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研究生:馬慧珊
研究生(外文):Hui-Shan Ma
論文名稱:比較三種泛線性模式標準化印度洋大目鮪族群豐度指標之適合度
論文名稱(外文):Comparison of standardized abundance index of bigeye tuna (Thunnus abesus) in the Indian Ocean by three general linear models
指導教授:許建宗許建宗引用關係
指導教授(外文):Chien-Chung Hsu
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:海洋研究所
學門:自然科學學門
學類:海洋科學學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:107
中文關鍵詞:印度洋大目鮪泛線性模式泛線性混合模式泛線性加乘模式Akaike information criterionBayesian information criterion
外文關鍵詞:Indian bigeye tunaGeneralized linear modelGeneralized linear mixed modelGeneralized additive modelAICBICCAIC
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印度洋大目鮪系群,主要是由台灣、日本等國家以鮪延繩釣船和西班牙及法國以圍網船進行開發利用。印度洋大目鮪從1993年的平均產量100,000公噸增加到1995年的150,000公噸;2000年到2004年之間的平均產量也大約維持在119,000公噸。其中,台灣的捕獲量約佔所有生產量的30%,為印度洋大目鮪主要利用國之一。為有效永續利用此系群,資源評估和管理是必要的手段。在進行系群評估時,標準化單位努力漁獲量是常被用來反應資源豐度的指標之一。由於單位努力漁獲量受到時空環境因子和漁業策略等的影響,需要藉由標準化單位努力漁獲量,將各種可能影響漁獲率的變數排除,才能獲得較符合真實狀況的資源量變動趨勢。因此,本研究採用三種不同模式:泛線性模式、泛線性混合性模式及泛線性加乘模式對相同之漁獲資料進行單位努力漁獲量的標準化。並利用Akaike information criterion (AIC)、Bayesian information criterion (BIC)及Consistent Akaike''s Information Criterion (CAIC)等三種統計值,作三種模式選擇之評估比較。標準化後的結果顯示,以泛線性加乘模式所得之模式統計值最小,故推斷此一模式為最適合做印度洋大目鮪系群資源量指標標準化模式。經標準化之單位努力漁獲量趨勢,自1980年代初期下降到1990年代初期的最低水準,再上升到1990年代中期的最高水準後,呈下降趨勢到2000年約為1990年之水準,後又再度上升至2002年之近十年來的次高水準。其後,三種模式有不同的資源量指標解讀,以泛線性模式和泛線性混合性模式標準化者,呈急速下降趨勢到2004年的歷年最低水準,而以泛線性加乘模式標準化者,則呈緩慢下降。
Indian bigeye tuna are mainly exploited by Taiwanese and Japanese longline and French and Spanish purse seiners. Total annual catch in creased from about 100,000 mt in 1993 to about 150,000 in 1995 and stayed at around 119,000 mt averaged between 2000 and 2004. Among the catch, Taiwan fleets took about 30%, which is one of the major fishing countries. In order to exploit the stock sustainably, standardized catch-per-unit-effort (CPUE) is the most common method used as an index to reflect stock abundance. To obtain the actual abundance trend, possible factors influencing the catch rate need to be removed by using standardized because CPUE may differ across time, space, and fishing stratege…etc. In this study, these statistical models, generalized linear models (GLM), generalized linear mixed models (GLMM), and generalized additive models (GAM), were applied to standaedize the common fishery catch effort data. Furthermore, model selection and comparison were conducted among the three models using Akaike information criterion (AIC), Bayesian information criterion (BIC), and Consistant Akaike’s information criterion (CAIC). These results indicated that the GAM with the smallest information criterion was selected as the best model to standardize CPUE for Indian bigeue tuna. The standardized CPUE decreased from the early 1980s to the lowest level in the early 1990s, increased to the peak level in the mid of 1990s, then declined to the 1990 level in 2000, and increased again in 2002, the second high level over past decade. After that, the trends of standardized CPUE were different. CPUE estimated by GLM and GLMM sharply dropped to the historical low level in 2004, but it tended to slowly decline by GAM.
目錄 頁次
口試委員會審定書
誌謝………………………………………………………………………………… i
中文摘要…………………………………………………………………………… ii
英文摘要…………………………………………………………………………… iii
目錄………………………………………………………………………………… iv
圖目錄……………………………………………………………………………… vii
表目錄……………………………………………………………………………… xi
1. 前言…………………………………………………………………… 1
2. 材料與方法…………………………………………………………………… 5
2.1 漁獲統計資料…………………………………………………………………… 5
2.2 來源資料篩選…………………………………………………………………… 5
2.3 資料檢定…………………………………………………………………… 6
2.4 顯著因子篩選…………………………………………………………………… 6
2.4.1 年別區分…………………………………………………………………… 6
2.4.2 作業漁區區分…………………………………………………………………… 6
2.4.3 季別區分…………………………………………………………………… 7
2.4.4 水溫區分…………………………………………………………………… 7
2.4.5 混獲魚種比例區分……………………………………………………… 8
2.5 豐度指標標準化…………………………………………………………………… 9
2.5.1 泛線性模式 (General linear models, GLM)…………………………………………………………………… 9
92.5.2 泛線性混合模式 (Generalized linear mixed models, GLMM)…………………………………………………………………… 12
2.5.3 泛線性加乘模式 (Generalized additive models, GAM)…………………………………………………………………… 14
2.6 模式的選取…………………………………………………………………… 16
3. 結果…………………………………………………………………… 17
3.1 來源資料篩選…………………………………………………………………… 17
3.2 檢定作業報表及觀察員資料之相關性……………………………… 17
3.3 各因子與名目單位努力漁獲量分布之關係……………………………… 18
3.3.1 年單位努力漁獲量分布……………………………………………………… 18
3.3.2 季節等級之單位努力漁獲量分布…………………………………………… 18
3.3.3 經度之單位努力漁獲量分布…………………………………………… 18
3.3.4緯度之單位努力漁獲量分布…………………………………………… 19
3.3.5作業漁區之單位努力漁獲量分布…………………………………………… 19
3.3.6海水表層溫度之單位努力漁獲量分布……………………………… 19
3.4 泛線性模式…………………………………………………………………… 20
3.4.1 印度洋全區…………………………………………………………………… 20
3.4.2 熱帶洋區…………………………………………………………………… 21
3.4.3 溫帶洋區…………………………………………………………………… 21
3.5泛線性混合模式……………………………………………… 22
3.5.1 印度洋全區…………………………………………………………………… 22
3.5.2 熱帶洋區…………………………………………………………………… 23
3.5.3 溫帶洋區…………………………………………………………………… 24
3.6 泛加乘性模式…………………………………………………………………… 25
3.6.1 印度洋全區…………………………………………………………………… 25
3.6.2熱帶洋區…………………………………………………………………… 26
3.6.3溫帶洋區…………………………………………………………………… 27
3.7 模式選取…………………………………………………………………… 28
4. 討論…………………………………………………………………… 29
4.1 標準化資源量指標……………………………………………………… 29
4.2 顯著因子的決定……………………………………………… 30
4.2.1 漁區…………………………………………………………………… 30
4.2.2 年間交感因子……………………………………………………… 31
4.2.3 溫度因子…………………………………………………………………… 31
4.2.4 季節因子…………………………………………………………………… 32
4.2.5 黃鰭鮪混獲比例因子……………………………………………………… 32
參考文獻…………………………………………………………………… 36
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