Additive tree methods are widely used in ecology. To date most ecologists have used boosted regression tree (BRT) methods. However, Bayesian additive regression tree (BART) models may offer advantages to ecologists previously unexamined.

Here we test whether BART has some benefits over the widely used BRT method. To do this we use two grassland data and 13 hydroclimatic and land use predictor variables. The dataset contained data from a period of drought as well as during a recovery phase after the drought. The response variable was the trend in the Enhanced Vegetation Index (EVI), which is an remotely sensed indicator of grassland degradation and recovery.

The settable parameters of both methods (BRT and BART) were varied to compare the performance of each method. BRT and BART models were evaluated using three prediction error statistics; root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). The best models across the two methods were assessed by inspecting the relative importance of predictor variables and the prediction error statistics.

BRT and BART models exhibited similar variable selection abilities, but the BART method generated models with similar or more favourable prediction error statistics than the BRT method (BART explained an additional 10.17% to 11.92% of the variation than BRT models). Our results indicate that BARTs may be more effective at modelling ecological data than BRTs.

BARTs also had shorter run times, more reasonable defaults in its software implementation, and greater functionality of said software implementation, beyond model building and prediction functions. Ecologists using additive regression approaches may benefit from using BART approaches and we suggest their use alongside more commonly used BRT methods in ecological studies.

加性树方法在生态学中被广泛使用。迄今为止，大多数生态学家已经使用了增强回归树（BRT）方法。但是，贝叶斯加性回归树（BART）模型可能为以前未经检查的生态学家提供优势。

在这里，我们测试BART是否比广泛使用的BRT方法具有某些优势。为此，我们使用了两个草地数据以及13个水文气候和土地利用预测变量。该数据集包含干旱期间以及干旱后恢复阶段的数据。响应变量是增强植被指数（EVI）中的趋势，该指数是草地退化和恢复的遥感指标。

改变两种方法（BRT和BART）的可设置参数以比较每种方法的性能。使用三个预测误差统计量评估了BRT和BART模型；均方根误差（RMSE），平均绝对误差（MAE）和确定系数（R ²）。通过检查预测变量的相对重要性和预测误差统计量，评估了两种方法中的最佳模型。

BRT和BART模型表现出相似的变量选择能力，但是BART方法生成的模型具有比BRT方法相似或更好的预测误差统计值（BART比BRT模型解释了另外的10.17％至11.92％的变化）。我们的结果表明，BARTs在建立生态数据方面可能比BRT更有效。

除了模型构建和预测功能之外，BART还具有更短的运行时间，更合理的默认软件实现以及更强大的功能。使用加性回归方法的生态学家可能会从使用BART方法中受益，我们建议在生态研究中将其与更常用的BRT方法一起使用。