arXiv 1908.098532019-08q-bio.PEcs.LGq-bio.QMstat.ML

生态网络中推断特征匹配和预测物种相互作用的机器学习算法

Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks

University of Regensburg（雷根斯堡大学）；University of Freiburg（弗莱堡大学）；Senckenberg Biodiversity and Climate Research Centre (SBiK-F)（森肯伯格生物多样性与气候研究中心）

Maximilian Pichler, Virginie Boreux, Alexandra-Maria Klein, Matthias Schleuning, Florian Hartig

备注：: 48 pages, 5 figures
DOI：: 10.1111/2041-210X.13329

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摘要：

长期以来，生态学家一直怀疑，如果物种的特征以某种特定的方式匹配，它们更有可能相互作用。例如，如果蜜蜂舌头的比例适合植物的花朵形状，授粉相互作用可能更有可能。然而，不同类型的生态网络对性状匹配在确定物种间相互作用中的重要性的经验估计存在显著差异。在这里，我们表明，模糊的经验特质匹配研究可能已经出现至少在使用过于简单的统计模型的一部分。使用模拟和真实数据，我们将传统的广义线性模型（GLM）与更灵活的机器学习（ML）模型（随机森林，Boosted回归树，深度神经网络，卷积神经网络，支持向量机，朴素贝叶斯和k-最近邻）进行对比，测试它们基于性状预测物种相互作用的能力，并推断出与物种相互作用有因果关系的性状组合。我们发现，最好的ML模型可以成功地预测植物-传粉者网络中的物种相互作用，大大优于GLM。我们的研究结果还表明，ML模型可以更好地识别因果关系负责的特质匹配组合比GLM。在两个案例研究中，最好的ML模型成功地预测了全球植物-传粉者数据库中的物种相互作用，并推断出植物-蜂鸟网络的生态学上合理的特征匹配规则，而无需任何先验假设。我们的结论是，灵活的ML模型在理解交互网络方面比传统的回归模型具有许多优势。我们期望这些结果可以外推到其他生态网络类型。更一般地说，我们的研究结果突出了机器学习和人工智能在生态学推理中的潜力，超越了图像或模式识别等标准任务。

英文摘要：

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait-matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait-matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naive Bayes, and k-Nearest-Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We find that the best ML models can successfully predict species interactions in plant-pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait-matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant-pollinator database and inferred ecologically plausible trait-matching rules for a plant-hummingbird network, without any prior assumptions. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition.

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