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1. The eruption of invasive wild pigs (IWPs) Sus scrofa throughout the world exemplifies the need to understand the influences of exotic and nonnative species expansions. In particular, the continental USA is precariously threatened by a rapid expansion of IWPs, and a better understanding of the rate and process of spread can inform strategies that will limit the expansion. 2. We developed a spatially and temporally dynamic model to examine three decades (1982-2012) of IWP expansion, and predict the spread of IWPs throughout the continental USA, relative to where IWPs previously inhabited. We used the model to predict where IWPs are likely to invade next. 3. The average rate of northward expansion increased from 6·5 to 12·6 km per year, suggesting most counties in the continental USA could be inhabited within the next 3-5 decades. The spread of IWPs was primarily associated with expansion into areas with similar environmental characteristics as their previous range, with the exception of spreading into colder regions. We identified that climate change may assist spread into northern regions by generating milder winters with less snow. Otherwise, the spread of IWPs was not dependent on agriculture, precipitation or biodiversity at the county level. The model correctly predicted 86% of counties that were invaded during 2012, and those predictions indicate that large portions of the USA are in immediate danger of invasion. 4. Synthesis and applications. Anti-invasion efforts should focus along the boundaries of current occupied range to stop natural expansion, and anti-invasion policies should focus on stopping anthropogenic transport and release of invasive wild pigs. Our results demonstrate the utility of a spatio-temporal examination to inform strategies for limiting the spread of invasive wild pigs.

Elucidating correlations between wild pig ( Sus scrofa ) behavior and landscape attributes can aid in the advancement of management strategies for controlling populations. Using GPS data from 49 wild pigs in the southeastern U.S., we used hidden Markov models to define movement path characteristics and assign behaviors (e.g., resting, foraging, travelling). We then explored the connection between these behaviors and resource selection for both sexes between two distinct seasons based on forage availability (i.e., low forage, high forage). Females demonstrated a crepuscular activity pattern in the high-forage season and a variable pattern in the low-forage season, while males exhibited nocturnal activity patterns across both seasons. Wild pigs selected for bottomland hardwoods and dense canopy cover in all behavioral states in both seasons. Males selected for diversity in vegetation types while foraging in the low-forage season compared to the high-forage season and demonstrated an increased use of linear anthropogenic features across seasons while traveling. Wild pigs can establish populations and home ranges in an array of landscapes, but our results demonstrate male and female pigs exhibit clear differences in movement behavior and there are key resources associated with common behaviors that can be targeted to improve the efficiency of management programs.

The spatiotemporal distribution of resources is a critical component of realized animal distributions. In agricultural landscapes, space use by generalist consumers is influenced by ephemeral resource availability that may produce behavioral differences across agricultural seasons, resulting in economic and production consequences and increased human-wildlife conflict. Our objective was to assess changes in habitat selection across seasons in an invasive generalist omnivore (feral pigs, Sus scrofa). Hypothesizing that pig space use is primarily driven by forage availability, we predicted strong selection for the most nutritionally beneficial crops and resource types as agricultural seasons progressed. We deployed GPS collars on 13 adult feral pigs in the Mississippi Alluvial Valley to study resource selection in a fragmented agricultural landscape. We estimated resource selection using mixed-effect logistic regression to assess variation in selection across planting, growing, harvest, and fallow seasons. We found that feral pigs varied resource selection across seasons, particularly for corn (Zea mais). We also detected seasonal dependencies in proportional coverage on the net probability of selection of a land unit (e.g., selection was generally strongest for locations composed of both agricultural and natural habitat), resulting in marked variation in predicted space use among agricultural seasons. These findings indicate behavioral changes in selection across agricultural seasons are driven by complex interactions between the availabilities of temporally dynamic resources and temporally static natural cover. Temporal variations in resource selection trends indicate seasonal responses to crop phenology which suggests a season-specific habitat functional response.

Many pathogens of humans and livestock also infect wildlife that can act as a reservoir and challenge disease control or elimination. Efficient and effective prioritization of research and management actions requires an understanding of the potential for new tools to improve elimination probability with feasible deployment strategies that can be implemented at scale. Wildlife vaccination is gaining interest as a tool for managing several wildlife diseases. To evaluate the effect of vaccinating white-tailed deer ( Odocoileus virginianus ), in combination with harvest, in reducing and eliminating bovine tuberculosis from deer populations in Michigan, we developed a mechanistic age-structured disease transmission model for bovine tuberculosis with integrated disease management. We evaluated the impact of pulse vaccination across a range of vaccine properties. Pulse vaccination was effective for reducing disease prevalence rapidly with even low (30%) to moderate (60%) vaccine coverage of the susceptible and exposed deer population and was further improved when combined with increased harvest. The impact of increased harvest depended on the relative strength of transmission modes, i.e., direct vs indirect transmission. Vaccine coverage and efficacy were the most important vaccine properties for reducing and eliminating disease from the local population. By fitting the model to the core endemic area of bovine tuberculosis in Michigan, USA, we identified feasible integrated management strategies involving vaccination and increased harvest that reduced disease prevalence in free-ranging deer. Few scenarios led to disease elimination due to the chronic nature of bovine tuberculosis. A long-term commitment to regular vaccination campaigns, and further research on increasing vaccines efficacy and uptake rate in free-ranging deer are important for disease management.

Body mass of wild pigs ( Sus scrofa ) can provide useful information regarding reproductive capacity of a population, and population health and resilience for this highly-destructive invasive species. Body mass of females is an indicator of whether they reproduce before 1 year of age, which could have substantial impacts on reproductive capacity of a population. Measuring body mass can be difficult because large wild pigs may require > 1 person to weigh, are often located in remote areas making equipment difficult to transport, or are often culled without access to the carcass (e.g., shooting from aircraft). We evaluated our ability to accurately estimate the body mass of wild pigs by visual inspection, and identified which factors (i.e., morphometrics and demographics) influenced the accuracy of our estimates. We visually estimated the body mass of wild pigs and then collected actual weights of 1,210 wild pigs across 5 regions (Alabama, Texas, Hawaii, Guam, Queensland). We also collected morphometric measurements and age to evaluate how these factors impacted our estimates. On average we found our estimates were accurate, averaging only -0.14 kg underestimated weights across all wild pigs weighed. However, our estimates were most severely underestimated (e.g., up to -20 kg) for younger wild pigs (i.e., < 1–3 years) that were heavier (i.e., > 30 kg). We also confirmed that although growth rates slowed after 1 year of age, wild pigs continued to grow in body length, head length, height, and girth as they aged, which explained why the age of an animal influenced our ability to generate accurate estimates. We surmised that young-yet-heavy wild pigs were disproportionally stouter than older animals, thus were underestimated due to their shortened appearance. Underestimating the body mass for young-yet-heavy females could misinform management plans, because these animals may have substantial influence on the reproductive capacity of a population. For visually estimating body mass of wild pigs, we recommend considering indicators of age (e.g., morphological proportions) to avoid underestimating young-yet-heavy animals with stout appearances. We also recommend calibrating observers regularly using known weights and morphometrics.

Effective management of widespread invasive species such as wild pigs (Sus scrofa) is limited by resources available to devote to the effort. Better insight of the effectiveness of different management strategies on population dynamics is important for guiding decisions of resource allocation over space and time. Using a dynamic population model, we quantified effects of culling intensities and time between culling events on population dynamics of wild pigs in the USA using empirical culling patterns and data-based demographic parameters. In simulated populations closed to immigration, substantial population declines (50-100%) occurred within 4 years when 20-60% of the population was culled annually, but when immigration from surrounding areas occurred, there was a maximum of 50% reduction, even with the maximum culling intensity of 60%. Incorporating hypothetical levels of fertility control with realistic culling intensities was most effective in reducing populations when they were closed to immigration and when intrinsic population growth rate was too high (> = 1.78) to be controlled by culling alone. However, substantial benefits from fertility control used in conjunction with culling may only occur over a narrow range of net population growth rates (i.e., where net is the result of intrinsic growth rates and culling) that varies depending on intrinsic population growth rate. The management implications are that the decision to use fertility control in conjunction with culling should rely on concurrent consideration of achievable culling intensity, underlying demographic parameters, and costs of culling and fertility control. The addition of fertility control reduced abundance substantially more than culling alone, however the effects of fertility control were weaker than in populations without immigration. Because these populations were not being reduced substantially by culling alone, fertility control could be an especially helpful enhancement to culling for reducing abundance to target levels in areas where immigration can't be prevented.

Rabies is an ancient viral disease that significantly impacts human and animal health throughout the world. In the developing parts of the world, dog bites represent the highest risk of rabies infection to people, livestock, and other animals. However, in North America, where several rabies virus variants currently circulate in wildlife, human contact with the raccoon rabies variant leads to the highest per capita population administration of post-exposure prophylaxis (PEP) annually. Previous rabies variant elimination in raccoons (Canada), foxes (Europe), and dogs and coyotes (United States) demonstrates that elimination of the raccoon variant from the eastern US is feasible, given an understanding of rabies control costs and benefits and the availability of proper tools. Also critical is a cooperatively produced strategic plan that emphasizes collaborative rabies management among agencies and organizations at the landscape scale. Common management strategies, alone or as part of an integrated approach, include the following: oral rabies vaccination (ORV), trap-vaccinate-release (TVR), and local population reduction. As a complement, mathematical and statistical modeling approaches can guide intervention planning, such as through contact networks, circuit theory, individual-based modeling, and others, which can be used to better understand and predict rabies dynamics through simulated interactions among the host, virus, environment, and control strategy. Strategies derived from this ecological lens can then be optimized to produce a management plan that balances the ecological needs and program financial resources. This paper discusses the management and modeling strategies that are currently used, or have been used in the past, and provides a platform of options for consideration while developing raccoon rabies virus elimination strategies in the US.

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 km 2 (± SE), F = 3.21 ± 0.16 km 2 ). 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.

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems [“GPS”]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous‐time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions—individuals occurring at the same location, but at different times—while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease‐relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30‐min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM‐Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine‐scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer–resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers. This study developed a method to estimate absolute animal interactions in space and time using continuous‐time movement models (CTMM) fit to GPS tracking data, which set up the foundation for understanding various ecological processes

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.