Radial growth of Cunninghamia lanceolata and its response to climate in Jiangle National Forest Farm, Fujian Province of eastern China
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摘要:目的
杉木是我国南方特有的用材树种，其具有良好的绿化效果，同时也具有较高的经济价值。本文主要对不同时间尺度上杉木径向生长动态及其与气候因子的关系展开研究，为科学经营研究区杉木提供参考依据。
方法本研究利用径向生长测量仪对福建省将乐国有林场杉木进行了为期4年（2017年7月—2021年6月）的连续监测，对其径向生长量进行分析，确定生长季，同时进行偏相关分析及滑动相关分析探究温度、湿度、降水量等气候因子对杉木径向生长的影响。
结果（1）杉木具有周期性的日径向改变，主要体现在3个阶段（第1个阶段是收缩、第2个阶段是恢复、第3个阶段是增长）；杉木径向变化的季节动态经历了低谷期、上升期和稳定期；杉木的径向变化年际间存在差异，但径增长的时间序列和累积变化趋势基本是一致的。（2）拟合并分析杉木累积径向生长曲线可以确定福建将乐杉木的生长季为每年2—9月。（3）平均相对湿度、降水量、最低温度和平均温度是主要的影响因子，且杉木净增量与降水量具有显著正相关的关系，温度在适宜杉木径向生长的范围内越高越利于生长，但高于或低于这个范围均会起抑制作用；滑动相关分析结果表明随着生长季时间的变化，相关系数呈现先增加后减少的趋势。
结论杉木径向生长与相对湿度、降水量、温度等气候因子有密切联系，因此研究杉木的径向变化特征和对气候因子的响应，为更好地规划目标树种的生存策略提供了重要的技术路径和参考模型。
Abstract:ObjectiveCunninghamia lanceolata is a unique timber species in southern China, which has good greening effect and also high economic value. In this article, the relationship between radial growth dynamics of C. lanceolata and climatic factors was investigated at different time scales to provide a basis for scientific management of C. lanceolata in the study area.
MethodIn this study, the radial growth of C. lanceolata trees was continuously monitored at the Jiangle State owned Forest Farm in Fujian Province of eastern China for 4 years (July 2017−June 2021) using a radial growth meter to analyze their radial growth and determine the growing seasons, while partial and sliding correlation analyses were conducted to investigate the effects of climate factors, such as temperature, humidity, and precipitation on C. lanceolata radial extension.
Result(1) C. lanceolata had periodic daily radial changes, which were mainly reflected in three stages, the first stage was contraction, the second stage was recovery, and the third stage was growth; the seasonal dynamics of radial changes in C. lanceolata underwent a trough period, a rise period and a stable period; there were interannual differences in radial changes in C. lanceolata, but the time series of diameter growth and cumulative change trends were basically the same. (2) The cumulative radial growth curves of C. lanceolata were combined to determine the growing season of C. lanceolata in Jiangle County, Fujian Province of eastern China, from February to September in each year. (3) The average relative humidity, precipitation, minimum temperature and average temperature were the main influencing factors, and the net increase of C. lanceolata had a significant positive correlation with precipitation. The sliding correlation analysis showed that the correlation coefficient increased and then decreased with the change of growing season time.
ConclusionThe radial growth of C. lanceolata is closely related to climate factors, such as relative humidity, precipitation and temperature, therefore, studying the radial variation characteristics of C. lanceolata and its response to climatic factors provides an important technical pathway and reference model for better planning of survival strategies for the target tree species.
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Keywords:
- Cunninghamia lanceolata /
- radial growth /
- growth season /
- climate factor
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图 1 将乐县2000—2020年的月最高温度、月平均温度、月最低温度与月平均降水量
Figure 1. Monthly maximum temperature, monthly mean temperature, monthly minimum temperature and monthly mean precipitation in Jiangle County of Fujian Province from 2000 to 2020
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图 3 研究区2018、2019、2020年气候特征
Figure 3. Climate characteristics of the study area in 2018, 2019 and 2020
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图 5 杉木年尺度径向变化动态
Figure 5. Annual scale radial variation dynamics of Cunninghamia lanceolata
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图 6 2018—2020年杉木径向生长累积拟合及径向生长速率
Figure 6. Cumulative fitting and radial growth rate of Cunninghamia lanceolata from 2018 to 2020
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图 7 径向生长与环境之间的动态相关性（21 d窗口期）
Figure 7. Dynamic correlation between radial growth and environment (21 d window period)
表 1 杉木生长季日径向变化与气象因子的Pearson偏相关性
Table 1 Pearson partial correlations between daily radial variation and meteorological factors inCunninghamia lanceolata growing season
项目
Item最高温度
Maximum
temperature最低温度
Minimum
temperature平均温度
Average
temperature降水量
Precipitation平均相对湿度
Average relative
humidity最小相对湿度
Minimum relative
humidity偏相关性系数
Partial correlation coefficient−0.004 −0.132* 0.11* 0.353** 0.139* 0.091 注：*表示P < 0.05，**表示P < 0.01。Notes: * means P < 0.05，** means P < 0.01. 
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