Ecological communities may present complex interactions, such that the standard Lotka-Volterra mathematical model may not describe the system adequately. Here, we address the question of how to detect automatically the presence of a particular set of ecological nonlinear interactions known as higher-order interactions (HOI). Our proposal is based on estimating standard Lotka-Volterra model parameters by fitting experimental data, and then on generating synthetic HOI samples by stochastic differential equations. These samples are then regarded as input of machine learning classifiers to train the models and predict if the experimental time series present HOI or not. We present a case study using experimental data for single-, pairwise- and three-species of Drosophila. We compare the classical statistical tests from literature against our proposed machine learning approach. For the testing sets, the classical statistical tests (additive test and multiplicative test) showed a lack of robustness, with best results of 0.75 accuracy, 0.50 sensitivity and 1.0 specificity. In contrast, our proposed machine learning HOI recognition approach showed robustness across the testing sets, achieving mean results of 0.93 accuracy, 0.88 sensitivity and 0.99 specificity. The advantage of our machine learning HOI recognition is that we are able to train the classifiers with noisy data that resembles experimental data, producing a more robust automatic tool for HOI detection than classical statistical tests.

生态群落可能呈现复杂的相互作用，因此标准的 Lotka-Volterra 数学模型可能无法充分描述系统。在这里，我们解决了如何自动检测一组特定生态非线性相互作用的存在的问题，称为高阶相互作用（HOI）。我们的提议基于通过拟合实验数据来估计标准 Lotka-Volterra 模型参数，然后通过随机微分方程生成合成 HOI 样本。然后将这些样本作为机器学习分类器的输入来训练模型并预测实验时间序列是否存在 HOI。我们提出了一个案例研究，使用单种、成对和三种果蝇的实验数据. 我们将文献中的经典统计测试与我们提出的机器学习方法进行比较。对于测试集，经典的统计检验（加法检验和乘法检验）缺乏稳健性，最佳结果为 0.75 准确度、0.50 敏感性和 1.0 特异性。相比之下，我们提出的机器学习 HOI 识别方法在测试集上表现出稳健性，实现了 0.93 准确度、0.88 敏感性和 0.99 特异性的平均结果。我们的机器学习 HOI 识别的优势在于，我们能够使用类似于实验数据的噪声数据训练分类器，从而产生比经典统计测试更强大的 HOI 检测自动工具。