001     885506
005     20220614190002.0
024 7 _ |a 10.1111/nph.16879
|2 doi
024 7 _ |a 0028-646X
|2 ISSN
024 7 _ |a 1469-8137
|2 ISSN
024 7 _ |a altmetric:88500226
|2 altmetric
024 7 _ |a pmid:33411342
|2 pmid
024 7 _ |a WOS:000579527300001
|2 WOS
024 7 _ |a 2128/31315
|2 Handle
037 _ _ |a FZJ-2020-03886
041 _ _ |a English
082 _ _ |a 580
100 1 _ |a Puglielli, Giacomo
|0 0000-0003-0085-4535
|b 0
|e Corresponding author
245 _ _ |a Global patterns of biomass allocation in woody species with different tolerance of shade and drought: evidence for multiple strategies
260 _ _ |a Oxford [u.a.]
|c 2021
|b Wiley-Blackwell
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1655205472_19268
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a The optimal partitioning theory predicts that plants of a given species acclimate to different environments by allocating a larger proportion of biomass to the organs acquiring the most limiting resource. Are similar patterns found across species adapted to environments with contrasting levels of abiotic stress?We tested the optimal partitioning theory by analysing how fractional biomass allocation to leaves, stems and roots differed between woody species with different tolerances of shade and drought in plants of different age and size (seedlings to mature trees) using a global dataset including 604 species.No overarching biomass allocation patterns at different tolerance values across species were found. Biomass allocation varied among functional types as a result of phenological (deciduous vs evergreen broad-leaved species) and broad phylogenetical (angiosperms vs gymnosperms) differences. Furthermore, the direction of biomass allocation responses between tolerant and intolerant species was often opposite to that predicted by the optimal partitioning theory.We conclude that plant functional type is the major determinant of biomass allocation in woody species. We propose that interactions between plant functional type, ontogeny and species-specific stress tolerance adaptations allow woody species with different shade and drought tolerances to display multiple biomass partitioning strategies
536 _ _ |a 217 - Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten (POF4-217)
|0 G:(DE-HGF)POF4-217
|c POF4-217
|f POF IV
|x 0
536 _ _ |a 2171 - Biological and environmental resources for sustainable use (POF4-217)
|0 G:(DE-HGF)POF4-2171
|c POF4-217
|f POF IV
|x 1
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Laanisto, Lauri
|0 0000-0003-2215-7298
|b 1
700 1 _ |a Poorter, Hendrik
|0 P:(DE-Juel1)129384
|b 2
700 1 _ |a Niinemets, Ülo
|0 0000-0002-3078-2192
|b 3
773 _ _ |a 10.1111/nph.16879
|g p. nph.16879
|0 PERI:(DE-600)1472194-6
|n 1
|p 308-322
|t The new phytologist
|v 229
|y 2021
|x 1469-8137
856 4 _ |u https://juser.fz-juelich.de/record/885506/files/New%20Phytologist%20-%202020%20-%20Puglielli%20-%20Global%20patterns%20of%20biomass%20allocation%20in%20woody%20species%20with%20different%20tolerances%20of.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:885506
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)129384
913 1 _ |a DE-HGF
|b Forschungsbereich Erde und Umwelt
|l Erde im Wandel – Unsere Zukunft nachhaltig gestalten
|1 G:(DE-HGF)POF4-210
|0 G:(DE-HGF)POF4-217
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-200
|4 G:(DE-HGF)POF
|v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten
|x 0
913 1 _ |a DE-HGF
|b Forschungsbereich Erde und Umwelt
|l Erde im Wandel – Unsere Zukunft nachhaltig gestalten
|1 G:(DE-HGF)POF4-210
|0 G:(DE-HGF)POF4-217
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-200
|4 G:(DE-HGF)POF
|v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten
|9 G:(DE-HGF)POF4-2171
|x 1
913 0 _ |a DE-HGF
|b Key Technologies
|l Key Technologies for the Bioeconomy
|1 G:(DE-HGF)POF3-580
|0 G:(DE-HGF)POF3-582
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Plant Science
|x 0
914 1 _ |y 2021
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1060
|2 StatID
|b Current Contents - Agriculture, Biology and Environmental Sciences
|d 2020-02-26
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b NEW PHYTOL : 2018
|d 2020-02-26
915 _ _ |a DEAL Wiley
|0 StatID:(DE-HGF)3001
|2 StatID
|d 2020-02-26
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-02-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
|d 2020-02-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
|d 2020-02-26
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2020-02-26
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NEW PHYTOL : 2018
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
|d 2020-02-26
915 _ _ |a National-Konsortium
|0 StatID:(DE-HGF)0430
|2 StatID
|d 2020-02-26
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-02-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-02-26
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IBG-2-20101118
|k IBG-2
|l Pflanzenwissenschaften
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IBG-2-20101118
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21