Invasive Wild pigs as primary nest predators for Wild turkeys

Depredation of wild turkey (Meleagris gallopavo) nests is a leading cause of reduced recruitment for the recovering and iconic game species. invasive wild pigs (Sus scrofa) are known to depredate nests, and have been expanding throughout the distributed range of wild turkeys in north America. We sought to gain better insight on the magnitude of wild pigs depredating wild turkey nests. We constructed simulated wild turkey nests throughout the home ranges of 20 GPS-collared wild pigs to evaluate nest depredation relative to three periods within the nesting season (i.e., early, peak, and late) and two nest densities (moderate = 12.5-25 nests/km2, high = 25-50 nests/km2) in south-central Texas, USA during March–June 2016. Overall, the estimated probability of nest depredation by wild pigs was 0.3, equivalent to native species of nest predators in the study area (e.g., gray fox [Urocyon cinereoargenteus], raccoon [Procyon lotor], and coyote [Canis latrans]). female wild pigs exhibited a constant rate of depredation regardless of nesting period or density of nests. However, male wild pigs increased their rate of depredation in areas with higher nest densities. Management efforts should remove wild pigs to reduce nest failure in wild turkey populations especially where recruitment is low

Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public

Background: Prevention and control of wildlife disease invasions relies on the ability to predict spatio-temporal dynamics and understand the role of factors driving spread rates, such as seasonality and transmission distance. Passive disease surveillance (i.e., case reports by public) is a common method of monitoring emergence of wildlife diseases, but can be challenging to interpret due to spatial biases and limitations in data quantity and quality. Methodology/Principal findings: We obtained passive rabies surveillance data from dead striped skunks (Mephitis mephitis) in an epizootic in northern Colorado, USA. We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling. We estimated the distance travelled per transmission event, direction of invasion, rate of spatial spread, and effects of infection density and season. We also estimated mean transmission distance and rates of spatial spread using a phylogeographic approach on a subsample of viral sequences from the same epizootic. Both the occupancy and phylogeographic approaches predicted similar rates of spatio-temporal spread. Estimated mean transmission distances were 2.3 km (95% Highest Posterior Density (HPD95): 0.02, 11.9; phylogeographic) and 3.9 km (95% credible intervals (CI95): 1.4, 11.3; occupancy). Estimated rates of spatial spread in km/year were: 29.8 (HPD95: 20.8, 39.8; phylogeographic, branch velocity, homogenous model), 22.6 (HPD95: 15.3, 29.7; phylogeographic, diffusion rate, homogenous model) and 21.1 (CI95: 16.7, 25.5; occupancy). Initial colonization probability was twice as high in spring relative to fall. Conclusions/Significance: Skunk-to-skunk transmission was primarily local (< 4 km) suggesting that if interventions were needed, they could be applied at the wave front. Slower viral invasions of skunk rabies in western USA compared to a similar epizootic in raccoons in the eastern USA implies host species or landscape factors underlie the dynamics of rabies invasions. Our framework provides a straightforward method for estimating rates of spatial spread of wildlife diseases

Refractive Structure-From-Motion Through a Flat Refractive Interface

Recovering 3D scene geometry from underwater images involves the Refractive Structure-from-Motion (RSfM) problem, where the image distortions caused by light refraction at the interface between different propagation media invalidates the single view point assumption. Direct use of the pinhole camera model in RSfM leads to inaccurate camera pose estimation and consequently drift. RSfM methods have been thoroughly studied for the case of a thick glass interface that assumes two refractive interfaces between the camera and the viewed scene. On the other hand, when the camera lens is in direct contact with the water, there is only one refractive interface. By explicitly considering a refractive interface, we develop a succinct derivation of the refractive fundamental matrix in the form of the generalised epipolar constraint for an axial camera. We use the refractive fundamental matrix to refine initial pose estimates obtained by assuming the pinhole model. This strategy allows us to robustly estimate underwater camera poses, where other methods suffer from poor noise-sensitivity. We also formulate a new four view constraint enforcing camera pose consistency along a video which leads us to a novel RSfM framework. For validation we use synthetic data to show the numerical properties of our method and we provide results on real data to demonstrate performance within laboratory settings and for applications in endoscopy

Invasive Wild pigs as primary nest predators for Wild turkeys

Depredation of wild turkey (Meleagris gallopavo) nests is a leading cause of reduced recruitment for the recovering and iconic game species. invasive wild pigs (Sus scrofa) are known to depredate nests, and have been expanding throughout the distributed range of wild turkeys in north America. We sought to gain better insight on the magnitude of wild pigs depredating wild turkey nests. We constructed simulated wild turkey nests throughout the home ranges of 20 GPS-collared wild pigs to evaluate nest depredation relative to three periods within the nesting season (i.e., early, peak, and late) and two nest densities (moderate = 12.5-25 nests/km2, high = 25-50 nests/km2) in south-central Texas, USA during March–June 2016. Overall, the estimated probability of nest depredation by wild pigs was 0.3, equivalent to native species of nest predators in the study area (e.g., gray fox [Urocyon cinereoargenteus], raccoon [Procyon lotor], and coyote [Canis latrans]). female wild pigs exhibited a constant rate of depredation regardless of nesting period or density of nests. However, male wild pigs increased their rate of depredation in areas with higher nest densities. Management efforts should remove wild pigs to reduce nest failure in wild turkey populations especially where recruitment is low

Low Secondary Risks for Captive Coyotes from a Sodium Nitrite Toxic Bait for Invasive Wild Pigs

An acute toxic bait is being developed to deliver micro‐encapsulated sodium nitrite (SN) to stimulate severe methemoglobinemia and humane death for invasive wild pigs (Sus scrofa), thereby providing a new tool for reducing their populations. During April 2016, we evaluated sensitivity to SN and outcomes of secondary consumption in the ubiquitous mammalian scavenger, coyote (Canis latrans), to determine secondary risks of consuming carcasses of wild pigs that died from consuming the SN toxic bait. At the National Wildlife Research Center in Fort Collins, Colorado, USA, we first evaluated whether coyotes fed carcasses of domestic pigs killed by consumption of SN bait showed signs of SN intoxication. Second, we conducted chemical analysis of residual SN in the coyotes for evidence of SN passing from pigs to coyotes. Last, we conducted an acute oral toxicity test (LD50) with SN for coyotes by feeding them meatballs containing capsules of SN. We found no evidence that captive coyotes experienced SN intoxication from consuming on carcasses that had been freshly poisoned with SN, despite consuming ¯ x = 1.6 kg of tissues/coyote within 24 hours. None of the captive coyotes consumed digestive tracts or stomach contents from poisoned carcasses, which contained the highest levels of residual SN. Chemical analysis indicated that only ≤34.14 mg/kg of residual SN were passed from the tissues of the pigs into the coyotes, confirming that SN does not bioaccumulate. All coyotes quickly vomited various doses of SN during the LD50 test and fully recovered, suggesting a natural defense against secondary poisoning from SN. Testing with captive coyotes indicates that the risks of secondary poisoning for free‐ranging coyotes are likely low, although field‐testing should be used to confirm

Dynamically balanced online random forests for interactive scribble-based segmentation

Interactive scribble-and-learning-based segmentation is attractive for its good performance and reduced number of user interaction. Scribbles for foreground and background are often imbalanced. With the arrival of new scribbles,the imbalance ratio may change largely. Failing to deal with imbalanced training data and a changing imbalance ratio may lead to a decreased sensitivity and accuracy for segmentation. We propose a generic Dynamically Balanced Online Random Forest (DyBa ORF) to deal with these problems,with a combination of a dynamically balanced online Bagging method and a tree growing and shrinking strategy to update the random forests. We validated DyBa ORF on UCI machine learning data sets and applied it to two different clinical applications: 2D segmentation of the placenta from fetal MRI and adult lungs from radiographic images. Experiments show it outperforms traditional ORF in dealing with imbalanced data with a changing imbalance ratio,while maintaining a comparable accuracy and a higher efficiency compared with its offline counterpart. Our results demonstrate that DyBa ORF is more suitable than existing ORF for learning-based interactive image segmentation

Detection of prion protein in the cerebrospinal fluid of elk (\u3ci\u3eCervus canadensis nelsoni\u3c/i\u3e) with chronic wasting disease using protein misfolding cyclic amplification

Cerebrospinal fluid (CSF) has been examined as a possible source for preclinical diagnosis of prion diseases in hamsters and sheep. The present report describes the detection of chronic wasting disease (CWD) in the CSF of elk and evaluates its usefulness as an antemortem test for CWD. The CSF from 6 captive and 31 free-ranging adult elk was collected at necropsy and evaluated for the presence of the abnormal isoform of the prion protein that has been associated with CWD (PrPCWD) via protein misfolding cyclic amplification. Additionally, the obex from each animal was examined by immunohistochemistry (IHC). Four out of 6 captive animals were CWD-positive and euthanized due to signs of terminal CWD. The remaining 2 were CWD negative. None of the 31 free-range animals showed overt signs of CWD, but 12 out of 31 tested positive for CWD by IHC. Protein misfolding cyclic amplification detected PrPCWD from 3 of the 4 captive animals showing clinical signs of CWD and none of the nonclinical animals that were CWD positive by IHC. The data suggests that CWD prions can be detected in the CSF of elk, but only relatively late in the course of the disease

Deep learning-based fetoscopic mosaicking for field-of-view expansion

PURPOSE: Fetoscopic laser photocoagulation is a minimally invasive surgical procedure used to treat twin-to-twin transfusion syndrome (TTTS), which involves localization and ablation of abnormal vascular connections on the placenta to regulate the blood flow in both fetuses. This procedure is particularly challenging due to the limited field of view, poor visibility, occasional bleeding, and poor image quality. Fetoscopic mosaicking can help in creating an image with the expanded field of view which could facilitate the clinicians during the TTTS procedure. METHODS: We propose a deep learning-based mosaicking framework for diverse fetoscopic videos captured from different settings such as simulation, phantoms, ex vivo, and in vivo environments. The proposed mosaicking framework extends an existing deep image homography model to handle video data by introducing the controlled data generation and consistent homography estimation modules. Training is performed on a small subset of fetoscopic images which are independent of the testing videos. RESULTS: We perform both quantitative and qualitative evaluations on 5 diverse fetoscopic videos (2400 frames) that captured different environments. To demonstrate the robustness of the proposed framework, a comparison is performed with the existing feature-based and deep image homography methods. CONCLUSION: The proposed mosaicking framework outperformed existing methods and generated meaningful mosaic, while reducing the accumulated drift, even in the presence of visual challenges such as specular highlights, reflection, texture paucity, and low video resolution