The eastern black rail (latteralus jamaicensis) is an elusive, poorly studied, and federally threatened (endangered species act, 2020) marsh bird that has undergone a massive range contraction since…

The eastern black rail (latteralus jamaicensis) is an elusive, poorly studied, and federally threatened (endangered species act, 2020) marsh bird that has undergone a massive range contraction since the beginning of the 20TH century. This contraction has largely been linked to habitat loss and degradation due to rising sea-level, woody encroachment, and urban development. However, because black rails are difficult to study using traditional survey methodology (i.e. they are rare, small ( 40 g), vocally active at night, and typically occupy tidal marsh habitat), very little is known about their basic ecology and population size. Therefore, considerable uncertainty exists as to the factors ultimately responsible for these presumed population declines that likely underlie this range contraction. Therefore, to develop a better understanding of their ecology and abundance we plan to develop and employ an automated passive acoustic monitoring protocol that leverages continuous monitoring with automated song classification using a recently developed machine learning algorithm - birdnet. Through this work, we aim to 1) develop a viable secretive marsh bird monitoring protocol tailored for black rails (and amenable towards other elusive species), and 2) contribute to our understanding of their basic ecology (phenology, calling behavior, and abundance) and 3) as a case study, implement this standardized protocol to monitor for black rails on proposed restoration project. To accomplish this work, we will deploy approximately 50 passive acoustic recorders in a gridded network at a natural and non-ditched tidal marsh at the edwin b. Forsythe national wildlife refuge where black rails have been previously encountered. This work will involve testing the efficacy and limitations of birdnet in identifying black rails, developing a standardize workflow for the storage, processing, and analysis of passive acoustic data for assessing focal species detections, and developing cost effective, accurate, and repeatable approaches to quantitatively estimating abundance from passive acoustic arrays. Ultimately, through these efforts we aim to fill a critical GAP in our current monitoring protocols that specifically target these elusive and difficult to detect species that are rapidly declining.