在基隆河上游五個樣站進行採樣,以瞭解水棲昆蟲的分佈及其與水質變數之關係。由單變質分析方法顯示,分類群豐度、密度、Shannon歧異度指標與優勢分類群比例,在上游3個樣站與下游2樣站間具有顯著之差異。多變質分析則選用典型對應分析(canonical correspondence analysis, CCA)及多維空間尺度(multidimensional scaling, MDS)二種方法比較,結果顯示在上游3個樣站間水棲昆蟲組成的差異僅能以多變質分析方法區辦。CCA指出在第一軸上的電導度與生化需氧量(biochemical oxygen demand, BOD)與第二軸上之水溫及酸鹼值為解釋此河段水棲昆蟲群聚變異最佳的水質變數,解釋的變異量約為42.9%。MDS顯示化學需氧量(chemical oxygen demand, COD)及BOD,與水棲昆蟲群聚所構成之排序圖具有最高之相似性。 由於在第3站自1997年8-9月間河道施工的影響,以Before-After Control- Impact with Paired-sampling (BACIP)之設計,計算第2、3站之間族群及群聚參數之差值。族群為基礎之參數中之Stenelmis formosana的豐度及群聚參數中之總豐度對於施工影響具有最佳之偵測能力。由聚類分析(clustering analysis)的結果顯示水棲昆蟲群聚在施工後兩個月已復原。 檢驗自1999年8月至2000年7月間基隆河水棲昆蟲群聚在空間序列性(seriation)及時間週期性(cyclicity)的趨勢。在多維空間尺度(MDS)分佈圖顯示水棲昆蟲分類群及功能群在樣站間有顯著的差異。多變質序列指標(index of multivariate seriation, IMS)在序列分析中的結果顯示,5個樣站的分類群縱走序列受水位上升的干擾而會中斷。功能群的縱走序列對水位上升較為敏感。由分類群構成之週期性對水位上升的干擾較具有抗性,而功能群組成的週期性則較易受干擾而中斷。第5樣站的施工所造成的干擾的反應則相反。 探討基隆河上游水棲昆蟲的歧異度包含alpha歧異度(alpha diversity)、分類歧異度(taxonomic diversity) 、beta歧異度(beta diversity)及歧異度劃分(partitioning of diversity)。結果顯示水棲昆蟲之alpha歧異度自上游往下游呈現遞減之趨勢。分類差異度(taxonomic distinctness, Δ*) 突顯在第2、3站的歧異度,而平均分類差異度(average taxonomic distinctness, Δ+) 提供統計檢測顯示第4、5站環境的劣化。以五種量測方法計算在空間上(縱走向)及時間上beta歧異度的變動。空間上βW及βT與βI及βE在整個採樣時間上具有相似的變動。歧異度劃分將分類群豐度(taxa richness, TR)、Shannon 歧異度(H’)及Simpson 歧異度(D)劃分到不同的尺度層級中。各樣站的平均樣點內TR皆低於各樣總TR之30%,而平均樣點內H’及D則分別高於各樣站總值之70%及80%。不同的方法其歧異度的劃分表現出不同之生態意義。歧異度劃分的結果顯示,使用Shannon或Simpson歧異度,以各樣站單一樣點可獲得相當清楚的呈現,但在分類群豐度方面,每一樣站樣本重複數及長時間的採樣,對區域的物種的全面瞭解是有其需要。物種歧異的劃分有助於對於物種歧異度在時間及空間分佈影響因子的瞭解,就保育生物學上相當具有應用的潛能。
Aquatic insects were sampled at five sites along the upper Keelung River to examine the community structures, seriation, cyclicity, and diversity patterns. The upstream sites 1 to 3 significantly differed from the downstream sites 4 and 5 in taxa richness, density, Shannon diversity index, and the proportion of dominant taxon in the univariate analyses. Differences in aquatic insect assemblages at the upstream three sites were found using the two ordination techniques, canonical correspondence analysis (CCA) and non-metric multidimensional scaling (MDS). In the CCA, conductivity, biochemical oxygen demand (BOD), water temperature, and pH were the best water variables that explained about 42.9% of the variance in aquatic insect assemblages on the first two axes. In the MDS, chemical oxygen demand (COD) and BOD showed the highest correlation with aquatic insect assemblages in the upper Keelung River. An engineering practice was proceeded at site 3 from August to September 1997. Differences in population- and community-based parameters between sites 2 and 3 were used in the Before-After Control-Impact with Paired-sampling (BACIP) design. The abundances of Stenelmis formosana in population-based parameters and the total abundances of aquatic insects in community-based parameters showed good abilities of impact detection. Result of the clustering analyses implied that it took two months for aquatic insects to recover from the construction. Spatial seriation and temporal cyclicity trends were examined in the aquatic insect assemblages in the upper Keelung River from August 1999 to July 2000. The MDS plot exhibited significantly different distribution patterns between sampling sites based on both taxonomic and functional data. The index of multivariate seriation (IMS) in the seriation analysis showed that the longitudinal patterns based on the taxonomic structure at the five sites broke down under the disturbances, mainly elevated discharges. The longitudinal patterns based on functional compositions also showed more sensitive to elevated discharge. The cyclic patterns based on taxonomic data were more resistant to the elevation of discharge, which was detrimental to the patterns based on functional data. The engineering practice at site 5 appeared to be more influential on cyclic patterns based on taxonomic data. Diversity patterns of aquatic insects were examined with different measures of alpha diversity, taxonomic diversity, beta diversity, and partitioning of diversity. The alpha diversity of aquatic insects decreased from upstream to downstream. The taxonomic distinctness (Δ*) accentuated the diversity at sites 2 and 3, and the average taxonomic distinctness (Δ+) provided a statistical test and indicated the impacted condition at sites 4 and 5. Similar spatial patterns of changes were recorded between βW and βT, and βI and βE over the sampling period. Partitioning of diversity divided taxa richness (TR), Shannon diversity (H’), and Simpson diversity (D) into components at each hierarchical level. For each sampling sites, mean within-point TR accounted for less than 30% of total TR at the five sites, while within-sample H’ and D accounted for more than 70% and 80% of total H’ and D. The results of partitioning of diversity suggested that the uses of Shannon and Simpson diversity for single sample at each site could give quite clear illustration. Replicated samples over a long period at each site were necessary for understanding the taxa composition. Partitioning of species diversity would be helpful in understanding of the factors controlling the spatial and temporal distribution of biodiversity, and of potential applications in conservation biology.