A mechanistic density functional theory for ecology across scales

Abstract

Our ability to predict the properties of a system typically diminishes as the number of its interacting constituents rises. This poses major challenges for understanding natural ecosystems, and humanity's effects on them. How do macroecological patterns emerge from the interplay between species and their environment? What is the impact on complex ecological systems of human interventions, such as extermination of large predators, deforestation, and climate change? The resolution of such questions is hampered in part by the lack of a holistic approach that unifies ecology across temporal and spatial scales. Here we use density functional theory, a computational method for many-body problems in physics, to develop a novel computational framework for ecosystem modelling. Our methods accurately fit experimental and synthetic data of interacting multi-species communities across spatial scales and can project to unseen data. Our mechanistic framework provides a promising new avenue for understanding how ecosystems operate and facilitates quantitative assessment of interventions.

Perspective: New directions in dynamical density functional theory

“…In the SIR-DDFT model [59,60], the considered 'particles' are humans. It even does not have to stop there, since a (quantum-based) DFT has been applied to entire ecosystems [322]. This brief list should make clear the particular advantage DDFT has in biology-the same concept can be applied across all length scales, making DDFT an ideal tool for multiscale modeling.…”