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研究生:林威任
研究生(外文):Lin, Wei-Ren
論文名稱:以機器學習法建構大西洋大目鮪資源豐度指標
論文名稱(外文):Developing abundance index for bigeye tuna (Thunnus obesus) in the Atlantic Ocean based on machine learning method
指導教授:蘇楠傑
指導教授(外文):Su, Nan-Jay
口試委員:葉信明江偉全王勝平
口試委員(外文):Ye, Hsin-MingChiang, Wei-ChuanWang, Sheng-Ping
口試日期:2020-06-18
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:2020
畢業學年度:108
語文別:中文
論文頁數:81
中文關鍵詞:大目鮪CPUE標準化機器學習豐度指標
外文關鍵詞:bigeye tunaCPUE standardizationmachine learningabundance index
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大目鮪(Thunnus obesus)廣泛分布於大西洋海域,1990年後成為大西洋臺灣遠洋鮪釣漁業主要目標魚種,臺灣身為大目鮪資源主要利用國家之一,基於保育與管理需求,急需建立可靠的漁業指標以提供資源評估分析。本研究透過漁區和作業型態分析,將不同作業特性區分不同作業漁船資料,利用傳統統計模式與機器學習方法,對大西洋臺灣鮪釣漁業資料進行CPUE標準化,以建構大目鮪資源指標。結果顯示以熱帶漁區資料分析的標準化趨勢與全洋區資料的趨勢相近,針對單一目標魚種(大目鮪或長鰭鮪)漁業資料進行標準化,結果亦與整體趨勢相似。比較傳統統計模式與機器學習方法發現,機器學習更能有效處理複雜的非線性關係,比起傳統統計模式有更佳的預測能力,本研究建議以機器學習之隨機森林法分析臺灣鮪釣漁業資料,建構大西洋大目鮪資源豐度指標,提供資源評估與漁業管理所需之科學依據。
Bigeye tuna (Thunnus obesus) is a pelagic species widely distributed in the tropical and temperate waters in the Atlantic Ocean. This species has become the major targeting for the Taiwanese tuna longline fishery in the Atlantic Ocean since 1990. The Taiwanese tuna longline fleet is one of the major fisheries that use this resource. To provide reliable fishery indicator for stock assessment and fishery management, we developed abundance index for Atlantic bigeye tuna using traditional statistical models and machine learning approaches based on the data from the Taiwanese tuna longline fishery. We separated the data set based on the information of fishing area and operating types. Results showed that the standardized CPUE trends of bigeye abundance index, based on the data set for whole Atlantic Ocean, are similar to those standardized with data for tropical areas, as well as the data for vessels that target bigeye tuna. Comparing with statistical models, the machine learning methods could deal well with complicated non-linear relationships in a proper and efficient way, particularly regarding to the model predictive ability. The random forest based on machine learning approach was applied in this study to develop abundance index for Atlantic bigeye tuna. The abundance indices for bigeye tuna derived from this study could be used as scientific basis for stock assessment and fishery management.
第一章、前言 - 1 -
1.1大目鮪生態習性與資源現況 - 1 -
1.2大西洋大目鮪漁業概況 - 2 -
1.3資源豐度指標標準化 - 3 -
1.4前人研究與文獻回顧 - 4 -
1.5研究動機與目的 - 5 -
第二章、 材料與方法 - 6 -
2.1大西洋臺灣鮪釣漁業資料彙整 - 6 -
2.2目標魚種船隊分析 - 6 -
2.2.1目標魚種得分 - 7 -
2.2.2目標魚種效應 - 8 -
2.2.3目標魚種船 - 8 -
2.3標準化模式 - 9 -
2.3.1傳統統計模式 - 9 -
2.3.2機器學習模式 - 11 -
2.4模式診斷與性能比較 - 14 -
2.4.1模式內診斷 - 14 -
2.4.2模式間預測性能比較 - 14 -
2.5 大目鮪資源豐度指標建構 - 16 -
第三章、結果 - 17 -
3.1大西洋臺灣遠洋鮪釣漁業時空分布 - 17 -
3.2臺灣鮪釣作業船隊資料分析 - 17 -
3.2.1漁區 - 17 -
3.2.2目標魚種船隊 - 18 -
3.3資源指標標準化模式分析 - 19 -
3.3.1 傳統統計模式 - 19 -
3.3.2 提升回歸樹 - 20 -
3.3.3 隨機森林 - 20 -
3.3.4模式間比較 - 21 -
3.4 標準化趨勢 - 21 -
第四章、討論 - 24 -
4.1作業船隊資料分析 - 24 -
4.1.1 時空變量 - 24 -
4.1.2目標魚種效應 - 26 -
4.2標準化模式比較 - 27 -
4.2.1模式設定 - 27 -
4.2.2模式性能比較 - 29 -
4.3 大西洋大目鮪資源豐度指標變動趨勢 - 30 -
4.3.1 年間變動的預測 - 30 -
4.3.2 機器學習模式間比較 - 31 -
第五章、結論與建議 - 32 -
參考文獻 - 33 -
附圖........ - 43 -
附表........ - 74 -
Abeare, S. (2009). Comparisons of boosted regression tree, GLM and GAM performance in the standardization of yellowfin tuna catch-rate data from the Gulf of Mexico lonline [sic] fishery.
Arocha, F., Ortiz, M., and Marcano, L. A. (2001). Standardized catch rates for yellowfin tuna (Thunnus albacares) from the Venezuelan pelagic longline fishery off the Caribbean Sea and the western central Atlantic. ICCAT Col. Vol. Sci. Pap, 52, 177-189.
Bangley, C. W., Paramore, L., Dedman, S. and Rulifson, R. A. (2018). Delineation and mapping of coastal shark habitat within a shallow lagoonal estuary. PloS one, 13(4).
Brill, R. W., Bigelow, K. A., Musyl, M. K., Fritsches, K. A. and Warrant, E. J. (2005). Bigeye tuna (Thunnus obesus) behavior and physiology and their relevance to stock assessments and fishery biology. Collective Volume Scientific Papers ICCAT, 57(2), 142-161.
Breiman, L., Friedman, J., Stone, C. J. and Olshen, R. A. (1984). Classification and regression trees. CRC press.
Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
Breiman, L. (2001a). Statistical modeling: the two cultures. Statistical Science, 16(3), 199–231.
Breiman, L. (2001b). Random forests. Machine Learning, 45(1), 5-32.
Brodie, S. J., Thorson, J. T., Carroll, G., Hazen, E. L., Bograd, S., Haltuch, M. A., Holsman, K.K., Kotwicki, S., Samhouri, J.F., Willis‐Norton, E. and Selden, R. L. (2020). Trade‐offs in covariate selection for species distribution models: a methodological comparison. Ecography, 43(1), 11-24.
Chai, T. and Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247-1250.
Carruthers, T.R., McAllister, M.K. and Ahrens, R.N.M. (2010). Simulating spatial dynamics to evaluate methods of deriving abundance indices for tropical tunas. Canadian Journal of Fisheries and Aquatic Sciences, 67(9), 1409-1427.
Carvalho, F., Ahrens, R., Murie, D., Ponciano, J. M., Aires-da-Silva, A., Maunder, M. N. and Hazin, F. (2014). Incorporating specific change points in catchability in fisheries stock assessment models: An alternative approach applied to the blue shark (Prionace glauca) stock in the south Atlantic Ocean. Fisheries Research, 154, 135-146.
Carvalho, F. C., Murie, D. J., Hazin, F. H., Hazin, H. G., Leite-Mourato, B., Travassos, P. and Burgess, G. H. (2010). Catch rates and size composition of blue sharks (Prionace glauca) caught by the Brazilian pelagic longline fleet in the southwestern Atlantic Ocean. Aquatic Living Resources, 23(4), 373-385.
Chang, S. K., Hoyle, S. and Liu, H. I. (2011). Catch rate standardization for yellowfin tuna (Thunnus albacares) in Taiwan's distant-water longline fishery in the Western and Central Pacific Ocean, with consideration of target change. Fisheries Research, 107(1-3), 210-220.
Chow, S., Okamoto, H., Miyabe, N., Hiramatsu, K. and Barut, N. (2000). Genetic divergence between Atlantic and Indo‐Pacific stocks of bigeye tuna (Thunnus obesus) and admixture around South Africa. Molecular Ecology, 9(2), 221-227.
Cooke, J. G. and Beddington, J. R. (1984). The relationship between catch rates and abundance in fisheries. Mathematical Medicine and Biology: A Journal of the IMA, 1(4), 391-405.
De'Ath, G. (2007). Boosted trees for ecological modeling and prediction. Ecology, 88(1), 243-251.
Dickson, K. A. (1995). Unique adaptations of the metabolic biochemistry of tunas and billfishes for life in the pelagic environment. Environmental Biology of Fishes, 42(1), 65-97.
Elith, J., Leathwick, J. R. and Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802-813.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 1189-1232.
Friedman, J. H. (2002). Stochastic gradient boosting. Computational statistics & data analysis, 38(4), 367-378.
Friedman, J. H. and Meulman, J. J. (2003). Multiple additive regression trees with application in epidemiology. Statistics in Medicine, 22(9), 1365-1381.
Fonteneau, A., Ariz, J., Delgado, A., Pallares, P. and Pianet, R. (2005). A comparison of bigeye (Thunnus obesus) stocks and fisheries in the Atlantic, Indian and Pacific Oceans. Collective Volume Scientific Papers ICCAT, 57(2), 41-66.
Forselledo, R., Mas, F., Pons, M. and Domingo, A. (2019). Standardized CPUE of bigeye tuna, Thunnus obesus, based on data gathered by the national observer program on board the Uruguayan longline fleet (2003-2012). Collective Volume Scientific Papers ICCAT, 75(7), 1935-1945.
Froeschke, J. T. and Froeschke, B. F. (2011). Spatio-temporal predictive model based on environmental factors for juvenile spotted seatrout in Texas estuaries using boosted regression trees. Fisheries Research, 111(3), 131-138.
Gonzalez, E. G., Beerli, P. and Zardoya, R. (2008). Genetic structuring and migration patterns of Atlantic bigeye tuna, Thunnus obesus (Lowe, 1839). BMC Evolutionary Biology, 8(1), 252.
Goodyear, C. P. (2002). Spatio-temporal distribution of longline CPUE and sea surface temperature for Atlantic marlins. Collective Volume Scientific Papers ICCAT, 54(3), 834-845.
Guisan, A., Edwards Jr, T. C. and Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling, 157(2-3), 89-100.
Hallier, J. P., Stequert, B., Maury, O. and Bard, F. X. (2005). Growth of bigeye tuna (Thunnus obesus) in the eastern Atlantic Ocean from tagging-recapture data and otolith readings. Collective Volume Scientific Papers ICATT, 57, 181-194.
Hampton, J., Bigelow, K. and Labelle, M. (1998). Effect of longline fishing depth, water temperature and dissolved oxygen on bigeye tuna (Thunnus obesus) abundance indices. Oceanic Fisheries Programme, Secretariat of the Pacific Community, New Caledonia, 18.
Hastie, T. J. and Tibshirani, R. J. (1990). Generalized additive models. CRC press.
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.
Hazin, H. G., Hazin, F., Travassos, P., Carvalho, F. C. and Erzini, K. (2007). Standardization of swordfish CPUE series caught by Brazilian longliners in the Atlantic Ocean, by GLM, using the targeting strategy inferred by cluster analysis. Collective Volume Scientific Papers ICCAT, 60(6), 2039-2047.
Hazin, H., Sant’Ana, R., Mourato, B. L., Travassos, P. and Hazin, G. S. F. (2019). Catch rate standardization for bigeye tuna caught by the Brazilian pelagic longline fleet (1978-2016). Collective Volume Scientific Papers ICCAT, 1968-1980.
He, X., Bigelow, K. A. and Boggs, C. H. (1997). Cluster analysis of longline sets and fishing strategies within the Hawaii-based fishery. Fisheries Research, 31(1-2), 147-158.
Hilborn, R. and Walters, C. J. (2013). Quantitative fisheries stock assessment: choice, dynamics and uncertainty. Springer Science & Business Media.
Hiraoka, Y., Kanaiwa, M., Ohshimo, S., Takahashi, N., Kai, M. and Yokawa, K. (2016). Relative abundance trend of the blue shark Prionace glauca based on Japanese distant-water and offshore longliner activity in the North Pacific. Fisheries science, 82(5), 687-699.
Holland, K. N., Kleiber, P. and Kajiura, S. M. (1999). Different residence times of yellowfin tuna, Thunnus albacares, and bigeye tuna, T. obesus, found in mixed aggregations over a seamount. Fishery Bulletin-National Oceanic and Atmospheric Administration, 97, 392-395.
Honma, M. and Suzuki, Z. (1977). Revised catch and effort statistics by area on Taiwanese tuna longline fleets in the Atlantic, 1967-1974. Collective Volume Scientific Papers ICCAT, 6, 175-180.
Hoyle, S. D., Huang, H., Kim, D. N., Lee, M. K., Matsumoto, T. and Walter, J. (2019a). Collaborative study of bigeye tuna CPUE from multiple Atlantic Ocean longline fleets in 2018. Collective Volume Scientific Papers ICCAT, 75(7), 2033-2080.
Hoyle, S. D., Lauretta, M., Lee, M. K., Matsumoto, T., Sant’Ana, R., Yokoi, H. and Su, N. J. (2019b). Collaborative study of yellowfin tuna CPUE from multiple Atlantic Ocean longline fleets in 2019. Collective Volume Scientific Papers ICCAT, 76(6), 241-293.
Hsu, C. C. (1999). Standardized abundance index of Taiwanese longline fishery for bigeye tuna in the Atlantic. Collective Volume Scientific Papers ICCAT, 49, 459-465.
Hsu, C. C. (2007). Preliminary analysis of standardized catch per unit effort of bigeye tuna (Thunnus obesus) caught by Taiwanese longline fleets in the Atlantic Ocean by general additive model. Collective Volume Scientific Papers ICCAT, 60, 102-116.
Hsu, C. C. (2008). Standardized catch per unit effort of bigeye tuna (Thunnus obesus) for Taiwanese longline fishery in the Atlantic Ocean by general additive model. Collective Volume Scientific Papers ICCAT, 62, 372-396.
Hsu, C. C. (2011). Verification of catch-effort data and standardization of abundance index of bigeye tuna by Taiwanese longline fishery in the Atlantic Ocean. Collective Volume Scientific Papers ICCAT, 66(1), 368-386.
Hsu, C. C. and Liu, H. C. (1992). Status of Taiwanese longline fisheries in the Atlantic. Collective Volume Scientific Papers ICCAT, 39, 258-264.
Hsu, C. C. and Lee, H. H. (2003). General linear mixed model analysis for standardication of Taiwanese longline CPUE for bigeye tuna in the Atlantic Ocean. Collective Volume Scientific Papers ICCAT, 1892-1915.
Hsu, C. C. and Lee, H. H. (2005). Standardized catch per unit effort of bigeye tuna (Thunnus obesus) caught by Taiwanese longline fleets in the Atlantic Ocean. Collective Volume Scientific Papers ICCAT, 58, 192-207.
Huang, H. W. (2019). Standardized CPUE of bigeye tuna (Thunnus obesus) of the Chinese Taipei longline fisheries operated in the Atlantic Ocean (1967-2017). Collective Volume Scientific Papers ICCAT, 75(7), 1946-1967.
ICCAT (2015). Report of the 2015 ICCAT bigeye tuna stock assessment session. Collective Volume Scientific Papers ICCAT, 72: 86-183.
ICCAT (2018). Report of the 2018 ICCAT bigeye tuna stock assessment session. Collective Volume Scientific Papers ICCAT, 75: 1634-1720.
Itano, D. G. and Holland, K. N. (2000). Movement and vulnerability of bigeye (Thunnus obesus) and yellowfin tuna (Thunnus albacares) in relation to FADs and natural aggregation points. Aquatic Living Resources, 13(4), 213-223.
Lam, C. H., Galuardi, B. and Lutcavage, M. E. (2014). Movements and oceanographic associations of bigeye tuna (Thunnus obesus) in the Northwest Atlantic. Canadian Journal of Fisheries and Aquatic Sciences, 71(10), 1529-1543.
Langley, A., Bigelow, K., Maunder, M. and Miyabe, N. (2005). Longline CPUE indices for bigeye and yellowfin in the Pacific Ocean using GLM and statistical habitat standardisation methods. WP SA-8, WCPFC-SC1, Noumea, New Caledonia, 8-19.
Li, M., Zhang, C., Xu, B., Xue, Y. and Ren, Y. (2017). Evaluating the approaches of habitat suitability modelling for whitespotted conger (Conger myriaster). Fisheries Research, 195, 230-237.
Li, Z., Ye, Z., Wan, R. and Zhang, C. (2015). Model selection between traditional and popular methods for standardizing catch rates of target species: a case study of Japanese Spanish mackerel in the gillnet fishery. Fisheries Research, 161, 312-319.
Liaw, A. and Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
Majkowski, J. (2007). Global fishery resources of tuna and tuna-like species. Food and Agriculture Organization, Rome, 54pp.
Mateo, I. and Hanselman, D. H. (2014). A comparison of statistical methods to standardize catch-per-unit-effort of the Alaska longline Sablefish fishery. NOAA Technical Memorandum NMFS-AFSC-269.
Martínez, P., González, E. G., Castilho, R. and Zardoya, R. (2006). Genetic diversity and historical demography of Atlantic bigeye tuna (Thunnus obesus). Molecular Phylogenetics and Evolution, 39(2), 404-416.
Matsumoto, T., Saito, H. and Miyabe, N. (2005). Swimming behavior of adult bigeye tuna using pop-up tags in the central Atlantic Ocean. Collective Volume Scientific Papers ICCAT, 57(1), 151-170.
Matsumoto, T. and Satoh, K. (2017). Standardization of bigeye tuna CPUE in the main fishing ground of Atlantic Ocean by the Japanese longline fishery using revised method. Collective Volume Scientific Papers ICCAT, 2013-2021.
Matsumoto, T., Satoh, K., Kitakado, T. and Hoyle, S. (2019). Standardization of bigeye tuna CPUE in the Atlantic Ocean by the Japanese longline fishery which includes cluster analysis. Collective Volume Scientific Papers ICCAT, 75(7), 2098-2116.
Maunder, M. N. and Punt, A. E. (2004). Standardizing catch and effort data: a review of recent approaches. Fisheries research, 70(2), 141-159.
Maunder, M. N., Sibert, J. R., Fonteneau, A., Hampton, J., Kleiber, P. and Harley, S. J. (2006). Interpreting catch per unit effort data to assess the status of individual stocks and communities. Ices Journal of Marine Science, 63(8), 1373-1385.
Maunder, M. N., Thorson, J. T., Xu, H., Oliveros-Ramos, R., Hoyle, S. D., Tremblay-Boyer, L., Lee, H.H., Kai, M., Chang, S.K., Kitakado, T. and Albertsen, C.M., (2020). The need for spatio-temporal modeling to determine catch-per-unit effort based indices of abundance and associated composition data for inclusion in stock assessment models. Fisheries Research, 229, 105594.
Milborrow, S. (2019). Plotting model residuals with plotres.
Montero, J. T., Chesney, T. A., Bauer, J. R., Froeschke, J. T. and Graham, J. (2016). Brown shrimp (Farfantepenaeus aztecus) density distribution in the Northern Gulf of Mexico: an approach using boosted regression trees. Fisheries Oceanography, 25(3), 337-348.
Mourato, B. L., Arfelli, C. A., Amorim, A. F., Hazin, H. G., Carvalho, F. C. and Hazin, F. H. (2011). Spatio-temporal distribution and target species in a longline fishery off the southeastern coast of Brazil. Brazilian Journal of Oceanography, 59(2), 185-194.
Naghibi, S. A., Pourghasemi, H. R. and Dixon, B. (2016). GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental monitoring and assessment, 188(1), 44.
Nelder, J. A. and Wedderburn, R. W. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370-384.
Nieto, K., Xu, Y., Teo, S. L., McClatchie, S. and Holmes, J. (2017). How important are coastal fronts to albacore tuna (Thunnus alalunga) habitat in the Northeast Pacific Ocean. Progress in Oceanography, 150, 62-71.
Okamura, H., Morita, S. H., Funamoto, T., Ichinokawa, M. and Eguchi, S. (2018). Target-based catch-per-unit-effort standardization in multispecies fisheries. Canadian Journal of Fisheries and Aquatic Sciences, 75(3), 452-463.
Ono, K., Punt, A. E. and Hilborn, R. (2015). Think outside the grids: An objective approach to define spatial strata for catch and effort analysis. Fisheries Research, 170, 89-101.
Ortiz, M. and Arocha, F. (2004). Alternative error distribution models for standardization of catch rates of non-target species from a pelagic longline fishery: billfish species in the Venezuelan tuna longline fishery. Fisheries Research, 70(2-3), 275-297.
Potts, S. E. and Rose, K. A. (2018). Evaluation of GLM and GAM for estimating population indices from fishery independent surveys. Fisheries Research, 208, 167-178.
Punt, A. E., Walker, T. I., Taylor, B. L. and Pribac, F. (2000). Standardization of catch and effort data in a spatially-structured shark fishery. Fisheries Research, 45(2), 129-145.
Quattrocchi, F., Mamouridis, V. and Maynou, F. (2016). Occurrence of adult anchovy in Catalonia (NW Mediterranean) in relation to sea surface conditions. Scientia Marina, 80(4), 457-466.
Quinn, T. J. and Deriso, R. B. (1999). Quantitative fish dynamics. Oxford university Press.
Reygondeau, G., Maury, O., Beaugrand, G., Fromentin, J. M., Fonteneau, A. and Cury, P. (2012). Biogeography of tuna and billfish communities. Journal of Biogeography, 39(1), 114-129.
Ridgeway, G. (2007). Generalized Boosted Models: A guide to the gbm package. Update, 1(1), 2007.
Runcie, R., Holts, D., Wraith, J., Xu, Y., Ramon, D., Rasmussen, R. and Kohin, S. (2016). A fishery-independent survey of juvenile shortfin mako (Isurus oxyrinchus) and blue (Prionace glauca) sharks in the Southern California Bight, 1994–2013. Fisheries Research, 183, 233-243.
Sacau, M., Pierce, G. J., Wang, J., Arkhipkin, A. I., Portela, J., Brickle, P., Santos, M.B., Zuur, A.F. and Cardoso, X. (2005). The spatio-temporal pattern of Argentine shortfin squid Illex argentinus abundance in the southwest Atlantic. Aquatic Living Resources, 18(4), 361-372.
Sagarese, S. R., Frisk, M. G., Cerrato, R. M., Sosebee, K. A., Musick, J. A. and Rago, P. J. (2014). Application of generalized additive models to examine ontogenetic and seasonal distributions of spiny dogfish (Squalus acanthias) in the Northeast (US) shelf large marine ecosystem. Canadian Journal of Fisheries and Aquatic Sciences, 71(6), 847-877.
Song, L., Xu, L. and Chen, X. (2004). Relationship between bigeye tuna vertical distribution and the temperature, salinity in the Central Atlantic Ocean. Journal of Fishery Sciences of China, 11(6), 561-566.
Stephens, A. and MacCall, A. (2004). A multispecies approach to subsetting logbook data for purposes of estimating CPUE. Fisheries Research, 70(2), 299-310.
Stock, B. C., Ward, E. J., Thorson, J. T., Jannot, J. E. and Semmens, B. X. (2019). The utility of spatial model-based estimators of unobserved bycatch. ICES Journal of Marine Science, 76(1), 255-267.
Su, N. J., Yeh, S. Z., Sun, C. L., Punt, A. E., Chen, Y. and Wang, S. P. (2008). Standardizing catch and effort data of the Taiwanese distant-water longline fishery in the western and central Pacific Ocean for bigeye tuna, Thunnus obesus. Fisheries Research, 90(1), 235-246.
Sun, C. L., Huang, C. L. and Yeh, S. Z. (2001). Age and growth of the bigeye tuna, Thunnus obesus, in the western Pacific Ocean. Fishery Bulletin, 99(3), 502-502.
Suykens, J. A. and Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293-300.
Tallis, H., Levin, P. S., Ruckelshaus, M., Lester, S. E., McLeod, K. L., Fluharty, D. L. and Halpern, B. S. (2010). The many faces of ecosystem-based management: making the process work today in real places. Marine Policy, 34(2), 340-348.
Tanaka, K. R., Chang, J. H., Xue, Y., Li, Z., Jacobson, L. and Chen, Y. (2019). Mesoscale climatic impacts on the distribution of Homarus americanus in the US inshore Gulf of Maine. Canadian Journal of Fisheries and Aquatic Sciences, 76(4), 608-625.
Thorson, J. T. (2019). Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fisheries Research, 210, 143-161.
Thorson, J. T., Fonner, R., Haltuch, M. A., Ono, K. and Winker, H. (2016). Accounting for spatiotemporal variation and fisher targeting when estimating abundance from multispecies fishery data. Canadian Journal of Fisheries and Aquatic Sciences, 74(11), 1794-1807.
Tidd, A. N. (2013). Effective fishing effort indicators and their application to spatial management of mixed demersal fisheries. Fisheries Management and Ecology, 20(5), 377-389.
Todorović, S., Juan-Jordá, M. J., Arrizabalaga, H. and Murua, H. (2019). Pelagic ecoregions: Operationalizing an ecosystem approach to fisheries management in the Atlantic Ocean. Marine Policy, 109, 103700.
Veall, M. R. and Zimmermann, K. F. (1994). Evaluating Pseudo-R 2's for binary probit models. Quality and Quantity, 28(2), 151-164.
Walsh, W. A. and Brodziak, J. (2015). Billfish CPUE standardization in the Hawaii longline fishery: model selection and multimodel inference. Fisheries Research, 166, 151-162.
Walters, C. (2003). Folly and fantasy in the analysis of spatial catch rate data. Canadian Journal of Fisheries and Aquatic Sciences, 60(12), 1433-1436.
Walters, C. J. and Martell, S. J. D. (2004). Fisheries Ecology and Management Princeton University Press. Princeton, New Jersey.
Wang, S. P. (2019). Data analysis and CPUE standardization of albacore caught by Taiwanese longline fishery in the Indian Ocean. IOTC–2019–WPTmT07 (DP)–14_Rev1, 46 pp.
Wang, S. P. and Nishida, T. (2014). CPUE standardization with targeting analysis for swordfish (Xiphias gladius) caught by Taiwanese longline fishery in the Indian Ocean. IOTC–2014–WPB12–22, 25 pp.
Yang, R. M., Zhang, G. L., Liu, F., Lu, Y. Y., Yang, F., Yang, F. and Li, D. C. (2016). Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecological Indicators, 60, 870-878.
Yang, S., Song, L., Zhang, Y., Fan, W., Zhang, B., Dai, Y. and Wu, Y. (2020). The Potential Vertical Distribution of Bigeye Tuna (Thunnus obesus) and Its Influence on the Spatial Distribution of CPUEs in the Tropical Atlantic Ocean. Journal of Ocean University of China, 19, 669-680.
Youssef, A. M., Pourghasemi, H. R., Pourtaghi, Z. S. and Al-Katheeri, M. M. (2016). Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides, 13(5), 839-856.
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