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研究生:賴詩涵
研究生(外文):Lai, Shi-Han
論文名稱:中西太平洋鮪延繩釣黃鰭鮪潛在棲地分布與預測模式建置之研究
論文名稱(外文):Potential habitat distributions and predicated model establishment of yellowfin tuna (Thunnus albacres) for longline fishery in the Western and Central Pacific Ocean
指導教授:藍國瑋
指導教授(外文):Lan, Kuo-Wei
口試委員:李明安葉信明張以杰
口試委員(外文):Lee, Ming-AnYeh, Hsin-MingChang,Yi-Jay
口試日期:2018-06-22
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:環境生物與漁業科學學系
學門:農業科學學門
學類:漁業學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:中文
論文頁數:74
中文關鍵詞:黃鰭鮪太平洋物種分布模式泛加成模式最大熵值模式
外文關鍵詞:Yellowfin tunaPacific OceanSpecies distribution modelGeneralized Additive ModelMaximum entropy model
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黃鰭鮪是遠洋鮪延繩釣漁業重要釣獲資源之一,本研究收集2009~2014年臺灣遠洋延繩釣漁船於太平洋作業之1度網格漁業數據,結合多衛星及模式演算之不同水文資料,利用Generalized Additive Model (GAM)及Maximum entropy model (MaxEnt)等物種分布模式,分析海洋環境因子對太平洋黃鰭鮪釣獲率的影響,探討在不同模式間黃鰭鮪之預測漁場變動關係,以及探討最適棲地、過渡區與非漁場區之分布趨勢與預測準確率。GAM分析結果顯示投入五項環境因子皆對黃鰭鮪之釣獲率有顯著影響(p<0.01),變異總解釋率為34%,且黃鰭鮪較偏好的水文環境範圍約介於海表水溫25~30(°C)、海面高度0.4~0.8(m)、葉綠素a濃度0 ~0.3 ( mg/m3)、混合層深度<40 (m)和表水溫鋒面0.4~0.6。另MaxEnt分析結果顯示現歷年四季之AUC值皆高達0.7以上,符合可被當作預測模式之AUC標準,且最適環境值約介於海表水溫25~29(°C),海面高度0.5~ 0.83(m)、葉綠素a濃度0.1 ~0.19( mg/m3),混合層深度5~32(m)和表水溫鋒面0.38~0.53,且兩種模式皆顯示以海表水溫與海面高度為主要影響釣獲率高低的因子。另探討不同模式預測黃鰭鮪釣獲率分布之準確性,結果顯示GAM於第二~四季三種棲地型態中皆能預測40%以上準確度,於第三季時預測過渡區之預測準確度達60%,而以MaxEnt模式建立之三種HSI模式,在預測能力上仍有差別。以AUC值最高模式(HSI_H)預測第二~三季最適棲地之預測準確度最高(64~100%),第四季則以AUC值最低模式(HSI_A)預測最適棲地之準確度達92%,另疊合兩模式結果,其預測準確度約介於16.67%~65.22%之間。另外最適棲地於各模式皆顯示黃鰭鮪之最適棲地主要位於中西太平洋海域及赤道太平洋海域為主,且呈現季節上之變動。
The physical environment directly influences the distribution, abundance, physiology and phenology of marine species. We used satellite-based oceanographic data of sea surface temperature (SST), sea surface chlorophyll-a concentration(SSC), and Sea surface elevation(SSE), Sea surface temperature front (JSD), Chlorophyll-a level 3(CHL) and Ocean mixed layer thickness (MLD) together with 1 degree resolution catch data of yellowfin tuna were collected during the period of 2009-2014. The study used two species distribution model, Generalized Additive Models (GAMs) and Maximum entropy model (MaxEnt) to investigate the relationship between yellowfin tuna fishing ground and oceanographic conditions and also to predict potential habitats for yellowfin tuna in the Pacific Ocean. We further explored the distribution and accuracy rate of three habitat types, suitable zone, buffer zone and core zone. The results revealed that the cumulative deviances obtained using the selected GAMs were 34%. The results suggest that areas with a higher SST approximately 25~30(°C), a SSE of approximately 0.4~0.8(m), a CHL of approximately 0 ~0.3 (mg/m3),a MLD of approximately<40 (m) and JSD of approximately 0.4~0.6 yield higher catch rates of yellowfin tuna in GAM model. The AUC were higher than 0.7 through the year in MaxEnt model, and the optimal range of hydrological variables were 25~29(°C) of SST, 0.5~0.83(m) of SSE, 0.1 ~0.19( mg/m3) of CHL, 5~32(m) of MLD, and 0.38~0.53 of JSD. Both models show that the sea surface water temperature and sea surface elevation are the main factors affecting the catch rate. In addition, we explored the accuracy of different models in predicting the distribution of catch rates. The results showed that GAM predict more than 40% accuracy in the three habitat types in second to fourth quarter, and the prediction accuracy of the buffer zone in third quarter approximately 60%. However, the three HSI models established using the MaxEnt model still differ in their prediction capabilities. The prediction of the optimal habitats in the second to third quarters with the highest AUC values predicted the highest accuracy (64 to 100%). In the fourth quarter, the accuracy of the most suitable habitat for the prediction of the lowest AUC value reached 92%. The prediction accuracy is between approximately 16.67% and 65.22% for the model of superimposing GAM and MaxEnt . In addition, the optimum habitat for the suitable habitat in all modes had seasonal changes and were mainly located in the Western and Central Pacific Ocean and in the equatorial Pacific Ocean, with seasonal changes.
摘要 I
ABSTRACT II
目錄 III
表目錄 V
圖目錄 VI
壹、 前言 1
1.1中西太平洋黃鰭鮪形態特徵與分布習性 1
1.2海洋環境變動對鮪類分布與資源之影響 1
1.3棲地預測模式之重要性 2
1.4棲地分布模式 3
1.5研究動機與目的 4
貳、 研究資料及方法 5
2.1研究範圍及流程 5
2.2漁獲資料收集 5
2.3海洋環境資料 6
2.3.1 MODIS-Aqua 海洋水色資料 6
2.3.2 混合座標海洋模式(Hybrid coordinate Ocean Model, HYCOM) 7
2.3.3 海面表水溫鋒面邊緣偵測法 7
2.4泛加成模式(GENERAL ADDITIVE MODEL, GAM) 8
2.4.1 GAM建構 8
2.4.2模式選擇及驗證 8
2.5 最大熵值模式(MAXIMUM ENTROPY METHOD, MAXENT) 9
2.5.1 MaxEnt建構 9
2.5.2 MaxEnt驗證 10
2.7模式之準確度分析 10
參、 結果 12
3.1太平洋黃鰭鮪之漁場概況 12
3.3 泛加成模式建置及驗證 13
3.3.1 GAM分析-釣獲率與環境因子之變動 13
3.3.2以GAM建構未來漁場預測模式 13
3.4 MAXENT 模式建置及驗證 14
3.4.1 模式選擇方式與擬合程度 14
3.4.2環境因子最適範圍與貢獻度 14
3.4.3 漁場空間分布預測結果 15
3.5 GAM與MAXENT模式最適棲地分布預測結果比較 15
肆、討論 17
4.1黃鰭鮪與海洋環境因子之關係 17
4.1.1 海表水溫(SST) 17
4.1.2 混合層深度(MLD) 18
4.1.3 葉綠素濃度(SSC) 18
4.1.4 海面高度(SSE) 19
4.1.5 水溫鋒面 19
4.2太平洋黃鰭鮪之時空分布變動 20
4.3 模式預測最適棲地準確率比較 21
4.3.1 GAM模式預測 21
4.3.2 MaxEnt 模式預測分布 22
4.4模式探討 23
伍、結論與未來展望 24
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