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Results for “Journal of North American Bat Research”

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USGS2025

Trade-offs in designing a participatory acoustic study of bats: Comparison of user engagement and data quality between two ultrasonic detectors

Technology for the acoustic detection of animals has advanced rapidly over the past few decades. Due to ease of use, consistency, and safety, acoustic methods are particularly useful for science applications that engage the public. In this study, we evaluated the technological and educational trade-offs between 2 acoustic bat detectors in a participatory science application along the Colorado River in the Grand Canyon, Arizona. Both devices were deployed simultaneously by commercial river guides in parallel with sampling insect prey for 1 h at dusk on 48 dates between April and October 2022. The detector with higher data quality capabilities recorded more bats overall (a mean of 231 more passes per hour) and more species (19 species, including 4 not detected by the lower quality detector). However, data from both detectors showed a decrease in total bat activity from spring to fall, despite the differences in recording capabilities. We conclude that detector quality matters, but user engagement is important when designing participatory research.

Journal of North American Bat Research↗

USGS2026

Landscape-scale drivers of bat acoustic activity in the interior mid-Atlantic

We assessed the landscape scale foraging habitat associations from a multi-year perspective using generalized linear mixed models for nine bat species using bat detectors in the interior mid-Atlantic region of Maryland and Pennsylvania, United States. The landscape use trends we detected included greater Eptesicus fuscus acoustic activity along field edges than in forested habitats, and greater Lasiurus borealis acoustic activity in xeric conditions rather than hydric/mesic conditions relative to aspect. Additionally, no bat species had a meaningful relationship with McNab’s Landform Index, atypical for Myotis septentrionalis. Our results also demonstrate that most bat species’ acoustic activity were stable over years though some counts were extremely low, attributable in part to population reductions from white-nose syndrome. We also observed variation of bat activity on the landscape throughout the active season. Eptesicus fuscus, M. lucifugus, M. septentrionalis, M. sodalis, and Perimyotis subflavus acoustic activity levels over the active season appear to be related to the annual reproductive cycle, i.e., pregnancy, lactation, and volancy of pups. Our study is indicative that bat acoustic activity can vary between regions and years, highlighting the need for more multi-year, locally focused research of bats.

Maryland, Pennsylvania↗

USGS2024

Reproductive trends in Little Brown Bats before and after the onset of white-nose syndrome in Virginia

Myotis lucifugus (Little Brown Bat) declines in Virginia following white-nose syndrome (WNS) prompted an investigation into reproductive behaviors of surviving individuals. To examine reproductive change, we examined female bats prior to, during and after endemism establishment. We also examined capture trends of juveniles at maternity colonies. Timing and proportion of reproductive conditions did not differ except for reductions in lactating females during WNS invasion relative to the pre-WNS period. There was no significant difference in the proportion of juveniles between WNS-endemic years, indicating population recovery is slow. Of 78 recaptures, 2 individuals survived >8 years, suggesting individual longevity after WNS endemism. Our findings emphasize the value of long-term datasets to assess reproductive status of Little Brown Bats.

Virginia↗

USGS2025

Multi-scale predictors of Northern Long-eared Bat (Myotis septentrionalis) occupancy in the United States

Historically, Myotis septentrionalis (Northern Long eared Bat) was among the most common forest-interior species in North America. Largely due to high mortality from white-nose syndrome, this species has experienced severe population declines across its range. To create an updated species distribution map representing summer occupancy probabilities from 2017 to 2022, we integrated stationary acoustic data with live-capture data from the database of the North American Bat Monitoring Program into a multi-scale, multi-method occupancy modeling framework. Our results provide data-driven predictions with quantified uncertainty for summer occupancy probabilities for Northern Long-eared Bats at 2 spatial scales across the range of the species, while also accounting for inherent observation biases (e.g., imperfect detection).

Journal of North American Bat Research↗

USGS2025

Acoustic response of bats to the Brood X Periodical Cicada (Magicicada spp.) emergence

Periodical cicada emergences can positively affect vertebrate reproduction and breeding behavior, yet this response is not well studied for bats. We examined the acoustic response of 6 species/phonic groups of bats in 2021 to the emergence of Brood X periodical cicadas, along the Potomac River corridor, in Maryland and Virginia. Using a before-after-control-impact study design, we deployed ultrasonic acoustic detectors during the summers of 2020–2022 within and just outside the range of the cicada emergence to document bat response. We observed significantly more echolocation passes during the 2021 emergence and the year following within the range of cicadas, relative to changes among years outside of the periodical cicada range. Our study demonstrates that periodical cicadas may serve as a resource that causes an increase in bat activity.

Maryland, Virginia, West Virginia↗

USGS2022

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 >600,000 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 <16 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.

Journal of Applied Ecology↗

USGS2020

Ambiguities in using telomere length for age determination in two North American bat species

The age of an animal, determined by time (chronological age) as well as genetic and environmental factors (biological age), influences the likelihood of mortality and reproduction and thus the animal’s contribution to population growth. For many long-lived species, such as bats, a lack of external and morphological indicators has made determining age a challenge, leading researchers to examine genetic markers of age for application to demographic studies. One widely studied biomarker of age is telomere length, which has been related both to chronological and biological age across taxa, but only recently has begun to be studied in bats. We assessed telomere length from the DNA of known-age and minimum known-age individuals of two bat species using a quantitative PCR assay. We determined that telomere length was quadratically related to chronological age in big brown bats ( Eptesicus fuscus ), although it had little predictive power for accurate age determination of unknown-age individuals. The relationship was different in little brown bats ( Myotis lucifugus ), where telomere length instead was correlated with biological age, apparently due to infection and wing damage associated with white-nose syndrome. Furthermore, we showed that wing biopsies currently are a better tissue source for studying telomere length in bats than guano and buccal swabs; the results from the latter group were more variable and potentially influenced by storage time. Refinement of collection and assessment methods for different non-lethally collected tissues will be important for longitudinal sampling to better understand telomere dynamics in these long-lived species. Although further work is needed to develop a biomarker capable of determining chronological age in bats, our results suggest that biological age, as reflected in telomere length, may be influenced by extrinsic stressors such as disease.

Journal of Mammalogy↗