长白山北坡云冷杉阔叶混交林的物种多度格局

doi:10.11707/j.1001-7488.20210509

摘要/Abstract

摘要：

目的: 研究长白山北坡云冷杉阔叶混交过伐林乔木层物种多度格局，阐明过伐林群落冠层结构维持的生态过程，探讨生境异质性、密度依赖、种间关联、扩散限制、更新限制和物种分化在针阔混交林群落装配中的作用，为揭示过伐林恢复过程中的物种多度分布机制提供理论指导。方法: 采用经验统计模型、生态位模型、群落中性理论模型以及复合生态位模型，对长白山北坡12块云冷杉阔叶混交过伐林样地进行物种多度格局分析。结果: 经验统计模型中对数正态模型和泊松对数正态模型拟合效果最佳，对数级数模型拟合效果较差，对数级数模型参数T和对数正态模型参数λ可作为表征群落多样性的重要参数；生态位模型中断棍模型拟合效果最佳，其次是生态位重叠模型和几何级数模型，再次为Zipf模型和Zipf-Mandelbrot模型，最后为随机分配模型和优势优先模型；复合生态位模型拟合效果较传统生态位模型有较大提高，复合模型可能在群落丰富种和稀有种呈现竞争等级多度分布时拟合效果最佳；以胸高断面积为指标的物种多度分布拟合与以个体数为指标的经验统计模型和生态位模型物种多度分布拟合表现基本一致，而在群落中性理论模型和复合模型拟合表现方面有一定差异，可能与稀有种断面积比重较大有关。结论: 复合模型可能成为连接经验模型、生态位模型和中性理论模型的纽带，为研究物种多度格局提供数学途径和思路。云冷杉阔叶混交过伐林群落物种等级多度分布某种程度上是生态位过程和随机干扰生态过程共同作用的结果。

关键词: 物种多度关系模型, 复合模型, 联合中性理论模型, 生态位模型, 随机分配模型

Abstract:

Objective: Species abundance distribution of community is the base for the biodiversity and community assemblage rules. Aim of the study was to fit the species abundance distribution of spruce-fir broad-leaved tree species mixed forest in overcutting forest zone in northeast of China and to interpret the ecological mechanisms behind the pattern. Method: Empirical statistical model, niche model, unified neutral theory model and niche composite model were used to analyze the species abundance data from the 12 communities of spruce-fir broad-leaved tree species mixed forest in overcutting forest zone in northeast of China. Result: 1) Logarithm normal distribution(ln)model and Poisson logarithm normal distribution(pl)model performed better than the logarithm series model(ls), the T parameter from the ls model and the λ parameter from the ln model characterized the biodiversity property of the 12 communities. 2) Among the 7 niche models, the random broken sticks model performed the best, followed by the overlapping niche model, geometrical series model, Zipf model and Zipf-Mandelbrot model, and the random assortment model and the dominance preemption model performed the worst. 3) The fitting capacity of the composite model performed better than the other traditional niche models listed above, especially when the abundances of common and rare species were in different competition hierarchies; the empirical model and the niche model exhibited the same performance when the basal area were used as generalized abundance indices, while the composite model and the unified neutral model had differences with that for individual number as abundance indices. Conclusion: Composite model was a combination of two simple niche model, it could span the bridge between empirical model, niche model and neutral theory model, and open up new ways for the species abundance pattern analysis. The species abundance of spruce-fir broad-leaved tree species mixed forest in overcutting forest zone in northeast of China was a combination of deterministic and stochastic ecological processes, and represented multiple mechanisms such as seed source dispersal, interspecific competition, forest cutting and random disturbance, overlogged forest community was in the stage of recovery succession, niche process may dominate.

Key words: species abundance relationship model, composite model, unified neutral theory model, niche model, random assortment model

中图分类号:

S718.5

郭跃东,张会儒,卢军,王卓晖. 长白山北坡云冷杉阔叶混交林的物种多度格局[J]. 林业科学, 2021, 57(5): 93-107.

Yuedong Guo,Huiru Zhang,Jun Lu,Zhuohui Wang. Species Abundance Distribution of Spruce-Fir Broad-Leaved Tree Species Mixed Forest in Northern Slope of Changbai Mountain in China[J]. Scientia Silvae Sinicae, 2021, 57(5): 93-107.

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样地 Sample plot	林分密度 Stand density/ (tree·hm^-2)	树种组成 Tree species composition	林分断面积 Basic area/m²	坡位 Slope position	海拔 Elevation/m	郁闭度 Canopy density
1	934	2椴树2杂2冷杉1云杉1色木1红松1落叶松+枫桦 2TI2MT2FI1SP1MA1KP1LA+YB	15.42	下Down	742	0.60
2	1 224	3杂2椴树2色木1冷杉1红松1枫桦+落叶松+云杉-榆树3 MT2TI2MA1FI1KP1YB+LA+SP-EL	15.26	中Middle	752	0.70
3	1 233	3杂2椴树2冷杉1色木1落叶松1榆树+红松+枫桦+云杉 3MT2TI2FI1MA1LA1EL+KP+YB+SP	16.87	中Middle	760	0.71
4	1 167	3椴树2杂2冷杉1色木1红松1榆树+云杉+枫桦-落叶松 3TI2MT2FI1MA1KP1EL+SP+YB-LA	17.20	上Up	773	0.66
5	1 328	2椴树2色木2杂2冷杉1红松1云杉+枫桦+榆树-落叶松 2TI2MA2MT2FI1KP1SP+YB+EL-LA	18.45	中Middle	780	0.72
6	1 422	3冷杉2椴树2色木1杂1枫桦1红松+云杉+榆树+落叶松 3FI2TI2MA1MT1YB1KP+SP+EL+LA	18.01	上Up	792	0.73
7	1 540	4冷杉2椴树1杂1色木1红松1枫桦+榆树+落叶松+云杉 4FI2TI1MT1MA1KP1YB+EL+LA+SP	18.60	上Up	771	0.76
8	1 310	3冷杉1杂1椴树1枫桦1云杉1色木1红松1白桦+落叶松 3FI1MT1TI1YB1SP1MA1KP1WB+LA	15.57	下Down	732	0.75
9	1 438	4冷杉2椴树1红松1色木1杂1枫桦+云杉+落叶松+榆树+杨树 4FI2TI1KP1MA1MT1YB+SP+LA+EL+PO	17.09	下Down	749	0.71
10	1 195	2椴树2杂2冷杉1红松1云杉1枫桦1色木+落叶松+榆树+杨树 2TI2MT2FI1KP1SP1YB1MA+LA+EL+PO	19.19	下Down	759	0.76
11	1 301	3椴树2色木2冷杉1红松1榆树1云杉+水曲柳+杂+枫桦+落叶松 3TI2MA2FI1KP1EL1SP+AS+MT+YB+LA	19.18	上Up	769	0.65
12	1 437	2椴树2杂1色木1榆树1冷杉1红松1落叶松1云杉+枫桦+水曲柳 2TI2MT1MA1EL1SP1KP1LA1SP+YB+AS	22.12	上Up	773	0.73

样地 Sample plot	ls			ln			pl
样地 Sample plot	χ²	D	AIC	χ²	D	AIC	χ²	D	AIC
1	5.39	0.40	162.14	6.99^*	0.30	159.10	7.14^*	0.30	159.21
2	3.46	0.60^*	143.53	0.01	0.60^*	129.84	0.02	0.60^*	129.82
3	2.70	0.60^*	156.34	15.95^**	0.40	153.38	9.69^**	0.40	152.67
4	2.74	0.60^*	154.48	12.54^**	0.40	152.12	7.64^*	0.40	151.51
5	3.78	0.50	154.22	0.04	0.40	143.46	0.96	0.40	143.36
6	2.57	0.36	169.17	7.94^*	0.36	171.53	5.53	0.36	171.26
7	4.05	0.45	162.73	0.24	0.36	155.58	0.34	0.36	155.64
8	1.46	0.54	163.47	4.62	0.36	167.61	3.04	0.36	167.22
9	1.13	0.42	158.09	6.79^*	0.42	160.62	4.69	0.41	160.37
10	1.85	0.50	162.21	5.73	0.30	161.24	3.51	0.30	160.92
11	4.17	0.55	164.65	0.03	0.45	151.31	0.55	0.45	150.88
12	1.30	0.45	171.84	8.90^*	0.36	173.76	6.50	0.36	173.51

样地 Sample plot	T	λ	个体数Number of individual				胸高断面积Basal area
样地 Sample plot	T	λ	Shannon- Wiener	Marglef	Simpson	Pielou	Shannon- Wiener	Marglef	Simpson	Pielou
1	1.94	3.39	2.24	2.04	0.87	0.82	2.14	1.67	0.85	0.79
2	1.67	4.39	2.18	1.41	0.87	0.91	1.99	1.15	0.83	0.83
3	2.03	3.92	2.22	1.69	0.88	0.87	1.91	1.36	0.81	0.75
4	2.05	3.86	2.23	1.70	0.88	0.87	2.06	1.40	0.85	0.80
5	1.82	4.30	2.24	1.53	0.88	0.90	2.04	1.26	0.84	0.82
6	2.34	3.56	2.18	1.93	0.86	0.80	2.06	1.58	0.84	0.76
7	1.95	4.13	2.12	1.64	0.84	0.83	1.99	1.35	0.82	0.78
8	2.38	3.38	2.14	1.95	0.85	0.79	2.09	1.61	0.84	0.77
9	2.15	3.55	1.96	1.79	0.79	0.74	1.86	1.51	0.76	0.71
10	2.23	3.72	2.27	1.83	0.88	0.86	2.12	1.54	0.85	0.80
11	2.01	4.23	2.29	1.67	0.88	0.89	2.10	1.42	0.84	0.82
12	2.33	3.65	2.19	1.92	0.85	0.81	2.31	1.66	0.89	0.85

	ls.T	ln.λ	Shannon- Wiener	Marglef	Simpson	Pielou	Shannon- Wiener.G	Marglef.G	Simpson.G
ln.λ	-0.76^**
Shannon-Wiener	-0.18	0.29
Marglef	0.77^**	-0.94^**	-0.06
Simpson	-0.29	0.37	0.96^**	-0.21
Pielou	-0.67^*	0.81^**	0.74^*	-0.71^**	0.83^**
Shannon-Wiener.G	0.37	-0.25	0.51	0.47	0.34	0.04
Marglef.G	0.80^**	-0.91^**	-0.08	0.98^**	-0.26	-0.71^**	0.54
Simpson.G	0.20	-0.05	0.70^**	0.26	0.60^*	0.32	0.93^**	0.30
Pielou.G	-0.28	0.46	0.65^*	-0.27	0.58^*	0.64^*	0.72^*	-0.18	0.81^*

样地 Sample plot	GS			RBS			Zipf			Mand			ONM			DPM			RAM
样地 Sample plot	χ²	D	AIC	χ²	D	AIC	χ²	D	AIC	χ²	D	AIC	χ²	D	AIC	χ²	D	AIC	χ²	D	AIC
1	2.16	0.10	5.36	1.92	0.10	3.36	9.96	0.25	14.35	2.50	0.10	8.70	6.50	0.20	4.12	7.00	0.50^**	10.99	16.00	0.50^**	4.70
2	6.33	0.10	6.70	6.83	0.15	4.70	9.00	0.11	10.29	6.33	0.10	8.70	3.25	0.10	6.58	8.00	0.30	12.24	26.00^**	0.45^*	9.56
3	4.00	0.16	3.82	7.50	0.10	4.70	8.00	0.24	9.55	4.00	0.16	5.82	2.25	0.10	4.12	3.00	0.30	13.35	25.00^**	0.30	9.56
4	3.67	0.20	5.36	3.25	0.11	-5.10	10.39	0.24	15.69	3.91	0.20	8.70	2.00	0.10	5.41	19.00^*	0.30	10.99	25.00^**	0.30	9.56
5	6.33	0.17	8.93	5.33	0.26	3.36	13.00	0.24	13.86	6.33	0.17	10.93	1.03	0.10	9.03	3.00	0.20	14.11	29.00^**	0.26	10.65
6	7.00	0.09	8.58	3.91	0.09	0.96	7.66	0.19	13.63	7.00	0.09	10.57	2.78	0.14	8.58	7.00	0.45^**	11.74	19.00^*	0.45^**	8.11
7	4.33	0.18	4.65	1.83	0.18	-3.50	6.41	0.27	13.04	4.33	0.18	6.65	0.75	0.09	2.65	6.00	0.27	8.58	17.00	0.27	4.79
8	21.16^*	0.18	19.09	13.17	0.18	15.25	21.25^*	0.19	21.82	21.16^*	0.27	22.66	7.55	0.21	4.12	37.00^**	0.27	14.74	41.00	0.27	15.50
9	8.00	0.22	9.27	4.67	0.19	0	4.80	0.17	7.27	7.00	0.17	10.12	4.13	0.22	7.11	14.00	0.42	10.17	17.00	0.42^*	6.72
10	4.00	0.30	5.36	7.83	0.10	5.88	22.50^**	0.34	18.56	4.00	0.30	7.36	4.46	0.20	1.84	4.00	0.40	12.53	22.00^**	0.40^*	8.33
11	1.17	0.09	-9.13	1.50	0.19	-11.12	0.67	0.18	-6.66	1.16	0.09	-7.13	2.33	0.12	4.12	10.00	0.27	11.04	23.00^*	0.32	8.11
12	7.00	0.27	10.58	4.67	0.19	4.12	8.67	0.18	14.20	9.00	0.18	19.25	5.13	0.21	8.58	28.00	0.36	14.74	31.00^**	0.36^*	12.73