Home-range fidelity and the effect of supplemental feeding on contact rates between white-tailed deer in southern Illinois

White-tailed deer (Odocoileous virginianus) are an important game animal and provide intrinsic value to many people. However, disease has become of great concern within white-tailed deer populations. Frequency of contract drives the establishment and spread of infectious diseases among susceptible hosts. Supplemental feed provided to increase white-tailed deer survival or create hunting opportunities, as well as bait stations to aid in capture of deer, may increase contact opportunities and disease transfer. My objective was to quantify the effects of bait sites on indirect contact between deer. I examined data from global positioning system (GPS) collars placed on 27 deer near Carbondale, Illinois, USA, from 2002 to 2005. Location data from GPS collars were used to ensure that I quantified contacts between deer in separate social groups, based on the volume of intersection of their spatial utilization distributions and correlation of movements. I matched 35 bait site locations and control sites not containing bait based on local land cover composition. Pairwise indirect contacts between deer were tabulated within a 10, 25, 50, 75, or 100-m buffer around each bait and control site. Indirect contact frequencies between bait and control sites were compared using mixed-model Poisson regression with deer pair as a random-effect variable and bait, joint utilization distribution (JUD), and year as fixed-effect variables. Contact frequencies did not differ significantly (P\u3c0.05) between bait sites and control sites at any buffer distance, implying that small bait piles used to capture deer have minimal effect on contact frequencies. However, the effect of more consistent and greater quantities of food distributed during supplemental feeding programs should be studied further to determine its impact on contact rates and spatial distribution of deer. Understanding the spatial distribution of white-tailed deer is important to implement effective disease and population management within localized areas. The objective of this study was to measure the home-range fidelity of female deer in an exurban deer herd in southern Illinois. I compared location data of 7 deer that had been collected in 2004-2005 and 2008. I used the volume of intersection (VI) and percent of home range overlap to statistically compare the two annual home ranges for each deer. Deer were located used ground-based radiotelemetry and home ranges were characterized using a fixed kernel utilization distribution. Comparing home ranges between years, the mean VI was 0.45 with little variation (range 0.35-0.55). I found the mean percent overlap of 50% isopleths to be 47.1% (range 31.3-71.7%) and the mean overlap of 95% isopleths to be 62.0% (range 44.3-68.6%). My results indicate that female white-tailed deer on our study area showed strong home-range fidelity, which could permit disease and population management by removing deer and reducing local deer densities

NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions. Here, we present NABat ML, an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and trained it on \u3e600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted-average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud-environment computing instance, the entire model training process took \u3c16 h. Synthesis and applications. Our convolutional neural network (CNN)-based model, NABat ML, classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species-level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open-source and reproducible, enabling future implementations as software on end-user devices and cloud-based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big-data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad-scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species