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Trait-Based Modeling of Terrestrial Ecosystems: Advances and Challenges Under Global Change

  • Advances and Future Directions in Earth System Modelling (I Simpson, Section Editor)
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Abstract

Purpose of Review

We summarize the general structure of modern terrestrial ecosystem models and investigate how advances in trait-based modeling approaches help to better constrain predictions for ecosystem sensitivity to global change.

Recent Findings

In ecosystem models, empirical parameters are increasingly being replaced with plant physiological trait-based parameters, which can be directly measured in the field. The needs to predict long-term terrestrial ecosystem dynamics under climate change have spurred novel model developments including the representation of (i) vegetation processes across the critical zone, (ii) wood and belowground ecophysiology, and (iii) the effects of physiological trait acclimation.

Summary

Trait-based modeling of terrestrial ecosystems allows for the direct integration of measured plant ecophysiology with model processes, increasing the potential to constrain uncertainty and improve predictions under novel climate regimes. However, such increased model complexity requires careful model design, standardized intercomparisons, and benchmarking for model responses to both climate extremes and long-term trends.

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Acknowledgments

X.X. acknowledges funding from Cornell CALS. A.T.T acknowledges funding from the USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Programme Grant No. 2018-67012-31496, the University of California Laboratory Fees Research Program Award No. LFR-20-652467, and the NSF Grant 2003205. We also thank two anonymous reviewers for providing insightful suggestions to improve the manuscript.

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This article is part of the Topical Collection on Advances and Future Directions in Earth System Modelling

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Xu, X., Trugman, A.T. Trait-Based Modeling of Terrestrial Ecosystems: Advances and Challenges Under Global Change. Curr Clim Change Rep 7, 1–13 (2021). https://doi.org/10.1007/s40641-020-00168-6

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