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.

中文翻译：

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使用基于树的集成学习模型估算自然死亡率

实证研究在估计鱼类自然死亡率方面很受欢迎（⁠$中号$⁠）。但是，这些经验方法是从其他生活史参数中得出M的，因此通常被认为不如直接方法可靠。为了提高经验方法的预测性能和可靠性，我们开发了集成学习模型，包括套袋树，随机森林和助推树，以进行预测$中号$基于软骨鱼类和鱼类鱼类的256条记录的数据集。三种常见的生活史参数用作预测因子：最大年龄和两个生长参数（生长系数和渐近长度）。此外，还包括分类学变量类，以区分软骨鱼类和软骨鱼类。结果表明，基于树的集成学习模型显着提高了$中号$与传统的统计回归模型和基本回归树模型相比，估计值更高。在整体学习模型中，助推树和随机森林在训练数据集上表现最佳，但前者在测试数据集上表现稍好。我们开发了四个助推树模型来进行估计$中号$ 基于变化的生活史参数，并提供了R包供感兴趣的读者估算 $中号$ 他们的新物种。