Inferring processes within dynamic forest models using hybrid modeling

TL;DR

This paper introduces Forest Informed Neural Networks (FINN), a hybrid approach combining mechanistic models and deep learning to improve forest dynamics predictions and infer ecological processes from data.

Contribution

The paper presents FINN, a novel hybrid modeling framework that integrates neural networks with process-based models for better inference and prediction of forest dynamics.

Findings

Replacing growth processes with DNNs enhances predictive accuracy.

The DNN learned an ecologically plausible growth function.

FINN improves forecasts of forest succession under changing conditions.

Abstract

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.