Bayesian Networks (BNs) are commonly used to model socio-ecological systems, as their graphical structure supports participatory modelling, they can integrate quantitative data and qualitative knowledge, and account for uncertainty. Although the spatial and temporal dimensions are important in socio-ecological systems, there is a lack of openly available and easy-to-use tools to run BNs with spatial data over time. Here, we present gBay (gbay.ethz.ch), an online platform where users can link their BNs to spatial data, run the network iteratively to incorporate dynamics and feedbacks, and add geo-processing calculations to account for spatial interactions. We demonstrate the use of this tool on the examples of a modelling a regulating ecosystem service, where we account for neighbourhood effects, and land-use decisions, where we include regional-level boundary conditions. The gBay platform supports users with its graphical interface and instructive wiki page, and provides a step towards more accessible and flexible socio-ecological modelling.

贝叶斯网络（BNs）通常用于对社会生态系统进行建模，因为它们的图形结构支持参与式建模，它们可以集成定量数据和定性知识，并解决不确定性。尽管空间和时间维度在社会生态系统中很重要，但是随着时间的推移，缺乏公开可用且易于使用的工具来运行带有空间数据的BN。在这里，我们介绍了gBay（gbay.ethz.ch），这是一个在线平台，用户可以在其中将其BN链接到空间数据，迭代地运行网络以合并动力学和反馈，并添加地理处理计算以解决空间相互作用。我们在对调节生态系统服务进行建模的示例中演示了该工具的使用，在该示例中我们考虑了邻里效应和土地使用决策，其中包括区域级别的边界条件。gBay平台通过其图形界面和说明性Wiki页面为用户提供支持，并朝着更易于访问和灵活的社会生态建模迈出了一步。