| 摘要: | 本研究應用統計方法判別林業試驗所六龜試驗林地區之樣區間與林型間之物種多樣性空間變異情形，其目的主要在比較不同的統計方法，並提出判別物種多樣性空間變異之最佳方法。研究方法首先是應用單變值統計方法測定樣區之物種多樣性，並提供多變值統計方法判別空間變異之用。由單變值統計方法之結果指出，不同樣區具有不同的物種多樣性，而林型間亦存在著差異，譬如天然林大於人工林；人工闊葉林大於人工針葉林，該差異經Shannon t-檢定結果顯示，在1%顯著水準時均呈顯著情形，此結果顯示單變值統計方法為測定物種多樣性之有效方法。至於應用多變值統計方法判別物種多樣性之空間變異的結果則指出，群落分析法，多維度序列法和主軸轉換法均為有用的空間判別技術，但多維度序列法比群落分析法較具判別性，同時比主軸轉換法較具解釋性。若將多維度序列法和群落分析法並用，則對於樣區間與林型間之空間變異解釋，將更具有判別性、一致性與代表性。由上述結果可得結論如下：多變值統計方法為判別物種多樣性空間變異之最有力工具，其中以多維度序列法為最佳，尤其是多維度序列法與群落分析法並用時，更能解釋樣區間與林型間之空間變異情形，故為本研究建議使用的方法。
This study applied statistical approaches to the discrimination of spatial variations between sites and between forest types in the upper area of the Liukuei Experimental Forest of Taiwan Forestry Research institute, Taiwan. The main purpose was to compare the effectiveness of various statistical approaches and then present the best strategy for discriminating the spatial variations of species diversity. The two methods used were (1) univariate methods by diversity measures, Shannon t-test, and (2) multivariate methods by cluster analysis, ordination by non-metric multi-dimensional scaling, and principal component analysis. The results by univariate methods indicate that diversity differences exist between sites and between forest types. Meanwhile, the natural forest has more diversity than the plantation, and the hardwood plantation has more diversity than the conifer plantation. The differences between forest types are very significant at the 100 significance level according to the Shannon t-test. The results indicate that univariate methods by diversity measures are a flexible way to reduce the complexity of "species by sites" matrices into a single coefficient. The results of using multivariate methods indicate that cluster analysis and ordination by non-metric multi-dimensional scaling and principal component analysis are useful techniques for discriminating spatial variations. However, ordination by non-metric multi-dimensional scaling discriminates better than principal component analysis. in addition, ordination by non-metric multi-dimensional scaling is a more informative summary than cluster analysis, and the combination of both the analyses is more effective than either alone for the mutual consistency of representations. it is concluded that the most powerful tools for discriminating the spatial variations of species diversity are in the multivariate category. Among multivariate methods, ordination by non-metric multidimensional scaling is preferable, and its superimposition with cluster analysis is recommended in order to obtain more information regarding the relationship between sites and between forest types. |
