Human dominated landscapes provide heterogeneous wildlife habitat. Conservation of habitat specialists, like red pandas Ailurus fulgens, inhabiting such landscapes is challenging. Therefore information on resource use across spatial and temporal scales could enable informed-decision making with better conservation outcomes. We aimed to examine the effect of geo-physical, vegetation, and disturbance variables on fine-scale habitat selection of red pandas in one such landscape. We equipped 10 red pandas with GPS collars in eastern Nepal in 2019 and monitored them for one year. Our analysis was based on a generalized-linear-mixed model. We found the combined effect of geo-physical, vegetation and disturbance variables resulted in differences in resource selection of red pandas and that the degree of response to these variables varied across seasons. Human disturbances, especially road and cattle herding activities, affected habitat utilization throughout the year whereas other variables were important only during restricted periods. For instance, geo-physical variables were influential in the premating and cub-rearing seasons while vegetation variables were important in all seasons other than premating. Red pandas selected steeper slopes with high solar insolation in the premating season while they occupied elevated areas and preferred specific aspects in the cub-rearing season. Furthermore, the utilized areas had tall bamboo in the birthing and cub-rearing seasons while they also preferred diverse tree species and high shrub cover in the latter. Our study demonstrates the significance of season-specific management, suggests the importance of specific types of vegetation during biologically crucial periods, and emphasizes the necessity to minimize disturbances throughout the year.