The use of empirical models to predict species distribution is recognized as an important tool in wildlife management. Tree-based methods gained considerable attention in the last years mostly due to their flexibility and robustness. Here, we provide an overview of tree-based methods by addressing some of their concepts, uses and limitations. For illustrative purposes, we modelled the distribution of a red deer (Cervus elaphus) population using fine-scale predictors while applying four modelling methods: three treebased methods (classification trees, random forests and boosted trees) and the generalized linear model by stepwise regression. In order to explore alternative trees and achieve the best model performance, a series of classifiers were run with different tuning parameters. The random forests and boosted trees models were the most accurate classifiers followed by classification trees and generalized linear model by stepwise regression. Despite differences in the predictive accuracy, the results of the four models were consistent with the species ecological requirements. Red deer occurred further away from disturbed areas (e.g. villages and other human settlements), agricultural fields and near shrubs and forest patches. Furthermore, the species often occurred in areas with gentle slopes, preferentially with a southern exposure. We observed that classification trees are easy to interpret but may produce unstable decision trees and unwieldy results in the presence of sharp discontinuities. We state that ensemble methods such as random forests and boosted trees are valuable tools in predicting species distributions. This study provides the necessary background for the understanding of tree-based methods, which will be of great help in further studies in ecological modelling, as it will shed light in the most appropriate technique to be used.

中文翻译：

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基于树的方法：资源选择模型框架下的概念、用途和限制

使用经验模型来预测物种分布被认为是野生动物管理的重要工具。基于树的方法在过去几年中获得了相当多的关注，主要是因为它们的灵活性和鲁棒性。在这里，我们通过解决一些概念、用途和局限性来概述​​基于树的方法。出于说明目的，我们使用细尺度预测器对马鹿（Cervus elaphus）种群的分布进行建模，同时应用四种建模方法：三种基于树的方法（分类树、随机森林和提升树）和通过逐步回归的广义线性模型。为了探索替代树并实现最佳模型性能，使用不同的调整参数运行了一系列分类器。随机森林和提升树模型是最准确的分类器，其次是分类树和逐步回归的广义线性模型。尽管预测准确性存在差异，但四种模型的结果均符合物种生态要求。马鹿出现在远离受干扰地区（例如村庄和其他人类住区）、农田以及靠近灌木和森林斑块的地方。此外，该物种经常出现在缓坡地区，优先向南暴露。我们观察到分类树很容易解释，但可能会产生不稳定的决策树，并在存在尖锐不连续性的情况下产生笨拙的结果。我们指出，诸如随机森林和增强树之类的集成方法是预测物种分布的宝贵工具。