种群分布格局的多尺度分析

MULTI-SCALE ANALYSES OF POPULATION DISTRIBUTION PATTERNS

种群分布格局的分析对于了解种群空间分布规律以及种内与种间关系具有重要的意义。最近邻体分析方法 (Nearestneighboranalysis,NNA)作为种群空间分布格局的重要分析方法 ,仅局限于种群格局的单尺度分析。改进NNA方法以应用于种群格局的多尺度分析 ,将有助于解决种群格局的尺度依赖性。该文在前人研究的基础上提出扩展最近邻体分析方法 (Extendednearestneighboranalysis ,ENNA) ,也即在传统Clark_Evans指数公式的基础上增加一个距离尺度参数d(m) ,并定义其所对应的Clark_Evans指数CE(d)的计算公式及其相应的显著性检验计算公式 (u(d) )分别为 :CE(d) =rdA/rdE=(1Nd∑Ndi=1 rdi) / (0 .5Ad/Nd+0 .0 5 14Pd/Nd+0 .0 4 1Pd/Nd3 /2 )和u(d) =(rdA-rdE) /σd,在距离尺度d(m)范围内 ,参数rdA指样地内各个体与其最近邻体间距离的平均值 (m)、rdE指相同环境中个体呈随机状态时最近邻体距离的平均值 (m)、Nd 为样地内个体总数、rdi为第i个个体与其最近邻体间的距离 (m)、Ad 为样地面积 (m2 )、Pd 为样地周长 (m)和σd 代表标准差。ENNA尺度变换采用与分形理论中计算沙盒维数相类似的过程 ,而格局类型判断的标准与传统最近邻体分析方法相同。传统最近邻体分析结果是EN NA中距离尺度d取最大值dmax时的一个特例。

The analyses of spatial distribution patterns of plant populations are useful for understanding pattern types and intra/inter-specific relationships. One of the most frequently employed methods in detecting spatial distribution patterns of populations is the nearest neighbor analysis proposed by Clark and Evans in 1954. This method has been highly successful for analyzing spatial patterns at a single scale but is rarely used for analyzing distribution patterns at multiple scales. We present the extended nearest neighbor analysis (ENNA) in this paper to solve the scale-dependent problem associated with the traditional method of nearest neighbor analysis. The Clark-Evans index was modified by using a distance scale parameter d (m), described in the following equation: CE(d)=r dA /r dE =(1N d∑N di=1r di )/(0.5A d/N d+0.051 4P d/N d+0.041P d/N d 3/2 ). Accordingly, the equation for testing the calculated CE index values against the significant deviation from 1 was changed into u(d)=(r dA -r dE )/σ d, where the parameters, r dA , r dE , N d, r di , A d, P d, σ d, refer to the mean distance between an individual and its nearest neighbor (m), the expected mean distance of the individuals of a population randomly scattered (m), the number of individuals in the current sample plot, distance between individual i and its nearest neighbor (m), surface of the current sample plot (m 2), circumference of the current sample plot (m), and the standard deviation, respectively. The procedure of scaling transformation in this approach was similar to that of the sandbox experiment in fractal theory, and the rule for detecting the pattern type was the same as that in the traditional nearest neighbor analysis. The traditional nearest neighbor analysis is a special case for the extended nearest neighbor analysis in which the minimum value of the distance scale parameter (d) is used. An example using the data from a needle and broad-leaved mixed forest community at Heishiding Nature Reserve, Guangdong Province was presented to explain the procedure. Five typical plant populations of this community, Pinus massoniana, Symplocos laurina, Castanopsis nigrescens, Itea chinensis and Rhodomyrtus tomentosa, were chosen for the multi-scale analysis of spatial distribution patterns. The results showed that spatial patterns of all five populations were scale-dependent with varying degrees of intensity. The Pinus massoniana population was randomly distributed at most scales examined, which may have been caused by the random self-thinning process in the population. The population of Itea chinensis was clumped at all scales examined. A simulation with the aid of geographic information system (GIS) also revealed that the distribution patterns of Symplocos laurina, Castanopsis nigrescens, Itea chinensis and Rhodomyrtus tomentosa were mainly clumped or random with an increase of distance scale. These results demonstrated that the ENNA method presented in this paper could be used for multi-scale analysis of spatial distribution patterns of plant populations that could not be solved using the traditional nearest neighbor analysis.