用神经网络改进森林模型，让预测更准、生态更合理。
Inferring processes within dynamic forest models using hybrid modeling
- 将生长过程替换为神经网络，与机制模型统一校准
- 在巴拿马岛屿数据上预测性能显著提升
- 可提取出符合生态规律的生长函数，适合生态建模者
在新型气候条件下模拟森林动态，需兼顾机制理解与经验灵活性。动态植被模型（DVM）虽以机理方式表示生态过程，但其表现易受功能形式假设错误影响。从数据中正确推断过程结构与功能形式仍是重大挑战，因现有方法如插件估计器效果不佳。我们提出森林感知神经网络（FINN），一种结合林隙模型与深度神经网络（DNN）的混合建模方法。FINN 将部分过程替换为 DNN，与其余机制组件一同在单一步骤中校准。在巴拿马巴罗科洛拉岛50公顷样地的案例研究中，用 DNN 替代生长过程后，预测性能与演替轨迹均优于机制版本的 FINN。此外，通过可解释AI技术从 DNN 中提取出一个生态合理的改进型生长函数。结论表明，该混合建模方法为从数据中推断森林动态提供了灵活途径，有助于提升极端环境变化下的生态系统轨迹预测能力。
原文摘要 · Abstract (English)
Modeling forest dynamics under novel climatic conditions requires a careful balance between process-based understanding and empirical flexibility. Dynamic Vegetation Models (DVM) represent ecological processes mechanistically, but their performance is prone to misspecified assumptions about functional forms. Inferring the structure of these processes and their functional forms correctly from data remains a major challenge because current approaches, such as plug-in estimators, have proven ineffective. We introduce Forest Informed Neural Networks (FINN), a hybrid modeling approach that combines a forest gap model with deep neural networks (DNN). FINN replaces processes with DNNs, which are then calibrated alongside the other mechanistic components in one unified step. In a case study on the Barro Colorado Island 50-ha plot we demonstrate that replacing the growth process with a DNN improves predictive performance and succession trajectories compared to a mechanistic version of FINN. Furthermore, we discovered that the DNN learned an ecologically plausible, improved functional form of the growth process, which we extracted from the DNN using explainable AI. In conclusion, our new hybrid modeling approach offers a versatile opportunity to infer forest dynamics from data and to improve forecasts of ecosystem trajectories under unprecedented environmental change.
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