پیش بینی پراکنش خرچنگ مانگرو ( 1852, Macrophthalmus (Macrophthalmus) sulcatus (H. Milne Edwards در پاسخ به گرمایش جهانی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه زیست شناسی دریا، دانشکده علوم و فنون دریایی، دانشگاه هرمزگان، بندرعباس، ایران

2 گروه شیلات، دانشکده علوم و فنون دریایی، دانشگاه هرمزگان، بندرعباس، ایران

3 گروه جانورشناسی دریا، موسسه تحقیقاتی و موزه تاریخ طبیعی Senckenberg، فرانکفورت، آلمان

4 مدیر داده های OBIS، بخش اعماق دریا، فرانکفورت، آلمان

چکیده

خرچنگ­ های خانواده Macrophthalmidae نقش اکولوژیکی مهم در زنجیره غذایی مانگروها ایفا می­ کنند. در این مطالعه، با استفاده از روش مدل ­سازی MaxEnt، پراکنش اخیر و آینده گونه Macrophthalmus (Macrophthalmus) sulcatus مدل­ سازی شد. مشاهدات پراکنش گونه از پایگاه ­های داده ­ای برخط، مقالات، و جمع ­آوری ­های موزه ­ای، به دست آمد. دمای سطح آب دریا و عمق مهم ­ترین محرک­ های پراکنش خرچنگ مانگرو بودند. در مدل­ سازی اخیر، مناسب ­ترین محیط­ ها عمق کم ­تر از 32/34 متر، دمای 27/82 (درجه سانتی گراد)، شوری 40/44-11/02 (پی پی ام) و سرعت جریان 0/04 (متر بر ثانیه) و در  مدل ­سازی آینده، نواحی ساحلی با عمق کم ­تر از 44/33 متر، دمای 28/86 (درجه سانتی گراد)، شوری 38/77 (پی پی ام) و سرعت جریان 0/01 (متر بر ثانیه) بود. مدل­  سازی آینده نشان داد که این گونه در پاسخ به تغییرات آب ­و هوایی، گستره پراکنش خود در آینده را محدود خواهد کرد و گسترش محیط­  های با شرایط زیستگاهی پایین برای گونه، از 23/52 به 27/27 درصد، و کاهش محیط­ های با شرایط زیستگاهی متوسط از 38/23 به 33/33 درصد در آینده، مشاهده خواهد شد. در نهایت تفسیر خروجی MaxEnt، آسیب­ پذیری خرچنگ­ مانگرو را به تغییرات آب­ و هوایی در آینده، با توجه به محدود شدن پراکنش آینده، نشان داد و این که آن­ ها­ چگونه ممکن هست به تغییرات در شرایط آب­ و هوایی، از طریق محدود شدن پراکنش خود، پاسخ دهند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

The prediction distribution of mangrove crab Macrophthalmus (Macrophthalmus) sulcatus (H. Milne Edwards, 1852) in response to global warming

نویسندگان [English]

  • Sana Sharifian 1
  • Ehsan Kamrani 2
  • Hanieh Saeedi 3 4
1 Department of Marine Biology, Faculty of Marine Science and Technology, University of Hormozgan, Bandar Abbas, Iran
2 Fishery Department, Faculty of Marine Science and Technology, University of Hormozgan, Bandar Abbas, Iran
3 Department of Marine Zoology, Senckenberg Research Institute and Natural History Museum, Frankfurt am Main, Germany|OBIS data manager, Deep-Sea Node, Frankfurt am Main, Germany
4 Department of Marine Zoology, Senckenberg Research Institute and Natural History Museum, Frankfurt am Main, Germany|OBIS data manager, Deep-Sea Node, Frankfurt am Main, Germany
چکیده [English]

Crabs family Macrophthalmidae play the importanat ecological role in food web of mangroves. In this study, using MaxEnt modeling technique, recent and future distributions of the crab M. sulcatus were modelled. The records on species distribution were obtained from the open-access databases, literatures and collections of museums. Depth and sea surface temperature (SST) were the most important drivers of distribution of mangrove crab. In present model, the most suitable environments were depths of less than 32.34 m, mean SST 27.82°C, salinity 40.11-44.02 (ppm), current velocity of 0.04m-1, and in future models, coastal area with depth less 44.33 m, SST between 28.86°C, salinity 38.77 (ppm), and current velocity between 0.01m-1. Future models showed that distribution of species will be contracted in response to climate change and it will be observed the expanding the low suitable environments from 23.52 to 27.27 percent and the decresing median suitable environments from 38.23 to 33.33 percent in future. In summary, the outputs of MaxEnt model present the vulnerability of mangrove crabs to climate change in future, in regards to the contraction of future distribtion and how they may response to change in climatic conditions through the contraction their distribution .

کلیدواژه‌ها [English]

  • Modeling MaxEnt
  • Global distribution
  • Temperature
  • Depth

مراجع

  1. Saupe, E.E., Hendricks, J.R., Townsend Peterson, A. and Lieberman, B.S., 2014. Climate change and marine molluscs of the western North Atlantic: future prospects and perils. J. Biogeogr. 41: 1352-1366.
  2. Cheung, W.W.L., Lam, V.W.Y., Sarmiento, J.L., Kearney, K., Watson, R. and Pauly, D., 2009. Projecting global marine biodiversity impacts under climate change scenarios. Fish Fish. 10: 235-251.
  3. Rombouts, I., Beaugrand, G. and Dauvin, J.C., 2012. Potential changes in benthic macrofaunal distributions from the English Channel simulated under climate change scenarios. Estuar. Coast. Shelf Sci. 99: 153-161.
  4. Sarà, G., Milanese, M., Prusina, I., Sarà, A., Angel, D.L., Glamuzina, B., Nitzan, T., Freeman, S., Rinaldi, A., Palmeri, V., Montalto, V., Lo Martire, M., Gianguzza, P., Arizza, V., Lo Brutto, S., De Pirro, M., Helmuth, B., Murray, J., De Cantis, S. and Williams, G.A., 2014. The impact of climate change on mediterranean intertidal communities: losses in coastal ecosystem integrity and services. Reg. Environ. Chang. 14: 5-17.
  5. Schoeman, D.S., Schlacher, T.A. and Defeo, O., 2014. Climate-change impacts on sandy-beach biota: crossing a line in the sand. Glob. Chang. Biol. 20: 2383-2392.
  6. Ashcroft, M.B., French, K.O. and Chisholm, L.A., 2011. An evaluation of environmental factors affecting species distributions. Ecol. Modell. 222: 524-531.
  7. Davies, A.J. and Guinotte, J.M., 2011. Global habitat suitability for framework-forming cold-water corals. PLoS One. 6: e18483.
  8. Pooyani, M.A., Shams-Esfandabad, B., Ahmadi, A. and Toranjzar, H., 2020. Modeling Habitability Pelecanus Crispus Using the Maximum Entropy Eethod (MaxEnt) in Iran. Journal of Animal Environmental. 12(2): 83-90. (In Persian) 
  9. Mirsanjari, M.M. and Sokhango, F., 2018. Habitat suitability modeling for Wild goat in Dena protected area by Maximum Entropy (MAXENT) model. Journal of Animal Environmental. 10(2): 23-30. (In Persian) 
  10. Weinert, M., Mathis, M., Kröncke, I., Neumann, H., Pohlmann, T. and Reiss, H., 2016. Modelling climate change effects on benthos: Distributional shifts in the North Sea from 2001 to 2099. Estuar. Coast. Shelf Sci. 175: 157-168.
  11. Saeedi, H., Basher, Z. and Costello, M.J., 2016. Modelling present and future global distributions of razor clams (Bivalvia: Solenidae). Helgol. Mar. Res. 70: 23.
  12. Drewnik, A., Węsławski, J.M. and Włodarska-Kowalczuk, M., 2017. Benthic Crustacea and Mollusca distribution in Arctic fjord- case study of patterns in Hornsund, Svalbard. Oceanologia. 59: 565-575.
  13. Basher, Z. and Costello, M.J., 2016. The past, present and future distribution of a deep-sea shrimp in the Southern Peer J. 4: e1713.
  14. Phillips, S.J. and Dudík, M., 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography (Cop.). 31: 161-175.
  15. Sharifian, S., Kamrani, E. and Saeedi, H., 2020. Global biodiversity and biogeography of mangrove crabs: Temperature, the key driver of latitudinal gradients of species richness. J. Therm. Biol. 92: 102692.
  16. Naderloo, R., Turkay, M. and Apel, M., 2011. Brachyuran crabs of the family Macrophthalmidae Dana, 1851 (Decapoda: Brachyura: Macrophthalmidae) of the Persian Gulf . Zootaxa. 2911: 19.
  17. Naderloo, R., 2017. Atlas of Crabs of the Persian Gulf. Springer International Publishing.
  18. Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F. and De Clerck, O., 2012. Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21: 272-281.
  19. Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E.A. and De Clerck, O., 2018. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27: 277-284.
  20. Basher, Z., Bowden, D.A. and Costello, M.J., 2018. Global Marine Environment Datasets (GMED). World Wide Web electronic publication. Version 2.0 (Rev.02.2018). Accessed at http://gmed.auckland.ac.nz on . [WWW Document].
  21. Phillips, S.J., Anderson, R.P. and Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190: 231-259.
  22. Dudík, M., SJ, S. and SJ, P., 2006. Correcting sample selection bias in maximum entropy density estimation. Adv Neural Inf Process Syst. 18: 323-330.
  23. Saeedi, H., Dennis, T.E. and Costello, M.J., 2017. Bimodal latitudinal species richness and high endemicity of razor clams (Mollusca). J. Biogeogr. 44: 592-604.
  24. Dambach, J., Thatje, S., Rödder, D., Basher, Z. and Raupach, M.J., 2012. Effects of Late-Cenozoic Glaciation on Habitat Availability in Antarctic Benthic Shrimps (Crustacea: Decapoda: Caridea). PLoS One. 7: e46283.
  25. Meißner, K., Fiorentino, D., Schnurr, S., Martinez Arbizu, P., Huettmann, F., Holst, S., Brix, S. and Svavarsson, J., 2014. Distribution of benthic marine invertebrates at northern latitudes- An evaluation applying multi-algorithm species distribution models. J. Sea Res. 85: 241-254.
  26. Kuhn, T., Cunze, S., Kochmann, J. and Klimpel, S., 2016. Environmental variables and definitive host distribution: a habitat suitability modelling for endohelminth parasites in the marine realm. Sci. Rep. 6: 30246.
  27. Reiss, H., Birchenough, S., Borja, A., Buhl-Mortensen, L., Craeymeersch, J., Dannheim, J., Darr, A., Galparsoro, I., Gogina, M., Neumann, H., Populus, J., Rengstorf, A.M., Valle, M., van Hoey, G., Zettler, M.L. and Degraer, S., 2014. Benthos distribution modelling and its relevance for marine ecosystem management. ICES J. Mar. Sci. 72: 297-315.
  28. Hattab, T., Albouy, C., Lasram, F.B.R., Somot, S., Le Loc’h, F. and Leprieur, F., 2014. Towards a better understanding of potential impacts of climate change on marine species distribution: a multiscale modelling approach. Glob. Ecol. Biogeogr. 23: 1417-1429.
  29. Weisshappel, J.B.F. and Svavarsson, J., 1998. Benthic amphipods (Crustacea: Malacostraca) in Icelandic waters: diversity in relation to faunal patterns from shallow to intermediate deep Arctic and North Atlantic Oceans. Mar. Biol. 131: 133-143.
  30. Jiménez-Valverde, A. and Lobo, J.M., 2007. Threshold criteria for conversion of probability of species presence to either–or presence–absence. Acta Oecologica. 31: 361-369.
  31. Lobo, J.M., Jiménez-Valverde, A. and Real, R., 2008. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17: 145-151.
  32. Pineda, E. and Lobo, J.M., 2009. Assessing the accuracy of species distribution models to predict amphibian species richness patterns. J. Anim. Ecol. 78: 182-190.
  33. Domisch, S., Araújo, M.B., Bonada, N., Pauls, S.U., Jähnig, S.C. and Haase, P., 2013. Modelling distribution in European stream macroinvertebrates under future climates. Glob. Chang. Biol. 19: 752-762.
  34. Record, S., Charney, N.D., Zakaria, R.M. and Ellison, A.M., 2013. Projecting global mangrove species and community distributions under climate change. Ecosphere. 4: art34.
فصلنامه محیط زیست جانوری
دوره 13، شماره 4
دی 1400
صفحه 253-258

فایل ها

هم رسانی

ارجاع به این مقاله

آمار