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Natural mortality estimation using tree-based ensemble learning models
Liu, Chanjuan Zhou, Shijie Wang, You-Gan Hu, Zhi-Hua
Liu, Chanjuan
Zhou, Shijie
Wang, You-Gan
Hu, Zhi-Hua
Abstract
Empirical studies are popular in estimating fish natural mortality rate (M). However, these empirical methods derive M from other life-history parameters and are often perceived as being less reliable than direct methods. To improve the predictive performance and reliability of empirical methods, we develop ensemble learning models, including bagging trees, random forests, and boosting trees, to predict M based on a dataset of 256 records of both Chondrichthyes and Osteichthyes. Three common life-history parameters are used as predictors: the maximum age and two growth parameters (growth coefficient and asymptotic length). In addition, taxonomic variable class is included to distinguish Chondrichthyes and Osteichthyes. Results indicate that tree-based ensemble learning models significantly improve the accuracy of M estimate, compared to the traditional statistical regression models and the basic regression tree model. Among ensemble learning models, boosting trees and random forests perform best on the training dataset, but the former performs a slightly better on the test dataset. We develop four boosting trees models for estimating M based on varying life-history parameters, and an R package is provided for interested readers to estimate M of their new species.
Keywords
empirical methods, ensemble learning methods, life-history parameters, natural mortality, regression tree, statistical learning
Date
2020
Type
Journal article
Journal
ICES Journal of Marine Science
Book
Volume
77
Issue
4
Page Range
1414-1426
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled