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

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Cited articles

Adler, P. B., HilleRisLambers, J., Kyriakidis, P. C., Guan, Q., and Levine, J. M.: Climate variability has a stabilizing effect on the coexistence of prairie grasses, P. Natl. Acad. Sci. USA, 103, 12793–12798,, 2006. 
Adler, P. B., Fajardo, A., Kleinhesselink, A. R., and Kraft, N. J. B.: Trait-based tests of coexistence mechanisms, Ecol. Lett., 16, 1294–1306,, 2013. 
Angert, A. L., Huxman, T. E., Chesson, P., and Venable, D. L.: Functional tradeoffs determine species coexistence via the storage effect, P. Natl. Acad. Sci. USA, 106, 11641–11645,, 2009. 
Antoniadis, A., Lambert-Lacroix, S., and Poggi, J.-M.: Random forests for global sensitivity analysis: A selective review, Reliab. Eng. Syst. Safe., 206, 107312,, 2020. 
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.