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
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 185485261
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Harmonizing nature's timescales in ecosystem models.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Groner%2C+Vivienne+P%2E%22">Groner, Vivienne P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vgroner@ic.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Cook%2C+Jacob%22">Cook, Jacob</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Orme%2C+C%2E+David+L%2E%22">Orme, C. David L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Amarasekare%2C+Priyanga%22">Amarasekare, Priyanga</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Comyn-Platt%2C+Edward%22">Comyn-Platt, Edward</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rallings%2C+Taran%22">Rallings, Taran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Joshi%2C+Jaideep%22">Joshi, Jaideep</searchLink><relatesTo>4,5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ewers%2C+Robert+M%2E%22">Ewers, Robert M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Trends+in+Ecology+%26+Evolution%22">Trends in Ecology & Evolution</searchLink>. Jun2025, Vol. 40 Issue 6, p575-585. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Order+picking+systems%22">Order picking systems</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorology%22">Meteorology</searchLink><br /><searchLink fieldCode="DE" term="%22Ecosystems%22">Ecosystems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>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.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=185485261
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.tree.2025.03.011
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 575
    Subjects:
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Knowledge transfer
        Type: general
      – SubjectFull: Order picking systems
        Type: general
      – SubjectFull: Meteorology
        Type: general
      – SubjectFull: Ecosystems
        Type: general
    Titles:
      – TitleFull: Harmonizing nature's timescales in ecosystem models.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Groner, Vivienne P.
      – PersonEntity:
          Name:
            NameFull: Cook, Jacob
      – PersonEntity:
          Name:
            NameFull: Orme, C. David L.
      – PersonEntity:
          Name:
            NameFull: Amarasekare, Priyanga
      – PersonEntity:
          Name:
            NameFull: Comyn-Platt, Edward
      – PersonEntity:
          Name:
            NameFull: Rallings, Taran
      – PersonEntity:
          Name:
            NameFull: Joshi, Jaideep
      – PersonEntity:
          Name:
            NameFull: Ewers, Robert M.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 01695347
          Numbering:
            – Type: volume
              Value: 40
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Trends in Ecology & Evolution
              Type: main
ResultId 1