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研究生:褚柏廷
研究生(外文):Po-Ting Chu
論文名稱:應用物種分布模型與River2D評估河川生態系統:以大屯溪為例
論文名稱(外文):Application of Species distribution models and River2D to assess riverine ecosystems- A case of the Datuan stream
指導教授:林裕彬林裕彬引用關係
口試委員:胡明哲王咏潔江莉琦
口試日期:2017-06-21
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生物環境系統工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:111
中文關鍵詞:物種分布模型集成模型River2D棲息地適宜性不確定性適宜性模型日本禿頭鯊台灣鏟頷魚
外文關鍵詞:SDMsensemble modelRiver2Dhabitat suitabilityuncertaintysuitability modelsDatun StreamSicyopterus JaponicusOnychostoma barbatulum
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河流生態系統常常會有受到人為污染和氣候變化下而污染的風險,為了改善這樣的狀況,制訂一些相關的法令政策是勢在必行的,且為了讓相關政府機關在制定時能有更好的準則,瞭解對於河流生態系統未來的發展就顯得是一件相當重要的事情。
物種分布模型是一個很好的模型可以用來理解環境變量和物種出現之間的關係,可以計算棲息地適宜度指數,依據不同的統計模式,已經發展有許多不同種類的物種分布模型且被運用在許多方面,但是對於不同物種與其物種不同的生長週期,應該選擇哪一種物種分布模型大多數人普遍沒有共識,且在河流生態系統,水圈和生物圈之間的關係是非常複雜的,不恰當的模型選擇不僅會擴大不確定性也會導致預測誤差。
本研究收集大屯溪河道的相關數據和日本禿頭鯊(Sicyopterus japonicus)與台灣鏟頷魚(Onychostoma barbatulum)在此區域出現的相關數據。透過利用River2D這個有效的工具去模擬此河段的二維水力模型,可以模擬出到這個河段每個點的流速與水深,接著我們將River2D得到的結果放入不同的物種分布模型,分別為廣義線性模型、廣義加法模型、隨機森林、支持向量機、人工類神經網路以及集成模型(其他五種模型的平均值)。且魚類資料我們隨機的取其中70%用來訓練模型,剩下的30%拿來驗證我們的結果,這個隨機取樣的過程我們會重複1000次。且透過運用相關係數、赤池信息量準則、均方根誤差和KL散度等驗證方式來比較哪個模型有較好的表現,最後計算信息熵數量化空間異質性與棲息地適宜度指數。
結果表明River2D的主要誤差大多會來自於測量的這個步驟。在我們的預測模式中,日本禿頭鯊偏好待在流速較快的位置,台灣鏟頷魚則偏好低水深低流速。最後透過不同驗證方式在四次模擬預測的結果,我們可以知道集成模型的性能優於其他五種,因此做類似的研究時,我們認為能夠先使用這種集成模型的方式,在我們還無法確定要使用哪種特地的物種分布模型之前,也能夠得到一個相當不錯的結果。此外我們認為這種結合了水力模型與物種分布模型的整合模式,可以成為政府規劃未來的生態工程的一個參考標準。
Riverine ecosystems are usually under the risk of anthropogenic contamination and climate change. With an eye to improving this situation, it is imperative to formulate corresponding policies. To provide the government with better policy-making standard, understanding of the future development of the ecosystems is quite necessary.
Species distribution models (SDMs) is a good tool to calculate habitat suitability index and understand the relationship between environmental variables and species occurrence. There are various species distribution models that have been developed and used in stream ecology. However, there is no consensus on the selection of different models and the relationship between hydrosphere and biosphere is highly complex. What is worse, inappropriate model selection not only expand the uncertainty but also cause the prediction error.
This study collects river channel data and fish data (Sicyopterus Japonic and Onychostoma barbatulum) in the Datuan stream. We use River2D as an efficient tool for simulating the two-dimensional flow condition of a stream segment. Then we combine six SDMs with the outputs of River2D and quantify the relationship between environmental variables and species occurrence by using six SDMs, which are generalized linear model (GLM), generalized additive model (GAM), random forest model (RF), support vector machine (SVM), artificial neural network model (ANN), and ensemble model (the average of other five SDMs). We randomly split the fish data to train(70%) and validate(30%), and each model repeats this step for 1000 times. Additionally, Through Correlation Coefficient, Akaike information criterion, root-mean-square error and Kullback–Leibler divergence, we can know which model has better performance. Moreover, we calculate the information entropy to quantify the spatial heterogeneity of flow conditions and habitat suitability index.
The results demonstrated the accuracy of River2D is affected by measurement deeply. Sicyopterus Japonic likes areas where the water velocity is rapid, and Onychostoma barbatulum likes areas where the velocity and the depth value is small. Moreover, through the result of Correlation Coefficient, root-mean-square error and Kullback–Leibler divergenc, we can know that the performance of the ensemble model is better than others. Therefore, next time we can use the ensemble model when we have not identified which model to use. Moreover, we consider this model for integrating hydraulic and species distribution models can become a criterion when government planning of Ecological Engineering in the future.
謝誌…………………………………………………………………………………….I
中文摘要……………………………………………………………………………...II
英文摘要……………………………………………………………………………..IV
目錄…………………………………………………………………………………..VI
圖目錄…………………………………………………………………………..……IX
表目錄………………………………………………………………………………XII
第一章 前言 1
1.1 背景說明 1
1.2 研究目的 4
1.3 研究流程 5
第二章 文獻回顧 8
2.1 河川環境因子與指標物種 8
2.1.1 河川生態系統之危機 8
2.1.2 指標生物 9
2.2 棲地適宜度指數 11
2.3 水理棲地模式 14
2.3.1 一維水理棲地模式 14
2.3.2 二維水理棲地模式 16
2.3.3 三維水理棲地模式 18
2.4 物種分布模型 20
第三章 研究方法 24
3.1 研究區域 24
3.1.1 大屯溪流域 24
3.1.2 研究河段位置 27
3.1.3 指標生物之選擇 28
3.2 現地調查與資料之取得 32
3.2.1 指標物種與採集方法 32
3.2.2 河床地形調查 33
3.2.3 物理棲地參數蒐集 34
3.2.4 流量資料 35
3.2.5 底質粗糙高度資料 36
3.3 二維水理棲地模式River2D 37
3.3.1 模式簡介 37
3.3.2 模式基本主要理論 37
3.3.3 流體方程式之數值模式 40
3.3.4 數值高程模型原理與應用 41
3.3.5 河床底質 42
3.4 物種分布模型 43
3.4.1 物種分布模型簡介 43
3.4.2 廣義線性模型 43
3.4.3 廣義加法模型 44
3.4.4 隨機森林 45
3.4.5 支持向量機 46
3.4.6 人工類神經網路 50
3.4.7 集成模型 54
3.5 模型整合與驗證 54
3.5.1 物種分布模型整合二維水理模式 54
3.5.2 模型比較與驗證 55
3.5.3 可用棲地面積 56
3.5.4 空間異質性之計算 56
第四章 結果與討論 57
4.1 棲地背景調查 57
4.1.1 水理資料 57
4.1.2 底質資料 59
4.1.3 魚類資料 59
4.2 二維水理模式 62
4.2.1 River2D模式建立 62
4.2.2 水理模擬結果 63
4.2.3 模式驗證 67
4.2.4 誤差來源探討 67
4.3 物種分布模型 70
4.3.1 日本禿頭鯊 70
4.3.2 台灣鏟頷魚 83
4.3.3 集成模型 96
第五章 結論與建議 97
5.1 結論 97
5.2 建議 99
第六章 文獻回顧 102
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