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首页> 外文期刊>Forest Ecology and Management >Application of response surface methodology and ANOVA to detect pollution effects on photosynthetic response under varying temperature and light regimes
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Application of response surface methodology and ANOVA to detect pollution effects on photosynthetic response under varying temperature and light regimes

机译：应用响应面方法和方差分析检测不同温度和光照条件下污染对光​​合作用响应的影响

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Studies of air pollution effects on plant physiological responses to diurnal changes in environmental conditions are complex, and thus, require efficient statistical design and valid data analysis. Here, we aim at demonstrating the use of a response-surface methodology (RSM) including least square regression and canonical analyses in combination with a two-way ANOVA to quantify net photosynthetic (A(n)) response of ponderosa pine (Pinus ponderosa Dougl. ex Laws) seedlings to different levels of rain acidity, ozone, temperature, and light conditions. Seedlings were exposed to four combinations of two rain (pH 5.1 and 3) and two ozone (ambient, and twice-ambient) regimes. The rain treatment was applied to foliage only on a weekly basis from January to April 1992. The ozone treatment was applied from September 1991 to November 1992. A(n) responses of current- and 1-year-old foliage to nine combinations of three temperatures (18, 25, 32 degreesC) and three light regimes (250, 500, 1000 mu mol photons m(-2) s(-1)) were measured between 0800 and 1900 h from 18 August to 7 September 1992. A completely randomized design, replicated twice, was used for the assignment of rain-by-ozone combinations (main plots). Foliage age classes were sub-plots within the main plots, temperature levels were sub-plots within foliage age classes, and light levels were sub-plots within temperature levels. Response surface models of A(n) response to temperature and light levels were constructed per rain-by-ozone combination for each foliage age. beta (Temp), beta (Temp2), beta (Light), A(n mean), A(n max), temperature at A(n max), and light at A(n max) were calculated or estimated. The effects of rain pH and ozone on these variables were then quantified using a two-way ANOVA. No significant rain-by-ozone or ozone effect was detected for any response. Rain of pH 3 decreased A(n mean) in current-year foliage only.
机译：空气污染对植物对环境条件日变化的生理反应的影响的研究非常复杂，因此需要有效的统计设计和有效的数据分析。在这里，我们旨在演示使用响应面方法（RSM），包括最小二乘回归和规范分析以及双向方差分析来量化黄松的净光合（A（n））反应（黄松。ex Laws）幼苗要承受不同程度的雨水酸度，臭氧，温度和光照条件。将幼苗暴露于两种降雨（pH 5.1和3）和两种臭氧（环境和两次环境）制度的四种组合下。从1992年1月至1992年4月，仅对叶子进行每周一次的雨水处理。从1991年9月至1992年11月，对臭氧进行了雨水处理。A（n）当前和1岁叶子对三种3种植物的9种组合的反应从1992年8月18日至9月7日在0800至1900 h之间测量了温度（18、25、32摄氏度）和三种光照状态（250、500、1000μmol光子m（-2）s（-1））。随机设计（重复两次）用于分配雨水与臭氧的组合（主图）。叶子年龄类别是主要地块内的子图，温度级别是叶子年龄类别内的子图，而光照水平是温度级别内的子图。对于每个叶子年龄，按雨水和臭氧的组合构建A（n）对温度和光照水平响应的响应表面模型。计算（或估算）beta（Temp），beta（Temp2），beta（Light），A（n平均值），A（n max），A（n max）处的温度和A（n max）处的光。然后使用双向方差分析定量雨水pH和臭氧对这些变量的影响。对于任何响应，均未检测到明显的臭氧对臭氧或臭氧的影响。 pH 3的雨仅降低了当年树叶的A（n平均值）。

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