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Journal Articles New Phytologist Year : 2021

Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models

1 SAGES - School of Archaeology, Geography and Environmental Sciences
2 Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University
3 IMBE - Institut méditerranéen de biodiversité et d'écologie marine et continentale
4 International Institute for Applied Systems Analysis (IIASA), Ecosystems Services and Management, Schlossplatz 1, A-2361 Laxenburg, Austria
5 SLU - Swedish University of Agricultural Sciences
6 Imperial College London
7 Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
8 Department of Mathematics and Mathematical Statistics, Umeå University, 901 87 Umeå, Sweden
9 Copernicus Institute of Sustainable Development, Environmental Sciences, Faculty of Geosciences, Utrecht University, Vening Meinesz building, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
10 Department of Evolutionary Studies of Biosystems, The Graduate University for Advanced Studies (Sokendai), Hayama, Kanagawa 240-0193, Japan
11 Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
12 UC Berkeley - University of California [Berkeley]
13 Stockholm University
14 CSIC - Consejo Superior de Investigaciones Cientificas = Spanish National Research Council
15 CREAF, Cerdanyola del Valles, 08193 Barcelona, Catalonia, Spain
16 BOKU - Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche]
17 Dept. Landscape Architecture and Rural Systems Engineering
18 Department of Biological Sciences, Texas Tech University, 2901 Main Street, Lubbock, TX 79409, USA
19 D-USYS - Department of Environmental Systems Science [ETH Zürich]
20 Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
Stefano Manzoni

Abstract

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studiesthat demonstrate how EEO generate parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration, and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.
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Dates and versions

hal-03265200 , version 1 (19-06-2021)

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Sandy P. Harrison, Wolfgang Cramer, Oskar Franklin, Iain Colin Prentice, Han Wang, et al.. Eco‐evolutionary optimality as a means to improve vegetation and land‐surface models. New Phytologist, 2021, 231 (6), pp.2125-2141. ⟨10.1111/nph.17558⟩. ⟨hal-03265200⟩
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