Detection And Growth Estimation Of Indo–Pacific Eel (Anguilla marmorata Quoy & Gaimard, 1824) Using Machine Learning In Central Vietnam
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Abstract
Context. The Indo–Pacific eel (Anguilla marmorata) is a widely distributed and commercially valuable species across ecological regions worldwide. Overfishing and habitat loss are leaving the Indo–Pacific eel in a risky situation and raising a high demand for conservation. Previous research has found relationships between the Indo–Pacific eel’s migration patterns and environmental factors. However, there is still a need to advance the discovery of its spatial distribution by using diverse environmental and ecological datasets and modelling its growth in terms of different environmental characterizations.
Aims & Methods. Here, we compared machine learning (ML) CatBoost (CB) and the multivariate linear model to investigate the relationship between spatial distribution, Indo–Pacific eel development stages, and environmental factors in central Vietnam.
Key results. Our results show that CB detected the Indo–Pacific eel at high accuracy (Overall Accuracy (OA) = 0.9, F1 = 0.88, AUC = 0.97) and estimated the total length at different confidence levels (R2 ranging from 0.51 to 0.70), demonstrating superior performance to the multivariate linear model.
Conclusions & implications. This study highlights the potential use of ML models in species distribution mapping and modelling growth patterns to support conservation efforts of Indo–Pacific eels in their natural habitats.
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References
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