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山毛榉树枝光合作用过程中的碳同位素分馏:贝叶斯建模方法。

Carbon isotope discrimination during branch photosynthesis of Fagus sylvatica: a Bayesian modelling approach.

机构信息

Institute of Agricultural Sciences, ETH Zurich, Universitätsstrasse 2, Zurich, 8092, Switzerland; UR1263 Ephyse, INRA, Villenave d'Ornon, 33140, France.

出版信息

Plant Cell Environ. 2014 Jul;37(7):1516-35. doi: 10.1111/pce.12262. Epub 2014 Feb 14.

DOI:10.1111/pce.12262
PMID:24372560
Abstract

Field measurements of photosynthetic carbon isotope discrimination ((13)Δ) of Fagus sylvatica, conducted with branch bags and laser spectrometry, revealed a high variability of (13)Δ, both on diurnal and day-to-day timescales. We tested the prediction capability of three versions of a commonly used model for (13)Δ [called here comprehensive ((13)(Δcomp)), simplified ((13) Δsimple) and revised ((13)(Δrevised)) versions]. A Bayesian approach was used to calibrate major model parameters. Constrained estimates were found for the fractionation during CO(2) fixation in (13)(Δcomp), but not in (13)(Δsimple), and partially for the mesophyll conductance for CO(2)(gi). No constrained estimates were found for fractionations during mitochondrial and photorespiration, and for a diurnally variable apparent fractionation between current assimilates and mitochondrial respiration, specific to (13)(Δrevised). A quantification of parameter estimation uncertainties and interdependencies further helped explore model structure and behaviour. We found that (13)(Δcomp) usually outperformed (13)(Δsimple) because of the explicit consideration of gi and the photorespiratory fractionation in (13)(Δcomp) that enabled a better description of the large observed diurnal variation (≈9‰) of (13)Δ. Flux-weighted daily means of (13)Δ were also better predicted with (13)(Δcomp) than with (13)(Δsimple).

摘要

利用枝套和激光光谱仪对欧洲山毛榉的光合碳同位素分馏((13)Δ)进行了田间测量,结果表明(13)Δ具有很高的日变化和日际变化变异性。我们测试了三种常用模型(综合模型((13)(Δcomp))、简化模型((13)Δsimple)和修正模型((13)(Δrevised))对(13)Δ的预测能力。使用贝叶斯方法对主要模型参数进行了校准。在综合模型中发现了 CO(2)固定过程中的分馏作用的约束估计值(13)(Δcomp),但在简化模型中没有,而在部分模型中发现了叶绿体导度对 CO(2)(gi)的约束估计值。对于线粒体和光呼吸过程中的分馏作用以及当前同化产物和线粒体呼吸之间日变化的明显分馏作用的约束估计值,对于修正模型来说是特定的(13)(Δrevised)。参数估计不确定性和相互依赖性的量化进一步有助于探索模型结构和行为。我们发现,综合模型(13)(Δcomp)通常优于简化模型(13)(Δsimple),因为(13)(Δcomp)明确考虑了 gi 和光呼吸分馏作用,从而能够更好地描述观察到的(13)Δ的大日变化(≈9‰)。与简化模型相比,综合模型(13)(Δcomp)还能更好地预测(13)Δ的通量加权日均值。

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