Automatic detection, classification, and quantification of sciaenid fish calls in an estuarine soundscape in the Southeast United States

• Published in: PloS one Vol. 14; no. 1; p. e0209914
• Main Authors: Monczak, Agnieszka, Ji, Yiming, Soueidan, Jamileh, Montie, Eric W
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.01.2019; Public Library of Science (PLoS)
• Subjects: Acoustics; Algorithms; Analysis; Animals; Anomalies; Biology and Life Sciences; Classification; Climate change; Communication; Courtship; Creeks & streams; Cynoscion nebulosus; Drum; Earth Sciences; Ecology and Environmental Sciences; Environmental aspects; Estuaries; Fish; Fishes - classification; Fishes - physiology; Monitoring; Noise; Phenology; Physical Sciences; Recorders; Researchers; Rivers; Sciaenidae; Sensors; Signal detectors; Signal processing; Sound; South Carolina; Spawning; Temperature anomalies; Vocalization, Animal; South Carolina; United States; United States > US
• DOI: 10.1371/JOURNAL.PONE.0209914
• ISSN: 1932-6203

In the Southeast USA, major contributors to estuarine soundscapes are the courtship calls produced by fish species belonging to the family Sciaenidae. Long-term monitoring of sciaenid courtship sounds may be valuable in understanding reproductive phenology, but this approach produces massive acoustic datasets. With this in mind, we designed a feature-based, signal detector for sciaenid fish calls and tested the efficacy of this detector against manually reviewed data. Acoustic recorders were deployed to collect sound samples for 2 min every 20 min at four stations in the May River estuary, South Carolina, USA from February to November, 2014. Manual analysis of acoustic files revealed that four fish species, belonging to the family Sciaenidae, were the major sound producers in this estuarine soundscape, and included black drum (Pogonias cromis), silver perch (Bairdiella chrysoura), spotted seatrout (Cynoscion nebulosus), and red drum (Sciaenops ocellatus). Recorded calls served as an acoustic library of signature features that were used to create a signal detector to automatically detect, classify, and quantify the number of calls in each acoustic file. Correlation between manual and automatic detection was significant and precision varied from 61% to 100%. Automatic detection provided quantitative data on calling rates for this long-term data set. Positive temperature anomalies increased calling rates of black drum, silver perch, and spotted seatrout, while negative anomalies increased calling rates of red drum. Acoustic monitoring combined with automatic detection could be an additional or alternative method for monitoring sciaenid spawning and changes in phenology associated with climate change.