Biodiversity Science ›› 2016, Vol. 24 ›› Issue (4): 407-420.doi: 10.17520/biods.2015353

Special Issue: Plant Diversity in the Dry Valleys of Southwest China

• Original Papers • Previous Article     Next Article

Quantitative classification and environmental interpretations for the structural differentiation of the plant communities in the dry valley of Jinshajiang River

Ye Liu1, Yue Xu2, Songlin Shi3, Peihao Peng4, Zehao Shen2, *()   

  1. 1 School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055.
    2 Department of Ecology, College of Urban and Environmental Sciences, the Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871.
    3 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085.
    4 College of Tourism and Urban-rural Planning, Chengdu University of Technology, Chengdu 610058
  • Received:2015-12-14 Accepted:2016-03-12 Online:2016-05-11
  • Shen Zehao

The structural differentiation of plant communities are associated with species traits, and interspecific interactions in heterogeneous environment. The comprehensive analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. Based on field investigation of 116 sites and 562 sampling points in the dry valley of Jinshajiang River, combined with vegetation classification by adaptive-affinity propagation, we used Moran’s Eigenvector Maps and variation partitioning to quantify the effects of spatial and environmental factors on the community structure. The results showed that: (1) the plant communities were divided into 30 groups by Adaptive-AP, and classified into 7 vegetation types, 23 formations. Savanna (30.0%) and warm deciduous broadleaved thicket (55.7%) were the main vegetation types. (2) Mean annual temperature (MAT) and aridity index (k) are two dominant climate factors limiting the distribution of plant community types in the dry valley of Jinshajiang River. Savanna, succulent thicket and evergreen broadleaved thicket are dominant vegetation types in typical dry-hot valley. Warm deciduous broadleaved thicket and evergreen sclerophyllous forest are dominant in dry-warm valley. Warm needle-leaved forest and deciduous broadleaved forest are more adaptive to lower temperature. (3) The pure environmental fraction can explain 5.5% of the species composition variation, the pure broad-scale spatial fraction can explain 22.5% of the species composition variation, 6.6% can be explained by the fraction corresponding to broad-scale structured environment and the unexplainable part was 65.4%. Among all the factors, MAT and k indicated the critical difference among the community habitats, which has prominent impact on the change of community composition. The broad-scale spatial factors played an important role in shaping the community structure by geographic isolation.

Key words: community structure, vegetation classification, structural differentiation, environmental factor, adaptive-affinity propagation, Moran’s eigenvector maps, variation partitioning

Table 1

Comparison of climatic features between dry-hot valley at Yuanmou and dry-warm valley at Benzilan in Yunnan. Following Jin (2002)"

Mean annual temperature
Mean temperature of the warmest month
Mean temperature of the coldest month
Mean annual precipitation
Aridity index
元谋 Yuanmou 21.8 27.0 15.0 614 3.30
奔子栏 Benzilan 16.7 24.3 7.7 286 4.75

Fig. 1

Study area and distribution of sampling sites"

Fig. 2

Dendrogram of the plant community classification for 524 plots in the dry valley of Jinshajiang River. N indicates the number of plots."

Fig. 3

Result of canonical correspondence analysis of different vegetation types. 1, Savanna; 2, Succulent thicket; 3, Evergreen broadleaved thicket; 4, Warm deciduous broadleaved thicket; 5, Evergreen sclerophyllous forest; 6, Warm needle-leaved forest; 7, Deciduous broadleaved forest. MAT, mean annual temperature; MTCM, mean temperature of the coldest month; TSN, temperature seasonality; MAP, mean annual precipitation; k, aridity index; PSN, precipitation seasonality."

Fig. 4

Vegetation ordination for the plots in Jinshajiang River. a,b, principal component analysis of original table; c,d, redundancy analysis with E as predictors; e,f, partial residual with E as covariables. The smoothed scalogram indicates the portion of variance R2 explained by each spatial scale. The dark gray is the highest R2. P values are given. The 95% confidence limit is also represented by the line of “+”."

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