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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許建宗 | |
dc.contributor.author | Chia-Lung Shih | en |
dc.contributor.author | 石佳隴 | zh_TW |
dc.date.accessioned | 2021-06-07T23:42:56Z | - |
dc.date.copyright | 2014-08-14 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-07-22 | |
dc.identifier.citation | Altman, P.L., Dittmer, D.S. 1962. Growth, including reproduction and morphological development. Federation of American Societies for Experimental Biology.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16656 | - |
dc.description.abstract | 大西洋黃鰭鮪與大目鮪主要棲息在熱帶海域,產卵場皆在新幾內亞灣夏季產卵,且稚魚階段混游在表水層。再則,胃內容物研究顯示此兩系群彼此間食性重疊。因此推測此兩魚種系群可能彼此間有生態競爭的影響。本研究目的是利用生產量模式評估競爭對大西洋黃鰭鮪與大目鮪資源之影響。台灣延繩釣漁獲統計資料顯示,漁業可分為兩種作業型態。一種是採用每筐鉤數比較少(8-11鉤)的作業型態,主要作業於緯度較高的海域(15oN以北與20oS以南),以捕獲長鰭鮪為主,此種作業型態稱為傳統式延繩釣。另外一種採用每筐鉤數較多(15-18鉤)的作業型態,主要在熱帶海域作業(15oN-20oS),以捕獲大目鮪為主且容易混獲大量黃鰭鮪,此種作業型態稱為深層式延繩釣。台灣延繩釣是主要捕獲大西洋大目鮪與黃鰭鮪重要漁業國家之一。本研究利用台灣延繩釣漁獲統計資料進行標準化大目鮪與黃鰭鮪CPUE序列。標準化黃鰭鮪與大目鮪CPUE序列在1994與1995年出現極高值,且隨時間序列變異很大。利用貝氏法估計生產量模式參數的敏感度分析中,發現不合理先驗參數設定範圍與有問題的國家漁業CPUE序列對資源評估影響很大。當輸入資料含有台灣延繩釣大目鮪CPUE序列時,會造成估計參數差異變大。評估黃鰭鮪與大目鮪彼此競爭對資源的影響,結果發現此兩系群彼此間存在競爭影響。黃鰭鮪系群會明顯降低大目鮪系群資源量(w2=0.2304),而大目鮪系群對黃鰭鮪系群的抑制影響很小(w1=0.0981)。比較模式是否有考慮競爭參數所估得生物參考點的差異,當模式考慮競爭參數時黃鰭鮪與大目鮪的估計值變小,其中大目鮪在考慮競爭參數模式下的最大持續生產量(7.3萬公噸)比未考慮競爭參數估值(最大持續生產量: 9.5萬公噸)小很多。本研究建議每年可捕獲量,黃鰭鮪可維持在目前漁獲量(11萬公噸),但大目鮪需由歷年設定的8.5萬公噸調降到4萬公噸,以避免過度利用此資源。 | zh_TW |
dc.description.abstract | Yellowfin tuna (Thunnus albacares) and bigeye tuna (Thunnus obesus) stocks in the Atlantic Ocean are mainly distributed in the tropical waters. These two stocks spawn in the Gulf of Guinea during summer. When they are in their juvenile stage, these two stocks mix in the surface waters. In addition, the stomach contents of these two stocks show dietary overlap. Thus, we hypothesized that these two stocks may show the effects of ecological competition on both. The objective of this study is to investigate the effects of ecological competition on assessing yellowfin tuna and bigeye tuna stocks by using the surplus production model with a Bayesian approach. In the analysis of Taiwanese longline fishery data, the results showed that there were two fishing types in the fishery. One fishing type was using lower number hooks of per basket (8-11 hooks), operating majorly in the higher latitude of waters (north of 15oN and south of 20oS), targeting on albacore and called as regular longline fishery. Another fishing type was using higher number hooks per basket (15-18 hooks), operating majorly in the lower latitude of waters (15oN-20oS), targeting on bigeye tuna and bycatching yellowfin tuna, and called as deep longline fishery. The standardized yellowfin tuna and bigeye tuna abundance indexes of this study showed extreme high CPUEs in 1994 and 1995, and highly temperal variation. In the sensitivity analysis of Bayesian approach, the results showed that unreasonable priors setting and questionable country fishery abundance indexes would result in bias of stock assessment. While input data included bigeye tuna abundance indexes of Taiwanese longline fihsery, the estimated parameters of production model showed a slight diference. In the analysis of the effects of competition on assessing yellowfin tuna and bigeye tuna stocks, the results showed that the competition exist in the two stocks. Yellowfin tuna stock could obviously decrease the biomass of bigeye tuna stock (w2=0.2304) and bigeye tuna stock would lightly decrease the biomass of yellowfin tuna stock (w1=0.0981). The estimated parameters of yellowfin tuna and bigeye tuna stocks estimated by the model with competition became smaller than these estimated by the single-species model, but the maximum sustainable yield (73,000 tons) of bigeye tuna estimated by the model with competition were smaller than that (maximum sustainable yield: 95,000 tons) estimated by the single-species model. Thus, we suggested that the total allowable catch of yellowfin tuna stock could be maintained at the current catch level (110,000 tons), whereas that of bigeye tuna stock should be decreased from 85,000 tons to 40,000 tons to avoid overexploiting this stock. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T23:42:56Z (GMT). No. of bitstreams: 1 ntu-103-D97241002-1.pdf: 5068621 bytes, checksum: 6585241b7d50ab4ec87323be02bfbd7c (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 口試委員會審定書 i
謝詞 ii 摘要 iii Abstract iv 第一章、 前言 1 1.1黃鰭鮪與大目鮪 1 1.1.1 黃鰭鮪 1 1.1.2 大目鮪 1 1.2大西洋黃鰭鮪與大目鮪漁業 2 1.2.1 黃鰭鮪漁業 2 1.2.2 大目鮪漁業 3 1.3歷年大西洋黃鰭鮪與大目鮪資源評估 3 1.3.1 黃鰭鮪 4 1.3.2 大目鮪 4 1.4 生態競爭之資源評估 5 1.5 台灣鮪延繩釣漁業 7 1.6 標準化台灣延繩釣CPUE 8 1.7 剩餘生產量模式 9 1.8 貝氏資源評估 10 1.9研究目的 11 第二章 材料與方法 12 2.1 材料 12 2.2 方法 13 2.2.1台灣延繩釣時空分布 13 2.2.2標準化台灣延繩釣CPUE 14 2.2.3 資源評估 15 第三章、結果 22 3.1 台灣延繩釣漁業漁獲統計資料 22 3.1.1 時空分布 22 3.1.2作業型態 22 3.1.3 熱帶與溫帶海域漁業資料的差異 22 3.2 標準化台灣延繩釣CPUE 23 3.2.1 漁區劃分 23 3.2.3 黃鰭鮪 24 3.2.4 大目鮪 25 3.3 貝氏剩餘生產量資源評估 26 3.3.1 黃鰭鮪 26 3.3.2 大目鮪 27 3.3.3 黃鰭鮪與大目鮪之生態競爭影響 29 第四章、討論 31 4.1 台灣延繩釣漁獲統計資料 31 4.2 標準化CPUE 33 4.3 資源評估 34 第五章、結論與建議 38 參考文獻 39 Fig. 1. Images of (a) yellowfin tuna (Thunnus albacares) and (b) bigeye tuna (Thunnus obesus). 49 Fig. 2. History annual catch of yellowfin tuna (YFT) and bigeye tuna (BET) in the Atlantic Ocean from 1950-2011 (ICCAT_database 2012) 50 Fig. 3. History catch of (a) yellowfin tuna and (b) bigeye tuna by fishing gear in the Atlantic Ocean from 1950-2011. 51 Fig.4. Historical total catch of yellowfin tuna (YFT) and bigeye tuna (BET) by Taiwanese longline fishery in the Atlantic Ocean from 1962-2011 (ICCAT_database 2012) 52 Fig.5. Distributions of fishing efforts, catch composition, bigeye tuna CPUE and yellowfin tuna CPUE of Taiwanese longline fishery in the Atlantic Ocean from 1981-1983, 1990-1992 and 2008-2010. 53 Fig. 6. Distributions of (a) cumulative fishing days and (b) catch species composition by number of hooks per basket for Taiwanese longline fishery in the Atlantic Ocean from 1995-2011. Note: The fishery data derived from Log file. 56 Fig. 7. Distributions of fishing type of Taiwanese longline fishery in the Atlantic Ocean from 1995-2011. Note: Green indicates the fishing type targeting bigeye tuna and red indicates the fishing type targeting albacore tuna. 57 Fig. 8. Annual fishing effort (hooks) by Taiwanese longline fishery operating in the temperate and tropical waters of Atlantic Ocean from 1981-2011. Note: The fishery data derived from Task II file. 58 Fig. 9. Nominal CPUE trends of bigeye and yellowfin tunas caught by Tainwanese longline fishery in the tropical and temperate waters of Atlantic Ocean from 1981-2011, respectively. Note: The fishery data derived from Task II file. 59 Fig. 10. Catch species compositions of Taiwanese longline fishery operating in the tropical waters (a) and temperate waters (b) of the Atlantic Ocean from 1981-2011. 60 Fig. 11. Nominal CPUE trend for (a) yellowfin tuna (b) bigeye tuna caught by Taiwnese longline fishery in the tropics of Atlantic Ocean from 1990-2011. Note: the dash lines indicate the vessels adopted hook<2,400 and hook>3,200 and the solid lines indicate the vessels adopted 2,400<=hook<=3,200. 61 Fig.12. The subareas adopted to standardize the Taiwanese longline CPUE trends operating in the Atlantic Ocean in which (a) is for this study, (b) is for yellowfin tuna (Hsu, 2012) and (c) is for bigyeye tuna (Hsu, 2011). 62 Fig. 13. The mean CPUE of bigeye tuna and yellowfin tuna by the five subareas. Note: Numbers in parentheses indicate sample size. 63 Fig. 14. (a). The Q-Q plots and (b) histogram of residuals with lognormal error structure in general linear model of positive CPUE for yellowfin tuna caught by Taiwanese longline fishery in the tropics of Atlantic Ocean from 1990-2011. 64 Fig. 15. The plots of residuals with lognormal error structure in general linear model of proportion of positive catch sets for yellowfin tuna caught by Taiwanese longline fishery in the tropics of Atlantic Ocean from 1990-2011. 65 Fig. 16. The standardized CPUE of yellowfin tuna caught by Taiwanese longline fishery in the tropics of the Atlantic Ocean from 1990-2011. 66 Fig. 17. The Q-Q plots (a) of histogram (b) of residuals with lognormal error structure in GLM of CPUE for bigeye tuna caught by Taiwanese longline fishery in the tropics of Atlantic Ocean from 1990-2011. 67 Fig. 18. Standardized CPUE of bigeye tuna caught by Taiwanese longline fishery in the tropics of the Atlantic Ocean from 1990-2011. 68 Fig. 19. Relative standardized CPUE of yellowfin tuna (a) and bigeye tuna (b) caught by Taiwanese and Japanese longline fishery in the Atlantic Ocean from 1990-2010. 69 Fig. 20. Sensitivity analysis of setting priors and all available CPUE trends for yellowfin tuna caught in the Atlantic Ocean using surplus production model. 70 Fig. 21. Prior and posterior densities generated from MCMC output of Run1 to Run3 for yellowfin tuna caught in the Atlantic Ocean. 71 Fig. 22. Iteration history of estimated parameters of yellowfin tuna in the Atlantic Ocean, (a) to (c) are Run 1, Run 2 and Run 3, respectively. 73 Fig. 23. Estimated biomass(solid lines) v.s. BMSY (dash lines) of yellowfin tuna in the Atlantic Ocean for 3 runs including the 95% confidential intervals (the thin lines) and the estimated mean values (the tick lines), (a) to (c) are Run 1, Run 2 and Run 3, respectively. 78 Fig. 24. Kobe matrix of yellowfin tuna caught in the Atlantic Ocean for 3 runs, (a) to (c) are Run 1, Run 2 and Run 3, respectively. Note: The first to fourth quadrants indicate overfishing and overfished, non-overfishing and overfished, overfishing and non-overfished, and non-overfishing and non-overfished, respectively. 79 Fig. 25. Catch (solid lines) and MSY (dash lines) including the 95% confidential intervals (the thin dash lines) and the estimated mean values (the tick dash lines) of yellowfin tuna in the Atlantic Ocean for 3 runs, (a) to (c) are Run 1, Run 2 and Run 3, respectively. 80 Fig. 26. The sensitivity analysis of priors setting and all available CPUE trends for bigeye tuna caught in the Atlantic Ocean using surplus production model. 81 Fig. 27. Prior and posterior densities generated from the MCMC output of Run 1 to Run 3 for bigeye tuna. 82 Fig. 28. Iteration history of MCMC of the estimated parameters of surplus production model for bigeye tuna caught in the Atlantic Ocean, (a) to (c) are Run 1, Run 2 and Run 3, respectively. 84 Fig. 29. Estimated biomass (solid lines) v.s. BMSY (dash lines) of bigeye tuna caught in the Atlantic Ocean for 3 runs including the 95% confidential intervals (the thin lines) and the estimated median values (the tick lines), (a) to (c) are Run 1, Run 2 and Run 3, respectively. 92 Fig. 30. Kobe matrix of bigeye tuna caught in the Atlantic Ocean estimated by surplus production model for 3 runs, (a) to (c) are Run 1, Run 2 and Run 3, respectively. Note: The first to fourth quadrants indicate overfishing and overfished, non-overfishing and overfished, overfishing and non-overfished, and non-overfishing and non-overfished, respectively. 93 Fig. 31. Catch (solid lines) and MSY (dash lines) including the 95% confidential intervals (the thin dash lines) and the estimated median values (the tick dash lines) of bigeye tuna caught in the Atlantic Ocean for 3 runs, (a) to (c) are Run 1, Run 2 and Run 3, respectively. 94 Fig. 32. Iteration history of MCMC for the estimated parameters of surplus production model considering competition parameters between yellowfin tuna and bigeye tuna. 95 Fig. 33. Estimated biomass (a) and fishing mortality (b) of surplus production model considering competition parameters for yellowfin tuna and bigeye tuna caught in the Atlantic Ocean from 1966 to 2009. 96 Fig. 34. Estimated biomass (solid lines) v.s. BMSY (dash lines) of (a) yellowfin tuna and (b) bigeye tuna in the Atlantic Ocean including the 95% confidential intervals (the thin lines) and the estimated median values (the tick lines) estimated by surplus production model considering competition. 97 Fig. 35. Kobe matric of (a) yellowfin tuna and (b) bigeye tuna in the Atlantic Ocean estimated by surplus production model considering competition. Note: The first to fourth quadrants indicate overfishing and overfished, non-overfishing and overfished, overfishing and non-overfished, and non-overfishing and non-overfished, respectively. 98 Fig. 36. Catch (solid lines) and MSY (dash lines) including the 95% confidential intervals (the thin dash lines) and the estimated median values (the tick dash lines) of (a) yellowfin tuna and (b) bigeye tuna in the Atlantic Ocean considering competition. 99 Fig. 37. Phase plane analysis of yellowfin tuna-bigeye tuna population in the Atlantic Ocean. The dashed and soild lines are the yellowfin tuna and bigeye tuna “isoclines” along which dPyft/dt=0 and dPbet/dt=0, respectively. 100 Fig. 38. The results of projection analysis under different catch levels for yellowfin tuna (a)-(d) and for bigeye tuna (e)-(h) from initial biomass (B0), as the production model considereing competition. 101 Fig. 39. The results of projection analysis of bigeye tuna for the next 10 years as yellowfin tuna catch was maintained at 110,000 tons, as the production model considereing competition. The straight lines indicate the BMSY of yellowfin tuna including the 95% confidential intervals (the thin dash lines) and the estimated median values (the tick dash lines). 102 Fig. 40. The results of projection analysis of yellowfin tuna for the next 10 years as bigeye tuna catch was maintained at 40,000 tons, as the production model considereing competition. The straight lines indicate the BMSY of yellowfin tuna including the 95% confidential intervals (the thin dash lines) and the estimated median values (the tick dash lines). 103 Fig. 1. Images of (a) yellowfin tuna (Thunnus albacares) and (b) bigeye tuna (Thunnus obesus). 49 Fig. 2. History annual catch of yellowfin tuna (YFT) and bigeye tuna (BET) in the Atlantic Ocean from 1950-2011 (ICCAT_database 2012) 50 Fig. 3. History catch of (a) yellowfin tuna and (b) bigeye tuna by fishing gear in the Atlantic Ocean from 1950-2011. 51 Fig.4. Historical total catch of yellowfin tuna (YFT) and bigeye tuna (BET) by Taiwanese longline fishery in the Atlantic Ocean from 1962-2011 (ICCAT_database 2012) 52 Fig.5. Distributions of fishing efforts, catch composition, bigeye tuna CPUE and yellowfin tuna CPUE of Taiwanese longline fishery in the Atlantic Ocean from 1981-1983, 1990-1992 and 2008-2010. 53 Fig. 6. Distributions of (a) cumulative fishing days and (b) catch species composition by number of hooks per basket for Taiwanese longline fishery in the Atlantic Ocean from 1995-2011. Note: The fishery data derived from Log file. 56 Fig. 7. Distributions of fishing type of Taiwanese longline fishery in the Atlantic Ocean from 1995-2011. Note: Green indicates the fishing type targeting bigeye tuna and red indicates the fishing type targeting albacore tuna. 57 Fig. 8. Annual fishing effort (hooks) by Taiwanese longline fishery operating in the temperate and tropical waters of Atlantic Ocean from 1981-2011. Note: The fishery data derived from Task II file. 58 Fig. 9. Nominal CPUE trends of bigeye and yellowfin tunas caught by Tainwanese longline fishery in the tropical and temperate waters of Atlantic Ocean from 1981-2011, respectively. Note: The fishery data derived from Task II file. 59 Fig. 10. Catch species compositions of Taiwanese longline fishery operating in the tropical waters (a) and temperate waters (b) of the Atlantic Ocean from 1981-2011. 60 Fig. 11. Nominal CPUE trend for (a) yellowfin tuna (b) bigeye tuna caught by Taiwnese longline fishery in the tropics of Atlantic Ocean from 1990-2011. Note: the dash lines indicate the vessels adopted hook<2,400 and hook>3,200 and the solid lines indicate the vessels adopted 2,400<=hook<=3,200. 61 Fig.12. The subareas adopted to standardize the Taiwanese longline CPUE trends operating in the Atlantic Ocean in which (a) is for this study, (b) is for yellowfin tuna (Hsu, 2012) and (c) is for bigyeye tuna (Hsu, 2011). 62 Fig. 13. The mean CPUE of bigeye tuna and yellowfin tuna by the five subareas. Note: Numbers in parentheses indicate sample size. 63 Fig. 14. (a). The Q-Q plots and (b) histogram of residuals with lognormal error structure in general linear model of positive CPUE for yellowfin tuna caught by Taiwanese longline fishery in the tropics of Atlantic Ocean from 1990-2011. 64 Fig. 15. The plots of residuals with lognormal error structure in general linear model of proportion of positive catch sets for yellowfin tuna caught by Taiwanese longline fishery in the tropics of Atlantic Ocean from 1990-2011. 65 Fig. 16. The standardized CPUE of yellowfin tuna caught by Taiwanese longline fishery in the tropics of the Atlantic Ocean from 1990-2011. 66 Fig. 17. The Q-Q plots (a) of histogram (b) of residuals with lognormal error structure in GLM of CPUE for bigeye tuna caught by Taiwanese longline fishery in the tropics of Atlantic Ocean from 1990-2011. 67 Fig. 18. Standardized CPUE of bigeye tuna caught by Taiwanese longline fishery in the tropics of the Atlantic Ocean from 1990-2011. 68 Fig. 19. Relative standardized CPUE of yellowfin tuna (a) and bigeye tuna (b) caught by Taiwanese and Japanese longline fishery in the Atlantic Ocean from 1990-2010. 69 Fig. 20. Sensitivity analysis of setting priors and all available CPUE trends for yellowfin tuna caught in the Atlantic Ocean using surplus production model. 70 Fig. 21. Prior and posterior densities generated from MCMC output of Run1 to Run3 for yellowfin tuna caught in the Atlantic Ocean. 71 Fig. 22. Iteration history of estimated parameters of yellowfin tuna in the Atlantic Ocean, (a) to (c) are Run 1, Run 2 and Run 3, respectively. 73 Fig. 23. Estimated biomass(solid lines) v.s. BMSY (dash lines) of yellowfin tuna in the Atlantic Ocean for 3 runs including the 95% confidential intervals (the thin lines) and the estimated mean values (the tick lines), (a) to (c) are Run 1, Run 2 and Run 3, respectively. 78 Fig. 24. Kobe matrix of yellowfin tuna caught in the Atlantic Ocean for 3 runs, (a) to (c) are Run 1, Run 2 and Run 3, respectively. Note: The first to fourth quadrants indicate overfishing and overfished, non-overfishing and overfished, overfishing and non-overfished, and non-overfishing and non-overfished, respectively. 79 Fig. 25. Catch (solid lines) and MSY (dash lines) including the 95% confidential intervals (the thin dash lines) and the estimated mean values (the tick dash lines) of yellowfin tuna in the Atlantic Ocean for 3 runs, (a) to (c) are Run 1, Run 2 and Run 3, respectively. 80 Fig. 26. The sensitivity analysis of priors setting and all available CPUE trends for bigeye tuna caught in the Atlantic Ocean using surplus production model. 81 Fig. 27. Prior and posterior densities generated from the MCMC output of Run 1 to Run 3 for bigeye tuna. 82 Fig. 28. Iteration history of MCMC of the estimated parameters of surplus production model for bigeye tuna caught in the Atlantic Ocean, (a) to (c) are Run 1, Run 2 and Run 3, respectively. 84 Fig. 29. Estimated biomass (solid lines) v.s. BMSY (dash lines) of bigeye tuna caught in the Atlantic Ocean for 3 runs including the 95% confidential intervals (the thin lines) and the estimated median values (the tick lines), (a) to (c) are Run 1, Run 2 and Run 3, respectively. 92 Fig. 30. Kobe matrix of bigeye tuna caught in the Atlantic Ocean estimated by surplus production model for 3 runs, (a) to (c) are Run 1, Run 2 and Run 3, respectively. Note: The first to fourth quadrants indicate overfishing and overfished, non-overfishing and overfished, overfishing and non-overfished, and non-overfishing and non-overfished, respectively. 93 Fig. 31. Catch (solid lines) and MSY (dash lines) including the 95% confidential intervals (the thin dash lines) and the estimated median values (the tick dash lines) of bigeye tuna caught in the Atlantic Ocean for 3 runs, (a) to (c) are Run 1, Run 2 and Run 3, respectively. 94 Fig. 32. Iteration history of MCMC for the estimated parameters of surplus production model considering competition parameters between yellowfin tuna and bigeye tuna. 95 Fig. 33. Estimated biomass (a) and fishing mortality (b) of surplus production model considering competition parameters for yellowfin tuna and bigeye tuna caught in the Atlantic Ocean from 1966 to 2009. 96 Fig. 34. Estimated biomass (solid lines) v.s. BMSY (dash lines) of (a) yellowfin tuna and (b) bigeye tuna in the Atlantic Ocean including the 95% confidential intervals (the thin lines) and the estimated median values (the tick lines) estimated by surplus production model considering competition. 97 Fig. 35. Kobe matric of (a) yellowfin tuna and (b) bigeye tuna in the Atlantic Ocean estimated by surplus production model considering competition. Note: The first to fourth quadrants indicate overfishing and overfished, non-overfishing and overfished, overfishing and non-overfished, and non-overfishing and non-overfished, respectively. 98 Fig. 36. Catch (solid lines) and MSY (dash lines) including the 95% confidential intervals (the thin dash lines) and the estimated median values (the tick dash lines) of (a) yellowfin tuna and (b) bigeye tuna in the Atlantic Ocean considering competition. 99 Fig. 37. Phase plane analysis of yellowfin tuna-bigeye tuna population in the Atlantic Ocean. The dashed and soild lines are the yellowfin tuna and bigeye tuna “isoclines” along which dPyft/dt=0 and dPbet/dt=0, respectively. 100 Fig. 38. The results of projection analysis under different catch levels for yellowfin tuna (a)-(d) and for bigeye tuna (e)-(h) from initial biomass (B0), as the production model considereing competition. 101 Fig. 39. The results of projection analysis of bigeye tuna for the next 10 years as yellowfin tuna catch was maintained at 110,000 tons, as the production model considereing competition. The straight lines indicate the BMSY of yellowfin tuna including the 95% confidential intervals (the thin dash lines) and the estimated median values (the tick dash lines). 102 Fig. 40. The results of projection analysis of yellowfin tuna for the next 10 years as bigeye tuna catch was maintained at 40,000 tons, as the production model considereing competition. The straight lines indicate the BMSY of yellowfin tuna including the 95% confidential intervals (the thin dash lines) and the estimated median values (the tick dash lines). 103 表目錄 Table 1. The base case of priors of the parameters in the production model for yellowfin tuna and bigeye tuna. 104 Table 2-1. The differences of CPUE of yellowfin tuna among subareas by Scheffe’s test. 104 Table 2-2. The differences of CPUE of bigeye tuna among subareas by Scheffe’s test. 106 Table 3. The effects of positive catch CPUE for yellowfin tuna selected by stepwise selection. 107 Table 4. Analysis of variance table for selection model of positive catch of yellowfin tuna CPUE in generalized linear model. 108 Table 5. The effects of proportion of positive catch sets for yellowfin tuna selected by stepwise selection. 109 Table 6. Analysis of variance table for selection model of proportion of positive catch sets for yellowfin tuna in general linear model. 110 Table 7. The effects of catch CPUE for bigeye tuna selected by stepwise selection. 111 Table 8. Analysis of variance table for selection model of catch CPUE for bigeye tuna in general linear model. 112 Table 9. Statistic output of yellowfin tuna caught in the Atlantic Ocean from surplus production model including Run 1, Run 2 and Run 3. 113 Table 10. Statistic output of bigeye tuna caught in the Atlantic Ocean from surplus production model including Run 1, Run 2 and Run 3. 114 Table 11. Statistic output with competition interaction between yellowfin tuna and bigeye tuna in the Atlantic Ocean from surplus production model. 115 | |
dc.language.iso | zh-TW | |
dc.title | 生態競爭對用貝氏估計生產量模式評估大西洋黃鰭鮪與大目鮪資源的影響 | zh_TW |
dc.title | Effects of Ecological Competition on Assessing Yellowfin tuna (Thunnus albacares) and Bigeye tuna (Thunnus obesus) Stocks in the Atlantic Ocean by Production Model with a Bayesian Approach | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 王慧瑜,葉裕民,陳志遠,陳孟仙,黃向文 | |
dc.subject.keyword | 生態競爭,delta泛線性模式,象平面分析,標準化豐度指標,總可漁獲量, | zh_TW |
dc.subject.keyword | ecological competition,delta generalized linear model,phase plane analysis,standardization abundance index,total allowable catch, | en |
dc.relation.page | 115 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2014-07-22 | |
dc.contributor.author-college | 理學院 | zh_TW |
dc.contributor.author-dept | 海洋研究所 | zh_TW |
顯示於系所單位: | 海洋研究所 |
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