Identifying the specific environmental features and associated density-dependent processes that limit population growth is central to both ecology and conservation. Comparative assessments of sympatric species allow for inference about how ecologically similar species differentially respond to their shared environment, which can be used to inform community-level conservation strategies. Comparative assessments can nevertheless be complicated by interactions and feedback loops among the species in question. We developed an integrated population model based on 61 years of ecological data describing the demographic histories of Canvasbacks (Aythya valisineria) and Redheads (Aythya americana), two species of migratory diving ducks that utilize similar breeding habitats and affect each other's demography through interspecific nest parasitism. We combined this model with a transient life table response experiment to determine the extent that demographic rates, and their contributions to population growth, were similar between these two species. We found that demographic rates and, to a lesser extent, their contributions to population growth covaried between Canvasbacks and Redheads, but the trajectories of population abundances widely diverged between the two species during the end of the twentieth century due to inherent differences between the species life histories and sensitivities to both environmental variation and harvest pressure. We found that annual survival of both species increased during years of restrictive harvest regulations; however, recent harvest pressure on female Canvasbacks may be contributing to population declines. Despite periodic, and often dramatic, increases in breeding abundance during wet years, the number of breeding Canvasbacks declined by 13% whereas the number of breeding Redheads has increased by 37% since 1961. Reductions in harvest pressure and improvements in submerged aquatic vegetation throughout the wintering grounds have mediated the extent to which populations of both species contracted during dry years in the Prairie Pothole Region. However, continued degradation of breeding habitats through climate-related shifts in wetland hydrology and agricultural conversion of surrounding grassland habitats may have exceeded the capacity for demographic compensation during the nonbreeding season.

Abstract While the quantity, quality, and variety of movement data has increased, methods that jointly allow for population‐ and species‐level movement parameters to be estimated are still needed. We present a formal data integration approach to combine individual‐level movement and population‐level distribution data. We show how formal data integration can be used to improve precision of individual and population‐level movement parameters and allow additional population‐level metrics (e.g., connectivity) to be formally quantified. We describe three components needed for an Integrated Movement Model (IMM): a model for individual movement, a model for among‐individual heterogeneity, and a model to quantify changes in species distribution. We outline a general IMM framework and develop and apply a specific stochastic differential equation model to a case study of telemetry and species distribution data for golden eagles in western North American during spring migration. We estimate eagle movements during spring migration from data collected between 2011 and 2019. Individual heterogeneity in migration behaviour was modelled for two subpopulations, individuals that make significant northward migrations and those that remained in the southern Rocky Mountain region through the summer. As is the case with most tracking studies, the sample population of individual telemetered birds is not representative of the population and underrepresents the proportion of long‐distance migrants in the population. The IMM was able to provide a more biological accurate subpopulation structure using joint estimation. In addition, the integrated approach (a) improves accuracy of other estimated movement parameters, (b) allows us to estimate the proportion of migratory and non‐migratory birds in a given location and time, and (c) estimate future spatiotemporal distributions of birds given a wintering location, which provide estimates of seasonal connectivity and migratory routes. We demonstrate how IMMs can be successfully used to address the challenge of estimating accurate population‐level movement parameters. Our approach can be generalized to a broad range of available movement models and data types, allowing us to significantly improve our knowledge of migration ecology across taxonomic groups, and address population and continental level information needs for conservation and management.

Anthropogenic disturbance, such as land conversion, recreation, and predation, can affect how wildlife select resources across the landscape. Prey species are thought to rely on both a cognitive map of risks (‘landscape of fear’) and a schedule of risks (‘schedule of fear’) to navigate their environment and make trade-offs among resources and habitats. Using a popular game species, the Canada goose (Branta canadensis), we aimed to expand our understanding of the landscape of fear by evaluating how resource selection and home range changed in response to predation threats during the hunting season. We used GPS receivers to track the movements of resident geese in Pennsylvania throughout two hunting seasons across two study sites. We fit resource selection functions and estimated home ranges at four different spatial and temporal scales. We found that the geese did not change their landscape use to avoid the predation threat at a coarse spatiotemporal scale but did modify their habitat use and resource selection at a finer spatiotemporal scale. Our results indicate that the geese perceived both a landscape of fear and a schedule of fear and used spatial and temporal partitioning to minimize their exposure to predation. When managing a heterogeneous landscape for both animal and human use, providing sufficient spatial refuge for prey species may help buffer the effects of predation threats.

GPS tags have become a common tool in ecological studies of animal behaviour and demography despite previous research indicating negative impacts on vital rates across a variety of taxa. Many researchers face tradeoffs when deciding whether they are an appropriate tool because GPS tags may impact vital rates, but they provide detailed data on movements and behaviour that often cannot be obtained in other ways. Using band recovery data, we evaluated the strength of effects induced by GPS tags on annual mortality of adult females across 13 waterfowl taxa, and examined whether taxa with a slower life-history strategy and larger body size were more resilient to GPS tag effects than their fast-lived counterparts with small body size. All species were exposed to hunting, which may interact with underlying processes affecting the impact of GPS tags on mortality, but also allowed for robust analysis of overall annual mortality. Hazard ratios, indicating the risk of death for individuals wearing GPS tags compared to those wearing only metal bands, ranged from 1.13 to 3.25 and the mean proportional difference in survival between marker types across species was 0.33. The magnitude of tag effects was surprisingly consistent across life-history tempo and body size, indicating that slower-lived species did not buffer the effect of wearing GPS tags. Our results highlight that even large, long-lived species, which are generally better at buffering their mortality against environmental adversity, are not immune to the effects GPS tags can have on survival and mortality. The results of our study emphasize the importance of testing for such effects across taxa in future research as technology advances.

Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance into unobserved areas or periods of time. However, such models often poorly predict the distribution of animal space‐use intensity beyond the place and time of data collection, presumably because space‐use behaviors vary between individuals and environmental contexts. Similarly, ecological inference based on habitat selection models could be muddied or biased due to unaccounted individual and context dependencies. Here, we present a modeling workflow designed to allow transparent variance‐decomposition of habitat‐selection patterns, and consequently improved inferential and predictive capacities. Using global positioning system (GPS) data collected from 238 individual pronghorn, Antilocapra americana, across three years in Utah, USA, we combine individual‐year‐season‐specific exponential habitat‐selection models with weighted mixed‐effects regressions to both draw inference about the drivers of habitat selection and predict space‐use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter‐ and intra‐individual components. We then used the results to predict population‐level, spatially and temporally dynamic, habitat‐selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30 × 30 m resolution but an extent of 220 000 km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models – variability in habitat selection – into a tool to understand and predict species‐habitat associations across space and time.

SNAPSHOT USA is a multicontributor, long-term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application (https://www.snapshot-usa.org/). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (https://www.wildlifeinsights.org/), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22-5.9 arrays per 100,000 km2), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Aerial surveys for large ungulates produce count data that often underrepresent the number of animals. Errors in count data can lead to erroneous estimates of abundance if they are not addressed. Our objective was to address imperfect detection probability by developing a framework that produces realistic and defensible estimates of bighorn sheep (Ovis canadensis) abundance. We applied our framework to a population of desert bighorn sheep (O. c. nelsoni) in the Great Basin, Nevada, USA. We captured and marked 24 desert bighorn sheep with global positioning system (GPS)-collars and then conducted helicopter surveys naive to the locations of collared animals. We developed a Bayesian integrated data model to leverage information from telemetry data, helicopter survey counts, and habitat characteristics to estimate abundance while accounting for availability and perception probability (i.e., detection given availability). Distance to ridgeline, terrain ruggedness, tree cover, and slope influenced perception probability of sheep given they were viewable from the helicopter. There was also annual variation in perception probability (2018: median = 0.64, credible interval [CrI] = 0.37-0.87; 2019: median = 0.81, CrI = 0.49-0.97). The abundance estimates from the integrated data model decreased from 2018 (594; 95% CrI = 537-656) to 2019 (487; 95% CrI = 436-551). In addition, accounting for availability and imperfect perception resulted in greater estimates of abundance compared to traditional directed search methods, which were 340 for 2018 and 320 for 2019. Our modeling framework can be used to generate more defensible population estimates of bighorn sheep and other large mammals that have been surveyed in a similar manner.

Abstract Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes ( $$n\approx 100+)$$ n ≈ 100 + ) and that multimodel inference can inform hypotheses about the configuration and length of survival stages needed to adequately describe mortality processes for tracked specimens. While we focus on survival estimation for tagged fish and wildlife populations, multistage time-to-event models could be used to understand other phenomena of interest such as migration, reproduction, or disease events across a range of taxa including plants and insects.

The parasitic mite Sarcoptes scabiei causes mange in nearly 150 species of mammals by burrowing under the skin, triggering hypersensitivity responses that can alter animals' behavior and result in extreme weight loss, secondary infections, and even death. Since the 1990s, sarcoptic mange has increased in incidence and geographic distribution in Pennsylvania black bear (Ursus americanus) populations, including expansion into other states. Recovery from mange in free-ranging wildlife has rarely been evaluated. Following the Pennsylvania Game Commission's standard operating procedures at the time of the study, treatment consisted of one subcutaneous injection of ivermectin. To evaluate black bear survival and recovery from mange, from 2018 to 2020 we fitted 61 bears, including 43 with mange, with GPS collars to track their movements and recovery. Bears were collared in triplicates according to sex and habitat, consisting of one bear without mange (healthy control), one scabietic bear treated with ivermectin when collared, and one untreated scabietic bear. Bears were reevaluated for signs of mange during annual den visits, if recaptured during the study period, and after mortality events. Disease status and recovery from mange was determined based on outward gross appearance and presence of S. scabiei mites from skin scrapes. Of the 36 scabietic bears with known recovery status, 81% fully recovered regardless of treatment, with 88% recovered with treatment and 74% recovered without treatment. All bears with no, low, or moderate mite burdens (<16 mites on skin scrapes) fully recovered from mange (n=20), and nearly half of bears with severe mite burden (≥16 mites) fully recovered (n=5, 42%). However, nonrecovered status did not indicate mortality, and mange-related mortality was infrequent. Most bears were able to recover from mange irrespective of treatment, potentially indicating a need for reevaluation of the mange wildlife management paradigm.

We trapped, anesthetized, and fit 16 female feral swine (Sus scrofa) with Global Positioning System (GPS) collars in Great Smoky Mountains National Park (GRSM) to develop predictive summer and winter models for more effective population control efforts. Given the highly diverse habitat and topography in GRSM and the spatial extent of our dataset, we employed Step Selection Function (SSF) to evaluate resource selection at the 3(rd)-order level and Resource Selection Function (RSF) models at the 2(nd)-order level for both summer and winter seasons. The summer SSF and RSF models suggested relatively similar levels of selection, whereas the winter models differed by method. We created a straightforward consensus model to better visualize the agreement and constraints of each set of models. In summer, feral swine used lower slopes regardless of elevation, especially those closer to human-dominated spaces such as along paved and gravel roadways. In winter, feral swine maintained preference for lower slopes but preferred oak-dominated forest areas and selection for human development was less than in summer. Wildlife managers can use these models to better focus feral swine surveillance and management in GRSM. Managers can identify areas of high use by season and plan control activities that are both accessible and highly efficient. The combination and consensus framework presented here can be applied to other systems where species' habitat selection may result in incongruous results across different levels of selection or seasons of interest.

We examined Henslow's Sparrow (Centronyx henslowii) response to prescribed fire at 32 grasslands at Big Oaks National Wildlife Refuge in southeastern Indiana from 1999 to 2009. We burned grasslands in the spring between 1999 and 2007 and monitored Henslow's Sparrows for up to 4 yr after treatment. We used linear mixed models to analyze our data. Henslow's Sparrow counts were correlated with time since prescribed fire and grassland size. The estimated changes in mean Henslow's Sparrow density relative to pre-burn densities were -0.19, 1.15, 0.74, and -0.68 birds ha(-1) for 1-4 breeding seasons after a spring burn, respectively. We found that Henslow's Sparrows preferred larger grasslands both during the first breeding season after prescribed fire, when vegetation was presumed to possess less litter and structural density, and during the fourth breeding season after fire, when vegetation was presumed to be a more ideal composition, but preferred smaller grasslands in the interim. Thus, grassland size shapes the magnitude of Henslow's Sparrow population response to fire, with populations in smaller grasslands experiencing greater amplitude changes. Larger grasslands might provide more habitat diversity following prescribed fire, attenuating population change. On average, Henslow's Sparrows responded positively to prescribed fire in a network of grasslands and cumulative net change in densities were highest three breeding seasons after a burn suggesting that maintaining this burn frequency would be beneficial to Henslow's Sparrow abundance. Lay Summary & BULL; Henslow's Sparrow (Centronyx henslowii) abundance has declined across much of its range, leading to its designation as one of the highest priorities for grassland bird conservation according to the U.S. Fish and Wildlife Service. & BULL; Research has shown that habitat loss and alteration, such as woody vegetation encroachment, is strongly associated with these population declines. Prescribed fire is often used to prevent woody encroachment into grasslands. & BULL; In southeastern Indiana, grasslands are organized as a network of patches distributed within forested habitat. These heterogeneous patches, which vary in size and degree of isolation, make it difficult to predict the response of sparrows to prescribed fire and therefore to develop an optimal burn schedule. & BULL; We studied Henslow's Sparrow response to a prescribed-fire regime by systematically burning grassland patches and performing Henslow's Sparrow counts for up to four breeding seasons post-burn. & BULL; Our results suggest that a 3-yr burn cycle (i.e., burning in years 1 and 4) is an optimal strategy for promoting sparrow density.

The ringtail (Bassariscus astutus) is a species of conservation concern. Yet, little is known about basic ringtail ecology at the northwestern edge of its range, where the habitat differs considerably from its primary range in the southwestern United States. Diurnal rest sites, such as cavities in live and standing-dead trees, are an essential habitat element for ringtails and co-occurring mesocarnivores. Ringtails use diurnal rest sites as shelter during adverse weather conditions, refugia from predators such as the co-occurring fisher (Pekania pennanti), and dens to raise young. Understanding the forest conditions associated with rest sites selected by ringtails can inform forest management practices. We fixed very-high-frequency radio collars to 16 ringtails on the Hoopa Valley Indian Reservation in northern California to better understand the relationships between forest characteristics and fisher presence on ringtail rest-site use. We found that ringtails were more likely to select rest sites in mature older forests compared to oak woodland and open areas, and were less likely to select rest sites closer to perennial water sources. We did not detect an effect of fishers on the selection of rest sites. These results indicate that both late-seral and some early-seral forest conditions provide suitable habitat for ringtail rest sites and ultimately demonstrate that ringtails use a mosaic of seral stages in the forests of the Pacific Northwest.

Climate and land use change are two of the primary threats to global biodiversity; however, each species within a community may respond differently to these facets of global change. Although it is typically assumed that species use the habitat that is advantageous for survival and reproduction, anthropogenic changes to the environment can create ecological traps, making it critical to assess both habitat selection (e.g. where species congregate on the landscape) and the influence of selected habitats on the demographic processes that govern population dynamics. We used a long-term (1958-2011), large-scale, multi-species dataset for waterfowl that spans the United States and Canada to estimate species-specific responses to climate and land use variables in a landscape that has undergone significant environmental change across space and time. We first estimated the effects of change in climate and land use variables on habitat selection and population dynamics for nine species. We then hypothesized that species-specific responses to environmental change would scale with life-history traits, specifically: longevity, nesting phenology and female breeding site fidelity. We observed species-level heterogeneity in the demographic and habitat selection responses to climate and land use change, which would complicate community-level habitat management. Our work highlights the importance of multi-species monitoring and community-level analysis, even among closely related species. We detected several relationships between life-history traits, particularly nesting phenology, and species' responses to environmental change. One species, the early-nesting northern pintail (Anas acuta), was consistently at the extreme end of responses to land use and climate predictors and has been a species of conservation concern since their population began to decline in the 1980s. They, and the blue-winged teal, also demonstrated a positive habitat selection response to the proportion of cropland on the landscape that simultaneously reduced abundance the following year, indicative of susceptibility to ecological traps. By distilling the diversity of species' responses to environmental change within a community, our methodological approach and findings will help improve predictions of community responses to global change and can inform multi-species management and conservation plans in dynamic landscapes that are based on simple tenets of life-history theory.

Zebrafish (Danio rerio) are widely accepted as a multidisciplinary vertebrate model for neurobehavioral and clinical studies, and more recently have become established as a model for exercise physiology and behavior. Individual differences in activity level (e.g., exploration) have been characterized in zebrafish, however, how different levels of exploration correspond to differences in motivation to engage in swimming behavior has not yet been explored. We screened individual zebrafish in two tests of exploration: the open field and novel tank diving tests. The fish were then exposed to a tank in which they could choose to enter a compartment with a flow of water (as a means of testing voluntary motivation to exercise). After a 2-day habituation period, behavioral observations were conducted. We used correlative analyses to investigate the robustness of the different exploration tests. Due to the complexity of dependent behavioral variables, we used machine learning to determine the personality variables that were best at predicting swimming behavior. Our results show that contrary to our predictions, the correlation between novel tank diving test variables and open field test variables was relatively weak. Novel tank diving variables were more correlated with themselves than open field variables were to each other. Males exhibited stronger relationships between behavioral variables than did females. In terms of swimming behavior, fish that spent more time in the swimming zone spent more time actively swimming, however, swimming behavior was inconsistent across the time of the study. All relationships between swimming variables and exploration tests were relatively weak, though novel tank diving test variables had stronger correlations. Machine learning showed that three novel tank diving variables (entries top/bottom, movement rate, average top entry duration) and one open field variable (proportion of time spent frozen) were the best predictors of swimming behavior, demonstrating that the novel tank diving test is a powerful tool to investigate exploration. Increased knowledge about how individual differences in exploration may play a role in swimming behavior in zebrafish is fundamental to their utility as a model of exercise physiology and behavior.

Background Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals. Methods Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events. Results For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events. Conclusions Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors.

ABSTRACTBanding waterfowl, in combination with the citizen science provided by hunters that report marks from harvested birds, is a long‐standing, institutionalized practice for estimating probabilities of survival and exploitation (i.e., legal harvest from such populations). Range‐wide population abundance can also be estimated by combining the number of banded individuals with the number harvested from the population. Waterfowl marking with uniquely identifiable bands done during late summer in North America is often referred to as pre‐season banding. For example, mass capture of arctic geese for pre‐season banding is normally done in July (nonbreeders) or August (failed breeders and breeders with young) during flightless molt of respective groups. An important assumption for proper inference about harvest probability provided from such samples is that there is no mortality, natural or otherwise, during the interval between when individuals are marked and when hunting seasons begin. We evaluated the effect of variable mortality that could occur between marking and subsequent hunting seasons on estimates of survival, recovery, and harvest probabilities using simulation pertinent to a typical waterfowl species. We fit a Brownie tag‐recovery model to the simulated data and calculated the estimator bias that resulted from various pre‐harvest mortality scenarios. There was no effect on survival probability during the interval between annual banding in subsequent years, but recovery probability, and thus estimated harvest probability, was directly and inversely related to pre‐harvest mortality of juveniles. The magnitude of negative bias in harvest probability of juveniles increased further as the fraction of the population sampled declined. If the probability of pre‐harvest mortality differs between marked and unmarked individuals, the negative bias in harvest probability results in overestimates of derived abundance that increases as the proportion of marked individuals in the population declines. We used our observed results to propose an explanation for occasional biologically improbable estimates of abundance of juvenile lesser snow geese (Anser caerulescens). © 2021 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.

Statistical models for animal movement provide tools that help ecologists and biologists learn how animals interact with their environment and each other. Efforts to develop increasingly realistic, implementable, and scientifically valuable methods for analyzing remotely observed trajectories have provided practitioners with a wide selection of models to help them understand animal behavior. Increasingly, researchers are interested in studying multiple animals jointly, which requires methods that can account for dependence across individuals. Dependence can arise for many reasons, including shared behavioral tendencies, familial relationships, and direct interactions on the landscape. We provide a synopsis of recent statistical methods for animal movement data applicable to settings in which inference is desired across multiple individuals. Highlights of these approaches include the ability to infer shared behavioral traits across a group of individuals and the ability to infer unobserved social networks summarizing dynamic relationships that manifest themselves in movement decisions. This article is categorized under: Statistical Models > Bayesian Models Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Data: Types and Structure > Social Networks

Anthropogenic landscape alteration and climate change can have multiscale and interrelated effects on ecological systems. Such changes to the environment can disrupt the connection between habitat quality and the cues that species use to identify quality habitat, which can result in an ecological trap. Ecological traps are typically difficult to identify without fine-scale information on individual survival and fitness, but this information is rarely available over large temporal and spatial scales. The Prairie Pothole Region (PPR) of the United States and Canada has undergone extensive changes in the latter half of the 20th century due to advancements in agricultural technologies, water management practices and climate change. Historically, the PPR has been a highly productive area for breeding waterfowl. While the overall trends for dabbling ducks in the PPR have exhibited increasing abundances since the late 1980s, some species, such as the northern pintail, have been declining in abundance. We used a long-term dataset of pintail counts across the PPR to separate count data into a demographic process and a habitat selection process using a hierarchical model. The hierarchical model provided an alternative way of identifying ecological traps in the absence of individual survival and fitness. Our model also allowed us to account for the indirect pathways by which climate and agriculture impact pintail through their additional contribution to wetland availability, which is a primary driver of pintail demography and habitat selection. Decoupling these processes allowed us to identify an ecological trap related to increasing cropland land cover, in which pintail selected for cropland over alternative nesting habitat, likely due to the similarities with productive native mixed-grass prairie. However, large proportions of cropland within a region resulted in fewer pintail the following year, likely due to nest failures from predation and agricultural practices. In addition, we identified several regions in Canada where this ecological trap is contributing significantly to mismatches between habitat selection and demographic processes.

As human populations continue to expand across the world, the need to understand and manage wildlife populations within the wildland-urban interface is becoming commonplace. This is especially true for large carnivores as these species are not always tolerated by the public and can pose a risk to human safety. Unfortunately, information on wildlife species within the wildland-urban interface is sparse, and knowledge from wildland ecosystems does not always translate well to human-dominated systems. Across western North America, cougars (Puma concolor) are routinely utilizing wildland-urban habitats while human use of these areas for homes and recreation is increasing. From 2007 to 2015, we studied cougar resource selection, human-cougar interaction, and cougar conflict management within the wildland-urban landscape of the northern Front Range in Colorado, USA. Resource selection of cougars within this landscape was typical of cougars in more remote settings but cougar interactions with humans tended to occur in locations cougars typically selected against, especially those in proximity to human structures. Within higher housing density areas, 83% of cougar use occurred at night, suggesting cougars generally avoided human activity by partitioning time. Only 24% of monitored cougars were reported for some type of conflict behavior but 39% of cougars sampled during feeding site investigations of GPS collar data were found to consume domestic prey items. Aversive conditioning was difficult to implement and generally ineffective for altering cougar behaviors but was thought to potentially have long-term benefits of reinforcing fear of humans in cougars within human-dominated areas experiencing little cougar hunting pressure. Cougars are able to exploit wildland-urban landscapes effectively, and conflict is relatively uncommon compared with the proportion of cougar use. Individual characteristics and behaviors of cougars within these areas are highly varied; therefore, conflict management is unique to each situation and should target individual behaviors. The ability of individual cougars to learn to exploit these environments with minimal human-cougar interactions suggests that maintaining older age structures, especially females, and providing a matrix of habitats, including large connected open-space areas, would be beneficial to cougars and effectively reduce the potential for conflict.