Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/66239
標題: 物種分布模式預測功效影響因子之評估
Evaluation of the Factors Affecting Predictive Performance of Species Distribution Models
作者: 陳厚昌
Chen, Hou-Chang
關鍵字: 物種分布模式
Species distribution model (SDM)
3S技術
影響因子
木荷
台灣杜鵑
潛在生育地
空間外推
3S technology
Influential factors
Schima superba var. superba
Rhododendron formosanum
Potential habitat
Spatial extrapolation.
出版社: 森林學系所
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摘要: 隨著社會變遷及人類生活需求重心的轉移,台灣森林政策及經營目標也導向森林保育及公益性為主。為了能永續利用生物資源及維持生態系功能,如何擬訂適當的經營管理原則與方案,以落實設置生態保護區或自然保留區,並發揮其最大效用,是當前最重要的課題。達成上述目標之關鍵是要能精確描述物種生態和地理分布的資訊。研究使用物種分布模式 (Species distribution model, SDM),協同遙測 (RS)、地理資訊系統 (GIS)、全球定位系統 (GPS) 結合之3S技術,執行分析、整合、預測及規劃的程序,量化物種已知分布位置與其周遭環境之關聯,並模擬及展示物種的空間分布型態,俾利於長期森林生態監測及森林經營。然而模式預測功效受到一些因子的影響,如: (1) 物種生態特性;(2) 模擬技術;(3) 資料品質 (資料解析度及樣本數);(4) 環境因子的選擇等。模式因為是以簡單的規則或數學函數來表示真實事件,所以物種重要的生態因子常被遺漏。有鑑於此,如何開發出健全的模式以減少預測誤差與評估因子對SDM功效的影響為研究重點。我們以GIS為平台,分別將GPS定位之木荷、台灣杜鵑樣株與地文、光譜預測變數圖層疊合,整併匯入最大熵值法 (MAXENT)、DOMAIN、BIOCLIM、抉擇樹 (DT)、區別分析 (DA)、廣義線性模式 (GLM)、最大概似法 (ML) 及倒傳遞類神經網路 (BPNN),建立八個模式,預測兩樹種於惠蓀林場之潛在生育地。同時採用分割樣本驗證法以及五種指標來評估模式功效。數據結果顯示,四項因子對模式預測皆有顯著的影響,其中資料品質是首項最重要的考量,居次為物種特性、統計方法,最後為環境因子選擇。研究發現,沒有一個方法能夠僅以少量樣本建立模式而依然維持良好表現,唯MAXENT模式,推測表現明顯著優於其他方法。模式於兩物種推測準確度初期隨主體樣本數量增加而小幅上升,當達到某一數量之後,其上揚幅度隨之減小,漸趨平緩並到達最大準確度。再者,採不同解析度所建模式,推測準確度有明顯的差異,尤其是模式採低解析資料會導致預測表現下降。此外,八模式預測台灣杜鵑整體平均表現均優於木荷,這意味著物種特性對模式預測有頗大的影響。再就各統計模式而言,DT、MAXENT及DOMAIN最為嚴謹且細緻,GLM、BPNN、ML居次,BIOCLIM與DA則殿後。MAXENT、DT及DOMAIN於首次模擬,即由全區篩選出佔其甚小比例的木荷與台灣杜鵑之高潛力區,大幅縮小實地調查面積,並節省可觀經費及人力。尤其重要的是,由於試區東峰溪與關刀溪集水區地文因子無共通性,使得模式受到建模樣本分布及微地形效應影響,無法跨越空間外推之障礙。後續研究建議加入直接作用因子 (如日照、氣溫、雨量等) 或它們的替代變數,或是從高空間解析度影像、高光譜影像及光達資料萃取預測變數,期能提升模式預測能力及適用更廣大的地理空間。
Today’s forest policies and management strategies have been leading to forest conservation and public welfare with transference of society change and human life needs. For the sustainable use of bio-resources and maintain ecosystem function, how to tackle these operating problems arising in the management process and design an appropriate management principle to set nature reserve become an importance issues. The key to achieve the goal is that we need accurately describe species ecological and species geographic distribution information. Then we used species distribution model (SDM) coupled with statistical methods, remote sensing (RS), geographic information system (GIS) and global position system (GPS) to implement the procedure of analysis, combination, prediction and planning. Then SDM could exhibit species distribution pattern spatially by quantifying the relationship between species and environment. However, model performance is often affected by some factors such as species ecological characteristics, modeling algorithms, data quality, choice of predictor variables and so on. As mentioned above, prediction errors frequently occur in SDMs because these models usually simplify the real world while ignore important aspects of species ecology. Therefore, how to develop a robust model and interpret the effects of factors on model performance became an essential part of this study. Samples of Schima superba var. superba (Chinese guger tree, CGT) and Rhododendron formosanum (Red-stripe rhododendron, RSR) were obtained by GPS. GIS technique was used to overlay the layers of two species with topographic variables and spectral response variables. Species distribution models were developed by eight algorithms, including maximum entropy (MAXENT), DOMAIN, BIOCLIM, decision, tree (DT), discriminant analysis (DA), generalized linear model (GLM), maximum likelihood (ML) and back-propagation neural network (BPNN) to predict the potential habitats of the two species in Huisun study area, respectively. The study took split-sample validation approach and evaluated SDMs in terms of five indicators for model accuracy comparison. According data results indicated that four factors had significant effects on model prediction. Data quality had the highest influence, followed by species traits and model algorithm, and predictor variable selection was the lowest among them. All algorithms could not keep good performance across different training sample sizes, except MAXENT. The accuracies of eight models progressively increased with the number of samples until reaching a certain number, beyond which accuracies started leveling off and eventually reached maximum accuracy. Data resolution affected model’s accuracy, especially lower resolution data significantly degraded model performance. Moreover, the overall accuracy of RSR species was higher than that of CGT species. This means that species traits had more influence on SDM performance than algorithms. In terms of modeling techniques, MAXENT, DT and DOMAIN had the highest accuracy, followed by GLM, BPNN, and ML, and BIOCLIM, and DA had the lowest accuracy. Furthermore, the predictions of MAXENT, DT and DOMAIN generated high potential areas of CGTs and RSRs from the entire study area at the first stage, and thereby saving both cost and labor. More importantly, the models merely based on sample distribution and topographic variables could not be applied to predict species distribution at a larger spatial scale because the topographic attributes of Tong-Feng and Kuan-Dau watersheds are quite different from each other. Consequently, the prediction from all models with topographic variables built only from Tong-Feng samples could not be accurately extrapolated to the Kuan-Dau watershed. For a future study, direct factors (e.g. solar radiation, temperature and rainfall) or their surrogates and spectral response variables extracted from hyperspectral image data should be incorporated into an SDM so that it can be applied over a larger geographic area.
URI: http://hdl.handle.net/11455/66239
其他識別: U0005-0508201314250100
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0508201314250100
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