2021
DOI: 10.1111/1365-2745.13762
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Phenotypic plasticity versus ecotypic differentiation under recurrent summer drought in two drought‐tolerant pine species

Abstract: This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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Cited by 17 publications
(13 citation statements)
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References 111 publications
(188 reference statements)
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“…For example, a late season drought can reduce the quality of buds through a lack of nutrient uptake, leading to shorter shoots and/or fewer new leaves in the following year. This reduces leaf area for 1 year (in the case of deciduous tree species) or multiple years (in the case of evergreens; Bachofen et al, 2021; Yang & Midmore, 2005; Zweifel et al, 2020; Zweifel & Sterck, 2018). Hence, a longer leaf area‐related lag effect is expected in evergreen species compared to deciduous species (Zweifel & Sterck, 2018).…”
Section: Discussionmentioning
confidence: 99%
“…For example, a late season drought can reduce the quality of buds through a lack of nutrient uptake, leading to shorter shoots and/or fewer new leaves in the following year. This reduces leaf area for 1 year (in the case of deciduous tree species) or multiple years (in the case of evergreens; Bachofen et al, 2021; Yang & Midmore, 2005; Zweifel et al, 2020; Zweifel & Sterck, 2018). Hence, a longer leaf area‐related lag effect is expected in evergreen species compared to deciduous species (Zweifel & Sterck, 2018).…”
Section: Discussionmentioning
confidence: 99%
“…We further assessed evidence for local adaptation in greenhouse‐measured seedling traits by evaluating the relationships between climatic moisture deficit (CMD) at the seed source and each trait, including SLA, SRL, root:shoot, total biomass, growth rate, and carbon‐13 composition (Bachofen et al, 2021). Seed source CMD integrates unmet water demand and temperature to reflect the average drought conditions of each population's source and thus is relevant for climate‐informed seed lot selection in the context of expected future climate change.…”
Section: Methodsmentioning
confidence: 99%
“…The models were estimated using prog curveFit in the software GAUSS (Aptech, 2022). All cases refer to the respective time series of the variables for the maximum common overlap period t = 1953-2020.…”
Section: Statistical Analysis and Modelingmentioning
confidence: 99%
“…The computational work was performed using the software GAUSS 23 (Aptech, 2022) together with the CVFIT and TSMT packages (Aptech, 2022), and the package DYNACLIM (van Deusen & Koretz, 1988) for chronology building. The Levenberg-Marquardt algorithm is used for solving the nonlinear least squares minimization problem.…”
Section: Statistical Analysis and Modelingmentioning
confidence: 99%