Distribution Patterns of Dominant Populations of Forest Communities in Pangquangou National Nature Reserve, Shanxi
QIN Hao;DONG Gang;ZHANG Feng;*
2013, 33(5):
605-609.
doi:10.7525/j.issn.1673-5102.2013.05.018
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Based on the dataset from the field survey, the distribution patterns of dominant populations of vegetation in Pangquangou National Nature Reserve, Shanxi, were studied by a quantitative research, including 5 indexes (Dispersal index (DI), Clump index (CI), Mean crowding (m*), Patchiness index (PAL) and Green index (GI)), Clump intensity (k), χ2-test for goodness-of-fit for Poisson and negative binomial distributions, respectively. In addition, the relationships among the six indexes were analyzed by correlation coefficient test. The results indicated that: (1) seven species in the arborous storey, Larix principis-rupprechtii, Betula platyphylla, Picea wilsonii, Quercus wutaishanica and Populus davidiana, were of great competitiveness and highly intensive distribution due to the wider ecological niche; while Picea meyeri and Pinustabulaef ormis were aggregated distribution with smaller clumping intensity; Betula albo-sinensis showed a trend of the random distribution. (2) The patterns of four species in the understory, including Lonicera maackii, Fragaria orientalis, Carex lanceolata and Carex stenophylloides, were of great aggregated intensity, and other species, such as Spiraea pubescens, Rosa bella, Dendranthema chanetii, Phlomis umbrosa ect., were aggregated of a slight degree; However, some species, such as Cotoneaster multiflorus, Ribes mandshuricum, Galium boreale, Kalimeris lautureana ect., were randomly distributed due to a relatively weak competitiveness and narrower ecological niche. (3) There was a significant positive correlation among Dispersal index (DI), Clump index (CI), Mean crowding (m*), Patchiness index (PAL) and Green index (GI). In order to reflect the pattern of certain species, the combined application of variance/mean ratio as well as the χ2 test for fitting Poisson and negative binomial distribution was more accurate.