蓮華池25公頃闊葉林長期動態樣區土壤性質之空間變異

Spatial Variation of Soil Properties in the Lienhuachih 25 ha Dynamic Plot in a Subtropical Evergreen Broad-Leaved Forest

林佳佳(Chia-Chia Lin)

指導教授 ： 陳尊賢

國立臺灣大學/生物資源暨農學院/農業化學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.10300

摘要

地形會影響植被的組成及土壤的化育，而植被經由吸收土壤中的養分並以有機物的形式散布到地表，同樣也會影響土壤之性質。土壤為植物生長所處的環境因子之一，其藉由影響植物之生理造成地上部植被之組成及結構之差異。若能對土壤特性之空間分布有所了解，我們便能從土壤的角度去更進一步的瞭解森林之空間結構。蓮華池25 公頃闊葉林長期動態樣區設置目的為探討台灣典型低海拔森林的組織結構，而本研究的目的則在建立此永久樣區內的土壤基本性質資料，並探討試驗區內土壤性質的變異與地形及植被間的關係。試驗以60 m × 60 m 的網格為採樣之單位，將樣區劃為64個採樣單位，另於試驗區西方以 30 m × 30 m 劃設 24 個網格進行更密集的採樣。之後以不同的地形位置 (稜線、坡地、溪溝) 及植被類型對採樣網格進行分組，進行各種土壤性質之統計分析與比較。土壤基本性質在研究樣區之空間分佈預測則利用地理統計方法之克利金法。
不同地形位置的比較中，土壤pH與可交換性鹽基在溪溝顯著較高，應為水分受地形的影響將被淋洗之離子帶至溪溝處累積的結果。有機碳在稜線顯著較高，可能為枯落物性質、微氣候等因子影響有機物分解速率而導致有機碳累積。不同植被類型間的比較則顯示生長於溪溝附近之植被類型 3 (大葉楠-石柃舅型)、植被類型4 (香桂-厚殼桂-狗骨仔型) 之土壤有較高的pH、可交換性鹽基、KCl extractable NH4+、可礦化氮及Mehlich K，顯示植被特性亦可能影響土壤性質。0-5 公分表土性質主成分分析結果顯示第一主成分 (可代表土壤養分) 與第二主成分 (可代表pH、有機碳) 兩者共可解釋約百分之七十七的總變異。從biplot可發現Mehlich K等土壤養分含量較低的採樣點多位於植被類型 1 (小葉赤楠-南投石櫟型)、植被類型2 (鵝掌柴-厚殼桂型) 所生長的區域，有機碳含量較高之樣點則多位於稜線上。由分析結果可確定土壤特性會隨不同地形位置及不同植被類型而改變，但由於地形會同時影響植被與土壤特性，土壤會影響植被分布，而植被又會影響土壤性質，因此最主要影響土壤特性之因子仍難以釐清。另外Bray No. 1 P以之60公尺為間距的分析結果發現空間相依性差，顯示在本試驗區Bray No. 1 P需做更密集的採樣 (如以30 m × 30 m 作為採樣單位) 才能獲得較佳的空間推估。

關鍵字

蓮華池25公頃闊葉林長期動態樣區 ； 土壤性質 ； 地形位置 ； 植被類型 ； 主成分分析 ； 地理統計法

並列摘要

Topography is one of the factors that affect both vegetation compositions and soil formation, and the vegetation type also influence the soil chemical characteristics for nutrient uptake and release in soil system. The soil can affect the plant species composition and structure types of a forest ecosystem which was produced by the differences of plant physiology. If we can understand the spatial variability and spatial patterns of soil characteristics, we would further understand the forest structure at a watershed level. One of the purposes of establishing Lienhuachih 25 ha dynamic plot in a subtropical evergreen broad-leaved forest is to analyze the forest structure. The objectives of this study were to establish the database of soil properties and to examine the relationships between the soil properties, landscape positions and vegetation types in the dynamic plot. The 25 ha plot was divided into 64 subplot for soil sampling. Each subplot was 60 m × 60 m in area and another 24 subplot was 30 m × 30 m in area in order to test different sampling methods located at the western area of plot. The study area was also divided into 3 different topographic positions and four vegetation types to compare the differences of soil properties at different topographic positions and among different vegetation types. The prediction of soil characteristics in the whole plot was conducted by Ordinary Kriging method of Geostatistics technique.
The results showed that soil pH and exchangeable cations were higher in gully, which revealed that landscape positions can affect the soil properties by redistribution of water and soluble soil nutrients. Organic carbon content was higher on the ridge which was the result of slow decomposition rate caused by microclimate or leaf residual properties. KCl extractable NH4+, mineralizable N, Mehlich K, exchangeable cations, and pH had significant differences between different vegetation types which indicated that vegetation type also can affect the soil properties in the plot. Principal component analysis (PCA) showed that PCA-1 factor (soil fertility) and PCA-2 factor (pH and organic carbon content) together can explain near 77% of total soil variability. Vegetation type 1 and 2 had lower soil fertility but higher organic carbon content in the ridge of plot than other vegetation types. We still cannot confirm the main factor to control the spatial variation of soil properties in this dynamic plot because the complicate relationships between soil characteristics, landscape position and vegetation types in the study area. For the estimation of spatial distribution of soil properties in the study area based on the grid sampling distance, 60 m × 60 m in area, the prediction of soil characteristics by Geostatistics method for soil pH, soil organic matter content, KCl extractable NH4+, mineralizable N, Mehlich K, and exchangeable cations, are acceptable, but the Bray No. 1 P showed that we need more denser sampling distance, such as 30 m × 30 m in area, to get more reliable database in this study plot.

並列關鍵字

Lienhuachih 25 ha dynamic plot ； Soil property ； Topographic position ； Vegetation type ； Principal component analysis (PCA) ； Geostatistics

參考文獻

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