中国东北的云冷杉林

本文从以下几方面对东北地区云冷杉林进行研究．１）环境背景;２）云冷杉林三个主要树种的生态属性;３）以红皮云杉、鱼鳞云杉和臭松为优势种的林分的每一植被类型的树种组成及其结构;４）影响林分的干扰因子;５）中国东北的云冷杉林的主要演替方式和过程．     

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.     

长白山北坡云冷杉林和落叶松林物种组成与群落结构

云冷杉林是长白山北坡保存最完整的森林植被,而落叶松林是长白山的隐域性森林植被.为了更好地了解其物种组成和群落结构等基本特征,于2010年在长白山北坡自然保护区内分别建立了4 hm2的云冷杉林和落叶松林长期监测样地,对样地内所有胸径≥1 cm的木本植物进行了定位、调查和挂牌.结果表明:云冷杉林样地木本植物有22种,隶属于6科12属;落叶松林样地木本植物有22种,隶属于8科16属.两样地物种组成差异不大,区系组成基本上都属于北温带成分.云冷杉林样地木本植物独立个体数为8640株,包括分支数为9257株;落叶松林样地木本植物独立个体数为3696株,包括分支数为4060株.两样地优势种明显,云冷杉林样地,臭冷杉和长白落叶松处于优势地位,其重要值分别占所有物种的38.7%和23.9%;落叶松林样地,长白落叶松占绝对优势,其重要值占所有物种的61.9%.两样地群落更新良好,径级结构均呈倒"J"型.云冷杉林样地,臭冷杉径级结构呈倒"J"型,长白落叶松径级结构呈正态分布;落叶松林样地,长白落叶松胸径≥10 cm个体的径级结构呈正态分布.主要物种空间分布在两个样地中随径级和空间尺度的变化表现出不同的格局,共有种在不同样地间表现出不同的格局.     

长白山云冷杉针阔混交林不同林隙下幼苗幼树密度及空间分布

2019年8月在云冷杉针阔混交林样地(0.36 hm²),对48个林隙及幼苗(0.2<更新高度RH<1 m)、幼树(RH≥1 m,胸径DBH<5 cm)进行调查,分析林隙大小(<20 m²,小;20～50 m²,中;50～120 m²,大;>120 m²,特大)对林隙内红松、鱼鳞云杉及冷杉幼苗幼树密度和生长指标(高、基径)的短期影响,并采用核密度估计法分析其空间分布规律。结果表明: 云冷杉更新的密度通常随林隙增大而降低,仅对幼树影响显著,小林隙下云冷杉幼树密度分别为0.34和1.74株·m^-2,红松密度不受林隙大小的影响。林隙大小对冷杉幼苗幼树生长指标的影响最大,对红松影响最小,平均最大值多出现在大林隙。红松和云杉幼树的基径和树高最大值均分布在小、中、大林隙东北部,在特大林隙中转移至冠空隙西北部。小林隙有助于幼苗的建立和萌发,可通过择伐冷杉创造小林隙,随后扩大林隙面积(>50 m²)促进幼树生长,需要持续监测来确定林隙大小对森林更新的长期影响。     

长白山云冷杉群落主要种群生态位特征

应用TWINSPAN将23块长白山云冷杉林样方划分为8个群落类型,以此作为一维资源位,应用Levins、Hurlbert生态位宽度公式和Pianka生态位重叠公式对长白山云冷杉林群落12种主要乔木、13种主要灌木和18种主要草本植物的生态位特征进行分析.结果 表明:(1)乔木层中,臭冷杉、鱼鳞云杉具有最大的生态位宽度,它们是该区森林群落的主要建群种.灌木层中,瘤枝卫矛与几种械树的生态位宽度较大.草本层中,粗茎鳞毛蕨、山酢浆草、二叶舞鹤草具有较大的生态位宽度.表明它们广布于云冷杉林郁闭的林冠下,对环境适应能力强.(2)生态位宽的种群可能产生较小的生态位重叠,生态位较窄的种群间也会产生较大的生态位重叠,这主要取决于物种的生物学特性和对环境资源的需求.(3)从生态位重叠分布格局来看,主要植物种群的生态位重叠较为普遍,但主要集中在较低水平,表明群落内因利用相同资源或占有同一资源而产生的种间竞争并不激烈,对环境资源的共享比较充分,长白山云冷杉群落处于相对稳定阶段.     

中国云冷杉林生物生产力格局及其数学模型

基于全国１００６块样地资料,从宏观上阐明了我国云冷杉林物生产力的格局规律,建立了联系叶面积指数分布规律和地植物学知识的生物生产力热相关模型与地理分布模型。在温度和降水分布空间上,云冷杉林净第一性生产力的格局呈现一种复合曲面的函数关系,即随着温度升高,林分生产力呈指数递增,其递增率随降水量的增加而线性增大。在正常分布范围内,云冷杉面积指数随温度变化呈递减函数关系。这可能是针叶的适光变态以及云冷杉林适     

长白山云冷杉林优势树种的竞争

优势种是植物群落各层次中占优势的植物种,混交林优势树种竞争关系的研究对合理经营混交林具有重要意义.本研究在吉林省汪清县金沟岭林场内,选择立地条件一致的云冷杉天然林,设置大小100 m×100 m样地.首先,用优势度分析法确定群落优势树种;其次,以优势树种为对象木,采用可描述单株林木侧方上方、种内种间竞争强度的林木竞争指数分析优势树种的竞争关系.结果表明: 该云冷杉天然林有3个优势树种:臭冷杉、红皮云杉、红松.样地中,小径级林木较多,群落林木趋于小龄化,3个优势树种的竞争树种主要有臭冷杉、红皮云杉、红松、枫桦、紫椴、青楷槭和白桦.3个优势树种受到的竞争最激烈的是臭冷杉(1412.48),其次是红皮云杉(439.17)、红松(245.28),都主要承受侧方挤占,臭冷杉、红皮云杉、红松的侧方挤占分别占各优势树种竞争强度的64.9%、65.2%、66.0%;3个优势树种侧方上方平均竞争强度大致随个体胸径的增大而减少,小径级林木的侧方上方平均竞争强度几乎相等,对象木径级越大,所承受的侧方挤占比例越大,大径级林木几乎不承受上方遮盖;3个优势树种的侧方上方竞争主要来源于臭冷杉、红皮云杉、红松、紫椴、枫桦、青楷槭和白桦.3个优势树种种间竞争均比种内竞争激烈,臭冷杉、红皮云杉、红松的种间竞争分别占各优势树种竞争强度的58.4%、87.1%、83.7%,且竞争强度大致随个体胸径的增大而减少.     

甘南地区紫果云杉、岷江冷杉生命表

本文以径级(胸径间隔)为基础,编制甘南地区紫果云杉、岷江冷杉生命表。 一、研究方法 本项研究于1986和1987年,在甘肃省南部云、冷杉林主要分布地带的白龙江林区和洮河林区进行。选择原始状态下的紫果云杉(Picea purpurea Mast.)、岷江冷杉(Abies faxoniana Rehd.et Wils)林,海拔3000米至3600米之间,位于白龙江中上游;同时选取择伐后的云杉(Picae asperata Mast.)、岷江冷杉林     

长白山白桦林中红松种群动态的研究

山地红松、云杉、冷杉林(简称红松云冷杉林),是山地云冷杉林与红松阔叶林相连接带上的垂直地带性植被。在长白山分布于海拔1100—1400m之间,构成窄狭的山地红松云冷杉林亚带(周以良、李景文,1964)。红松云冷杉林受干扰后,白桦林是最为主要的派生森林类型之一。所以,红松云冷杉林演替过程中。白桦林阶段以及林下红松的更新居于重要的位置。本世纪以来,由于东北山地森林的大规模开发,红松云冷杉林面积急剧减小;而作为红松云冷杉林演替先锋阶段的白桦林比重相     

长白山北坡天然次生林典型建群种的种群结构及动态特征

分析长白山林区典型天然次生林的建群种种群结构及动态特征,揭示关键种群的生存现状和发展趋势,以期为研究区的天然林保护与修复提供基础资料和理论依据。基于24块1 hm²的固定样地数据,通过编制种群静态生命表,拟合并绘制种群存活曲线,运用生存分析、种群数量化分析和时间序列分析,定量研究2种典型天然次生林4个建群种的种群结构与动态特征。结果显示,4个种群的存活曲线总体均趋于Deevey-Ⅱ型,但所属亚型有所区别。臭冷杉（Abies nephrolepis）种群死亡率波动较大,在不同龄级出现了多次死亡高峰;鱼鳞云杉（Picea jezoensis var. microsperma）和蒙古栎（Quercus mongolica）种群死亡率随龄级增大逐步递增;红松（Pinus koraiensis）种群在各龄级上的死亡率均较高。数量化动态分析表明,4个种群均属于增长型,增长潜力为红松 > 臭冷杉 > 鱼鳞云杉 > 蒙古栎;4个种群受外界干扰的敏感程度均较高,其中红松种群受干扰的概率最大。时间序列预测表明,臭冷杉和红松种群个体数量在未来2、4、6、8个龄级后均呈现不同幅度的增加趋势,增长势态稳定。鱼鳞云杉和蒙古栎种群在幼、中龄级表现出衰退迹象。结论表明,臭冷杉和红松种群的自然更新较好,增长潜力较大,但同时受外界干扰的敏感程度也较高。鱼鳞云杉和蒙古栎种群的自然更新不足,增长潜力小,群落存在偏离稳定状态的风险。建议严格保护臭冷杉和红松种群的生境,适度开展疏伐抚育;改善鱼鳞云杉和蒙古栎种群空间格局,及时实施人工促进天然更新,促进群落进展演替。     

An additive edge correction for the influence potential of trees

The influence potential on a quadrat (IPQ) is an index for measuring the ecological effect that trees have on understory vegetation observed in a quadrat of a plot. IPQ is defined as the sum of the effect of every trees in the plot, where the effect depends on the size of the tree and the distance between the tree and the quadrat. Since only the trees in the plot have been observed and not the trees outside the plot, the true IPQ may be underestimated. Existing edge corrections are not appropriate for this case. We propose a correction that consists of adding the expected IPQ due to effects of trees outside the plot to the observed IPQ. The expectation is obtained by applying the Campbell theorem for stationary marked point processes. Data from the 1985-86 National Forest Inventory of Finland was used to calculate IPQ for six quadrats systematically allocated to each of 1240 plots. The implementation of the correction for this data is described. The distributions of IPQ with and without the correction proved the existence of edge effects and the effectiveness of the correction to eliminate the bias. This method has the potential to be applied to other additive functions.     

岷江上游干旱河谷农林边界影响域的研究

对岷江上游农林边界的影响域进行研究,以提高该区管理农田和林地的水平．共调查3种类型农林边界10条样带,采用移动窗口法对植物多样性的数据进行分析,结果表明,当窗口宽度达到6～10时。SED曲线的变化趋向稳定,并且在曲线上有一或两个峰值出现．不同类型边界的影响域是不同的,但均在距边界50m内．各类型边界的影响域多在12～30m之间．6条林地样带只有M2和M6样带林地的影响域被确定,而4条农田样带的影响域均被确定．影响域的大小取决于边界两侧斑块类型和地形以及小气候等因子,但坡向对其影响不大;移动窗口法能有效地刻画边界动态,是一种分析边界简单而有力的工具．这些结果有利于进一步理解干旱河谷区农林间的相互作用．     

On explicit formulas of edge effect correction for Ripley's K-function

Abstract. The analysis of spatial pattern in plant ecology usually implies the solution of some edge effect problems. We present in this paper some explicit formulas of edge effect correction that should enable plant ecologists to analyse a wider range of real field data. We consider the local correcting factor of edge effect for Ripley's K-function, that can also be used for other statistics of spatial analysis based on the counting of neighbours within a given distance. For both circular and rectangular study areas, we provide a review of explicit formulas and an extension of these formulas for long and narrow plots. In the case of irregular-shaped study plots, we propose a generalization of the method that computes edge effect correction by excluding triangular surfaces from a simple (rectangular or circular) initial shape. An example in forest ecology, where the soil characteristics determine a study plot of complex shape, illustrates how this edge effect correction can be effective in avoiding misinterpretations.     

云南哀牢山主峰两侧沉积物孢粉区系成分对比分析

哀牢山作为滇东高原与横断山系南段滇西南山地的分界线,其高大山脉造就了山体两侧气候、植被差异显著。本研究以沉积物孢粉为媒介,对山脉主峰两侧的植物物种区系成分进行对比分析。结果显示,哀牢山西坡样地受印度夏季风影响程度高于东坡样地,沉积物孢粉中热带成分大于东坡,分别为6.8%和4.7%;哀牢山东坡样地则受北来东亚冬季风影响显著,造成东坡样地孢粉中北温带成分远大于西坡样地,分别为26.4%和13.4%。哀牢山成为印度夏季风与东亚冬季风的重要分界之一。     

√2 Rule for Controlling the Tree Pattern in Forest Cut

A forest's productivity can be optimized by the application of rules derived from monopolized circles.A monopolized circle is defined as a circle whose center is a tree and whose radius is half of the distance between the tree itself and its nearest neighbor.Three characteristics of monopolized circle are proved.(1) Monopolized circles do not overlay each other,the nearest relationship being tangent.(2)"Full uniform pattern"means that the grid of trees (a×=N) covers the whole plot,so that the distance between each tree in a row is the same as the row spacing.The total monopolized circle area with a full uniform pattern is independent on the number of trees and π/4 times the plot area.(3) If a tree is removed,the area of some trees'monopolized circle will increase without decreasing the monopolized circles of the other trees.According to the above three characteristics,"uniform index"is defined as the total area of monopolized circles in a plot divided by the total area of the monopolized circles,arranged in a uniform pattern in the same shaped plot.According to the definition of monopolized circle,the distribution of uniform index (L) = x2(2n)/2πn for a random pattern and E(L)=1/π;the variance of L is D(L)=1/nπ2.It is evident that E(L) is independent on N and the plot area;hence,L is a relative index.L can be used to compare the uniformity among plots with different areas and the numbers of trees.In a random pattern,where L is equivalent to the tree density of the plot in which the number of trees is 1 and the area is π,the influence of tree number and plot area to L is eliminated.When n→∞,D(L)→0 and L→1/π= 0.318;it indicates that the greater the number of tree is in the plots,the smaller the difference between the uniform indices will be.There are three types of patterns for describing tree distribution (aggregated,random,and uniform patterns).Since the distribution of L in the random pattern is accurately derived,L can be used to test the pattern types.The research on Moarshan showed that the whole plot has an aggregated pattern;the first,third,and sixth parts have an aggregated pattern;and the second,fourth,and fifth parts have a random pattern.None of the uniform indices is more than 0.318 (1/Ⅱ),which indicates that uniform patterns are rare in natural forests.The rules of uniform index can be applied to forest thinning.If you want to increase the value of uniform index,you must increase the total area of monopolized circles,which can be done by removing select trees."Increasing area trees"are the removed trees and can increase the value of the uniform index.A tree is an increasing area tree if the distance between the tree and its second nearest neighbor is √2 times longer than that between the tree itself and its first nearest neighbor,which is called the √2 rule.It was very interesting to find that when six plots were randomly separated from the original plot,the proportion of increasing area trees in each plot was always about 0.5 without exception.In random pattern,the expected proportion of increasing area trees is about 0.35-0.44,which is different from the sampling value of 0.5.The reason is very difficult to explain,and further study is needed.Two criteria can be used to identify which trees should be removed to increase the uniform index during forest thinning.Those trees should be (1) trees whose monopolized circle areas are on the small side and (2) increasing area trees,which are found via the √2 rule.     

Spatial point pattern analysis of available and exploited resources

A patchy spatial distribution of resources underpins many models of population regulation and species coexistence, so ecologists require methods to analyse spatially‐explicit data of resource distribution and use. We describe a method for analysing maps of resources and testing hypotheses about species' distributions and selectivity. The method uses point pattern analysis based on the L‐function, the linearised form of Ripley's K‐function. Monte Carlo permutations are used for statistical tests. We estimate the difference between observed and expected values of L(t), an approach with several advantages: 1) The results are easy to interpret ecologically. 2) It obviates the need for edge correction, which has largely precluded the use of L‐functions where plot boundaries are “real”. Including edge corrections may lead to erroneous conclusions if the underlying assumptions are invalid. 3) The null expectation can take many forms, we illustrate two models: complete spatial randomness (to describe the spatial pattern of resources in the landscape) and the underlying pattern of resource patches in the landscape (akin to a neutral landscape approach). The second null is particularly useful to test whether spatial patterns of exploited resource points simply reflect the spatial patterns of all resource points. We tested this method using over 100 simulated point patterns encompassing a range of patterns that might occur in ecological systems, and some very extreme patterns. The approach is generally robust, but Type II decision errors might arise where spatial patterns are weak and when trying to detect a clumped pattern of exploited points against a clumped pattern of all points. An empirical example of an intertidal lichen growing on barnacle shells illustrates how this technique might be used to test hypotheses about dispersal mechanisms. This approach can increase the value of survey data, by permitting quantification of natural resource patch distribution in the landscape as well as patterns of resource use by species.     

Frequency and distance of pollen dispersal from transgenic oilseed rape (Brassica napus)

The objective of this study was to evaluate pollen dispersal inBrassica napus (oilseed rape). The selectable marker, used to follow pollen movement, was a dominant transgene (bar) conferring resistance to the herbicide glufosinate-ammonium. Transgenic and non-transgenic plants of the cultivar Westar were planted in a 1.1 ha field trial, with the transgenic plants in a 9 m diameter circle at the centre, surrounded by non-transgenic plants to a distance of at least 47 m in all directions. A 1 m circle of non-transgenic plants was sown in the centre of the transgenic area to allow estimation of the level of pollen dispersal when plants were in close contact. Honeybee hives were placed at the trial site to optimize the opportunity for cross-pollination. During the flowering period, regular observations were made of the number of plants flowering and the number and type of insects present in 60 1 m² areas. These areas were located uniformly around the plot at distances of 1, 3, 6, 12, 24, 36 and 47 m from the edge of the 9 m circle of transgenic plants. Seed samples were harvested from each of the 7 distances so that approximately 20% of the circumference of the plot was sampled at each distance. The centre non-transgenic circle was also sampled. Plants were grown from the seed samples and sprayed with glufosinate to estimate the frequency of pollen dispersal at each distance. In order to screen enough samples to detect low frequency cross-pollination events, seed samples were tested in the greenhouse and on a larger scale in the field. Results were confirmed by testing progeny for glufosinate resistance and by Southern blot analysis. The estimated percentage of pollen dispersal in the non-transgenic centre circle was 4.8%. The frequency was estimated to be 1.5% at a distance of 1 m and 0.4% at 3 m. The frequency decreased sharply to 0.02% at 12 m and was only 0.00033% at 47 m. No obvious directional effects were detected that could be ascribed to wind or insect activity.     

\sqrt 2 Rule for Controlling the Tree Pattern in Forest Cut

A forest’s productivity can be optimized by the application of rules derived from monopolized circles. A monopolized circle is defined as a circle whose center is a tree and whose radius is half of the distance between the tree itself and its nearest neighbor. Three characteristics of monopolized circle are proved. (1) Monopolized circles do not overlay each other, the nearest relationship being tangent. (2) “Full uniform pattern” means that the grid of trees (a×b=N) covers the whole plot, so that the distance between each tree in a row is the same as the row spacing. The total monopolized circle area with a full uniform pattern is independent on the number of trees and $\frac{\pi }{4}$ times the plot area. (3) If a tree is removed, the area of some trees’ monopolized circle will increase without decreasing the monopolized circles of the other trees. According to the above three characteristics, “uniform index” is defined as the total area of monopolized circles in a plot divided by the total area of the monopolized circles, arranged in a uniform pattern in the same shaped plot. According to the definition of monopolized circle, the distribution of uniform index $(L) = \frac{{\chi ^2 (2n)}}{{2\pi n}}$ for a random pattern and $E(L) = \frac{1}{\pi }$ the variance of L is $D(L) = \frac{1}{{n\pi ^2 }}$ . It is evident that E(L) is independent on N and the plot area; hence, L is a relative index. L can be used to compare the uniformity among plots with different areas and the numbers of trees. In a random pattern, where L is equivalent to the tree density of the plot in which the number of trees is 1 and the area is π, the influence of tree number and plot area to L is eliminated. When n→∞, D(L)→0 and $L \to \frac{1}{\pi } = 0.318$ it indicates that the greater the number of tree is in the plots, the smaller the difference between the uniform indices will be. There are three types of patterns for describing tree distribution (aggregated, random, and uniform patterns). Since the distribution of L in the random pattern is accurately derived, L can be used to test the pattern types. The research on Moarshan showed that the whole plot has an aggregated pattern; the first, third, and sixth parts have an aggregated pattern; and the second, fourth, and fifth parts have a random pattern. None of the uniform indices is more than 0.318 (1/∏), which indicates that uniform patterns are rare in natural forests. The rules of uniform index can be applied to forest thinning. If you want to increase the value of uniform index, you must increase the total area of monopolized circles, which can be done by removing select trees. “Increasing area trees” are the removed trees and can increase the value of the uniform index. A tree is an increasing area tree if the distance between the tree and its second nearest neighbor is $\sqrt 2 $ times longer than that between the tree itself and its first nearest neighbor, which is called the $\sqrt 2 $ rule. It was very interesting to find that when six plots were randomly separated from the original plot, the proportion of increasing area trees in each plot was always about 0.5 without exception. In random pattern, the expected proportion of increasing area trees is about 0.35–0.44, which is different from the sampling value of 0.5. The reason is very difficult to explain, and further study is needed. Two criteria can be used to identify which trees should be removed to increase the uniform index during forest thinning. Those trees should be (1) trees whose monopolized circle areas are on the small side and (2) increasing area trees, which are found via the $\sqrt 2 $ rule.     

Effects of quadrat size and data measurement on the detection of boundaries

Abstract. With sampled field data, the accuracy of delineation of ecotones is directly related to the resolution of the data (i.e. spatial and measurement type) and to the edge detection algorithm used. In the present study the reliability is investigated of an edge detection algorithm (lattice-wombling) to delimit vegetation boundaries when different spatial resolutions (quadrat sizes) are used. To quantify whether the edge detection algorithm is robust, it was applied to data from woody species in a second-growth woodland measured at different spatial resolutions with different data types. Boundaries were found at similar locations using any reasonable quadrat size (ca. 200 m²) but there were some slight differences when using different vegetation measures (density versus presence/absence data).