Nonconsumptive effects of predators potentially have negative fitness consequences on prey species through changes in prey behavior. Coyotes (Canis latrans) recently expanded into the eastern United States, and raccoons (Procyon lotor) are a common mesocarnivore that potentially serve as competitors and food for coyotes. We used camera traps at baited sites to quantify vigilance behavior of feeding raccoons and used binomial logistic regression to analyze the effects of social and environmental factors. Additionally, we created raccoon and coyote activity patterns from the camera trap data by fitting density functions based on circular statistics and calculating the coefficient of overlap (Δ). Overall, raccoons were vigilant 46% of the time while foraging at baited sites. Raccoons were more vigilant during full moon and diurnal hours but less vigilant as group size increased and when other species were present. Raccoons and coyotes demonstrated nocturnal activity patterns, with coyotes more likely to be active during daylight hours. Overall, raccoons did not appear to exhibit high levels of vigilance. Activity pattern results provided further evidence that raccoons do not appear to fear coyotes, as both species were active at the same time and showed a high degree of overlap (Δ = 0.75) with little evidence of temporal segregation in activity. Thus, our study indicates that nonconsumptive effects of coyotes on raccoons are unlikely, which calls into question the ability of coyotes to initiate strong trophic cascades through some mesocarnivores.

A recent study of Sitka black‐tailed deer Odocoileus hemionus sitkensis demonstrated that opportunistic fawn capture yielded left‐truncated data and ultimately resulted in overestimating fawn survival and spurious ecological model inference compared to neonates captured via vaginal implant transmitters (VITs). Given the ecological and economic value of ungulates worldwide and the importance of neonate survival to understanding population dynamics, the potential biases in survival estimates and causes of mortality caused by left‐truncation must be transparent. Herein, we used a VIT‐based dataset from white‐tailed deer Odocoileus virginianus to examine potential problems with left‐truncated data. We manipulated our original VIT‐based dataset by randomly assigning age‐at‐capture to create three hypothetical opportunistic samples. We used the Kaplan—Meier estimator to quantify fawn survival to 16 weeks of age for the original and hypothetical datasets. Additionally, we compared the relative importance of mortality causes between the datasets. Survival for the original, VIT‐based dataset was 0.121 (SE = 0.043), while hypothetical datasets yielded overestimates (ranging from 0.191 to 0.234). The hypothetical opportunistic samples overestimated coyote predation as a source of mortality, while underestimating starvation. Because management actions rely on accurate estimates of survival and causes of mortality, we recommend that neonatal survival studies consider biases caused by capture method. For robust estimates of survival, VIT‐based samples appear to provide better estimates of survival, as opportunistic samples are biased high. We encourage future work to elucidate the potential for neonate capture technique to affect cause‐specific mortality.

In conjunction with efforts to assess pathogen exposure in feral pigs from the southeastern United States, we amplified Bartonella henselae, B. koehlerae, and B. vinsonii subsp. berkhoffii from blood samples. Feral pigs may represent a zoonotic risk for hunters or butchers and pose a potential threat to domesticated livestock.

We compared survival of neonate white-tailed deer Odocoileus virginianus captured using vaginal-implant transmitters (VITs) and traditional ground searches to determine if capture method affects neonate survival. During winter 2003, 14 adult female radio-collared deer were fitted with VITs to aid in the spring capture of neonates; neonates were captured using VITs (N=14) and traditional ground searches (N=7). Of the VITs, seven (50%) resulted in the location of birth sites and the capture of 14 neonates. However, seven (50%) VITs were prematurely expelled prior to parturition. Predation accounted for seven neonate mortalities, and of these, five were neonates captured using VITs. During summer 2003, survival for neonates captured using VITs one, two, and three months post capture was 0.76 (SE=0.05; N=14), 0.64 (SE=0.07; N=11) and 0.64 (SE=0.08; N=9), respectively. Neonate survival one, two and three months post capture for neonates captured using ground searches was 0.71 (SE=0.11; N=7), 0.71 (SE=0.15; N=5) and 0.71 (SE=0.15; N=5), respectively. Although 71% of neonates that died were captured <24 hours after birth using VITs, survival did not differ between capture methods. Therefore, use of VITs to capture neonate white-tailed deer did not influence neonate survival. VITs enabled us to capture neonates in dense habitats which would have been difficult to locate using traditional ground searches.

Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf’s ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid.