The ecotropic viral integration site-1 (Evi1) locus was initially identified as a common site of retroviral integration in myeloid tumors of the AKXD-23 recombinant inbred mouse strain. The full-length Evi1 transcript encodes a putative transcription factor, containing ten zinc finger motifs found within two domains of the protein. To determine the biological function of the Evi1 proto-oncogene, the full-length, but not an alternately spliced, transcript was disrupted using targeted mutagenesis in embryonic stem cells. Evi1 homozygous mutant embryos die at approximately 10.5 days post coitum. Mutants were distinguished at 10.5 days post coitum by widespread hypocellularity, hemorrhaging, and disruption in the development of paraxial mesenchyme. In addition, defects in the heart, somites, and cranial ganglia were detected and the peripheral nervous system failed to develop. These results correlated with whole-mount in situ hybridization analyses of embryos which showed expression of the Evi1 proto-oncogene in embryonic mesoderm and neural crest-derived cells associated with the peripheral nervous system. These data suggest that Evi1 has important roles in general cell proliferation, vascularization, and cell-specific developmental signaling, at midgestation.

Invasive Sus scrofa, a species commonly referred to as wild pig or feral swine, is a destructive invasive species with a rapidly expanding distribution across the United States. We used artificial wallows and small waterers to determine the minimum amount of time needed for pig eDNA to accumulate in the water source to a detectable level. We removed water from the artificial wallows and tested eDNA detection over the course of 2 weeks to understand eDNA persistence. We show that our method is sensitive enough to detect very low quantities of eDNA shed by a terrestrial mammal that has limited interaction with water. Our experiments suggest that the number of individuals shedding into a water system can affect persistence of eDNA. Use of an eDNA detection technique can benefit management efforts by providing a sensitive method for finding even small numbers of individuals that may be elusive using other methods.

Contact rates vary widely among individuals in socially structured wildlife populations. Understanding the interplay of factors responsible for this variation is essential for planning effective disease management. Feral swine (Sus scrofa) are a socially structured species which pose an increasing threat to livestock and human health, and little is known about contact structure. We analyzed 11 GPS data sets from across the United States to understand the interplay of ecological and demographic factors on variation in co‐location rates, a proxy for contact rates. Between‐sounder contact rates strongly depended on the distance among home ranges (less contact among sounders separated by >2 km; negligible between sounders separated by >6 km), but other factors causing high clustering between groups of sounders also seemed apparent. Our results provide spatial parameters for targeted management actions, identify data gaps that could lead to improved management and provide insight on experimental design for quantitating contact rates and structure.

Evaluation of the progress of management programs for invasive species is crucial for demonstrating impacts to stakeholders and strategic planning of resource allocation. Estimates of abundance before and after management activities can serve as a useful metric of population management programs. However, many methods of estimating population size are too labor intensive and costly to implement, posing restrictive levels of burden on operational programs. Removal models are a reliable method for estimating abundance before and after management using data from the removal activities exclusively, thus requiring no work in addition to management. We developed a Bayesian hierarchical model to estimate abundance from removal data accounting for varying levels of effort, and used simulations to assess the conditions under which reliable population estimates are obtained. We applied this model to estimate site-specific abundance of an invasive species, feral swine (Sus scrofa), using removal data from aerial gunning in 59 site/time-frame combinations (480-19,600 acres) throughout Oklahoma and Texas, USA. Simulations showed that abundance estimates were generally accurate when effective removal rates (removal rate accounting for total effort) were above 0.40. However, when abundances were small (<50) the effective removal rate needed to accurately estimates abundances was considerably higher (0.70). Based on our post-validation method, 78% of our site/time frame estimates were accurate. To use this modeling framework it is important to have multiple removals (more than three) within a time frame during which demographic changes are minimized (i.e., a closed population; ≤3 months for feral swine). Our results show that the probability of accurately estimating abundance from this model improves with increased sampling effort (8+ flight hours across the 3-month window is best) and increased removal rate. Based on the inverse relationship between inaccurate abundances and inaccurate removal rates, we suggest auxiliary information that could be collected and included in the model as covariates (e.g., habitat effects, differences between pilots) to improve accuracy of removal rates and hence abundance estimates.

Knowledge of population density is necessary for effective management and conservation of wildlife, yet rarely are estimators compared in their robustness to effects of ecological and observational processes, which can greatly influence accuracy and precision of density estimates. In this study, we simulate biological and observational processes using empirical data to assess effects of animal scale of movement, true population density, and probability of detection on common density estimators. We also apply common data collection and analytical techniques in the field and evaluate their ability to estimate density of a globally widespread species. We find that animal scale of movement had the greatest impact on accuracy of estimators, although all estimators suffered reduced performance when detection probability was low, and we provide recommendations as to when each field and analytical technique is most appropriately employed. The large influence of scale of movement on estimator accuracy emphasizes the importance of effective post-hoc calculation of area sampled or use of methods that implicitly account for spatial variation. In particular, scale of movement impacted estimators substantially, such that area covered and spacing of detectors (e.g. cameras, traps, etc.) must reflect movement characteristics of the focal species to reduce bias in estimates of movement and thus density.

An estimate or index of target species density is important in determining oral rabies vaccination (ORV) bait densities to control and eliminate specific rabies variants. From 1997–2011, we indexed raccoon (Procyon lotor) densities 253 times based on cumulative captures on 163 sites from Maine to Alabama, USA, near ORV zones created to prevent raccoon rabies from spreading to new areas. We conducted indexing under a common cage trapping protocol near the time of annual ORV to aid in bait density decisions. Unique raccoons (n = 8,415) accounted for 68.0% of captures (n = 12,367). We recaptured raccoons 2,669 times. We applied Schnabel and Huggins mark‐recapture models on sites with ≥3 years of capture data and ≥25% recaptures as context for raccoon density indexes (RDIs). Simple linear relationships between RDIs and mark‐recapture estimates supported application of our index. Raccoon density indexes ranged from 0.0–56.9 raccoons/km2. For bait density decisions, we evaluated RDIs in the following 4 raccoon density groups, which were statistically different: (0.0–5.0 [n = 70], 5.1–15.0 [n = 129], 15.1–25.0 [n = 31], and >25.0 raccoons/km2 [n = 23]). Mean RDI was positively associated with a higher percentage of developed land cover and a lower percentage of evergreen forest. Non‐target species composition (excluding recaptured raccoons) accounted for 32.0% of captures. Potential bait competitors accounted for 76.5% of non‐targets. The opossum (Didelphis virginiana) was the primary potential bait competitor from 27°N to 44°N latitude, north of which it was numerically replaced by the striped skunk (Mephitis mephitis). We selected the RDI approach over mark‐recapture methods because of costs, geographic scope, staff availability, and the need for supplemental serologic samples. The 4 density groups provided adequate sensitivity to support bait density decisions for the current 2 bait density options. Future improvements to the method include providing random trapping locations to field personnel to prevent trap clustering and marking non‐targets to better characterize bait competitors. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.

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 emptyanimal 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 | 10375 TABAK eT Al.

Context Management of overabundant or invasive species is a constant challenge because resources for management are always limited and relationships between management costs, population density and damage costs are complex and difficult to predict. Metrics of management success are often based on simple measures, such as counts, which may not be indicative of impacts on damage reduction or cost-effectiveness under different management plans. Aims The aims of this study were to evaluate the effectiveness of aerial gunning for the management of wild pigs (Sus scrofa), and to evaluate how cost-effectiveness would vary under different relationships between levels of damage and densities of wild pigs. Methods Repeated reduction events were conducted by aerial gunning on three consecutive days at three study sites. Using a removal model, the proportion of the population removed by each flight was estimated and population modelling was used to show the time it would take for a population to recover. Three possible damage–density relationships were then used to show the level of damage reduction (metric of success) from different management intensities and levels of population recovery, and these relationships were expressed in terms of total costs (including both damage and management costs). Key results Populations were typically reduced by ~31% for the first flight, ~56% after two flights and ~67% after three flights. When the damage relationship suggests high damage even at low densities, the impact of one, two or three flights would represent a reduction in damage of 2%, 19% and 60% respectively after 1 year. Different damage relationships may show considerable damage reduction after only one flight. Removal rates varied by habitat (0.05 per hour in open habitats compared with 0.03 in shrubby habitats) and gunning team (0.03 versus 0.05). Conclusions Monitoring the efficacy of management provides critical guidance and justification for control activities. The efficacy of different management strategies is dependent on the damage–density relationship and needs further study for effective evaluation of damage reduction efforts. Implications It is critically important to concurrently monitor density and damage impacts to justify resource needs and facilitate planning to achieve a desired damage reduction goal.