Articles | Volume 16, issue 14
Development and technical paper
17 Jul 2023
Development and technical paper |  | 17 Jul 2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1286', Anonymous Referee #1, 01 Feb 2023
    • AC1: 'Reply on RC1', Lingcheng Li, 31 Mar 2023
  • RC2: 'Comment on egusphere-2022-1286', Gregory Duveiller, 15 Feb 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Lingcheng Li on behalf of the Authors (31 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (12 Apr 2023) by Klaus Klingmüller
RR by Anonymous Referee #2 (25 May 2023)
ED: Publish subject to technical corrections (07 Jun 2023) by Klaus Klingmüller
AR by Lingcheng Li on behalf of the Authors (09 Jun 2023)  Author's response   Manuscript 
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.