CAJ | 학술논문

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CO_2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.
운용LI-6400편휴식광합측정계통,측정롱랍봉총와지불동연체계단우세충백모、황형、홍배산마간、황기화청강적광합생리생태특성,병비교타문적정광합작용속솔、증등작용속솔、기공도도、수분이용효솔、표관양자효솔등생리생태지표급기생경중대응적광합유효복사、대기CO_2농도、기온、상대습도적일변화.결과표명,봉총와지생태배경인자일변화복잡,황기유명현적"오휴"현상,선봉충황형、홍배산마간적정광합작용속솔、증등속솔화기공도도교대,선봉충중C4식물백모수분이용효솔최고,연체후기우세충청강화황기적표관광양자효솔、이용암용구수분적효솔화괄응성경강.대정광합작용속솔여생리생태영향인자진행상관성분석,표명생리화생태인자공동영향식물적광합작용,불동연체우세충적영향인자유차이.이축보회귀방식건립정광합작용속솔화영향인자지간적회귀방정모형,5개방정균유현저성의의화량호적예측성.
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 CO_2 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.