杉木生长性状的空间与竞争效应及其对遗传参数估计的影响

doi:10.11707/j.1001-7488.20220508

Abstract

Abstract:

Objective: Genetic testing is an important approach for genetic improvement of forest trees and can be used to estimate genetic parameters and select elite trees. This study is to explore the effects of different models by combining spatial and competition effects on the estimation of genetic parameters of growth traits in Chinese fir (Cunninghamia lanceolate) at different forest age stages, in order to provide a theoretical basis for improving the accuracy of breeding value prediction and selection of elite trees. Method: This study was conducted in a 40-year-old open-pollinated progeny trial forest in the Yangkou State Forest Farm in Fujian Province, and a mixed linear model was used to fit the effects of experimental design, spatial, competition, spatial-competition effects for diameter at breast height (DBH) and tree heights measured at different tree ages from the 40-year-old Chinese fir trial. The Akaike information criterion (AIC) was used to evaluate different models, and compare genetic parameters and breeding values of the different models. Result: 1) The spatial-competition effect of fit increased by an average of 39.72% of the narrow heritability estimates of growth traits. The additive genetic variance component of DBH in the mixed spatial-competition model increased 4.46%, 26.95% and 35.87% compared to that of the standard mixed linear model (MLM), spatial model, and competition model, respectively. While the residual variances of DBH in the mixed spatial-competition model decreased by 21.95%, 26.81% and 20.19% compared to those of the standard mixed linear model, spatial model, and competition model, respectively. The additive genetic variance of the tree height in the spatial-competition model was 10.73% and 1.22% higher than that in MLM in 1991 and 2020, respectively, and the residual variance decreased by 12.75% and 33.83%, respectively. 2) The intensity of competition was related to the distance between stand individuals. The residual correlation among individuals showed that the competition effect of growth traits in the same row and column was significantly higher than the competition effect of individuals in the diagonal direction. 3) There was a competitive effect on growth traits, and diameter growth was greater than tree height growth. The additive-competitive effect correlation of the same trait showed that the growth traits of Chinsese fir showed a competitive effect at different forest ages, and the competitive effect of diameter growth was greater than that of high growth at the young forest age stage; the growth traits at the middle forest age stage showed a strong competitive correlation, and combined with the residual correlation results, it showed that the growth traits of Chinsese fir showed a strong competitive effect in the middle forest age. The competitive influence among individual chest diameters was greater than the competitive influence between tree height, and DBH was more sensitive to competition than tree height. 4) The accuracy of the breeding values was 0.559 under 5% selection rate, and 23 individuals were selected, among them, 78.3% of individuals were replicated. The mean values of DBH of standard MLM, spatial model, competition model and spatial-competition were 37.3, 37.2, 37.3, 37.3 cm, respectively. and genetic gains of DBH for the selected individuals were 12.73%, 13.86%, 13.32% and 14.37%, respectively. Conclusion: Using competition-spatial model improves the accuracy of genetic parameter estimation. It is showed that the competition of individual plants in the diagonal direction is weaker than that of individual plants in the row and column directions. The growth traits at middle stand ages are most affected by competition, DBH is more sensitive to competition than tree height.

Key words: competition effects, spatial effects, mixed linear model, genetic parameters, Chinese fir

CLC Number:

S718.46

Yuedong Shi,Hong Zheng,Daiquan Ye,Jisen Shi,Liming Bian. Spatial and Competition Effects for Growth Traits of Chinese Fir and Their Impacts on Estimations of Genetic Parameters[J]. Scientia Silvae Sinicae, 2022, 58(5): 75-84.

Figures/Tables 6

Table 1

Table 2

Table 3

Comparison of four models and their estimated values of genetic parameters"

性状/年份 Traits/year	模型 Models	AIC	σ_p²	σ_ad²	σ_ac²	σ_adac	σ_ξ²	σ_e²	σ_η²	h²
胸径 DBH/1985	MLM	3 639	0.010(0.02)^ns	0.560(0.20)	—	—	—	1.476(0.18)		0.275(0.09)
	AR1	3 395	0.010(0.01)^ns	0.574(0.20)	—	—	0.310(0.10)		1.221(0.18)	0.319(0.10)
	CM	3 422	0.013(0.02)^ns	0.564(0.20)	0.342(0.15)	0.005	—	1.154(0.20)		0.440(0.14)
	CSM	3 398	0.011(0.01)^ns	0.585(0.20)	0.056(0.06)	-0.059	0.317(0.10)		1.152(0.18)	0.358(0.18)
胸径 DBH/1 991	MLM	5 073	0.004(0.02)^ns	2.712(0.10)	—	—	—	7.017(0.03)		0.286(0.10)
	AR1	5 075	0.000(0.00)^ns	2.713(0.10)	—	—	0.000(0.00)^ns		7.021(0.03)	0.278(0.01)
	CM	5 061	0.001(0.00)^ns	3.044(0.18)	1.160(0.07)	-1.408	—	5.695(0.02)		0.425(0.15)
	CSM	5 052	0.001(0.00)^ns	3.443(0.20)	0.857(0.05)	-1.470	0.543(0.03)		5.136(0.02)	0.456(0.16)
胸径 DBH/2 020	MLM	4 111	0.487(0.58)^ns	8.567(4.58)	—	—	—	24.320(4.30)		0.260(0.13)
	AR1	4 105	0.207(0.50)^ns	11.150(5.30)	—	—	2.600(1.73)		20.520(4.80)	0.333(0.16)
	CM	4 115	0.412(0.49)^ns	9.026(4.70)	0.980(1.55)	-0.185	—	23.030(4.17)		0.303(0.15)
	CSM	4 109	0.478(0.53)^ns	9.894(4.92)	0.519(1.13)	-0.616	4.442(2.27)		18.16(3.17)	0.364(0.18)
树高 Tree height/1 985	MLM	2 255	0.005(0.00)^ns	0.170(0.06)	—	—	—	0.324(0.05)		0.344(0.11)
	AR1	2 183	0.005(0.00)^ns	0.170(0.05)	—	—	0.098(0.02)		0.245(0.04)	0.409(0.12)
	CM	2 222	0.004(0.00)^ns	0.180(0.06)	0.029(0.02)	0.075	—	0.245(0.04)		0.460(0.15)
	CSM	2 197	0.000(0.00)^ns	0.166(0.06)	0.063(0.03)	0.004	0.106(0.02)		0.250(0.04)	0.478(0.15)
树高 Tree height/1 991	MLM	3 657	0.010(0.02)^ns	0.708(0.25)	—	—	—	1.475(0.21)		0.320(0.10)
	AR1	3 642	0.004(0.01)^ns	0.715(0.24)	—	—	0.186(0.06)		1.313(0.21)	0.352(0.11)
	CM	3 654	0.000(0.00)^ns	0.703(0.24)	0.196(0.12)	-0.050	—	1.301(0.20)		0.409(0.13)
	CSM	3 655	0.000(0.00)^ns	0.717(0.25)	0.171(0.12)	-0.059	0.046(0.03)		1.287(0.20)	0.408(0.13)
树高 Tree height/2 020	MLM	3 448	0.019(0.19)^ns	2.545(1.23)	—	—	—	5.660(1.12)		0.310(0.13)
	AR1	3 410	0.025(0.10)^ns	2.808(1.26)	—	—	1.876(0.63)		3.841(1.11)	0.422(0.17)
	CM	3 438	0.006(0.10)^ns	2.975(1.33)	1.090(0.80)	0.819	—	4.238(0.96)		0.490(0.21)
	CSM	3 415	0.025(0.10)^ns	2.860(1.70)	0.094(0.24)	0.054	1.815(0.62)		3.745(1.09)	0.441(0.19)

Table 3

Table 4

Fig.1

Table 5

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性状 Traits	年份 Year	平均值±标准误差 Mean ± SE	最小值 Minimum	最大值 Maximum	变异系数 Coefficient of variation (%)	株数 Tree number
胸径 DBH/cm	1985	7.30±0.05	2.5	10.9	20.14	974
	1991	15.90±0.10	5.6	26.1	19.47	948
	2020	25.75±0.27	9.0	42.4	22.04	471
树高 Tree height/m	1985	4.81±0.02	2.1	7.2	14.80	974
	1991	12.21±0.05	6.8	16.5	12.15	948
	2020	19.98±0.13	9.5	25.5	14.32	471

性状/年份 Trait/year	ρ_Row	ρ_Col	r_Row	r_Col	r_Dia	r_All	r_{a_ic_i}
胸径DBH/1985	0.71	0.94	0.06^**	-0.01	0.06^**	0.06^**	-0.30^**
树高Tree height/1985	0.77	0.92	0.01	0.06^**	0.06^**	0.08^**	0.14
胸径DBH/1991	0.13	0.13	-0.21^**	-0.15^**	-0.07^**	-0.24^**	-0.92^**
树高Tree height/1991	0.27	0.95	-0.13^**	-0.08^**	-0.05^**	-0.12^**	-0.71^**
胸径DBH/2020	-0.19	0.84	-0.15^**	-0.05^**	-0.09^**	-0.18^**	-0.30^**
树高Tree height/2020	0.67	0.95	-0.17^**	-0.05^**	-0.11^**	-0.04^**	-0.09

年份Year		a_{Tree height}	c_{Tree height}	c_DBH
1985	a_DBH	0.50^**	0.36^**	-0.30^**
	c_DBH	-0.40^**	0.55^**	—
	c_{Tree height}	0.14	—	—
1991	a_DBH	0.91^**	0.74^**	-0.92^**
	c_DBH	-0.88^**	0.82^**	—
	c_{Tree height}	-0.71^**	—	—
2020	a_DBH	0.95^**	0.32^**	-0.30^**
	c_DBH	-0.14	0.56^**	—
	c_{Tree height}	-0.09	—	—

	基本模型MLM			空间模型SM			竞争模型CM			竞争-空间模型CSM
排名 Rank	单株编号 Tree ID	育种值 Breeding value/cm	r	单株编号 Tree ID	育种值 Breeding value/cm	r	单株编号 Tree ID	育种值 Breeding value/cm	r	单株编号 Tree ID	育种值 Breeding value/cm	r
1	165	4.24	0.523	165	4.74	0.553	165	4.41	0.537	165	4.93	0.563
2	887	3.94	0.514	887	4.52	0.545	887	4.08	0.528	738	4.64	0.564
3	228	3.79	0.522	738	4.39	0.553	738	3.99	0.537	887	4.63	0.556
4	738	3.75	0.523	688	4.24	0.547	230	3.91	0.531	688	4.31	0.557
5	230	3.73	0.518	353	3.95	0.553	228	3.90	0.536	353	4.13	0.564
6	353	3.54	0.523	198	3.77	0.547	198	3.73	0.530	198	3.93	0.558
7	198	3.52	0.516	1 024	3.59	0.546	353	3.70	0.537	488	3.73	0.555
8	688	3.38	0.516	488	3.52	0.545	231	3.56	0.529	1 024	3.68	0.557
9	231	3.37	0.515	585	3.52	0.543	688	3.51	0.530	369	3.64	0.559
10	585	3.32	0.512	129	3.47	0.546	585	3.44	0.526	129	3.59	0.557
11	227	3.19	0.516	369	3.47	0.548	135	3.37	0.531	585	3.57	0.553
12	129	3.19	0.515	664	3.42	0.545	227	3.35	0.530	840	3.52	0.564
13	135	3.19	0.517	840	3.39	0.553	488	3.31	0.529	664	3.52	0.556
14	488	3.11	0.515	216	3.37	0.548	129	3.28	0.529	216	3.46	0.559
15	369	3.06	0.517	228	3.36	0.554	369	3.22	0.531	228	3.45	0.565
16	840	3.02	0.522	135	3.31	0.549	840	3.17	0.537	135	3.44	0.560
17	1 024	3.02	0.516	525	3.29	0.550	1 024	3.16	0.530	525	3.42	0.560
18	525	2.99	0.518	600	3.28	0.550	525	3.11	0.532	600	3.32	0.560
19	1 002	2.92	0.519	715	3.17	0.553	1 002	3.05	0.532	715	3.30	0.564
20	664	2.86	0.515	230	3.14	0.550	664	3.01	0.528	230	3.25	0.561
21	485	2.77	0.513	1 002	3.13	0.549	43	2.92	0.527	231	3.23	0.559
22	43	2.74	0.513	876	3.10	0.545	54	2.89	0.532	1 002	3.21	0.560
23	600	2.74	0.518	1 012	3.04	0.543	476	2.88	0.531	1 012	3.14	0.553

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Spatial and Competition Effects for Growth Traits of Chinese Fir and Their Impacts on Estimations of Genetic Parameters

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