Chin Bull Bot ›› 2017, Vol. 52 ›› Issue (4): 496-510.doi: 10.11983/CBB16115

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Vegetation Structure and Internal Relationship Between Distribution Patterns of Vegetation and Environment in Ecological Service Forest of Rui’an City in Zhejiang Province

Nuonan Ye1, Naping Shen2, Tianqi Shang1, Hongdi Gao3, Jieran Guan1, Lita Yi1*   

  1. 1School of Forestry and Biotechnology, Zhejiang Agriculture and Forestry University, Lin’an 311300, China;
    2The East China Institute of Forest Inventory and Planning of the State Forestry Administration, Hangzhou 310019, China
    3Ecological Management Center, Forestry Department of Zhejiang Province, Hangzhou 310020, China
  • Received:2016-05-26 Accepted:2016-10-11 Online:2017-05-05 Published:2017-07-01
  • Contact: Yi Lita E-mail:yilita@126.com
  • About author:

    # Co-first authors

Abstract:

With the construction of an ecological service forest (ESF) in Zhejiang province, the ESF has provided many ecological and social benefits to humans; thus, the number of studies about ESF has also increased. This study describes how to analyze the vegetation structure and the internal relationship between distribution patterns of vegetation and environment in 92 permanent sample plots of the ESF in Rui’an city, Zhejiang province. The vegetation structure and the internal relationship between the distribution patterns of vegetation and environment were studied by using canonical correspondence analysis (CCA), two-way indicator species analysis (TWINSPAN), biodiversity and interspecies association. (1) The elevation, slop aspect and slop position were main environment factors in the community distribution. (2) Combined with the results of CCA, the 92 plots could be divided into 13 groups by TWINSPAN, and the associations of Pinus massoniana, Cunninghamia lanceolata and Cryptomeria fortunei were the most constructive species. (3) The diversity of vegetation was high, and the groups with P. massonian, C. lanceolata and C. fortunei as dominant species had higher diversity. The species diversities of different layers in the community were in the order of shrub layer>tree layer>herb layer. (4) The stability of the community was not high (less than 2), but the tree layer was relatively stable because the lower ratios of positive and negative association theoretically implied the instable vegetation structure. Therefore, the internal relationship between the distribution patterns of vegetation and environment and the dominant species association could be explained by a combination of all methods; these methods could provide a scientific foundation for the classification management of a regional ESF.

Table 1

Importance values of the main tree, shrub and herb on 92 plots in ecological service forest of Rui’an city in Zhejiang province"

Layer Number Species Importance value Layer Number Species Importance value
Tree 1 Pinus massoniana 30.1251 48 Syzygium buxifolium 2.6616
2 Cunninghamia lanceolata 19.1471 49 Camellia oleifera 2.6044
3 Cryptomeria fortunei 11.7255 50 Pinus massoniana 2.4924
4 Phyllostachys heterocycla 10.4433 51 Camellia sinensis 2.3143
5 Schima superba 6.4891 52 Pleioblastus amarus 2.1880
6 Lithocarpus glaber 2.7277 53 Smilax china 2.1092
7 Liquidambar formosana 2.5786 54 Smilax sieboldii 1.9226
8 Cyclobalanopsis glauca 2.1817 55 Mallotus japonicus 1.8279
9 Castanopsis sclerophylla 2.0855 56 Liquidambar formosana 1.7230
10 Castanopsis eyrei 1.5263 57 Litsea cubeba 1.6368
11 Symplocos sumuntia 1.4795 58 Schima superba 1.4864
12 Myrica rubra 1.0891 59 Rubus phoenicolasius 1.1355
13 Eurya japonica 0.8298 60 Cyclobalanopsis glauca 0.9140
14 Pinus taiwanensis 0.8262 61 Symplocos stellaris 0.8884
15 Machilus thunbergii 0.7395 62 Rubus buergeri 0.7625
16 Ilex chinensis 0.6225 63 Camellia japonica 0.7053
17 Toxicodendron succedaneum 0.4550 64 Castanopsis sclerophylla 0.6913
18 Cinnamomum camphora 0.4451 65 Toxicodendron succedaneum 0.6529
19 Litsea cubeba 0.3609 66 Trachelospermum jasminoides 0.5940
20 Vernicia montana 0.3327 67 Adinandra millettii 0.5748
21 Ulmus pumila 0.3149 68 Dendrocalamopsis oldhami 0.5642
22 Camellia japonica 0.2716 69 Cryptomeria fortunei 0.5083
23 Elaeocarpus decipiens 0.2526 70 Lindera aggregata 0.4966
24 Loropetalum chinense 0.2059 71 Rubus xanthocarpus 0.4675
25 Ficus microcarpa 0.2039 72 Quercus fabri 0.4185
26 Mallotus japonicus 0.1838 73 Myrica rubra 0.4185
27 Cyclobalanopsis myrsinifolia 0.1728 74 Lespedeza bicolor 0.4092
28 Eucalyptus camaldulensis 0.1678 75 Pinus taiwanensis 0.4092
29 Diospyros kaki 0.1445 76 Rosa laevigata 0.4092
30 Rhus chinensis 0.1296 77 Rhus chinensis 0.3742
31 Albizia kalkora 0.1260 78 Neocinnamomum chekiangense 0.3602
32 Paulownia tomentosa 0.1199 79 Osmanthus fragrans 0.3509
33 Machilus leptophylla 0.1132 80 Glochidion puberum 0.3101
34 Adinandra millettii 0.1109 81 Ulmus pumila 0.3019
35 Quercus fabri 0.1099 82 Gardenia jasminoides 0.2985
Shrub 36 Eurya japonica 13.5139 83 Vernicia fordii 0.2845
37 Rhododendron simsii 7.6406 84 Toxicodendron sylvestre 0.2553
38 Loropetalum chinense 5.6483 85 Eucalyptus camaldulensis 0.2437
39 Phyllostachys heteroclada 5.5905 86 Diospyros kaki 0.2437
40 Cunninghamia lanceolata 4.3673 87 Dalbergia hupeana 0.2145
41 Symplocos sumuntia 4.3183 88 Photinia serratifolia 0.2029
42 Brachystachyum densiflorum 4.3177 89 Melia azedarach 0.1655
43 Castanopsis eyrei 3.6525 90 Phyllostachys edulis 0.1655
44 Vaccinium bracteatum 3.3764 91 Pinus elliottii 0.1655
45 Lithocarpus glaber 3.1092 92 Machilus leptophylla 0.1364
46 Ilex chinensis 3.0220 93 Lindera glauca 0.1073
47 Smilax corbularia 2.7980 94 Phoebe sheareri 0.1073
Herb 95 Osmunda japonica 60.1798 105 Boehmeria nivea 0.6881
96 Dryopteris fuscipes 6.8201 106 Dicranopteris pedata 0.5281
97 Miscanthus floridulus 6.7660 107 Juncus setchuensis 0.3454
98 Cyperus difformis 6.5565 108 Onychium japonicum 0.2944
99 Imperata cylindrica 4.9332 109 Pteris dispar 0.2816
100 Miscanthus sinensis 4.0732 110 Carex brunnea 0.1684
101 Polytrichum sp. 3.7467 111 Woodwardia japonica 0.1472
102 Pueraria montana 1.7609 112 Boehmeria spicata 0.1472
103 Diplopterygium glaucum 1.4828 113 Yucca gloriosa 0.1387
104 Chrysanthemum indicum 0.8028 114 Lygodium japonicum 0.1387

Table 2

Coefficients between the first 4 axes in canonical correspondence analysis (CCA) ordination and environment factors of 92 plots in ecological service forest of Rui’an city in Zhejiang province"

Environmental factors Axis 1 Axis 2 Axis 3 Axis 4
Elevation 0.7595* -0.1964 0.1744 0.0626
Slope -0.2506 -0.6853* -0.1253 0.0902
Aspect -0.1357 -0.1096 0.6827* 0.0054
Position 0.3322 0.0569 0.0666 0.5886*
Eigenvalues 0.2900 0.1980 0.1110 0.1070
Species-environment correlations 0.8200 0.7410 0.7000 0.6500
Cumulative percentage variance of
species-environment relation (%)
41.00 69.10 84.40 100.00

Figure 1

CCA ordination of 114 dominant species in ecological service forest of Rui’an city in Zhejiang province^Species number see Table 1. ELE: Elevation; SL: Slope; ASP: Aspect; POS: Position"

Figure 2

Dendrogram of TWINSPAN classification of 92 plots in ecological service forest of Rui’an city in Zhejiang province^Ninety-seven plots were surveyed, and the data of the plot 1, 2, 19, 41, and 71 were missing."

Figure 3

13 groups of species diversity index of communi- ties in ecological service forest of Rui’an city in Zhejiang pro- vince"

Figure 4

Shannon-Wiener (A), Pielou (B), and Simpson (C) index of the 3 vertical levels in ecological service forest of Rui’an city in Zhejiang province"

Table 3

Difference analysis of tree layer, shrub layer and herb layer in ecological service forest of Rui’an city in Zhejiang pro- vince"

Index Shannon-Wiener Simpson Pielou
Layer Tree Shrub Herb Tree Shrub Herb Tree Shrub Herb
Mean 1.885 2.871 1.460 0.548 0.759 0.491 0.832 1.114 0.815
SE 0.257 0.298 0.159 0.066 0.050 0.054 0.098 0.062 0.075

Table 4

Chi-square test, association coefficient (AC) and percentage co-occurrence (PC) in tree layer of 13 ecological associations in ecological service forest of Rui’an city in Zhejiang province"

Test methods Positive correlations Negative correlations None correlations
χ2 P<0.01 0.01<P<0.05 P>0.05 P<0.01 0.01<P<0.05 P>0.05 ad=bc
χ2-test 7 4 33 0 1 21 0
AC AC≥0.60 0.20≤AC<0.60 0.05≤AC<0.20 AC≤-0.60 -0.60≤AC<-0.20 -0.20≤AC<-0.05 -0.05≤AC<0.05
AC values 2 17 10 3 11 5 18
PC PC≥0.7 0.4≤PC<0.7 0.2≤PC<0.4 - - - 0≤PC<0.2
PC values 0 4 30 - - - 10

Figure 5

Association coefficient (AC) of dominant species in tree layer in ecological service forest of Rui’an city in Zhejiang province"

Table 5

Chi-square test, association coefficient (AC) and percentage co-occurrence (PC) in shrub layer of 13 ecological associations in ecological service forest of Rui’an city in Zhejiang province"

Test methods Positive correlations Negative correlations None correlations
χ2 P<0.01 0.01<P<0.05 P>0.05 P<0.01 0.01<P<0.05 P>0.05 ad=bc
χ2-test 4 4 104 0 0 141 20
AC AC≥0.60 0.20≤AC<0.60 0.05≤AC<0.20 AC≤-0.60 -0.60≤AC<-0.20 -0.20≤AC<-0.05 -0.05≤AC<0.05
AC values 0 22 44 60 46 29 55
PC PC≥0.7 0.4≤PC<0.7 0.2≤PC<0.4 - - - 0≤PC<0.2
PC values 0 1 22 - - - 92

Figure 6

Association coefficient (AC) of dominant species in shrub layer in ecological service forest of Rui’an city in Zhejiang province"

Table 6

Chi-square test, association coefficient (AC) and percentage co-occurrence (PC) in herb layer of 13 ecological associations in ecological service forest of Rui’an city in Zhejiang province"

Test methods Positive correlations Negative correlations None correlations
χ2 P<0.01 0.01<P<0.05 P>0.05 P<0.01 0.01<P<0.05 P>0.05 ad=bc
χ2-test 2 1 12 0 0 21 0
AC AC≥0.60 0.20≤AC<0.60 0.05≤AC<0.20 AC≤-0.60 -0.60≤AC<-0.20 -0.20≤AC<-0.05 -0.05≤AC<0.05
AC values 3 6 5 8 4 6 2
PC PC≥0.7 0.4≤PC<0.7 0.2≤PC<0.4 - - - 0≤PC<0.2
PC values 0 0 3 - - - 12

Figure 7

Association coefficient (AC) of dominant species of herb layer in ecological service forest of Rui’an city in Zhejiang province"

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