Effects of forest tree species diversity on soil carbon and nitrogen contents in China
-
摘要:目的
揭示不同气候带森林生态系统中土壤碳氮含量对树种多样性的响应差异,并探讨影响这种响应的关键生物和非生物因素。
方法收集中国热带、亚热带、暖温带和中温带4个森林生态系统树种多样性,土壤碳、氮含量和土壤真菌多样性数据,针对处理组(多个树种)和对照组(单一树种)共计214组数据进行整合分析,使用随机效应模型计算多样性效应值,并分析不同气候带森林生态系统影响树种多样性效应值的生物和非生物变量的解释权重。
结果不同气候带森林土壤碳、氮含量对树种多样性存在差异性响应,随着纬度梯度增高,树种多样性对土壤碳、氮含量的影响逐渐减弱。热带和亚热带森林较强的树种多样性效应主要源于海拔和土壤pH,而非土壤真菌群落多样性,但是暖温带和中温带森林土壤真菌多样性则是调控树种多样性对土壤碳、氮含量影响的重要因素之一。
结论中国不同气候带森林生态系统的土壤碳、氮含量对树种多样性的响应格局一定程度上解释了局域尺度研究中森林树种多样性对土壤碳库影响机制的分歧,同时也说明树种多样性的变化对于热带和亚热带森林土壤碳库的影响可能更为剧烈。
Abstract:ObjectiveTo reveal the differences in the response of soil carbon and nitrogen content to tree species diversity in forest ecosystems in different climatic zones, and to explore the key biotic and abiotic factors affecting this response.
MethodWe collected the data of altitude, air temperature, soil properties (including soil carbon (C), nitrogen (N), C/N ratio), as well as fungal diversity from sample plots with both tree species mixtures and monocultures. In total, 214 sets of data were used to conduct meta analysis, with a random-effect model calculating effect size. Additionally, the explanatory weights of biotic and abiotic variables influencing the effect of tree richness were analyzed.
ResultWe found that the responses of soil C and N contents to tree richness varied across forest ecosystems, with the strength of tree richness effects on soil C and N contents gradually diminishing from tropical to mid-temperate forest ecosystems. Moreover, the effects of tree richness on soil C and N contents in tropical and subtropical forest ecosystems were primarily driven by altitude and soil pH, rather than soil fungal diversity. In contrast, cascading effects of tree richness on soil C and N contents were observed via modulating soil fungal diversity in warm-temperate and mid-temperate forest ecosystems.
ConclusionThe response patterns of soil carbon and nitrogen contents in forest ecosystems of different climate zones in China to tree species diversity, to some extent, explain the variability mechanisms of forest tree species diversity on soil carbon pools in local scale studies, and also indicate that changes in tree species diversity may have a more severe impact on soil carbon pools in tropical and subtropical forests.
-
油用牡丹(Paeonia suffruticosa)是我国特有的木本油料树种,属于多年生小灌木[1-2],对其开发利用具有很高的经济效益、生态效益和社会效益[3-4]。现阶段,油用牡丹植株修剪和果实采摘机械化水平较低[5-6]。已有研究表明:根据油用牡丹植株的生长特点和农艺条件,采用切割方式切断茎秆能有效的进行果实采摘[7],但油用牡丹茎秆切割机理尚未明确。
目前,关于灌木切割的研究主要集中在力学参数与茎秆物理特性、微观组织、化学成分的关系上[8]。同时,刀具角度如滑切角[9]、斜切角[10-11]对茎秆切割有显著影响,并且茎秆本身的物理特性也会影响其切割性能[12-13]。研究表明:灌木微观结构属于典型的多孔胞元结构,这种微观组织成分和排列模式导致宏观动态力学性能与加载速率相关[14-16]。针对作物茎秆切割,许多研究基于Johnson-Cook模型[17-18]使用有限元方法模拟分析了其切割特性。如廖宜涛等[19-20]对芦竹(Arundo donax)切割的研究,郭茜[21]对藤茎类秸秆的切割特性研究,苏工兵等[22-23]对苎麻(Boehmeria nivea)茎秆切割研究。柳爱群等[24]基于准静态单轴拉伸和单轴扭转试验给出了材料参数的识别方法,季玉辉[25]提出了Johnson-Cook材料参数估计方法和估计程序。但茎秆切割研究中计算未考虑应变率效应,也未对本构方程参数的测定方法进行研究。
对处于果实成熟期的“紫斑”油用牡丹茎秆切割特性进行相关研究,提出以Johnson-Cook模型作为茎秆切割本构模型。通过电子万能试验机进行准静态拉伸试验和动态拉伸试验,得到油用牡丹茎秆材料的Johnson-Cook本构方程参数,基于ANSYS/LS-DYNA软件仿真计算油用牡丹茎秆的切割过程,并对其应变率效应和应变硬化效应进行分析。通过模拟和试验结果的对比验证了模型的可行性和正确性,为后续采摘机械研究提供依据。
1. 材料与方法
1.1 材 料
材料均选自北京市海淀区鹫峰国家森林公园牡丹园种植的“紫斑”油用牡丹,随机选取当年生长的新枝,取样时间为牡丹植株的果实成熟期。选择生长良好,无病虫害或机械损伤的茎秆,从果实果柄向下30 ~ 50 cm处剪下(保证与果实采摘时切割位置一致),茎秆经手工去除叶和侧枝,装入保鲜袋密封。并于当天在北京林业大学工学院实验室(26 ℃空调环境)进行茎秆力学试验。牡丹茎秆截面形状如图1,其形状近似为圆形,每次试验前用游标卡尺测量其截面尺寸,按照近似圆形进行面积计算。
由于茎秆材料的非均一性,茎秆进行拉伸试验时断裂位置较为随机,而应变测量需要保证断裂位置为茎秆两夹持端中央的有效标距内。因此,每个茎秆在试验前均预先进行中部去皮处理,如图2所示。由于茎秆表皮很薄,影响茎秆机械性能的厚壁组织和维管束组织主要分布在韧皮部和髓心,仅去除表皮对茎秆机械性能影响不大,预试验结果也表明茎秆去皮后拉伸无明显差异。选取的油用牡丹茎秆直径分为3级:细(直径2 ~ 3 mm)、中(直径3 ~ 4 mm)、粗(直径4 ~ 5 mm),试验材料平均分配到各个试验组,共进行12次单轴拉伸试验。经测定,试验中茎秆的平均含水率为56.7%。
1.2 准静态拉伸试验
为分析材料的应变效应,采用准静态拉伸试验可获得应变率为10−5 ~ 10−2 s−1时,油用牡丹茎秆的应力–应变曲线。准静态拉伸试验采用电子万能力学试验机(M4050 深圳市瑞格尔仪器有限公司,图3)。试样尺寸为标距10 mm,加载速率5 mm/min,应变率为8.4 × 10−3 s−1。
试验中,使用CCD相机记录茎秆拉伸至断裂的变形过程,采用视频引伸计[26-28]测量拉伸应变。这是基于机器视觉的一种应变测量方法,其基本原理是利用标定好的相机追踪被测对象上的标记点或纹理特征,通过计算其位移来确定试件的变形量。其基本工作原理如图4所示。
1.3 动态拉伸试验
材料的处理和装置与准静态拉伸试验相同。试验在常温中进行,按照加载速率的不同将茎秆材料分为4组,每组同样分配3个等级直径的茎秆并进行12次试验。4组加载速率分别为25、50、100和200 mm/min,对应应变率分别为4.20 × 10−2、8.40 × 10−2、1.68 × 10−1和3.36 × 10−1 s−1。
2. 结果与分析
2.1 准静态力学性能
油用牡丹茎秆在室温和准静态拉伸条件下的真实应力–应变曲线如图5所示。从图5可以看出:油用牡丹茎秆在准静态拉伸过程中,流动应力随应变增加迅速升高,当应力达到一定值后(A点),茎秆进入稳定塑性流动状态,应变强化率(Δσ/Δε)基本不变,随着应变的继续增大,茎秆流动应力近似直线增加(BC段),呈现显著的应变硬化效应。
2.2 茎秆的应变率效应
图6为油用牡丹茎秆在常温下不同应变率时的真实应力–应变曲线。图中黑色实线为准静态拉伸(应变率为8.40 × 10−3 s−1)结果,其余曲线为不同应变率下动态拉伸结果。由图6可知:茎秆在动态拉伸条件下的曲线明显高于准静态拉伸,在应变相同时,茎秆拉伸应变率越高,应力值越大。当茎秆应变率由8.40 × 10−3 s−1(准静态)增大为3.36 × 10−1 s−1,拉伸应变ε = 12%时,茎秆流动应力由7.78 MPa 增大至10.58 MPa,增加约36%,表明茎秆存在应变率强化效应。而且随着应变率升高,产生相同应变需要更大的应力,即茎秆产生相同的塑性变形需要更大的力,导致茎秆的塑性变形功(材料发生塑性变形所消耗的功W,W =
∫ε0σdε )[29-30]增加。3. 建立本构模型
Johnson-Cook模型是一个能反映应变率强化效应的理想刚塑性强化模型[17-18],其表达式如式(1)所示:
σy=(A+Bεn)(1+Cln˙ε˙ε0)[1−(T−TrTm−Tr)m] (1) 式中:
σy 表示材料塑性变形时的流动应力(MPa);ε 为等效塑性应变(%);˙ε 为试验应变率(s−1);˙ε0 为准静态参考应变率(s−1),取˙ε0 为8.40 × 10−3 s−1;T为试验温度(℃);Tr 为室温(℃);Tm 为材料熔点(℃);A、B、n、C、m为材料参数,其中,A、B和n为应变硬化参数,A为材料屈服强度(MPa),B和n分别为材料应变硬化的硬化模量(MPa)和硬化指数,C表示材料应变率系数,m为材料温升软化指数。在式(1)中,流动应力
σy 的计算包括3部分:第一个括号表达的是室温下,准静态加载时材料的本构关系,体现了材料的应变硬化现象;第二个括号表达的是应变率强化效应的影响;第三个括号表示材料的温升软化效应[31-32]。一方面,由式(1)可知Johnson-Cook模型是针对材料塑性变形中应力与应变关系的本构模型,虽然茎秆材料和金属材料材性差异明显,但从破坏形式上来说茎秆材料破坏过程也要经历塑性变形阶段直至材料断裂,因此本构模型需要能够描述茎秆材料塑性变形中应力–应变关系,这一点Johnson-Cook模型能够满足;另一方面,茎秆准静态拉伸和动态拉伸试验的结果表明茎秆材料呈现显著的应变硬化和应变率效应,这符合式(1)所描述的材料塑性变形的流动应力主要影响因素。因此,Johnson-Cook本构模型可以作为茎秆切割本构模型。同时,由于茎秆剪切过程不会释放大量热量使温度急剧上升,试验温度约等于室温(
T≈Tr ),因而忽略温度的影响,方程(1)可简化为:σy=(A+Bεn)(1+Cln˙ε˙ε0) (2) 根据式(2),通过油用牡丹茎秆拉伸试验,可以拟合得到模型中的各参数,从而建立能反映油用牡丹茎秆切割性能的Johnson-Cook本构模型。
本研究进行了准静态拉伸试验,此时
˙ε=˙ε0 ,流动应力σy=(A+Bεn) ,将其两边取对数后得到:ln(σy−A)=nlnε+lnB (3) 这在以
ln(σy−A) 为纵坐标,以lnε 横坐标的对数坐标中表示为斜率n、截距lnB的一条直线,通过试验数据的拟合,即可得到B、n的对应值。A表示材料的屈服强度,可以直接由准静态试验的应力–应变曲线读取。拟合得到茎秆的应变硬化参数:A = 4.75 MPa,B = 3.404 MPa,n = 0.147。为得到茎秆应变率系数C,令K = 4.75 + 3.404
ε0.147 ,取试验中茎秆以不同应变率拉伸时的极限强度σi,则Johnson-Cook本构方程可简化为:σi=K(1+Cln˙ε˙ε0) (4) 令
Y=σiK−1 ,X=ln˙ε˙ε0 ,式(4)可转换成Y = CX。根据动态拉伸试验结果,采用最小二乘法拟合得到应变率系数C = 0.103。建立油用牡丹茎秆Johnson-Cook本构方程为
σy=(4.75+3.404ε0.147)(1+0.103ln˙ε) (5) 通过准静态拉伸试验和动态加载试验,得到油用牡丹茎秆材料的本构模型参数(表1)。
表 1 油用牡丹茎秆本构模型参数Table 1. Constitutive model parameters of oil tree peony stem参数
Parameter屈服强度
Yield strength (A)/MPa应变硬化模量
Strain hardening modulus (B)/MPa应变硬化指数
Strain hardening index (n)应变率系数
Strain rate coefficient (C)值 Value 4.75 3.404 0.147 0.103 按公式(5)中对应材料参数进行拟合,图7是计算结果和试验结果的对比图。图中实线为试验结果,虚线为计算结果。由图7可以看到由Johnson-Cook模型拟合得到的本构曲线与试验结果吻合较好,这说明拟合得到的各材料参数是正确的,Johnson-Cook模型能有效地表达油用牡丹茎秆在不同应变率下的塑性本构关系,能预测不同应变率下茎秆塑性流动应力。
4. 数值仿真分析
4.1 茎秆剪切试验
试验采用自制的夹具与刀具,在电子万能试验机上进行(图8a)。试验时,按2 cm间距标记剪切点,测量并计算剪切点处截面积,装夹好试件后进行试验,如图8b所示。
4.2 茎秆剪切有限元仿真
采用式(5) 油用牡丹茎秆本构方程,基于ANSYS/LS-DYNA建立了有限元模型如图9a所示,计算得到油用牡丹茎秆剪切过程中不同时刻的应力场,分别如图9b和9c所示。模拟结果表明刀具与茎秆的接触面产生了应力集中,存在明显的局部变形,模拟结果与茎秆切割的实际受力和变形情况一致。
4.3 分析与讨论
为了验证茎秆本构模型的正确性和模型参数的准确性,本文将模拟得到的结果与试验结果进行对比分析。
图10是茎秆最大切割力的仿真结果与试验结果的对比图,为了进一步检验两者的相关性,使用SPSS软件进行配对t检验,结果如表2 ~ 4所示。
表 2 峰值切割力仿真结果与试验结果的成对样本相关系数Table 2. Correlation coefficient between simulation results and test results of cutting force样本数量 Sample number 相关系数 Correlation coefficient P值 P value 10 0.937 < 0.000 1 表 4 切割能量仿真结果与试验结果的配对t检验Table 4. Paired t test between simulation results and test results of cutting energy成对差分 Paired difference t值
t value自由度
dfP值
P value均值
Mean标准差
SD均值的标准差
SD of mean差分的95%置信区间 95% confidence interval of difference 下限 Lower limit 上限 Upper limit 2.975 3.535 1.543 −1.691 4.201 −1.625 9 0.086 从图10可以看出:茎秆的峰值切割力随茎秆的直径增大而增大,仿真结果与试验结果较一致。而且表2和表3的配对t检验结果表明:两者的相关系数达到了0.937,且P值小于0.5,说明两组数据显著相关;同时,t检验结果的P值为0.912,大于0.05,说明置信区间为95%的情况下,两组样本没有显著性差异。表4说明切割能量和剪切强度的仿真结果与实际结果也没有显著性差异。
表 3 峰值切割力仿真结果与试验结果的配对t检验Table 3. Paired t test between simulation results and test results of cutting force成对差分 Paired difference t值
t value自由度
dfP值
P value均值
Mean标准差
SD均值的标准差
SD of mean差分的95%置信区间 95% confidence interval of difference 下限 Lower limit 上限 Upper limit 0.158 4.409 1.394 −2.996 3.312 0.113 9 0.912 通过分析表明:茎秆剪切数值仿真结果和试验结果是一致的,两者无显著差异。本文改进的Johnson-Cook模型可以作为茎秆切割本构模型,提出的模型参数测定方法是准确的。
5. 结 论
本研究提出以Johnson-Cook方程作为油用牡丹茎秆切割本构方程,通过准静态拉伸试验和动态拉伸试验确定了茎秆材料参数,并进行了茎秆切割试验研究和数值模拟,得到以下结论:
(1)油用牡丹茎秆切割过程存在明显的应变率效应,塑性变形过程中茎秆流动应力随应变率增大而增大,塑性变形功也随之增加。
(2)对于油用牡丹茎秆,可以通过准静态拉伸试验和动态拉伸试验的方式测定Johnson-Cook模型的静态和动态材料参数。
(3)采用改进的Johnson-Cook模型模拟茎秆切割过程,仿真结果与试验结果一致。表明该模型可以较好地预测茎秆材料的切割过程及其性能。
-
表 1 森林生态系统树种多样性效应值的影响因素
Table 1 Influencing factors of tree species diversity effect values in forest ecosystems
指标 土壤有机碳效应值 土壤全氮效应值 土壤碳氮比效应值 土壤真菌香农指数效应值 海拔 −0.010 1*** −0.005 1*** −0.000 3** 0.003 2* 纬度 −0.667 9* −0.627 1*** 0.019 3 −0.553 8** 年平均温度 −0.848 1* −0.201 7 −0.026 9 −0.721 7** 土壤真菌香农指数 0.960 4 −0.256 7 0.097 9* 土壤pH 2.123 1** 2.248 6*** −0.048 7 −0.529 1 土壤有机碳 −0.044 8 土壤全氮 −1.168 5 土壤碳氮比 0.031 2 注: **表示影响极显著(P < 0.01)。 表 2 树种多样性效应值影响因子的解释权重
Table 2 Explanatory weight of the factors affecting the effect values of tree species diversity
指标 土壤有机碳效应值 土壤全氮效应值 土壤碳氮比效应值 土壤真菌香农指数效应值 总体 热带 亚热带 暖温带 中温带 总体 热带 亚热带 暖温带 中温带 总体 热带 亚热带 暖温带 中温带 总体 热带 亚热带 暖温带 中温带 海拔 0.993 0.500 0.989 1.000 0.281 1.000 0.500 0.291 0.998 0.986 0.960 0.500 0.279 0.783 0.261 0.690 0.500 0.249 0.306 0.413 土壤pH 0.965 0.141 0.831 1.000 0.194 1.000 0.291 0.386 1.000 0.940 0.352 0.102 0.422 0.272 0.222 0.320 0.722 0.420 0.640 0.725 土壤真菌
香农指数0.420 0.078 0.271 1.000 0.354 0.323 0.085 0.586 0.335 0.964 0.713 0.090 0.259 0.726 0.187 年平均温度 0.500 0.500 0.240 0.379 0.433 0.370 0.500 0.681 0.272 0.431 0.590 0.500 0.244 0.349 0.285 0.410 0.500 0.953 0.339 0.392 纬度 0.545 0.996 0.623 0.970 土壤有机碳 0.960 0.738 0.960 0.966 0.559 土壤全氮 0.896 0.120 0.290 0.834 0.471 土壤碳氮比 0.326 0.044 0.303 0.317 0.325 -
[1] Hansen M C, Potapov P V, Moore R, et al. High-resolution global maps of 21st-century forest cover change[J]. Science, 2013, 342: 850−853. doi: 10.1126/science.1244693
[2] Food and Agriculture Organization of the United Nations, Forestry Department. Global forest resources assessment 2020: main report[M]. Rome: Food and Agriculture Organization of the United Nations, 2020.
[3] Huang L F, Zhu Z C, Huang M T, et al. Projection of gross primary productivity change of global terrestrial ecosystem in the 21st century based on optimal ensemble averaging of CMIP6 models[J]. Climate Change Research, 2021, 17(5): 514−524.
[4] Liu X J, Trogisch S, He J S, et al. Tree species richness increases ecosystem carbon storage in subtropical forests[J/OL]. Biological Sciences, 2018, 285: 20181240[2023−02−12]. https://doi.org/10.1098/rspb.2018.1240.
[5] Qiao X T, Geng Y, Zhang C, et al. Spatial asynchrony matters more than alpha stability in stabilizing ecosystem productivity in a large temperate forest region[J]. Global Ecology and Biogeography, 2022, 31(6): 1133−1146. doi: 10.1111/geb.13488
[6] Chen X L, Chen H Y, Chen C, et al. Effects of plant diversity on soil carbon in diverse ecosystems: a global meta-analysis[J]. Biological Reviews, 2020, 95(1): 167−183. doi: 10.1111/brv.12554
[7] Wang Q, Bai W, Sun Z X, et al. Does reduced intraspecific competition of the dominant species in intercrops allow for a higher population density?[J]. Food and Energy Security, 2021, 10(2): 285−298.
[8] Jia Y F, Zhai G Q, Zhu S S, et al. Plant and microbial pathways driving plant diversity effects on soil carbon accumulation in subtropical forest[J/OL]. Soil Biology & Biochemistry, 2021, 161: 108375[2023−02−12]. https://doi.org/10.1016/j.soilbio.2021.108375.
[9] Gillespie L M, Hättenschwiler S, Milcu A, et al. Tree species mixing affects soil microbial functioning indirectly via root and litter traits and soil parameters in European forests[J]. Functional Ecology, 2021, 35: 2190−2204. doi: 10.1111/1365-2435.13877
[10] Domeignoz-Horta L A, Pold G, Liu X J, et al. Microbial diversity drives carbon use efficiency in a model soil[J/OL]. Nature Communications, 2020, 11, 3684[2020−07−23]. https://doi.org/10.1038/s41467-020-17502-z.
[11] Wu Y T, Deng M F, Huang J S, et al. Global patterns in mycorrhizal mediation of soil carbon storage, stability, and nitrogen demand: a meta-analysis[J/OL]. Soil Biology & Biochemistry, 2022, 166, 108578[2022−03−01]. https://doi.org/10.1016/j.soilbio.2022.108578.
[12] Wei Y Q, Xiong X, Ryo M, et al. Repeated litter inputs promoted stable soil organic carbon formation by increasing fungal dominance and carbon use efficiency[J]. Biology and Fertility of Soils, 2022, 58(6): 619−631. doi: 10.1007/s00374-022-01647-8
[13] Jing X, Chen X, Fang J Y, et al. Soil microbial carbon and nutrient constraints are driven more by climate and soil physicochemical properties than by nutrient addition in forest ecosystems[J/OL]. Soil Biology & Biochemistry, 2020, 141[2020−02−01]. https://doi.org/10.1016/j.soilbio.2019.107657.
[14] Li J, Pei J, Pendall E, et al. Rising temperature may trigger deep soil carbon loss across forest ecosystems[J/OL]. Advanced Science, 2020, 6; 7(19): 2001242[2023−02−12]. https://doi.org/10.1002/advs.202001242.
[15] Wolfgang V. Conducting meta-analyses in R with the metafor package[J]. Journal of Statistical Software, 2010, 36: 1−48.
[16] Lange M, Eisenhauer N, Sierra C, et al. Plant diversity increases soil microbial activity and soil carbon storage[J/OL]. Nature Communications, 2015, 6, 6707[2015−04−07]. https://doi.org/10.1038/ncomms7707.
[17] Cong W F, van Ruijven J, Mommer L, et al. Plant species richness promotes soil carbon and nitrogen stocks in grasslands without legumes[J]. Journal of Ecology, 2014, 102(5): 1163−1170. doi: 10.1111/1365-2745.12280
[18] Xu S, Eisenhauer N, Ferlian O, et al. Species richness promotes ecosystem carbon storage: evidence from biodiversity-ecosystem functioning experiments[J/OL]. Proceedings of the Royal Society B-Biological Sciences, 2020, 287: 20202063[2020−11−25]. https://doi.org/10.1098/rspb.2020.2063.
[19] Hong S, Yin G, Piao S, et al. Divergent responses of soil organic carbon to afforestation[J]. Nature Sustainability, 2020, 3: 694−700. doi: 10.1038/s41893-020-0557-y
[20] Sayer E, Heard M, Grant H, et al. Soil carbon release enhanced by increased tropical forest litterfall[J]. Nature Climate Change, 2011, 1: 304−307. doi: 10.1038/nclimate1190
[21] Crowther T W, Riggs C, Lind E M, et al. Sensitivity of global soil carbon stocks to combined nutrient enrichment[J]. Ecology Letters, 2019, 22: 936−945. doi: 10.1111/ele.13258
[22] Wang Y F, Du J Q, Pang Z, et al. Unimodal productivity-biodiversity relationship along the gradient of multidimensional resources across Chinese grasslands[J]. National Science Review, 2022, 9(12): 1−14.
[23] 李婷婷, 唐永彬, 周润惠, 等. 云顶山不同人工林林下植物多样性及其与土壤理化性质的关系[J]. 生态学报, 2021, 41(3): 1168−1177. Li T T, Tang Y B, Zhou R H, et al. Understory plant diversity and its relationship with soil physicochemical properties in different plantations in Yunding Mountain[J]. Acta Ecologica Sinica, 2021, 41(3): 1168−1177.
[24] Palandrani C, Alberti G. Tree derived soil carbon is enhanced by tree species richness and functional diversity[J]. Plant and Soil, 2020, 446: 457−469. doi: 10.1007/s11104-019-04381-7
[25] 徐文仕. 植物多样性对亚热带森林土壤可溶性有机碳氮含量的影响[D]. 上海: 华东师范大学, 2022. Xu W S. Effects of plant diversity on soil labile carbon and nitrogen contents in a subtropical forest[D]. Shanghai: East China Normal University, 2022.
[26] Campo J, Merino A. Variations in soil carbon sequestration and their determinants along a precipitation gradient in seasonally dry tropical forest ecosystems[J]. Global Change Biology, 2016, 22(5): 1942−1956. doi: 10.1111/gcb.13244
[27] Chen S P, Wang W T, Xu W T, et al. Plant diversity enhances productivity and soil carbon storage[J]. Proceedings of the National Academy of Sciences, 2018, 115(16): 4027−4032.
[28] 胡靓达, 周海菊, 黄永珍, 等. 不同杉木林分类型植物多样性及其土壤碳氮关系的研究[J]. 生态环境学报, 2022, 31(3): 451−459. Hu L D, Zhou H J, Huang Y Z, et al. A study on plant species diversity and soil carbon and nitrogen in different Cunninghamia lanceolata stand types[J]. Ecology and Environment Sciences, 2022, 31(3): 451−459.
[29] Feng J, Tang M, Zhu B. Soil priming effect and its responses to nutrient addition along a tropical forest elevation gradient[J]. Global Change Biology, 2021, 27: 2793−2806. doi: 10.1111/gcb.15587
[30] 于文睿南, 潘畅, 郭佳欢, 等. 杉木人工林表土有机质含量及其对土壤养分的影响[J]. 中国生态农业学报(中英文), 2021, 29(11): 1931−1939. Yu-Wen R N, Pan C, Guo J H, et al. Topsoil organic matter and its effect on the soil nutrients contents of Cunninghamia lanceolata plantations[J]. Chinese Journal of Eco-Agriculture, 2021, 29(11): 1931−1939.
[31] Fu H, Yuan G, Ge D, et al. Cascading effects of elevation, soil moisture and soil nutrients on plant traits and ecosystem multi-functioning in Poyang Lake wetland, China[J/OL]. Aquatic Sciences, 2020, 82(2) [2020−03−04]. https://doi.org/10.1007/s00027-020-0711-7.
[32] Nobuhiko S, Kiyoshi U, Toshihide H. Plant functional diversity and soil properties control elevational diversity gradients of soil bacteria[J/OL]. FEMS Microbiology Ecology, 2019, 95(4) [2019−02−28]. https://doi.org/10.1093/femsec/fiz025.
[33] Dai Z, Zang H, Chen J, et al. Metagenomic insights into soil microbial communities involved in carbon cycling along an elevation climosequences[J]. Environmental Microbiology, 2021, 23: 4631−4645. doi: 10.1111/1462-2920.15655
[34] Hättenschwiler S. Effects of tree species diversity on litter quality and decomposition[M]// Scherer-Lorenzen M, Körner C, Schulze E D. Forest diversity and function: temperate and boreal systems. Berlin: Springer, 2005: 176.
[35] Handa I, Aerts R, Berendse F, et al. Consequences of biodiversity loss for litter decomposition across biomes[J]. Nature, 2014, 509: 218−221. doi: 10.1038/nature13247
[36] Shen C C, Wang J, He J Z, et al. Plant diversity enhances soil fungal diversity and microbial resistance to plant invasion [J/OL]. Applied and Environmental Microbiology, 2021, 87(11)[2021−05−11]. https://doi.org/10.1128/AEM.00251-21.
[37] Zeng X M, Feng J, Chen J, et al. Microbial assemblies associated with temperature sensitivity of soil respiration along an altitudinal gradient [J/OL]. Science of the Total Environment, 2022, 820, 153257[2022−05−10]. https://doi.org/10.1016/j.scitotenv.2022.153257.
[38] Zhu X F, Jackson R D, de Lucia E H, et al. The soil microbial carbon pump: from conceptual insights to empirical assessments[J]. Global Change Biology, 2020, 26(11): 6032−6039. doi: 10.1111/gcb.15319
[39] Ye X D, Luan J W, Wang H, et al. Tree species richness and N-fixing tree species enhance the chemical stability of soil organic carbon in subtropical plantations [J/OL]. Soil Biology & Biochemistry, 2022, 174, 108828 [2022−11−01]. https://doi.org/10.1016/j.soilbio.2022.108828.
[40] Xie L L, Yin C Y. Seasonal variations of soil fungal diversity and communities in subalpine coniferous and broadleaved forests [J/OL]. Science of the Total Environment, 2022, 846, 157409[2022−11−10]. https://doi.org/10.1016/j.scitotenv.2022.157409.
[41] Orracha S, Gernot B, Christoph R, et al. Fungal biomass and microbial necromass facilitate soil carbon sequestration and aggregate stability under different soil tillage intensities [J/OL]. Applied Soil Ecology, 2022, 179 [2022−11−01]. 104599, https://doi.org/10.1016/j.apsoil.2022.104599.
[42] Xu Y, Gao X, Pei J, et al. Crop root vs. shoot incorporation drives microbial residue carbon accumulation in soil aggregate fractions[J]. Biology and Fertility of Soils, 2022, 58: 843−854. doi: 10.1007/s00374-022-01666-5
[43] Ma X Y, Wang T X, Shi Z, et al. Long-term nitrogen deposition enhances microbial capacities in soil carbon stabilization but reduces network complexity [J/OL]. Microbiome, 2022, 10(1): 112[2022−07−28]. https://doi.org/10.1186/s40168-022-01309-9.
[44] Mishra S, Hättenschwiler S, Yang X D, et al. The plant microbiome: a missing link for the understanding of community dynamics and multifunctionality in forest ecosystems [J/OL]. Applied Soil Ecology, 2020, 145, 103345 [2020−01−01]. https://doi.org/10.1016/j.apsoil.2019.08.007.
[45] Ren C J, Wang J Y, Bastida F, et al. Microbial traits determine soil C emission in response to fresh carbon inputs in forests across biomes[J]. Global Change Biology, 2022, 28(4): 1516−1528. doi: 10.1111/gcb.16004
[46] Zhong Y L, Chu C J, Myers J A, et al. Arbuscular mycorrhizal trees influence the latitudinal beta-diversity gradient of tree communities in forests worldwide [J/OL]. Nature Communications, 2021, 12(1), 3137[2021−05−25]. https://doi.org/10.1038/s41467-021-23236-3.
[47] Craig M E, Geyer K M, Beidler K V, et al. Fast-decaying plant litter enhances soil carbon in temperate forests but not through microbial physiological traits [J/OL]. Nature Communications, 2022, 13, 1229[2022−03−09]. https://doi.org/10.1038/s41467-022-28715-9.
[48] Gutierrez C C, Sánchez F D, Velasco J, et al. Similarity in the difference: changes in community functional features along natural and anthropogenic stress gradients[J]. Ecology, 2015, 96(9): 2458−2466. doi: 10.1890/14-1447.1
-
期刊类型引用(10)
1. 王艺,刘思思,张彤赫,黄儒强. 高良姜多糖提取工艺的优化及抗氧化活性研究. 农产品加工. 2023(03): 34-38 . 百度学术
2. 周佳悦,夏晓雨,候艳丽,王凡予,李芳菲,郭庆启. 不同发酵方式蓝莓果酒发酵过程中理化指标和抗氧化能力的动态变化. 中国酿造. 2023(05): 132-138 . 百度学术
3. 杨丽婷,赵珊,李明玉,杨薇潼,郑志强,符群. 黑果腺肋花楸分级提取物成分分析及抗氧化活性比较. 食品工业. 2022(07): 129-134 . 百度学术
4. 国田,张娜,符群,柴洋洋,郭庆启. 几种辅助提取方式对蓝莓原花青素浸提效果及抗氧化活性的影响. 北京林业大学学报. 2020(09): 139-148 . 本站查看
5. 李珊,梁俭,冯群,刘真珍. 桂七青芒果皮多糖提取工艺的响应面优化及其体外抗氧化活性. 食品工业科技. 2019(04): 220-225+231 . 百度学术
6. 高嘉敏,邓剑平,王一飞,王治平. 黄连与人参协同抗氧化活性的研究. 现代食品科技. 2019(06): 110-118+199 . 百度学术
7. 曹叶霞,王泽慧,贺金凤,左鑫. 静乐黑枸杞多糖的提取及抗氧化性分析. 食品工业科技. 2019(14): 196-202 . 百度学术
8. 夏晓雨,王凤娟,符群,张娜,郭庆启. 几种单元操作对蓝莓果汁饮料酚类物质含量及抗氧化活性的影响. 中南林业科技大学学报. 2019(11): 125-131 . 百度学术
9. 姚佳,李世正,杜煜,侯鹏鹏. 大孔树脂分离纯化罗勒叶总黄酮及抗氧化活性研究. 食品研究与开发. 2018(20): 63-68 . 百度学术
10. 黄娟,黄燕燕,刘冬梅,陈素芹,潘伟才. 响应面法优化多汁乳菇多糖提取工艺及抗氧化活性研究. 食品工业科技. 2017(11): 55-60 . 百度学术
其他类型引用(10)
-
其他相关附件
-
DOCX格式
2022-0400附录 1 点击下载(44KB)
-