【作者】 宁鹏；

【导师】 高润梅；

【作者基本信息】 山西农业大学 ， 林学， 2023， 博士

【摘要】 在全球气候变化背景下,研究气候变化对树木生长的影响及树木对气候变化的生长响应具有重要理论意义。中条山森林资源丰富,在涵养水源、保护物种多样性、维系区域生态平衡等方面都发挥着不可替代的作用。本区属暖温带向亚热带过渡区域,树木生长对气候变化的响应更为敏感。本文以中条山林区的4个优势树种,油松(Pinus tabuliformis)、华山松(Pinus armandii)、辽东栎(Quercus mongolica)、栓皮栎(Quercus variabilis)等为研究对象,分别选择27株优势木(共108株)作解析木,以5个典型生长方程拟合4个优势树种的胸径、树高和材积的最优生长模型,并分析其生长规律;利用多元逐步回归和随机森林两种算法构建包含气候因子的最优生长模型,分析不同因子对各优势树种生长的贡献率;以最优生长模型为基础,结合国家气候中心BCC-CSM2-MR气候系统模式下的4种气候情景(SSP1-2.6、SSP2-4.5、SSP3-7.0、SSP5-8.5),预测未来气候情景下4个优势树种的生长动态。本文主要结论如下:(1)利用5个典型生长方程拟合4个优势树种的胸径、树高和材积生长:油松材积最优生长模型为Gompertz方程,除此之外,其他最优生长模型均为Richards方程。利用Richards方程拟合各树种的生长曲线,发现油松胸径、树高和材积的生长高峰期分别是10-50a、10-30a和20-70a,数量成熟龄为70a;华山松胸径和树高的生长高峰期均为5-30a,材积生长高峰期是20-90a,数量成熟龄为90a;辽东栎胸径、树高和材积的生长高峰期分别是10-30a、5-35a、10-70a,数量成熟龄为70a;栓皮栎胸径和树高的生长高峰期均为10-30a,材积的生长高峰期是20-95a,数量成熟龄为95a。(2)对于胸径来说,多元逐步回归算法和随机森林算法的拟合精度因树种而异:多元逐步回归算法拟合的油松胸径生长模型优于随机森林算法,而对其他三个优势树种的胸径生长模型而言,随机森林算法优于多元逐步回归算法,但精度不高。显著影响各优势树种胸径生长的气候因子为年均最高气温、相对湿度、风速、年降雨量、标准化降水蒸散指数等,胸径生长与年均最高气温、相对湿度、风速等三个气候因子呈正相关,与年降雨量和标准化降水蒸散指数等表现为负相关。(3)4个优势树种构建的树高生长模型均以随机森林算法为优,即随机森林算法比多元逐步回归算法更适用于本区优势树种树高生长的拟合。两种算法中,气候因子对树高生长的贡献率排序基本一致,依次为年均气温、年均最低气温、年最低气温、日照时数、相对湿度、风速等。其中,树高生长与年均气温表现为负相关,而与年均最低气温、年最低气温、日照时数、相对湿度和风速等气候因子都表现为正相关。(4)与胸径和树高的拟合相似,随机森林算法构建的材积生长模型总体优于多元逐步回归算法,且材积模型的R~2总体高于胸径和树高模型。两种算法下,气候因子对材积生长贡献率排序基本一致,且与温度有关的气候因子的影响作用大于与水分有关的气候因子。材积生长与年均最高气温、标准化降水蒸散指数呈正相关,而与日照时数呈负相关。(5)未来气候情景均不同程度地抑制4个优势树种的胸径、树高和材积生长。区分树种来看,对油松和华山松的胸径、树高和材积总生长量的抑制作用均强于辽东栎和栓皮栎。就胸径和树高总生长量而言,SSP1-2.6情景的抑制作用最强,SSP2-4.5情景的抑制作用最弱,但对各优势树种高生长的影响小于对胸径的影响;就材积总生长量来说,SSP1-2.6情景的抑制作用最强,SSP5-8.5情景的抑制作用最弱。本文利用生长模型研究树木生长规律,分析气候因子对中条山优势树种生长的影响,预测未来气候情景下树木的生长趋势,以期为气候变化背景下制定科学的森林经营管理方案提供科学依据,推动区域森林可持续发展。更多还原

【Abstract】 In the context of global climate change,studying the impact of climate change on tree growth and their response to climate change has become a scientific issue.Zhongtiao Mountains plays an important role in maintaining water conservation,species diversity and regional ecological balance.This area belongs to the warm-temperate to subtropical climate transition zone,where tree growth is more sensitive to climate change.This paper takes four dominant tree species: Pinus tabuliformis,Pinus armandii,Quercus mongolica,Quercus variabilis in the Zhongtiao mountain as the research object.We selected five typical growth equations,and fitted the growth models of the diameter at breast height,height,and volume of each dominant tree species,then analyzed the growth laws.At the same time,based on the data of nearest meteorological stations,a growth model of single tree diameter at breast height,height and volume included climate factors was constructed by using multiple stepwise regression and random forest.The impact of different climate factors on the growth of each dominant tree species was analyzed,and the relative importance of different climate factors on the growth of dominant tree species was calculated.Meanwhile,climate factors for four different climate scenarios(SSP1-2.6,SSP2-4.5,SSP3-7.0,SSP5-8.5)under the BCC-CSM2-MR climate system model of the National Meteorological Center were selected to predict the growth trends of various dominant tree species under future climate scenarios by using the above conclusions.(1)Using five typical growth equations to fit the growth of dominant tree species,including diameter at breast height,height,and volume.The optimal growth model for volume of P.tabuliformis is the Gompertz equation,while the optimal growth models for the others are Richards equations.By using the Richards equation to fit the growth curves of various dominant tree species,it was found that the peak growth period of the DBH、height and volume of P.tabulaeformis was 10-50 years、10-30 years、20-70 years,and the mature age of quantity is 70 years.The peak growth period of the DBH and height of P.armandii is 5-30 years,the peak growth period for volume is 20-90 years,and the mature age for quantity is 90 years.The peak growth period of the DBH、height and volume of Q.mongolica is 10-30 years、5-35 years、10-70 years,and the mature age of quantity is 70 years.The peak growth period of the DBH and height of Q.variabilis is 10-30 years,and the volume is 20-95 years,and the mature age of quantity is 95 years.(2)For the DBH,the accuracy of fitting by the multivariate stepwise regression and the random forest is different.To the P.tabuliformis,the multivariate stepwise regression is superior to the random forest,but the others is the random forest better than the multiple stepwise regression,however the accuracy is not high.The relative importance of climate factors affecting the growth of DBH is consisted,for example mean maximum annual temperature,relative humidity,wind speed,annual precipitation,and standardized precipitation evapotranspiration index have a significant impact on the growth of DBH.The mean maximum annual temperature,relative humidity,and wind speed have a positive effect on the DBH of dominant tree species.The annual precipitation and standardized precipitation evapotranspiration index exhibit a negative correlation.(3)The tree height growth models constructed by the random forest are superior to the models constructed by the multiple stepwise regression for the four dominant tree species.The contribution of climatic factors to tree height growth was basically the same in both algorithms,in the order of mean annual temperature,mean minimum annual temperature,annual minimum temperature,mean annual sunshine hours,relative humidity and wind speed.Tree height growth was negatively correlated with mean annual temperature,and positively correlated with mean minimum annual temperature,annual minimum temperature,mean annual sunshine hours,relative humidity and wind speed.(4)Similar to the fits for DBH and height,the random forest generally is superior to the multiple stepwise regression for volume growth models.The R2 of volume growth models was generally higher than that of the DBH and height.The contribution of climatic factors to volume growth was generally consistent between the random forest and the multiple stepwise regression.Temperature-related climatic factors had a greater effect than moisture-related climatic factors.The mean maximum annual temperature and standardized precipitation-evapotranspiration index were positively correlated with volume growth,while mean annual sunshine hours was negatively correlated.(5)Under the future climate scenarios,the DBH,height,and volume growth of the four dominant species were restrained in varying degrees.By species,the inhibition of total growth at DBH,height and volume was stronger for P.tabuliformis and P.armandii,than Q.mongolica and Q.variabilis.In terms of total growth at DBH and height,the SSP1-2.6 scenario had the strongest inhibiting effect and the SSP2-4.5 scenario had the weakest,but the effect on height growth of the dominant tree species was less than that on DBH.For total volume growth,the SSP1-2.6 scenario had the strongest inhibiting effect and the SSP5-8.5 scenario had the weakest.This paper uses growth models to study the law of the tree growth,attempting to analysis the effects of climate factors on the growth of dominant tree species in the Zhongtiao Mountains and to predict tree growth trends under future climate scenarios.This paper aims to provide a scientific basis for formulating scientific forest management program under the background of climate change and promote sustainable development of the regional foresst.更多还原