Articles | Volume 9, issue 1
https://doi.org/10.5194/essd-9-163-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/essd-9-163-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Standardization of a geo-referenced fishing data set for the Indian Ocean bigeye tuna, Thunnus obesus (1952–2014)
Teja A. Wibawa
CORRESPONDING AUTHOR
Marine Ecosystem Department, Space Oceanography Division, CLS, 8-10
rue Hermès, 31520, France
Institute for Marine Research and Observations IMRO, Prancak Jembrana Bali, 82218, Indonesia
Patrick Lehodey
Marine Ecosystem Department, Space Oceanography Division, CLS, 8-10
rue Hermès, 31520, France
Inna Senina
Marine Ecosystem Department, Space Oceanography Division, CLS, 8-10
rue Hermès, 31520, France
Related authors
No articles found.
Derek P. Tittensor, Tyler D. Eddy, Heike K. Lotze, Eric D. Galbraith, William Cheung, Manuel Barange, Julia L. Blanchard, Laurent Bopp, Andrea Bryndum-Buchholz, Matthias Büchner, Catherine Bulman, David A. Carozza, Villy Christensen, Marta Coll, John P. Dunne, Jose A. Fernandes, Elizabeth A. Fulton, Alistair J. Hobday, Veronika Huber, Simon Jennings, Miranda Jones, Patrick Lehodey, Jason S. Link, Steve Mackinson, Olivier Maury, Susa Niiranen, Ricardo Oliveros-Ramos, Tilla Roy, Jacob Schewe, Yunne-Jai Shin, Tiago Silva, Charles A. Stock, Jeroen Steenbeek, Philip J. Underwood, Jan Volkholz, James R. Watson, and Nicola D. Walker
Geosci. Model Dev., 11, 1421–1442, https://doi.org/10.5194/gmd-11-1421-2018, https://doi.org/10.5194/gmd-11-1421-2018, 2018
Short summary
Short summary
Model intercomparison studies in the climate and Earth sciences communities have been crucial for strengthening future projections. Given the speed and magnitude of anthropogenic change in the marine environment, the time is ripe for similar comparisons among models of fisheries and marine ecosystems. We describe the Fisheries and Marine Ecosystem Model Intercomparison Project, which brings together the marine ecosystem modelling community to inform long-term projections of marine ecosystems.
P. Lehodey, I. Senina, A.-C. Dragon, and H. Arrizabalaga
Earth Syst. Sci. Data, 6, 317–329, https://doi.org/10.5194/essd-6-317-2014, https://doi.org/10.5194/essd-6-317-2014, 2014
Related subject area
Biosphere – Biodiversity
New historical data for long-term swordfish ecological studies in the Mediterranean Sea
An 18S V4 rRNA metabarcoding dataset of protist diversity in the Atlantic inflow to the Arctic Ocean, through the year and down to 1000 m depth
Integrated ecological monitoring in Wales: the Glastir Monitoring and Evaluation Programme field survey
Multi-scale data on intertidal macrobenthic biodiversity and environmental features in three New Zealand harbours
The Arctic Traits Database – a repository of Arctic benthic invertebrate traits
Freshwater fish fauna of rivers of the southern Western Ghats, India
Copepod species abundance from the Southern Ocean and other regions (1980–2005) – a legacy
Land cover and vegetation data from an ecological survey of "key habitat" landscapes in England, 1992–1993
Growth characteristics of natural and planted Dahurian larch in northeast China
Ecological landscape elements: long-term monitoring in Great Britain, the Countryside Survey 1978–2007 and beyond
Seabed images from Southern Ocean shelf regions off the northern Antarctic Peninsula and in the southeastern Weddell Sea
Long-term vegetation monitoring in Great Britain – the Countryside Survey 1978–2007 and beyond
Ecological survey of the native pinewoods of Scotland 1971
Survey of the terrestrial habitats and vegetation of Shetland, 1974 – a framework for long-term ecological monitoring
Woodland Survey of Great Britain 1971–2001
Brian R. MacKenzie, Teresa Romeo, Piero Addis, Pietro Battaglia, Pierpaolo Consoli, Franco Andaloro, and Gianluca Sarà
Earth Syst. Sci. Data, 13, 5867–5877, https://doi.org/10.5194/essd-13-5867-2021, https://doi.org/10.5194/essd-13-5867-2021, 2021
Short summary
Short summary
Management of marine fisheries and ecosystems is limited by knowledge based on datasets which are short and recent. We recovered new long-term catch and size data for swordfish in the Mediterranean Sea. Our new data series cover the period 1896–2010, which predates most other Mediterranean swordfish datasets. The data allow scientists to investigate long-term effects of fishing and ocean–climate conditions on swordfish ecology in the Mediterranean Sea.
Elianne Egge, Stephanie Elferink, Daniel Vaulot, Uwe John, Gunnar Bratbak, Aud Larsen, and Bente Edvardsen
Earth Syst. Sci. Data, 13, 4913–4928, https://doi.org/10.5194/essd-13-4913-2021, https://doi.org/10.5194/essd-13-4913-2021, 2021
Short summary
Short summary
Here we present a dataset of DNA sequences obtained from size-fractionated seawater samples from the Arctic Ocean that are used to identify taxonomic groups of unicellular plankton. This dataset can be used to investigate the diversity and distribution of plankton groups both by season and by depth and thus increase our understanding of the factors influencing the dynamics of this important part of the Arctic marine ecosystem.
Claire M. Wood, Jamie Alison, Marc S. Botham, Annette Burden, François Edwards, R. Angus Garbutt, Paul B. L. George, Peter A. Henrys, Russel Hobson, Susan Jarvis, Patrick Keenan, Aidan M. Keith, Inma Lebron, Lindsay C. Maskell, Lisa R. Norton, David A. Robinson, Fiona M. Seaton, Peter Scarlett, Gavin M. Siriwardena, James Skates, Simon M. Smart, Bronwen Williams, and Bridget A. Emmett
Earth Syst. Sci. Data, 13, 4155–4173, https://doi.org/10.5194/essd-13-4155-2021, https://doi.org/10.5194/essd-13-4155-2021, 2021
Short summary
Short summary
The Glastir Monitoring and Evaluation Programme (GMEP) ran from 2013 until 2016, as a national programme of ecological study in Wales. GMEP included a large field survey component, collecting data on a range of elements including vegetation, land cover and land use, soils, freshwater, birds, and insect pollinators. GMEP was designed so that surveys could be repeated at regular intervals to monitor the Welsh environment. Data from GMEP have been used to address many applied policy questions.
Casper Kraan, Barry L. Greenfield, and Simon F. Thrush
Earth Syst. Sci. Data, 12, 293–297, https://doi.org/10.5194/essd-12-293-2020, https://doi.org/10.5194/essd-12-293-2020, 2020
Short summary
Short summary
Understanding how the plants and animals that live in the sea floor vary in their spatial patterns of diversity and abundance is fundamental to gaining insight into the role of biodiversity in maintaining ecosystem functioning in coastal ecosystems. Yet data are lacking. Therefore, we collected multi-scale high-resolution data on macrobenthic biodiversity in New Zealand marine sandflats. For 1200 sampling locations we provide data on benthic biodiversity and associated environmental variables.
Renate Degen and Sarah Faulwetter
Earth Syst. Sci. Data, 11, 301–322, https://doi.org/10.5194/essd-11-301-2019, https://doi.org/10.5194/essd-11-301-2019, 2019
Short summary
Short summary
Trait-based approaches (such that consider the life history, morphological, physiological and behavioral characteristics of species) promise new insights in ecology. To facilitate these approaches also in polar regions, we provide the free and easily accessible Arctic Traits Database to the scientific community. Trait information was collected from literature and via communication with experts. At present the database holds trait information for more than 1900 arctic taxa.
Anbu Aravazhi Arunkumar and Arunachalam Manimekalan
Earth Syst. Sci. Data, 10, 1735–1752, https://doi.org/10.5194/essd-10-1735-2018, https://doi.org/10.5194/essd-10-1735-2018, 2018
Astrid Cornils, Rainer Sieger, Elke Mizdalski, Stefanie Schumacher, Hannes Grobe, and Sigrid B. Schnack-Schiel
Earth Syst. Sci. Data, 10, 1457–1471, https://doi.org/10.5194/essd-10-1457-2018, https://doi.org/10.5194/essd-10-1457-2018, 2018
Short summary
Short summary
Copepods are the predominant taxon in marine zooplankton and play an important role in the pelagic food web as intermediators between primary producers, the microbial loop and higher trophic levels. Here, we provide 33 data sets with abundances for a total of 312 copepod taxa from the Southern Ocean, the Magellan region, the Great Meteor Bank and the northern Red Sea, and the Gulf of Aqaba.
Claire M. Wood, Robert G. H. Bunce, Lisa R. Norton, Simon M. Smart, and Colin J. Barr
Earth Syst. Sci. Data, 10, 899–918, https://doi.org/10.5194/essd-10-899-2018, https://doi.org/10.5194/essd-10-899-2018, 2018
Short summary
Short summary
In the 1990s, ecological survey work was carried out in English landscapes containing semi-natural habitats that were perceived to be under threat, or which represented areas of concern ("key habitats"), complementing the national Countryside Survey of Great Britain. The landscapes were lowland heath, chalk and limestone grasslands, coasts and uplands. Standardised procedures were used to record ecological data from representative 1 km squares throughout England in 1992 and 1993.
Bingrui Jia and Guangsheng Zhou
Earth Syst. Sci. Data, 10, 893–898, https://doi.org/10.5194/essd-10-893-2018, https://doi.org/10.5194/essd-10-893-2018, 2018
Short summary
Short summary
Dahurian larch (Larix gmelinii) is the dominant species in northeast China, which is situated in the southernmost part of the global boreal forest and is undergoing great climate change. Its growth characteristics (tree height, diameter at breast height, tree volume and/or stand volume) were collected from published studies from 1965 to 2015. The data set (N=743) provides a quantitative reference for plantation management practices and boreal forest growth prediction under future climate change.
Claire M. Wood, Robert G. H. Bunce, Lisa R. Norton, Lindsay C. Maskell, Simon M. Smart, W. Andrew Scott, Peter A. Henrys, David C. Howard, Simon M. Wright, Michael J. Brown, Rod J. Scott, Rick C. Stuart, and John W. Watkins
Earth Syst. Sci. Data, 10, 745–763, https://doi.org/10.5194/essd-10-745-2018, https://doi.org/10.5194/essd-10-745-2018, 2018
Short summary
Short summary
The Countryside Survey (CS) of Great Britain consists of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was first undertaken in 1978 to monitor ecological and land use change in Britain using standardised procedures for recording ecological data from representative 1 km squares throughout the country. The mapping of ecological landscape elements has subsequently been repeated in 1984, 1990, 1998 and 2007.
Dieter Piepenburg, Alexander Buschmann, Amelie Driemel, Hannes Grobe, Julian Gutt, Stefanie Schumacher, Alexandra Segelken-Voigt, and Rainer Sieger
Earth Syst. Sci. Data, 9, 461–469, https://doi.org/10.5194/essd-9-461-2017, https://doi.org/10.5194/essd-9-461-2017, 2017
Short summary
Short summary
An ocean floor observation system (OFOS) was used to collect seabed imagery on two cruises of the RV Polarstern, ANT-XXIX/3 (PS81) to the Antarctic Peninsula from January to March 2013 and ANT-XXXI/2 (PS96) to the Weddell Sea from December 2015 to February 2016. We report on the image and data collections gathered during these cruises. Seabed images, including metadata, are available from the data publisher PANGAEA via https://doi.org/10.1594/PANGAEA.872719 (PS81) and https://doi.org/10.1594/PANGAEA.862097 (PS96).
Claire M. Wood, Simon M. Smart, Robert G. H. Bunce, Lisa R. Norton, Lindsay C. Maskell, David C. Howard, W. Andrew Scott, and Peter A. Henrys
Earth Syst. Sci. Data, 9, 445–459, https://doi.org/10.5194/essd-9-445-2017, https://doi.org/10.5194/essd-9-445-2017, 2017
Short summary
Short summary
The Countryside Survey (CS) of Great Britain consists of an extensive set of repeated ecological measurements at a national scale, covering a time span of 29 years. CS was first undertaken in 1978 to monitor ecological and land use change in Britain using standardised procedures for recording ecological data from representative 1 km squares throughout the country. The vegetation component has subsequently been repeated in 1990, 1998 and 2007, and changes may be related to a range of drivers.
Claire M. Wood and Robert G. H. Bunce
Earth Syst. Sci. Data, 8, 177–189, https://doi.org/10.5194/essd-8-177-2016, https://doi.org/10.5194/essd-8-177-2016, 2016
Short summary
Short summary
In 1971, an ecological survey of the native pinewoods of Scotland was carried out. This unique habitat is widely recognised, not only by ecologists for its inherent biodiversity but also by the general public for its cultural and amenity value. The repeatable survey collected information on ground flora, soils, forest structure and general site information from 27 major pinewood sites. The results from the survey helped to set the conservation agenda for the old Caledonian pinewoods.
Claire M. Wood and Robert G. H. Bunce
Earth Syst. Sci. Data, 8, 89–103, https://doi.org/10.5194/essd-8-89-2016, https://doi.org/10.5194/essd-8-89-2016, 2016
Short summary
Short summary
A survey of the natural environment was undertaken in Shetland in 1974, after concern was expressed that large-scale development from the new oil industry could threaten the natural features of the islands. A framework was constructed by the Institute of Terrestrial Ecology on which to select samples for the survey. The vegetation and habitat data that were collected, along with the sampling framework, have recently been made public.
C. M. Wood, S. M. Smart, and R. G. H. Bunce
Earth Syst. Sci. Data, 7, 203–214, https://doi.org/10.5194/essd-7-203-2015, https://doi.org/10.5194/essd-7-203-2015, 2015
Short summary
Short summary
The Woodland Survey of Great Britain is a unique data set, consisting of a detailed range of ecological measurements at a national scale, covering a time span of 30 years. A set of 103 semi-natural woods spread across Britain were first surveyed in 1971, which were again surveyed in 2000-2003. Standardised methods of describing the trees, shrubs, ground flora, soils and general habitats present were used for both sets of surveys.
Cited articles
Agnew, D. J., Pearce, J., Pramod, G., Peatman, T., Watson, R., Beddington, J. R., and Pitcher, T. J.: Estimating the worldwide extent of illegal fishing, PLoS One, 4, e4570, https://doi.org/10.1371/journal.pone.0004570, 2009.
Allen, R.: International management of tuna fisheries – Arrangements, challenges and a way forward, FAO Fisheries And Aquaculture Technical Paper No. 536, available at: http://www.fao.org/docrep/012/i1453e/i1453e00.pdf, last access: 18 October 2013, 2010.
Campbell, R., Dowling, N., and Polaheck, T.: Exploratory analyses of Japanese longline catch and effort data in the Indian Ocean, IOTC Proceedings no. 4, 402–417, available at: http://www.iotc.org/sites/default/files/documents/proceedings/2001/wptt/IOTC-2001-WPTT-13.pdf, last access: 28 August 2014, 2001.
Chassot, E., Floch, L., Dewals, P., Chavance, P., and Fonteneau, V.: Re-processing of the fisheries statistics for the French purse seine fishing fleet during 1981–1990, IOTC-2013-SC16-INF12, 1–10, available at: http://wqww.iotc.org/sites/default/files/documents/2014/02/IOTC-2013-SC16-INF12_-_Reprocessing-French-PS-1981-1990.pdf, last access: 18 August 2014, 2013.
Chassot, E., Assan, C., Soto, M., Damiano, A., Delgado de Molina, A., Joachim, L. D., Cauquil, P., Lesperance, F., Curpen, M., Lucas, J., and Floch, L.: Statistics of the European Union and associated flags purse seine fishing fleet targeting tropical tunas in the Indian Ocean during 1981–2014, IOTC-2015-WPTT17-12, available at: http://iotc.org/sites/default/files/documents/2015/10/IOTC-2015-WPTT17-12_-_EU_PS_statistics.pdf, last access: 10 February 2017, 2015.
Davies, L. and Gather, U.: The identification of multiple outliers, J. Am. Stat. Assoc., 88, 782–792, 1993.
Davies, T. K., Mees, C. C., and Milner-Gulland, E. J.: The past, present and future use of drifting fish aggregating devices (FADs) in the Indian Ocean, Mar. Policy, 45, 163–170, https://doi.org/10.1016/j.marpol.2013.12.014, 2014.
de Jong, R., Verbesselt, J., Schaepman, M. E., and de Bruin, S.: Trend changes in global greening and browning: Contribution of short-term trends to longer-term change, Glob. Change Biol., 18, 642–655, https://doi.org/10.1111/j.1365-2486.2011.02578.x, 2012.
Dueri, S., Faugeras, B., and Maury, O.: Modelling the skipjack tuna dynamics in the Indian Ocean with APECOSM-E: Part 1. Model formulation, Ecol. Model., 245, 41–54, https://doi.org/10.1016/j.ecolmodel.2012.02.007, 2012.
Fonteneau, A., Pallarés, P., and Pianet, R.: A worldwide review of purse seine fisheries on FADs, in: Peche thoniere et dispositifs de concentration de poisons, edited by: Le Gall, J.-Y., Cayre, P., and Taquet, M., Ifremer, Plouzane, France, 15–34, available at: http://archimer.ifremer.fr/doc/00042/15278/12664.pdf, last access: 15 June 2016, 2000.
Fonteneau, A., Chassot, E., and Bodin, N.: Global spatio-temporal patterns in tropical tuna purse seine fisheries on drifting fish aggregating devices (DFADs): Taking a historical perspective to inform current challenges, Aquat. Living Resour., 48, 37–48, https://doi.org/10.1051/alr/2013046, 2013.
Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M. D., Neigh, C. S. R., and Reichstein, M.: Trend Change detection in NDVI time series: Effects of inter-annual variability and methodology, Remote Sens., 5, 2113–2144, https://doi.org/10.3390/rs5052113, 2013.
Hampton, J. and Fournier, D. A.: A spatially disaggregated, length-based, age-structured population model of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean, Mar. Freshwater Res., 52, 937–963, https://doi.org/10.1071/MF01049, 2001.
Haward, M. and Bergin, A.: Taiwan's distant water tuna fisheries, Mar. Policy, 24, 33–43, 2000.
Haward, M. and Bergin, A.: The political economy of Japanese distant water tuna fisheries, Mar. Policy, 25, 91–101, 2001.
Hoyle, S. D., Okamoto, H., Yeh, Y.-M., Kim, Z. G., Lee, S., and Sharma, R.: Report of the Second IOTC CPUE Workshop on Longline Fisheries, Taipei, 30 April–2 May 2015, IOTC-2015-CPUEWS02-R[E], 128 pp., available at: http://iotc.org/sites/default/files/documents/2015/10/IOTC-2015-WPTT17-23_-_CPUEWS02_Report.pdf, last access: 10 February 2017, 2015.
IOTC: Status of the Indian Ocean bigeye tuna (BET: Thunnus obesus) resource, IOTC-2015-SC18-ES02[E], available at: http://iotc.org/sites/default/files/documents/2015/11/IOTC-2015-SC18-ES02E_-_Bigeye_tuna.pdf, last access: 10 February 2017, 2015.
Joseph, J.: Managing fishing capacity of the world tuna fleet, FAO Fisheries Circular No 982, Food and Agriculture Organization of the United Nations, Rome, available at: http://www.fao.org/docrep/field/003/ab825f/AB825F00.htm#TOC, last access: 15 November 2016, 2003.
Kaplan, D. M., Dueri, S., Chassot, E., Amande, J. M., Dueri, S., Herve, D., Dagorn, L., and Fonteneau, A.: Spatial management of Indian Ocean tropical tuna fisheries?: potential and perspectives, ICES J. Mar. Sci., 71, 1728–1749, https://doi.org/10.1093/icesjms/fst233, 2014.
Kolody, D. S., Herrera, M., and Million, J.: Exploration of Indian Ocean Bigeye Tuna Stock Assessment Sensitivities 1952–2008 using Stock Synthesis (updated to include 2009), IOTC-2010-WPTT-04(Rev1), 1–93, available at: http://www.iotc.org/sites/default/files/documents/proceedings/2010/wptt/IOTC-2010-WPTT-04.pdf, last access: 10 November 2013, 2010.
Lambert, J., Drenou, C., Denux, J., Balent, G., and Cheret, V.: Monitoring forest decline through remote sensing time series analysis, GISCI Remote Sens., 50, 437–457, https://doi.org/10.1080/15481603.2013.820070, 2013.
Langley, A.: Stock assessment of bigeye tuna in the Indian Ocean for 2016-model development and evaluation, IOTC-2016-WPTT18-20, available at: http://www.iotc.org/sites/default/files/documents/2016/10/IOTC-2016-WPTT18-20_Stock_assessment_of_IO_BET_in_2016.pdf, last access: 24 November 2016.
Langley, A., Herrera, M., and Sharma, R.: Stock assessment of bigeye tuna in the Indian Ocean for 2012, IOTC-2013-WPTT15-30 Rev_1, 1–36, available at: http://iotc.org/sites/default/files/documents/2014/04/IOTC-2013-WPTT15-30 Rev_1 - BET stock assessment Langley.pdf, last access: 10 February 2017, 2013.
Lee, S. I., Kim, Z. G., Ku, J. E., Lee, M. K., Park, H. W., Yoon, S. C., and Lee, D. W.: Review of catch and effort for albacore tuna by Korean tuna longline fishery in the Indian Ocean (1965–2013), IOTC-2014-WPTmT05-17 Rev_1, 1–11, available at: http://www.iotc.org/sites/default/files/documents/proceedings/2012/wptmt/IOTC-2012-WPTmT04-15.pdf, last access: 27 May 2016, 2014.
Lee, Y.-C., Nishida, T., and Mohri, M.: Separation of the Taiwanese regular and deep tuna longliners in the Indian Ocean using bigeye tuna catch ratios, Fisheries Sci., 71, 1256–1263, https://doi.org/10.1111/j.1444-2906.2005.01091.x, 2005.
Lehodey, P., Senina, I., Dragon, A.-C., and Arrizabalaga, H.: Spatially explicit estimates of stock size, structure and biomass of North Atlantic albacore tuna (Thunnus alalunga), Earth Syst. Sci. Data, 6, 317–329, https://doi.org/10.5194/essd-6-317-2014, 2014.
Lehodey, P., Senina, I., Nicol, S., and Hampton, J.: Modelling the impact of climate change on South Pacific albacore tuna, Deep-Sea Res. Pt. II, 113, 246–259, https://doi.org/10.1016/j.dsr2.2014.10.028, 2015.
Lopez, J., Moreno, G., Sancristobal, I., and Murua, J.: Evolution and current state of the technology of echo-sounder buoys used by Spanish tropical tuna purse seiners in the Atlantic , Indian and Pacific Oceans, Fish. Res., 155, 127–137, https://doi.org/10.1016/j.fishres.2014.02.033, 2014.
Majowski, J.: Global fishery resources of tuna and tuna-like species, FAO Fisheries Technical Paper No. 483, Food and Agriculture Organization of the United Nations, available at: ftp://ftp.fao.org/docrep/fao/010/a1291e/a1291e.pdf, last access: 10 February 2017, 2007.
Matsumoto, T., Satoh, K., and Okamoto, H.: Japanese longline CPUE for bigeye tuna in the Indian Ocean standardized by GLM, IOTC-2013-WPTT15-25, 1–28 available at: http://www.iotc.org/sites/default/files/documents/2014/01/IOTC-2013-WPTT15-25.pdf, last access: 31 March 2016, 2013.
Matsumoto, T., Ochi, D., and Satoh, K.: Japanese longline CPUE for bigeye tuna in the Indian Ocean standardized by GLM, IOTC-2015-WPTT17-34, 1–26, available at: http://www.iotc.org/sites/default/files/documents/2015/10/IOTC-2015-WPTT17-34_-_BET_CPUE_JPN_LL.pdf, last access: 19 November 2015.
Maunder, M. N. and Punt, E. A.: Standardizing catch and effort data?: a review of recent approaches, Fish. Res., 70, 141–159, https://doi.org/10.1016/j.fishres.2004.08.002, 2004.
Maunder, M. N., Sibert, J. R., Fonteneau, A., Hampton, J., Kleiber, P., and Harley, S. J.: Interpreting catch per unit effort data to assess the status of individual stocks and communities, ICES J. Mar. Sci., 65, 1373–1385, https://doi.org/10.1016/j.icesjms.2006.05.008, 2006.
Maunder, M. N., Crone, P., Valero, J., and Semmens, B. X.: Selectivity: Theory, estimation, and application in fishery stock assessment models, Fish. Res., 158, 1–4, https://doi.org/10.1016/j.fishres.2014.03.0170165-783610.1016/j.fishres.2014.03.0170165-7836, 2014.
Menard, F., Marsac, F., Bellier, E., and Cazelles, B.: Climatic oscillations and tuna catch rates in the Indian Ocean?: a wavelet approach to time series analysis, Fish. Oceanogr., 16, 95–104, https://doi.org/10.1111/j.1365-2419.2006.00415.x, 2007.
Miyake, M. P., Miyabe, N., and Nakano, H.: Historical trends of tuna catches in the world, FAO Fisheries Technical Paper 467, 74 pp., available at: ftp://ftp.fao.org/docrep/fao/007/y5428e/y5428e.zip, last access: 10 February 2017, 2004.
Mohri, M. and Nishida, T.: Distribution of bigeye tuna and its relationship to the environmental conditions in the Indian Ocean based on the Japanese longline fisheries information, IOTC Proceedings no. 2, 221–230, available at: http://iotc.org/sites/default/files/documents/proceedings/1999/wptt/IOTC-1999-WPTT-11.pdf, last access: 10 February 2017, 1999.
Nakamura, E. L. and Uchiyama, J. H.: Length-weight relations of Pacific Tunas, in: Proceedings, Governor's Conference on central Pacific fishery resources, edited by: Manar, T. A., State of Hawaii, Honolulu, 197–201, 1966.
Okamoto, H. and Shono, H.: Japanese longline CPUE for bigeye tuna in the Indian Ocean up to 2004 standardized by GLM applying gear material information in the model, IOTC Working Party on Tropical Tunas, IOTC-2006-WPTT-17, 1–17, available at: http://www.iotc.org/sites/default/files/documents/proceedings/2006/wptt/IOTC-2006-WPTT-17.pdf, last access: 10 February 2017, 2006.
Okamoto, H., Miyabe, N., and Inagake, D.: Interpretation of high catch rates of bigeye tuna in 1977 and 1978 observed in the Japanese longline fishery in the Indian Ocean, IOTC Proceedings no. 4, 169–190, available at: http://www.iotc.org/sites/default/files/documents/proceedings/2001/wpm/IOTC-2001-WPM-01.pdf, last access: 21 August 2014, 2001.
Okamoto, H., Miyabe, N., and Shono, H.: Standardized Japanese longline CPUE for bigeye tuna in the Indian Ocean up to 2002 with consideration on gear categorization, IOTC Working Party on Tropical Tunas, IOTC-2004-WPTT-18, 1–14, available at: http://www.iotc.org/sites/default/files/documents/proceedings/2004/wptt/IOTC-2004-WPTT-18.pdf, last access: 10 February 2017, 2004.
Pearson, R. K.: Exploring Data in Engineering, the Sciences, and Medicine, Oxford University Press, New York, 2011.
Pianet, R., Delgado de Molina, A., Doriso, J., Bretaudeau, P., Herve, A., and Ariz, J.: Statistics of the main purse seine fleets fishing in the Indian Ocean (1981–2007), IOTC-2008-WPTT-05, available at: http://www.iotc.org/sites/default/files/documents/proceedings/2008/wptt/IOTC-2008-WPTT-05.pdf, last access: 5 April 2016, 2008.
Punt, A. E., Hurtado-Ferro, F., and Whitten, A. R.: Model selection for selectivity in fisheries stock assessments, Fish. Res., 158, 124–134, https://doi.org/10.1016/j.fishres.2013.06.003, 2014.
Sakagawa, G. T., Coan, A. L., and Bartoo, N. W.: Patterns in Longline Fishery Data and Catches of Bigeye Tuna, Thunnus obesus, Mar. Fish. Rev., 49, 57–66, available at: http://spo.nmfs.noaa.gov/mfr494/mfr4949.pdf, last access: 6 March 2014, 1987.
Sharma, R., Langley, A., Herrera, M., Geehan, J., and Hyun, S.: Investigating the influence of length – frequency data on the stock assessment of Indian Ocean bigeye tuna, Fish. Res., 158, 50–62, https://doi.org/10.1016/j.fishres.2014.01.012, 2014.
Verbesselt, J., Hyndman, R., Newnham, G., and Culvenor, D.: Detecting trend and seasonal changes in satellite image time series, Remote Sens. Environ., 114, 106–115, https://doi.org/10.1016/j.rse.2009.08.014, 2010a.
Verbesselt, J., Hyndman, R., Zeileis, A., and Culvenor, D.: Phenological change detection while accounting for abrupt and gradual trends in satellite image time series, Remote Sens. Environ., 114, 2970–2980, https://doi.org/10.1016/j.rse.2010.08.003, 2010b.
Verbesselt, J., Achim, Z., and Hyndman, R.: Package 'bfast', available at: https://cran.r-project.org/web/packages/bfast/bfast.pdf, last access: 23 November 2015.
Ward, P. and Hindmarsh, S.: An overview of historical changes in the fishing gear and practices of pelagic longliners , with particular reference to Japan's Pacific fleet, Rev. Fish Biol. Fisher., 17, 501–516, https://doi.org/10.1007/s11160-007-9051-0, 2007.
Watts, L. M. and Laffan, S. W.: Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region, Remote Sens. Environ., 154, 234–245, https://doi.org/10.1016/j.rse.2014.08.023, 2014.
Wibawa, T. A., Lehodey, P., and Senina, I.: Standardize geo-referenced catch, fishing effort and length-frequency data for the Indian Ocean bigeye tuna, Thunnus obesus (1952–2014), https://doi.org/10.1594/PANGEA.864154, 2016.
Yeh, Y.-M. and Chang, S.-T.: CPUE standardization for bigeye tuna caught by Taiwanese longline fishery in the Indian Ocean using Generalized Linear Model, IOTC-2013-WPTT15-26, 1–21, 2013.
Yeh, Y.-M. and Chang, S.-T.: Updated CPUE standardizations for bigeye and yellowfin tuna caught by Taiwanese longline fishery in the Indian Ocean using Generalized Linear Model, IOTC-2015-WPTT17-25, available at: http://www.iotc.org/sites/default/files/documents/2015/10/IOTC-2015-WPTT17-25_-_Taiwan-China_LL_CPUE_STD_YFT_BET.docx.pdf, last access: 31 March 2016, 2015.
Short summary
Geo-referenced catch and fishing effort data of the bigeye tuna fisheries in the Indian Ocean over 1952–2014 were analyzed and standardized to facilitate population dynamics modeling studies. A total of 30 fisheries were finally determined from longline, purse seine and other-gears data sets. Nevertheless, still one-third of total nominal catch is not included due to a total lack of geo-referenced information and would need to be processed separately.
Geo-referenced catch and fishing effort data of the bigeye tuna fisheries in the Indian Ocean...
Altmetrics
Final-revised paper
Preprint