A stomatal control model based on optimization of carbon gain versus hydraulic risk predicts aspen sapling responses to drought.
Identifieur interne : 001071 ( Main/Exploration ); précédent : 001070; suivant : 001072A stomatal control model based on optimization of carbon gain versus hydraulic risk predicts aspen sapling responses to drought.
Auteurs : Martin D. Venturas [États-Unis] ; John S. Sperry [États-Unis] ; David M. Love [États-Unis] ; Ethan H. Frehner [États-Unis] ; Michael G. Allred [États-Unis] ; Yujie Wang [États-Unis] ; William R L. Anderegg [États-Unis]Source :
- The New phytologist [ 1469-8137 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
- métabolisme : Carbone, Eau.
- physiologie : Populus, Stomates de plante.
- Modèles biologiques, Pression, Sécheresses.
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : Carbon, Water.
- physiology : Plant Stomata, Populus.
- Droughts, Models, Biological, Pressure.
Abstract
Empirical models of plant drought responses rely on parameters that are difficult to specify a priori. We test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk. We applied four drought treatments to aspen (Populus tremuloides) saplings in a research garden. First we tested the optimization algorithm by using predawn xylem pressure as an input. We then tested the full model which calculates root-zone water budget and xylem pressure hourly throughout the growing season. The optimization algorithm performed well when run from measured predawn pressures. The per cent mean absolute error (MAE) averaged 27.7% for midday xylem pressure, transpiration, net assimilation, leaf temperature, sapflow, diffusive conductance and soil-canopy hydraulic conductance. Average MAE was 31.2% for the same observations when the full model was run from irrigation and rain data. Saplings that died were projected to exceed 85% loss in soil-canopy hydraulic conductance, whereas surviving plants never reached this threshold. The model fit was equivalent to that of an empirical model, but with the advantage that all inputs are specific traits. Prediction is empowered because knowing these traits allows knowing the response to climatic stress.
DOI: 10.1111/nph.15333
PubMed: 29998567
Affiliations:
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Venturas</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112</wicri:regionArea><wicri:noRegion>84112</wicri:noRegion></affiliation></author><author><name sortKey="Sperry, John S" sort="Sperry, John S" uniqKey="Sperry J" first="John S" last="Sperry">John S. Sperry</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112</wicri:regionArea><wicri:noRegion>84112</wicri:noRegion></affiliation></author><author><name sortKey="Love, David M" sort="Love, David M" uniqKey="Love D" first="David M" last="Love">David M. Love</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112</wicri:regionArea><wicri:noRegion>84112</wicri:noRegion></affiliation></author><author><name sortKey="Frehner, Ethan H" sort="Frehner, Ethan H" uniqKey="Frehner E" first="Ethan H" last="Frehner">Ethan H. Frehner</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112</wicri:regionArea><wicri:noRegion>84112</wicri:noRegion></affiliation></author><author><name sortKey="Allred, Michael G" sort="Allred, Michael G" uniqKey="Allred M" first="Michael G" last="Allred">Michael G. Allred</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112</wicri:regionArea><wicri:noRegion>84112</wicri:noRegion></affiliation></author><author><name sortKey="Wang, Yujie" sort="Wang, Yujie" uniqKey="Wang Y" first="Yujie" last="Wang">Yujie Wang</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112</wicri:regionArea><wicri:noRegion>84112</wicri:noRegion></affiliation></author><author><name sortKey="Anderegg, William R L" sort="Anderegg, William R L" uniqKey="Anderegg W" first="William R L" last="Anderegg">William R L. Anderegg</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112</wicri:regionArea><wicri:noRegion>84112</wicri:noRegion></affiliation></author></analytic><series><title level="j">The New phytologist</title><idno type="eISSN">1469-8137</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Carbon (metabolism)</term><term>Droughts (MeSH)</term><term>Models, Biological (MeSH)</term><term>Plant Stomata (physiology)</term><term>Populus (physiology)</term><term>Pressure (MeSH)</term><term>Water (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Carbone (métabolisme)</term><term>Eau (métabolisme)</term><term>Modèles biologiques (MeSH)</term><term>Populus (physiologie)</term><term>Pression (MeSH)</term><term>Stomates de plante (physiologie)</term><term>Sécheresses (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Carbon</term><term>Water</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Carbone</term><term>Eau</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Populus</term><term>Stomates de plante</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Plant Stomata</term><term>Populus</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Droughts</term><term>Models, Biological</term><term>Pressure</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Modèles biologiques</term><term>Pression</term><term>Sécheresses</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Empirical models of plant drought responses rely on parameters that are difficult to specify a priori. We test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk. We applied four drought treatments to aspen (Populus tremuloides) saplings in a research garden. First we tested the optimization algorithm by using predawn xylem pressure as an input. We then tested the full model which calculates root-zone water budget and xylem pressure hourly throughout the growing season. The optimization algorithm performed well when run from measured predawn pressures. The per cent mean absolute error (MAE) averaged 27.7% for midday xylem pressure, transpiration, net assimilation, leaf temperature, sapflow, diffusive conductance and soil-canopy hydraulic conductance. Average MAE was 31.2% for the same observations when the full model was run from irrigation and rain data. Saplings that died were projected to exceed 85% loss in soil-canopy hydraulic conductance, whereas surviving plants never reached this threshold. The model fit was equivalent to that of an empirical model, but with the advantage that all inputs are specific traits. Prediction is empowered because knowing these traits allows knowing the response to climatic stress.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29998567</PMID><DateCompleted><Year>2019</Year><Month>09</Month><Day>27</Day></DateCompleted><DateRevised><Year>2020</Year><Month>09</Month><Day>30</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1469-8137</ISSN><JournalIssue CitedMedium="Internet"><Volume>220</Volume><Issue>3</Issue><PubDate><Year>2018</Year><Month>11</Month></PubDate></JournalIssue><Title>The New phytologist</Title><ISOAbbreviation>New Phytol</ISOAbbreviation></Journal><ArticleTitle>A stomatal control model based on optimization of carbon gain versus hydraulic risk predicts aspen sapling responses to drought.</ArticleTitle><Pagination><MedlinePgn>836-850</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/nph.15333</ELocationID><Abstract><AbstractText>Empirical models of plant drought responses rely on parameters that are difficult to specify a priori. We test a trait- and process-based model to predict environmental responses from an optimization of carbon gain vs hydraulic risk. We applied four drought treatments to aspen (Populus tremuloides) saplings in a research garden. First we tested the optimization algorithm by using predawn xylem pressure as an input. We then tested the full model which calculates root-zone water budget and xylem pressure hourly throughout the growing season. The optimization algorithm performed well when run from measured predawn pressures. The per cent mean absolute error (MAE) averaged 27.7% for midday xylem pressure, transpiration, net assimilation, leaf temperature, sapflow, diffusive conductance and soil-canopy hydraulic conductance. Average MAE was 31.2% for the same observations when the full model was run from irrigation and rain data. Saplings that died were projected to exceed 85% loss in soil-canopy hydraulic conductance, whereas surviving plants never reached this threshold. The model fit was equivalent to that of an empirical model, but with the advantage that all inputs are specific traits. Prediction is empowered because knowing these traits allows knowing the response to climatic stress.</AbstractText><CopyrightInformation>© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Venturas</LastName><ForeName>Martin D</ForeName><Initials>MD</Initials><Identifier Source="ORCID">0000-0001-5972-9064</Identifier><AffiliationInfo><Affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sperry</LastName><ForeName>John S</ForeName><Initials>JS</Initials><AffiliationInfo><Affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Love</LastName><ForeName>David M</ForeName><Initials>DM</Initials><Identifier Source="ORCID">0000-0002-0582-6990</Identifier><AffiliationInfo><Affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Frehner</LastName><ForeName>Ethan H</ForeName><Initials>EH</Initials><AffiliationInfo><Affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Allred</LastName><ForeName>Michael G</ForeName><Initials>MG</Initials><AffiliationInfo><Affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Yujie</ForeName><Initials>Y</Initials><Identifier Source="ORCID">0000-0002-3729-2743</Identifier><AffiliationInfo><Affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Anderegg</LastName><ForeName>William R L</ForeName><Initials>WRL</Initials><AffiliationInfo><Affiliation>Department of Biology, University of Utah, 257 S 1400E, Salt Lake City, UT, 84112, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>CNH-1714972</GrantID><Agency>Division of Environmental Biology</Agency><Country>International</Country></Grant><Grant><GrantID>2017-05521</GrantID><Agency>National Institute of Food and Agriculture</Agency><Country>International</Country></Grant><Grant><Agency>University of Utah Global Change and Sustainability Center</Agency><Country>International</Country></Grant><Grant><GrantID>IOS-1450560</GrantID><Agency>Division of Integrative Organismal Systems</Agency><Country>International</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>07</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>New Phytol</MedlineTA><NlmUniqueID>9882884</NlmUniqueID><ISSNLinking>0028-646X</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>059QF0KO0R</RegistryNumber><NameOfSubstance UI="D014867">Water</NameOfSubstance></Chemical><Chemical><RegistryNumber>7440-44-0</RegistryNumber><NameOfSubstance UI="D002244">Carbon</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D002244" MajorTopicYN="N">Carbon</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D055864" MajorTopicYN="Y">Droughts</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008954" MajorTopicYN="Y">Models, Biological</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D054046" MajorTopicYN="N">Plant Stomata</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011312" MajorTopicYN="N">Pressure</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014867" MajorTopicYN="N">Water</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">drought mortality</Keyword><Keyword MajorTopicYN="Y">gas exchange</Keyword><Keyword MajorTopicYN="Y">hydraulic limitations</Keyword><Keyword MajorTopicYN="Y">modeling</Keyword><Keyword MajorTopicYN="Y">photosynthesis optimization</Keyword><Keyword MajorTopicYN="Y">plant drought responses</Keyword><Keyword MajorTopicYN="Y">stomatal control</Keyword><Keyword MajorTopicYN="Y">xylem cavitation</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>02</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>06</Month><Day>08</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>7</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>9</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2018</Year><Month>7</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">29998567</ArticleId><ArticleId IdType="doi">10.1111/nph.15333</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country></list><tree><country name="États-Unis"><noRegion><name sortKey="Venturas, Martin D" sort="Venturas, Martin D" uniqKey="Venturas M" first="Martin D" last="Venturas">Martin D. Venturas</name></noRegion><name sortKey="Allred, Michael G" sort="Allred, Michael G" uniqKey="Allred M" first="Michael G" last="Allred">Michael G. Allred</name><name sortKey="Anderegg, William R L" sort="Anderegg, William R L" uniqKey="Anderegg W" first="William R L" last="Anderegg">William R L. Anderegg</name><name sortKey="Frehner, Ethan H" sort="Frehner, Ethan H" uniqKey="Frehner E" first="Ethan H" last="Frehner">Ethan H. Frehner</name><name sortKey="Love, David M" sort="Love, David M" uniqKey="Love D" first="David M" last="Love">David M. Love</name><name sortKey="Sperry, John S" sort="Sperry, John S" uniqKey="Sperry J" first="John S" last="Sperry">John S. Sperry</name><name sortKey="Wang, Yujie" sort="Wang, Yujie" uniqKey="Wang Y" first="Yujie" last="Wang">Yujie Wang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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