Temporal dynamics in photosynthetic electron transfer rate of Artemisia ordosica in growing season and its response to environmental factors in Mu Us Sandy Land of northwestern China
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摘要:
目的 探究干旱半干旱地区植被光合生理在生长季动态变化及其对各个影响因子的响应机制，对进一步了解该区植被对波动环境的适应性具有重要意义。 方法 该研究于2019年5—9月，对典型沙生植被油蒿的光合电子传递速率（ETR）进行长期原位连续监测，同步观测光合有效辐射（PAR）、空气温度（Ta）、相对湿度（RH）和土壤含水量（SWC）等环境因子，同时测定叶绿素含量（SPAD）。分析ETR的动态变化规律及其对主要影响因子的响应。 结果 ETR与PAR、Ta、SPAD显著相关（P < 0.05），ETR月均值7月份达到最大，9月份最小。ETR随光强的变化呈上升趋势，且在弱光条件下（PAR ≤ 800 μmol/（m2·s））对光照响应强度大于强光。ETR随Ta的变化呈先上升后下降的趋势，5月（展叶期）和9月（落叶期）的高低温胁迫阈值分别为5和20 ℃，6—8月（成熟期）为10和25 ℃。ETR与SPAD呈线性正相关关系。成熟期ETR的稳定性小于展叶期和落叶期。 结论 通过以上研究发现PAR、Ta、SPAD是影响ETR的3个主要因子。在展叶期ETR主要受SPAD的影响，成熟期主要受温度和光强的影响，落叶期主要受植物生理的影响。油蒿在不同环境因子的交替作用下均体现出了良好的适应性。此外适当的升温能够促进光合电子的传递，增强植物光合能力。该研究结果可以为全球气候变暖下植物光合生理对环境响应的研究提供一定的理论指导，同时也为荒漠植被的恢复提供参考。 Abstract:Objective Understanding the dynamics of electron transfer rate of typical sandy speices Artemisia ordosica and its response mechanism to environmental factors in desert area is very important to manage desert ecosystem to adapt to the fluctuating environment. Method This study conducted a long-term in situ continuous monitoring of photosynthetic electron transfer rate (ETR) in Artemisia ordosica in May−September of 2019 using chlorophyll fluorescence observation method. Photosynthetically active radiation (PAR), air temperature (Ta), chlorophyll content (SPAD) were simultaneously measured. Result ETR was significantly correlated with PAR, Ta and SPAD at a significant level of 0.05, with the monthly average reaching the maximum in July and the smallest in September. The change of ETR with light intensity showed an upward trend, and the response intensity to light under low light condition of PAR ≤ 800 μmol/(m2·s) was greater than that of strong light. The change of ETR with Ta showed a trend of first increasing and then decreasing. The thresholds of high and low temperature stress in May (leaf-leaning stage) and September (deciduous stage) were 5 ℃ and 20 ℃, respectively, and June−August (mature stage) was 10 ℃ and 25 ℃. The relationship between ETR and SPAD was linear and positive, and the stability of ETR in mature stage was less than that in leaf-expansion stage and leaf-falling stage. Conclusion Through the above research, it was found that PAR, Ta, and SPAD were the three main factors affecting ETR. ETR was mainly affected by SPAD during the leaf-expansion stage, temperature and light intensity during the mature stage, and plant physiology during the defoliation stage. We found that Artemisia ordosica ETR showed a good adaptability under the alternating effect of the main influencing factors. Besides proper temperature increase can promote the transfer of photosynthetic electrons, thus enhance the photosynthetic capacity of plants. The results can not only provide some theoretical guidance for the response of plants’ photosynthetic physiology to the environment under global warming, but also a reference for the restoration of desert vegetation. -
图 1 2019年5—9月油蒿环境因子在生长季的动态变化
PAR. 光合有效辐射；Ta. 空气温度；RH. 相对湿度；P. 降雨量；SWC. 土壤含水量；SWC10. 10 cm 处土壤含水量；SWC30. 30 cm 处土壤含水量。下同。PAR, photosynthetically active radiation; Ta, air temperature; RH, relative humidity; P, precipitation; SWC, soil water content; SWC10, soil water content in 10 cm; SWC30, soil water content in 30 cm. Same as below.
Figure 1. Dynamics in environmental factors in Artemisia ordosica from May to September in 2019
图 2 2019年5—9月油蒿叶绿素含量在生长季的动态变化
SPAD. 叶绿素相对含量。下同。SPAD, relative chlorophyll content. Same as below.
Figure 2. Dynamics of chlorophyll content (SPAD) in Artemisia ordosica from May to September in 2019
图 3 2019年5—9月油蒿光合电子传递速率在生长季的动态变化
ETR. 光合电子传递速率。下同。ETR, photosynthetic electron transfer rate. The same below.
Figure 3. Seasonal changes in ETR in Artemisia ordosica from May to September in 2019
图 4 2019年5—9月油蒿光合电子传递速率与光合有效辐射的关系
Figure 4. Relationship between ETR and PAR for Artemisia ordosica from May to September in 2019
图 5 2019年5—9月油蒿光合电子传递速率与空气温度的关系
Figure 5. Relationship between ETR and air temperature (Ta) for Artemisia ordosica from May to September in 2019
图 6 2019年5—9月油蒿光合电子传递速率与叶绿素含量的关系
Figure 6. Relationship between ETR and chlorophyll content for Artemisia ordosica from May to September in 2019
表 1 油蒿光合电子传递速率与影响因子相关性
Table 1. Correlations between ETR and influencing factors in Artemisia ordosica
相关指标 Relevant index ETR PAR Ta RH PPT SWC10 SWC30 SPAD ETR 1 PAR 0.646** 1 Ta 0.376** 0.422** 1 RH −0.321 −0.684** −0.310** 1 PPT 0.345 −0.486 0.179* 0.488** 1 SWC10 0.001 0.200* 0.183* −0.010 −0.046 1 SWC30 0.077 0.280** 0.310** −0.113 −0.086 0.588** 1 SPAD 0.818** 0.125* 0.417* 0.351 0.032 0.397 0.597** 1 注：**表示在0.01水平(双侧)上极显著相关；*表示在0.05水平(双侧)上显著相关。Notes: ** means a very significant correlation at 0.01 level (bilateral); * means a significant correlation at 0.05 level (bilateral). 
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表 2 2019年5—9月油蒿光合电子传递速率与光合有效辐射回归模型
Table 2. Regression models between ETR and PAR for Artemisia ordosica from May to September in 2019
月份
Month回归拟合模型 Regression fitting model
(PAR ≤ 800 μmol/(m2·s))R1 2 回归拟合模型 Regression fitting model
(PAR > 800 μmol/(m2·s))R2 2 5 y = −0.678 + 0.200x 0.95 y = 118.474 + 0.061x 0.96 6 y = 4.209 + 0.209x 0.97 y = 138.194 + 0.052x 0.95 7 y = 1.500 + 0.218x 0.96 y = 136.220 + 0.051x 0.93 8 y = 2.563 + 0.215x 0.95 y = 135.831 + 0.049x 0.95 9 y = −0.065 + 0.197x 0.96 y = 115.271 + 0.053x 0.95
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表 3 2019年5—9月油蒿光合电子传递速率与空气温度回归模型
Table 3. Regression models between ETR and Ta for Artemisia ordosica from May to September in 2019
月份
Month温度变化区间
Temperature changing interval回归拟合模型
Regression fitting modelR2 5 5 ℃ < Ta ≤ 11 ℃ y = 0.273 + 1.833x 0.95 11 ℃ < Ta ≤ 21 ℃ y = 0.266 + 9.265x 0.96 22 ℃ < Ta < ≤ 25 ℃ y = 1.134 − 3.410x 0.95 6 10 ℃ < Ta ≤ 15 ℃ y = 0.121 + 1.835x 0.92 15 ℃ < Ta ≤ 24 ℃ y = 0.415 + 12.648x 0.95 24 ℃ < Ta ≤ 29 ℃ y = 0.701 − 5.620x 0.81 7 9 ℃ < Ta ≤ 16 ℃ y = 0.136 + 1.855x 0.94 16 ℃ < Ta ≤ 26 ℃ y = 0.579 + 12.250x 0.96 26 ℃ < Ta ≤ 29 ℃ y = −1.241 − 7.534x 0.92 8 9 ℃ < Ta ≤ 15 ℃ y = 0.135 + 1.832x 0.91 15 ℃ < Ta ≤ 5 ℃ y = 0.456 + 12.159x 0.93 25 ℃ < Ta ≤ 29 ℃ y = 1.135 − 6.523x 0.79 9 6 ℃ < Ta ≤ 10 ℃ y = 0.108 + 1.813x 0.94 10 ℃ < Ta ≤ 20 ℃ y = 0.410 + 9.639x 0.96 20 ℃ < Ta ≤ 25 ℃ y = 0.109 − 4.015x 0.94
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