Assessing the influence of socials calls on bat mist-netting success in North America

Since the introduction of the fungal disease White-Nose Syndrome in 2006, millions of North American bats have perished. For many species, the disease has caused over a 90 percent decline in abundance. With populations fluctuating as the pathogen spreads, biologists require improved methods of estimating bat demographics and abundance. Previous research indicates that mist netting success may be improved with the use of acoustic lures at mist-netting locations. Our research investigates which type of social calls improve the capture rates of North American bats, including the big brown bat (Eptesicus fuscus). Social call types used include antagonistic buzzes, distress calls, advertising calls, mother-to-offspring calls, and cohesion calls. We deployed acoustic lures at each netting site from 15 May 2017 to 15 August 2017. We created 5-hour long playlists using 10-minute blocks of each of the 5 call types, including a block of silence as control. We recorded the time of each bat capture to indicate the call block each individual entered the net. We utilized maximum likelihood analysis in program R to identify if call type had an influence on bat captures. Analysis indicated that European distress calls negatively impacted big brown bat captures. Overall, this suggests that researchers should utilize North American bat calls to improve capture rates of big brown bats

Impacts of White-Nose Syndrome Observed During Long-Term Monitoring of a Midwestern Bat Community

White-nose syndrome (WNS) is an emerging fungal disease suspected to have infected Indiana caves in the winter of 2010–2011. This disease places energetic strains on cave-hibernating bats by forcing them to wake and use energy reserves. It has caused \u3e5.5 million bat deaths across eastern North America, and may be the driving force for extinction of certain bat species. White-nose syndrome infection can be identified in hibernacula, but it may be difficult to determine whether bats in a particular area are affected if no known hibernacula exist. Thus, our aim was to use long-term monitoring data to examine changes in a summer population away from hibernacula that may be attributable to WNS effects during winter. We used capture data from a long-term bat-monitoring project in central Indiana with data from 10 repeatedly netted sites consistent across all reproductive periods. We modeled capture data by WNS exposure probability to assess changes in relative abundance of common species and reproductive classes as WNS exposure probability increases. We base exposure probability on a cokriging spatial model that interpolated WNS infection from hibernaculum survey data. The little brown bat Myotis lucifugus, the Indiana bat M. sodalis, and the tri-colored bat Perimyotis subflavus suffered 12.5–79.6% declines; whereas, the big brown bat Eptesicus fuscus, the eastern red bat Lasiurus borealis, and the evening bat Nycticeius humeralis showed 11.5–50.5% increases. We caught more nonreproductive adult females and postlactating females when WNS exposure probabilities were high, suggesting that WNS is influencing reproductive success of affected species. We conclude that, in Indiana, WNS is causing species-specific declines and may have caused the local extinction of M. lucifugus. Furthermore, WNS-affected species appear to be losing pups or forgoing pregnancy. Ongoing long-term monitoring studies, especially those focusing on reproductive success, are needed to measure the ultimate impacts of WNS

Impacts of White-Nose Syndrome Observed During Long-Term Monitoring of a Midwestern Bat Community

White-nose syndrome (WNS) is an emerging fungal disease suspected to have infected Indiana caves in the winter of 2010–2011. This disease places energetic strains on cave-hibernating bats by forcing them to wake and use energy reserves. It has caused \u3e5.5 million bat deaths across eastern North America, and may be the driving force for extinction of certain bat species. White-nose syndrome infection can be identified in hibernacula, but it may be difficult to determine whether bats in a particular area are affected if no known hibernacula exist. Thus, our aim was to use long-term monitoring data to examine changes in a summer population away from hibernacula that may be attributable to WNS effects during winter. We used capture data from a long-term bat-monitoring project in central Indiana with data from 10 repeatedly netted sites consistent across all reproductive periods. We modeled capture data by WNS exposure probability to assess changes in relative abundance of common species and reproductive classes as WNS exposure probability increases. We base exposure probability on a cokriging spatial model that interpolated WNS infection from hibernaculum survey data. The little brown bat Myotis lucifugus, the Indiana bat M. sodalis, and the tri-colored bat Perimyotis subflavus suffered 12.5–79.6% declines; whereas, the big brown bat Eptesicus fuscus, the eastern red bat Lasiurus borealis, and the evening bat Nycticeius humeralis showed 11.5–50.5% increases. We caught more nonreproductive adult females and postlactating females when WNS exposure probabilities were high, suggesting that WNS is influencing reproductive success of affected species. We conclude that, in Indiana, WNS is causing species-specific declines and may have caused the local extinction of M. lucifugus. Furthermore, WNS-affected species appear to be losing pups or forgoing pregnancy. Ongoing long-term monitoring studies, especially those focusing on reproductive success, are needed to measure the ultimate impacts of WNS

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