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dc.contributor.authorAnderegg, William R. L.
dc.contributor.authorWolf, Adam
dc.contributor.authorArango-Velez, Adriana
dc.contributor.authorChoat, Brendan
dc.contributor.authorChmura, Daniel J.
dc.contributor.authorJansen, Steven
dc.contributor.authorKolb, Thomas
dc.contributor.authorLi, Shan
dc.contributor.authorMeinzer, Frederick
dc.contributor.authorPita, Pilar
dc.contributor.authorResco de Dios, Víctor
dc.contributor.authorSperry, John S.
dc.contributor.authorWolfe, Brett T.
dc.contributor.authorPacala, Stephen
dc.date.accessioned2017-11-20T10:20:19Z
dc.date.available2017-11-20T10:20:19Z
dc.date.issued2017
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/10459.1/60511
dc.description.abstractClimate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.ca_ES
dc.description.sponsorshipFunding for this research was provided by NSF DEB EF-1340270 and the Climate Mitigation Initiative at the Princeton Environmental Institute, Princeton University. SL acknowledges financial support from the China Scholarship Council (CSC). VRD acknowledges funding from Ramón y Cajal fellowship (RYC-2012-10970). BTW was supported by the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. DJC acknowledges funding from the National Science Centre, Poland (NN309 713340). WRLA was supported in part by NSF DEB 1714972.ca_ES
dc.language.isoengca_ES
dc.publisherPublic Library of Scienceca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1371/journal.pone.0185481ca_ES
dc.relation.ispartofPlos One, 2017, vol.12, núm. 10, p.1-17ca_ES
dc.rightscc-by (c) Anderegg et al., 2017ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titlePlant water potential improves prediction of empirical stomatal modelsca_ES
dc.typearticleca_ES
dc.identifier.idgrec026212
dc.type.versionpublishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0185481


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