Automated acoustic detection of river herring (Alewife and Blueback Herring) spawning activity

Abstract

Objective We used passive acoustic monitoring (PAM) and automatic detection of spawning splashes to examine the timing and environmental drivers of spawning in river herring (Alewife Alosa pseudoharengus and Blueback Herring A. aestivalis). Methods Acoustic recordings of spawning splashes were collected from March to May 2021 in the Choptank River, Maryland, using an AudioMoth recorder. Recordings were analyzed using a random forest model on the Rainforest Connection ARBIMON platform to determine hourly presence–absence of splashes. Result At a seasonal scale, our results suggested two peaks in spawning activity: early March and mid‐April, corresponding with the known phenologies of Alewife and Blueback Herring. Hourly patterns in spawning activity suggested distinct diel cycles, with spawning most concentrated at dawn. In contrast, sonar fish counts collected for 1 week during the season indicated that migration occurred throughout daylight hours. We also found a potential relationship between spawning activity and the presence of great blue herons Ardea herodias. Conclusion Overall, our results demonstrate that PAM can be an efficient and affordable method for studying the spawning ecology of anadromous fish.

A video monitoring and computational system for estimating migratory juvenile fish abundance in river systems

“…Climate-related changes in phenology may also shift populations out of their usual habitats or migration times (Parmesan and Yohe 2003;Nye et al 2009), which may make it difficult to use traditional monitoring methods to capture population trends. Technologies like Global Positioning System collars (Bassing et al 2023) and Passive Integrated Transponder (PIT) tags (Keefer et al 2008;McCartin et al 2019;Alcott et al 2021) allow continuous tracking of tagged individuals while acoustic (Staples et al 2023) and camera monitoring (Weinstein 2018;Ditria et al 2020) allow continuous tracking of individuals that pass an installed recording device. Cost-effective, noninvasive, continuous monitoring methods may improve data quality for assessing population dynamics, especially for species where capture efficiency is low using direct observation (Pimm et al 2015;Weinstein 2018;Malde et al 2020;Goodwin et al 2022).…”

“…Water flow, water quality, light availability, turbidity, and moving debris make the deployment of video and camera monitoring equipment and machine learning systems more difficult in aquatic environments than terrestrial environments (Mandal et al 2018;Ditria et al 2020;Salman et al 2020). Within aquatic environments, applications of neural network technologies have been used for fish species classification (Salman et al 2016(Salman et al , 2020Villon et al 2018;Saleh et al 2022), fish species enumeration (Ditria et al 2020), plankton monitoring (Lombard et al 2019), and acoustic monitoring (Deng and Yu 2014;Staples et al 2023). Many of these approaches have been limited to short-term monitoring in high-visibility tropical reef systems (Salman et al 2016(Salman et al , 2020Villon et al 2018), although some have been implemented in estuarine systems (Ditria et al 2020).…”