Harmonizing nature's timescales in ecosystem models.

Area of Science:

• Ecology
• Computational Biology
• Environmental Modeling

Background:

• Ecosystem models struggle to represent complex, nonlinear processes across diverse timescales.
• Key challenges include selecting appropriate timesteps and sequencing simulations for interconnected processes.
• Existing frameworks often involve trade-offs between biological realism and computational efficiency.

Purpose of the Study:

• To identify and synthesize common challenges and solutions for integrating multiple timescales in ecosystem modeling.
• To explore methods for selecting representative timesteps and ordering process simulations.
• To propose transdisciplinary approaches for enhancing timescale integration in ecological research.

Main Methods:

• Synthesized existing ecosystem modeling frameworks to identify common strategies.
• Categorized approaches for representative timestep selection (e.g., 'selective elimination of timescales', 'capture the unseen').
• Identified various processing order strategies (e.g., hierarchical, logical, iterative, random).

Main Results:

• Identified four distinct approaches for representative timestep selection and four for processing order.
• Highlighted shared compromises between biological realism and computational performance in current models.
• Demonstrated potential benefits of transferring methods from other scientific disciplines.

Conclusions:

• Addressing timescale integration requires innovative, transdisciplinary solutions.
• Smarter computing and cross-disciplinary method transfer can improve ecological modeling.
• Future research should focus on developing integrated approaches for multi-timescale ecosystem dynamics.