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Cluster analysis methods applied to daily vessel location data to identify cooperative fishing among tuna purse-seiners

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Abstract

Management of large-scale pelagic fisheries relies heavily on fishery data to provide information on tuna population status because, for widely distributed populations, the cost of collecting survey data is often prohibitively high. However, fishery data typically do not provide direct information on interactions among fishing vessels, and thus methods of analysis often assume that vessels operate independently, despite the belief that cooperative fishing occurs. Cluster analysis methods were applied to daily vessel location data collected by onboard fisheries observers to identify groups of tuna purse-seine vessels searching for fish close to each other in space. Some vessel groups were found to reoccur through time, both on daily and monthly or longer time scales. This temporal persistence and reoccurrence are interpreted as an indication of cooperative fishing. Results indicate that there may be multiple layers of vessel interactions, from groups of a few vessels to networks of larger numbers of vessels. The use of reoccurring vessel group characteristics to study the temporal and spatial persistence of areas of high tuna abundance is discussed.

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Notes

  1. The area of the Pacific Ocean between the coast of the Americas to 150° W from 50° S to 50° N.

  2. Bigeye tuna, skipjack tuna, and yellowfin tuna.

  3. Interacting cooperatively (i.e., “cooperative fishing”), refers to the sharing of information among vessels that may improve their fishing success. Such information can include the specific locations and dates of the purse-seine sets each vessel made, and its catch amounts, or it may be more general spatial–temporal information on the distribution of schools of tunas seen during the fishing trip. This information is shared via radio or satellite phone, for example. The names of vessels with whom another vessel communicates, as well as what is said, is strictly confidential information and not recorded by onboard observers.

  4. Agreement on the International Dolphin Conservation Program: http://www.iattc.org/IDCPENG.htm.

  5. Purse-seine vessels with > 363 metric tons fish-carrying capacity.

  6. Marine mammal sightings are typically the product of active searching and observers must record position information for every marine mammal sighting.

  7. There are two fishery closures each year and vessels must choose to participate in one: July 29–October 8 and November 9 to January 19 of the following year.

  8. Centroid = [average (latitudes), average(longitudes)].

  9. For example, the great circle distance between two points at 25°N separated by one degree of longitude is 54 nm or 90% of the distance at the equator, which is 60 nm.

References

  • Arakawa JRA (2013) A behavioral ecology of fishermen: hidden stories from trajectory data in the northern Humboldt Current System. Thesis, Institut de Recherche Pour le Development, Montpellier. http://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers14-02/010060304.pdf

  • Barnes ML, Lynham J, Kalberg K, Leung P (2016) Social networks and environmental outcomes. Proc Natl Acad Sci United States of America 113:6466–6471

    Article  CAS  Google Scholar 

  • Bayliff WH (2001) Organization, functions and achievements of the Inter-American Tropical Tuna Commission. Inter-American Tropical Tuna Commission Special Report 13. http://www.iattc.org/PDFFiles/SpecialReports/_English/No-13-2001-BAYLIFF,%20WILLIAM%20H_Organization,%20functions,%20and%20achievements%20of%20the%20Inter-American%20Tropical%20Tuna%20Commission.pdf

  • Bertrand S, Bertrand A, Guevara-Carrasco R, Gerlotto F (2007) Scale-invariant movements of fishermen: the same foraging strategy as natural predators. Ecol Appl 17:331–337

    Article  Google Scholar 

  • Dreyfus-Leon M, Gaertner D (2006) Modeling performance and information exchange between fishing vessels with artificial neural networks. Ecol Model 195:30–36

    Article  Google Scholar 

  • Dreyfus-León MJ, Martínez-Olvera R, Hernández-Walls R (2011) Numeric simulation of fishing effort and strategies (stochastic and cartesian) using cellular automata. Ciencias Marinas 37:393–402

    Article  Google Scholar 

  • Duron JN, Chassot E, Floch L, Maufroy A (2015) Preferred habitat of tropical tuna species in the eastern Atlantic and western Indian Oceans: a comparative analysis between FAD-associated and free-swimming schools. Indian Ocean Tuna Commission 17th Working Party on Tropical Tunas Document IOTC-2015-WPTT17-31, October 9, 2015. https://www.iotc.org/documents/preferred-habitat-tropical-tuna-species-eastern-atlantic-and-western-indian-oceans

  • Edelsbrunner H, Kirkpatrick DG, Seidel R (1983) On the shape of a set of points in the plane. IEEE Trans Inf Theory 29(4):551–559

    Article  Google Scholar 

  • Fonteneau A, Lucas V, Tewaki E, Delgado A (2008) Mesocale exploitation of a major tuna concentration in the Indian Ocean. Aquat Living Resour 21:109–121

    Article  Google Scholar 

  • Gaertner D, Dreyfus-Leon M (2004) Analysis of non-linear relationships between catch per unit effort and abundance in a tuna purse-seine fishery simulated with artificial neural networks. ICES J Mar Sci 61:812–820

    Article  Google Scholar 

  • IATTC (2019a). Report on the Tuna Fishery, Stocks and Ecosystem in the Eastern Pacific Ocean in 2018. Inter-American Tropical Tuna Commission Fishery Status Report 17. http://www.iattc.org/PDFFiles/FisheryStatusReports/_English/No-17-2019_Tuna%20fishery,%20stocks,%20and%20ecosystem%20in%20the%20eastern%20Pacific%20Ocean%20in%202018.pdf

  • IATTC (2019b). Status of Tuna and Billfish Stocks in 2018. Inter-American Tropical Tuna Commission Stock Assessment Report 20. http://www.iattc.org/PDFFiles/StockAssessmentReports/_English/No-20-2019_Status%20of%20the%20tuna%20and%20billfish%20stocks%20in%202018.pdf

  • Jeung H, Yiu ML, Zhou X, Jensen CS, Shen HT (2008) Discovery of convoys in trajectory databases. Proc VLDB Endowment VLDB Endowment 1:1068–1080. https://doi.org/10.14778/1453856.1453971

    Article  Google Scholar 

  • Kalnis P, Mamoulis N, Bakiras S (2005) On discovering moving clusters in spatio-temporal data. In: Bauzer Medeiros C, Egenhofer MJ, Bertino E. (eds) Advances in spatial and temporal databases. SSTD 2005. Lecture Notes in Computer Science, vol 3633. pp. 364–381. Springer, Berlin, Heidelberg

  • Kaufman L, Rousseeuw PJ (1990) Finding groups in data. Wiley, New Jersey

    Book  Google Scholar 

  • Kessler WS (2006) The circulation of the eastern tropical Pacific: a review. Prog Oceanogr 69:181–217

    Article  Google Scholar 

  • Khaing HS, Thein T (2014) An efficient clustering algorithm for moving object trajectories. Proceedings of the 3rd International Conference on Computational Techniques and Artificial Intelligence (ICCTAI2014) February 11–12, 2014, Singapore, 74–78

  • Kisilevich S, Mansmann F, Nanni M, Rinzivillo S (2010) Spatio-temporal clustering. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, New York, pp 855–874

    Google Scholar 

  • 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. https://doi.org/10.3390/rs9050444

    Article  Google Scholar 

  • Li Z, Ding B, Han J, Kays R (2010) Swarm: mining relaxed temporal moving object clusters. Proceedings of the 36th International conference on Very Large Data Bases, September 13–17, 2010, Singapore, vol 3, p 273–734

  • Mao Y, Zhong H, Qi H, Ping P, Li X (2017) An adaptive trajectory clustering method based on grid and density in mobile pattern analysis. Sensors 17(1):19

    Google Scholar 

  • National Research Council (NRC) (1992) Dolphins and the tuna industry. National Academy Press, Washington, DC, p 192

    Google Scholar 

  • Pateiro-Lopez B, Rodriguez-Casal A (2019) alphahull: generalization of the Convex Hull of a Sample of Points in the Plane. R package version 2.2. https://CRAN.R-project.org/package=alphahull

  • Pella JJ (1969) A stochastic model for purse seining in a two-species fishery. J Theor Biol 22:209–226

    Article  CAS  Google Scholar 

  • R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/

  • Rooker JR, Kitchens LL, Dance MA, Wells RD, Falterman B, Cornic M (2013) Spatial, temporal, and habitat-related variations in abundance of pelagic fishes in the Gulf of Mexico: potential implications of the Deep-water Horizon oil spill. PLoS ONE 8:e76080

    Article  CAS  Google Scholar 

  • Waring T, Acheson J (2018) Evidence of cultural group selection in territorial lobstering in Maine. Sustain Sci 13:21–34

    Article  Google Scholar 

  • Wilson J, Yan L, Wilson C (2007) The precursors of governance in the Maine lobster fishery

  • Wise L, Murta AG, Carvalho JP, Mesquita M (2012) Qualitative modelling of fishermen’s behavior in a pelagic fishery. Ecol Modeling 228:112–122

    Article  Google Scholar 

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Correspondence to Cleridy E. Lennert-Cody.

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Handling Editor: Bryan F. J. Manly.

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Lennert-Cody, C.E., Maunder, M.N., Román, M.H. et al. Cluster analysis methods applied to daily vessel location data to identify cooperative fishing among tuna purse-seiners. Environ Ecol Stat 27, 649–664 (2020). https://doi.org/10.1007/s10651-020-00451-7

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