Influence of temporary emigration on wood turtle (Glyptemys insculpta) detectability, with implications for abundance estimation.

Reliable population estimates are important for making informed management decisions about wildlife species. Standardized survey protocols have been developed for monitoring population trends of the wood turtle (Glyptemys insculpta), a semi-aquatic freshwater turtle species of conservation concern throughout its distribution in east-central North America. The protocols use repeated active search surveys of defined areas, allowing for estimation of survey-specific detection probability (p) and site-specific abundance. These protocols assume population closure within the survey area during the survey period, which is unlikely to be met as wood turtles are a highly mobile species. Additionally, current protocols use a single-pass design that does not allow for separation of availability (pa) and detectability (pd). If there are systematic influences on pa or pd that are not accounted for in the survey design or data analysis, then resulting abundance estimates could be biased. The objectives of this study were to determine if pa is a random process and if pa and pd are influenced by demographic characteristics. We modified the wood turtle survey protocol used in the upper Midwest to include a double-pass design, allowing us to estimate pa and pd using a robust design capture-recapture model. The modified protocol was implemented at 14 wood turtle monitoring sites in Minnesota and Wisconsin between 2017 and 2022. Our results indicated that pa was non-random and that pd increased with turtle carapace length. Our study suggests that model assumptions for current wood turtle population models may be violated, likely resulting in an overestimation of abundance. We discuss possible protocol and modeling modifications that could result in more accurate wood turtle abundance estimates.

Statistical population reconstruction of moose (Alces alces) in northeastern Minnesota using integrated population models.

Given recent and abrupt declines in the abundance of moose (Alces alces) throughout parts of Minnesota and elsewhere in North America, accurately estimating statewide population trends and demographic parameters is a high priority for their continued management and conservation. Statistical population reconstruction using integrated population models provides a flexible framework for combining information from multiple studies to produce robust estimates of population abundance, recruitment, and survival. We used this framework to combine aerial survey data and survival data from telemetry studies to recreate trends and demographics of moose in northeastern Minnesota, USA, from 2005 to 2020. Statistical population reconstruction confirmed the sharp decline in abundance from an estimated 7,841 (90% CI = 6,702-8,933) in 2009 to 3,386 (90% CI = 2,681-4,243) animals in 2013, but also indicated that abundance has remained relatively stable since then, except for a slight decline to 3,163 (90% CI = 2,403-3,718) in 2020. Subsequent stochastic projection of the population from 2021 to 2030 suggests that this modest decline will continue for the next 10 years. Both annual adult survival and per-capita recruitment (number of calves that survived to 1 year per adult female alive during the previous year) decreased substantially in years 2005 and 2019, from 0.902 (SE = 0.043) to 0.689 (SE = 0.061) and from 0.386 (SE = 0.030) to 0.303 (SE = 0.051), respectively. Sensitivity analysis revealed that moose abundance was more sensitive to fluctuations in adult survival than recruitment; thus, we conclude that the steep decline in 2013 was driven primarily by decreasing adult survival. Our analysis demonstrates the potential utility of using statistical population reconstruction to monitor moose population trends and to identify population declines more quickly. Future studies should focus on providing better estimates of per-capita recruitment, using pregnancy rates and calf survival, which can then be incorporated into reconstruction models to help improve estimates of population change through time.

EVALUATING THE THRESHOLD DENSITY HYPOTHESIS FOR MOOSE (ALCES ALCES), WHITE-TAILED DEER (ODOCOILEUS VIRGINIANUS), AND PARELAPHOSTRONGYLUS TENUIS.

Despite the importance of the Parelaphostrongylus tenuis infection for moose (Alces alces) and white-tailed deer (Odocoileus virginianus) management, only one peer-reviewed study has evaluated the relationship between deer and moose densities and the potential for parasite-mediated competition between the species. Using polynomial-regression modeling, that study identified a deer-density threshold above which moose populations declined; however, the nature of the data and apparent outliers suggests the approach used to develop that threshold may not have been appropriate. We used the data from the original study to test whether alternative models, including linear models and negative binomial models would be less sensitive to outliers and could better explain that relationship. We found no evidence that moose density decreases as deer density increases. We concluded that, although the proposed moose-deer-P. tenuis relationship could be partially density dependent, additional factors, such as frequency dependence of disease transmission, gastropod abundance, and shared use of resources by moose and deer should also be considered.

Moose movement rates are altered by wolf presence in two ecosystems.

Predators directly impact prey populations through lethal encounters, but understanding nonlethal, indirect effects is also critical because foraging animals often face trade-offs between predator avoidance and energy intake. Quantifying these indirect effects can be difficult even when it is possible to monitor individuals that regularly interact. Our goal was to understand how movement and resource selection of a predator (wolves; Canis lupus) influence the movement behavior of a prey species (moose; Alces alces). We tested whether moose avoided areas with high predicted wolf resource use in two study areas with differing prey compositions, whether avoidance patterns varied seasonally, and whether daily activity budgets of moose and wolves aligned temporally. We deployed GPS collars on both species at two sites in northern Minnesota. We created seasonal resource selection functions (RSF) for wolves and modeled the relationship between moose first-passage time (FPT), a method that discerns alterations in movement rates, and wolf RSF values. Larger FPT values suggest rest/foraging, whereas shorter FPT values indicate travel/fleeing. We found that the movements of moose and wolves peaked at similar times of day in both study areas. Moose FPTs were 45% lower in areas most selected for by wolves relative to those avoided. The relationship between wolf RSF and moose FPT was nonlinear and varied seasonally. Differences in FPT between low and high RSF values were greatest in winter (-82.1%) and spring (-57.6%) in northeastern Minnesota and similar for all seasons in the Voyageurs National Park ecosystem. In northeastern Minnesota, where moose comprise a larger percentage of wolf diet, the relationship between moose FPT and wolf RSF was more pronounced (ave. across seasons: -60.1%) than the Voyageurs National Park ecosystem (-30.4%). These findings highlight the role wolves can play in determining moose behavior, whereby moose spend less time in areas with higher predicted likelihood of wolf resource selection.

Moose at their bioclimatic edge alter their behavior based on weather, landscape, and predators.

Populations inhabiting the bioclimatic edges of a species' geographic range face an increasing amount of stress from alterations to their environment associated with climate change. Moose Alces alces are large-bodied ungulates that are sensitive to heat stress and have exhibited population declines and range contractions along their southern geographic extent. Using a hidden Markov model to analyze movement and accelerometer data, we assigned behaviors (rest, forage, or travel) to all locations of global positioning system-collared moose (n = 13, moose-years = 19) living near the southern edge of the species' range in and around Voyageurs National Park, MN, USA. We assessed how moose behavior changed relative to weather, landscape, and the presence of predators. Moose significantly reduced travel and increased resting behaviors at ambient temperatures as low as 15 °C and 24 °C during the spring and summer, respectively. In general, moose behavior changed seasonally in association with distance to lakes and ponds. Moose used wetlands for travel throughout the year, rested in conifer forests, and foraged in shrublands. The influence of wolves Canis lupus varied among individual moose and season, but the largest influence was a reduction in travel during spring when near a wolf home range core, primarily by pregnant females. Our analysis goes beyond habitat selection to capture how moose alter their activities based on their environment. Our findings, along with climate change forecasts, suggest that moose in this area will be required to further alter their activity patterns and space use in order to find sufficient forage and avoid heat stress.

Erratum: Moose at their bioclimatic edge alter their behavior based on weather, landscape, and predators.

[This corrects the article DOI: 10.1093/cz/zox047.]