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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.

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.

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 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.

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.

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.

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.

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.