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长白山阔叶红松林生态系统生产力与温度的关系

花匠小妙招
2024-12-27 14:51

摘要:

目的气候变暖背景下生态系统碳循环的温度敏感性研究是全球变化生态学的主要研究内容之一，森林生态系统生产力对温度的响应和适应机制是理解生态系统的温度敏感性的重要手段，长白山阔叶红松林作为典型的温带森林生态系统及重要的碳汇，研究其生产力对环境温度的响应，对提升中国森林植被碳循环模拟的准确性至关重要。

方法本研究以长白山阔叶红松林为对象，收集长白山通量站2003—2011年共9年的观测数据，通过进行整合分析，量化了生态系统碳循环的3个关键过程：生态系统总初级生产力、净生态系统生产力和生态系统呼吸的温度响应曲线，并进一步分析环境因子对其最适温的影响。

结果研究发现总初级生产力、净生态系统生产力的温度响应均表现为一条峰值曲线，并存在最适温，GPP的最适温(tGPP)与NEP的最适温(tNEP)存在显著线性正相关关系。在年际尺度上，一年中最高空气温度的改变是引起tGPP和tNEP变化的主要因素，而年均温和夏季温度对tGPP和tNEP的变化没有显著影响。当最高温度升高1 ℃时，tGPP和tNEP分别增加0.41和0.66 ℃。降水、光和有效辐射、饱和蒸汽压差等环境因子对tGPP和tNEP无显著影响，但夏季降水能够降低温度对tGPP的影响。

结论通过上述研究说明，生态系统的初级生产力及净生产力存在温度适应现象，当研究碳循环与气候变化相互作用的模型时需要充分考虑生态系统生产力的温度适应，从而更加准确地预测碳循环对气候变暖的响应和反馈。

Abstract:

ObjectiveStudying the temperature sensitivity of ecosystem carbon cycle in the climate change scenario is one of the major subjects of global change ecology, which demands the incorporation of temperature acclimation of ecosystem productivity. Exploring the response of productivity to ambient temperature in the broadleaved Korean pine forest, a typical temperate forest ecosystem and an important carbon sink, is helpful for better understanding the fundamental processes of ecosystems in a changing environment, which will promote the accuracy of carbon cycle simulation of forest vegetation in China.

MethodIn this study, we investigated the temperature response of gross primary productivity, net ecosystem productivity and ecosystem respiration using flux data of a Korean pine forest in northeastern China from 2003 to 2011, and further explored the influence of environmental factors on the above three carbon processes.

ResultThe results suggested that both the temperature responses of gross primary productivity and net ecosystem productivity were one-peaked curves with their optimum temperatures (tGPP and tNEP) positively correlated with the maximum temperature in a year. For 1 ℃ increase in the maximum temperature, tGPP and tNEP increased by 0.41 ℃ and 0.66 ℃ in the interannual scale, respectively. Environmental factors such as annual precipitation, photosynthetically active radiation, and vapor pressure deficit had no significant effect on tGPP and tNEP, while summer precipitation might have the ability to mediate the effects of tGPP caused by the rising temperature.

ConclusionTherefore, there was a thermal acclimation of photosynthesis at the ecosystem-level. Previous models might exaggerate the impact of climate change on carbon fluxes if ignoring the influence of photosynthesis thermal acclimation.

图 1 2003—2011年长白山涡度站年平均空气温度的变化趋势

Figure 1. Interannual pattern of annual mean air temperature during 2003 to 2011

图 2 2003—2011年长白山涡度站总初级生产力(GPP)，生态系统呼吸(ER)，净生态系统生产力(NEP)在对温度的响应曲线

年均温范围为3.46~5.04 ℃

Figure 2. General pattern of peak-curve temperature response of gross primary productivity (GPP), ecosystem respiration (ER) and net ecosystem productivity (NEP) at Changbai Mountain site over different temperature years

Mean annual temperature ranges from 3.46 ℃ to 5.04 ℃

图 3 GPP的最适温和NEP的最适温的关系

Figure 3. Relationship between tNEP and tGPP

图 4 GPP的最适温和NEP的最适温与年最高温(a)，年最低温(b)，年均温(c)，夏季平均温度(d)和生长季平均温度(e)的关系

Figure 4. Relationship between tNEP and tGPP with the maximum temperature (a), the minimum temperature (b), daily mean air temperature (c), daily mean air temperature in summer (d) and daily mean air temperature in growing season(e)

图 5 GPP的最适温和夏季降水(7—9月)的关系

Figure 5. Relationship between tNEP and tGPP with precipitation during the period from July to September

表 1 2003—2011年长白山阔叶红松林观测站tNEP和tGPP的值

Table 1 Values of tNEP and tGPP at flux site of Korean pine forest during 2003 and 2011

因子Factor年份Year 20032004200520062007200820092010 tGPP21.8322.1822.5821.4222.1221.9921.7922.88 tNEP21.8321.1621.3920.7421.9921.3120.5422.88 注：tGPP，GPP的最适温；tNEP，NEP的最适温。Notes: tGPP, the optimum temperature of GPP；tNEP, the optimum temperature of NEP.

表 2 tNEP和tGPP与气候变量之间回归分析的决定系数(R2)和P值

Table 2 Coefficient of determination (R2) and P value of the regression analysis between tNEP and tGPP with climatic variables

因子Factor年降水量MAP饱和蒸汽压差VPD光合有效辐射PAR年降水量，饱和蒸汽压差，光合有效辐射MAP, VPD, PAR R2P valueR2P valueR2P valueR2P value tGPP0.100.450.340.080.290.100.120.39 tNEP0.230.110.070.500.27 0.080.070.40 [1]

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