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首页> 外文期刊>Applications in plant sciences. >A new phenological metric for use in pheno‐climatic models: A case study using herbarium specimens of Streptanthus tortuosus
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A new phenological metric for use in pheno‐climatic models: A case study using herbarium specimens of Streptanthus tortuosus

机译:一种新的用于物候气候模型的物候指标:以曲霉链霉菌的植物标本室标本为例

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Premise Herbarium specimens have been used to detect climate‐induced shifts in flowering time by using the day of year of collection (DOY) as a proxy for first or peak flowering date. Variation among herbarium sheets in their phenological status, however, undermines the assumption that DOY accurately represents any particular phenophase. Ignoring this variation can reduce the explanatory power of pheno‐climatic models (PCMs) designed to predict the effects of climate on flowering date. Methods Here we present a protocol for the phenological scoring of imaged herbarium specimens using an ImageJ plugin, and we introduce a quantitative metric of a specimen's phenological status, the phenological index (PI), which we use in PCMs to control for phenological variation among specimens of Streptanthus tortuosus (Brassicaceeae) when testing for the effects of climate on DOY. We demonstrate that including PI as an independent variable improves model fit. Results Including PI in PCMs increased the model R sup2/sup relative to PCMs that excluded PI; regression coefficients for climatic parameters, however, remained constant. Discussion Our protocol provides a simple, quantitative phenological metric for any observed plant. Including PI in PCMs increases R sup2/sup and enables predictions of the DOY of any phenophase under any specified climatic conditions.
机译:通过使用采集日(DOY)作为第一或峰值开花日期的代表,室内植物标本室的标本已用于检测气候引起的开花时间变化。然而,植物标本室的物候状态之间的差异破坏了DOY准确代表任何特定物相的假设。忽略这种变化会降低旨在预测气候对开花期影响的表型气候模型(PCM)的解释能力。方法在这里,我们介绍了使用ImageJ插件对成像植物标本室标本进行物候评分的协议,并介绍了标本物候状态的定量指标,即物候指数(PI),我们在PCM中使用该指标来控制标本之间的物候变化。在测试气候对DOY的影响时,曲霉链霉菌(Brassicaceeae)的数量。我们证明了将PI作为自变量可以改善模型拟合。结果相对于不包含PI的PCM,将PCM包含PI可以增加模型R 2 。但是,气候参数的回归系数保持不变。讨论我们的协议为任何观察到的植物提供了一种简单的,定量的物候指标。在PCM中包含PI会增加R 2 ,并能够在任何指定的气候条件下预测任何表相的DOY。

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