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

Rakiura Maori (New Zealand's southernmost group of indigenous peoples) have harvested the chicks of burrow-nesting Sooty Shearwaters (Titi; Puffinus griseus) for generations. As part of the harvest process, some families have maintained annual harvest diaries, some dating back to the l950s. We...

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Bibliographic Details
Published in:Ecological applications 2014-12, Vol.24 (8), p.2107-2121
Main Authors: Humphries, G R W, Bragg, C, Overton, J, Lyver, B, Moller, H
Format: Article
Language:English
Subjects:
Puffinus griseus
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Summary:Rakiura Maori (New Zealand's southernmost group of indigenous peoples) have harvested the chicks of burrow-nesting Sooty Shearwaters (Titi; Puffinus griseus) for generations. As part of the harvest process, some families have maintained annual harvest diaries, some dating back to the l950s. We used generalized boosted regression models, a machine-learning algorithm, to calculate a harvest index that takes into account factors that could impact the numbers of birds taken on any given hunt. The use of machine learning to correct for extraneous factors (e.g., hunting effort, skill level, or weather) and to create standardized measures could be applied to other systems such as fisheries or terrestrial resource management.
ISSN:1051-0761
DOI:10.1890/13-2023.1.sm