人工长白落叶松冠层光合作用-光响应曲线最优模型

以黑龙江省帽儿山林场15年生人工长白落叶松为研究对象,采用直角双曲线模型(RH)、非直角双曲线模型(NRH)、指数模型(EM)、修正直角双曲线模型(MRH)和修正指数模型(MEM)分别对4种不同光响应特征的光合作用-光响应曲线(光抑制型光响应曲线,PLC_i;光饱和型光响应曲线,PLC_s;未饱和型光响应曲线,PLC_u;弱光环境下植被的光响应曲线,PLC_w)进行拟合,计算出光饱和时的最大净光合速率(P_{n max})、暗呼吸速率(R_d)、光补偿点(LCP)、光饱和点(LSP)及表观量子效率(AQY)等重要的光合生理指标,综合对比5个候选模型对不同响应曲线的拟合优度和对光合生理指标的估计精度.结果表明: MEM模型仅适用于拟合光抑制型曲线,MRH对光抑制型曲线和光饱和型曲线的拟合效果最好(R_a²分别为0.9986和0.9978),NRH最适合拟合未饱和型曲线和弱光环境型曲线(R_a²分别为0.9996和0.9963).在所有类型曲线中,MRH模型估计P_{n max}时,平均相对误差绝对值(MAPE)最低(0.1%R_d表现出更准确的估计(MAPE分别为1.8%、0.1%和3.9%);RH模型对光抑制型曲线的LCP及光饱和型曲线的R_d有更好的估计效果(MAPE分别为1.0%和2.7%);EM模型适用于估计弱光环境型曲线的LCP(MAPE为0.2%).MRH在保证较好的模型拟合效果及光合生理指标估计精度以外,还在拟合不同类型曲线时表现出极高的稳定性,因此,本文选择MRH模型作为拟合人工长白落叶松冠层光合作用-光响应曲线的最优模型.

Optimal model of photosynthesis-light response curve in canopy of planted Larix olgensis tree.

Rectangle hyperbola model (RH), nonrectangle hyperbola model (NRH), exponential model (EM), modified rectangle hyperbola model (MRH), and modified exponential model (MEM) were applied respectively for modeling the photosynthesis-light response curves (PLC) based on four types of curves (photosynthesis-light response curve of inhibition, PLC_i, photosynthesis-light response curve of saturation, PLC_s, photosynthesis-light response curve of unsaturation, PLC_u andphotosynthesis-light response curve in weak light, PLC_w) in canopy of 15 years old planted Larix olgensis tree in the Maoershan Forest Farm, Heilongjiang Province. The major photosynthetic physiological indexes including maximum net photosynthetic rate (P_{n max}), dark respiration rate (R_d), light compensation point (LCP), light saturation point at saturated light intensity (LSP) and apparent quantum yield (AQY) were calculated. All the five candidate models were comprehensively compared by the model goodness-of-fit and the precision estimations of photosynthetic physiological indexes in the four types of curves. The results showed that MEM model was only suitable for fitting the PLC_i. MRH model showed the best goodness-of-fit for PLC_i and PLC_s(R_a²was 0.9986 and 0.9978, respectively). Meanwhile, NRH model expressed the best fitting result in PLC_u and PLC_w (R_a² was 0.9996 and 0.9963, respectively). MRH had the lowest mean absolute value of relative error (MAPE) when evaluating P_{n max} in different types of curves (0.1%s and PLC_u, as well as R_d of PLC_u(MAPE was 1.8%, 0.1%, and 3.9%, respectively). RH model represented better prediction effect for LCP of PLC_i and R_d of PLC_s(MAPE was 1.0% and 2.7%, respectively). EM was more sui-table for estimating LCP of PLC_w(MAPE was 0.2%). Comprehensive analysis showed that MRH model not only proved well model goodness-of-fit and accurate estimation of photosynthetic physiological indexes, but also showed the highest stability when fitting different types of curves. As a result, MRH model was finally selected as the optimal canopy photosynthesis-light response model of planted L. olgensis tree.