Japanese encephalitis virus (JEV), a zoonotic, mosquito-borne virus, has broad circulation across the Central Indo-Pacific biogeographical region (CIPBR) and has recently expanded dramatically within...

Autonomous recording units (ARUs) are an emerging technology that allows for passive monitoring of soniferous animals and soundscapes. Over the past decade, ARUs have become a popular tool for monitoring birds for their potential to reduce the labor and costs of traditional in-person sampling procedures. However, uncertainty surrounding factors affecting detection of avian taxa using ARUs can inhibit their monitoring efficacy. Eastern Black Rails (Laterallus jamaicensis jamaicensis) are a secretive marsh bird listed as a federally threatened species in the U.S.A. Eastern Black Rail vocalizations are difficult to detect by field personnel, and numerous in-person surveys can be required to confirm their presence at a site. While ARUs are an alternative for detecting Eastern Black Rails, it is unknown at what maximum distance an ARU can detect their vocalizations. We evaluated factors affecting the detection distance of simulated vocalizations for ARUs in four marsh vegetation types under a range of environmental conditions. Detection distances varied across models, vocalization and vegetation types, and call volume. Kickeedo vocalizations were detected at greater distances, and detection distances increased for all vocalization types in open vegetation. High relative humidity increased detection distances, while louder background noise decreased detection distances. High wind speeds in cordgrass (Spartina spp.) decreased detection probability disproportionately relative to other vegetation types. Based on these results, considerations of survey area, vegetation type, and site condition can allow land managers and researchers to optimize Eastern Black Rail monitoring using ARUs. Given the substantial staff time needed to monitor this species, ARUs may increase the likelihood of detection and provide an efficient alternative to in-person monitoring.

YouTube video by Hany Hossameldin