In Journal of fish biology
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, we tagged 48 bloater Coregonus hoyi with acoustic telemetry predation tags and tracked them 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 (± SD) 3.1 ± 2.1 days after release. Post-predation detections of predators occurred for an average (± SD) 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 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 is widely applicable to inform future stocking and other management efforts. This article is protected by copyright. All rights reserved.
Klinard Natalie V, Matley Jordan K, Ivanova Silviya V, Larocque Sarah M, Fisk Aaron T, Johnson Timothy B