峰丛洼地恢复演替系列优势种光合生理生态特征日变化研究——以广西马山弄拉峰丛洼地为例

Daily changs of photosynthetic ecophysiological characteristics of dominant species of restoration sere in a peak cluster depression region:a case study of Nongla, Mashan County, Guangxi

关键词:  峰丛洼地  恢复演替  优势种  净光合作用速率  生理生态因子

Key words:  peak cluster depression  resuming succession  dominant species  net photosynthetic rate  ecophysiological factors

摘要:

运用LI-6400便携式光合测定系统,测定弄拉峰丛洼地不同演替阶段优势种白茅、黄荆、红背山麻杆、黄杞和青冈的光合生理生态特性,并比较它们的净光合作用速率、蒸腾作用速率、气孔导度、水分利用效率、表观量子效率等生理生态指标及其生境中对应的光合有效辐射、大气CO₂浓度、气温、相对湿度的日变化。结果表明,峰丛洼地生态环境因子日变化复杂,黄杞有明显的“午休”现象,先锋种黄荆、红背山麻杆的净光合作用速率、蒸腾速率和气孔导度较大,先锋种中C4植物白茅水分利用效率最高,演替后期优势种青冈和黄杞的表观光量子效率、利用岩溶区水分的效率和适应性更强。对净光合作用速率与生理生态影响因子进行相关性分析,表明生理和生态因子共同影响植物的光合作用,不同演替优势种的影响因子有差异。以逐步回归方式建立净光合作用速率和影响因子之间的回归方程模型,5个方程均有显著性意义和良好的预测性。

Abstract:

Photosynthetic ecophysiological characteristics of different dominant species were investigated by LI-6400 in the Nongla peak cluster depression. The daily changes of some indexes were measured including PAR(Photosynthetically Radiation),air CO₂ concentration(Ca),air temperature(Ta),relative humidity(RH),net photosynthetic rate(Pn),transpiration rate(E),stomatal conductance(Gs),water use efficiency(WUE)and apparent quanta efficiency(AQE)etc. It's indicated that daily changes in environment factors were complicated. Engelhardia roxburhgiana showed“midday depression”of photosynthesis obviously. Pn,E and Gs of Vitex negundo and Alchornea trewioides were higher in values. Imperata cylindrica,a C4 species,had the highest WUE. Engelhardia roxburhgiana and Cyclobalanopsis glauca had higher AQE and WUE in the karst region and were more adaptive in the karst environment. The correlation matrix of net photosynthetic rate and various factors was established by SPSS13.0. It showed that both plant physiological and environmental factors affected photosynthesis,but these factors were variational in different dominant species of restoration sere. Stepwise regression method was used to establish linear regression equations for net photosynthetic rate and various effecting factors. The 5 linear regression equations were statistically significant and could predict effectively.