View in EDS

Harmonizing nature's timescales in ecosystem models.

Saved in:
Bibliographic Details
Title: Harmonizing nature's timescales in ecosystem models.
Authors: Groner, Vivienne P.1 (AUTHOR) vgroner@ic.ac.uk, Cook, Jacob1 (AUTHOR), Orme, C. David L.1 (AUTHOR), Amarasekare, Priyanga2 (AUTHOR), Comyn-Platt, Edward3 (AUTHOR), Rallings, Taran1 (AUTHOR), Joshi, Jaideep4,5,6 (AUTHOR), Ewers, Robert M.1 (AUTHOR)
Source: Trends in Ecology & Evolution. Jun2025, Vol. 40 Issue 6, p575-585. 11p.
Subjects: Artificial intelligence, Knowledge transfer, Order picking systems, Meteorology, Ecosystems
Abstract: A central challenge in ecosystem modeling is accurately representing complex biotic and abiotic processes across different timescales, specifically finding a common model timestep and determining the computing sequence. Existing ecosystem models either collapse or omit timescales, which can compromise biological accuracy, or include all timesteps, which reduces computational performance and increases uncertainty. These choices can lead to qualitative or quantitative errors and weaken predictive accuracy. This temporal scaling problem pervades a diversity of disciplines – such as astrophysics, engineering, finance, meteorology, and artificial intelligence – generating the potential for knowledge transfer. We argue that transdisciplinary solutions are key to harmonizing timescales. We illustrate one such approach, demonstrating concrete progress on multiple timesteps, and highlight opportunities for future research. Modeling complex, nonlinear ecosystem processes across different timescales presents a significant challenge. We identify two key issues: selecting a representative timestep that captures interconnected processes across various timescales, and simulating these processes in an appropriate sequence. By synthesizing existing ecosystem frameworks, we find shared compromises between biological realism and computational performance. For the representative timestep, these include 'selective elimination of timescales', 'biting the bullet', 'each in their own time', and 'capture the unseen'. For processing order, we identify hierarchical, logical, iterative, and random approaches. Similar challenges exist in other disciplines, and we show how transferring methods from multiple fields, along with smarter computing, can improve timescale integration. Overcoming these challenges requires innovative transdisciplinary solutions, and we outline directions for future research. [ABSTRACT FROM AUTHOR]
Copyright of Trends in Ecology & Evolution is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:A central challenge in ecosystem modeling is accurately representing complex biotic and abiotic processes across different timescales, specifically finding a common model timestep and determining the computing sequence. Existing ecosystem models either collapse or omit timescales, which can compromise biological accuracy, or include all timesteps, which reduces computational performance and increases uncertainty. These choices can lead to qualitative or quantitative errors and weaken predictive accuracy. This temporal scaling problem pervades a diversity of disciplines – such as astrophysics, engineering, finance, meteorology, and artificial intelligence – generating the potential for knowledge transfer. We argue that transdisciplinary solutions are key to harmonizing timescales. We illustrate one such approach, demonstrating concrete progress on multiple timesteps, and highlight opportunities for future research. Modeling complex, nonlinear ecosystem processes across different timescales presents a significant challenge. We identify two key issues: selecting a representative timestep that captures interconnected processes across various timescales, and simulating these processes in an appropriate sequence. By synthesizing existing ecosystem frameworks, we find shared compromises between biological realism and computational performance. For the representative timestep, these include 'selective elimination of timescales', 'biting the bullet', 'each in their own time', and 'capture the unseen'. For processing order, we identify hierarchical, logical, iterative, and random approaches. Similar challenges exist in other disciplines, and we show how transferring methods from multiple fields, along with smarter computing, can improve timescale integration. Overcoming these challenges requires innovative transdisciplinary solutions, and we outline directions for future research. [ABSTRACT FROM AUTHOR]
ISSN:01695347
DOI:10.1016/j.tree.2025.03.011