Including microbial communities in soil carbon-nitrogen cycling modeling via a hybrid neural-mechanistic modeling approach

A recurring challenge in ecosystem science is modeling the variance of biogeochemical process rates in connection with local microbial community composition. Mechanistic models usually relies on fixed parameters that ignore such ecological variations. Purely statistical approaches require extensive data and, lacking process-based information, often overfit to training conditions, limiting their ability to generalize. We present here a hybrid modeling framework that combines these approaches, allowing mechanistic biogeochemical models to adapt their parameters based on local microbial community structure.

Our approach uses neural networks to translate microbial community composition (bacterial and fungal taxonomic data) into site-specific key parameters in a mechanistic carbon-nitrogen cycling equations. Since these intermediate parameters likely capture multiple processes, we view them as functional parameters that allow the mechanistic model to flexibly incorporate the variance of decomposition rates due to local microbial communities, while still maintaining the interpretable structure of process-based equations and retaining the deterministic information for the processes we know how to model.

The innovation lies in including community composition from sequencing directly as a driver of parameter variation within established biogeochemical theory, preserving information that would otherwise be lost (for example assembling the sequencing data into diversity indicators). Literature-derived constraints ensure parameters remain within physically plausible ranges, but the neural components learns how microbial community structure modulates these values locally to improve predictions.

This methodological framework demonstrates that we can link communities with decomposition processes without requiring a complete mechanistic understanding (with consequent biases due to likely missing processes) of every intermediate step. This approach is broadly applicable, solving the difficulties coming from knowing that functional diversity influences biogeochemical processes but with an incomplete understanding of all the underlying mechanistic complexity, embedding the paradigm of soil decomposition kinetics as emergent ecological properties rather than as fixed intrinsic characteristics.