Automatic detection of unidentified fish sounds: a comparison of traditional machine learning with deep learning

Many species of fishes around the world are soniferous. The types of sounds fishes produce vary among species and regions but consist typically of low-frequency (<1.5 kHz) pulses and grunts. These sounds can potentially be used to monitor fishes non-intrusively and could complement traditional monitoring techniques. However, the significant time required for human analysts to manually label fish sounds in acoustic recordings does not yet allow passive acoustics to be used as a viable tool for monitoring fishes. In this paper, we compare two different approaches to automatically detect fish sounds. One is a more traditional machine learning technique based on the detection of acoustic transients in the spectrogram and the classification using Random Forest (RF). The other is using a deep learning approach and is based on the classification of overlapping segments (0.2 s) of spectrogram using a ResNet18 Convolutional Neural Network (CNN). Both algorithms were trained using 21,950 manually annotated fish and non-fish sounds collected from 2014 to 2019 at five different locations in the Strait of Georgia, British Columbia, Canada. The performance of the detectors was tested on part of the data from the Strait of Georgia that was withheld from the training phase, data from Barkley Sound, British Columbia, and data collected in the Port of Miami, Florida, United States. The CNN performed up to 1.9 times better than the RF (F1 score: 0.82 vs. 0.43). In some cases, the CNN was able to find more faint fish sounds than the analyst and performed well in environments different from the one it was trained in (Miami F1 score: 0.88). Noise analysis in the 20–1,000 Hz frequency band shows that the CNN is still reliable in noise levels greater than 130 dB re 1 μPa in the Port of Miami but becomes less reliable in Barkley Sound past 100 dB re 1 μPa due to mooring noise. The proposed approach can efficiently monitor (unidentified) fish sounds in a variety of environments and can also facilitate the development of species-specific detectors. We provide the software FishSound Finder, an easy-to-use open-source implementation of the CNN detector with detailed documentation

Pacific Canada's Rockfish Conservation Areas: using Ostrom's design principles to assess management effectiveness

International declines in marine biodiversity have lead to the creation of marine protected areas and fishery reserve systems. In Canada, 164 Rockfish Conservation Areas (RCAs) were implemented between 2003 and 2007 and now cover 4847.2 km² of ocean. These reserves were created in response to widespread concern from fishers and nongovernmental organizations about inshore rockfish (genus Sebastes) population declines. We used the design principles for effective common-pool resource management systems, originally developed by Elinor Ostrom, to assess the social and ecological effectiveness of these conservation areas more than 10 years after their initial implementation. We assessed the relative presence or absence of each design principle within current RCA management. We found that 2 of the 11 design principles were moderately present in the recreational fishery. All other design principles were lacking for the recreational sector. We found that 2 design principles were fully present and 5 were moderately present in the commercial sector. Four design principles were lacking in the commercial sector. Based on this analysis, we highlight 4 main areas for management improvement: (1) create an education and outreach campaign to explain RCA rules, regulations, boundaries, and the need for marine conservation; (2) increase monitoring of users and resources to discourage noncompliance and gather the necessary data to create social buy-in for marine conservation; (3) encourage informal nested governance through stakeholder organizations for education and self-regulation (e.g. fisher to fisher); and (4) most importantly, create a formal, decadal RCA review process to gather stakeholder input and make amendments to regulations and RCA boundaries. This information can be used to inform spatial management systems both in Canada and internationally. This analysis also contributes to a growing literature on effectively scaling up small-scale management techniques for large-scale, often federally run, common-pool resource systems