Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario, Canada.
Ontario Ministry of Natural Resources and Forestry, Glenora Fisheries Station, Picton, Ontario, Canada.
J Fish Biol. 2021 Jan;98(1):237-250. doi: 10.1111/jfb.14574. Epub 2020 Nov 3.
Understanding predator-prey interactions and food web dynamics is important for ecosystem-based management in aquatic environments, as they experience increasing rates of human-induced changes, such as the addition and removal of fishes. To quantify the post-stocking survival and predation of a prey fish in Lake Ontario, 48 bloater Coregonus hoyi were tagged with acoustic telemetry predation tags and were tracked on an array of 105 acoustic receivers from November 2018 to June 2019. Putative predators of tagged bloater were identified by comparing movement patterns of six species of salmonids (i.e., predators) in Lake Ontario with the post-predated movements of bloater (i.e., prey) using a random forests algorithm, a type of supervised machine learning. A total of 25 bloater (53% of all detected) were consumed by predators on average (± S.D.) 3.1 ± 2.1 days after release. Post-predation detections of predators occurred for an average (± S.D.) of 78.9 ± 76.9 days, providing sufficient detection data to classify movement patterns. Tagged lake trout Salvelinus namaycush provided the most reliable classification from behavioural predictor variables (89% success rate) and was identified as the main consumer of bloater (consumed 50%). Movement networks between predicted and tagged lake trout were significantly correlated over a 6 month period, supporting the classification of lake trout as a common bloater predator. This study demonstrated the ability of supervised learning techniques to provide greater insight into the fate of stocked fishes and predator-prey dynamics, and this technique is widely applicable to inform future stocking and other management efforts.
了解捕食者-猎物相互作用和食物网动态对于水生环境的基于生态系统的管理很重要，因为它们经历了越来越多的人为变化，例如鱼类的增加和移除。为了量化安大略湖中一种猎物鱼类的放养后生存和捕食情况，将 48 条白鲑 Coregonus hoyi 用声学遥测捕食标签标记，并在 2018 年 11 月至 2019 年 6 月期间在 105 个声学接收器阵列上进行追踪。通过将安大略湖中六种鲑鱼（即捕食者）的运动模式与白鲑（即猎物）的捕食后运动模式进行比较，使用随机森林算法（一种监督机器学习）来确定标记白鲑的潜在捕食者。平均而言（±标准偏差），在释放后 3.1±2.1 天，有 25 条白鲑（所有检测到的白鲑的 53%）被捕食者消耗。平均而言（±标准偏差），捕食者的捕食后检测发生在 78.9±76.9 天，提供了足够的检测数据来对运动模式进行分类。标记的湖鳟 Salvelinus namaycush 提供了最可靠的分类（89%的成功率），被确定为白鲑的主要消费者（消耗了 50%）。在 6 个月的时间内，预测的和标记的湖鳟之间的运动网络显著相关，支持将湖鳟归类为常见的白鲑捕食者。这项研究证明了监督学习技术能够更深入地了解放养鱼类的命运和捕食者-猎物动态，并且这种技术广泛适用于为未来的放养和其他管理工作提供信息。
