Differentiable Land Model Reveals Global Environmental Controls on Ecological Parameters

Abstract

Do ecosystems primarily reflect evolutionary history or current environment? Predicting land-atmosphere exchange hinges on this unresolved question. Plant traits adapt to particular environments over evolutionary timescales, yet their individual relationships with current climate and soils are often obscured by limited sampling, plant-type effects, and multiple adaptive strategies that can yield similar outcomes. Crucially, it is the coordination of traits, rather than any single trait, that governs vegetation dynamics and ecosystem fluxes, yet such multivariate relationships cannot be directly observed. We present DifferLand, a differentiable hybrid model that integrates process understanding with machine learning to uncover latent trait-environment relationships from global satellite and in-situ observations (2001-2023). DifferLand explains up to 88% of the variance in canopy structure, photosynthesis, and carbon exchange by learning latent ecological axes-leaf economics, plant stature, and cropland distribution-that link long-term adaptation with short-term dynamics. Interpretable machine learning shows that these coordinated axes emerge from nonlinear interactions between plant-type attributes and local environment. Embedding such relationships into terrestrial models establishes a pathway toward adaptive models that better predict ecosystem resilience under climate change.