Species Suitability Modeling of Caspian kutum (Rutilus frisii kutum) based on A Multi-Criteria Evaluation for in Southern Caspian Sea

Document Type : Ecology

Authors

1 Environment Department, Faculty of Natural Resources and Marine Science, University of Tarbiat Modares, Noor, Iran

2 Fisheries Department, Faculty of Natural Resources and Marine Science, University of Tarbiat Modares, Noor, Iran

Abstract

Habitat loss is one of the most serious threats to the recovery of fish stocks and can jeopardize the ability of marine coastal areas to support artisanal fisheries. On the other hand, habitat species modeling is a practicable solution for conservation and protection of marine ecosystems and habitats. In the present study, species suitability modeling was applied to determine habitat of Caspian kutum in southern Caspian Sea that five environmental variables include depth, chlorophyll a, photosynthetically active radiation, sea level anomaly and sea surface temperature were used. The results show that the most habitat suitability of Caspian kutum was in southwest Caspian Sea (include 8c, 25 and 15b indices) and depth and chlorophyll a are the most important environmental variables for the Caspian kutum to modeling its habitats. The results of accuracy assessment for weighted scenarios show that best scenario considering the AUC ROCs of 0.69. According to suggests that output of present study used a criteria for identifying marine protected areas, site selection of aquaculture and others Ecosystem Services Models in southern Caspian Sea.

Keywords

References

  1. Abdolhay, H.; Daud, S.; Rezvani Ghilkolahi, S.; Pourkazemi, M.; Siraj, S. and Abdul Satar, M., 2011. Fingerling production and stock enhancement of Mahisefid (Rutilus frisii kutum) lessons for others in the south of Caspian Sea. Reviews in Fish Biology and Fisheries.  Vol. 21, pp: 247-257.
  2. Anderson, M.C.; Watts, J.M.; Freilich, J.E.; Yool, S.R.; Wakefield, G.L.; Mccaulery, J.F. and Fahnestock,
    A., 2000.     Regression- tree modeling of desert tortoise habitat in the central Mojave Desert. Ecological pplications.
    Vol. 10, pp: 890-897.
  3. Bani, A. and Haghi Vayghan, A., 2011. Temporal variations in haematological and biochemical indices of the Caspian kutum, Rutilusfrisiikutum. Ichthyological Research. Vol. 58, No. 2, pp: 126-133.
  4. Bartoszewicz, M.; Okarma, H.; Zalewski, A. and Szczęsna, J., 2008. Ecology of the raccoon (Procyon lotor) from western Poland. Annales Zoologici Fennici. Vol. 45, No. 4, pp: 291-298.
  5. Birol Kara, A.; Wallcraft, A.J.; Metzger, E.J. and Gunduz, M., 2010. Impacts of freshwater on the seasonal variations of surface salinity and circulation in the Caspian Sea. Continental Shelf Research. Vol. 30, pp: 1211-1225.
  6. Burrough, P.A., 1989. Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil Science. Vol. 40, pp: 477-492.
  7. Crec’hriou, R.; Bonhomme, P.; Criquet, G.; Cadiou, G.; Lenfant, P.; Bernard, G.; Roussel, E.; Le Dire ´ach L. and Planes, S., 2008. Spatial patterns and GIS habitat modelling of fish in two French Mediterranean coastal areas. Hydrobiologia. Vol. 612, pp: 135-153.
  8. Eastman, R.J., 2015. TerrSet Manual, Worcester, MA: Clark University. 392 p.
  9. Eastman, R.J., 2012. Idrisi guid to GIS and Image processing. Accessed in Idrisi Selva 17.00, Worcester, MA: Clark University. 354 p.
  10. Eckert, S.A.; Moore, J.E.; Dunn, D.C.; Buiten, R.S.; Eckert, K.L. and Halpin, P.N., 2008. Modeling loggerhead turtle movement in the mediterranean: importance of body size and oceanography. Ecological Applications, Vol. 18, pp: 290-308.
  11. Fazli, H.; Daryanabard, G.; Salmanmahiny, A.; Abdolmaleki, S.; Bandani, G. and Afraei Bandpei,
    M., 2012.     Fingerling release program, biomass trend and evolution of the condition factor of Caspian Kutum during the 1991-2011 period. Cybium. Vol. 36, pp: 545-550.
  12. Foley, M.M.; Halpern, B.S.; Micheli, F.; Armsby, M.H.; Caldwell, M.R.; Crain, C.M.; Prahler, E.; Rohr, N.; Sivas, D.; Beck, M.W.; Carr, M.H.; Crowder, L.B.; Emmett, J.; Hacker, S.D.; McLeod, K.L.; Palumbi, S.R.; Peterson, C.H.; Regan, H.M.; Ruckelshaus, M.H.; Sandifer, P.A. and Steneck, R.S., 2010. Guiding ecological principles for marine spatial planning. Mar Policy. Vol. 34, pp: 955-966.
  13. Frouin, R. and Murakami, H., 2007. Estimating photosynthetically available radiation at the ocean surface from ADEOS-II global imager data. Journal of Oceanography. Vol. 63, No. 3, pp: 493-503.
  14. Granados-Dieseldorff, P., 2009. A GIS-based spatial model for predicting habitat suitability for juvenile mutton snapper (Lutjanus analis) in Belize. In: Proceedings of the Gulf and Caribbean Fisheries Institute. Vol. 61, pp: 191-197.
  15. Hall, S.J., 1998. The effects of fishing on marine ecosystems and communities. Blackwell Science, Oxford.
  16. Karpinsky, M.G., 2010. Review: the Caspian Sea benthos: unique fauna and community formed under strong grazing pressure. Marine Pollution Bulletin. Vol. 61, No. 4-6, pp: 156-161.
  17. Kostianoy A.G. and Kosarev, A.N., 2005. The Caspian Sea Environment, Springer Science and Business Media. Vol. 5.
  18. Mack, E.L.; Firbank, L.G.; Bellary, P.E.; Hinsley, S.A. and Veitch, N., 1997. The comparison of remotely sensed and ground based habitat area data using species area models. applied ecology. Vol. 34, pp: 1222-1228.
  19. MacMillana, H.; Moorea, A.; Augéa, A. and Chilversd, L., 2016. IS-based multi-criteria analysis of breeding habitats for recolonising species: New Zealand sea lions. Ocean and Coastal Management. Vol. 130, pp: 162-171.
  20. Mahini, A.S. and Gholamalifard, M., 2006. Siting MSW landfills with a weighted linear combination methodology in a GIS environment. International Journal of Environmental Science and Technology. Vol. 3, No. 4, pp: 435-445.
  21. Mehri, A.; Salmanmahiny, A.; Mirkarimi, S.H. and Rezaei, H.R., 2014. Use of optimization algorithms to prioritize protected areas in Mazandaran Province of Iran. Journal for Nature Conservation. Vol. 22, No. 5, pp: 462-470.
  22. Nasrollahzadeh, A., 2010. Caspian Sea and its ecological challenges. Caspian Journal of Environmental Sciences. Vol. 8, pp: 97-104.
  23. Ouraji, H.; Khalili, K.; Ebrahimi, G. and Jafarpour, S., 2011. Determination of the optimum transfer time of kutum (Rutilus frisii kutum) larvae from live food to artificial dry feed. Aquaculture International. Vol. 19, pp: 683-691.
  24. Paudel, P.K.; Hais, M. and Kindlmann, P., 2015, Zoological Studies,54:37. Doi:10.1186/s40555-015-0116-9.
  25. Pikitch, E.K.; Santora, C.; Babcock, E.A.; Bakun, A.; Bonfil, R. and Conover, D.O., 2004. Ecosystem based fishery management. Science. Vol. 305, pp: 346-347.
  26. Roberts, J.J.; Best, B.D.; Dunn, D.C.; Treml, E.A. and Halpin, P.N., 2010. Marine geospatial ecology tools: An integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++. Environmental Modelling and Software. Vol. 25, pp: 1197-1207.
  27. Saravi Nasrollahzadeh, H.; Din, Z.B.; Foong, S.Y. and Makhlough, A., 2008. Trophic status of the Iranian Caspian Sea based on water quality parameters and phytoplankton diversity. Continental Shelf Research. Vol. 28,
    pp: 1153-1165.
  28. Schmucker, K.J., 1982. Fuzzy Sets, natural language computations and risk analysis (Computer Science Press).
    pp: 7.
  29. Sparrevohn, C.R.; Dinesen, G.E.; Andersen, J.H. and Reker, J.B., 2008. Spatial prediction of nursery grounds for juvenile flatfish in the Danish Kattegat: From: Mapping and modeling of marine habitats in the Baltic Sea region. BALANCE. BALANCE Interim Report. No. 27.
  30. Stevick, P.T.; McConnell, B.J. and Hammond, P.S., 2002. Patterns of movement. In: Hoelzel, A.R. (Ed.), Marine Mammal Biology: An Evolutionary Approach. Blackwell Science, Oxford.
  31. Taheri, M.; Foshtomi, M.; Noranian, M. and Mira, S., 2012. Spatial distribution and biodiversity of macrofauna in the southeast of the Caspian Sea, Gorgan Bay in relation to environmental conditions. Ocean Science Journal. Vol. 47, pp: 113-122.
  32. Valavanis, V.D.; Georgakarakos, S.; Kapantagakis, A.; Palialexis, A. and Katara, I., 2004. A GIS environmental modelling approach to essential fish habitat designation. Ecological Modelling. Vol. 178, No. 3-4, pp: 417-427.
  33. Valavanis, V.; Pierce, G.; Zuur, A.; Palialexis, A.; Saveliev, A.; Katara, I. and Wang, J., 2008. Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS. Hydrobiologia. Vol. 612, No. 1, pp: 5-20.
  34. Valavanis, V.D.; Georgakarakos, S.; Koutsoubas, D.; Arvanitidis, C. and Haralabous, J., 2002. Development of a marine information system for Cephalopod fisheries in the Greek Seas (Eastern Mediterranean). Bull Mar Sci. Vol. 71, pp: 867-882.
  35. Vayghan, A.H.; Zarkami, R.; Sadeghi, R. and Fazli, H., 2016. Modeling habitat preferences of Caspian kutum, Rutilus frisii kutum (Kamensky, 1901) (Actinopterygii, Cypriniformes) in the Caspian Sea. Hydrobiologia. Vol. 766, No. 1, pp: 103-119.
  36. Vayghan, A.H.; Poorbagher, H.; Shahraiyni, H.T.; Fazli, H. and Saravi, H.N., 2013. Suitability indices and habitat suitability index model of Caspian kutum (Rutilus frisii kutum) in the southern Caspian Sea. Aquatic Ecology. Vol. 47, No. 4, pp: 441-451.
  37. Voogd, H., 1983. Multicriteria evaluation for urban and regional planning. Pion, Ltd., London.
Journal of Animal Environment
Volume 9, Issue 4
January 2018
Pages 235-246

Files

Share

How to cite

Statistics