Geology topics

Emily Ferrall

Publications and source records attributed to Emily Ferrall.

5 recordsLinked to original sources

USGS2024

Disparities in Perimyotis subflavus body mass between cave and culvert hibernacula in Georgia, USA

The tricolored bat ( Perimyotis subflavus ), once common in the eastern United States, has experienced significant mortality due to white-nose syndrome (WNS), a fungal disease that primarily affects bats hibernating in caves and mines. In coastal regions of the southeastern United States, where caves and mines are scarce, tricolored bats often use roadway culverts as hibernacula. However, WNS infection dynamics in culverts are poorly understood. Previous research indicated that bats with higher body mass at the onset of hibernation have a higher probability of surviving repeated arousal events from WNS. Therefore, we compared tricolored bat winter body mass between cave and culvert hibernacula and identified culvert characteristics influencing body mass during hibernation in Georgia, USA. From 2018 to 2022, we measured body mass of 754 individuals in early and late hibernation across 32 culverts ( n = 497) and four caves ( n = 257). Our study revealed a southward spread of the fungus over multiple years, with the first confirmed case of WNS in a Georgia culvert in 2022. Overall, tricolored bats in caves weighed more in early hibernation than those in culverts, but bats in culverts weighed more in late hibernation. Across all sites, female tricolored bats entering and leaving hibernation had greater mass than males but lost more mass during hibernation, possibly due to differences in torpor-arousal patterns and WNS infection rates. Additionally, all bats lost more mass in longer culverts. Understanding culvert characteristics affecting bat body mass will inform management strategies to mitigate WNS effects. Identifying risk factors for specific tricolored bat hibernacula can guide managers on where to focus winter WNS monitoring efforts and potential treatments.

Georgia↗

USGS2022

North American Bat Monitoring Program (NABat) mobile acoustic transect surveys standard operating procedure 1—Locating and establishing mobile transect routes

This document is the first of three standard operating procedures (SOPs) providing instructions and considerations for conducting mobile acoustic surveys along road transects to collect bat acoustic data following the North American Bat Monitoring Program (NABat) protocol and sample design. This SOP focuses specifically on selecting NABat grid cells and establishing mobile transect survey routes using online tools available through the NABat Partner Portal. Intended audiences for this document include those in charge of facilitating surveys within their region (for example, State or provincial managers, and NABat regional hub coordinators), and project leaders or survey coordinators responsible for setting up and organizing NABat mobile transect monitoring for their organization or area.

Techniques and Methods↗

USGS2022

North American Bat Monitoring Program (NABat) Mobile Acoustic Transect Surveys Standard Operating Procedure 2—Field Season and Survey Preparation

This document is the second of three standard operating procedures providing instructions and considerations for conducting mobile acoustic surveys along road transects to collect bat acoustic data following the North American Bat Monitoring Program (NABat) protocol and sample design. This standard operating procedure focuses specifically on considerations for establishing the field survey season and preparing to conduct mobile acoustic transect surveys. Intended audiences for this document include those in charge of facilitating surveys within their region (for example, state or provincial mangers and NABat regional hub coordinators), project leaders or survey coordinators responsible for setting up and organizing NABat mobile transect monitoring for their organization or area, and field staff preparing to conduct surveys within the field.

Techniques and Methods↗

USGS2022

North American Bat Monitoring Program (NABat) mobile acoustic transect surveys standard operating procedure 3—Conducting mobile transect surveys

This standard operating procedure (SOP) provides instructions and considerations for conducting mobile acoustic surveys along road transects to collect bat acoustic data following the North American Bat Monitoring Program (NABat) protocol and sample design. This report discusses measures for ensuring the safety of surveyors and efficiency of mobile transect surveys. This guidance is intended to aid surveyors in equipment setup and operation, including how to mount and orient equipment. Finally, this document provides guidance on driving the route, performing end of survey procedures, and steps for data management.

Techniques and Methods↗

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↗