A density functional theory for ecology across scales.

Area of Science:

• Ecology
• Computational Biology
• Physics

Background:

• Ecology struggles with holistic, cross-scale modeling due to complex interacting systems.
• Understanding human impacts on ecosystems is hindered by current modeling limitations.

Purpose of the Study:

• To develop a unified computational framework for ecosystem modeling.
• To enable accurate predictions across spatial and temporal scales.
• To assess the impact of human interventions on ecological systems.

Main Methods:

• Applied density functional theory (DFT), a physics-based computational method.
• Developed a novel cost function to represent ecosystem component trade-offs.
• Validated the framework with experimental and synthetic multi-species community data.

Main Results:

• The DFT framework accurately fits and projects data for interacting communities.
• The cost function effectively encodes ecological trade-offs.
• The model performs comparably to specialized approaches across diverse systems.

Conclusions:

• The density functional framework offers a promising, generalizable approach for ecological modeling.
• This method advances the understanding of complex ecosystems and their responses to change.
• Facilitates holistic modeling of ecological phenomena across scales.