YangKangZhaoEtAl2021

Référence

Yang, Y., Kang, L., Zhao, J., Qi, N., Li, R., Wen, Z., Kassout, J., Peng, C., Lin, G., Zheng, H. (2021) Quantifying leaf trait covariations and their relationships with plant adaptation strategies along an aridity gradient. Biology, 10(10). (Scopus )

Résumé

A trait-based approach is an effective way to quantify plant adaptation strategies in response to changing environments. Single trait variations have been well depicted before; however, multi-trait covariations and their roles in shaping plant adaptation strategies along aridity gradients remain unclear. The purpose of this study was to reveal multi-trait covariation characteristics, their controls and their relevance to plant adaptation strategies. Using eight relevant plant functional traits and multivariate statistical approaches, we found the following: (1) the eight studied traits show evident covariation characteristics and could be grouped into four functional dimensions linked to plant strategies, namely energy balance, resource acquisition, resource investment and water use efficiency; (2) leaf area (LA) together with traits related to the leaf economic spectrum, including leaf nitrogen content per area (Narea ), leaf nitrogen per mass (Nmass ) and leaf dry mass per area (LMA), covaried along the aridity gradient (represented by the moisture index, MI) and dominated the trait–environmental change axis; (3) together, climate, soil and family can explain 50.4% of trait covariations; thus, vegetation succession along the aridity gradient cannot be neglected in trait covariations. Our findings provide novel perspectives toward a better understanding of plant adaptations to arid conditions and serve as a reference for vegetation restoration and management programs in arid regions. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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@ARTICLE { YangKangZhaoEtAl2021,
    AUTHOR = { Yang, Y. and Kang, L. and Zhao, J. and Qi, N. and Li, R. and Wen, Z. and Kassout, J. and Peng, C. and Lin, G. and Zheng, H. },
    JOURNAL = { Biology },
    TITLE = { Quantifying leaf trait covariations and their relationships with plant adaptation strategies along an aridity gradient },
    YEAR = { 2021 },
    NOTE = { cited By 0 },
    NUMBER = { 10 },
    VOLUME = { 10 },
    ABSTRACT = { A trait-based approach is an effective way to quantify plant adaptation strategies in response to changing environments. Single trait variations have been well depicted before; however, multi-trait covariations and their roles in shaping plant adaptation strategies along aridity gradients remain unclear. The purpose of this study was to reveal multi-trait covariation characteristics, their controls and their relevance to plant adaptation strategies. Using eight relevant plant functional traits and multivariate statistical approaches, we found the following: (1) the eight studied traits show evident covariation characteristics and could be grouped into four functional dimensions linked to plant strategies, namely energy balance, resource acquisition, resource investment and water use efficiency; (2) leaf area (LA) together with traits related to the leaf economic spectrum, including leaf nitrogen content per area (Narea ), leaf nitrogen per mass (Nmass ) and leaf dry mass per area (LMA), covaried along the aridity gradient (represented by the moisture index, MI) and dominated the trait–environmental change axis; (3) together, climate, soil and family can explain 50.4% of trait covariations; thus, vegetation succession along the aridity gradient cannot be neglected in trait covariations. Our findings provide novel perspectives toward a better understanding of plant adaptations to arid conditions and serve as a reference for vegetation restoration and management programs in arid regions. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. },
    AFFILIATION = { State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China; East China Inventory and Planning Institute of the State Administration of Forestry and Grassland, Hangzhou, 310019, China; China Aero Geophysical Survey & Remote Sensing Center for Natural Resources, Beijing, 100083, China; School of Information Science & Technology, Beijing Forestry University, Beijing, 100083, China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, 712100, China; Laboratory of Applied Botany, BioAgrodiversity Team, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan, 93002, Morocco; Department of Biological Sciences, Institute of Environmental Sciences, University of Quebec at Montreal, Montréal, QC H3C 3P8, Canada; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China },
    ART_NUMBER = { 1066 },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.3390/biology10101066 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118125990&doi=10.3390%2fbiology10101066&partnerID=40&md5=a8c17228be92ce7177359c17bbbcb57c },
}

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