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Author(s): Teton, Ben Scott | Advisor(s): Young, Hillary S | Abstract: Estimating population abundance through time is an essential component of wildlife conservation and management. However, accurate population monitoring can be difficult and expensive for many elusive species occurring across large, dynamic landscapes. Thus, wildlife managers require methods that accurately estimate population abundance, while also minimizing field effort and cost. We estimated abundance of invasive wild pigs (Sus scrofa) on Tejon Ranch in the Tehachapi Mountains of California, using natural markings to identify individuals for mark-resight population estimation. Wild pigs in this region, like many species not traditionally identified using natural marks, are generally homogeneous in appearance with distinctive features that range dramatically in relative visibility, uniqueness and permanence. We developed a method based on standardized thresholds of image quality and animal flank distinctiveness to account for the inherent variability of natural markings between individuals. This method was tested over a fifteen-month period between March 2015 and June 2016, using an array of 48 camera traps across a 48km² survey grid. With 18.5% of wild pigs meeting our conservative standard of identifiability, we generated absolute estimates of abundance across five consecutive three-month sampling periods using the Poisson log-normal estimator under Pollocks robust design. Using left-flank photos of both naturally marked and unmarked pigs, we generated abundance estimates ranging from 352 (SE + 56) individuals in summer 2015 to 157 (SE + 43) individuals in spring 2016. These results suggest an overall decline in the wild pig population on Tejon Ranch from 2015 to 2016, which is supported by a simultaneous decline in Ranch-wide hunter harvest totals during this period. As this mark-resight method requires no trapping or tagging of any kind, it may be utilized as a cost and resource efficient alternative to traditional mark-resight techniques that rely on ear-tags or neck-bands for individual identification of species traditionally considered unidentifiable using natural marks alone.

Reliable and efficient population estimates are a critical need for effective management of invasive wild pigs (Sus scrofa). We evaluated the use of 10‐day camera grids for rapid population assessment (RPA) of wild pigs at 3 study sites that varied in vegetation communities and wild pig densities. Study areas included Buck Island Ranch, Florida; Tejon Ranch, California; and the Savannah River Site, South Carolina, USA, during 2016–2018. Rapid population assessments grids were composed of baited camera traps spaced approximately 500 or 750 m apart. Two RPA grids were deployed per study site and each grid was deployed twice (4–6 months apart) to assess changes in response to season or population control efforts. We assessed the ability of RPA grids to track population trends, how camera number influenced estimate precision, and how relative abundance indices related to density estimates. We detected changes in occupancy probability, detection probability, and N‐mixture estimates following removal operations and between seasons, but the ability of RPA grids to track population trends was dependent on the statistical method used and number of cameras traps. Increasing the number of cameras traps used in RPA grids increased precision, and these results can be used in determining survey design and estimate choice. We found that estimates of occupancy probability, detection probability, and N‐mixture estimates were positively correlated with spatially explicit capture–recapture density estimates. Thus, these less labor‐intensive estimates from RPA grids showed potential to index the relative abundance of wild pigs in some systems. Our evaluation of RPAs indicates that using study‐specific combinations of statistical method and number of cameras can provide a useful tool for monitoring wild pig presence, tracking population trends, and evaluating the effectiveness of management actions. © 2020 The Wildlife Society.

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 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.

18 pages, 5 figures, supplementary data https://doi.org/10.1016/j.biocon.2021.109175.-- The data supporting the findings of this study are available in the Supplementary Materials (Appendix 3–5, Table A3-A5). Raw datasets (where available) and results summary tables for each analysis of human mobility and empirical datasets are deposited in a github repository: https://github.com/rjcommand/PAN-Environment, The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness, The Canada Research Chairs program provided funding for the core writing team. Field research funding was provided by A.G. Leventis Foundation; Agence Nationale de la Recherche, [grant number ANR-18-32–0010CE-01 (JCJC PEPPER)]; Agencia Estatal de Investigaci; Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação (ARDITI), [grant number M1420-09-5369-FSE-000002]; Alan Peterson; ArcticNet; Arkadaşlar; Army Corp of Engineers; Artificial Reef Program; Australia's Integrated Marine Observing System (IMOS), National Collaborative; Research Infrastructure Strategy (NCRIS), University of Tasmania; Australian Institute of Marine Science; Australian Research Council, [grant number LP140100222]; Bai Xian Asia Institute; Batubay Özkan; BC Hydro Fish and Wildlife Compensation Program; Ben-Gurion University of the Negev; Bertarelli Foundation; Bertarelli Programme in Marine Science; Bilge Bahar; Bill and Melinda Gates Foundation; Biology Society of South Australia; Boston University; Burak Över; California State Assembly member Patrick O'Donnell; California State University Council on Ocean Affairs, Science & Technology; California State University Long Beach; Canada Foundation for Innovation (Major Science Initiative Fund and funding to Oceans Network Canada), [grant number MSI 30199 for ONC]; Cape Eleuthera Foundation; Centre National d'Etudes Spatiales; Centre National de la Recherche Scientifique; Charles Darwin Foundation, [grant number 2398]; Colombian Institute for the Development of Science and Technology (COLCIENCIAS), [grant number 811–2018]; Colombian Ministry of Environment and Sustainable Development, [grant number 0041–2020]; Columbia Basin Trust; Commission for Environmental Cooperation; Cornell Lab of Ornithology; Cultural practices and environmental certification of beaches, Universidad de la Costa, Colombia, [grant number INV.1106–01–002-15, 2020–21]; Department of Conservation New Zealand; Direction de l'Environnement de Polynésie Française; Disney Conservation Fund; DSI-NRF Centre of; Excellence at the FitzPatrick Institute of African Ornithology; Ecology Project International; Emin Özgür; Environment and Climate Change Canada; European Community: RTD programme - Species Support to Policies; European Community's Seventh Framework Programme; European Union; European Union's Horizon 2020 research and innovation programme, Marie Skłodowska-Curie, [grant number 798091, 794938]; Faruk Eczacıbaşı; Faruk Yalçın Zoo; Field research funding was provided by King Abdullah University of Science and Technology; Fish and Wildlife Compensation Program; Fisheries and Oceans Canada; Florida Fish and Wildlife Conservation Commission, [grant numbers FWC-12164, FWC-14026, FWC-19050]; Fondo Europeo de Desarrollo Regional; Fonds québécois de la recherche nature et technologies; Foundation Segré; Fundação para a Ciência e a Tecnologia (FCT Portugal); Galapagos National Park Directorate research, [grant number PC-41-20]; Gordon and Betty Moore Foundation, [grant number GBMF9881 and GBMF 8072]; Government of Tristan da Cunha; Habitat; Conservation Trust Foundation; Holsworth Wildlife Research Endowment; Institute of Biology of the Southern Seas, Sevastopol, Russia; Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; Instituto Nacional de Pesquisas Espaciais (INPE), Brazil; Israeli Academy of Science's Adams Fellowship; King Family Trust; Labex, CORAIL, France; Liber Ero Fellowship; LIFE (European Union), [grant number LIFE16 NAT/BG/000874]; María de Maeztu Program for Units of Excellence in R&D; Ministry of Science and Innovation, FEDER, SPASIMM,; Spain, [grant number FIS2016–80067-P (AEI/FEDER, UE)]; MOE-Korea, [grant number 2020002990006]; Mohamed bin Zayed Species Conservation Fund; Montreal Space for Life; National Aeronautics and Space Administration (NASA) Earth and Space Science Fellowship Program; National Geographic Society, [grant numbers NGS-82515R-20]; National Natural Science Fund of China; National Oceanic and Atmospheric Administration; National Parks Board, Singapore; National Science and Technology Major Project of China; National Science Foundation, [grant number DEB-1832016]; Natural Environment Research Council of the UK; Natural Sciences and Engineering Research Council of Canada (NSERC), Alliance COVID-19 grant program, [grant numbers ALLRP 550721–20, RGPIN-2014-06229 (year: 2014), RGPIN-2016-05772 (year: 2016)]; Neiser Foundation; Nekton Foundation; Network of Centre of Excellence of Canada: ArcticNet; North Family Foundation; Ocean Tracking Network; Ömer Külahçıoğlu; Oregon State University; Parks Canada Agency (Lake Louise, Yoho, and Kootenay Field Unit); Pew Charitable Trusts; Porsim Kanaf partnership; President's International Fellowship Initiative for postdoctoral researchers Chinese Academy of Sciences, [grant number 2019 PB0143]; Red Sea Research Center; Regional Government of the Azores, [grant number M3.1a/F/025/2015]; Regione Toscana; Rotary Club of Rhinebeck; Save our Seas Foundation; Science & Technology (CSU COAST); Science City Davos, Naturforschende Gesellschaft Davos; Seha İşmen; Sentinelle Nord program from the Canada First Research Excellence Fund; Servizio Foreste e Fauna (Provincia Autonoma di Trento); Sigrid Rausing Trust; Simon Fraser University; Sitka Foundation; Sivil Toplum Geliştirme Merkezi Derneği; South African National Parks (SANParks); South Australian Department for Environment and Water; Southern California Tuna Club (SCTC); Spanish Ministry for the Ecological Transition and the Demographic Challenge; Spanish Ministry of Economy and Competitiveness; Spanish Ministry of Science and Innovation; State of California; Sternlicht Family Foundation; Suna Reyent; Sunshine Coast Regional Council; Tarea Vida, CEMZOC, Universidad de Oriente, Cuba, [grant number 10523, 2020]; Teck Coal; The Hamilton Waterfront Trust; The Ian Potter Foundation, Coastwest, Western Australian State NRM; The Red Sea Development Company; The Wanderlust Fund; The Whitley Fund; Trans-Anatolian Natural Gas Pipeline; Tula Foundation (Hakai Institute); University of Arizona; University of Pisa; US Fish and Wildlife Service; US Geological Survey; Valencian Regional Government; Vermont Center for Ecostudies; Victorian Fisheries Authority; VMRC Fishing License Fund; and Wildlife Warriors Worldwide, With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S

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.

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.

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.

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.

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.

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.