Biomass stability and influencing factors of mixed coniferous and broadleaved forests at different scales in Jiaohe, Jilin Province of northeastern China
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摘要:目的
探讨不同空间尺度下物种多样性、结构多样性、物种异步性、林分密度和环境因素对群落生物量稳定性的影响及相互之间的作用路径，旨在解析生物量的稳定性在不同空间尺度上的主要驱动因素，为森林的可持续经营及科学管理提供理论基础。
方法以吉林蛟河针阔混交林为研究对象，利用2010—2020年的样地群落调查数据，通过结构方程模型探讨20 m × 20 m和40 m × 40 m两个空间尺度上生物因素（物种多样性、结构多样性、物种异步性、林分密度）和非生物因素（地形因子、土壤理化性质）与群落生物量稳定性间的关系及作用机制。
结果在20 m × 20 m空间尺度上，生物量稳定性与物种异步性、结构多样性、林分密度均有显著正向关系，非生物因素中地形因子（坡度、凹凸度）与生物量稳定性呈正相关关系；而在40 m × 40 m空间尺度上，生物量稳定性与物种异步性呈现显著正相关，非生物因素中土壤理化性质（速效钾、全磷和速效氮）与生物量稳定性呈现负相关关系。结构方程模型分析表明，在20 m × 20 m空间尺度上，物种异步性对生物量稳定性的影响高于林分密度和结构多样性，路径系数为0.40。土壤理化性质（全钾、全氮和速效氮）通过显著影响物种多样性间接作用于生物量稳定性，路径系数为0.10。在40 m × 40 m空间尺度上，生物因素中只有物种异步性对生物量稳定性有显著正向影响，路径系数为0.64。地形因子（坡度、凹凸度）通过调整结构多样性间接作用于林分生物量稳定性，路径系数为0.35。
结论在不同的空间尺度上，虽然生物因素与非生物因素对生物量稳定性的作用路径和影响力不尽相同，但物种异步性均为生物量稳定性的主要驱动因子。在20 m × 20 m空间尺度上，物种异步性、林分密度、结构多样性等生物因素通过直接正向效应来影响生物量稳定性，非生物因素通过间接效应作用于生物量稳定性；在40 m × 40 m空间尺度上，林分生物量稳定性主要影响因子则是物种异步性和土壤理化性质。
Abstract:ObjectiveThis paper aims to explore the effects of species diversity, structural diversity, species asynchronism, stand density and environmental factors on community biomass stability and their interaction paths at different spatial scales, and to analyze the main driving factors of biomass stability at different spatial scales, so as to provide a theoretical basis for sustainable forest management and scientific management.
MethodBased on the survey data of coniferous and broadleaved mixed forest in Jiaohe, Jilin Province of northeastern China from 2010 to 2020, the relationship between biotic factors (species diversity, structural diversity, species asynchronism, stand density) and abiotic factors (topographic factors, soil physical and chemical properties) and community biomass stability at 20 m × 20 m and 40 m × 40 m spatial scales and their mechanism were investigated by structural equation model.
ResultAt the scale of 20 m × 20 m, biomass stability was significantly positively correlated with species asynchrony, structural diversity, and stand density. Among abiotic factors, topographic factors (slope and convexity) were positively correlated with biomass stability. At the scale of 40 m × 40 m, biomass stability was positively correlated with species asynchrony, while soil physicochemical properties (available potassium, total phosphorus and available nitrogen) were negatively correlated with biomass stability. The structural equation model analysis showed that the effect of species asynchrony on biomass stability was higher than that of stand density and structural diversity at the scale of 20 m × 20 m, and the path coefficient was 0.40. Soil physicochemical properties indirectly affected stand biomass stability by significantly affecting species diversity, and the path coefficient was 0.10. At the scale of 40 m × 40 m, only species asynchrony had a significant positive effect on biomass stability, with a path coefficient of 0.64. Topographic factors (slope and convexity) indirectly affected stand biomass stability by adjusting structural diversity, and the path coefficient was 0.35.
ConclusionAt different spatial scales, although biotic factors and abiotic factors have different paths and influences on biomass stability, species asynchrony is the main driving factor for biomass stability. At the scale of 20 m × 20 m, biological factors such as species asynchrony, stand density, and structural diversity affect biomass stability through direct positive effects, while environmental factors affect biomass stability through indirect effects. At the scale of 40 m × 40 m, species asynchrony and soil physicochemical properties (available potassium, total phosphorus and available nitrogen) are the main influencing factors for biomass stability.
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Keywords:
- biomass stability /
- spatial scale /
- species asynchrony /
- community structure /
- environmental factor
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森林作为最重要的陆地生态系统，具有消减洪峰、涵养水源等生态服务功能，通常被称为“森林水库”[1]。森林水源涵养的能力主要体现在其枯落物层和土壤层[2]，是森林发挥水源涵养功能的主体部分[3]。枯落物是森林生态系统结构中重要的一环[4]。作为降水降落至地表面先于土壤接触到的部分，枯落物具有涵养水源、拦蓄降水与径流、维持土壤湿度等重要作用[5]。林地土壤层的水文效则通过自身蓄水能力和入渗特性所体现，对降水分配、水分循环和土壤流失等过程具有显著作用[6]。研究枯落物和土壤的水文特征，揭示枯落物和土壤与生态环境要素之间的定性与定量关系，对研究森林生态系统的水土保持能力，合理规划和利用水资源方面具有重要意义[7]。
国内外学者对不同区域、不同森林植被类型的枯落物和土壤水文效应进行了大量研究[8-10]，不同林分枯落物及土壤水文效应随立地条件、树种配置以及林分结构的变化而产生显著差异。近年来对冀北地区林地水源涵养功能的研究也取得一定成果，但多以完全郁闭的成熟林为研究对象，重点集中在不同林分类型、林分密度的比较研究[11-12]，相较于人工成熟林，人工幼龄林处于植被恢复的初期，也是目前人工抚育和经营管理的主要作业阶段，了解人工幼龄林的水源涵养能力，后期通过适宜的抚育方式和经营措施，优化林分结构，促进林木生长，抑制不利因素的发生，可为其发挥水源涵养主导功能提供支持，而目前对植被恢复下的不同人工幼龄林及其与灌木混交配置模式下的水源涵养功能研究甚少。
河北省崇礼区西沟流域植被稀少，大部分为荒山秃岭，且裸岩率较高[13]，加上人为不合理开垦、放牧，使得该地区已有植被被破坏，水土流失问题严重，导致该地区植被水源涵养等生态服务功能难以充分发挥，严重制约区域社会和经济发展。为恢复和改善生态环境，西沟流域自2009年以来，先后通过人工营造、自然恢复等手段，建设了大面积的针叶纯林及其与灌木的乔灌混交林。由于地处崇礼区冬奥场馆周边，如何快速且充分发挥人工针叶幼龄林及乔灌混交林的水源涵养与水土保持功能，对于保障崇礼赛区冬奥场馆的正常运营、改善冬奥场馆周边小流域生态环境尤为重要。因此，亟需对人工针叶幼龄林及其不同混交配置模式的生态服务功能进行深入研究。为研究项目实施后冬奥周边小流域的林地水源涵养能力，以崇礼区西沟−羊草沟流域的5种典型配置模式的人工针叶幼龄林为研究对象，定量分析和比较其枯落物层及土壤层的水源涵养能力，为冬奥会崇礼赛区乃至整个冀北地区人工林的恢复、经营和水源涵养、水土保持功能的研究提供理论依据和科学参考。
1. 研究区概况
研究区位于河北省张家口市崇礼区驿马图乡的羊草沟流域（图1），地处冀北接坝山区，属于清水河支流−崇礼西沟流域，清水河（永定河水系上游）源头即起源于此，地理坐标为41°04′05″ ~ 41°08′30″N，114°58′30″ ~ 115°02′30″E，海拔在1 084 ~ 1 575 m之间，属温带大陆性季风气候，地形大部分为山地，地势东高西低、北高南低，受地形影响，结霜期较晚，年均降雨量456.8 mm，全年降雨集中在6—9月，降雨时空分布不均。土壤以山地褐土和栗钙土为主。
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图 1 研究区地理位置图Ⅰ.落叶松纯林，Ⅱ.樟子松纯林，Ⅲ.落叶松柠条混交林，Ⅳ.樟子松柠条混交林，Ⅴ.樟子松落叶松柠条混交林。下同。Ⅰ, Larix gmelinii pure forest；Ⅱ, Pinus sylvestris pure forest；Ⅲ, Larix gmelinii and Caragana korshins mixed forest；Ⅳ, Pinus sylvestris and Caragana korshins mixed forest；Ⅴ, Pinus sylvestris, Larix gmelinii and Caragana korshins mixed forest. Same as below.Figure 1. Geographical location map of the study area2010年开始在荒山荒坡内实施封山育林和人工造林相结合的植被恢复与重建，坝头山地以营造水土保持林、防风固沙林和水源涵养林为主；阴坡、半阴坡土层较厚的坡面以落叶松（Larix gmelinii）、樟子松（Pinus sylvestris var. mongolica）为主；阳坡、半阳坡土层较薄，树种设计以樟子松、油松（Pinus tabuliformis）为主；沟壑设计栽植沙棘（Hippophae rhamnoides）、杨树（Populus simonii var. przewalskii），乔灌混交达到7∶3。混交方式以不规则块状混交和班间混交为主，现已形成华北落叶松（Larix gmelinii var. principis-rupprechtii）针叶纯林、樟子松针叶纯林、华北落叶松柠条（Caragana korshinskii）混交林、樟子松柠条混交林、华北落叶松樟子松柠条混交林5种主要人工林地。灌木林地主要有山杏（Armeniaca sibirica）、沙棘（Hippophae rhamnoides）等。
2. 研究方法
2.1 样地调查
于2021年5月初，在流域内选取了代表该区域管理后植被恢复都为12年的5块面积为20 m × 20 m的标准样地，包括Ⅰ落叶松纯林、Ⅱ樟子松纯林、Ⅲ落叶松柠条混交林、Ⅳ樟子松柠条混交林、Ⅴ樟子松落叶松柠条混交林（图1），并对5种不同配置模式的造林地进行了野外调查，包括GPS定位、每木检尺调查，并记录了海拔、坡向、坡度、郁闭度以及林分密度等，生长季初期林下无草本生长。表1记录了5个采样点的植物种类和地形信息。
表 1 样地类型和基本特征Table 1. Sample plot types and basic characteristics林分类型
Forest stand type海拔
Altitude/m坡度
Slope/（°）林龄/a
Stand age/year树高
Tree height/mDBH/cm 林分密度/（株·hm−2）
Forest density/（tree·ha−1）Ⅰ 1 521 20 12 2.35 3.75 950 Ⅱ 1 493 22 12 2.21 4.17 1 075 Ⅲ 1 286 22 12 3.24 3.55 1 250 Ⅳ 1 569 22 12 3.52 4.37 1 275 Ⅴ 1 249 23 12 2.68 3.86 1 125 2.2 枯落物调查
枯落物储量调查在每个样地内都选取3个（坡上、坡中、坡下）面积为0.2 m × 0.2 m的小样方，用钢尺分别测量未分解层和半分解层的厚度并记录，分层取样后装入牛皮纸袋中进行称鲜质量，然后带回放入烘箱在105 ℃下烘12 h后称干质量。
枯落物持水测定采用室内浸泡法[14]进行枯落物持水量和持水速率的测定。
枯落物有效拦蓄量计算通过枯落物持水、蓄积量以及自然含水率[15]进行推算，得到枯落物的有效拦蓄量。
2.3 土壤调查
土壤物理性质测定采用剖面法，在每块样地内选取3个样点挖取土壤剖面，由于该地区土层较薄，且多为砾石，加上每个样地土层深度不同，为保证所有样地取土层相同，所以仅在0 ~ 10 cm的土层取环刀土样，并用环刀法[16]测定土壤密度、孔隙度等物理性质。
土壤入渗测定采用原状土双环法[17]测定土壤入渗，在每块样地随机选取3个样点进行试验。
2.4 数据处理
采用Excel 2016和SPSS 22.0软件进行数据处理，使用ArcGIS 10.4.1和Origin 2021进行做图，采用单因素方差分析进行差异显著性分析（P < 0.05）。
3. 结果与分析
3.1 不同配置模式的枯落物蓄积量
由表2可知：5种配置模式的枯落物总厚度处于5.10 ~ 6.70 mm之间，总蓄积量处于2.55 ~ 4.50 t/hm2之间，其大小排序为樟子松纯林（4.50 t/hm2） > 樟子松落叶松柠条混交林（3.81 t/hm2） > 樟子松柠条混交林（3.76 t/hm2） > 落叶松纯林（3.64 t/hm2） > 落叶松柠条混交林（2.55 t/hm2）。5种配置模式枯落物的半分解层蓄积量及厚度均小于对应未分解层的蓄积量及厚度，半分解层枯落物的蓄积量大小排序为樟子松纯林 > 樟子松柠条混交林 > 落叶松纯林 > 樟子松落叶松柠条混交林 > 落叶松柠条混交林；未分解层枯落物的蓄积量大小排序为樟子松纯林 > 樟子松落叶松柠条混交林 > 樟子松柠条混交林 > 落叶松纯林 > 落叶松柠条混交林，落叶松柠条混交林与3种有樟子松的林地都表现为差异显著（P < 0.05）。
表 2 不同配置模式的枯落物厚度及持水情况Table 2. Litter thickness and water holding capacity of different configuration models林分类型
Stand type枯落物层
Litter layer枯落物蓄积量/（t·hm−2）
Litter volume/（t·ha−1）枯落物厚度
Litter thickness/mmⅠ 半分解层 Semi-decomposed layer 1.68 ± 0.15a 2.60 ± 0.34ab Ⅱ 2.03 ± 0.32a 2.90 ± 0.29a Ⅲ 1.08 ± 0.06b 2.30 ± 0.15b Ⅳ 1.69 ± 0.49a 2.80 ± 0.17a Ⅴ 1.61 ± 0.35ab 2.70 ± 0.14ab Ⅰ 未分解层 Undecomposed layer 1.96 ± 0.12ab 3.30 ± 0.11b Ⅱ 2.47 ± 0.15a 3.80 ± 0.06a Ⅲ 1.47 ± 0.11b 2.80 ± 0.09c Ⅳ 2.07 ± 0.63a 3.30 ± 0.26b Ⅴ 2.20 ± 0.08a 3.50 ± 0.05b 注：同列不同小写字母表示同一分解状态下各处理间差异显著（P < 0.05）；表中数据为平均值 ± 标准差。下同。Notes: different lowercase letters in the same column indicate significant differences between treatments at the same decomposed layer(P < 0.05); the data in the table are mean ± standard deviation. The same below. 3.2 不同配置模式的枯落物持水能力
3.2.1 枯落物最大持水量和最大持水率
由表3可知：5种配置模式的枯落物总的最大持水率处于231.20% ~ 333.05%之间，其大小排序为樟子松落叶松柠条混交林 > 落叶松纯林 > 落叶松柠条混交林 > 樟子松柠条混交林 > 樟子松纯林。在半分解层和未分解层中，樟子松落叶松柠条混交林的枯落物最大持水率都为最大，分别是161.42%和171.63%，樟子松纯林的枯落物最大持水率都为最小，分别是126.70%和104.50%。
表 3 不同配置模式枯落物层的拦蓄能力Table 3. Interception capacity of litter layer of different configuration models林分类型
Stand type枯落物层 Litter layer 自然含水率
Natural moisture content/%最大持水率
Maximum water holding rate/%最大持水量/（t·hm−2）
Maximum water holding capacity/(t·ha−1)有效拦蓄率
Effective interception rate/%有效拦蓄量/（t·hm−2）
Effective interception capacity/(t·ha−1)Ⅰ 半分解层
Semi-decomposed layer15.09 ± 0.02ab 149.46 ± 40.02a 0.95 ± 0.32b 107.58 ± 40.33a 0.63 ± 0.04a Ⅱ 17.76 ± 0.03a 126.70 ± 33.18a 1.48 ± 0.34ab 92.60 ± 25.81a 0.70 ± 0.04a Ⅲ 15.09 ± 0.02ab 159.93 ± 50.38a 1.46 ± 0.35ab 123.52 ± 41.39a 0.71 ± 0.05a Ⅳ 17.76 ± 0.03a 151.23 ± 8.96a 1.30 ± 0.61a 123.59 ± 9.97a 0.68 ± 0.04a Ⅴ 11.01 ± 0.01b 161.42 ± 33.51a 1.33 ± 0.22ab 127.85 ± 27.67a 0.68 ± 0.05a Ⅰ 未分解层 Undecomposed layer 11.97 ± 0.04a 147.65 ± 27.28a 1.00 ± 0.07b 113.41 ± 25.04a 0.54 ± 0.02c Ⅱ 16.78 ± 0.06a 104.50 ± 11.61b 1.46 ± 0.41ab 74.56 ± 8.92b 0.71 ± 0.03b Ⅲ 11.97 ± 0.04a 120.11 ± 3.94b 1.17 ± 0.04ab 92.44 ± 2.58b 0.65 ± 0.03b Ⅳ 16.78 ± 0.06a 106.44 ± 5.14b 1.59 ± 0.33a 83.94 ± 3.93b 0.78 ± 0.04a Ⅴ 10.06 ± 0.02a 171.63 ± 9.42a 1.37 ± 0.16ab 137.33 ± 6.79a 0.69 ± 0.04b 枯落物总的最大持水量处于1.95 ~ 2.94 t/hm2之间，其大小排序为樟子松纯林 > 樟子松柠条混交林 > 樟子松落叶松柠条混交林 > 落叶松柠条混交林 > 落叶松纯林。在半分解层中，樟子松纯林的枯落物最大持水量最大，为1.48 t/hm2，而落叶松纯林最小，为0.95 t/hm2，5种配置模式之间无显著差异；在未分解层中，樟子松 柠条混交林的枯落物最大持水量最大，为1.59 t/hm2，而落叶松纯林最小，为1.00 t/hm2，樟子松柠条混交林和落叶松纯林差异显著（P < 0.05）。
3.2.2 枯落物有效拦蓄量
由表3可知：5种配置模式的有效拦蓄量处于1.17 ~ 1.46 t/hm2之间，其大小排序为樟子松柠条混交林 > 樟子松纯林 > 樟子松落叶松柠条混交林 > 落叶松柠条混交林 > 落叶松纯林。在半分解层中落叶松柠条混交林的枯落物有效拦蓄量最大，落叶松纯林的最小；在未分解层中樟子松柠条混交林的枯落物有效拦蓄量最大，落叶松纯林的最小。方差分析表明5种配置模式的有效拦蓄量在半分解层和未分解层的显著性与最大持水量一致，即5种配置模式在半分解层之间无显著差异，在未分解层樟子松纯林，落叶松柠条混交林和樟子松落叶松 柠条混交林之间无显著差异，但他们与另外两种配置模式之间差异显著（P < 0.05）。
3.2.3 枯落物持水过程
5种配置模式的枯落物半分解层和未分解层的持水过程变化趋势总体上大致相同（图2），在2 h内都快速上升，2 h后上升的速率逐渐减小，直到8 h后逐渐趋于稳定，在浸泡12 h时都已趋于饱和状态。对不同配置模式枯落物持水量和浸水时间进行统计分析，得出5种林分的关系式为（表4）Q = alnt + b，式中：Q为枯落物持水量（g/kg），t为枯落物浸水时间（h），a、b为方程系数[18]。
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图 2 5种配置模式枯落物持水过程Figure 2. Water-holding process of litter in five configuration models表 4 不同配置模式枯落物层持水量、吸水速率与浸水时间的关系式Table 4. Relationship between water holding capacity, water absorption rate and immersion time ofdifferent configuration models in litter layers林分类型
Stand type枯落物层
Litter layer持水过程
Water holding procedure吸水过程
Water absorption procedure回归方程
Regression equationR2 回归方程
Regression equationR2 Ⅰ 半分解层 Semi-decomposed layer Q = 138.28lnt + 1 087.4 0.989 4 V = 5 632.4t−1.822 0.951 1 Ⅱ Q = 137.38lnt + 883.7 0.964 1 V = 4 335.3t−1.770 0.947 1 Ⅲ Q = 96.27lnt + 1 310.4 0.980 1 V = 5 740.4t−1.758 0.944 5 Ⅳ Q = 121.49lnt + 1 051.0 0.979 1 V = 1 379.5t−0.935 0.999 7 Ⅴ Q = 191.92lnt + 1 185.7 0.951 3 V = 5 560.5t−1.837 0.948 5 Ⅰ 未分解层 Undecomposed layer Q = 133.70lnt + 1 100.7 0.976 6 V = 5 780.1t−1.829 0.948 7 Ⅱ Q = 107.70lnt + 745.9 0.971 9 V = 3 714.6t−1.786 0.946 0 Ⅲ Q = 88.59lnt + 920.2 0.958 7 V = 6 793.0t−1.840 0.953 6 Ⅳ Q = 86.81lnt + 824.8 0.977 2 V = 5 102.8t−1.887 0.957 3 Ⅴ Q = 153.35lnt + 1 286.5 0.970 5 V = 4 444.9t−1.862 0.951 9 3.2.4 枯落物吸水过程
由图3可知：5种配置模式的枯落物吸水速率变化规律基本一致，在2 h内最大且快速下降，2 h后吸水速率逐渐减慢，到6 h后逐渐趋于稳定，24 h时已经接近零。对不同配置模式枯落物吸水速率和浸水时间进行统计分析，得出5种配置模式的关系式为（表4）V = mtn，式中：V为枯落物吸水速率，g/（kg·h）；t为枯落物浸水时间，h；m为方程系数；n为指数[19]。
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图 3 枯落物层吸水速率与浸水时间的关系Figure 3. Relationship between water absorption rate and immersion time in litter layer3.3 土壤水文物理性质
3.3.1 土壤物理性质及持水能力
由表5可知：5种配置模式的土壤密度大小排序为落叶松纯林 > 樟子松纯林 > 落叶松柠条混交林 > 樟子松柠条混交林 > 樟子松落叶松柠条混交林。5种配置模式的土壤总孔隙度大小排序为：樟子松落叶松柠条混交林 > 樟子松柠条混交林 > 落叶松柠条混交林 > 樟子松纯林 > 落叶松纯林。樟子松落叶松柠条混交林的非毛管孔隙度和毛管孔隙度都最大，分别为7.93%和33.16%，落叶松纯林的非毛管孔隙度和毛管孔隙度都最小，分别为3.25%和28.19%。
表 5 不同配置模式土壤层的土壤持水及物理性质Table 5. Soil water holding capacity and physical properties of soil layers in different configuration models林分类型
Stand type土壤密度
Soil density/
（g·cm−3）非毛管孔隙度
Non-capillary porosity/%毛管孔隙度
Capillary porosity/%总孔隙度
Total porosity/%最大持水量/（t·hm−2）
Maximum water holding capacity/(t·ha−1)毛管持水量/（t·hm−2）
Capillary water holding capacity/(t·ha−1)有效持水量/（t·hm−2）
Effective water holding capacity/（t·ha−1）Ⅰ 1.62 ± 0.07a 3.25 ± 0.26b 28.19 ± 0.46b 31.44 ± 0.63b 516.21 ± 6.04b 468.45 ± 2.54b 47.76 ± 4.60b Ⅱ 1.56 ± 0.12ab 3.58 ± 0.52b 28.33 ± 1.28b 31.91 ± 1.80b 529.40 ± 13.21ab 477.28 ± 13.79ab 52.12 ± 2.88b Ⅲ 1.44 ± 0.02bc 5.14 ± 0.28ab 28.51 ± 0.87b 34.11 ± 1.51ab 550.89 ± 20.75ab 474.45 ± 18.70ab 76.44 ± 2.05a Ⅳ 1.42 ± 0.02bc 5.60 ± 0.75ab 29.27 ± 0.54ab 34.42 ± 0.33ab 557.10 ± 7.57ab 475.87 ± 3.43ab 81.23 ± 4.18a Ⅴ 1.20 ± 0.03c 7.93 ± 0.38a 33.16 ± 0.44a 41.09 ± 0.63a 645.36 ± 10.07a 558.57 ± 6.99a 86.79 ± 6.10a 5种配置模式土壤的最大持水量处于516.21 ~ 645.36 t/hm2，其大小排序为：樟子松落叶松柠条混交林 > 樟子松柠条混交林 > 落叶松柠条混交林 > 樟子松纯林 > 落叶松纯林，这与土壤总孔隙度、毛管孔隙度和非毛管孔隙度的变化规律一致。林地内有效持水量处于47.76 ~ 86.79 t/hm2之间，其中樟子松落叶松柠条混交林的有效持水量最大，是落叶松纯林的有效持水量的1.82倍。
方差分析表明：整体上，樟子松落叶松柠条混交林和2种纯林配置模式的土壤水文物理性质差异显著（P < 0.05），混交配置模式表现为樟子松落叶松柠条混交林和其他2种混交林配置模式的土壤水文物理性质无显著差异。
3.3.2 土壤入渗
由表6可知：5种配置模式的初渗速率和稳渗速率分别处于9.60 ~ 16.57 mm/min和2.10 ~ 5.34 mm/min之间。5种配置模式初渗速率和稳渗速率的大小排序都为：樟子松落叶松柠条混交林（Ⅴ） > 樟子松柠条混交林（Ⅳ） > 落叶松柠条混交林（Ⅲ） > 樟子松纯林（Ⅱ） > 落叶松纯林（Ⅰ）。樟子松落叶松柠条混交林初渗速率和稳渗速率分别是落叶松纯林初渗速率和稳渗速率的1.73倍和2.54倍。方差分析表明2种纯林和3种混交林配置模式的初渗速率差异显著（P < 0.05），且3种混交林配置模式的初渗速率之间差异显著（P < 0.05），落叶松纯林的稳渗速率与3种混交林配置模式都存在显著性差异（P < 0.05）。对土壤入渗的时间和速率进行拟合，得出二者符合幂函数关系：y = at−b，式中：y为入渗速率（mm/min）；a、b都为方程系数；t为入渗时间（min）。
表 6 不同配置模式的土壤渗透速率及模型Table 6. Soil infiltration rate and model of different configuration models林分类型
Stand type初渗速率
Initial infiltration rate/（mm·min−1）稳渗速率
Steady infiltration rate/（mm·min−1）回归方程
Regression equationR2 Ⅰ 9.60 ± 0.28d 2.10 ± 0.26c y = 8.97t−0.47 0.932 9 Ⅱ 10.04 ± 0.34d 3.39 ± 0.33bc y = 9.82t−0.31 0.929 1 Ⅲ 11.71 ± 0.36c 3.54 ± 0.31b y = 10.43t−0.35 0.955 3 Ⅳ 13.91 ± 0.42b 4.61 ± 0.39ab y = 10.65t−0.26 0.919 8 Ⅴ 16.57 ± 0.38a 5.34 ± 0.35a y = 11.65t−0.24 0.865 8 4. 讨 论
4.1 不同配置模式下枯落物的蓄积量
枯落物储量的大小受树种组成、林龄、水热环境、凋落量、分解速率、地表累积时间等要素的综合影响[20-21]。本研究中，5种配置模式的枯落物厚度和蓄积量各不相同，樟子松纯林由于自身结构较优且适应能力强，其厚度和蓄积量均为最大（表2）；5种配置模式下，枯落物的半分解层厚度和蓄积量均小于未分解层，这可能是由于所有树种都处于幼龄林阶段且分解时间较短，加上研究区总体降水量较低（401.6 mm/a），平均气温偏冷（3.5 ℃），且处于高寒半干旱地区，特定的地理气候环境导致枯落物累积和分解速率均较慢，这也与公博等[12]的研究结果一致。
4.2 不同配置模式下枯落物层的水文特征
枯落物的持水能力受树种、枯落物组成、蓄积量和分解速率等多重影响[22]。本研究中，5种配置模式的枯落物最大持水量、最大持水率以及有效拦蓄量的变化规律并不一致，这与枯落物的生物量及其自身结构有关[9]。总体而言，不论乔灌混交林还是纯林，樟子松林的枯落物持水量均偏大（表5），这可能是由于同龄樟子松本身适应能力强，生长状况更好，加之同龄樟子松叶片轮廓均大于落叶松，使得樟子松的外型与枯落物厚度较之同龄的落叶松均更大；另外一个可能原因是，樟子松林地的林分密度较高（表1），受林内环境因素的影响，樟子松林地的持水量均普遍较大。尽管枯落物持水量和吸水速率都与浸水时间呈现出较好的函数关系，但樟子松和落叶松林地持水和吸水变化规律与邓继峰等[23]、孙拥康等[24]的研究结果不同，这种差异可能与树种的生长阶段不同直接相关，也可能是区域、树种生物学特性等差异的影响所导致。
4.3 不同配置模式下土壤层的水文特征
由于不同森林植被类型生态学特性的差异，同一区域相同生境内，不同配置模式的土壤层蓄水渗透性能也表现出差异[6]。本研究中，5种配置模式的土壤水文物理性质变化规律一致，即3种混交配置模式的土壤物理性质和入渗性能均优于纯林，其中，樟子松落叶松柠条混交林在5种配置模式中均为最优，可能是由于樟子松 落叶松柠条混交林相对于其他4种配置，其树种组成，土壤层的腐殖物质以及根系生物量更为复杂多样（本研究测得樟子松落叶松柠条混交林的平均根质量密度最大，为2.13 kg/m3，落叶松纯林的平均根质量密度最小，为1.93 kg/m3）。腐殖物质和根系对土壤的改良调控作用导致土壤理化性质和入渗性能的变化显著。一方面，说明与柠条混交的配置模式对于林地土壤密度、孔隙度等土壤物理性质的提升显著，另一方面，说明樟子松和落叶松的混交配置模式产生的枯落物及其分解过程对林地蓄水能力提升有一定改善，三者的相互耦合，使得林地土壤的蓄水能力得到显著提高，从而为混交林地中不同植被的生长生存提供了更有利的土壤水文条件。这也与廖军[25]、公博等[12]的研究结论一致，不同配置模式的土壤水文物理性质存在一定的差异，混交林配置模式由于树种、枯落物组成以及根系分布更加复杂，比人工纯林有更强的水源涵养能力，可以有效地延缓地表径流的产生，减少水土流失，改善生态质量[26]。
5. 结 论
综合来看，樟子松落叶松柠条混交林的枯落物层和土壤层的水文性能都高于其他林地，即该人工林的水源涵养能力最强，足以说明树种选择和配置方式在人工林重建和恢复过程中的重要性，可以作为该地区首选的植被恢复模式。恢复方式对于新营造的森林结构和功能至关重要[27]，然而当前研究区人工林经营和抚育作业开展仍较少，大多以单次人工林营造为主，并且仍存在林分结构单一、不合理的现象。目前的恢复方式侧重于树种组成、结构、自然栖息地、生态系统过程和服务的恢复[28]。人工造林对生态系统服务功能的构建不仅与造林区域的气候与土壤有关[29]，也与造林树种、造林密度和经营管理措施的差异有关[30]。森林结构的复杂性在调节森林生态系统功能方面起着至关重要的作用，并强烈影响生物多样性[31]。林分空间结构是林分特征的重要研究内容之一，而林分结构对森林功能的发挥有重要影响作用[32]，混交林的林分结构相较于纯林更为复杂，物种更加丰富多样。因此，迫切需要加强人工林经营管理，根据不同的环境条件，充分考虑树种和配置模式，通过结构化森林经营技术[33-34]，实施针叶纯林改造技术，改善林分状态，提高林分稳定性。
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图 1 20 m × 20 m（a）和 40 m × 40 m（b）空间尺度上土壤理化性质重要性排序
TK. 全钾；AN. 速效氮；TN. 全氮；pH. 土壤pH值；OM. 有机质含量；TP. 全氮；AP. 速效磷；AK. 速效钾。下同。TK, total potassium; AN, available nitrogen; TN, total nitrogen; pH, soil pH value; OM, organic matter content; TP, total nitrogen; AP, available phosphorus; AK, available potassium. The same below.
Figure 1. Importance ranking of soil physicochemical properties at spatial scale of 20 m × 20 m (a) and 40 m × 40 m (b)
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图 2 20 m × 20 m空间尺度上结构方程模型
灰色和黑色箭头分别代表负效应和正效应，实心箭头代表显著路径，虚线箭头代表非显著路径。线条的粗细反映了标准化预测系数的大小。下同。Gray and black arrows represent negative and positive effects, respectively, solid arrows represent significant paths, and dashed arrows represent non-significant paths. Thickness of lines reflects the size of standardized prediction coefficients. The same below.
Figure 2. Structural equation models at 20 m × 20 m spatial scale
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图 3 40 m × 40 m空间尺度上结构方程模型
Figure 3. Structural equation model at 40 m × 40 m spatial scale
表 1 物种多样性和结构多样性各指数计算公式
Table 1 Calculation formulas for each index of species diversity and structural diversity
多样性 Diversity 指数 Index 计算公式 Formula 物种多样性
Species diversity物种丰富度
Species richness (S)S=Ns 物种Shannon-Wiener指数
Species Shannon-Wiener index (HS)Hs=−Ns∑i=1niN×lnniN 物种均匀度
Species evenness (ES)Es=Hs/ln Ns 物种Simpson指数
Species Simpson index (DS)Ds=1−NS∑i=1(niN)2 结构多样性
Structural diversity胸径Shannon-Wiener指数
DBH Shannon-Wiener index (Hd)Hd=−Nd∑j=1njN×lnnjN 胸径均匀度
DBH evenness (Ed)Ed=Hd/ln Nd 胸径Simpson指数
DBH Simpson index (Dd)Dd=1−Nd∑j=1(njN)2 胸径变异系数
Coefficient of variation of DBH (Dvar)Dvar=√1N(Dk−μ)2μ×100% 注：NS.样方内总物种数；N.样方内的总个体数；ni.样方内第i个物种的数量；nj.样方内第j个胸径等级的数量；Nd.样方内胸径等级总数；Dk.样方内第k个个体的胸径值；μ.样方内所有个体的胸径平均值。Notes: NS, total number of species in a quadrat; N, total number of individuals in a quadrat; ni, number of the ith species in the quadrat; nj, number of the jth DBH grade in the quadrat; Nd, total number of DBH grades in the quadrat; DBHk, DBH value of the kth individual in the quadrat; μ, average DBH of all individuals in the quadrat. 
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表 2 不同空间尺度样方环境因子数据
Table 2 Environmental factor data from different spatial scales of sample plots
环境因子
Environmental factor20 m × 20 m 40 m × 40 m 均值
Mean范围
Range标准差
Standard deviation均值
Mean范围
Range标准差
Standard deviation土壤pH Soil pH 6.07 5.31 ~ 6.87 0.20 6.07 5.31 ~ 6.87 0.32 土壤全氮
Soil total nitrogen/(g·kg−1)1.31 0.53 ~ 2.70 0.17 1.31 0.53 ~ 2.70 0.29 土壤全磷
Soil total phosphorus/(g·kg−1)1.24 0.77 ~ 1.74 0.12 1.24 0.77 ~ 1.74 0.20 土壤全钾
Soil total potassium/(g·kg−1)58.38 23.78 ~ 114.41 11.60 58.49 23.78 ~ 114.41 17.07 土壤速效氮
Soil available nitrogen/(mg·kg−1)569.24 49.99 ~ 961.57 87.80 563.67 49.98 ~ 961.58 144.73 土壤速效磷
Soil available phosphorus/(μg·g−1)36.80 20.83 ~ 54.11 4.74 36.87 20.83 ~ 54.11 6.94 土壤速效钾
Soil available potassium/(μg·g−1)507.74 236.37 ~ 1 060.28 84.45 512.95 236.35 ~ 1 060.29 141.60 土壤有机质
Soil organic matter/(mg·g−1)117.87 41.95 ~ 236.88 27.68 118.06 41.94 ~ 236.88 38.97 海拔 Elevation/m 665.32 581.46 ~ 780.39 51.17 662.52 580.06 ~ 776.94 51.96 坡度 Slope/(°) 19.91 5.56 ~ 40.86 6.45 16.52 5.45 ~ 26.40 4.35 坡向 Aspect/(°) 190.75 72.81 ~ 299.93 35.06 231.57 0.18 ~ 359.85 151.28 凹凸度Convexity degree 0.05 −4.36 ~ 5.71 1.41 0.12 −16.40 ~ 10.82 3.74
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表 3 物种多样性和结构多样性与生物量稳定性线性回归模型评价
Table 3 Evaluation of linear regression models for species diversity, structural diversity and biomass stability
解释变量
Explanatory variable20 m × 20 m 40 m × 40 m a1 a2 AIC a3 a4 AIC S + Hd 0.054 −0.173 116.013 −0.116 −0.102 97.965 Hs + Hd −0.304 0.153 123.193 −3.326* 0.998 105.945 Es + Hd −2.558 0.141 135.139 −8.093* 0.509 99.045 Ds + Hd −1.676 0.206 131.031 −12.827** 0.691 103.339 S + Ed 0.034 −3.593 116.428 −0.121 −4.565 90.188 Hs + Ed −0.165 −3.612 125.472 −2.934* 0.082 97.426 Es + Ed −2.145 −2.016 122.134 −7.675 0.181 91.882 Ds + Ed −1.193 −3.181 128.699 −12.074* 0.059 96.008 S + Dd 0.053 −2.036 108.747 −0.112 −4.543 96.433 Hs + Dd −0.254 −0.121 111.810 −3.094* 5.004 103.938 Es + Dd −2.577 0.894 120.351 −7.807* 2.113 97.280 Ds + Dd −1.587 0.611 117.907 −12.337* 2.949 101.759 S + Dvar 0.037 3.119*** 104.015 −0.079 3.570* 89.060 Hs + Dvar 0.134 3.163*** 111.159 −2.143 2.774 96.282 Es + Dvar −0.578 3.053*** 115.869 −5.400 3.006 89.630 Ds + Dvar −0.159 3.113*** 116.664 −9.577 2.759 94.836 注：a1和a2、a3和a4分别为20 m × 20 m、40 m × 40 m尺度上构建的回归模型中物种多样性指数和结构多样性指数所对应的解释变量系数。*、**、***分别表示在P < 0.05、P < 0.01和P < 0.001的水平上显著。下同。Notes: a1, a2 as well as a3 and a4 are coefficients of explanatory variables corresponding to the species diversity index and structural diversity index in the regression model constructed under 20 m × 20 m scale and 40 m × 40 m scale, respectively, *, **, *** indicate significance at the levels of P < 0.05, P < 0.01, and P < 0.001, respectively. The same below.
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表 4 20 m × 20 m空间尺度上生物因素和非生物因素对稳定性的标准化影响效应
Table 4 Standardized effects of biotic and abiotic factors on stability at 20 m × 20 m spatial scale
影响因子
Influencing factor直接效应
Direct effect间接效应
Indirect effect总效应
Total effect土壤 Soil 0 −0.002 −0.002 地形 Topography 0 0.164 0.164 林分密度 Stand density 0.229 −0.179 0.050 物种多样性
Species diversity−0.053 −0.008 −0.061 结构多样性
Structural diversity0.148 0.010 0.158 物种异步性
Species asynchrony0.398 0 0.398
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表 5 40 m × 40 m空间尺度上生物因素和非生物因素对生物量稳定性的标准化影响效应
Table 5 Standardized effects of biotic and abiotic factors on biomass stability at a spatial scale of 40 m × 40 m
影响因子
Influencing factor直接效应
Direct effect间接效应
Indirect effect总效应
Total effect土壤 Soil −0.518 0.016 −0.502 地形 Topography 0 0.247 0.247 林分密度 Stand density 0.122 −0.180 −0.058 物种多样性
Species diversity0.008 −0.022 −0.014 结构多样性
Structural diversity0.119 −0.010 0.109 物种异步性
Species asynchrony0.637 0 0.637
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[1] Ferreira J, Lennox G D, Gardner T A, et al. Carbon-focused conservation may fail to protect the most biodiverse tropical forests[J]. Nature Climate Change, 2018, 8(8): 744−749. doi: 10.1038/s41558-018-0225-7
[2] Allan E, Manning P, Alt F, et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition[J]. Ecology Letters, 2015, 18(8): 834−843. doi: 10.1111/ele.12469
[3] García-Palacios P, Gross N, Gaitán J, et al. Climate mediates the biodiversity–ecosystem stability relationship globally[J]. Proceedings of the National Academy of Sciences, 2018, 115(33): 8400−8405. doi: 10.1073/pnas.1800425115
[4] Schnabel F, Liu X, Kunz M, et al. Species richness stabilizes productivity via asynchrony and drought-tolerance diversity in a large-scale tree biodiversity experiment[J]. Science Advances, 2021, 7(51): eabk1643. doi: 10.1126/sciadv.abk1643
[5] Hautier Y, Tilman D, Isbell F, et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity[J]. Science, 2015, 348: 336−340. doi: 10.1126/science.aaa1788
[6] Bai Y, Han X, Wu J, et al. Ecosystem stability and compensatory effects in the Inner Mongolia grassland[J]. Nature, 2004, 431: 181−184. doi: 10.1038/nature02850
[7] Xu Z, Ren H, Li M H, et al. Environmental changes drive the temporal stability of semi-arid natural grasslands through altering species asynchrony[J]. Journal of Ecology, 2015, 103(5): 1308−1316. doi: 10.1111/1365-2745.12441
[8] Hillebrand H, Bennett D M, Cadotte M W. Consequences of dominance: a review of evenness effects on local and regional ecosystem processes[J]. Ecology, 2008, 89(6): 1510−1520. doi: 10.1890/07-1053.1
[9] Albrich K, Rammer W, Thom D, et al. Trade-offs between temporal stability and level of forest ecosystem services provisioning under climate change[J]. Ecological Applications, 2018, 28(7): 1884−1896. doi: 10.1002/eap.1785
[10] White L, O’Connor N E, Yang Q, et al. Individual species provide multifaceted contributions to the stability of ecosystems[J]. Nature Ecology & Evolution, 2020, 4(12): 1594−1601.
[11] Hautier Y, Seabloom E W, Borer E T, et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands[J]. Nature, 2014, 508: 521−525. doi: 10.1038/nature13014
[12] Isbell F, Craven D, Connolly J, et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes[J]. Nature, 2015, 526: 574−577. doi: 10.1038/nature15374
[13] Tilman D, Downing J A. Biodiversity and stability in grasslands[J]. Nature, 1994, 367: 363−365. doi: 10.1038/367363a0
[14] Craven D, Eisenhauer N, Pearse W D, et al. Multiple facets of biodiversity drive the diversity–stability relationship[J]. Nature Ecology & Evolution, 2018, 2(10): 1579−1587.
[15] Mazzochini G G, Fonseca C R, Costa G C, et al. Plant phylogenetic diversity stabilizes large-scale ecosystem productivity[J]. Global Ecology and Biogeography, 2019, 28(10): 1430−1439. doi: 10.1111/geb.12963
[16] Hector A, Hautier Y, Saner P, et al. General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding[J]. Ecology, 2010, 91(8): 2213−2220. doi: 10.1890/09-1162.1
[17] Loreau M, de Mazancourt C. Biodiversity and ecosystem stability: a synthesis of underlying mechanisms[J]. Ecology Letters, 2013, 16: 106−115. doi: 10.1111/ele.12073
[18] Loreau M, de Mazancourt C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments[J]. The American Naturalist, 2008, 172(2): E48−E66. doi: 10.1086/589746
[19] Loreau M, Hector A. Partitioning selection and complementarity in biodiversity experiments[J]. Nature, 2001, 412: 72−76. doi: 10.1038/35083573
[20] Lehman C L, Tilman D. Biodiversity, stability, and productivity in competitive communities[J]. The American Naturalist, 2000, 156(5): 534−552. doi: 10.1086/303402
[21] van der Plas F. Biodiversity and ecosystem functioning in naturally assembled communities[J]. Biological Reviews, 2019, 94(4): 1220−1245.
[22] Satdichanh M, Ma H, Yan K, et al. Phylogenetic diversity correlated with above-ground biomass production during forest succession: Evidence from tropical forests in Southeast Asia[J]. Journal of Ecology, 2019, 107(3): 1419−1432. doi: 10.1111/1365-2745.13112
[23] Poorter L, van der Sande M T, Arets E J M M, et al. Biodiversity and climate determine the functioning of Neotropical forests[J]. Global Ecology and Biogeography, 2017, 26(12): 1423−1434. doi: 10.1111/geb.12668
[24] Forrester D I , Bauhus J . A review of processes behind diversity: productivity relationships in forests[J]. Current Forestry Reports, 2016, 2(1): 45−61.
[25] Zhang Y, Chen H Y H. Individual size inequality links forest diversity and above-ground biomass[J]. Journal of Ecology, 2015, 103(5): 1245−1252. doi: 10.1111/1365-2745.12425
[26] Vilà M, Vayreda J, Gracia C, et al. Does tree diversity increase wood production in pine forests?[J]. Oecologia, 2003, 135(2): 299−303. doi: 10.1007/s00442-003-1182-y
[27] Yuan Z, Ali A, Wang S, et al. Abiotic and biotic determinants of coarse woody productivity in temperate mixed forests[J]. Science of the Total Environment, 2018, 630: 422−431. doi: 10.1016/j.scitotenv.2018.02.125
[28] Ali A, Lin S L, He J K, et al. Climate and soils determine aboveground biomass indirectly via species diversity and stand structural complexity in tropical forests[J]. Forest Ecology and Management, 2019, 432: 823−831. doi: 10.1016/j.foreco.2018.10.024
[29] Lei X, Wang W, Peng C. Relationships between stand growth and structural diversity in spruce-dominated forests in New Brunswick, Canada[J]. Canadian Journal of Forest Research, 2009, 39(10): 1835−1847. doi: 10.1139/X09-089
[30] Weiner J, Solbrig O T. The meaning and measurement of size hierarchies in plant populations[J]. Oecologia, 1984, 61(3): 334−336. doi: 10.1007/BF00379630
[31] Schnabel F, Schwarz J A, Dănescu A, et al. Drivers of productivity and its temporal stability in a tropical tree diversity experiment[J]. Global Change Biology, 2019, 25(12): 4257−4272. doi: 10.1111/gcb.14792
[32] Soares A A V, Leite H G, Souza A L, et al. Increasing stand structural heterogeneity reduces productivity in Brazilian Eucalyptus monoclonal stands[J]. Forest Ecology and Management, 2016, 373: 26−32. doi: 10.1016/j.foreco.2016.04.035
[33] Jucker T, Sanchez A C, Lindsell J A, et al. Drivers of aboveground wood production in a lowland tropical forest of West Africa: teasing apart the roles of tree density, tree diversity, soil phosphorus, and historical logging[J]. Ecology and Evolution, 2016, 6(12): 4004−4017. doi: 10.1002/ece3.2175
[34] Ouyang S, Xiang W, Wang X, et al. Effects of stand age, richness and density on productivity in subtropical forests in China[J]. Journal of Ecology, 2019, 107(5): 2266−2277. doi: 10.1111/1365-2745.13194
[35] Forrester D I, Ammer C, Annighöfer P J, et al. Effects of crown architecture and stand structure on light absorption in mixed and monospecific Fagus sylvatica and Pinus sylvestris forests along a productivity and climate gradient through Europe[J]. Journal of Ecology, 2018, 106(2): 746−760. doi: 10.1111/1365-2745.12803
[36] Morin X. Species richness promotes canopy packing: a promising step towards a better understanding of the mechanisms driving the diversity effects on forest functioning[J]. Functional Ecology, 2015, 29(8): 993−994.
[37] Barry K E, Mommer L, van Ruijven J, et al. The future of complementarity: disentangling causes from consequences[J]. Trends in Ecology & Evolution, 2019, 34(2): 167−180.
[38] Ryan M G, Stape J L, Binkley D, et al. Factors controlling Eucalyptus productivity: how water availability and stand structure alter production and carbon allocation[J]. Forest Ecology and Management, 2010, 259(9): 1695−1703. doi: 10.1016/j.foreco.2010.01.013
[39] Jucker T, Avăcăriței D, Bărnoaiea I, et al. Climate modulates the effects of tree diversity on forest productivity[J]. Journal of Ecology, 2016, 104(2): 388−398. doi: 10.1111/1365-2745.12522
[40] Conradi T, van Meerbeek K, Ordonez A, et al. Biogeographic historical legacies in the net primary productivity of Northern Hemisphere forests[J]. Ecology Letters, 2020, 23(5): 800−810. doi: 10.1111/ele.13481
[41] Callaway R M, Brooker R W, Choler P, et al. Positive interactions among alpine plants increase with stress[J]. Nature, 2002, 417: 844−848. doi: 10.1038/nature00812
[42] Paquette A, Messier C. The effect of biodiversity on tree productivity: from temperate to boreal forests[J]. Global Ecology and Biogeography, 2011, 20(1): 170−180. doi: 10.1111/j.1466-8238.2010.00592.x
[43] Guo Y, Wang B, Mallik A U, et al. Topographic species–habitat associations of tree species in a heterogeneous tropical karst seasonal rain forest, China[J]. Journal of Plant Ecology, 2017, 10(3): 450−460.
[44] Bohlman S A, Laurance W F, Laurance S G, et al. Importance of soils, topography and geographic distance in structuring central Amazonian tree communities[J]. Journal of Vegetation Science, 2008, 19(6): 863−874. doi: 10.3170/2008-8-18463
[45] Guo Q, Ren H. Productivity as related to diversity and age in planted versus natural forests[J]. Global Ecology and Biogeography, 2014, 23(12): 1461−1471. doi: 10.1111/geb.12238
[46] Luizão R C C, Luizão F J, Paiva R Q, et al. Variation of carbon and nitrogen cycling processes along a topographic gradient in a central Amazonian forest[J]. Global Change Biology, 2004, 10(5): 592−600. doi: 10.1111/j.1529-8817.2003.00757.x
[47] Takyu M, Aiba S I, Kitayama K. Effects of topography on tropical lower montane forests under different geological conditions on Mount Kinabalu, Borneo[J]. Plant Ecology, 2002, 159(1): 35−49. doi: 10.1023/A:1015512400074
[48] Xu Y, Franklin S B, Wang Q, et al. Topographic and biotic factors determine forest biomass spatial distribution in a subtropical mountain moist forest[J]. Forest Ecology and Management, 2015, 357: 95−103. doi: 10.1016/j.foreco.2015.08.010
[49] 王媚臻, 毕浩杰, 金锁, 等. 林分密度对云顶山柏木人工林林下物种多样性和土壤理化性质的影响[J]. 生态学报, 2019, 39(3): 981−988. Wang M Z, Bi H J, Jin S, et al. Effect of stand density on understory species diversity and soil physicochemical properties of a Cupressus funebris plantation in Yunding Mountain[J]. Acta Ecologica Sinica, 2019, 39(3): 981−988.
[50] Kabzems R, Haeussler S. Soil properties, aspen, and white spruce responses 5 years after organic matter removal and compaction treatments[J]. Canadian Journal of Forest Research, 2005, 35(8): 2045−2055. doi: 10.1139/x05-175
[51] Xiang W, Zhou J, Ouyang S, et al. Species-specific and general allometric equations for estimating tree biomass components of subtropical forests in southern China[J]. European Journal of Forest Research, 2016, 135(5): 963−979. doi: 10.1007/s10342-016-0987-2
[52] Quesada C A, Phillips O L, Schwarz M, et al. Basin-wide variations in Amazon forest structure and function are mediated by both soils and climate[J]. Biogeosciences, 2012, 9(6): 2203−2246. doi: 10.5194/bg-9-2203-2012
[53] Russo S E, Davies S J, King D A, et al. Soil-related performance variation and distributions of tree species in a Bornean rain forest[J]. Journal of Ecology, 2005, 93(5): 879−889. doi: 10.1111/j.1365-2745.2005.01030.x
[54] He H, Zhang C, Zhao X, et al. Allometric biomass equations for 12 tree species in coniferous and broadleaved mixed forests, Northeastern China[J]. PLoS ONE, 2018, 13(1): e0186226. doi: 10.1371/journal.pone.0186226
[55] 谭凌照, 范春雨, 范秀华. 吉林蛟河阔叶红松林木本植物物种多样性及群落结构与生产力的关系[J]. 植物生态学报, 2017, 41(11): 1149−1156. doi: 10.17521/cjpe.2016.0321 Tan L Z, Fan C Y, Fan X H. Relationships between species diversity or community structure and productivity of woody-plants in a broad-leaved Korean pine forest in Jiaohe, Jilin, China[J]. Chinese Journal of Plant Ecology, 2017, 41(11): 1149−1156. doi: 10.17521/cjpe.2016.0321
[56] 刘家畅, 唐斌, 邹源. 应用GIS分析影响森林火灾发生的因子和时空分布特征: 以美国加利福尼亚州为例[J]. 东北林业大学学报, 2020, 48(7): 70–74. Liu J C, Tang B, Zou Y. GIS-based spatial and temporal distribution characteristics and factor analysis of forest fires-taking California, USA as an example [J]. Journal of Northeast Forestry University, 2020, 48(7): 70–74.
[57] Lohbeck M, Poorter L, Martínez-Ramos M, et al. Biomass is the main driver of changes in ecosystem process rates during tropical forest succession[J]. Ecology, 2015, 96(5): 1242−1252.
[58] del Río M, Pretzsch H, Ruíz-Peinado R, et al. Species interactions increase the temporal stability of community productivity in Pinus sylvestris–Fagus sylvatica mixtures across Europe[J]. Journal of Ecology, 2017, 105(4): 1032−1043.
[59] Sasaki T, Lauenroth W K. Dominant species, rather than diversity, regulates temporal stability of plant communities[J]. Oecologia, 2011, 166(3): 761−768.
[60] Jucker T, Bouriaud O, Avacaritei D, et al. Stabilizing effects of diversity on aboveground wood production in forest ecosystems: linking patterns and processes[J]. Ecology Letters, 2014, 17(12): 1560−1569.
[61] Dănescu A, Albrecht A T, Bauhus J. Structural diversity promotes productivity of mixed, uneven-aged forests in southwestern Germany[J]. Oecologia, 2016, 182(2): 319−333. doi: 10.1007/s00442-016-3623-4
[62] Forrester D I, Rodenfels P, Haase J, et al. Tree-species interactions increase light absorption and growth in Chinese subtropical mixed-species plantations[J]. Oecologia, 2019, 191(2): 421−432. doi: 10.1007/s00442-019-04495-w
[63] Bohn F J, May F, Huth A. Species composition and forest structure explain the temperature sensitivity patterns of productivity in temperate forests[J]. Biogeosciences, 2018, 15(6): 1795−1813. doi: 10.5194/bg-15-1795-2018
[64] Ali A, Lin S L, He J K, et al. Big-sized trees overrule remaining trees’ attributes and species richness as determinants of aboveground biomass in tropical forests[J]. Global Change Biology, 2019, 25(8): 2810−2824. doi: 10.1111/gcb.14707
[65] van der Sande M T, Peña-Claros M, Ascarrunz N, et al. Abiotic and biotic drivers of biomass change in a Neotropical forest[J]. Journal of Ecology, 2017, 105(5): 1223−1234. doi: 10.1111/1365-2745.12756
[66] Belote R T. Species-rich national forests experience more intense human modification, but why?[J]. Forests, 2018, 9(12): 753. doi: 10.3390/f9120753
[67] Nüchel J, Bøcher P K, Svenning J C. Topographic slope steepness and anthropogenic pressure interact to shape the distribution of tree cover in China[J]. Applied Geography, 2019, 103: 40−55. doi: 10.1016/j.apgeog.2018.12.008
[68] Ouyang S, Xiang W, Gou M, et al. Stability in subtropical forests: The role of tree species diversity, stand structure, environmental and socio-economic conditions[J]. Global Ecology and Biogeography, 2021, 30(2): 500−513. doi: 10.1111/geb.13235
[69] Prado J A, Schiavini I, Vale V S, et al. Conservative species drive biomass productivity in tropical dry forests[J]. Journal of Ecology, 2016, 104(3): 817−827. doi: 10.1111/1365-2745.12543
[70] Gonzalez A, Germain R M, Srivastava D S, et al. Scaling-up biodiversity-ecosystem functioning research[J]. Ecology Letters, 2020, 23(4): 757−776. doi: 10.1111/ele.13456
[71] Dolezal J, Fibich P, Altman J, et al. Determinants of ecosystem stability in a diverse temperate forest[J]. Oikos, 2020, 129(11): 1692−1703. doi: 10.1111/oik.07379
[72] Forrester D I, Kohnle U, Albrecht A T, et al. Complementarity in mixed-species stands of Abies alba and Picea abies varies with climate, site quality and stand density[J]. Forest Ecology and Management, 2013, 304: 233−242. doi: 10.1016/j.foreco.2013.04.038
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