Modelling the spatial distribution of the yellowfin tuna, Thunnus Albacares in the Persian Gulf using a fuzzy rule-based classification
Downloads
Yellowfin tuna, Thunnus albacares, are the most important ecological and economic fishes in the Persian Gulf. In recent decades, their populations have faced overfishing, environmental problems and climate change. In this study, using some environmental variables affecting the habitat of tuna fish, i.e. sea surface temperature at night and day, reflection of 645 nm wavelength as a water turbidity, angstrom view of aerosol 443 to 965 nm, aerosol optic thickness at 869 nm, organic and inorganic particle carbon, photosynthetic active radiation, absorption by phytoplankton at 443 nm and chlorophyll-a concentration from 2002 to 2018, on the spatial distribution of yellow-fin tuna has been modelled by fuzzy rule-based classification. Over the years, the variables had different degrees of importance in the models. There was a great variation in the spatial distribution of the species from year to year.
Downloads
Alavian Petroody S, Ashrafi S., Eagderi S., Khazaee M. (2013). Investigation of body size effect on bioaccumulation pattern of Cd, Pb and Ni in the soft tissue of rock oyster Saccostrea cucullata from Laft Port. Journal of the Persian Gulf, 4(14): 39-45.
Amiri K., Shabanipour N., Eagderi S. (2017). Using kriging and co-kriging to predict distributional areas of Kilka species (Clupeonella spp.) in the southern Caspian Sea. International Journal of Aquatic Biology, 5(2): 108-113.
Amiri K., Shabanipour N., Eagderi S. (2018). Forecasting the catch of kilka species (Clupeonella spp.) using Time Series SARIMA models in the Southern Caspian Sea. Caspian Journal of Environmental Sciences, 16(4): 349-358.
Chassot E., Bonhommeau S., Bonhommeau G., Nieto K., Polovina J.J., Huret M., Dulvy N.K., Demarcq H. (2011). Satellite remote sensing for an ecosystem approach to fisheries management. ICES Journal of Marine Science, 68(4): 651–666.
Cheung W.W.L., Oyinlola M.A. (2018). Vulnerability of flatï¬sh and their ï¬sheries to climate change. Sea Research, 140: 1–10.
Cheung W.W.L., Close C., Lam V., Watson R., Pauly D. (2008). Application of macroecological theory to predict effects of climate change on global ï¬sheries potential. Marine Ecology Progress Series, 365: 187–197.
Coro G., Large S., Magliozzi1 C., Pagano P. (2016). Analysing and forecasting ï¬sheries time series: purse seine in Indian Ocean as a case study. Marine Science, 73(10): 2552–2571.
Eagderi S., Fricke R., Esmaeili H.R., Jalili P. (2019). Annotated checklist of the fishes of the Persian Gulf: Diversity and conservation status. Iranian Journal of Ichthyology, 6(Suppl.1): 1–171.
Guillen A., Honryo T., Ibarra J., Cano A., Margulies D., Scholey P.V., Wexler B.J., Stein S.M., Kobayashi T., Sawada Y. (2014). Effect of water temperature on embryonic development of yellowfin tuna Thunnus albacares inhabiting the Eastern Pacific Ocean. Aquaculture Science, 62(3): 319-322.
Gunarso W. (1985). Fish behavior in relation to tools, methods and arrest tactics. Faculty of Fisheries, Istitut Pertanian Bogor, Bogor. 149 p.
Kim D.K., Jeong K.S., McKay R.I.B.,Chon T.S., Joo G.J. (2012). Machine learning for predictive management: short and long term prediction of phytoplankton biomass using genetic algorithm based recurrent neural networks. International Journal of Environmental Research, 6(1): 95-108.
Klein E.S., Smith S.L., Kritzer J.P. (2017). Effects of climate change on four New England groundï¬sh species. Reviews in Fish Biology and Fisheries, 27(2): 317-338.
Kuhn M., Johnson K. (2013). Applied predictive modelling. Springer. 560 p.
Miguel A., Santos P. (2000). Fisheries oceanography using satellite and airborne remote sensing methods: a review. Fisheries Research, 49(1): 1-20.
Moran P., Cal L., Cobelo-Garcí A., Almecijab C., Caballero P., Garcia de Leaniz C. (2018). Historical legacies of river pollution reconstructed from fish scales. Environmental Pollution, 234: 253-259.
Nishidaa T., Chenb D.G., Moh M. (2007). Fuzzy logic analyses for the spawner-recruitment relationship of bigeye tuna (Thunnus obesus) in the Indian Ocean incorporating the environmental regime shift. Ecological Modelling, 203(1): 132–140.
Nurdina S., Mustapha M.A., Lihan T. (2013). The relationship between sea surface temperature and chlorophyll-a concentration in fisheries aggregation area in the archipelagic waters of Spermonde using satellite images. AIP Conference Proceedings, 1571: 466-472.
Pekcan-Hekim Z. (2007). Effects of turbidity on feeding and distribution of ï¬sh. Ph.D. Thesis, University of Helsinki, Department of Biological and Environmental Sciences.
Poorbagher H., Eagderi S. (2017). Modeling the effects of sea surface temperature and aerosols on presence of Epinephelus (Perciformes: Serranidae) in the Persian Gulf using remotely sensed data. The Fifth Iranian Conference of Ichthyology, Islamic Azad University of Babol, 13-14 December 2017. pp: 71-721.
Putri A.R.S., Zainuddin M., Putri R.S. (2018). Effect of climate change on the distribution of skipjack tuna Katsuwonus pelamis catch in the Bone Gulf, Indonesia, during the southeast monsoon. AACL Bioflux, 11(2): 439-451.
Rezagholinejad S., Arshad A., Nurul Amin S.M., Ehteshami F. (2016). The influence of environmental parameters on fish larval distribution and abundance in the mangrove estuarine area of Marudu bay, Sabah, Malaysia. Survey in Fisheries Sciences, 2(2): 67-78.
Riza S.L., Bergmeir CH., Herrera F., Benıtez M. (2015). frbs: Fuzzy rule-based systems for classiï¬cation and regression in R. Statistical Software. Journal of Statistical Software, 65(6): 1-30.
Sadeghi N. (2001). Biological and morphological characteristics of southern Iranian Fish. Naghsh Mehr, Tehran. 432 p.
Worm B., Hilborn R., Baum J.K., Branch T.A., Collie J.S., Costello C., Fogarty M.J., Fulton E.A., Hutchings J.A., Jennings S., Jensen O.P. (2009). Rebuilding Global Fisheries. Science, 578-585.