| 摘要: |
| 以9种轻型屋顶绿化植物为材料，通过人工气候室模拟高温，以25℃为对照，设置35℃、40℃、45℃、55℃ 4个胁迫处理，研究不同温度胁迫下9种植物的形态变化和丙二醛、脯氨酸、可溶性蛋白质和可溶性糖4种生理指标对不同温度的的响应，计算各指标的耐热系数、进行相关性及主成分分析，并基于主成分分析法综合评价其耐热性强弱，筛选出适宜广州气候的轻型屋顶绿化植物。结果表明：随着胁迫温度的升高，丙二醛与可溶性糖整体呈先升后降的趋势、脯氨酸呈上升趋势，可溶性蛋白主要在55℃有不同程度的下降，植物间4个指标的变化幅度存在显著差异；通过综合分析得出9种植物的耐热性强弱依次为：铺地锦竹草>大花马齿苋>假紫万年青>红趾草>重瓣大花马齿苋>紫米粒>凹叶景天>大苞水竹叶>薄雪万年草，筛选出铺地锦竹草、大花马齿苋、假紫万年青、红趾草、重瓣大花马齿苋作为广州轻型屋顶绿化植物。 |
| 关键词: 轻型屋顶绿化 耐热性 生理指标 主成分分析 |
| DOI： |
| 分类号: |
| 基金项目:广州市科技计划项目“广州园林植物科技资源圃”、广东省重大科技专项“节能减排屋顶绿化配套产品研发和示范”（2013A011401004） |
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| Response of Physiology of 9 Extensive Green Roof Plants to High Temperature Stress |
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huxing, zhonglimei, wangwei, daiseping
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Guangzhou Institute of Forestry and Landscape Architecture
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| Abstract: |
| The aim of this study was to select the most heat-tolerant plant from 9 species optimum
for extensive green roof. The contents of malondialdehyde (MDA), proline, soluble protein, soluble sugar in leaves and plant appearance were observed under high temperature (35℃, 40℃, 45℃ and 55℃, while 25℃ as control) in the phytotron that simulate high temperature environment.The heat-tolerance coefficients, correlation and principal components of measured indexes were calculated and analyzed before the heat tolerance of tested plants were comprehensively evaluated.The contents of MDA and soluble sugar showed a rise and then reduce with high temperature rising in general, the contents of proline showed a rise, whereas the contents of soluble protein have decreased in varying degrees of 9 spcies at 55 ℃. The rangeabilities between 4 physiological indexes were significantly different. At the circumstance of 55℃, The heat resistance rank of 9 plants was Callisia repens > Portulaca grandiflora > Belosynapsis ciliata > C. repens ‘Bolivian Jew’> P. grandiflora ‘Double Peony’ > P. gilliesii > Sedum emarginatum > Murdannia bracteata > S. hispanicum. According to our experiments, the first five taxa were suitable for use as extensive green roof plant of Guangzhou. |
| Key words: extensive green roof heat tolerance physiological indexes principal component analysis |
