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Abstract Urbanization that occurs across a gradient from low‐ to high‐density development, is a primary driver of landscape change that can affect biodiversity. Animals balance trade‐offs in obtaining resources and avoiding anthropogenic disturbances across the gradient of urbanization to maximize their fitness. However, additional research is necessary to understand seasonal variations in how animals respond to urbanization, particularly in arid regions, where resource availability shifts drastically across seasons. Our objective was to evaluate the response of a suite of bat species to urbanization and whether species shift their response to urbanization across seasons. We predicted that the response of bats to urbanization would differ among species, with some species being more sensitive to urbanization than others. We also predicted that bat species would increase the use of moderate and highly urbanized areas in the summer season where food and water resources were assumed to be greater compared with wildland areas. To evaluate these predictions, we used a stratified random sampling design to sample 50 sites with stationary acoustic bat monitors across the gradient of urbanization in the Phoenix metropolitan area, Arizona, USA during four seasons. We identified a total of 14 bat species during 1000 survey nights. Consistent with predictions, bat species exhibited different responses to urbanization, with most species exhibiting a negative relationship with urbanization, and some species exhibiting a quadratic or positive relationship with urbanization. Counter to predictions, most species did not appear to shift their response to urbanization across seasons. Consistent with predictions, plant productivity and water were important for some species in the summer season. Differences in the response of bat species to urbanization was likely related to species traits (e.g., wing morphology and echolocation call characteristics) and behavioral strategies that influence a species' sensitivity to anthropogenic disturbances and ability to access available resources in urbanized areas. Ultimately, to promote the management and conservation of bats, it is likely important to maintain resources in urbanized areas for bats that are more tolerant of urbanization and to conserve areas of undeveloped high‐quality habitat with low anthropogenic disturbance in wildland areas for bats that are sensitive to urbanization.

Abstract Cities support abundant human and wildlife populations that are shaped indirectly and directly by human decisions, often resulting in unequal access to environmental services and accessible open spaces. Urban land cover drives biodiversity patterns across metropolitan areas, but at smaller scales that matter to local residents, neighborhood socio‐cultural factors can influence the presence and abundance of wildlife. Neighborhood income is associated with plant and animal diversity in some cities, but the influence of other social variables is less well understood, especially across desert ecosystems. We explored wildlife distribution across gradients of neighborhood ethnicity in addition to income and landscape characteristics within residential areas of metropolitan Phoenix, Arizona, USA. Utilizing data from 38 wildlife cameras deployed in public parks and undeveloped open spaces within or near suburban neighborhoods, we estimated occupancy and activity patterns of common mammal species, including species native to the Sonoran Desert (coyote [Canis latrans] and desert cottontail rabbit [Sylvilagus audubonii]), and non‐native domestic cat (Felis catus). Neighborhood ethnicity (percentage of Latino residents) appeared to exhibit a negative relationship with occupancy for coyotes and cottontail rabbits. Additionally, daily activity patterns of coyotes occurred later in the evenings and mornings in neighborhoods with higher proportions of Latino residents, but activity was unaffected by differences in neighborhood income. This study is one of the first to show that social‐ecological mechanisms associated with patterns of neighborhood ethnicity as well as income may help to shape wildlife distribution in cities. These findings have implications for equitable management and provisioning of ecosystem services for urban residents and highlight the importance of considering a range of social covariates to better understand biodiversity outcomes in urban and urbanizing areas.

Abstract Daily activity patterns of animals can be dynamic across seasons in response to changing environmental conditions. Daily activity, though, has rarely been evaluated in relation to multiple factors (e.g., seasons, demographics, and data collection methods), which could be important for understanding what drives activity patterns. Here, we evaluated the daily activity patterns of a widespread invasive species, wild pigs (Sus scrofa), across two ecologically different study areas at Buck Island Ranch, FL, and Tejon Ranch, CA, from 2015 to 2018. Using GPS telemetry data (62 individuals in FL, 21 individuals in CA) and detections from grids of systematically placed motion‐activated cameras (44 cameras in FL, 48 cameras in CA), we compared activity patterns among seasons, sexes, and data collection methods (GPS and camera traps). Overall, wild pigs were mostly active during crepuscular and nocturnal periods, with their lowest activity during diurnal periods; however, activity patterns varied due to several factors. Daily activity patterns were similar between methods, among seasons, and between sexes in the subtropical climate of Florida. In contrast, daily activity patterns exhibited greater differences between methods and among seasons in California, where seasonal differences in temperature and precipitation were more pronounced. Overall, daily activity patterns estimated using GPS telemetry and camera‐based methods not only exhibited a high degree of overlap in several comparisons but also exhibited marked differences that should be recognized. Given the increasing evaluation of daily activity patterns of animals using multiple types of datasets, our study provides ecologists with valuable information to consider when designing ecological studies, interpreting their results, and comparing research across systems and studies.

IntroductionHuman-wildlife coexistence in cities depends on how residents perceive and interact with wildlife in their neighborhoods. An individual’s attitudes toward and responses to wildlife are primarily shaped by their subjective cognitive judgments, including multi-faceted environmental values and perceptions of risks or safety. However, experiences with wildlife could also positively or negatively affect an individual’s environmental attitudes, including their comfort living near wildlife. Previous work on human-wildlife coexistence has commonly focused on rural environments and on conflicts with individual problem species, while positive interactions with diverse wildlife communities have been understudied. MethodsGiven this research gap, we surveyed wildlife attitudes of residents across twelve neighborhoods in the Phoenix Metropolitan Area, AZ to ask: how do the environments in which residents live, as well as their values, identities, and personal characteristics, explain the degree to which they are comfortable living near different wildlife groups (coyotes, foxes, and rabbits)? ResultsWe found that residents who were more comfortable living near wildlife commonly held pro-wildlife value orientations, reflecting the expectation that attitudes toward wildlife are primarily driven be an individual’s value-based judgements. However, attitudes were further influenced by sociodemographic factors (e.g., pet ownership, gender identity), as well as environmental factors that influence the presence of and familiarity with wildlife. Specifically, residents living closer to desert parks and preserves were more likely to have positive attitudes toward both coyotes and foxes, species generally regarded by residents as riskier to humans and domestic animals. DiscussionBy improving understanding of people’s attitudes toward urban wildlife, these results can help managers effectively evaluate the potential for human-wildlife coexistence through strategies to mitigate risk and facilitate stewardship.

Abstract 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 between 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). Among sexes, home range size did not vary seasonally, and males exhibited larger home ranges compared to females (M = 10.36 ± 0.79 km2 (± SE), F = 3.21 ± 0.16 km2). Strength of overlap varied by season with wild pig home ranges overlapping more during the dry season. Males interacted with a greater number of individuals of both sexes, compared to females, and exhibited greater strength of overlap during the dry season. 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. Our framework can be used to understand patterns of space use and territoriality in populations, which has important implications in understanding intraspecific interactions and population processes, such as how pathogens and parasites might spread within and among populations.

Abstract Cities are seen as quintessentially human; however, because they can offer viable habitat to many plants, animals and other forms of life, cities are also dynamic ecosystems. As urban areas expand to house more of the global human population and reduce natural habitat for wildlife, the need for wildlife‐inclusive urban planning and design becomes increasingly pressing. The 2019 Urban Wildlife Information Network Summit responded to this need by connecting a group of 80 scientists, urban planners and designers to examine the role of cities in combating the global biodiversity crisis. The Summit focused on identifying and addressing barriers to transdisciplinary work between these communities, such as disciplinary silos, varying incentive structures, funding, differences in spatio‐temporal scale, existing infrastructure and values and bias. We explore the challenges to network building for wildlife‐inclusive design and planning revealed by the Summit and offer potential solutions for overcoming these obstacles for more effective collaboration around wildlife‐inclusive cities. A free Plain Language Summary can be found within the Supporting Information of this article.

ABSTRACT 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.

Abstract 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.

Abstract 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.

Wild pigs cause around $1 billion of damage to agriculture in the United States each year — foraging on crops, breaking branches and vines, and damaging irrigation lines and fences — but little is known about how and when they access agricultural fields. We used wildlife camera traps to document and describe wild pig access to two fenced southern San Joaquin Valley farms. Pigs breached fences around agricultural fields, especially during the harvest period when crops were ripe, and almost exclusively at night, outside of the regulated, daytime recreational pig hunting period. GPS data from an adult boar revealed that pigs may travel long distances from wildlands to reach crops. The results of our case study suggest that increasing monitoring and maintenance of fences during the harvest season and removing pigs that have learned to access farms may help reduce pig damage to agricultural fields. The results also suggest a formal scientific investigation of risk factors and strategies to reduce wild pig damage is warranted.

Abstract Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors.

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.

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.

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 km2 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.

Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal's movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population‐level occurrence distributions and precluding the use of low‐frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse GPS data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 h from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2‐h data. We then subsampled the 2‐h data to one location every 12 h (a proxy for sparse data) and fitted BBMMs to the 12‐h data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species‐specific range (mule deer: 400–1200 m2; elk: 600–1600 m2) successfully delineated migration corridors similar to the 2‐h baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m2 (mule deer) and 1000 m2 (elk) decreased the inflation of the 12‐h corridors relative to the 2‐h corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe. [ABSTRACT FROM AUTHOR]

Aim: The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location: Conterminous United States. Time Period: 2010–2021. Major Taxa Studied: Twenty‐five species of mammals. Methods: We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results: Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard‐mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine‐scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions: Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa. [ABSTRACT FROM AUTHOR]

Animals adjust their habitat use patterns in response to changes in their physiological needs and environmental conditions. Understanding the mechanisms underlying resource selection and space use across time and space reveals effects of the environment on animals' decisions. We explored the effects of habitat availability and heterogeneity on the seasonal and annual space use and resource selection of two free‐roaming feral burro (Equus asinus) populations in the United States within distinct climate and habitat conditions: the Sonoran Desert and the Colorado Plateau. As an introduced yet protected species in the United States, understanding burros' interactions with habitat elements is important for their conservation and management, as well as the conservation of sympatric wildlife. We used GPS locations of female burros (72 animals across both study areas) to delineate annual and seasonal ranges and resource selection patterns. We evaluated effects of mean and CV of habitat covariates, including forage, distance to water, and topography, representing availability and heterogeneity of resources, on seasonal and annual range size of burros. Moreover, we explored how burro seasonal and annual resource selection patterns were affected by availability and heterogeneity of resources. In the Sonoran Desert study area, burros had smaller seasonal and annual ranges and constant resource selection patterns across a year, likely due to a freshwater lake in the area, making water a nonlimiting resource. Human presence was the greatest factor affecting range size and resource selection in the Sonoran Desert, where burros selected for areas near roads and human recreation. In the Colorado Plateau study area, where resources were more seasonal, we found larger range sizes and fluctuating resource selection patterns compared to the Sonoran Desert population. Spatial variation in forage, water, and topography significantly affected range size of burros inhabiting the Colorado Plateau study area. Productive habitats with available water support smaller ranges and a more consistent pattern of resource selection. Our results highlight the positive effect of habitat heterogeneity and the negative effect of habitat productivity on range size of animals. Our findings contribute to an improved understanding of habitat requirements for free‐roaming burros that currently live under various climate and habitat conditions globally. [ABSTRACT FROM AUTHOR]

High individual detection success enables precise estimates of density and the ability to monitor trends in abundance for wolverine and other low‐density species, information that is critical for the implementation and assessment of conservation measures. We evaluated a dataset that included six different wolverine capture–recapture studies over a large gradient in wolverine (Gulo gulo) density to provide recommendations for increasing detection. We examined factors related to bait station components, habitat, and seasonal timing. Accounting for variation in wolverine density and trap duration, our results suggest that bait stations setups having a run pole, frame, and camera to photograph unique ventral color patterns, in addition to hair snag devices, identify more individual wolverine than those without. The presence of snow is a habitat feature that also increases individual detection. Female detection rates were lower than male detection rates at the onset of the reproductive denning season in late February and early March compared with January and early February. We found latency to detection was independent of wolverine density, but greater in areas with human influence. Relatively high rates of genotyping success (55%) were predicted by even a single guard hair left at bait stations, while underfur required ~15 hairs for similar success. Longer sampling intervals reduced genotyping success in spring, more so for underfur than guard hair. Hair samples acquired from barbwire were of higher quality than those from either alligator clips or gun brushes. To improve individual detection for wolverine inventory and monitoring, we recommend deploying run pole setups in areas with low human disturbance that will retain snow into late spring. Extending the winter trapping effort into April and May could increase the chances of detecting denning females. Latency to detection suggests that traps should be active for more than a month, especially in human‐influenced areas, but genotyping success suggests that traps should also be cleared of hair samples at smaller intervals of a month or less, during late winter/spring. [ABSTRACT FROM AUTHOR]

Non‐native species can affect ecosystems by influencing native predator‐prey dynamics. Therefore, management interventions designed to remove non‐natives may inadvertently lead to increased predation on native species. Feral horses are widely distributed throughout the arid parts of western North America. A growing body of research indicates that horses can be an important prey species to mountain lions in ecosystems where they overlap. In December 2020, the Bureau of Land Management removed 455 horses from the Delamar Mountains, Nevada, USA. We leveraged this management intervention to implement a before–after–control–impact study to test hypotheses about predation on horses and native ungulates. We predicted (1) that horses would comprise an important part of the diet in this mixed‐prey community, (2) following removal, the proportion of horses in the diet would decrease and native ungulates would increase, and (3) mountain lion home ranges overlapping the treatment areas would increase in response to decreased prey availability. From 2018 to 2022, we investigated 1360 clusters from 29 GPS‐collared lions and identified 1056 prey items. To model the probability of a predation event (a kill), we fit a mixed‐effects logistic regression model for ungulate prey as a function of lion sex, treatment area (in/out), and treatment period (pre‐/post‐removal). We used a log‐linear regression model to evaluate changes in home range size. The most common prey were mule deer (55%), feral horses (32%), and coyotes (4%). Twenty‐two of 29 lions consumed horses, although the rate of horse consumption was highly variable across individuals. Horses of both sexes and all age classes were predated. In contrast to predictions, our models detected no effect of removals on diet composition (βinteraction = 0.30 ± 1.1), nor did the removal influence home range size (βinteraction = 0.02 ± 0.02). Despite a 46% reduction in horse abundance, we found no evidence for prey‐switching following the horse removal treatment. Removal magnitude, rapid horse immigration, and/or behavioral specialization of individual mountain lions may help explain these results. Our findings have important implications for mountain lion and feral horse management in arid environments characterized by high prey diversity, but low prey abundance. [ABSTRACT FROM AUTHOR]

Urbanization that occurs across a gradient from low‐ to high‐density development, is a primary driver of landscape change that can affect biodiversity. Animals balance trade‐offs in obtaining resources and avoiding anthropogenic disturbances across the gradient of urbanization to maximize their fitness. However, additional research is necessary to understand seasonal variations in how animals respond to urbanization, particularly in arid regions, where resource availability shifts drastically across seasons. Our objective was to evaluate the response of a suite of bat species to urbanization and whether species shift their response to urbanization across seasons. We predicted that the response of bats to urbanization would differ among species, with some species being more sensitive to urbanization than others. We also predicted that bat species would increase the use of moderate and highly urbanized areas in the summer season where food and water resources were assumed to be greater compared with wildland areas. To evaluate these predictions, we used a stratified random sampling design to sample 50 sites with stationary acoustic bat monitors across the gradient of urbanization in the Phoenix metropolitan area, Arizona, USA during four seasons. We identified a total of 14 bat species during 1000 survey nights. Consistent with predictions, bat species exhibited different responses to urbanization, with most species exhibiting a negative relationship with urbanization, and some species exhibiting a quadratic or positive relationship with urbanization. Counter to predictions, most species did not appear to shift their response to urbanization across seasons. Consistent with predictions, plant productivity and water were important for some species in the summer season. Differences in the response of bat species to urbanization was likely related to species traits (e.g., wing morphology and echolocation call characteristics) and behavioral strategies that influence a species' sensitivity to anthropogenic disturbances and ability to access available resources in urbanized areas. Ultimately, to promote the management and conservation of bats, it is likely important to maintain resources in urbanized areas for bats that are more tolerant of urbanization and to conserve areas of undeveloped high‐quality habitat with low anthropogenic disturbance in wildland areas for bats that are sensitive to urbanization. [ABSTRACT FROM AUTHOR]

Human disturbance has the potential to alter competitive interactions, favoring species better able to adapt to areas used by humans. One such species is the spotted hyena (Crocuta crocuta), which has been successful in human‐dominated areas throughout Africa. In addition, hyenas are frequently successful in kleptoparasiting prey brought down by other carnivore species (e.g., leopards [Panthera pardus]). In the Udzungwa Mountains of Tanzania, leopard density declines sharply close to human settlements, and hyenas are their only competitors. Using camera trap data and a spatio‐temporal occupancy model, we assessed the relative dominance of each species through spatial co‐occurrence, altered activity patterns and temporary site avoidance. We tested the hypothesis that hyenas gain a competitive advantage over leopards in human‐dominated areas due to their relatively higher tolerance for anthropogenic activities. We found that while hyena occupancy was best predicted by prey occupancy and not strongly affected by landscape factors associated with humans, leopards, especially male leopards, were less likely to be detected close to human settlements. Female leopards, which are smaller than males, exhibited activity shifts and temporary site avoidance in response to hyenas. In response to male leopards, hyenas shifted their activity patterns to become more active around dusk and dawn. These results suggest that while hyenas may be behaviorally dominant over female leopards, they are subordinate to male leopards. We found that male leopards and hyenas were less likely to co‐occur closer to people, especially where prey was scarce, suggesting subordinance of hyenas to male leopards may be mitigated by human disturbance. Furthermore, young male leopards shifted their activity patterns to be more diurnal in response to hyena presence, suggesting that dominance relationship between hyenas and leopards develops with age and is probably related to body size. These results indicate that human disturbance has the potential to affect the competitive relationship between leopards and hyenas in the Udzungwa Mountains, but that relationships will vary with sex and body size. [ABSTRACT FROM AUTHOR]

Cities support abundant human and wildlife populations that are shaped indirectly and directly by human decisions, often resulting in unequal access to environmental services and accessible open spaces. Urban land cover drives biodiversity patterns across metropolitan areas, but at smaller scales that matter to local residents, neighborhood socio‐cultural factors can influence the presence and abundance of wildlife. Neighborhood income is associated with plant and animal diversity in some cities, but the influence of other social variables is less well understood, especially across desert ecosystems. We explored wildlife distribution across gradients of neighborhood ethnicity in addition to income and landscape characteristics within residential areas of metropolitan Phoenix, Arizona, USA. Utilizing data from 38 wildlife cameras deployed in public parks and undeveloped open spaces within or near suburban neighborhoods, we estimated occupancy and activity patterns of common mammal species, including species native to the Sonoran Desert (coyote [Canis latrans] and desert cottontail rabbit [Sylvilagus audubonii]), and non‐native domestic cat (Felis catus). Neighborhood ethnicity (percentage of Latino residents) appeared to exhibit a negative relationship with occupancy for coyotes and cottontail rabbits. Additionally, daily activity patterns of coyotes occurred later in the evenings and mornings in neighborhoods with higher proportions of Latino residents, but activity was unaffected by differences in neighborhood income. This study is one of the first to show that social‐ecological mechanisms associated with patterns of neighborhood ethnicity as well as income may help to shape wildlife distribution in cities. These findings have implications for equitable management and provisioning of ecosystem services for urban residents and highlight the importance of considering a range of social covariates to better understand biodiversity outcomes in urban and urbanizing areas. [ABSTRACT FROM AUTHOR]

While there is increasing recognition that social processes in cities like gentrification have ecological consequences, we lack nuanced understanding of the ways gentrification affects urban biodiversity. We analyzed a large camera trap dataset of mammals (>500 g) to evaluate how gentrification impacts species richness and community composition across 23 US cities. After controlling for the negative effect of impervious cover, gentrified parts of cities had the highest mammal species richness. Change in community composition was associated with gentrification in a few cities, which were mostly located along the West Coast. At the species level, roughly half (11 of 21 mammals) had higher occupancy in gentrified parts of a city, especially when impervious cover was low. Our results indicate that the impacts of gentrification extend to nonhuman animals, which provides further evidence that some aspects of nature in cities, such as wildlife, are chronically inaccessible to marginalized human populations. [ABSTRACT FROM AUTHOR]

Daily activity patterns of animals can be dynamic across seasons in response to changing environmental conditions. Daily activity, though, has rarely been evaluated in relation to multiple factors (e.g., seasons, demographics, and data collection methods), which could be important for understanding what drives activity patterns. Here, we evaluated the daily activity patterns of a widespread invasive species, wild pigs (Sus scrofa), across two ecologically different study areas at Buck Island Ranch, FL, and Tejon Ranch, CA, from 2015 to 2018. Using GPS telemetry data (62 individuals in FL, 21 individuals in CA) and detections from grids of systematically placed motion‐activated cameras (44 cameras in FL, 48 cameras in CA), we compared activity patterns among seasons, sexes, and data collection methods (GPS and camera traps). Overall, wild pigs were mostly active during crepuscular and nocturnal periods, with their lowest activity during diurnal periods; however, activity patterns varied due to several factors. Daily activity patterns were similar between methods, among seasons, and between sexes in the subtropical climate of Florida. In contrast, daily activity patterns exhibited greater differences between methods and among seasons in California, where seasonal differences in temperature and precipitation were more pronounced. Overall, daily activity patterns estimated using GPS telemetry and camera‐based methods not only exhibited a high degree of overlap in several comparisons but also exhibited marked differences that should be recognized. Given the increasing evaluation of daily activity patterns of animals using multiple types of datasets, our study provides ecologists with valuable information to consider when designing ecological studies, interpreting their results, and comparing research across systems and studies. [ABSTRACT FROM AUTHOR]

Animals commonly form groups with conspecifics. Hypotheses on the drivers of group size often (but not always) pertain to fear of predation and food availability or quality. However, for a more complete understanding of group forming, multiple, possibly interacting mechanisms should be considered. We used camera traps, detectability estimates from distance sampling, and hierarchical Bayesian modeling to index group size and test multiple additive and interacting group‐formation hypotheses in white‐tailed deer (Odocoileus virginianus), within Indiana, USA. We found a strong relationship between our index of group size and (1) a three‐way interaction between spatiotemporal pulses in fear of natural predation risk and anthropogenic development and (2) a three‐way interaction between the local cover (defined as the amount of vegetative cover in the immediate area of the camera), the area of concealment (defined as forests or wetlands) within the larger landscape, and time of day. Specifically, we documented larger groups in areas near anthropogenic development, in areas with high predator use intensity, and when predators were active. Additionally, we found larger groups during dawn and dusk when deer were most active in locations with sparse local cover when the area of concealment in the surrounding landscape was small, and within dense local cover when the area of concealment in the surrounding landscape was large. Our methods can aid future behavioral and ecological studies, as we present a clear and easily replicable strategy that infers group membership from spatiotemporal proximity, corrects for differences in detectability, and enables sampling across large spatial and long temporal scales. Our findings indicate that fear of natural predators and anthropogenic disturbance interactively explain variation in group size. [ABSTRACT FROM AUTHOR]

This study aimed to predict the habitat suitability of Asian elephants in Madhesh Province, Nepal, by using maximum entropy (MaxEnt) modeling based on the occurrence data and environmental variables, including bioclimatic, topographic, vegetation‐related, and anthropogenic variables. The study was conducted under current and future climate scenarios for the year 2100. Among the districts of Madhesh Province, the largest suitable habitats for elephants are located in Rautahat district (30%) followed by Bara district (21%), with Dhanusha district having the smallest suitable area (1%). Elevation, slope, annual precipitation, precipitation of the driest quarter, and temperature seasonality were identified as the most important variables affecting habitat suitability. A total of 1037.3 km2 was identified as the current highly suitable habitat for elephants, primarily in grasslands and shrublands. The results of the study depict a slight increase in highly suitable areas under the emission scenarios shared socioeconomic pathways (SSP) 2–4.5 and SSP 5–8.5 but a severe decrease in suitable habitats and a drastic increase in unsuitable habitats. The research emphasizes the possible influence of human activities and land use on the living environment of elephants. The study, therefore, suggests that the authorities should prioritize future land use management to lessen the potential harm to the habitats of elephants and other endangered species in Nepal. It is also imperative to identify how elephants utilize their spatial habitat within their range to help park authorities devise efficient management strategies. [ABSTRACT FROM AUTHOR]

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 between 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). Among sexes, home range size did not vary seasonally, and males exhibited larger home ranges compared to females (M = 10.36 ± 0.79 km2 (± SE), F = 3.21 ± 0.16 km2). Strength of overlap varied by season with wild pig home ranges overlapping more during the dry season. Males interacted with a greater number of individuals of both sexes, compared to females, and exhibited greater strength of overlap during the dry season. 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. Our framework can be used to understand patterns of space use and territoriality in populations, which has important implications in understanding intraspecific interactions and population processes, such as how pathogens and parasites might spread within and among populations. [ABSTRACT FROM AUTHOR]

Cities are seen as quintessentially human; however, because they can offer viable habitat to many plants, animals and other forms of life, cities are also dynamic ecosystems.As urban areas expand to house more of the global human population and reduce natural habitat for wildlife, the need for wildlife‐inclusive urban planning and design becomes increasingly pressing.The 2019 Urban Wildlife Information Network Summit responded to this need by connecting a group of 80 scientists, urban planners and designers to examine the role of cities in combating the global biodiversity crisis.The Summit focused on identifying and addressing barriers to transdisciplinary work between these communities, such as disciplinary silos, varying incentive structures, funding, differences in spatio‐temporal scale, existing infrastructure and values and bias.We explore the challenges to network building for wildlife‐inclusive design and planning revealed by the Summit and offer potential solutions for overcoming these obstacles for more effective collaboration around wildlife‐inclusive cities. A free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

Most of Earth's terrestrial carbon is stored in the soil and can be released as carbon dioxide (CO2) when disturbed. Although humans are known to exacerbate soil CO2 emissions through land‐use change, we know little about the global carbon footprint of invasive species. We predict the soil area disturbed and resulting CO2 emissions from wild pigs (Sus scrofa), a pervasive human‐spread vertebrate that uproots soil. We do this using models of wild pig population density, soil damage, and their effect on soil carbon emissions. Our models suggest that wild pigs are uprooting a median area of 36,214 km2 (mean of 123,517 km2) in their non‐native range, with a 95% prediction interval (PI) of 14,208 km2–634,238 km2. This soil disturbance results in median emissions of 4.9 million metric tonnes (MMT) CO2 per year (equivalent to 1.1 million passenger vehicles or 0.4% of annual emissions from land use, land‐use change, and forestry; mean of 16.7 MMT) but that it is highly uncertain (95% PI, 0.3–94 MMT CO2) due to variability in wild pig density and soil dynamics. This uncertainty points to an urgent need for more research on the contribution of wild pigs to soil damage, not only for the reduction of anthropogenically related carbon emissions, but also for co‐benefits to biodiversity and food security that are crucial for sustainable development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For saltmarsh habitats, we draw from a uniform distribution with a minimum of -7.3 and a maximum of 0, which represents the proportion change of soil respiration after wild pig soil disturbance. First, Don (2021) points out that we did not differentiate between two pathways of CO SB 2 sb emissions - those that occur from the decomposition of SOC (heterotrophic respiration) and those from plant roots (autotrophic respiration) under wild pig soil disturbance. GLO:EVN/01feb22:gcb15992-fig-0001.jpg PHOTO (COLOR): 1 Model results of CO2 emissions in million metric tonnes (MMT; log10 scale) from wild pig (Sus scrofa) uprooting in their non-native geographic range derived from 10,000 simulations of wild pig density, soil disturbance, and proportion change in soil CO2 emissions from wild pig soil disturbance (see O'Bryan et al., 2021). [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Motion‐activated cameras ("camera traps") are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, 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) that allows the users to (a) use 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 analysing images. Our r package makes these methods accessible to ecologists. [ABSTRACT FROM AUTHOR]

The list of the reviewers who contributed to the journal's manuscript evaluation is presented including Ramiro Aguilar, Susana Agusti, and T. Mitchell Aide.