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Abstract

Delineating marine fishes' distribution and home range may help manage and set marine protected areas. While single-species distribution models provide some information about the home range of species, they do not incorporate the interactions among species and may not provide an accurate picture of community distribution in a given habitat. Hence, the present study modeled the distribution of marine fish communities in the Persian Gulf using joint species distribution modeling. In addition, this study converted the predicted presence of every species to dichotomized presence or absence output to facilitate the interpretation of the results. Most species had wide or sporadic distribution across the Persian Gulf, but some indicated contagious distribution mostly confined to coastal areas close to the Hormuz Strait. The presence study showed that joint species distribution has high accuracy and facilitates predicting fish communities across large geographical areas, allowing us to predict such communities simultaneously.

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Distribution Modeling Latent variable model Persian Gulf Fish Communities

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The authors transfer the copyrights of their papers to the Iranian Society of Ichthyology. However, the information could be used in accordance with the Creative Commons licence (Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

How to Cite
POORBAGHER, H., & EAGDERI, S. (2024). Predicting the distribution of fish community in the Persian Gulf using joint species distribution modelling with a latent variable model. Iranian Journal of Ichthyology, 11(1), 69–78. https://doi.org/10.22034/iji.v11i1.1029
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References

  1. Abdelbary, E.M.M. & Al Ashwal, A.A. 2021. Distribution and abundance of seagrasses in Qatar marine zone. The Arabian Seas: Biodiversity, Environmental Challenges and Conservation Measures pp: 327-362.
  2. Ben-Hasan, A. & Daliri, M. 2023. Persian Gulf artisanal fisheries: Magnitude, threats, and opportunities. Reviews in Fish Biology and Fisheries 33(3): 541-559.
  3. Cabral, J.S.; Wiegand, K. & Kreft, H. 2019. Interactions between ecological, evolutionary and environmental processes unveil complex dynamics of insular plant diversity. Journal of Biogeography 46(7): 1582-1597.
  4. Chen, Z.; Hu, C. & Muller-Karger, F. 2007. Monitoring turbidity in Tampa Bay using MODIS/Aqua 250-m imagery. Remote Sensing of Environment 109(2): 207-220.
  5. Clément, J. & Vieilledent, G. 2023. jSDM: Joint Species Distribution Models. https://cran.r-project.org.
  6. 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: 1-171.
  7. Edmonds, N.J.; Al-Zaidan, A.S.; Al-Sabah, A.A.; Quesne, W.L.; Devlin, M.; Davison, P. & Lyons, B.P. 2021. Kuwait’s marine biodiversity: Qualitative assessment of indicator habitats and species. Marine Pollution Bulletin 163: 111915.
  8. Fatemi, S.M.R. & Shokri, M. R. 2001. Iranian coral reefs status with particular reference to Kish Island, Persian Gulf. In: Proceedings of international coral reef initiative (ICRI) regional workshop for the Indian Ocean, Maputo, Mozambique. pp: 26-28.
  9. Ghaitaranpour, M., Poorbagher, H.; Eagderi, S. & Feghhi, J. 2019. Modelling the spatial distribution of the yellowfin tuna, Thunnus albacares in the Persian Gulf using a fuzzy rule-based classification. International Journal of Aquatic Biology 7(6): 351-356.
  10. Haiduc-Dale, N. 2018. Fishing in the Persian Gulf: The Merits of Mediocrity. Journal of Arabian Studies 8(1): 99-117.
  11. Hijmans, R.J. 2021. raster: Geographic Data Analysis and Modeling.
  12. Karimpour, M.; Harlioglu, M.M.; Khanipour, A.A.; Abdolmalaki, S. & Aksu, Ö. 2013. Present status of fisheries in Iran. Journal of FisheriesSciences.com. 7(2): 161.
  13. Kuhn, M. & Johnson, K. 2013. Applied predictive modeling. Springer Science and Business Media. 600 p.
  14. Lenoir, S.; Beaugrand, G. & Lecuyer, E. 2011. Modelled spatial distribution of marine fish and projected modifications in the North Atlantic Ocean. Global Change Biology 17(1): 115-129.
  15. 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.
  16. Mugo, R.; Saitoh, S-I.; Nihira, A. & Kuroyama, T. 2011. Application of multi-sensor satellite and fishery data, statistical models and marine-GIS to detect habitat preferences of skipjack tuna’, Handbook of Satellite Remote Sensing Image Interpretation: Applications for Marine Living Resources Conservation and Management, EU PRESPO and IOCCG, Dartmouth, Canada.
  17. NASA Goddard Space Flight Center, Ocean Ecology Laboratory, O. B. P. G. .2021. MODIS-Aqua Ocen Color Data NASA OB.DAAC, Greenbelt, MD, USA.
  18. Scherrer, D. et al. (2018. How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer. Methods in Ecology and Evolution 9(10): 2155-2166.
  19. Scherrer, D.; Mod, H.K. & Guisan, A. 2020. How to evaluate community predictions without thresholding? Methods in Ecology and Evolution 11(1): 51-63.
  20. Tikhonov, G.; Opedal, Ø.H.; Abrego, N.; Lehikoinen, A.; de Jonge, M.M.J.; Oksanen, J. & Ovaskainen, O. 2020. Joint species distribution modelling with the R-package Hmsc. Methods in Ecology and Evolution 11(3): 442-447.
  21. Warton, D.F. Blanchet, G.; O’Hara, Otso Ovaskainen, O.; Taskinen, S.; Walker, S.C. & Hui F.K.C. 2015. So many variables: joint modeling in community ecology. Trends in Ecology and Evolution 30(12): 766-779.
  22. Watson, R.A. 2016. Global Fisheries Landings V2.0. Institute for Marine and Antarctic Studies (IMAS), University of Tasmania (UTAS). doi: 10.4226/77/58293083b0515.
  23. Wilkinson, D.P.; Golding, N.; Gurutzeta Guillera-Arroita, G.; Tingley, R. & McCarthy M.A. 2019. A comparison of joint species distribution models for presence-absence data. Methods in Ecology and Evolution 10(2): 198-211.
  24. Williams, J.N.; Seo, C.; Thorne, J.; Nelson, J.K.; Erwin, S.; O’Brien, J.M & Schwartz, M.W. 2009. Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions 15(4): 565-576.