Abstract

Fish spawning aggregations (FSA) consist of the temporary gathering of a large number of fishes at a specific location to spawn. Monitoring of FSA is typically conducted by divers, but surveys are often restricted to a limited area and dependent upon sea conditions, thus our knowledge of FSA dynamics is extremely limited. Fisheries independent research strives for new technology that can help remotely and unobtrusively quantify fish biomass and abundance. Since some fish species, such as groupers, produce sounds during reproductive behaviors, Eulerian passive acoustic monitoring provides information when divers cannot access the FSA site. Fish sounds provide an innovative approach to assess fish presence and potentially their numbers during reproductive events. However, this technology is limited by the sound propagation range, hence the distance between the fish emitting sounds the hydrophone location. As such, this Eulerian monitoring approach implicitly creates a knowledge gap about what happens beyond the range of the recorders. Furthermore, the large datasets make the detection process by human ears and eyes very tedious and inconsistent. This paper reports on two innovative approaches to overcome these limitations. To facilitate fish call detections, we have developed an algorithm based on machine learning and voice recognition methods to identify and classify the sounds known to be produced by certain species during FSA. This algorithm currently operates on a SV3 Liquid Robotics wave glider, an autonomous surface vehicle which has been fitted to accommodate a passive acoustic listening device and can cover large areas under a wide range of sea conditions. Fish sounds detections, classification results, and locations along with environmental data are transmitted in real-time enabling verification of the sites with high detections by divers or other in-situ methods. Recent surveys in the US Virgin Islands with the SV3 Wave Glider are revealing for the first time the spatial and temporal distribution of fish calls surrounding known FSA sites. These findings are critical to understanding the dynamics of fish populations because calling fish were detected several kilometers away from the known FSAs. These courtship associated sounds from surrounding areas suggest that other FSAs may exist in the region.

Highlights

  • Fisheries sustainability and ecosystem health rely on habitat quality and biodiversity and on reproductive success (Mumby et al, 2006)

  • In Ibrahim et al (2018a), we revealed the effectiveness of using convolutional neural networks (CNNs) and Long short-term memory (LSTM) networks for classifying fish calls and we evaluated the performance of such methods against the mel frequency cepstral coefficients (MFCC) approach

  • In order to interpret the wave glider courtship associated sounds (CASs) rate patterns, we propose to use the CAS rate patterns observed at both Red Hind Bank Marine Conservation District (RHB) and Grammanik Bank (GB) fish spawning aggregation (FSA) as reference patterns associated with red hind reproductive behavior

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Summary

Introduction

Fisheries sustainability and ecosystem health rely on habitat quality and biodiversity and on reproductive success (Mumby et al, 2006). FSAs are challenging to observe and monitor because they are mostly found at remote locations near the shelf break (Claro and Lindeman, 2003; Kobara and Heyman, 2010; Kobara et al, 2013), in water depths between 30 and 80 m while spawning activities occur mostly at dusk. While many of these sites are known to fishers and represent areas of intensive harvest, not all FSA locations have been documented. There may be a significant number of unreported FSAs, which, if located, could provide a better estimate of the status of certain populations of grouper species such as Nassau, Warsaw (Hyporthodus nigritus), black, red hind, goliath (Epinephelus itajara) and their critical habitats used for spawning

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