Leaf trait network architecture shifts with species-richness and climate across forests at continental scale.
adaptation
community
forest
leaf trait network
network
photosynthesis
trait
Journal
Ecology letters
ISSN: 1461-0248
Titre abrégé: Ecol Lett
Pays: England
ID NLM: 101121949
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
revised:
02
03
2022
received:
24
09
2021
accepted:
22
03
2022
pubmed:
10
4
2022
medline:
7
6
2022
entrez:
9
4
2022
Statut:
ppublish
Résumé
Variation in the architecture of trait networks among ecosystems has been rarely quantified, but can provide high resolution of the contrasting adaptation of the whole phenotype. We constructed leaf trait networks (LTNs) from 35 structural, anatomical and compositional leaf traits for 394 tree species in nine forests from tropical to cold-temperate zones in China. Our analyses supported the hypothesis that LTNs would increase in modular complexity across forests in parallel with species-richness and climatic warmth and moisture, due to reduced phenotypic constraints and greater opportunities for niche differentiation. Additionally, we found that within LTNs, leaf economics traits including leaf thickness would have central importance, acting as hub traits with high connectivity due to their contributions to multiple functions. Across the continent, the greater species richness and trait diversity observed in forests under resource-rich climates enable greater complexity in whole phenotype structure and function as indicated by the trait network architecture.
Types de publication
Letter
Langues
eng
Sous-ensembles de citation
IM
Pagination
1442-1457Subventions
Organisme : National Natural Science Foundation of China
ID : 31988102
Organisme : National Natural Science Foundation of China
ID : 42141004
Organisme : National Natural Science Foundation of China
ID : 32171544
Organisme : National Science and Technology
ID : 2019FY101300
Organisme : China Postdoctoral Science Foundation
ID : 2020M680663
Organisme : China Postdoctoral Science Foundation
ID : 2021M693147
Informations de copyright
© 2022 John Wiley & Sons Ltd.
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