DECOD, L'Institut Agro, IFREMER, INRAE, 56100, Lorient, France.
Université Côte d'Azur, CNRS, ECOSEAS, Nice, France.
Sci Rep. 2024 Jun 7;14(1):13110. doi: 10.1038/s41598-024-63929-5.
Low-impact fishing gear, such as fish pots, could help reduce human's impact on coastal marine ecosystems in fisheries but catch rates remain low and the harvest of resources used for baiting increases their environmental cost. Using black seabreams (Spondyliosoma cantharus) as target species in the Bay of Biscay, we developed and assessed the efficiency of biodegradable biopolymer-based baits (hereafter bio-baits) made of cockles (Cerastoderma edule) and different biopolymer concentrations. Through a suite of deep and machine learning models, we automatized both the tracking and behavior classification of seabreams based on quantitative metrics describing fish motion. The models were used to predict the interest behavior of seabream towards the bait over 127 h of video. All behavior predictions categorized as interested to the bait were validated, highlighting that bio-baits have a much weaker attractive power than natural bait yet with higher activity after 4 h once natural baits have been consumed. We also show that even with imperfect tracking models, fine behavioral information can be robustly extracted from video footage through classical machine learning methods, dramatically lifting the constraints related to monitoring fish behavior. This work therefore offers new perspectives both for the improvement of bio-baits and automatic fish behavior recognition.
低影响渔具，如鱼篓，可以帮助减少渔业对沿海海洋生态系统的人为影响，但渔获率仍然较低，用于诱饵的资源的收获增加了它们的环境成本。我们以比斯开湾的黑鲷（Spondyliosoma cantharus）为目标物种，开发并评估了由贻贝（Cerastoderma edule）和不同生物聚合物浓度制成的可生物降解生物聚合物基诱饵（简称生物诱饵）的效率。通过一系列深度学习和机器学习模型，我们根据描述鱼类运动的定量指标，自动跟踪和分类鲷鱼的行为。这些模型用于预测鲷鱼对诱饵的兴趣行为超过 127 小时的视频。所有被归类为对诱饵感兴趣的行为预测都得到了验证，这表明生物诱饵的吸引力比天然诱饵弱得多，但在 4 小时后，一旦天然诱饵被消耗，其活性更高。我们还表明，即使使用不完善的跟踪模型，也可以通过经典的机器学习方法从视频片段中稳健地提取精细的行为信息，极大地减轻了与监测鱼类行为相关的限制。因此，这项工作为生物诱饵的改进和自动鱼类行为识别提供了新的视角。
