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Competition is a widespread interaction among carnivores, ultimately manifested through one or more dimensions of the species' ecological niche. One of the most explicit manifestations of competitive interactions regards spatial displacement. Its interpretation under a theoretical context provides an important tool to deepen our understanding of biological systems and communities, but also for wildlife management and conservation. We used Bayesian multispecies occupancy models on camera-trapping data from multiple sites in Southwestern Europe (SWE) to investigate competitive interactions within a carnivore guild, and to evaluate how species' ecological traits are shaping coexistence patterns. Seventeen out of 26 pairwise interactions departed from a hypothesis of independent occurrence, with spatial association being twice as frequent as avoidance. Association behaviors were only detected among mesocarnivores, while avoidance mainly involved mesocarnivores avoiding the apex predator (n = 4) and mesocarnivore-only interactions (n = 2). Body mass ratios, defined as the dominant over the subordinate species body mass, revealed an important negative effect (β̂ = −0.38; CI95 = −0.81 to −0.06) on co-occurrence probability, and support that spatially explicit competitive interactions are mostly expressed by larger species able to dominate over smaller ones, with a threshold in body mass ratios of ∼4, above which local-scale intraguild coexistence is unlikely. We found a weak relationship between pairwise trophic niche overlap and the probability of coexistence (β̂ = −0.19; CI95 = −0.58 to 0.21), suggesting that competition for feeding resources may not be a key driver of competition, at least at the scale of our analysis. Despite local-scale avoidance, regional-scale coexistence appears to be maintained by the spatial structuring of the competitive environment. We provide evidence that SWE ecosystems consist of spatially structured competitive environments, and propose that coexistence among near-sized species is likely achieved through the interplay of “facultative” and “behavioral” character displacements. Factors influencing carnivore coexistence likely include context-dependent density and trait-mediated effects, which should be carefully considered for a sound understanding of the mechanisms regulating these communities.

Abundance and density estimates are central to the field of ecology and are an important component of wildlife management. While many methods exist to estimate abundance from individually identifiable animals, it is much more difficult to estimate abundance of unmarked animals. One step toward noninvasive abundance estimation is the use of passive detectors such as remote cameras or acoustic recording devices. However, existing methods for estimating abundance from cameras for unmarked animals are limited by variable detection probability and have not taken full advantage of the information in camera trapping rate. We developed a time to event (TTE) model to estimate abundance from trapping rate. This estimate requires independent estimates of animal movement, so we collapsed the sampling occasions to create a space to event (STE) model that is not sensitive to movement rate. We further simplified the STE model into an instantaneous sampling (IS) estimator that applies fixed‐area counts to cameras. The STE and IS models utilize time‐lapse photographs to eliminate the variability in detection probability that comes with motion‐sensor photographs. We evaluated the three methods with simulations and performed a case study to estimate elk (Cervus canadensis) abundance from remote camera trap data in Idaho. Simulations demonstrated that the TTE model is sensitive to movement rate, but the STE and IS methods are unbiased regardless of movement. In our case study, elk abundance estimates were comparable to those from a recent aerial survey in the area, demonstrating that these new methods allow biologists to estimate abundance from unmarked populations without tracking individuals over time.

Most examples of seasonal mismatches in phenology span multiple trophic levels, with timing of animal reproduction, hibernation, or migration becoming detached from peak food supply. The consequences of such mismatches are difficult to link to specific future climate change scenarios because the responses across trophic levels have complex underlying climate drivers often confounded by other stressors. In contrast, seasonal coat color polyphenism creating camouflage against snow is a direct and potentially severe type of seasonal mismatch if crypsis becomes compromised by the animal being white when snow is absent. It is unknown whether plasticity in the initiation or rate of coat color change will be able to reduce mismatch between the seasonal coat color and an increasingly snow-free background. We find that natural populations of snowshoe hares exposed to 3 y of widely varying snowpack have plasticity in the rate of the spring white-to-brown molt, but not in either the initiation dates of color change or the rate of the fall brown-to-white molt. Using an ensemble of locally downscaled climate projections, we also show that annual average duration of snowpack is forecast to decrease by 29–35 d by midcentury and 40–69 d by the end of the century. Without evolution in coat color phenology, the reduced snow duration will increase the number of days that white hares will be mismatched on a snowless background by four- to eightfold by the end of the century. This novel and visually compelling climate change-induced stressor likely applies to >9 widely distributed mammals with seasonal coat color.

As duration of snow cover decreases owing to climate change, species undergoing seasonal colour moults can become colour mismatched with their background. The immediate adaptive solution to this mismatch is phenotypic plasticity, either in phenology of seasonal colour moults or in behaviours that reduce mismatch or its consequences. We observed nearly 200 snowshoe hares across a wide range of snow conditions and two study sites in Montana, USA, and found minimal plasticity in response to mismatch between coat colour and background. We found that moult phenology varied between study sites, likely due to differences in photoperiod and climate, but was largely fixed within study sites with only minimal plasticity to snow conditions during the spring white-to-brown moult. We also found no evidence that hares modify their behaviour in response to colour mismatch. Hiding and fleeing behaviours and resting spot preference of hares were more affected by variables related to season, site and concealment by vegetation, than by colour mismatch. We conclude that plasticity in moult phenology and behaviours in snowshoe hares is insufficient for adaptation to camouflage mismatch, suggesting that any future adaptation to climate change will require natural selection on moult phenology or behaviour.

Monitoring is an essential component of wildlife management and conservation. However, the usefulness of monitoring data is often undermined by the lack of 1) coordination across organizations and regions, 2) meaningful management and conservation objectives, and 3) rigorous sampling designs. Although many improvements to avian monitoring have been discussed, the recommendations have been slow to emerge in large-scale programs. We introduce the Integrated Monitoring in Bird Conservation Regions (IMBCR) program designed to overcome the above limitations. Our objectives are to outline the development of a statistically defensible sampling design to increase the value of large-scale monitoring data and provide example applications to demonstrate the ability of the design to meet multiple conservation and management objectives. We outline the sampling process for the IMBCR program with a focus on the Badlands and Prairies Bird Conservation Region (BCR 17). We provide two examples for the Brewer's sparrow (Spizella breweri) in BCR 17 demonstrating the ability of the design to 1) determine hierarchical population responses to landscape change and 2) estimate hierarchical habitat relationships to predict the response of the Brewer's sparrow to conservation efforts at multiple spatial scales. The collaboration across organizations and regions provided economy of scale by leveraging a common data platform over large spatial scales to promote the efficient use of monitoring resources. We designed the IMBCR program to address the information needs and core conservation and management objectives of the participating partner organizations. Although it has been argued that probabilistic sampling designs are not practical for large-scale monitoring, the IMBCR program provides a precedent for implementing a statistically defensible sampling design from local to bioregional scales. We demonstrate that integrating conservation and management objectives with rigorous statistical design and analyses ensures reliable knowledge about bird populations that is relevant and integral to bird conservation at multiple scales.

Variation in habitat quality affects individual fitness through the accumulation of benefits and costs over time. Although an individual's fitness and susceptibility to mortality are consequences of these past experiences, current analytical models do not quantify the cumulative effects of resources, risks, and environmental conditions on survival. We developed the Survival and Habitat Quality model (SHQ), which redefines survival as a cumulative process and measures habitat quality by its aggregate effect on survival through time. SHQ is an autoregressive time‐series model that uses fine‐scale tracking data, remotely sensed environmental data, and computational power to quantify the cumulative effects of spatial variation in habitat quality on survival without relying on subjective, user‐defined lag effects. We tested SHQ on simulated data and on pronghorn data in South Dakota, USA. Compared to a traditional survival model, SHQ was more precise and accurate at estimating cumulative effects of habitat on survival. Using model output, we were also able to generate maps predicting areas of high and low pronghorn survival. SHQ is a conceptual and methodological advance that explicitly integrates individuals' day‐to‐day interactions with their surroundings to identify ultimate sources of mortality. The model is a novel and accurate tool for assessing habitat quality and identifying management actions that increase individual survival and population growth. More broadly, SHQ's flexible mathematical framework captures the full spatial and temporal scope of processes affecting survival, providing a powerful means for understanding the environmental basis of fitness.

Recent declines in eastern wild turkeys (Meleagris gallopavo silvestris) have prompted increased interest in management and research of this important game species. However, the mechanisms underlying these declines are unclear, leaving uncertainty in how best to manage this species. Foundational to effective management of wildlife species is understanding the biotic and abiotic factors that influence demographic parameters and the contribution of vital rates to population growth. Our objectives for this study were to (1) conduct a literature review to collect all published vital rates for eastern wild turkey over the last 50 years, (2) perform a scoping review of the biotic and abiotic factors that have been studied relative to wild turkey vital rates and highlight areas that require additional research, and (3) use the published vital rates to populate a life‐stage simulation analysis (LSA) and identify the vital rates that make the greatest contribution to population growth. Based on published vital rates for eastern wild turkey, we estimated a mean asymptotic population growth rate (λ) of 0.91 (95% CI = 0.71, 1.12). Vital rates associated with after‐second‐year (ASY) females were most influential in determining population growth. Survival of ASY females had the greatest elasticity (0.53), while reproduction of ASY females had lower elasticity (0.21), but high process variance, causing it to explain a greater proportion of variance in λ. Our scoping review found that most research has focused on the effects of habitat characteristics at nest sites and the direct effects of harvest on adult survival, while research on topics such as disease, weather, predators, or anthropogenic activity on vital rates has received less attention. We recommend that future research take a more mechanistic approach to understanding variation in wild turkey vital rates as this will assist managers in determining the most appropriate management approach. Using a life‐stage simulation analysis, we show survival and reproduction of adult female wild turkeys had the greatest influence on population trajectories for this species. However, these life stages showed differing patterns in elasticity and variability, with adult survival having high elasticity but low process variance, and reproduction having lower elasticities but greater process variance. Additionally, we highlight several critical knowledge gaps regarding vital rates for different life stages and in the factors regulating or limiting wild turkeys.

Monitoring the abundance of rare carnivores is a daunting task for wildlife biologists. Many carnivore populations persist at relatively low densities, public interest is high, and the need for population estimates is great. Recent advances in trail camera technology provide an unprecedented opportunity for biologists to monitor rare species economically. Few studies, however, have conducted rigorous analyses of our ability to estimate abundance of low‐density carnivores with cameras. We used motion‐triggered trail cameras and a space‐to‐event model to estimate gray wolf (Canis lupus) abundance across three study areas in Idaho, USA, 2016–2018. We compared abundance estimates between cameras and noninvasive genetic sampling that had been extensively tested in our study areas. Estimates of mean wolf abundance from camera and genetic surveys were within 22% of one another and 95% CIs overlapped in 2 of the 3 years. A single camera with many detections appeared to bias camera estimates high in 2018. A subsequent bootstrapping procedure produced a population estimate from cameras equal to that derived from genetic sampling, however. Camera surveys were less than half the cost of genetic surveys once initial camera purchases were made. Our results suggest that cameras can be a viable method for estimating wolf abundance across broad landscapes (>10,000 km2).

For species of conservation concern, assessing population dynamics consistently across different populations is of paramount importance to effective conservation and restoration planning. This effort presents a challenge for wide‐ranging species with considerable variation in both abundance and demographic rates. Raw counts of individuals are typically used to assess population trends at broad scales, but the demographic rates that explain changes in population size can only feasibly be measured at local scales. We developed a generalized integrated population model, which combines the strengths of these two data types, for the greater sage‐grouse ( Centrocercus urophasianus ). We used N ‐mixture models to estimate annual abundance from counts of males at breeding leks and constructed a two‐sex, demographic matrix model using published vital rate estimates for male and female sage‐grouse from across their range. We applied the model to 13 years of statewide lek counts for Montana, USA . Then, we applied the model to local, annually varying vital rate estimates and lek count data from a population in the Powder River Basin in Montana and Wyoming. We demonstrate potential for this modeling approach to improve our understanding of sage‐grouse population dynamics in a consistent and robust framework, especially as data quality and quantity increases. We use our results to highlight the need for better data on sex and age ratios, female population size, and the proportion of active leks being monitored each year. While our model was focused on the greater sage‐grouse, this approach could be applied to a variety of sensitive species to compare population dynamics across a species’ range.

A suite of recently developed statistical methods to estimate the abundance and density of unmarked animals from camera traps require accurate estimates of the area sampled by each camera. Although viewshed area is fundamental to achieving accurate abundance estimates, there are no established guidelines for collecting this information in the field. Furthermore, while the complexities of the detection process from motion sensor photography are generally acknowledged, viewable area (the common factor between motion sensor and time lapse photography) on its own has been underemphasized. We establish a common set of terminology to identify the component parts of viewshed area, contrast the photographic capture process and area measurements for time lapse and motion sensor photography, and review methods for estimating viewable area in the field. We use a case study to demonstrate the importance of accurate estimates of viewable area on abundance estimates. Time lapse photography combined with accurate measurements of viewable area allow researchers to assume that capture probability equals 1. Motion sensor photography requires measuring distances to each animal and fitting a distance sampling curve to account for capture probability of <1. We provide recommendations for best practices when implementing methods developed in recent years that allow abundance or density estimation from unmarked animals with camera traps. We establish a common set of terminology to identify the component parts of viewshed area, contrast the detection process and area measurements for time lapse and motion sensor photography, review methods for estimating viewable area in the field, and use a case study to demonstrate the importance of accurate estimates of viewable area on abundance estimates.