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Quantifying plant interactions: Independent reference is critical for standardising the importance indices

机译:量化植物相互作用:独立参考对于标准化重要性指数至关重要

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Mathematical tools for quantifying plant-plant interactions are continuously improving, for example by attaining desirable statistical properties such as symmetry around zero (positive and negative effects have the same distribution). Standardisation is another such important property, making indices comparable between independent experiments, and can be achieved by standardisation for size. Using simulated data, here we show that an approach to standardisation by size that works well for indices of intensity is not appropriate for those of importance (intensity indices measure the absolute size of interaction effect, whilst importance indices quantify this effect as a proportion of the impact of the environment overall); our analyses also show that importance values can be overestimated in unproductive environments. These issues arise because importance indices use a reference value that is the "maximum growth on the gradient". This causes problems when comparing the results from studies that examine different sections of an environmental gradient: the maximum growth of plants within these sections is different and so the indices are not easy to compare between different sections of a gradient. Although this may sound like an obvious point, such issues can often be overlooked and a general solution adopted. One such solution is to report raw data from separate studies so that values can be recomputed for combined datasets and thus standardised comparisons. Another solution is to use an off-gradient reference that is the maximum growth measured under optimal conditions for a model target species (phytometer).
机译:用于量化植物 - 植物相互作用的数学工具是连续改进的,例如通过获得所需的统计特性,例如零零的对称性(正和负效应具有相同的分布)。标准化是另一种这样的重要属性,使独立实验之间的指数进行比较,并且可以通过标准化来实现。使用模拟数据,我们展示了一种规模的标准化方法,适用于强度的指标不合适(强度指数测量相互作用效果的绝对尺寸,而重要性指数量化此效果作为比例整体环境的影响);我们的分析还表明,在非生产性环境中可以高估重要值。出现这些问题,因为重要性指数使用参考值,即“梯度的最大增长”。当从研究环境梯度的不同部分的研究比较时,这会导致问题:这些部分内的植物的最大增长是不同的,因此指数不容易比较梯度的不同部分之间。虽然这可能听起来很明显,但这些问题通常被忽视,并且采用了一般的解决方案。一种这样的解决方案是从单独的研究报告原始数据,以便可以重新计算组合数据集,从而将值进行标准化的比较。另一种解决方案是使用脱梯度参考,即在模型目标物种(媒体流动仪)的最佳条件下测量的最大增长。

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