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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. [ABSTRACT FROM AUTHOR]

Abundance estimation is a critical component of conservation planning, particularly for exploited species where managers set regulations to restrict harvest based on current population size. An increasingly common approach for abundance estimation is through integrated population modeling (IPM), which uses multiple data sources in a joint likelihood to estimate abundance and additional demographic parameters. Lincoln estimators are one commonly used IPM component for harvested species, which combine information on the rate and total number of individuals harvested within an integrated band‐recovery framework to estimate abundance at large scales. A major assumption of the Lincoln estimator is that banding and recoveries are representative of the whole population, which may be violated if major sources of spatial heterogeneity in survival or harvest rates are not incorporated into the model. We developed an approach to account for spatial variation in harvest rates using a spatial predictive process, which we incorporated into a Lincoln estimator IPM. We simulated data under different configurations of sample sizes, harvest rates, and sources of spatial heterogeneity in harvest rate to assess potential model bias in parameter estimates. We then applied the model to data collected from a field study of wild turkeys (Meleagris gallapavo) to estimate local and statewide abundance in Maine, USA. We found that the band recovery model that incorporated a spatial predictive process consistently provided estimates of adult and juvenile abundance with low bias across a variety of spatial configurations of harvest rate and sampling intensities. When applied to data collected on wild turkeys, a model that did not incorporate spatial heterogeneity underestimated the harvest rate in some subregions. Consistent with simulation results, this led to overestimation of both local and statewide abundance. Our work demonstrates that a spatial predictive process is a viable mechanism to account for spatial variation in harvest rates and limit bias in abundance estimates. This approach could be extended to large‐scale band recovery data sets and has applicability for the estimation of population parameters in other ecological models as well. [ABSTRACT FROM AUTHOR]

Understanding population dynamics is central to population management, particularly for game species that experience human harvest and non-harvestmortality. Ruffed grouse (Bonasa umbellus) are a widely distributed and common game species in North America that have experienced population declines along their southern range margins, including portions of New England, primarily in response to forest succession and habitat loss. In the state of Maine, ruffed grouse are generally considered abundant, but there is a lack of state-specific knowledge of ruffed grouse survival and harvest rates to inform harvest management. We estimated seasonal and annual survival rates, harvest rates, and documented cause-specific mortality of 248 radio-marked ruffed grouse at 2 study areas in central Maine from 2014 to 2016. We used Program MARK to evaluate sources of spatial, temporal, and individual variation that may affect ruffed grouse survival and harvest. Survival was lowest during October and during winter, and adult ruffed grouse had a higher survival probability than juveniles throughout the year with mean annual survival probabilities of 0.28±0.01 (SE) and 0.13±0.003, respectively. Harvest rates were greater in a state-owned Wildlife Management Area and were lower within commercially managed private forest that was open to public hunting. Harvest results suggest harvest (H) was greatest at the beginning of the hunting season (Oct; Frye Mountain HOct=0.14±0.02; Stud Mill HOct=0.07±0.02), and was lower later in the season (Nov and Dec; Frye Mountain HNov-Dec=0.07±0.02; Stud Mill HNov-Dec=0.03±0.01). Pooled across all years and study areas, the ruffed grouse harvest rate was 0.16 (95% CI=0.14-0.18). Our results are comparable to other range-wide studies and suggest that current hunting regulations for ruffed grouse in Maine are consistent with sustainable population management. [ABSTRACT FROM AUTHOR]