Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54–77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.

机器学习的最新发展已扩展了数据驱动建模（DDM）功能，允许人工智能通过计算和利用系统中观察到的变量之间的相关性来推断系统的行为。机器学习算法可以启用日益增多的“大数据”的使用，并协助跨规模应用生态系统服务模型，分析和预测这些服务向分散受益者的流动。我们使用Weka和ARIES软件生成了DDM的两个示例：分别是南非使用的柴火和西西里岛的生物多样性价值。我们在南非的示例表明，DDM（准确度为64-91％）可以识别出柴火使用率最高的四分位数内的区域，其准确度可与传统建模技术相媲美（准确度为54-77％）。西西里人的例子强调了决策者如何使DDM更加易于使用，他们既显示了参与不确定性信息的能力，又显示了他们的意愿。作为DDM流程一部分而产生的不确定性估计值，使决策者可以确定他们可以接受的不确定性水平，并利用自己的专业知识来进行可能引起争议的决策。我们得出的结论是，在对生态系统服务进行建模时，DDM扮演着明显的角色，有助于为复杂的社会生态问题提供跨学科模型和整体解决方案。允许决策者确定他们可以接受的不确定性水平，并利用自己的专业知识来进行可能引起争议的决策。我们得出的结论是，在对生态系统服务进行建模时，DDM扮演着明显的角色，有助于为复杂的社会生态问题提供跨学科模型和整体解决方案。允许决策者确定他们可以接受的不确定性水平，并利用自己的专业知识来进行可能引起争议的决策。我们得出的结论是，在对生态系统服务进行建模时，DDM扮演着明显的角色，有助于为复杂的社会生态问题提供跨学科模型和整体解决方案。