Agent-based vegetation models are a widely used tool in ecology, for example, to understand and predict the response of vegetation to environmental change. Models are based on well-established descriptions of processes such as vegetation establishment, growth and mortality. However, they are often developed from scratch, which can be inefficient. Here we present pyMANGA, a free and open-source platform for plant growth modelers. pyMANGA's modular design allows for the combination of different concepts and theories of how plants establish, grow or compete in response to above- and below-ground resource availability. New or alternative modules describing, e.g., competition or facilitation, can be easily added. The interchangeability of modules supports the systematic testing of different hypotheses, e.g., on dominant processes in soil-plant feedback loops. Here we further present the thorough benchmarking strategy to maintain the platform and how pyMANGA can be used to compare models with different levels of abstraction and complexity.

中文翻译：

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pyMANGA：一个模块化、开放且可扩展的软件平台，用于森林和植被动态建模

基于代理的植被模型是生态学中广泛使用的工具，例如，用于理解和预测植被对环境变化的响应。模型基于对植被建立、生长和死亡等过程的完善描述。然而，它们通常是从头开始开发的，效率可能很低。在这里，我们介绍 pyMANGA，一个针对植物生长建模者的免费开源平台。pyMANGA 的模块化设计允许结合植物如何建立、生长或竞争以响应地上和地下资源可用性的不同概念和理论。可以容易地添加描述例如竞争或促进的新的或替代的模块。模块的可互换性支持不同假设的系统测试，例如土壤-植物反馈回路中的主导过程。在这里，我们进一步介绍了维护平台的全面基准测试策略，以及如何使用 pyMANGA 来比较具有不同抽象和复杂程度的模型。