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Reconciling the optimal and empirical approaches to modelling stomatal conductance: RECONCILING OPTIMAL AND EMPIRICAL STOMATAL MODELS
Open accessAbstract
Models of vegetation function are widely used to predict the effects of climate change on carbon, water and nutrient cycles of terrestrial ecosystems, and their feedbacks to climate. Stomatal conductance, the process that governs plant water use and carbon uptake, is fundamental to such models. In this paper, we reconcile two long-standing theories of stomatal conductance. The empirical approach, which is most commonly used in vegetation models, is phenomenological, based on experimental observations of stomatal behaviour in response to environmental conditions. The optimal approach is based on the theoretical argument that stomata should act to minimize the amount of water used per unit carbon gained. We reconcile these two approaches by showing that the theory of optimal stomatal conductance can be used to derive a model of stomatal conductance that is closely analogous to the empirical models. Consequently, we obtain a unified stomatal model which has a similar form to existing empirical models, but which now provides a theoretical interpretation for model parameter values. The key model parameter, g1, is predicted to increase with growth temperature and with the marginal water cost of carbon gain. The new model is fitted to a range of datasets ranging from tropical to boreal trees. The parameter g1 is shown to vary with growth temperature, as predicted, and also with plant functional type. The model is shown to correctly capture responses of stomatal conductance to changing atmospheric CO2, and thus can be used to test for stomatal acclimation to elevated CO2. The reconciliation of the optimal and empirical approaches to modelling stomatal conductance is important for global change biology because it provides a simple theoretical framework for analyzing, and simulating, the coupling between carbon and water cycles under environmental change.
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Cites
A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species
Farquhar, G. D., von Caemmerer, S., & Berry, J. A. (1980) · Planta · doi:10.1007/bf00386231 · Cited by 8,796
A climate-change risk analysis for world ecosystems
Scholze, M., Knorr, W., Arnell, N. W., & Prentice, I. C. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601816103 · Cited by 756
Self-citation
Cited by
Plantecophys - An R Package for Analysing and Modelling Leaf Gas Exchange Data
Duursma, R. A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0143346 · Cited by 624
Self-citationThe fate of carbon in a mature forest under carbon dioxide enrichment
Jiang, M., Medlyn, B. E., Drake, J. E., Duursma, R. A., Anderson, I. C., Barton, C. V. M., Boer, M. M., Carrillo, Y., Castañeda-Gómez, L., Collins, L., et al. (2020) · Nature · doi:10.1038/s41586-020-2128-9 · Cited by 448
Self-citationReduced streamflow in water-stressed climates consistent with CO2 effects on vegetation
Ukkola, A. M., Prentice, I. C., Keenan, T. F., van Dijk, A. I. J. M., Viney, N. R., Myneni, R. B., & Bi, J. (2016) · Nature Climate Change · doi:10.1038/nclimate2831 · Cited by 359
Self-citationFisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell, N. G., Knox, R. G., Koven, C., Holm, J., Rogers, B. M., et al. (2015) · Geoscientific Model Development · doi:10.5194/gmd-8-3593-2015 · Cited by 337
Physics‐Constrained Machine Learning of Evapotranspiration
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C., Li, X., & Qiu, G. Y. (2019) · Geophysical Research Letters · doi:10.1029/2019gl085291 · Cited by 315
Metadata
- Authors
- MEDLYN, B. E., DUURSMA, R. A., EAMUS, D., ELLSWORTH, D. S., PRENTICE, I. C., BARTON, C. V. M., CROUS, K. Y., DE ANGELIS, P., FREEMAN, M., & WINGATE, L.
- Venue
- Global Change Biology
- Publisher
- Wiley
- Published
- 2011-01-11
- Volume
- 17
- Issue
- 6
- Pages
- 2134-2144
- ISSN
- 1354-1013 (ISSN-L)
- OpenAlex
- W2100174116
- Cited by
- 1,343
- References
- 2
- FWCI
- 17.15
- Type
- journal article
- Language
- en
- Open access
- bronze
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- MEDLYN, B. E., DUURSMA, R. A., EAMUS, D., ELLSWORTH, D. S., PRENTICE, I. C., BARTON, C. V. M., CROUS, K. Y., DE ANGELIS, P., FREEMAN, M., & WINGATE, L. (2011). Reconciling the optimal and empirical approaches to modelling stomatal conductance: RECONCILING OPTIMAL AND EMPIRICAL STOMATAL MODELS. Global Change Biology. https://doi.org/10.1111/j.1365-2486.2010.02375.x
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.