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Pattern recognition in long‐term Sooty Shearwater data: applying machine learning to create a harvest index.

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    Rakiura Māori harvest diaries reveal machine learning models accurately predict Sooty Shearwater (Tītī) chick harvests. The rama harvest data shows consistency, making it ideal for long-term population monitoring.

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    Area of Science:

    • Ecology
    • Conservation Biology
    • Indigenous Knowledge Systems

    Background:

    • Rakiura Māori have a multi-generational tradition of harvesting Sooty Shearwater (Tītī; Puffinus griseus) chicks.
    • Annual harvest diaries, some originating in the 1950s, provide a historical data source for Tītī populations.
    • Traditional ecological knowledge from Rakiura Māori is integral to understanding the harvest.

    Observation:

    • Generalized boosted regression models, a machine-learning approach, were employed to analyze harvest data.
    • Harvest indices were calculated, accounting for variables influencing catch numbers.
    • Model performance was assessed by comparing predicted versus observed harvest values.

    Findings:

    • Machine learning models demonstrated strong predictive accuracy for both nanao (daytime burrow harvest) and rama (nighttime surface harvest) seasons.
    • The 'day of season' was a significant predictor for rama harvests, with peak catches around 10-15 days into the season.
    • Rama harvest data exhibited greater consistency, suggesting its suitability for long-term Sooty Shearwater population monitoring.

    Implications:

    • Machine learning offers a robust method for standardizing harvest data, correcting for extrinsic factors like effort and weather.
    • This approach can be adapted for monitoring other harvested resources, including fisheries and terrestrial wildlife.
    • Integrating indigenous knowledge with advanced analytical techniques enhances conservation and resource management strategies.