Natural mortality estimation using tree-based ensemble learning models

Journal article


Liu, Chanjuan, Zhou, Shijie, Wang, You-Gan and Hu, Zhi-Hua. (2020). Natural mortality estimation using tree-based ensemble learning models. ICES Journal of Marine Science. 77(4), pp. 1414-1426. https://doi.org/10.1093/icesjms/fsaa058
AuthorsLiu, Chanjuan, Zhou, Shijie, Wang, You-Gan and 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.

Keywordsempirical methods; ensemble learning methods; life-history parameters; natural mortality; regression tree; statistical learning
Year2020
JournalICES Journal of Marine Science
Journal citation77 (4), pp. 1414-1426
PublisherOxford University Press
ISSN1054-3139
Digital Object Identifier (DOI)https://doi.org/10.1093/icesjms/fsaa058
Scopus EID2-s2.0-85091626847
Page range1414-1426
FunderAustralian Research Council (ARC)
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online05 Jun 2020
Publication process dates
Accepted13 Mar 2020
Deposited14 Dec 2022
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDDP160104292
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