Chin J Plant Ecol ›› 2009, Vol. 33 ›› Issue (1): 71-80.DOI: 10.3773/j.issn.1005-264x.2009.01.008
• Research Articles • Previous Articles Next Articles
YU Hong, YANG Xiao-Hui*(), CI Long-Jun
Received:
2008-06-10
Accepted:
2008-07-10
Online:
2009-06-10
Published:
2009-01-30
Contact:
YANG Xiao-Hui
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[J]. Chin J Plant Ecol, 2009, 33(1): 71-80.
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) |
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) |
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) |
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) |
内容 Attribute | B06-1 | B06-2 | ||
---|---|---|---|---|
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 |
Table 3 Tree species and its composition in two plots surveyed
内容 Attribute | B06-1 | B06-2 | ||
---|---|---|---|---|
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 |
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
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
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
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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