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Using strong inference to falsify differential equation models of sugar maple height growth

机译:用强推论证伪造糖枫生长微分方程模型

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Platt’s research strategy called 'strong inference' is often studied, but is difficult to apply. Here strong inference is applied in selecting differential equation models of sugar maple, Acer saccharum M., height growth. Two model groups proposed by Zeide (1993; Zeide, B. 1993. Analysis of growth equations. For. Sci. 39(3):594-616) are supplemented with two additional groups, 1) size decline and 2) second order differential equations, nearly exhausting the possible height growth models currently in the literature. A ‘crucial experiment’ was to fit a simultaneous system of equations to height – age data collected from a cohort of trees felled for stem analysis. Models for cohort members are identical in right-hand-sides, have common parameters, but have tree-specific initial heights. Common parameters and tree-specific initial heights are estimated during fitting. Results, based on stem analysis data for a cohorts of from three to five sugar maple growing on 54 plots in the Lake States, showed that all cohort members were predicted by logarithm of time decline (LTD) models to have extremely similar initial heights (less than 0.01 m range), which contradicts experience and leads to their falsification. Three of four models in the time decline (TD) class predict a very small range in final heights, but a large range in initial heights (from 6.4 to 2.9m), hence can also be considered falsified. Size decline and second order models could not be falsified using the height – age cohort data available. MCFNS 2(1):1-11.
机译:普氏(Platt)的研究策略称为“强推论”,通常会进行研究,但很难应用。在这里,强烈的推论被用于选择糖枫,宏cer,生长高度的微分方程模型。 Zeide(1993; Zeide,B. 1993.增长方程分析。For Sci。39(3):594-616)提出的两个模型组补充了两个额外的组,即1)尺寸减小和2)二阶微分。方程,几乎用尽了目前文献中可能的高度增长模型。一项“关键实验”是使方程组同时适用于身高-从一组砍伐的树木中收集的年龄数据用于茎分析。同类群组的模型在右侧是相同的,具有相同的参数,但是具有特定于树的初始高度。在拟合过程中会估算出常用参数和特定于树的初始高度。结果基于对湖州54个样地上生长的3至5个糖枫的队列的茎分析数据,表明使用时间下降对数(LTD)模型预测的所有队列成员都具有非常相似的初始高度(较小小于0.01 m的范围),这与经验相矛盾并导致其伪造。时间下降(TD)类中的四个模型中的三个模型预测最终高度的范围很小,但是初始高度的范围很大(从6.4到2.9m),因此也可以认为是伪造的。尺寸下降和二阶模型无法使用可用的身高–年龄队列数据进行伪造。 MCFNS 2(1):1-11。

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