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Trait-based plant ecology a flawed tool in climate studies? The leaf traits of wild olive that pattern with climate are not those routinely measured

机译:基于性状的植物生态学是气候研究中有缺陷的工具吗?与气候有关的野生橄榄的叶片特征不是常规测量的

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摘要

Climate-related studies have generally focussed upon physiologically well-defined ‘mechanistic’ traits rather than ‘functional’ ones relating indirectly to resource capture. Nevertheless, field responses to climate are likely to typically include both ‘mechanistic’ specialization to climatic extremes and ‘functional’ strategies that optimize resource acquisition during less climatically-severe periods. Here, this hypothesis was tested. Seventeen traits (six ‘functional’, six ‘mechanistic’ and five ‘intermediate’) were measured from 19 populations of oleaster (wild olive) along a climatic gradient in Morocco. Principal components analysis of the trait dataset identified size and the ‘worldwide leaf economics spectrum’ as PCA axes 1 and 2. However, contrary to our prediction, these axes, and commonly-measured ‘functional’ traits, were little correlated with climate. Instead, PCA 3, perhaps relating to water-use and succulence, together stomatal density, specific leaf water content and leaf shape, patterned with altitude, aridity, rainfall and temperature. We concluded that, at least for slow-growing species, such as oleaster, ‘mechanistic’ traits are key to identifying mechanisms of climatic restriction. Meaningful collaboration between ‘mechanistic’ and ‘functional’ disciplines provides the best way of improving our understanding of the global impacts of climate change on species distribution and performance.
机译:与气候相关的研究通常集中于生理学上定义明确的“机械”特征,而不是与资源捕获间接相关的“功能”特征。但是,现场对气候的反应通常可能包括针对极端气候的“机械”专业化和“在气候严重程度较低的时期内优化资源获取的”功能性策略。在这里,检验了该假设。在摩洛哥的气候梯度条件下,从19个橄榄果种群(野生橄榄)中测量了17个特征(六个“功能”,六个“机械”和五个“中级”)。特征数据集的主成分分析确定了PCA轴1和2的大小和“全球烟叶经济谱”。然而,与我们的预测相反,这些轴以及通常测量的“功能”特征与气候几乎没有关联。取而代之的是PCA 3(可能与用水和肉质有关),以及气孔密度,特定叶片含水量和叶片形状,并以海拔,干旱,降雨和温度为特征。我们得出的结论是,至少对于缓慢生长的物种(例如,夹竹桃)而言,“机械”特性是确定气候限制机制的关键。 “机械”和“功能”学科之间有意义的合作,为增进我们对气候变化对物种分布和性能的全球影响的理解提供了最好的方法。

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