Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction

2026-03-03

AI Summary

This study addresses the challenge of predicting ecosystem carbon fluxes, which is hindered by strong spatiotemporal heterogeneity and the limited generalizability of existing methods that treat environmental covariates as homogeneous inputs. To overcome this, we propose the Role-Aware Conditional Inference (RACI) framework, which explicitly differentiates the functional roles of environmental variables for the first time. RACI employs a hierarchical temporal encoder to decouple slow-varying mechanisms from fast-changing drivers and introduces a role-aware spatial retrieval mechanism that integrates contextual information from functionally similar and geographically proximate sites. This approach enables unified modeling across diverse ecological regimes without requiring multiple local models or reliance on fixed spatial structures. Evaluated on wetlands, croplands, and other ecosystems for predicting CO₂, GPP, and CH₄ fluxes, RACI significantly outperforms current baselines, achieving higher prediction accuracy and improved cross-regional generalization.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Accurate prediction of terrestrial ecosystem carbon fluxes (e.g., CO$_2$, GPP, and CH$_4$) is essential for understanding the global carbon cycle and managing its impacts. However, prediction remains challenging due to strong spatiotemporal heterogeneity: ecosystem flux responses are constrained by slowly varying regime conditions, while short-term fluctuations are driven by high-frequency dynamic forcings. Most existing learning-based approaches treat environmental covariates as a homogeneous input space, implicitly assuming a global response function, which leads to brittle generalization across heterogeneous ecosystems. In this work, we propose Role-Aware Conditional Inference (RACI), a process-informed learning framework that formulates ecosystem flux prediction as a conditional inference problem. RACI employs hierarchical temporal encoding to disentangle slow regime conditioners from fast dynamic drivers, and incorporates role-aware spatial retrieval that supplies functionally similar and geographically local context for each role. By explicitly modeling these distinct functional roles, RACI enables a model to adapt its predictions across diverse environmental regimes without training separate local models or relying on fixed spatial structures. We evaluate RACI across multiple ecosystem types (wetlands and agricultural systems), carbon fluxes (CO$_2$, GPP, CH$_4$), and data sources, including both process-based simulations and observational measurements. Across all settings, RACI consistently outperforms competitive spatiotemporal baselines, demonstrating improved accuracy and spatial generalization under pronounced environmental heterogeneity.

Problem

• spatiotemporal heterogeneity
• ecosystem carbon flux prediction
• environmental covariates
• global response function
• generalization

Innovation

• Role-Aware Conditional Inference
• spatiotemporal heterogeneity
• hierarchical temporal encoding
• role-aware spatial retrieval
• ecosystem carbon flux prediction