Abstract
Mortality and predation of tagged fishes present a serious challenge to interpreting results of acoustic telemetry studies. There is a need for standardized methods to identify predated individuals and reduce the impacts of “predation bias” on results and conclusions. Here, we use emerging approaches in machine learning and acoustic tag technology to classify out-migrating Atlantic salmon (Salmo salar) smolts into different fate categories. We compared three methods of fate classification: predation tag pH sensors and detection data, unsupervised k-means clustering, and supervised random forest combined with tag pH sensor data. Random forest models increased predation estimates by 9–32% compared to relying solely on pH sensor data, while clustering reduced estimates by 3.5–30%. The greatest changes in fate class estimates were seen in years with large class imbalance (one or more fate classes underrepresented compared to the others) or low model accuracy. Both supervised and unsupervised approaches were able to classify smolt fate; however, in-sample model accuracy improved when using tag sensor data to train models, emphasizing the value of incorporating such sensors when studying small fish. Sensor data may not be sufficient to identify predation in isolation due to Type I and Type II error in predation sensor triggering. Combining sensor data with machine learning approaches should be standard practice to more accurately classify fate of tagged fish.
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Data availability
The data that support the findings of this study will be made publicly available on the Ocean Tracking Network database following publication of the data.
Code availability
Associated R code for analyses is publicly available on GitHub: https://github.com/danielanotte
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Acknowledgements
We would like to acknowledge Cindy Hawthorne, Jeff Reader, Alana Ransome, George Nau, and other members of Fisheries and Oceans Canada and the Mi’kmaw Conservation Group for assistance in the field. As well as Darren and Erica Porter for receiver deployment and retrieval in the Minas Basin. We also thank Jake Brownscombe for answering questions about machine learning model tuning and members of the Ocean Tracking Network Data Centre for data acquisition and coding help.
Funding
Funding provided by NSERC Strategic Partnership Grant No. 521256. R.J. Lennox was supported by the NFR project LaKES (#320726).
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DVN: performed field work, contributed to concept development, performed analyses, and wrote initial manuscript drafts. RJL: contributed to concept development and manuscript drafts. DCH: performed field work, developed study design, and contributed to manuscript drafts. GTC: contributed to manuscript drafts.
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All animal experiments were approved by the Canadian Committee on Animal Care, via permits issued by Fisheries and Oceans Canada (Maritimes Region Animal Care Committee Animal Utilization Protocols 17–16, 18–13, 19–10) and by Dalhousie University (University Committee on Lab Animals permit 18–126).
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Communicated by Yannis Papastamatiou.
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Notte, D.V., Lennox, R.J., Hardie, D.C. et al. Application of machine learning and acoustic predation tags to classify migration fate of Atlantic salmon smolts. Oecologia 198, 605–618 (2022). https://doi.org/10.1007/s00442-022-05138-3
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DOI: https://doi.org/10.1007/s00442-022-05138-3