林分、土壤及空间因子对谷地云冷杉林叶面积指数空间异质性的影响

叶面积指数(LAI)的空间异质性对研究植物的生长状况、分布格局及其对气候变化的响应机制至关重要,然而关于不同因素对解释LAI空间变异相对贡献率的报道尚少。该研究依托小兴安岭9.12 hm~2 (380 m×240 m)谷地云冷杉林固定样地,采用LAI-2200植物冠层分析仪测定了228个小样方(20m×20m)的LAI,基于地统计学方法分析了LAI的空间异质性;测定了每个小样方的28个林分因子和10个土壤因子,利用主轴邻距法(PCNM)量化了空间因子,并采用方差分解的方法解析了林分、土壤、空间因子及其相互作用对LAI空间变异的相对贡献率。结果表明:LAI在37 m尺度内具有强烈的空间自相关,且在不同方向上LAI呈现相异的空间格局;3种因子及其相互作用共同解释了LAI空间变异的50.4%,其中空间因子的贡献率最大,单独解释了LAI空间变异的25.5%;中等树(5cm胸径≤10cm)的密度和主要树种(冷杉(Abiesnephrolepis)和云杉(Piceaspp.))的胸高断面积均与LAI显著正相关,质量含水率与LAI显著负相关。总体来看,空间自相关对小兴安岭谷地云冷杉林LAI空间异质性的决定作用明显强于林分因子和土壤因子。     

小兴安岭5种林型土壤呼吸时空变异

原始阔叶红松林、谷地云冷杉林、阔叶红松择伐林、次生白桦林、人工落叶松林是小兴安岭乃至东北地区的重要森林类型。采用红外气体分析法比较测定了这几种森林类型的土壤呼吸及其相关环境因子,分析探讨了这几种森林类型土壤呼吸的时空变异。结果表明:各林型土壤呼吸与5 cm深土壤温度(T5)呈显著的指数相关,并且土壤呼吸与土壤温度、土壤湿度及其相互作用的回归模型可以解释各林型土壤呼吸约71%的季节变异。生长季平均土壤呼吸速率为次生白桦林(3.59μmolCO.2m-.2s-1)>谷地云冷杉林(3.52μmolCO.2m-.2s-1)>阔叶红松择伐林(3.44μmolCO.2m-.2s-1)>原始阔叶红松林(2.58μmolCO.2m-.2s-1)>人工落叶松林(2.29μmolCO.2m-.2s-1),说明土壤呼吸对原始阔叶红松林人为干扰的响应是不同的。各林型Q10值介于1.84(人工落叶松林)—2.32(次生白桦林)之间。在整个生长季,各林型之间土壤呼吸的变异系数变化幅度为19.74%—37.39%,而各林型内土壤环间其变化幅度为32.13%—60.20%,显著大于样地间的变化幅度14.28%—35.70%(P<0.001),说明土壤呼吸在细微尺度上的差异更大。土壤湿度可以解释各林型(阔叶红松林除外)内部土壤呼吸15.8%—33.5%的空间异质性。     

小兴安岭谷地云冷杉林粗木质残体碳密度特征

以小兴安岭谷地云冷杉林9.12 hm~2固定样地为研究对象,分析粗木质残体(CWD)碳密度的基础特征,揭示其与林分因子和物种多样性的关系。结果表明:(1)谷地云冷杉林CWD碳密度为13.25 t C/hm~2,其中云杉(Picea spp.)、冷杉(Abies nephrolepis)、兴安落叶松(Larix gmelinii)和未知种的CWD碳密度分别为3.59、2.61、3.06和2.85 t C/hm~2。(2)不同腐烂等级下CWD碳密度呈近正态分布,多集中在Ⅱ和Ⅲ等级,分别占总量的42.7%和35.4%。不同径级的CWD碳密度也呈近正态分布,主要分布在30—40 cm和40—50 cm径级上。干中折断、拔根倒、枯立木和干基折断为谷地云冷杉林CWD碳密度的主要存在方式。腐烂等级为Ⅰ和Ⅴ的CWD中,拔根倒的碳密度最高,其他腐烂等级中均为干中折断的碳密度最高。(3)CWD碳密度表现出较强的空间异质性,其随着林分平均胸径、最大胸径和胸高断面积的增加而下降,呈显著负相关关系(P0.05);而与林分密度、多样性指数和均匀度指数均无显著相关性(P0.05)。     

冻融期去根处理对小兴安岭6种林型土壤微生物量的影响

春季冻融期,在小兴安岭的阔叶红松(Pinus koraiensis)林、谷地云冷杉(Picea koraiensis-Abies nephrolepis)林、阔叶红松择伐林、白桦(Betula platyphylla)次生林、红松人工林、兴安落叶松(Larix gmelinii)人工林的去根处理样地和对照样地进行野外取土实验,分析了根去除对上述林型土壤微生物量的影响以及与土壤环境因子的关系。结果表明:冻融循环期间对照样地和去根处理样地的林型、土壤层次、取样时间均显著地影响土壤微生物量碳(MBC)(P0.05),对照样地中各林型的土壤微生物量氮(MBN)差异显著,而去根处理样地中各林型的MBN没有显著差异(P0.05);冻融循环期间去根处理显著地减少了大部分林型及土层(谷地云冷杉林0—10 cm及择伐林外)的MBC,而去根处理对大部分林型及土层(阔叶红松林0—10 cm,谷地云冷杉林和择伐林的10—20 cm除外)的MBN没有显著影响。说明在小兴安岭春季冻融期根系对土壤微生物量的影响不可忽视。     

云冷杉针阔混交林叶面积指数的空间异质性

叶面积指数(Leaf area index,LAI)是森林生态过程的关键参数和描述森林冠层结构的重要指标。用半球摄影技术对吉林省汪清林业局金沟岭林场的10块1 hm2云冷杉针阔混交林的LAI进行测定,采用地统计学的半变异函数和普通克里格法对研究区的LAI的空间异质性进行了分析。结果表明:10块样地的10 m×10 m小样方内以及样地间的LAI离散程度较小,但分布有一定的规律。LAI的空间相关性存在不确定性,可由线性、孔状、高斯3种模型有效的描述,空间相关性存在的尺度范围变异大,中等和强空间相关性的影响范围在15—155m之间,10块样地LAI的空间相关性的影响范围平均为65.637m。普通克里格插值结果显示,LAI的空间分布呈明显的条带状和斑块状的梯度变化。LAI与林分平均胸径、林分平均高和林分平均冠长呈显著负相关,与林分平均冠幅、林分密度以及树种个数呈显著正相关。研究结果可为不同尺度云冷杉针阔混交林LAI的估计提供依据。     

长白山云冷杉针阔混交林天然更新空间分布格局及其异质性

2012年8月在长白山林区云冷杉针阔混交林中设置一块60 m×60 m典型更新样地,采用地统计学的半方差函数分析方法、分形维数和Kriging插值方法,分析林下天然更新幼苗幼树的空间分布格局及其异质性.结果表明:云冷杉针阔混交林更新层幼苗幼树以冷杉和色木槭为主,占更新总数的87.4%;样地中更新幼苗幼树呈聚集分布,聚集斑块最大半径为9.93 m;更新幼苗幼树之间具有很强的空间自相关性,其88.7%的变异由结构性因素(生物学、生态学特性和环境异质性)引起,11.3%的变异由随机性因素引起;更新幼苗幼树的空间分布表现出各向异性,南北方向上的分形维数最小,异质性最高,东北-西南方向上的分形维数最大,异质性最低;更新幼苗幼树树高比地径的空间异质性强;更新幼苗幼树地径和树高的空间自相关范围分别为29.97和31.86 m,随机因素和结构因素对其空间异质性的影响具有同等作用.     

小兴安岭7种典型林型林分生物量碳密度与固碳能力

森林生物碳储量作为森林生态系统碳库的重要组成部分, 在全球碳循环中发挥着重要作用。以小兴安岭7种典型林型为研究对象, 通过外业样地调查与室内实验分析相结合的方法, 从林分尺度对林分生物量与碳密度进行计量, 分析了林分生物碳储量的空间分配格局, 并对林分年固碳能力与碳汇潜力进行了探讨。结果表明: 小兴安岭不同林型从幼龄林到成熟林的乔木层碳密度增长速率为: 蒙古栎(Quercus mongolica)林>兴安落叶松(Larix gmelinii)林>云冷杉(Picea-Abies)林>樟子松(Pinus sylvestris var. mongolica)林>山杨(Populus davidiana)林>红松(Pinus koraiensis)林>白桦(Betula platyphylla)林。7种典型林型不同龄组(幼龄林、中龄林、近熟林和成熟林)林分生物量碳密度分别为: 红松林31.4、74.7、118.4和130.2 t·hm^-2; 兴安落叶松林28.9、44.3、74.2和113.3 t·hm^-2; 樟子松林22.8、52.0、71.1和92.6 t·hm^-2; 云冷杉林23.1、44.1、77.6和130.3 t·hm^-2; 白桦林18.8、35.3、66.6和88.5 t·hm^-2; 蒙古栎林25.0、20.0、47.5和68.9 t·hm^-2; 山杨林19.8、28.7、43.7和76.6 t·hm^-2。红松林、兴安落叶松林、樟子松林和蒙古栎林在幼龄林时林分年固碳量较高, 其他林型在成熟林时林分年固碳量较高。7种典型林型不同龄组的林分生物量碳密度均随林龄增长而增加, 但不同林型的碳汇功能存在差异, 同一林型不同林龄的生物量碳密度增幅差异也较大。林分年固碳量在0.4-2.8 t·hm^-2之间, 碳汇能力较强、碳汇潜力较大。尤其是小兴安岭目前林分质量较差, 幼龄林和中龄林所占的比重较大, 具有较大的碳汇潜力。研究结果可为森林经营管理及碳汇功能评价提供参考。     

小兴安岭谷地云冷杉林主要种群及林隙形成木的空间格局

调查了小兴安岭谷地云冷杉林1.5 hm²(100 m×150 m)固定样地内的物种组成和径级结构,用点格局方法分析样地内主要种群的空间格局和空间关联性以及林隙形成木的空间格局.结果表明： 样地内胸径≥2 cm的乔木有13种,种群密度差异大,重要值在前4位的为臭冷杉、红皮云杉、白桦和花楷槭.种群的径级结构近似倒“J”形.臭冷杉和红皮云杉空间分布相似,随空间尺度的变化趋势为聚集 随机 均匀分布.白桦在≤40 m尺度为聚集分布,>40 m尺度变为随机分布,而花楷槭在整个研究尺度上均为聚集分布.除白桦和花楷槭两者间在整个研究尺度上均为负相关以外,其余种群间均为小尺度上正相关、大尺度上负相关.臭冷杉和白桦种群间正相关的显著性强,其他种群间正相关性普遍较弱.样地内林隙形成木的空间格局表现为中小尺度聚集分布,随尺度增大趋于随机分布.掘根形成的林隙形成木的空间点格局呈单峰型分布,随机、聚集和均匀3种分布均有出现.折干形成的林隙形成木的空间点格局分布整体波动不大,小尺度上在随机和聚集分布间交替出现,到大尺度则归于随机分布.两者间的空间关联性分析表明,≤32 m尺度为显著正相关,>32 m尺度为不显著的负相关.     

不同林龄阔叶红松林林下簇毛槭的性比格局及雌雄个体的空间分布

为探讨不同发育阶段林分下的雌雄异株植物与性别相关的性比格局和空间分布, 以5.2 ha的中龄林和1.0 ha的老龄林固定监测样地内簇毛槭(Acer barbinerve)雌、雄植株的定位观测数据为基础, 对比分析了长白山不同林龄的阔叶红松林中的已花簇毛槭的性比格局、空间分布及其与环境因子间的关系。研究结果表明: 中龄林和老龄林中雌树的胸径均显著大于雄树, 总体上性比极显著偏离1:1。随着树木的生长, 性比由偏雄性转变为不再偏离1:1, 这可能是因为雄树始花胸径较小所致。O-ring单变量点格局分析显示中龄林样地中的雌树和雄树符合异质性泊松分布, 老龄林样地中的雌树和雄树均完全随机分布。O-ring双变量点格局分析显示, 在随机标签假设下, 中龄林中的雌树和雄树在1-4 m尺度上空间负相关, 在4-100 m尺度上空间独立, 老龄林中的雌树和雄树在所有尺度上空间独立。簇毛槭在中龄林和老龄林中不同的空间分布格局说明中龄林中簇毛槭分布的斑块性相对明显, 随着林分的发育, 郁闭度较高的老龄林样地中环境异质性降低, 环境因子对簇毛槭分布的影响减弱。典范冗余分析(redundancy analysis, RDA)表明在中龄林中, 林分密度只能解释3.73%的雌树分布的变异, 与雄树分布的相关性不显著, 叶面积指数和非生物因子对雌树和雄树的影响均较弱; 老龄林中簇毛槭的分布与生物因子和非生物因子的相关性均不显著。     

小兴安岭三种林型叶面积指数的估测

利用Winscanopy2006冠层分析仪测定2009年7月初至11月初小兴安岭白桦次生林、谷地云冷杉林、阔叶红松林的有效叶面积指数(LAI_e),并将经过木质部分所占比率、冠层水平集聚和簇内集聚校正的11月初LAI_e作为真实叶面积指数(LAI_t),结合凋落物法测定3种林型的LAI_t及其季节动态.结果表明： 调查期间,白桦次生林的LAI_e在7月达到峰值(2.21),谷地云冷杉林、阔叶红松林的LAI_e在8月达到峰值,分别为2.57、2.68.白桦次生林、谷地云冷杉林、阔叶红松林的LAI_t均在7月达到峰值,分别为3.44、3.86、6.93;相对于本文探讨的方法,光学仪器所测定的LAI_e在最高叶面积指数期分别低估33.1%、32.9%、66.0%;而在整个调查期内,谷地云冷杉林和阔叶红松林LAI_e平均低估22.8%和56.5%,白桦次生林平均高估13.2%.
     

Measuring seasonal dynamics of leaf area index in a mixed conifer-broadleaved forest with direct and indirect methods

利用光学仪器法能够快速、高效地测定森林生态系统的叶面积指数(leaf area index, LAI)。然而, 评估该方法测定针阔混交林LAI季节动态准确性的研究较少。该研究基于凋落物法测定了小兴安岭地区阔叶红松(Pinus koraiensis)林LAI的季节动态, 其结果可代表真实的LAI。参考真实的LAI, 对半球摄影法(digital hemispherical photography, DHP)和LAI-2000植物冠层分析仪测定的有效叶面积指数(effective LAI, L_e)进行了评估。首先对DHP测定LAI过程中采用的不合理曝光模式(自动曝光)进行了系统校正。同时, 测定了光学仪器法估测LAI的主要影响因素(包括木质比例(woody-to-total area ratio, α)、集聚指数(clumping index, Ω_E)和针簇比(needle-to-shoot area ratio, γ_E))的季节变化。结果表明: 3种不同方法测定的LAI均表现为单峰型的季节变化, 8月初达到峰值。从5月至11月, DHP测定的L_e比真实的LAI低估50%-59%, 平均低估55%; 而LAI-2000植物冠层分析仪测定的L_e比真实的LAI低估19%-35%, 平均低估27%。DHP测定的L_e 经过自动曝光, α、Ω_E和γ_E校正后, 精度明显提高, 但仍比真实的LAI低估6%-15%, 平均低估9%; 相对而言, LAI-2000植物冠层分析仪测定的L_e经过α、Ω_E和γ_E校正后, 精度明显提高, 各时期与真实的LAI的差异均小于9%。研究结果表明, 考虑木质部和集聚效应对光学仪器法的影响后, DHP和LAI-2000植物冠层分析仪均能相对准确地测定针阔混交林LAI的季节动态, 其中, DHP的测定精度高于85%, 而LAI-2000植物冠层分析仪的测定精度高于91%。     

Spatiotemporal variation and scale effect of canopy leaf area index of larch plantation on a slope of the semi-humid Liupan Mountains,Ningxia, China

Aims Leaf area index (LAI) is an important canopy structure parameter characterizing ecological and hydrological processes, such as forest growth, canopy interception and transpiration. Forest LAI is limited by both light and soil water availability, thus may vary with slope position and seasonality. This study is aimed at the spatiotemporal variation of LAI and its relationship with environmental variables. Methods A 34-years-old Larix gmelinii var. principis-rupprechtii planted forest situated on a typical slope located in a small watershed of Xiangshuihe within Liupan Mountains was selected for LAI observations. Sixteen plots along a 30 m wide transect along the slope was surveyed from May to October of 2015 to measure the monthly canopy LAI. Important findings It showed there was a remarkable difference of LAI among slope positions. The LAI in May decreased toward downslope direction with a scale effect of -0.02/100 m. Whereas for the period from June to August, LAI showed a nonlinear variation along slope positions: increasing from to top slope downward, reaching its maximum at the middle slope, and then decreasing to the slope foot. The scale effect of LAI was +0.15/100, +0.16/100, and +0.18/100 m in the slope range (downward positive) of 0-244.2 m, but -0.09/100, -0.08/100, and -0.07/100 m in the slope range of 244.2-425.1 m for June, July and August, respectively. The LAI increased toward downslope in September and October, with a slope scale effect of +0.03/100 m and +0.09/100 m, respectively. The seasonal variation of LAI-slope relationship showed a shift from the light and temperature control in the early growing season, to the soil water resources control in the mid growing season, and then to an integrated control of many factors in the late growing season. In the early growing season when soil moisture and nutrients were abundant, terrain shading limited the leaf growth in middle and downslope. From early to the mid growing season, the soil moisture on the slope was quickly depleted due to fast evapotranspiration and poor moisture retention of the coarse soil. On the other hand, average solar height increased, and allowed direct light radiation to penetrate to the middle then downslope. The result is that the leaf growth in the middle slope was the strongest in the mid growing season. During the late growing season, the temperature decreased fast in the mountain top to incur earlier leaf fall than the mountain foot. Thus the LAI exhibited the increasing trend toward the downslope.     

Exploring the influence of soil types underneath the canopy in winter wheat leaf area index remote estimating

Aims Remote sensing is an effective and nondestructive way to retrieve leaf area index (LAI) from plot, regional and global range. Soil background is one of the confounding factors limiting remotely estimating LAI. And soil type contains a large proportion of soil background information, which can influence the optical properties of vegetation canopy and soil. However, our knowledge on the effects stemmed from soil types underneath the canopy on LAI remote estimating have been in shortage. Thus, this study aims to explore the influences of soil types underneath the canopy on winter wheat LAI remote estimating. Methods We analyzed the sensitivity variation of eight spectral indices, named normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption ratio index 2 (MCARI₂), red edge inflection point (REIP), red edge amplitude (Dr), red edge area (SDr), red edge symmetry (RES), normalized difference spectral index (NDSI), to LAI in different soil types, and then we identified some spectral intervals or parameters that were insensitive to soil type variations underneath the canopy. We also compared the accuracy of two commonly used regression models, partial least squares regression (PLSR) and random forest regression (RFR), in estimating LAI for different soil types. We also explored the problems arising from applying the regression model developed in single soil type area to complex soil types area in retrieving LAI. Important findings This paper demonstrates the effects of soil types underneath the canopy on LAI retrieving. 1) The sensitivity of spectral indices to LAI is significantly different due to the soil type variation, but REIP has the least effects from soil type variation among the eight spectral indices. Meanwhile, the band selection algorithm of lambda-by-lambda not only chooses the most sensitive spectral interval for LAI, but also provides a feasible way to construct the spectral index that exhibits strong resistances to the effects of soil types underneath the canopy. 2) The accuracy of LAI estimation by regression models differs under soil type considered or not. So we suggest that in small scale researches, especially in a field scale, the ability of regression models in explaining variables is the priority consideration, while the PLSR is superior to RFR in this respect. Under the premise of unknown priori knowledge of land surfaces, the RFR is more suitable for retrieving LAI than PLSR, but land surface priori knowledge is still necessary. These findings provide the theoretical basis and methods for developing remotely sensing estimating LAI models adapted to various land surfaces. Further analysis is needed in applying the findings in more crop types, cultivars and growth stages.     

利用直接法和间接法测定针阔混交林叶面积指数的季节动态

利用光学仪器法能够快速、高效地测定森林生态系统的叶面积指数(leaf area index, LAI)。然而, 评估该方法测定针阔混交林LAI季节动态准确性的研究较少。该研究基于凋落物法测定了小兴安岭地区阔叶红松(Pinus koraiensis)林LAI的季节动态, 其结果可代表真实的LAI。参考真实的LAI, 对半球摄影法(digital hemispherical photography, DHP)和LAI-2000植物冠层分析仪测定的有效叶面积指数(effective LAI, L_e)进行了评估。首先对DHP测定LAI过程中采用的不合理曝光模式(自动曝光)进行了系统校正。同时, 测定了光学仪器法估测LAI的主要影响因素(包括木质比例(woody-to-total area ratio, α)、集聚指数(clumping index, Ω_E)和针簇比(needle-to-shoot area ratio, γ_E))的季节变化。结果表明: 3种不同方法测定的LAI均表现为单峰型的季节变化, 8月初达到峰值。从5月至11月, DHP测定的L_e比真实的LAI低估50%-59%, 平均低估55%; 而LAI-2000植物冠层分析仪测定的L_e比真实的LAI低估19%-35%, 平均低估27%。DHP测定的L_e 经过自动曝光, α、Ω_E和γ_E校正后, 精度明显提高, 但仍比真实的LAI低估6%-15%, 平均低估9%; 相对而言, LAI-2000植物冠层分析仪测定的L_e经过α、Ω_E和γ_E校正后, 精度明显提高, 各时期与真实的LAI的差异均小于9%。研究结果表明, 考虑木质部和集聚效应对光学仪器法的影响后, DHP和LAI-2000植物冠层分析仪均能相对准确地测定针阔混交林LAI的季节动态, 其中, DHP的测定精度高于85%, 而LAI-2000植物冠层分析仪的测定精度高于91%。     

中国典型陆地生态系统波文比特征及影响因素

波文比(β)是陆面过程中的重要参数, 影响着地表和大气间的能量交换, 明确β的空间变异规律和影响因素有助于对地表能量平衡和气候间反馈关系的预测。该研究收集了在中国不同生态系统类型开展的用涡度相关法(EC)测量地表能量平衡的公开发表文献, 构建了β和气象环境因子数据库, 分析了β在生态系统之间的差异、空间变异特征及影响因素。主要结果: (1)所有生态系统β平均值为0.95 ± 0.64, 变异系数67%, 偏度1.58, 峰度3.07, 整体服从对数正态分布, β平均值最高为灌木生态系统(1.26), 最低为湿地生态系统(0.49)。(2) β在生态系统类型间差异显著: 森林和湿地生态系统β无显著差异, 灌木生态系统β >草地生态系统 β >森林和湿地生态系统 β, 农田生态系统β介于草地生态系统与森林和湿地生态系统之间。(3) β随着纬度的增加而增加, 不随经度和海拔变化。纬度每增加1°,β增加0.038。(4) β随着年降水量(MAP)、年平均气温(MAT)、净辐射(R_n)、当年降水量(PPT)、当年平均气温(T_a)和叶面积指数(LAI)的增加而降低。(5)不同生态系统中β对生物和非生物因素的响应存在显著差异: 草地、森林和灌木生态系统的β对生物和非生物因素变化较为敏感, 而农田和湿地生态系统的β与所有生物和非生物因素均无显著相关关系。(6) MAP和R_n是β变化的直接影响因素, LAI通过影响R_n间接影响β。结果表明了植被类型与气候因素之间具有交互作用, 能量分配最主要的影响因素是降水, 叶面积对能量分配的调节作用并不显著。     

Geostatistical analysis of spatial variations in leaf traits of woody plants in Tiantong,Zhejiang Province

AimsExploring spatial variations in leaf traits and their relationships with environmental properties is crucial for understanding plant adaptation strategies and community assembly. This study aimed to reveal how leaf traits varied spatially and the role of environmental factors.MethodsThe study was conducted in a 5-hm² forest plot in Tiantong, Zhejiang Province. Three leaf traits, including individual leaf area (ILA), specific leaf area (SLA), and leaf dry matter content (LDMC) were measured for 20253 individual trees with diameter at breast height (DBH) ≥1 cm. Soil properties measured included contents of soil total nitrogen, soil total phosphorus, soil total carbon, soil pH value, soil volumetric water content, bulk density, and humus depth. Topographic variables measured included elevation, slope and convexity. We used geostatistical analysis to reveal spatial variations of the three leaf traits. Relationships between leaf variability and environmental factors were analyzed using principal component analysis (PCA) and Pearson’s correlation.Important findings Spatial variability followed the order of ILA > SLA > LDMC. Spatial autocorrelation of three leaf traits was weak within a distance of 5.16 m. The optimal model of the semi-variogram function was Gaussian model for ILA, and exponential model for SLA and LDMC. ILA showed the largest variability at the direction of northeast-southwest, and smallest variability at the direction of northwest-southeast. In contrast, SLA and LDMC had the highest variability at the direction of northwest-southeast and least variability at the direction of northeast-southwest. There were significantly negative relationships between ILA and topographic factors (r = -0.12, p < 0.0001), and between SLA and soil nutrients (r = -0.16, p < 0.0001). In contrast, LDMC was positively correlated with soil nutrients (r = 0.13, p < 0.0001). Relative to soil nutrients, topographic factors affected much more variations in ILA, SLA and LDMC at the direction of northeast-southwest. Distinctly, at the direction of northwest-southeast, variability of ILA was affected mainly by topographic factors, while soil nutrients resulted in the most variability of SLA and LDMC. In conclusion, leaf traits varied considerably with spatial direction in the studied forest plot. Associations between leaf traits and topographic factors and soil nutrients indirectly indicated effects of environmental filtering on community assembly.     

Spatial pattern of riparian vegetation in desert of the lower Tarim River basin

AimsRevealing the spatial pattern of riparian vegetation in hyper-arid regions can improve our understanding on the water relations of riparian vegetation in the desert watershed ecosystem, and also can provide valuable scientific guidance for desertification control and water resources management of watershed of the arid region in northwestern China. This research objective is to show the spatial distribution and structures of typical riparian vegetation in hyper-arid desert watershed from regional and overall perspective.Methods Based on Landsat-8 OLI remote sensing images and a large number of field vegetation surveys, the supervised classification method was used to distinguish three main vegetation categories in the lower Tarim River basin: Tamarix thicket, Populus euphratica woodland, and Phragmites australis grassland. The leaf area index (LAI) of Tamarix thickets and Populus euphratica woodlands were inverted by using the remote-sensed LAI inversion empirical model that we developed.Important findings Supervised classification supporting abundant information of ground objects by remote sensing was an effective method to determine desert riparian vegetation categories in arid desert regions. The area was 336.4 km² for the Populus euphratica woodlands and 405.3 km² for the Tamarix thickets, respectively. The Tamarix thickets had a wider distribution range while the Populus euphratica woodlands grew near the river channel. The overall LAI of the riparian vegetation was low. The average LAI value was 0.253 for the Tamarix thickets and 0.252 for the Populus euphratica woodlands. The areas of vegetation with the LAI value of less than 0.5 accounted for 92.4% and 90.1% of the total area of the Tamarix thickets and the Populus euphratica woodlands, respectively. The statistic results showed that large spatial variability of the riparian vegetation LAI existed. The spatial variability of the Populus euphratica woodlands was larger than that of the Tamarix thickets. The LAI values of the riparian vegetation had a significant negative exponential relationship with the distances away from the river channel. The LAI values declined rapidly within the distance of 1 km from the river channel and they were generally lower than 0.1 when the distances beyond 1 km, which indicated that the riparian vegetation was mainly distributed within 1 km from both side of the river. This research indicated three basic characteristics of the spatial pattern in riparian vegetation from hyper-arid desert regions, including overall sparse spatial distribution, high spatial variability and negative exponential relationship between LAI and distance away from the river channel.     

Spatial variation and controlling factors of temperature sensitivity of soil respiration in forest ecosystems across China

土壤呼吸的温度敏感性(Q₁₀)是陆地碳循环与气候系统间相互作用的关键参数。尽管已有大量关于不同类型森林Q₁₀季节和年际变化规律的研究, 但是对Q₁₀在区域尺度的空间变异特征及其影响因素仍认识不足, 已有结果缺乏一致结论。该研究通过整合已发表论文, 构建了中国森林生态系统年尺度Q₁₀数据集, 共包含399条记录、5种森林类型(落叶阔叶林(DBF)、落叶针叶林(DNF)、常绿阔叶林(EBF)、常绿针叶林(ENF)、混交林(MF))。分析了不同森林类型Q₁₀的空间变异特征及其与地理、气候和土壤因素的关系。结果显示, 1) Q₁₀介于1.09到6.24之间, 平均值(±标准误差)为2.37 (± 0.04), 且在不同森林类型之间无显著差异; 2)当考虑所有森林类型时, Q₁₀随纬度、海拔、土壤有机碳含量(SOC)和土壤全氮含量(TN)的增加而增大, 随经度、年平均气温(MAT)、平均年降水量(MAP)的增加而减小。气候(MAT、MAP)和土壤(SOC、TN)因素间存在相互作用, 共同解释了33%的Q₁₀空间变异, 其中MAT和SOC是Q₁₀空间变异的主要驱动因素; 3)不同类型森林Q₁₀对气候和土壤因素的响应存在差异。在DNF中Q₁₀随MAP的增加而减小, 而其他类型森林中Q₁₀与MAP无显著相关性; 在EBF、DBF、ENF中Q₁₀随TN的增加而增大, 但Q₁₀对TN的敏感性在EBF中最高, 在ENF中最低。这些结果表明, 尽管Q₁₀有一定的集中分布趋势, 但仍有较大范围的空间变异, 在进行碳收支估算时应注意尺度问题。Q₁₀的主要驱动因素和Q₁₀对环境因素的响应随森林类型而变化, 在气候变化情景下, 不同森林类型间Q₁₀可能发生分异。因此, 未来的碳循环-气候模型还应考虑不同类型森林碳循环关键参数对气候变化的响应差异。     

青海森林生态系统中灌木层和土壤生态化学计量特征

灌木层作为森林生态系统的重要组成部分, 了解其生态化学计量特征将有助于揭示森林生态系统物质周转和养分循环等生态功能。该研究选取青海省7种主要优势林分——白桦(Betula platyphylla)林、毛白杨(Populus tomentosa)林、红桦(Betula albosinensis)林、青扦(Picea wilsonii)林、山杨(Populus davidiana)林、圆柏(Sabina chinensis)林、云杉(Picea asperata)林为研究对象, 采用野外取样和室内实验分析相结合的方法, 研究了不同林分林下灌木层不同器官(叶、枝干、根)及其表层(0-10 cm)土壤的碳(C)、氮(N)、磷(P)含量及其相关性。结果表明: 7种林分间灌木(叶、枝干、根) P含量、C:P均没有明显差异性; 山杨林、圆柏林、云杉林的林下灌木(叶、枝干、根) N含量、N:P高于白桦林、毛白杨林、红桦林和青扦林, C:N则相反。圆柏林的林下灌木生长受P限制, 其余6种林分的林下灌木生长受N限制。7种林分间土壤有机碳(SOC)和总氮(TN)含量呈现出明显差异性, 而总磷(TP)含量则差异不明显。相关性分析表明, 林下灌木(叶、枝干、根) N含量、C:N、N:P与土壤TN含量、C:N、N:P呈极显著相关性, 而P含量、C:P与土壤TP含量呈显著相关性。冗余分析表明, 林下灌木层植被C、N、P含量及生态化学计量特征受到土壤化学计量特征及各环境因子的共同影响, 其中土壤C:N、海拔、年平均气温、年降水量为主要影响因子。     

Current stocks and potential of carbon sequestration of the forest tree layer in Qinghai Province,China

为阐明青海省森林生态系统乔木层植被碳储量现状及其分布特征, 该研究利用240个标准样地实测的乔木数据, 估算出青海省森林生态系统不同林型处于不同龄级阶段的平均碳密度, 并结合青海省森林资源清查资料所提供的不同龄级的各林型面积, 估算了青海省森林生态系统乔木层的固碳现状、速率和潜力。结果表明: 1) 2011年青海省森林乔木层平均碳密度为76.54 Mg·hm ^-2, 总碳储量为27.38 Tg。云杉(Picea spp.)林、柏木(Cupressus funebris)林、桦木(Betula spp.)林、杨树(Populus spp.)林是青海地区的主要林型, 占青海省森林面积的96.23%, 占青海省乔木层碳储量的86.67%, 其中云杉林的碳储量(14.78 Tg)和碳密度(106.93 Mg·hm ^-2)最高。按龄级划分, 乔木层碳储量表现为过熟林>中龄林>成熟林>近熟林>幼龄林。2)青海省乔木层总碳储量从2003年的23.30 Tg增加到2011年的27.38 Tg, 年平均碳增量为0.51 Tg·a ^-1。乔木层固碳速率为1.06 Mg·hm ^-2·a ^-1, 其中柏木林的固碳速率最大(0.44 Mg·hm ^-2·a ^-1); 桦木林的固碳速率为负值(-1.06 Mg·hm ^-2·a ^-1)。3)青海省乔木层植被固碳潜力为8.50 Tg, 其中云杉林固碳潜力最高(3.40 Tg)。该研究结果表明青海省乔木层具有较大的固碳潜力, 若对现有森林资源进行合理管理和利用, 将会增加青海省森林的碳固存能力。