Predicting Potential Conflict Areas Between Wind Energy Development and Eastern Red Bats (Lasiurus borealis) in Indiana

Wind turbines pose threats to bats due to the risk of collisions, barotrauma, habitat loss, and environmental changes. To assess potential conflicts between wind energy development and the summer habitat of the eastern red bat (Lasiurus borealis) in Indiana, we used a species distribution modeling approach (MaxEnt) to generate two predictive models. We created a model representing areas with the potential for future wind energy development based on six environmental characteristics along with the locations of wind turbines. To create models of habitat suitability for summer resident eastern red bats, we used detections of eastern red bats collected via mobile acoustic surveys. We modeled these with 20 environmental variables that characterize potentially suitable eastern red bat summer habitat. Wind power at a height of 50 m, wind speed at a height of 100 m, and land cover type were the most influential predictors of wind energy development. Proportion of forest within 500 m and 1 km and forest edge within 5 km were the most important variables for predicting suitable summer habitat for red bats. Overlaid maps revealed that approximately three-quarters of the state was unsuitable for both wind development and red bats. Less than 1% of the state showed areas suitable for both wind development and red bats, which made up an area of about 4 km2. Primarily, these were rural areas where cropland was adjacent to forest patches. Predicting areas with potential conflicts can be an invaluable source for reducing impacts of wind energy development on resident red bats

Landscape Features Associated with the Roosting Habitat of Indiana Bats and Northern Long-Eared Bats

Context Bat conservation in the eastern United States faces threats from white nose syndrome, wind energy, and fragmentation of habitat. To mitigate population declines, the habitat requirements of species of concern must be established. Assessments that predict habitat quality based upon landscape features can aid species management over large areas. Roosts are critical habitat for many bat species including the endangered Indiana bat (Myotis sodalis) and the threatened northern long-eared bat (M. septentrionalis). Objectives While much is known about the microhabitat requirements of roosts, translating such knowledge into landscape-level management is difficult. Our goal was to determine the landscape-scale environmental variables necessary to predict roost occupancy for both species. Methods Using MaxLike, a presence-only occupancy modeling approach, with known roost sites, we identified factors associated with roosting habitat. Spatially independent roost locations were particularly limited for northern long-eared bats resulting in differences in study areas and sample sizes between the two species. Results Occupancy of Indiana bat roosts was greatest in areas with \u3e80 % local forest cover within broader landscapes (1 km) with1 km from intermittent streams and in areas with poor foraging habitat. Northern long-eared roost occupancy was greatest in areas with \u3e80 % regional but fragmented forest cover with greater forest edge approximately 4 km from the nearest major road. Conclusions Landscape features associated with roost occupancy differed greatly between species suggesting disparate roosting needs at the landscape scale, which may require independent management of roost habitat for each species

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