地表火对红花尔基沙地樟子松种群空间分布格局的影响

doi:10.3773/j.issn.1005-264x.2009.01.008

摘要/Abstract

摘要：

利用全林木定位的方法, 对地表火干扰1年后的樟子松(Pinus sylvestris L. var. mongolica Litv.)林进行调查, 并通过假设检验和成对相关函数对其林火及林分结构特征和空间格局进行分析。结果表明, 林火强度相似的同一场地表火干扰下, 不同林分的密度均大大降低, 胸高断面积仅略有下降, 林分结构特征则有趋同的态势。不同林分的空间格局也有相似的变化趋势, 烧死木均表现为显著的双尺度聚集分布及显著的正相关, 活立木也表现出显著的正相关; 地表火干扰前后, 樟子松林的空间格局均为显著的聚集分布, 但地表火干扰后其聚集分布的尺度范围变小; 存活林木中, 大树和幼树则呈现出相互独立或略微排斥的关系。显然, 地表火驱动下, 不同樟子松林的空间格局呈现出相似的变化趋势, 并推动其向着成熟林方向演替, 这对天然樟子松林的资源保护和经营管理有着重要意义。

关键词: 樟子松, 成对相关函数, 空间格局, 林分结构, 地表火

Abstract:

Aims Our objectives were to 1) explore variations of stand structure and spatial pattern after surface fire in Mongolian pine forest of the Hulun Buir sand region and 2) elucidate surface fire as a dynamic driving force in stand thinning and succession.
Methods Fire-killed and live trees were mapped in two 1-hm² plots one year postfire. Differences of fire intensities and stand structures were tested by the Kruskal-Wallis procedure, and spatial patterns were explored using the pair correlation function g(r).
Important findings Tree densities decreased greatly and basal areas were decreased slightly by surface fire with similar intensities. Fire-killed trees showed a nested, double-cluster, positively correlated pattern. Stems were aggregated spatially pre- and postfire; however, the ranges of significant scales were narrower postfire. Adults and saplings were independent or slightly negatively associated at small scales. We conclude that surface fire is an important driving force of stand thinning and variations in spatial pattern that influence succession to mature forest.

Key words: Mongolian pine, pair correlation function, spatial pattern, stand structure, surface fire

喻泓, 杨晓晖, 慈龙骏. 地表火对红花尔基沙地樟子松种群空间分布格局的影响. 植物生态学报, 2009, 33(1): 71-80. DOI: 10.3773/j.issn.1005-264x.2009.01.008

YU Hong, YANG Xiao-Hui, CI Long-Jun. VARIATIONS OF SPATIAL PATTERN IN FIRE-MEDIATED MONGOLIAN PINE FOREST, HULUN BUIR SAND REGION, INNER MONGOLIA, CHINA. Chinese Journal of Plant Ecology, 2009, 33(1): 71-80. DOI: 10.3773/j.issn.1005-264x.2009.01.008

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https://www.plant-ecology.com/CN/Y2009/V33/I1/71

图/表 8

图1 调查样地的立木点图 (a) B06-1和(b) B06-2为2006年地表火干扰的樟子松林样地。黑色(●)为高度大于1.3 m的烧死木、灰色(●)为高度小于1.3 m的烧死木、黑色(○)为树高大于1.3 m的活立木、灰色(○)为树高小于1.3 m的存活幼树。胸径大于10 cm立木的符号与其胸径大小成比例, 最小的符号为胸径小于或等于10 cm及高度小于1.3 m的立木 Symbols are fire-enhanced dead trees with heights not less than 1.3 m (●), fire-enhanced dead trees with heights less than 1.3 m (●), living trees with heights not less than 1.3 m (○), and living trees with heights less than 1.3 m (○). Symbol sizes are proportional to the DBH not less than 10 cm. The smallest symbols are agents with DBH not more than 10 cm or not more than 1.3 m in height

Fig. 1 Maps of (a) B06-1 (plot 1 burned in 2006) and (b) B06-2 (plot 2 burned in 2006)

表1 空间点格局分析的零假设模型及其代码

Table 1 Null models and its codes in spatial point pattern analysis

假设 Hypothesis	零模型 Null model	代码 Code	点格局分析 Point pattern analysis
双变量的两个格局先后顺序出现, 格局1独立于格局2, 但是后者受前者的影响 The two types of points were not created at the same time, but in sequence, pattern 2 did not influence the development of pattern 1, but pattern 1 may influence the development of pattern 2	先决条件假设 Antecedent condition	AC	火后存活大树(格局1) 和幼树(格局2) Survival trees (pattern 1) and survival saplings (pattern 2)
格局的点间没有相互作用且是相互独立的, 其密度在研究区内也是相对均一的 There were no interactions between the points of the patterns and they were independent each other, the first-order intensity of the pattern is constant over the study region	完全空间随机假设 Complete spatial randomness	CSR	火后存活立木(点的密度是相对均一的) Survivals (the first-order intensity of the pattern is relative constant over the region)
在两个大、小不同的尺度上表现为Neyman-Scott过程 The pattern shows Neyman-Scott process at the small and large scales	双尺度集聚过程 Double-cluster process	DC	烧死木(空间点形成明显的大小不同的聚块体) Fire-enhanced dead trees (the points of the pattern follows a double-clustered structure at the small and large scales)
点的密度在不同区域上存在差异(在本研究中, 探测空间异质性的滑动窗口的半径为30 m) The first-order intensity of the pattern is not constant over the study region (the radius of the moving window to explore the heterogeneous pattern is 30 m in the study)	空间异质性泊松过程 Heterogeneous Poisson process	HP	林火干扰前林木、火后存活的幼树(点的密度不是相对均一的) Trees in prefire stand, survival saplings (the intensity of points is not constant over the region)
研究对象和对照产生于同一随机过程, 分别是其共同分布格局的一个随机子样本 The pattern of controls and targets (“case”) are created by the same stochastic process, and each of them represents a random sub-sample of the joined pattern of the control and the case points	随机标识假设 Random labeling	RL	火后存活立木(格局1)和烧死木(格局2)(二者同时受林火的影响) Survivals (pattern 1) and fire-enhanced dead trees (pattern 2) (the two patterns were simultaneously attacked by fire)

表1 空间点格局分析的零假设模型及其代码

Table 1 Null models and its codes in spatial point pattern analysis

假设 Hypothesis	零模型 Null model	代码 Code	点格局分析 Point pattern analysis
双变量的两个格局先后顺序出现, 格局1独立于格局2, 但是后者受前者的影响 The two types of points were not created at the same time, but in sequence, pattern 2 did not influence the development of pattern 1, but pattern 1 may influence the development of pattern 2	先决条件假设 Antecedent condition	AC	火后存活大树(格局1) 和幼树(格局2) Survival trees (pattern 1) and survival saplings (pattern 2)
格局的点间没有相互作用且是相互独立的, 其密度在研究区内也是相对均一的 There were no interactions between the points of the patterns and they were independent each other, the first-order intensity of the pattern is constant over the study region	完全空间随机假设 Complete spatial randomness	CSR	火后存活立木(点的密度是相对均一的) Survivals (the first-order intensity of the pattern is relative constant over the region)
在两个大、小不同的尺度上表现为Neyman-Scott过程 The pattern shows Neyman-Scott process at the small and large scales	双尺度集聚过程 Double-cluster process	DC	烧死木(空间点形成明显的大小不同的聚块体) Fire-enhanced dead trees (the points of the pattern follows a double-clustered structure at the small and large scales)
点的密度在不同区域上存在差异(在本研究中, 探测空间异质性的滑动窗口的半径为30 m) The first-order intensity of the pattern is not constant over the study region (the radius of the moving window to explore the heterogeneous pattern is 30 m in the study)	空间异质性泊松过程 Heterogeneous Poisson process	HP	林火干扰前林木、火后存活的幼树(点的密度不是相对均一的) Trees in prefire stand, survival saplings (the intensity of points is not constant over the region)
研究对象和对照产生于同一随机过程, 分别是其共同分布格局的一个随机子样本 The pattern of controls and targets (“case”) are created by the same stochastic process, and each of them represents a random sub-sample of the joined pattern of the control and the case points	随机标识假设 Random labeling	RL	火后存活立木(格局1)和烧死木(格局2)(二者同时受林火的影响) Survivals (pattern 1) and fire-enhanced dead trees (pattern 2) (the two patterns were simultaneously attacked by fire)

表2 调查样地树皮熏黑高的统计特征

Table 2 Statistics of bark char heights in two surface fire-mediated plots

样地 Plot	样本量 Samples	平均值 (标准误差) Mean (standard error)	中位数 Median	标准偏差 Standard deviation	最小值 Minimum	最大值 Maximum	正态性检验 Tests for normality
B06-1	2 256	1.501 6 (0.026 8)^a	1.07	1.273 4	0	8.80	0.150 2 (<0.010 0)
B06-2	929	1.397 5 (0.046 5)^a	0.77	1.417 8	0	7.88	0.791 8 (<0.000 1)

表3 调查样地的树种组成及其结构特征

Table 3 Tree species and its composition in two plots surveyed

内容 Attribute	B06-1		B06-2
内容 Attribute	林火前 Prefire	林火后 Postfire	林火前 Prefire	林火后 Postfire
密度 Density (No.·hm^-2)
PiMo	2 870 (78.26)	428 (74.05)	3 144 (78.99)	343 (84.07)
BePl	761 (20.75)	142 (24.57)	2 (0.05)	0 (0)
CrDa	1 (0.03)	1 (0.17)	1 (0.03)	0 (0)
MaBa	34 (0.93)	7 (1.21)	821 (20.63)	65 (15.93)
PaAv	1 (0.03)	0 (0)	12 (0.30)	0 (0)
合计 Total	3667	578	3 980	408
平均胸径 Mean DBH (cm)
PiMo	8.4 (0.32)	26.7 (0.64)	14.7 (0.65)	23.4 (0.80)
BePl	3.5 (0.09)	5.8 (0.23)	1.1 (0)	0 (0)
CrDa	1.5 (0)	1.5 (0)	129 (0)	0 (0)
MaBa	1.1 (0.11)	36.3 (3.62)	3.1 (0.10)	5.20 (0.22)
PaAv	57	0 (0)	2.1 (1.40)	0 (0)
合计 Total	7.1 (0.24)	21.5 (0.62)	10.1 (0.43)	20.4 (0.75)
胸高断面积 Area at breast height (m²)
PiMo	30.465 3 (97.297 4)	29.943 6 (98.546 6)	19.800 8 (93.648 7)	19.667 8 (99.22)
BePl	0.845 6 (2.7)	0.441 4 (1.452 7)	0.950 3 (4.494 5)	0 (0)
CrDa	0.000 2 (0.000 6)	0.000 2 (0.000 7)	0 (0)	0 (0)
MaBa	0.000 6 (0.002)	0 (0)	0.391 6 (1.852 1)	0.153 9 (0.78)
PaAv	0 (0)	0 (0)	0.001 (0.004 7)	0 (0)
合计 Total	31.311 7	30.385 2	21.143 7	19.821 7

图2 地表火干扰前(a: B06-1, b: B06-2)后(a, b的插入图)的单变量空间格局分析地表火干扰前的林分包括了烧死木和活立木, 图中大写字母为点格局分析所采用的零假设模型的代码(表1), “+”符号及其后的数字分别表示显著性聚集分布及其相应的尺度范围; 黑色和灰色实线分别为g(r)函数值及其99次Monte Carlo检验的99%置信区间

Fig. 2 Univariate spatial pattern analysis of pre- (a, b) and post-fire (inset figures in a, b) in B06-1 and B06-2, respectively Fire-enhanced dead trees are also included in spatial pattern analysis prefire. Capital letters are the codes of null hypothesis models (Table 1). “+” and numbers followed indicate significant aggregation at the corresponding range of scales. Black and grey solid lines indicate g(r) and their corresponding lower and upper 99% confidence limits constructed by 99 Monte Carlo simulations, respectively

图3 烧死木的单变量空间格局分析 a)、b)及其插入图分别为B06-1和B06-2样地在大、小不同尺度上双尺度聚集分布的点格局分析。图中大写字母为点格局分析所采用的零假设模型的代码(表1), “+”符号及其后的数字分别表示显著性双尺度聚集分布及其聚块的半径; 黑色和灰色实线分别为g(r)函数值及其99次Monte Carlo检验的99%置信区间

Fig. 3 Univariate spatial pattern analysis of fire-enhanced dead trees in B06-1 (a), B06-2 (b), respectively a) and b) and its inset figures are double-cluster pattern analysis of fire-enhanced dead trees at smaller and larger scales, respectively. Capital letters are codes of null hypothesis models (Table 1). “+” and numbers followed stand significant aggregation and the radii of the corresponding clusters, respectively. Black and grey solid lines stand g(r) and their corresponding lower and upper 99% confidence limits constructed by 99 Monte Carlo simulations, respectively

图4 火后存活大树(格局1)与幼树(格局2)间的双变量空间格局分析 B06-1(a)和B06-2(b)的大树是指胸径大于或等于10 cm 的活立木, 幼树是指胸径小于10 cm和树高低于1.3 m的活立木。图中大写字母为点格局分析所采用的零假设模型的代码(表1), “+”和“-”符号及其后的数字分别表示显著性聚集和均匀分布及其相应的尺度范围; 黑色和灰色实线分别为g12(r)函数值和99次Monte Carlo检验的99%置信区间

Fig. 4 Bivariate spatial pattern analysis of adults (pattern 1) and saplings (pattern 2) in B06-1 (a) and B06-2 (b), respectively Adults are survivals not less than 10 cm in DBH, and saplings survivals not more than 10 cm in DBH and not more than 1.3 m in height in B06-1 and B06-2, respectively. Capital letters are the codes of null hypothesis models (Table 1). “+” and “-” and numbers followed stand significant aggregation and repulsion at the corresponding range of scales, respectively. Black and grey solid lines stand g12(r) function and their corresponding lower and upper 99% confidence limits constructed by 99 Monte Carlo simulations, respectively

图5 活立木(格局1)和烧死木(格局2)的双变量空间格局分析 a)和b)及其插入图分别为B06-1和B06-2活立木和烧死木的点格局分析。图中大写字母为点格局分析所采用的零假设模型的代码(表1), “-”符号及其后的数字分别表示显著性均匀分布及其相应的尺度范围; 黑色和灰色实线为函数值及其99次Monte Carlo检验的99%置信区间

Fig. 5 Bivariate spatial pattern analysis of survivals (pattern 1) and fire-enhanced dead trees (pattern 2) in B06-1 (a) and B06-2 (b) and its inset figures, respectively Capital letters are the codes of null hypothesis models (Table 1). “-” and numbers followed stand significant repulsion at the corresponding range of scales. Black and grey solid lines stand function values and their corresponding lower and upper 99% confidence limits constructed by 99 Monte Carlo simulations

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