正鰹為臺灣中西太平洋鰹鮪圍網的主要漁獲目標，在鮪類中雖是體型最小卻是數量最多之物種，其具有重要的社會經濟價值，佔據約6成全球鮪類漁獲。然而氣候變遷下海洋環境迅速變動造成搜索魚群上困難，以及日益增加的入漁費用及飆升的油價，大幅增加遠洋漁業運營之成本。環境變遷不穩定性增加，魚類將面臨適宜棲地不復存在。為掌握物種分布和生態棲地偏好特徵，本研究初始收集大規模2012至2016年中西太平洋鰹鮪圍網作業及臺灣漁獲日誌，結合高空間解析度之衛星遙測資料(海表水溫與其鋒面、海表鹽度、海表高度、混合層深度及有限尺度李雅普諾夫指數)，利用棲地適合度模式探究正鰹棲地偏好，並以此基礎建立潛力漁場預測。第二階段增加收集資料至2019年，並加入最大熵值模式分析空間解析度對模式之影響，拓展至未來12個月魚群預測圖資。
研究成果顯示，高時空解析度資料成功建立漁場預測模式，並證實水溫鋒面與有限尺度李雅普諾夫指數加入有助於預測準確率，68.3%的作業位置位於潛力漁場5km內。另一方面，真實漁撈紀錄相對於努力量較適合建立物種分布模式，能夠掌握關鍵海洋環境參數；經0.1°網格處理訓練資料，能夠減少原始解析度下巨量資料計算負擔且潛力漁場預測表現較佳。身為遠洋開發國的一員，我國必須面對氣候變遷下漁業資源管理等需持續關注的議題。為確保漁業資源之永續經營，本研究對於正鰹棲地之研究，深入探討棲地與海洋環境之關係，將有助於魚群棲地的監測，掌握氣候變遷下物種調適機制，進一步提供科學上的判斷以限制過高的漁獲能力，並直接的減少漁撈成本，以及漁業管理機關作為政策上之參考依據。

The skipjack tuna is one of the most harvest commercial tuna in the world, especially in Taiwanese distance water fishery. However, with the rapid change of climate, the unstable marine environment was affected the habitat distribution of skipjack tuna. To address this problem, this study aims to present the species distribution models for habitat modelling and fishing grounds prediction by analyzing historic fishing records and satellite remote sensing data. The study matched the fishing spots and satellite-derived images as environments factors included sea surface temperature and its front, sea surface height, sea surface salinity, mixed layer depth, and finite-size Lyapunov exponents. The fishing grounds prediction model were developed based on two simulating methods: habitat suitability index with geometric mean model (HSIGMM) and maximum entropy model (MaxEnt). The initial study analyzed the data period of 2012 to 2016, and second stage extended to 2019. The results showed that the statistic-based empirical model could benefit from high-spatial-temporal-resolution data. Sea surface temperature fronts and finite-size Lyapunov exponents can enhance the predictability of skipjack tuna habitat. The prediction capability of model based on catch data was higher than the model based on effort data. Additionally, the moderate resolution of 0.1° was suitable for training machine-learning based model. Overall, 68.3% fishing events could be found within 5 km of predicted fishing grounds. Further, results from this study would be used as a reference to monitor habitat and to create an effective tool for managing sustainable fishery.

Abernethy, K. E., Allison, E. H., Molloy, P. P., & Côté, I. M. (2007). Why do fishers fish where they fish? Using the ideal free distribution to understand the behaviour of artisanal reef fishers. Canadian Journal of Fisheries and Aquatic Sciences, 64(11), 1595-1604.
Alabia, I. D., Saitoh, S. I., Mugo, R., Igarashi, H., Ishikawa, Y., Usui, N., . . . Seito, M. (2015). Seasonal potential fishing ground prediction of neon flying squid (Ommastrephes bartramii) in the western and central North Pacific. Fisheries Oceanography, 24(2), 190-203.
Amoroso, R. O., Parma, A. M., Pitcher, C. R., McConnaughey, R., & Jennings, S. (2018). Comment on “Tracking the global footprint of fisheries”. Science, 361(6404), eaat6713.
Anderson, G., Lal, M., Stockwell, B., Hampton, J., Smith, N., Nicol, S., & Rico, C. (2020). No population genetic structure of skipjack tuna (Katsuwonus pelamis) in the tropical Western and Central Pacific assessed using single nucleotide polymorphisms. Frontiers in Marine Science, 7, 570760.
Arrizabalaga, H., Dufour, F., Kell, L., Merino, G., Ibaibarriaga, L., Chust, G., . . . Fraile, I. (2015). Global habitat preferences of commercially valuable tuna. Deep Sea Research Part II: Topical Studies in Oceanography, 113, 102-112.
Artetxe-Arrate, I., Fraile, I., Marsac, F., Farley, J. H., Rodriguez-Ezpeleta, N., Davies, C. R., . . . Murua, H. (2021). A review of the fisheries, life history and stock structure of tropical tuna (skipjack Katsuwonus pelamis, yellowfin Thunnus albacares and bigeye Thunnus obesus) in the Indian Ocean. Advances in Marine Biology, 88, 39-89.
Ashida, H. (2020). Spatial and temporal differences in the reproductive traits of skipjack tuna Katsuwonus pelamis between the subtropical and temperate western Pacific Ocean. Fisheries Research, 221, 105352.
Baidai, Y., Dagorn, L., Amande, M., Gaertner, D., & Capello, M. (2020). Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data. Fisheries Research, 229, 105613.
Barbet‐Massin, M., Jiguet, F., Albert, C. H., & Thuiller, W. (2012). Selecting pseudo‐absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3(2), 327-338.
Baudena, A., Ser-Giacomi, E., d’Onofrio, D., Capet, X., Cotté, C., Cherel, Y., & d’Ovidio, F. (2019). Fine-scale fronts as hotspots of fish aggregation in the open ocean. BioRxiv.
Belkin, I. M. (2021). Remote sensing of ocean fronts in marine ecology and fisheries. Remote Sensing, 13(5), 883.
Belkin, I. M., & O'Reilly, J. E. (2009). An algorithm for oceanic front detection in chlorophyll and SST satellite imagery. Journal of Marine Systems, 78(3), 319-326.
Bell, J. D., Senina, I., Adams, T., Aumont, O., Calmettes, B., Clark, S., . . . Hampton, J. (2021). Pathways to sustaining tuna-dependent Pacific Island economies during climate change. Nature sustainability, 4(10), 900-910.
Bettencourt, J. H., López, C., & Hernández-García, E. (2013). Characterization of coherent structures in three-dimensional turbulent flows using the finite-size Lyapunov exponent. Journal of Physics A: Mathematical and Theoretical, 46(25), 254022.
Bordalo-Machado, P. (2006). Fishing effort analysis and its potential to evaluate stock size. Reviews in Fisheries Science, 14(4), 369-393.
Boria, R. A., & Blois, J. L. (2018). The effect of large sample sizes on ecological niche models: Analysis using a North American rodent, Peromyscus maniculatus. Ecological modelling, 386, 83-88.
Boyra, G., Moreno, G., Sobradillo, B., Pérez-Arjona, I., Sancristobal, I., & Demer, D. A. (2018). Target strength of skipjack tuna (Katsuwanus pelamis) associated with fish aggregating devices (FADs). ICES Journal of Marine Science, 75(5), 1790-1802.
Brooks, R. P. (1997). Improving habitat suitability index models. Wildlife Society Bulletin, 163-167.
Brouwer, S., Pilling, G., Hampton, J., Williams, P., McKechnie, S., & Tremblay-Boyer, L. (2018). The Western and Central Pacific tuna fishery: 2017 overview and status of stocks. Tuna Fisheries Assessment Report, 18.
Casey, K. S., & Adamec, D. (2002). Sea surface temperature and sea surface height variability in the North Pacific Ocean from 1993 to 1999. Journal of Geophysical Research: Oceans, 107(C8), 14-11-14-12.
Chang, S.-K., & Lu, H.-J. (1998). Taiwan tuna fisheries in the western-central Pacific Ocean, 1997. Eleventh Meeting of the Standing Committee on Tuna and Billfish, Honolulu, HI, USA,
Chang, Y., Chan, J.-W., Huang, Y.-C. A., Lin, W.-Q., Lee, M.-A., Lee, K.-T., . . . Kuo, Y.-C. (2014). Typhoon-enhanced upwelling and its influence on fishing activities in the southern East China Sea. International Journal of Remote Sensing, 35(17), 6561-6572.
Chen, X., Cao, J., Chen, Y., Liu, B., & Tian, S. (2012). Effect of the Kuroshio on the spatial distribution of the red flying squid Ommastrephes bartramii in the Northwest Pacific Ocean. Bulletin of Marine Science, 88(1), 63-71.
Chen, X., & Lei, Y. (2012). Effects of sample size on accuracy and stability of species distribution models: A comparison of GARP and Maxent. In Recent Advances in Computer Science and Information Engineering: Volume 2 (pp. 601-609). Springer.
Chen, X., Li, G., Feng, B., & Tian, S. (2009). Habitat suitability index of Chub mackerel (Scomber japonicus) from July to September in the East China Sea. Journal of oceanography, 65(1), 93-102.
Chen, X., Tian, S., Chen, Y., & Liu, B. (2010). A modeling approach to identify optimal habitat and suitable fishing grounds for neon flying squid (Ommostrephes bartramii) in the Northwest Pacific Ocean. Fishery Bulletin, 108(1).
Coletto, J. L., Pinho, M. P., & Madureira, L. S. P. (2019). Operational oceanography applied to skipjack tuna (Katsuwonus pelamis) habitat monitoring and fishing in south‐western Atlantic. Fisheries Oceanography, 28(1), 82-93.
Correa, G., Kaplan, D., Grande, M., Uranga, J., Ramos Alonso, M., Alyón, P., . . . Santiago, J. (2024). Standardized catch per unit effort of yellowfin tuna in the Atlantic Ocean for the European purse seine fleet operating on floating objects. ICCAT Standing Committee On Research and Statistics (SCRS).
d'Ovidio, F., Fernández, V., Hernández‐García, E., & López, C. (2004). Mixing structures in the Mediterranean Sea from finite‐size Lyapunov exponents. Geophysical Research Letters, 31(17).
Dagorn, L., Holland, K. N., Restrepo, V., & Moreno, G. (2013). Is it good or bad to fish with FAD s? What are the real impacts of the use of drifting FAD s on pelagic marine ecosystems? Fish and fisheries, 14(3), 391-415.
de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A., & Iudicone, D. (2004). Mixed layer depth over the global ocean: An examination of profile data and a profile‐based climatology. Journal of Geophysical Research: Oceans, 109(C12).
Dell, J., Wilcox, C., & Hobday, A. J. (2011). Estimation of yellowfin tuna (Thunnus albacares) habitat in waters adjacent to Australia’s East Coast: making the most of commercial catch data. Fisheries Oceanography, 20(5), 383-396.
Druon, J.-N., Chassot, E., Murua, H., & Lopez, J. (2017). Skipjack tuna availability for purse seine fisheries is driven by suitable feeding habitat dynamics in the Atlantic and Indian Oceans. Frontiers in Marine Science, 4, 315.
Druon, J., Chassot, E., Murua, H., & Soto, M. (2016). Preferred feeding habitat of skipjack tuna in the eastern central Atlantic and western Indian Oceans: relations with carrying capacity and vulnerability to purse seine fishing. IOTC Proceedings. IOTC.
Ducharme-Barth, N. D. (2021). What is CPUE standardisation and why is it important for stock assessments?
Duflot, R., Avon, C., Roche, P., & Bergès, L. (2018). Combining habitat suitability models and spatial graphs for more effective landscape conservation planning: An applied methodological framework and a species case study. Journal for nature conservation, 46, 38-47.
Díaz-Barroso, L., Hernández-Carrasco, I., Orfila, A., Reglero, P., Balbín, R., Hidalgo, M., . . . Álvarez-Berastegui, D. (2022). Singularities of surface mixing activity in the Western Mediterranean influence bluefin tuna larval habitats. Marine Ecology Progress Series, 685, 69-84.
Elith, J., Graham, C., Anderson, R., Dudík, M., Ferrier, S., Guisan, A., . . . Lehmann, A. (2006). Novel methods improve prediction of species' distributions from occurrence data. Ecography 29, 129e151. In.
Elith, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., . . . Lehmann, A. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129-151.
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and distributions, 17(1), 43-57.
Ely, B., Viñas, J., Alvarado Bremer, J. R., Black, D., Lucas, L., Covello, K., . . . Thelen, E. (2005). Consequences of the historical demography on the global population structure of two highly migratory cosmopolitan marine fishes: the yellowfin tuna (Thunnus albacares) and the skipjack tuna (Katsuwonus pelamis). BMC Evolutionary Biology, 5, 1-9.
Escalle, L., Scutt Phillips, J., Brownjohn, M., Brouwer, S., Sen Gupta, A., Van Sebille, E., . . . Pilling, G. (2019). Environmental versus operational drivers of drifting FAD beaching in the Western and Central Pacific Ocean. Scientific reports, 9(1), 1-12.
Evans, K., Young, J., Nicol, S., Kolody, D., Allain, V., Bell, J., . . . Hunt, B. (2015). Optimising fisheries management in relation to tuna catches in the western central Pacific Ocean: A review of research priorities and opportunities. Marine Policy, 59, 94-104.
Eveson, J. P., Hobday, A. J., Hartog, J. R., Spillman, C. M., & Rough, K. M. (2015). Seasonal forecasting of tuna habitat in the Great Australian Bight. Fisheries Research, 170, 39-49.
Fang, B., Zhao, Q., Qin, Q., & Yu, J. (2022). Prediction of Potentially Suitable Distribution Areas for Prunus tomentosa in China Based on an Optimized MaxEnt Model. Forests, 13(3), 381.
FAO. (2017). Fishery and Aquaculture Statistics 2017.
FAO. (2020). The State of World Fisheries and Aquaculture 2020. https://doi.org/10.4060/ca9229en
Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental conservation, 24(1), 38-49.
Fonteneau, A. (2003). Biological overview of tuna stocks and overfishing. Report and Documentation of the International Workshop on the Implementation of International Fisheries Instruments and Factors of Unsustainability and Overexploitation in Fisheries, Mauritius,
Fonteneau, A., Chassot, E., & Bodin, N. (2013). Global spatio-temporal patterns in tropical tuna purse seine fisheries on drifting fish aggregating devices (DFADs): Taking a historical perspective to inform current challenges⋆. Aquatic Living Resources, 26(1), 37-48.
Fonteneau, A., Pallares, P., & Pianet, R. (2000). A worldwide review of purse seine fisheries on FADs. Pêche thonière et dispositifs de concentration de poissons, Caribbean-Martinique, 15-19 Oct 1999.
Forget, F., & Muir, J. (2021). The critically endangered bowmouth guitarfish (Rhina ancylostoma) in the open ocean with an associated tuna school. Marine Biodiversity, 51(4), 69.
Georgian, S. E., Anderson, O. F., & Rowden, A. A. (2019). Ensemble habitat suitability modeling of vulnerable marine ecosystem indicator taxa to inform deep-sea fisheries management in the South Pacific Ocean. Fisheries Research, 211, 256-274.
Georgii, H.-O. (2011). Gibbs measures and phase transitions. In Gibbs Measures and Phase Transitions. de Gruyter.
Global Fishing Watch, I. (2022). Global Fishing Watch. www.globalfishingwatch.org
Graham, J. B., & Dickson, K. A. (2004). Tuna comparative physiology. Journal of experimental biology, 207(23), 4015-4024.
Grebenkov, A., Lukashevich, A., Linkov, I., & Kapustka, L. A. (2006). A habitat suitability evaluation technique and its application to environmental risk assessment. In Ecotoxicology, Ecological Risk Assessment and Multiple Stressors (pp. 191-201). Springer.
Greiner, M., Pfeiffer, D., & Smith, R. D. (2000). Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Preventive veterinary medicine, 45(1-2), 23-41.
Griffiths, S. P., Allain, V., Hoyle, S. D., Lawson, T. A., & Nicol, S. J. (2019). Just a FAD? Ecosystem impacts of tuna purse‐seine fishing associated with fish aggregating devices in the western Pacific Warm Pool Province. Fisheries Oceanography, 28(1), 94-112.
Hale, R., Colton, M. A., Peng, P., & Swearer, S. E. (2019). Do spatial scale and life history affect fish–habitat relationships? Journal of Animal Ecology, 88(3), 439-449.
Hartog, J. R., Hobday, A. J., Matear, R., & Feng, M. (2011). Habitat overlap between southern bluefin tuna and yellowfin tuna in the east coast longline fishery–implications for present and future spatial management. Deep Sea Research Part II: Topical Studies in Oceanography, 58(5), 746-752.
Hernandez, P. A., Graham, C. H., Master, L. L., & Albert, D. L. (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29(5), 773-785.
Hidayat, R., Zainuddin, M., Safruddin, S., Mallawa, A., & Farhum, S. (2019). Skipjack Tuna (Katsuwonus pelamis) catch in relation to the Thermal and Chlorophyll-a Fronts during May–July in the Makassar Strait. IOP Conference Series: Earth and Environmental Science,
Hobday, A. J., Hartog, J. R., Spillman, C. M., & Alves, O. (2011). Seasonal forecasting of tuna habitat for dynamic spatial management. Canadian Journal of Fisheries and Aquatic Sciences, 68(5), 898-911.
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.
Hoyle, S. D., Campbell, R. A., Ducharme-Barth, N. D., Grüss, A., Moore, B. R., Thorson, J. T., . . . Maunder, M. N. (2024). Catch per unit effort modelling for stock assessment: A summary of good practices. Fisheries Research, 269, 106860.
Hsu, T.-Y., & Chang, Y. (2017). Modeling the habitat suitability index of skipjack tuna (Katsuwonus pelamis) in the Western and Central Pacific Ocean. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),
Hsu, T.-Y., Chang, Y., Lee, M.-A., Wu, R.-F., & Hsiao, S.-C. (2021). Predicting Skipjack Tuna Fishing Grounds in the Western and Central Pacific Ocean Based on High-Spatial-Temporal-Resolution Satellite Data. Remote Sensing, 13(5), 861.
Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical review, 106(4), 620.
Kai, E. T., Rossi, V., Sudre, J., Weimerskirch, H., Lopez, C., Hernandez-Garcia, E., . . . Garçon, V. (2009). Top marine predators track Lagrangian coherent structures. Proceedings of the National Academy of Sciences, 106(20), 8245-8250.
Kim, J., Na, H., Park, Y.-G., & Kim, Y. H. (2020). Potential predictability of skipjack tuna (Katsuwonus pelamis) catches in the Western Central Pacific. Scientific reports, 10(1), 3193.
Kim, Y.-H., & Park, M.-C. (2009). The simulation of the geometry of a tuna purse seine under current and drift of purse seiner. Ocean engineering, 36(14), 1080-1088.
Kiyofuji, H., Aoki, Y., Kinoshita, J., Okamoto, S., Masujima, M., Matsumoto, T., . . . Sugimoto, N. (2019). Northward migration dynamics of skipjack tuna (Katsuwonus pelamis) associated with the lower thermal limit in the western Pacific Ocean. Progress in Oceanography, 175, 55-67.
Kroodsma, D. A., Mayorga, J., Hochberg, T., Miller, N. A., Boerder, K., Ferretti, F., . . . Block, B. A. (2018a). Response to Comment on “Tracking the global footprint of fisheries”. Science, 361(6404), eaat7789.
Kroodsma, D. A., Mayorga, J., Hochberg, T., Miller, N. A., Boerder, K., Ferretti, F., . . . Block, B. A. (2018b). Tracking the global footprint of fisheries. Science, 359(6378), 904-908.
Lan, K.-W., Kawamura, H., Lee, M.-A., Lu, H.-J., Shimada, T., Hosoda, K., & Sakaida, F. (2012). Relationship between albacore (Thunnus alalunga) fishing grounds in the Indian Ocean and the thermal environment revealed by cloud-free microwave sea surface temperature. Fisheries Research, 113(1), 1-7.
Lan, K.-W., Shimada, T., Lee, M.-A., Su, N.-J., & Chang, Y. (2017). Using remote-sensing environmental and fishery data to map potential yellowfin tuna habitats in the tropical Pacific Ocean. Remote Sensing, 9(5), 444.
Lauver, C. L., Busby, W. H., & Whistler, J. L. (2002). Testing a GIS model of habitat suitability for a declining grassland bird. Environmental management, 30(1), 88-97.
Lee, D., Son, S. H., Lee, C.-I., Kang, C.-K., & Lee, S. H. (2019). Spatio-temporal variability of the habitat suitability index for the Todarodes pacificus (Japanese common squid) around South Korea. Remote Sensing, 11(23), 2720.
Lee, M.-A., Weng, J.-S., Lan, K.-W., Vayghan, A. H., Wang, Y.-C., & Chan, J.-W. (2020). Empirical habitat suitability model for immature albacore tuna in the North Pacific Ocean obtained using multisatellite remote sensing data. International Journal of Remote Sensing, 41(15), 5819-5837.
Lehodey, P. (2001). The pelagic ecosystem of the tropical Pacific Ocean: dynamic spatial modelling and biological consequences of ENSO. Progress in Oceanography, 49(1-4), 439-468.
Lehodey, P. (2004). Climate and fisheries: an insight from the central Pacific Ocean. Marine Ecosystems and Climate Variation: The North Atlantic. A Comparative Perspective, 137.
Lehodey, P., Bertignac, M., Hampton, J., Lewis, A., & Picaut, J. (1997). El Niño Southern Oscillation and tuna in the western Pacific. Nature, 389(6652), 715-718.
Lehodey, P., Senina, I., Calmettes, B., Hampton, J., & Nicol, S. (2013). Modelling the impact of climate change on Pacific skipjack tuna population and fisheries. Climatic change, 119, 95-109.
Lehodey, P., Senina, I., & Murtugudde, R. (2008). A spatial ecosystem and populations dynamics model (SEAPODYM)–Modeling of tuna and tuna-like populations. Progress in Oceanography, 78(4), 304-318.
Leroy, B., Nicol, S., Lewis, A., Hampton, J., Kolody, D., Caillot, S., & Hoyle, S. (2015). Lessons learned from implementing three, large-scale tuna tagging programmes in the western and central Pacific Ocean. Fisheries Research, 163, 23-33.
Li, Y., & Ding, C. (2016). Effects of sample size, sample accuracy and environmental variables on predictive performance of MaxEnt model. Polish Journal of Ecology, 64(3), 303-312.
Liao, C.-P., & Huang, H.-W. (2016). The cooperation strategies of fisheries between Taiwanese purse seiners and Pacific Island Countries. Marine Policy, 66, 67-74.
Liu, C., Berry, P. M., Dawson, T. P., & Pearson, R. G. (2005). Selecting thresholds of occurrence in the prediction of species distributions. Ecography, 28(3), 385-393.
Lobo, J. M., Jiménez‐Valverde, A., & Real, R. (2008). AUC: a misleading measure of the performance of predictive distribution models. Global ecology and Biogeography, 17(2), 145-151.
Lopez, J., Lennert-Cody, C., Maunder, M., Xu, H., Brodie, S., Jacox, M., & Hartog, J. (2019). Developing alternative conservation measures for bigeye tuna in the Eastern Pacific Ocean: a dynamic ocean management approach. American Fisheries Society & The Wildlife Society 2019 Joint Annual Conference,
Loukos, H., Monfray, P., Bopp, L., & Lehodey, P. (2003). Potential changes in skipjack tuna (Katsuwonus pelamis) habitat from a global warming scenario: modelling approach and preliminary results. Fisheries Oceanography, 12(4‐5), 474-482.
Lowe, T., Brill, R., & Cousins, K. (2000). Blood oxygen-binding characteristics of bigeye tuna (Thunnus obesus), a high-energy-demand teleost that is tolerant of low ambient oxygen. Marine Biology, 136, 1087-1098.
Lynch, P. D., Shertzer, K. W., & Latour, R. J. (2012). Performance of methods used to estimate indices of abundance for highly migratory species. Fisheries Research, 125, 27-39.
Madureira, L., Coletto, J., Pinho, M., Weigert, S., Varela, C., Campello, M., & Llopart, A. (2017). Skipjack (Katsuwonus pelamis) fishery improvement project: From satellite and 3D oceanographic models to acoustics, towards predator-prey landscapes. 2017 IEEE/OES Acoustics in Underwater Geosciences Symposium (RIO Acoustics),
Maes, C., Ando, K., Delcroix, T., Kessler, W. S., McPhaden, M. J., & Roemmich, D. (2006). Observed correlation of surface salinity, temperature and barrier layer at the eastern edge of the western Pacific warm pool. Geophysical Research Letters, 33(6).
Maes, C., & Belamari, S. (2011). On the impact of salinity barrier layer on the Pacific Ocean mean state and ENSO. Sola, 7, 97-100.
Manel, S., Williams, H. C., & Ormerod, S. J. (2001). Evaluating presence–absence models in ecology: the need to account for prevalence. Journal of applied Ecology, 38(5), 921-931.
Marín-Enríquez, E., Moreno-Sánchez, X. G., Urcádiz-Cázares, F. J., Morales-Bojórquez, E., & Ramírez-Pérez, J. S. (2020). A spatially explicit model for predicting the probability of occurrence of zero-catch quadrants in the tuna purse seine fishery of the Eastern Tropical Pacific Ocean. Ciencias marinas, 46(1), 19-38.
Matear, R., Chamberlain, M., Sun, C., & Feng, M. (2015). Climate change projection for the western tropical Pacific Ocean using a high-resolution ocean model: Implications for tuna fisheries. Deep Sea Research Part II: Topical Studies in Oceanography, 113, 22-46.
Matsumoto, W. M. (1974). The skipjack tuna, Katsuwonus pelamis, an underutilized resource. Mar Fish Rev, 26-33.
McKechnie, S., Harley, S., Davies, N., Rice, J., Hampton, J., & Berger, A. (2014). Basis for regional structures used in the 2014 tropical tuna assessments, including regional weights. WCPFC Scientific Committee, 10th Regular Session,
Medoff, S., Lynham, J., & Raynor, J. (2022). Spillover benefits from the world’s largest fully protected MPA. Science, 378(6617), 313-316.
Melo-Merino, S. M., Reyes-Bonilla, H., & Lira-Noriega, A. (2020). Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecological modelling, 415, 108837.
Merckx, B., Steyaert, M., Vanreusel, A., Vincx, M., & Vanaverbeke, J. (2011). Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling. Ecological modelling, 222(3), 588-597.
Miyabe, N., & Nakano, H. (2004). Historical trends of tuna catches in the world. Food & Agriculture Org.
Miyake, M. P., Guillotreau, P., Sun, C.-H., & Ishimura, G. (2010). Recent developments in the tuna industry: stocks, fisheries, management, processing, trade and markets. Food and Agriculture Organization of the United Nations Rome, Italy.
Mondal, S., Vayghan, A. H., Lee, M.-A., Wang, Y.-C., & Semedi, B. (2021). Habitat Suitability Modeling for the Feeding Ground of Immature Albacore in the Southern Indian Ocean Using Satellite-Derived Sea Surface Temperature and Chlorophyll Data. Remote Sensing, 13(14), 2669. https://www.mdpi.com/2072-4292/13/14/2669
Mondal, S., Wang, Y.-C., Lee, M.-A., Weng, J.-S., & Mondal, B. K. (2022). Ensemble Three-Dimensional Habitat Modeling of Indian Ocean Immature Albacore Tuna (Thunnus alalunga) Using Remote Sensing Data. Remote Sensing, 14(20), 5278.
Mugo, R., & Saitoh, S.-I. (2020). Ensemble modelling of skipjack tuna (Katsuwonus pelamis) habitats in the western north pacific using satellite remotely sensed data; a comparative analysis using machine-learning models. Remote Sensing, 12(16), 2591.
Mugo, R., Saitoh, S. i., Nihira, A., & Kuroyama, T. (2010). Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective. Fisheries Oceanography, 19(5), 382-396.
Muhling, B. A., Brill, R., Lamkin, J. T., Roffer, M. A., Lee, S.-K., Liu, Y., & Muller-Karger, F. (2017). Projections of future habitat use by Atlantic bluefin tuna: mechanistic vs. correlative distribution models. ICES Journal of Marine Science, 74(3), 698-716.
Núñez‐Riboni, I., Akimova, A., & Sell, A. F. (2021). Effect of data spatial scale on the performance of fish habitat models. Fish and fisheries, 22(5), 955-973.
Orue, B., Lopez, J., Moreno, G., Santiago, J., Soto, M., & Murua, H. (2019). Aggregation process of drifting fish aggregating devices (DFADs) in the Western Indian Ocean: Who arrives first, tuna or non-tuna species? PLoS One, 14(1), e0210435.
Park, J.-Y., Stock, C. A., Dunne, J. P., Yang, X., & Rosati, A. (2019). Seasonal to multiannual marine ecosystem prediction with a global Earth system model. Science, 365(6450), 284-288.
Pennino, M. G., Conesa, D., López-Quílez, A., Munoz, F., Fernández, A., & Bellido, J. M. (2016). Fishery-dependent and-independent data lead to consistent estimations of essential habitats. ICES Journal of Marine Science, 73(9), 2302-2310.
Phillip Jr, N. B., & Escalle, L. (2020). SCIENTIFIC COMMITTEE SIXTEENTH REGULAR SESSION.
Phillips, S. J. (2005). A brief tutorial on Maxent. AT&T Research, 190(4), 231-259.
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3-4), 231-259.
Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161-175.
Phillips, S. J., Dudík, M., & Schapire, R. E. (2022). Maxent software for modeling species niches and distributions (Version 3.4.1). http://biodiversityinformatics.amnh.org/open_source/maxent/
Pravin, P. (2002). Purse seine and its operation. In. Central Institute of Fisheries Technology.
Pravin, P. (2009). Purse seines and their operation. In. Central Institute of Fisheries Technology, Cochin, India.
Qu, T., & Yu, J.-Y. (2014). ENSO indices from sea surface salinity observed by Aquarius and Argo. Journal of oceanography, 70, 367-375.
Rathnasuriya, M. I. G. (2016). Environmental effect on the skipjack tuna (Katsuwonus pelamis) fishery in the Sri Lankan waters Pukyong national university].
Receveur, A., Nicol, S., Tremblay-Boyer, L., Menkes, C., Senina, I., & Lehodey, P. (2016). Using SEAPODYM to better understand the influence of El Niño Southern Oscillation on Pacific tuna fisheries. Spc Fish. Newsl, 149, 31-36.
Ridgeway, G. (2007). Generalized Boosted Models: A guide to the gbm package. Update, 1(1), 2007.
Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E., & Lundquist, C. J. (2017). A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Frontiers in Marine Science, 4, 421.
Saitoh, S.-I., Mugo, R., Radiarta, I. N., Asaga, S., Takahashi, F., Hirawake, T., . . . Shima, S. (2011). Some operational uses of satellite remote sensing and marine GIS for sustainable fisheries and aquaculture. ICES Journal of Marine Science, 68(4), 687-695.
Santiago, J., Uranga, J., Quincoces, I., Grande, M., Murua, H., Merino, G., . . . Boyra, G. (2021). INDEX OF ABUNDANCE OF SKIPJACK TUNA IN THE ATLANTIC OCEAN DERIVED FROM ECHOSOUNDER BUOYS (2010-2020). Collect. Vol. Sci. Pap. ICCAT, 79(1), 158-175.
Santos, A. M. P. (2000). Fisheries oceanography using satellite and airborne remote sensing methods: a review. Fisheries Research, 49(1), 1-20.
Schaefer, K. M., & Fuller, D. W. (2019). Spatiotemporal variability in the reproductive dynamics of skipjack tuna (Katsuwonus pelamis) in the eastern Pacific Ocean. Fisheries Research, 209, 1-13.
Semedi, B., Hardoko, H., Dewi, C. S. U., Syam’s, N. D. S., Diza, N. F., & Bayuaji, G. D. A. P. (2023). Seasonal Migration Zone of Skipjack Tuna (Katsuwonus pelamis) in the South Java Sea Using Multisensor Satellite Remote Sensing. Journal of Marine Sciences, 2023.
Senina, I., Lehodey, P., Calmettesa, B., Nicol, S., Caillot, S., Hampton, J., & Williams, P. (2016). Predicting skipjack tuna dynamics and effects of climate change using SEAPODYM with fishing and tagging data. Scientific Committee Twelfth Regular Session. Bali, Indonesia, 3.
Sharifian, S., Mortazavi, M. S., & Mohebbi-Nozar, S. L. (2022). Modeling Present Distribution Commercial Fish and Shrimps Using MaxEnt. Wetlands, 42(5), 1-9.
Sharma, R., & Herrera, M. (2019). Using effort control measures to implement catch limits in IOTC purse seine fisheries. 20th Working Party on Tropical Tunas, IOTC–2019–WPTT21–23.
Siregar, E., Siregar, V., Jhonnerie, R., Alkayakni, M., & Samsul, B. (2019). Prediction of potential fishing zones for yellowfin tuna (Thunnus albacares) using maxent models in Aceh province waters. IOP Conference Series: Earth and Environmental Science,
Skirtun, M., Tidd, A., Reid, C., Kirchner, C., Hampton, J., & Pilling, G. (2018). Target Reference Points and MEY in the Western and Central Pacific Purse Seine Fishery.
Stelzenmüller, V., Ehrich, S., & Zauke, G.-P. (2005). Effects of survey scale and water depth on the assessment of spatial distribution patterns of selected fish in the northern North Sea showing different levels of aggregation. Marine Biology Research, 1(6), 375-387.
Stephenson, F., Rowden, A. A., Anderson, O. F., Pitcher, C. R., Pinkerton, M. H., Petersen, G., & Bowden, D. A. (2021). Presence-only habitat suitability models for vulnerable marine ecosystem indicator taxa in the South Pacific have reached their predictive limit. ICES Journal of Marine Science, 78(8), 2830-2843.
Swain, D. P., & Wade, E. J. (2003). Spatial distribution of catch and effort in a fishery for snow crab (Chionoecetes opilio): tests of predictions of the ideal free distribution. Canadian Journal of Fisheries and Aquatic Sciences, 60(8), 897-909.
Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285-1293.
Syah, A. F., Gaol, J. L., Zainuddin, M., Apriliya, N. R., Berlianty, D., & Mahabror, D. (2020). Detection of potential fishing zones of Bigeye tuna (Thunnus obesus) at profundity of 155 M in the eastern Indian Ocean. The Indonesian Journal of Geography, 52(1), 29-35.
Syah, A. F., Siregar, E. S. Y., Siregar, V. P., & Agus, S. B. (2020). Application of remotely sensed data and maximum entropy model in detecting potential fishing zones of Yellowfin tuna (Thunnus albacares) in the eastern Indian Ocean off Sumatera. Journal of Physics: Conference Series,
Tang, H., Xu, L.-x., Chen, X.-j., Zhu, G.-p., Zhou, C., & Wang, X.-f. (2013). Effects of spatiotemporal and environmental factors on the fishing ground of Skipjack Tuna (Katsuwonus pelamis) in the western and central Pacific Ocean based on generalized additive model. Marine Environmental Science, 32(4), 518-522.
Tang, H., Xu, L., Zhou, C., Wang, X., Zhu, G., & Hu, F. (2017). The effect of environmental variables, gear design and operational parameters on sinking performance of tuna purse seine setting on free-swimming schools. Fisheries Research, 196, 151-159.
Tew Kai, E., Rossi, V., Sudre, J., Weimerskirch, H., Lopez, C., Hernandez-Garcia, E., . . . Garçon, V. (2009). Top marine predators track Lagrangian coherent structures. Proceedings of the National Academy of Sciences, 106(20), 8245-8250.
Tidd, A. N., Reid, C., Pilling, G. M., & Harley, S. J. (2016). Estimating productivity, technical and efficiency changes in the Western Pacific purse-seine fleets. ICES Journal of Marine Science, 73(4), 1226-1234. https://doi.org/10.1093/icesjms/fsv262
Torres-Irineo, E., Gaertner, D., Chassot, E., & Dreyfus-León, M. (2014). Changes in fishing power and fishing strategies driven by new technologies: The case of tropical tuna purse seiners in the eastern Atlantic Ocean. Fisheries Research, 155, 10-19.
Torres-Irineo, E., Gaertner, D., de Molina, A. D., & Ariz, J. (2011). Effects of time-area closure on tropical tuna purse-seine fleet dynamics through some fishery indicators. Aquatic Living Resources, 24(4), 337-350.
Townhill, B. L., Couce, E., Bell, J., Reeves, S., & Yates, O. (2021). Climate change impacts on Atlantic oceanic island tuna fisheries. Frontiers in Marine Science, 8, 634280.
Uenaka, T., Sakamoto, N., & Koyamada, K. (2014). Visual analysis of habitat suitability index model for predicting the locations of fishing grounds. 2014 IEEE Pacific Visualization Symposium,
Van der Lee, G. E., Van der Molen, D. T., Van den Boogaard, H. F., & Van der Klis, H. (2006). Uncertainty analysis of a spatial habitat suitability model and implications for ecological management of water bodies. Landscape Ecology, 21(7), 1019-1032.
Vayghan, A. H., Lee, M.-A., Weng, J.-S., Mondal, S., Lin, C.-T., & Wang, Y.-C. (2020). Multisatellite-based feeding habitat suitability modeling of albacore tuna in the southern atlantic ocean. Remote Sensing, 12(16), 2515.
Venne, S., & Currie, D. J. (2021). Can habitat suitability estimated from MaxEnt predict colonizations and extinctions? Diversity and distributions, 27(5), 873-886.
Wang, J., Chen, X., & Chen, Y. (2016). Spatio-temporal distribution of skipjack in relation to oceanographic conditions in the west-central Pacific Ocean. International Journal of Remote Sensing, 37(24), 6149-6164.
Wang, X., Chen, Y., Truesdell, S., Xu, L., Cao, J., & Guan, W. (2014). The large-scale deployment of fish aggregation devices alters environmentally-based migratory behavior of skipjack tuna in the Western Pacific Ocean. PLoS One, 9(5), e98226.
Watson, J. R., Fuller, E. C., Castruccio, F. S., & Samhouri, J. F. (2018). Fishermen follow fine-scale physical ocean features for finance. Frontiers in Marine Science, 5, 46.
WCPFC. (2008). Conservation and management measure for bigeye and yellowfin tuna in the Western and Central Pacific Ocean. Conservation Management Measure, 1.
WCPFC. (2018). Conservation and management measure for bigeye, yellowfin and skipjack tuna in the Western and Central Pacific Ocean. WCPFC CMM, 1.
WCPFC. (2022). WCPFC Tuna Fishery Yearbook 2022. W. a. C. P. F. Commission. https://www.wcpfc.int/doc/wcpfc-tuna-fishery-yearbook-2022
Williams, A. J., Allain, V., Nicol, S. J., Evans, K. J., Hoyle, S. D., Dupoux, C., . . . Dubosc, J. (2015). Vertical behavior and diet of albacore tuna (Thunnus alalunga) vary with latitude in the South Pacific Ocean. Deep Sea Research Part II: Topical Studies in Oceanography, 113, 154-169.
Wilson, C., & Coles, V. J. (2005). Global climatological relationships between satellite biological and physical observations and upper ocean properties. Journal of Geophysical Research: Oceans, 110(C10).
Woodson, C., McManus, M., Tyburczy, J., Barth, J., Washburn, L., Caselle, J., . . . Menge, B. (2012). Coastal fronts set recruitment and connectivity patterns across multiple taxa. Limnology and Oceanography, 57(2), 582-596.
Wu, X., Chang, Y., Liao, T., Ding, M., & Ke, C. (2023). Real-time multi-month forecasting of skipjack tuna (Katsuwonus pelamis) habitat in the western and central Pacific Ocean for improved fishing efficiency and fisheries management. ICES Journal of Marine Science, 80(10), 2490-2503.
Wu, Y.-L., Lan, K.-W., Evans, K., Chang, Y.-J., & Chan, J.-W. (2022). Effects of decadal climate variability on spatiotemporal distribution of Indo-Pacific yellowfin tuna population. Scientific reports, 12(1), 1-13.
Xiao, C., Chen, N., Hu, C., Wang, K., Gong, J., & Chen, Z. (2019). Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sensing of Environment, 233, 111358.
Xue, Y., Guan, L., Tanaka, K., Li, Z., Chen, Y., & Ren, Y. (2017). Evaluating effects of rescaling and weighting data on habitat suitability modeling. Fisheries Research, 188, 84-94.
Yackulic, C. B., Chandler, R., Zipkin, E. F., Royle, J. A., Nichols, J. D., Campbell Grant, E. H., & Veran, S. (2013). Presence‐only modelling using MAXENT: when can we trust the inferences? Methods in Ecology and Evolution, 4(3), 236-243.
Yang, S., Yu, L., Wang, F., Chen, T., Fei, Y., Zhang, S., & Fan, W. (2023). The Environmental Niche of the Tuna Purse Seine Fleet in the Western and Central Pacific Ocean Based on Different Fisheries Data. Fishes, 8(2), 78.
Yang, S., Zhang, H., Fan, W., Shi, H., Fei, Y., & Yuan, S. (2022). Behaviour Impact Analysis of Tuna Purse Seiners in the Western and Central Pacific Based on the BRT and GAM Models. Frontiers in Marine Science, 850.
Yen, K.-W., Lu, H.-J., Chang, Y., & Lee, M.-A. (2012). Using remote-sensing data to detect habitat suitability for yellowfin tuna in the Western and Central Pacific Ocean. International Journal of Remote Sensing, 33(23), 7507-7522.
Yen, K.-W., Su, N.-J., Teemari, T., Lee, M.-A., & Lu, H.-J. (2016). Predicting the catch potential of Skipjack tuna in the western and central Pacific Ocean under different climate change scenarios. Journal of Marine Science and Technology, 24(6), 2.
Zainuddin, M., Farhum, A., Safruddin, S., Selamat, M. B., Sudirman, S., Nurdin, N., . . . Saitoh, S.-I. (2017). Detection of pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, southwestern Coral Triangle tuna, Indonesia. PLoS One, 12(10), e0185601.
Zhang, H., Yang, S.-L., Fan, W., Shi, H.-M., & Yuan, S.-L. (2021). Spatial analysis of the fishing behaviour of tuna purse seiners in the western and central Pacific based on vessel trajectory data. Journal of Marine Science and Engineering, 9(3), 322.
Zhang, T., Song, L., Yuan, H., Song, B., & Ebango Ngando, N. (2021). A comparative study on habitat models for adult bigeye tuna in the Indian Ocean based on gridded tuna longline fishery data. Fisheries Oceanography, 30(5), 584-607.
Zhou, C., Hu, Y., Cao, J., Xu, L., Wang, X., Wan, R., . . . Tang, H. (2022). Comparison of nominal and standardized catch per unit effort data in quantifying habitat suitability of skipjack tuna in the equatorial Pacific Ocean. Acta Oceanologica Sinica, 41(3), 1-10.
Zhou, C., Wan, R., Cao, J., Xu, L., Wang, X., & Zhu, J. (2021). Spatial variability of bigeye tuna habitat in the Pacific Ocean: Hindcast from a refined ecological niche model. Fisheries Oceanography, 30(1), 23-37.
漁業署 (2022)，民國111年(2022)漁業統計年報。行政院農業委員會漁業署。