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Urban biodiversity provides critical ecosystem services and is a key component to environmentally and socially sustainable cities. However, biodiversity varies greatly within and among cities, leading to human communities with changing and unequal experiences with nature. The "luxury effect," a hypothesis that predicts a positive correlation between wealth, typically measured by per capita income, and species richness may be one indication of these inequities. While the luxury effect is well studied for some taxa, it has rarely been investigated for mammals, which provide unique ecosystem services (e.g., biological pest control) and exhibit significant potential for negative human-wildlife interactions (e.g., nuisances or conflicts). We analyzed a large dataset of mammal detections across 20 North American cities to test whether the luxury effect is consistent for medium- to large-sized terrestrial mammals across diverse urban contexts. Overall, support for the luxury effect, as indicated by per capita income, was inconsistent; we found evidence of a luxury effect in approximately half of our study cities. Species richness was, however, highly and negatively correlated with urban intensity in most cities. We thus suggest that economic factors play an important role in shaping urban mammal communities for some cities and species, but that the strongest driver of urban mammal diversity is urban intensity. To better understand the complexity of urban ecosystems, ecologists and social scientists must consider the social and political factors that drive inequitable human experiences with nature in cities.

Invasive wild pigs (Sus scrofa) have been spread by humans outside of their native range and are now established on every continent except Antarctica. Through their uprooting of soil, they affect societal and environmental values. Our recent article explored another threat from their soil disturbance: greenhouse gas emissions (O'Bryan et al., Global Change Biology, 2021). In response to our paper, Don (Global Change Biology, 2021) claims there is no threat to global soil carbon stocks by wild pigs. While we did not investigate soil carbon stocks, we examine uncertainties regarding soil carbon emissions from wild pig uprooting and their implications for management and future research.

To control invasive species and prioritize limited resources, managers need information about population size to evaluate the current state of the problem, the trend in population growth through time, and to understand the potential magnitude of the problem in the absence of management actions. This information is critical for informing management actions and allocating resources. We used two national-scale data sets to estimate historical, current, and future potential population size of invasive wild pigs (Sus scrofa; hereafter wild pigs) in the United States. Between 1982 to present, the Southeastern Cooperative Wildlife Disease Study mapped the distribution of wild pigs in the United States. In addition, recent research has predicted potential population density of wild pigs across the United States by evaluating broad-scale landscape characteristics. We intersected these two data sets to estimate the population size of wild pigs in 1982, 1988, 2004, 2010, 2013, and 2016. In addition, we estimated potential population size if wild pigs were present at equilibrium conditions in all available habitat in each state. We demonstrate which states have experienced recent population growth of wild pigs and are predicted to experience the greatest population increase in the future without sufficient management actions and policy implementation. Regions in the western, northern, and northeastern United States contain no or few wild pig populations, but could potentially support large numbers of these animals if their populations become established. This information is useful in identifying regions at greatest risk if wild pigs become established, which can assist in prioritizing management actions aimed at controlling or eliminating this invasive species across broad to local scales.

Human activity affects plant and animal populations across local to global scales, and the management of recreation areas often aims to reduce such impacts. Specifically, by understanding patterns of human activity and its influence on animal populations, parks and recreation areas can be managed to provide spatial and temporal refuge to wildlife most sensitive to this type of human disturbance. However, additional research is necessary to understand how human activity influences wildlife populations, habitat use, and activity patterns for a diversity of wildlife species. We studied the potential impacts of human activity (as measured by nonmotorized recreationists) on populations and activity patterns of 12 mammal species, including herbivores and carnivores, from 63 motion‐activated cameras that sampled game trails and human trails with varying degrees of human activity along the Front Range of Colorado. Human activity was greatest during the day and minimal or absent during the night. All wildlife species in our study used human trails, although the extent to which human recreation altered the occupancy, relative habitat use, and activity patterns of wildlife varied across species, where some animals appeared to be more influenced by human activity than others. Some species (e.g., fox squirrel, red fox, and striped skunk) did not demonstrate a response to human activity. Other species (e.g., black bear, coyote, and mule deer) altered their activity patterns on recreation trails to be more active at night. Across all wildlife, the degree to which animals altered activity patterns on human trails was related to their natural activity patterns and how active they were during the day when human activity was greatest; species that exhibited greater overlap in natural activity patterns with humans demonstrated the greatest shifts in their activity, often exhibiting increased nocturnal activity. Further, some species (e.g., Abert’s squirrel, bobcat, and mountain lion) exhibited reduced occupancy and/or habitat use in response to human recreation. Managing spatial and temporal refuges for wildlife would likely reduce the impacts of human recreation on animals that use habitat in proximity to trail networks.

An individual’s spatial behavior is shaped by social and environmental factors and provides critical information about population processes to inform conservation and management actions. Heterogeneity in spatial overlap among conspecifics can be evaluated using estimates of home ranges and core areas and used to understand factors influencing space use and territoriality. To understand and test predictions about spatial behavior in an invasive large mammal, the wild pig (Sus scrofa), we examined variation in space use between sexes and seasons. We predicted that if animals were territorial that there would be a reduction in space-use overlap when comparing overlap of home ranges (HR-HR), to home ranges and core areas (HR-CA), and in-turn to core areas (CA-CA). Home ranges and core areas were estimated for 54 wild pigs at Buck Island Ranch, FL from GPS telemetry data. Overlap indices were calculated to estimate the strength (space-use overlap) and number of potential interactions within three wet seasons (June-October) and two dry seasons (December-April). Across all seasons, males exhibited larger home ranges compared to females (M = 10.36±0.79 km² (±SE), F = 3.21±0.16 km²) and interacted with a greater number of individuals than females. Home range size and strength of interactions did not vary between wet and dry seasons. Consistent with our predictions, wild pigs appeared to exhibit territorial behavior, where strength of overlap decreased when comparing HR-HR to HR-CA and HR-CA to CA-CA. Strength of overlap varied by sex, which was lowest between females, intermediate between females and males, and greatest between males. Our framework can be used to understand patterns of space use and territoriality in populations, which has important implications in understanding population processes and how pathogens and parasites might spread within and among populations.

Human activity and land use change impact every landscape on Earth, driving declines in many animal species while benefiting others. Species ecological and life history traits may predict success in human-dominated landscapes such that only species with "winning" combinations of traits will persist in disturbed environments. However, this link between species traits and successful coexistence with humans remains obscured by the complexity of anthropogenic disturbances and variability among study systems. We compiled detection data for 24 mammal species from 61 populations across North America to quantify the effects of (1) the direct presence of people and (2) the human footprint (landscape modification) on mammal occurrence and activity levels. Thirty-three percent of mammal species exhibited a net negative response (i.e., reduced occurrence or activity) to increasing human presence and/or footprint across populations, whereas 58% of species were positively associated with increasing disturbance. However, apparent benefits of human presence and footprint tended to decrease or disappear at higher disturbance levels, indicative of thresholds in mammal species' capacity to tolerate disturbance or exploit human-dominated landscapes. Species ecological and life history traits were strong predictors of their responses to human footprint, with increasing footprint favoring smaller, less carnivorous, faster-reproducing species. The positive and negative effects of human presence were distributed more randomly with respect to species trait values, with apparent winners and losers across a range of body sizes and dietary guilds. Differential responses by some species to human presence and human footprint highlight the importance of considering these two forms of human disturbance separately when estimating anthropogenic impacts on wildlife. Our approach provides insights into the complex mechanisms through which human activities shape mammal communities globally, revealing the drivers of the loss of larger predators in human-modified landscapes.

During the worldwide shutdown in response to the COVID‐19 pandemic, many reports emerged of urban wildlife sightings. While these images garnered public interest and declarations of wildlife reclaiming cities, it is unclear whether wildlife truly reoccupied urban areas or whether there were simply increased detections of urban wildlife during this time. Here, we detail key questions and needs for monitoring wildlife during the COVID‐19 shutdown and then link these with future needs and actions with the intent of improving conservation within urban ecosystems. We discuss the tools ecologists and conservation scientists can use to safely and effectively study urban wildlife during the shutdown. With a coordinated, multicity effort, researchers and community scientists can rigorously investigate the responses of wildlife to changes in human activities, which can help us address long‐standing questions in urban ecology, inspire conservation of wildlife, and inform the design of sustainable cities.

Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths., We provide an R package (MLWIC2), which includes two models with 97% validation accuracy, one that recognizes 58 animal species from camera trap images and another that removes empty images. The R package includes Shiny Applications, allowing users to apply trained models and train new models using a point‐and‐click interface.

Motion-activated cameras (camera traps) are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or out-of-distribution in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

Recreational use of public land is high and rising. Thus, it is critical to understand the dynamics of land use at the wildland—urban interface so managers can simultaneously meet varying human needs while mitigating environmental impacts caused by recreationists and their canine companions. Using motion-activated camera surveys along official trails near the urban edge of Boulder, Colorado, we quantified daily patterns and the relative frequency of human recreational activities, with particular focus on activities involving domestic dogs (Canis lupus familiaris) and human compliance with dog regulations. We also tested hypotheses regarding potential predictors of human activity as well as predictors of human accompaniment by dogs, both on-leash and off, and user compliance with leash laws. Pedestrians used the trail system most frequently, followed by dog-walkers, mountain bikers, and recreationists of other types, with activity peaking in the mornings and evenings. Use of open space lands was primarily, and positively, predicted by the presence of scenic vistas or trails on which users could be accompanied by their dogs. Compliance with dog regulations depended on the type of policy imposed, with stricter and more consistent restrictions resulting in fewer violations. This study suggests that to maximize compliance with leash laws, consistent policies may be more effective than temporally varying leash laws. Additionally, it may be beneficial to increase enforcement at peak times for all trails, and at all times on trails where violations are more likely, such as those with seasonal leash laws.

Functional responses describe how changing resource availability affects consumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro-ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and availability. We linked heterogeneous movement data across different agro-ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs (Sus scrofa), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental-scale movement data within agro-ecosystems to quantify the functional response of agricultural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as distance to crops altered the magnitude of the functional response. There was a strong agricultural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non-agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found significant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depending on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our understanding of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro-ecosystems.

1. Motion-activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or “out-of-distribution” in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies.4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

Ongoing global landscape change resulting from urbanization is increasingly linked to changes in species distributions and community interactions. However, relatively little is known about how urbanization influences competitive interactions among mammalian carnivores, particularly related to wild felids. We evaluated interspecific interactions between medium‐ and large‐sized carnivores across a gradient of urbanization and multiple scales. Specifically, we investigated spatial and temporal interactions of bobcats and pumas by evaluating circadian activity patterns, broad‐scale seasonal interactions, and fine‐scale daily interactions in wildland–urban interface (WUI), exurban residential development, and wildland habitats. Across levels of urbanization, interspecific interactions were evaluated using two‐species and single‐species occupancy models with data from motion‐activated cameras. As predicted, urbanization increased the opportunity for interspecific interactions between wild felids. Although pumas did not exclude bobcats from areas at broad spatial or temporal scales, bobcats responded behaviorally to the presence of pumas at finer scales, but patterns varied across levels of urbanization. In wildland habitat, bobcats avoided using areas for short temporal periods after a puma visited an area. In contrast, bobcats did not appear to avoid areas that pumas recently visited in landscapes influenced by urbanization (exurban development and WUI habitat). In addition, overlap in circadian activity patterns between bobcats and pumas increased in exurban development compared to wildland habitat. Across study areas, bobcats used sites less frequently as the number of puma photographs increased at a site. Overall, bobcats appear to shape their behavior at fine spatial and temporal scales to reduce encounters with pumas, but residential development can potentially alter these strategies and increase interaction opportunities. We explore three hypotheses to explain our results of how urbanization affected interspecific interactions that consider activity patterns, landscape configuration, and animal scent marking. Altered competitive interactions between animals in urbanized landscapes could potentially increase aggressive encounters and the frequency of disease transmission.

Urbanization is a primary driver of landscape conversion, with far-reaching effects on landscape pattern and process, particularly related to the population characteristics of animals. Urbanization can alter animal movement and habitat quality, both of which can influence population abundance and persistence. We evaluated three important population characteristics (population density, site occupancy, and species detection probability) of a medium-sized and a large carnivore across varying levels of urbanization. Specifically, we studied bobcat and puma populations across wildland, exurban development, and wildland-urban interface (WUI) sampling grids to test hypotheses evaluating how urbanization affects wild felid populations and their prey. Exurban development appeared to have a greater impact on felid populations than did habitat adjacent to a major urban area (i.e., WUI); estimates of population density for both bobcats and pumas were lower in areas of exurban development compared to wildland areas, whereas population density was similar between WUI and wildland habitat. Bobcats and pumas were less likely to be detected in habitat as the amount of human disturbance associated with residential development increased at a site, which was potentially related to reduced habitat quality resulting from urbanization. However, occupancy of both felids was similar between grids in both study areas, indicating that this population metric was less sensitive than density. At the scale of the sampling grid, detection probability for bobcats in urbanized habitat was greater than in wildland areas, potentially due to restrictive movement corridors and funneling of animal movements in landscapes influenced by urbanization. Occupancy of important felid prey (cottontail rabbits and mule deer) was similar across levels of urbanization, although elk occupancy was lower in urbanized areas. Our study indicates that the conservation of medium- and large-sized felids associated with urbanization likely will be most successful if large areas of wildland habitat are maintained, even in close proximity to urban areas, and wildland habitat is not converted to low-density residential development.

Transmission of pathogens among animals is influenced by demographic, social, and environmental factors. Anthropogenic alteration of landscapes can impact patterns of disease dynamics in wildlife populations, increasing the potential for spillover and spread of emerging infectious diseases in wildlife, human, and domestic animal populations. We evaluated the effects of multiple ecological mechanisms on patterns of pathogen exposure in animal populations. Specifically, we evaluated how ecological factors affected the prevalence of Toxoplasma gondii (Toxoplasma), Bartonella spp. (Bartonella), feline immunodeficiency virus (FIV), and feline calicivirus (FCV) in bobcat and puma populations across wildland-urban interface (WUI), low-density exurban development, and wildland habitat on the Western Slope (WS) and Front Range (FR) of Colorado during 2009-2011. Samples were collected from 37 bobcats and 29 pumas on the WS and FR. As predicted, age appeared to be positively related to the exposure to pathogens that are both environmentally transmitted (Toxoplasma) and directly transmitted between animals (FIV). In addition, WS bobcats appeared more likely to be exposed to Toxoplasma with increasing intraspecific space-use overlap. However, counter to our predictions, exposure to directly-transmitted pathogens (FCV and FIV) was more likely with decreasing space-use overlap (FCV: WS bobcats) and potential intraspecific contacts (FIV: FR pumas). Environmental factors, including urbanization and landscape covariates, were generally unsupported in our models. This study is an approximation of how pathogens can be evaluated in relation to demographic, social, and environmental factors to understand pathogen exposure in wild animal populations.

Competitive interactions between species are fundamental to understanding species assemblages, community dynamics, and ecological processes. Anthropogenic landscape change, particularly resulting from urbanization, can alter interspecific interactions; however, different forms of urbanization are predicted to have contrasting effects on competitive interactions. We developed contact networks between bobcats and pumas to evaluate (1) the potential for interspecific interactions between wild felids and (2) how space-use metrics might change along the urban gradient, including low-density exurban development, wildland–urban interface, and wildland habitat, at both the population and individual level. We used an extensive telemetry data set for bobcats and pumas across multiple study areas to evaluate four space-use metrics: space-use overlap (used to define potential interactions among animals) and three additional contact network metrics, including degree (the number of potentially interacting animals), in-strength (sum of space-use overlap for animals), and equivalent social connectivity (ESC; considering both space-use extent and the amount of space-use overlap). Space-use extent was an important predictor of potential social interactions as measured by space-use metrics. Bobcats appeared to have a greater opportunity to interact with female pumas based on space-use overlap, degree, and in-strength, which demonstrates that relative scale of space-use extent among animals could be important for understanding interactions; ESC, however, was greater between bobcats and male pumas, likely due to the larger space-use extent by male compared to female pumas and the positive relationship between space-use extent and ESC. In addition, pumas and male bobcats exhibited a greater opportunity to interact, based on space-use overlap, degree, and in-strength, and demonstrated higher ESC compared to female bobcats. Counter to our predictions, felids associated with urbanized grids or with greater amounts of urbanization in their extent of space use did not appear to exhibit greater values of space-use metrics compared to animals with less exposure to urbanization; these results appear consistent with previous research evaluating population characteristics of felids across broad scales in our study areas. Greater ESC for male pumas and male bobcats suggests that males could be particularly important for facilitating connectivity of some ecological processes, such as the transmission of disease, through interspecific contact networks.

There have been few assessments of the performance of alternative resistance surfaces, and little is known about how connectivity modeling approaches differ in their ability to predict organism movements. In this paper, we evaluate the performance of four connectivity modeling approaches applied to two resistance surfaces in predicting the locations of highway crossings by American black bears in the northern Rocky Mountains, USA. We found that a resistance surface derived directly from movement data greatly outperformed a resistance surface produced from analysis of genetic differentiation, despite their heuristic similarities. Our analysis also suggested differences in the performance of different connectivity modeling approaches. Factorial least cost paths appeared to slightly outperform other methods on the movement-derived resistance surface, but had very poor performance on the resistance surface obtained from multi-model landscape genetic analysis. Cumulative resistant kernels appeared to offer the best combination of high predictive performance and sensitivity to differences in resistance surface parameterization. Our analysis highlights that even when two resistance surfaces include the same variables and have a high spatial correlation of resistance values, they may perform very differently in predicting animal movement and population connectivity.

Carrying behavior is exhibited for myriad purposes by a variety of animals, including mammalian carnivores, but it has been poorly studied. We used wildlife camera data to document and describe carrying behavior in a suite of native and domestic mammalian carnivores in Colorado, USA. Our objectives were to estimate the carrying rate for each carnivore species, assess the relationship between carrying rate and carnivore body mass, compare items carried to known diets and with proximity to urbanization, and explore seasonal variation in carrying rate. We documented carrying behavior in red foxes (Vulpes vulpes), coyotes (Canis latrans), bobcats (Lynx rufus), gray foxes (Urocyon cinereoargenteus), pumas (Puma concolor), and domestic cats (Felis catus). These carnivores carried objects at relatively low rates (range 0.44%–3.40%), although smaller-bodied carnivores carried items at greater rates than larger-bodied species. Prey items recorded by remote cameras were consistent with vertebrates known to be consumed by each species, and anthropogenic and synanthropic items were more common near the wildland-urban interface. For red fox, the species with the largest sample of carrying events, the rate of carrying varied by season, with increased rates during spring and summer months. Systematic exploration of carrying behavior has the potential to lend insight into carnivore diet in space and time and into shifts in dietary patterns along the wildland-urban interface. We demonstrate the power of camera trap data to examine these relationships and encourage further research of carrying behavior.

Feline immunodeficiency virus (FIV) classically infects felid species with highly divergent species-specific FIVs. However, recent studies have detected an FIV strain infecting both bobcats (Lynx rufus) and pumas (Puma concolor) in California and Florida. To further investigate this observation, we evaluated FIV from bobcats in Florida (n=25) and Colorado (n=80) between 2008 and 2011. Partial viral sequences from five Florida bobcats cluster with previously published sequences from Florida panthers. We did not detect FIV in Colorado bobcats.

Understanding how landscape, host, and pathogen traits contribute to disease exposure requires systematic evaluations of pathogens within and among host species and geographic regions. The relative importance of these attributes is critical for management of wildlife and mitigating domestic animal and human disease, particularly given rapid ecological changes, such as urbanization. We screened > 1000 samples from sympatric populations of puma (Puma concolor), bobcat (Lynx rufus), and domestic cat (Felis catus) across urban gradients in six sites, representing three regions, in North America for exposure to a representative suite of bacterial, protozoal, and viral pathogens (Bartonella sp., Toxoplasma gondii, feline herpesvirus-1, feline panleukopenea virus, feline calicivirus, and feline immunodeficiency virus). We evaluated prevalence within each species, and examined host trait and land cover determinants of exposure; providing an unprecedented analysis of factors relating to potential for infections in domesticated and wild felids. Prevalence differed among host species (highest for puma and lowest for domestic cat) and was greater for indirectly transmitted pathogens. Sex was inconsistently predictive of exposure to directly transmitted pathogens only, and age infrequently predictive of both direct and indirectly transmitted pathogens. Determinants of pathogen exposure were widely divergent between the wild felid species. For puma, suburban land use predicted increased exposure to Bartonella sp. in southern California, and FHV-1 exposure increased near urban edges in Florida. This may suggest interspecific transmission with domestic cats via flea vectors (California) and direct contact (Florida) around urban boundaries. Bobcats captured near urban areas had increased exposure to T. gondii in Florida, suggesting an urban source of prey Bobcats captured near urban areas in Colorado and Florida had higher FIV exposure, possibly suggesting increased intraspecific interactions through pile-up of home ranges. Beyond these regional and pathogen specific relationships, proximity to the wildland-urban interface did not generally increase the probability of disease exposure in wild or domestic felids, empha- sizing the importance of local ecological determinants. Indeed, pathogen exposure was often negatively associated with the wildland-urban interface for all felids. Our analyses suggest cross-species pathogen transmission events around this interface may be infrequent, but followed by self-sustaining propagation within the new host species. virus; puma (Puma concolor); Toxoplasma gondii; urbanization.

Activity patterns of animals can vary depending on both endogenous and environmental factors. Although black bears exhibit substantial variation in activity across the year, relatively little is known about how daily activity patterns and associated movement rates differ among sex and age classes across seasons. We used fine-scale movement data from black bears fitted with GPS collars to evaluate movement and activity patterns across sex and age classes for daily and seasonal time periods in relation to mating and foraging behavior. Black bears were most active during crepuscular time periods, moderately active during the day, and least active at night, which is consistent with previous research on black bear activity patterns in areas where bears were not greatly influenced by human activities. However, during spring and early summer, adult males moved significantly more during the crepuscular time period than they did during late summer and fall. Female and subadult males exhibited very similar activity patterns between seasons; and during late summer and fall, all bear classes exhibited similar activity patterns across daily time periods. Differences in activity patterns of males between seasons were potentially related to extensive movements undertaken during the mat- ing season. Although adult males exhibited lower movement rates in late summer and fall compared to spring and early summer, females and subadult males did not appear to reduce movement rates during the late-summer berry season. Our research reveals that, although bear classes exhibit similar temporal patterns of daily activity and inactivity, the magnitude of movement rates revealed differences in activity patterns among bear classes, which allowed us to better understand factors influencing activity in animal populations. RESUMEN.—Los patrones de actividad de los animales pueden variar dependiendo, tanto los factores endogenos, como los ambientales. A pesar de que los osos negros exhiben gran variacion en su actividad durante el ano, se sabe rela- tivamente poco acerca de como los patrones de actividad cotidiana y las tasas de movimientos asociados difieren entre los sexos y clases de edad a lo largo de las estaciones. Usamos datos de escala fina del movimiento de los osos negros, a traves de collares con sistema de posicionamiento global (SPG), para evaluar los patrones de movimiento y actividad por sexo y por clases de edad, durante periodos de tiempo diarios y estacionales con relacion al comportamiento de apareamiento y de forrajeo. Los osos negros fueron mas activos durante los periodos de tiempo crepuscular; moderadamente activos durante el dia y menos activos durante la noche, lo cual es consistente con las investigaciones previas en las que se evaluaban los patrones de actividad del oso negro en lugares donde no eran fuertemente influenciados por las actividades humanas. Sin embargo, durante la primavera y el comienzo del verano, los machos adultos se movieron significantemente mas durante los periodos crepusculares, comparado con el final del verano y el otono. Las hembras y los machos subadultos mostraron patrones de actividad muy similares entre las estaciones; y para finales del verano y el otono, todas las clases de osos exhibieron patrones de actividad similares durante los periodos de tiempo diarios. Las diferencias en los patrones de actividad de los machos entre estaciones se relacionaron potencialmente a los extensos movimientos emprendidos durante la epoca de apareamiento. A pesar de que los machos adultos exhibieron tasas bajas de movimiento a finales del verano y en el otono, en comparacion con la primavera y principios del verano, las hembras y los machos subadultos no aparentaron disminuir las tasas de movimiento durante la epoca de bayas al final del verano. Nuestra investigacion muestra que, a pesar de que las clases de osos exhiben patrones temporales similares de actividad e inactividad diaria, la magnitud de las tasas de movimiento revelo diferencias en los patrones de actividad entre las clases de osos, lo cual nos permitio entender mejor los factores que influyen en la actividad de las poblaciones de animales.

Roads and associated human development are prevalent features in many landscapes and can have significant impacts on wildlife populations, including the creation of barriers to movement that can reduce connectivity within and among populations. We evaluated how a highway in the northern Rocky Mountains influenced movements of American black bears (Ursus americanus) in a landscape modified by humans, resulting from human residences, recreation, and resource extraction. The objectives of the study were to determine habitat characteristics that were selected at locations where bears crossed the highway and develop and evaluate a predictive model for road-crossing locations based on habitat characteristics. Eleven out of 23 black bears fitted with GPS collars crossed highway 95 at least once. When bears crossed the highway they selected for specific habitat attributes, at both roadside and landscape scales, characterized by forested areas away from human development, with additional important habitat features including distance to cover, amount of shrub along the highway, and distance to water. Validation of our model with 24 independent highway crossing events from 2 years demonstrated high model predictability. We described how animal movement data can be used to identify and predict important road crossing areas for wildlife, which can assist in designing planning strategies for roadways and landscapes to facilitate wildlife movement to maintain connectivity among populations and to reduce wildlife–vehicle collisions.

Individual-based landscape genetic analyses provide empirically based models of gene flow. It would be valuable to verify the predictions of these models using independent data of a different type. Analyses using different data sources that produce consistent results provide strong support for the generality of the findings. Mating and dispersal movements are the mechanisms through which gene flow operates in animal populations. The best means to verify landscape genetic predictions would be to use movement data to independently predict landscape resistance. We used path-level, conditional logistic regression to predict landscape resistance for American black bear (Ursus americanus) in a landscape in which previous work predicted population connectivity using individual-based landscape genetics. We found consistent landscape factors influence genetic differentiation and movement path selection, with strong similarities between the predicted landscape resistance surfaces. Genetic differentiation in American black bear is driven by spring movement (mating and dispersal) in relation to residential development, roads, elevation and forest cover. Given the limited periods of the year when gene flow events primarily occur, models of landscape connectivity should carefully consider temporal changes in functional landscape resistance.

By studying animal movements, researchers can gain insight into many of the ecological characteristics and processes important for understanding population-level dynamics. We developed a Brownian bridge movement model (BBMM) for estimating the expected movement path of an animal, using discrete location data obtained at relatively short time intervals. The BBMM is based on the properties of a conditional random walk between successive pairs of locations, dependent on the time between locations, the distance between locations, and the Brownian motion variance that is related to the animal's mobility. We describe two critical developments that enable widespread use of the BBMM, including a derivation of the model when location data are measured with error and a maximum likelihood approach for estimating the Brownian motion variance. After the BBMM is fitted to location data, an estimate of the animal's probability of occurrence can be generated for an area during the time of observation. To illustrate potential applications, we provide three examples: estimating animal home ranges, estimating animal migration routes, and evaluating the influence of fine-scale resource selection on animal movement patterns.

Summary 1 Global positioning system (GPS) technology enables researchers to evaluate wildlife movements, space use and resource selection in detail for extended periods of time. Two types of errors, missed location fixes and location error, are inherent to GPS telemetry and can bias location data sets. Habitat characteristics can influence both types of errors, but no studies have reported how continuous ranges of canopy cover and terrain simultaneously affect location error at different positional dilution of precision (PDOP) and signal quality levels. This information can assist in developing a protocol for removing large location errors from GPS data sets. 2 The objectives of this study were to quantify how canopy cover and terrain affected GPS collar performance within a mountainous region of northern Idaho, USA, and evaluate different data-screening options for GPS location data sets from stationary test collars and free-ranging black bears Ursus americanus. 3 The fix rate for test collars was very high in all habitats (mean 99·5%, SE 0·14, range 97·9–100%) and was not related to canopy cover or terrain obstruction. However, habitat variables strongly influenced location error, PDOP values and proportion of three-dimensional (3-D) fixes. The 95% circular error probable (CEP) equalled 106·8 m for locations at all test sites, and varied substantially with canopy cover, terrain obstruction and signal quality categories, ranging from 14·3 m to 557·0 m. Location errors for two-dimensional (2-D) fixes were more variable at higher PDOP values and were significantly larger compared with 3-D fixes. 4 Data screening increased the accuracy of test collar location data sets by removing large location errors that were associated with high PDOP values. Data-screening options that focused on screening 2-D locations were most effective in reducing location error and retaining the greatest number of locations. For black bear data sets, the four data screening options resulted in data reduction ranging from 8% to 35%. 5 Synthesis and applications. We have demonstrated how location data can be analysed and screened based on 2-D and 3-D fixes in relation to PDOP values to eliminate locations with potentially large location errors. This information can be applied to GPS location data for individual animals to increase data accuracy for analyses.

Motion-activated cameras are a versatile tool that wildlife biologists can use for sampling wild animal populations to estimate species occurrence. Occupancy modelling provides a flexible framework for the analysis of these data; explicitly recognizing that given a species occupies an area the probability of detecting it is often less than one. Despite the number of studies using camera data in an occupancy framework, there is only limited guidance from the scientific literature about survey design trade-offs when using motion-activated cameras. A fuller understanding of these trade-offs will allow researchers to maximise available resources and determine whether the objectives of a monitoring program or research study are achievable. We use an empirical dataset collected from 40 cameras deployed across 160 km(2) of the Western Slope of Colorado, USA to explore how survey effort (number of cameras deployed and the length of sampling period) affects the accuracy and precision (i.e., error) of the occupancy estimate for ten mammal and three virtual species. We do this using a simulation approach where species occupancy and detection parameters were informed by empirical data from motion-activated cameras. A total of 54 survey designs were considered by varying combinations of sites (10-120 cameras) and occasions (20-120 survey days). Our findings demonstrate that increasing total sampling effort generally decreases error associated with the occupancy estimate, but changing the number of sites or sampling duration can have very different results, depending on whether a species is spatially common or rare (occupancy = ψ) and easy or hard to detect when available (detection probability = p). For rare species with a low probability of detection (i.e., raccoon and spotted skunk) the required survey effort includes maximizing the number of sites and the number of survey days, often to a level that may be logistically unrealistic for many studies. For common species with low detection (i.e., bobcat and coyote) the most efficient sampling approach was to increase the number of occasions (survey days). However, for common species that are moderately detectable (i.e., cottontail rabbit and mule deer), occupancy could reliably be estimated with comparatively low numbers of cameras over a short sampling period. We provide general guidelines for reliably estimating occupancy across a range of terrestrial species (rare to common: ψ = 0.175-0.970, and low to moderate detectability: p = 0.003-0.200) using motion-activated cameras. Wildlife researchers/managers with limited knowledge of the relative abundance and likelihood of detection of a particular species can apply these guidelines regardless of location. We emphasize the importance of prior biological knowledge, defined objectives and detailed planning (e.g., simulating different study-design scenarios) for designing effective monitoring programs and research studies.

Background Reliable predictions of regional-scale population connectivity are needed to prioritize conservation actions. However, there have been few examples of regional connectivity models that are empirically derived and validated. The central goals of this paper were to (1) evaluate the effectiveness of factorial least cost path corridor mapping on an empirical resistance surface in reflecting the frequency of highway crossings by American black bear, (2) predict the location and predicted intensity of use of movement corridors for American black bear, and (3) identify where these corridors cross major highways and rank the intensity of these crossings. Results We used factorial least cost path modeling coupled with resistant kernel analysis to predict a network of movement corridors across a 30.2 million hectare analysis area in Montana and Idaho, USA. Factorial least cost path corridor mapping was associated with the locations of actual bear highway crossings. We identified corridor-highway intersections and ranked these based on corridor strength. We found that a major wildlife crossing overpass structure was located close to one of the most intense predicted corridors, and that the vast majority of the predicted corridor network was “protected” under federal management. However, narrow, linear corridors connecting the Greater Yellowstone Ecosystem to the rest of the analysis area had limited protection by federal ownership, making these additionally vulnerable to habitat loss and fragmentation. Conclusions Factorial least cost path modeling coupled with resistant kernel analysis provides detailed, synoptic information about connectivity across populations that vary in distribution and density in complex landscapes. Specifically, our results could be used to quantify the structure of the connectivity network, identify critical linkage nodes and core areas, map potential barriers and fracture zones, and prioritize locations for mitigation, restoration and conservation actions. Electronic supplementary material The online version of this article (doi:10.1186/2051-3933-1-12) contains supplementary material, which is available to authorized users.