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ارزیابی جریان محیط زیستی رودخانه بشار به روش هیدرولوژیک تنانت بر اساس نیازهای زیستی ماهیان شاخص Evaluating the Environmental Flow of Beshar River, Using Tennant's Hydrological Method Based on the Biological Requirements of Indicator Fishes 1 1 در این مطالعه ارزیابی جریان محیط زیستی رودخانه بشار با استفاده از روش هیدرولوژیک تنانت و مقایسه مشخصات عمق و سرعت جریان آب در مقاطع عرضی رودخانه با نیاز‌های مراحل مختلف چرخه زندگی ماهیان شاخص رودخانه انجام شد. برای پایش وضعیت ماهیان رودخانه بشار و برداشت مقاطع عرضی رودخانه پیمایش‌های صحرایی در مهرماه و دی‌ماه سال 1400 انجام شد. انتخاب گونه‌های شاخص براساس نظرسنجی از متخصصان انجام گرفت. از بین 23 گونه ماهی رودخانه بشار، 5 گونه از خانواده کپورماهیان بیشترین امتیاز را کسب کردند. نیازهای زیستی این گونه‌ها به عنوان گونه‌های شاخص استخراج گردید و مبنای مقایسه با داده‌های مقاطع عرضی رودخانه بشار قرار گرفت. نتایج بدست آمده نشان داد که در بالادست رودخانه بشار (محدوده ایستگاههای هیدرومتری قلات و شاهمختار) طبقه خوب روش تنانت، شامل 40 درصد متوسط دبی سالانه برای دوره پرآبی (آذر تا اردیبهشت) و 20 درصد متوسط دبی سالانه برای دوره کم آبی (خرداد تا آبان)، به ترتیب مناسب تامین نیازهای زیستگاهی برای دو مرحله زندگی بالغ و لارو سیاه‌ماهیان رودخانه بشار است. 2 Environmental flow of the Beshar River was evaluated using Tennant's hydrological method, the comparison of the depth and speed of the water in the transverse sections of the river, and the needs of indicator fish species in different stages of the life cycle. Field samplings were conducted in October and January 2021 to monitor the status of fishes in the Beshar River and to collect cross-sections of the river. Indicator species were selected based on the expert judgement survey method. Among 23 fish species of the Beshar River, five species from the Cyprinidae family recieved the highest scores. The biological requirements of these species were extracted and used as the basis of comparison with the data of cross sections of the river. The obtained results showed that in the upper reaches of the river (between the two hydrometric stations of Qalat and Shah Mokhtar) the good class of the Tennant method, that includes 40% of the average annual discharge for the period of high flow (April to May) and 20% of the average annual discharge for the period of low flow (June to November), is suitable to provide the biological requirements of adult and larva of cyprinid species of the Beshar River, respectively. 1 19 2022/09/16 1401/6/25 2022/11/30 1401/9/9 زهرا مظلومی Z. Mazloomi دانشکده منابع طبیعی دانشگاه صنعتی اصفهان ایران mazlomiz@na.iut.ac.ir محمد نعمتی ورنوسفادرانی M. Nemati Varnosfaderany دانشکده منابع طبیعی دانشگاه صنعتی اصفهان ایران nemati@iut.ac.ir رضا مدرس R. Modarres دانشکده منابع طبیعی دانشگاه صنعتی اصفهان ایران reza.modarres@iut.ac.ir Beshar River Tennant method Indicator species Habitat suitability رودخانه بشار روش تنانت گونه‌ های شاخص مطلوبیت زیستگاه 1. Assadullah Nasrabadi, S. 2008. Reproductive biology and growth of Siahmahi, Capoeta damascina, in Zayandeh-Rud River. MSc Thesis, Isfahan University of Technology, Isfahan, Iran. (In Persian) ##2. Asadi, H., M. Sattari and S. Igdari. 2014. The determinant factors underlying habitat selectivity and preference for Black fish Capoeta capoeta gracilis (Keyserling 1891) in Siyahrud River (a tributary of Sefidrud River basin). Iran Fisheries Science Research Institute, 23 (3): 1-9. (In Persian)##3. Ahmadpari, H., B. Rigi Ladez, E. Sadat Shokooh and M. Kheiry Ghojeh Biglou. 2019. Evaluation of environmental flows in Dalfard River using hydrological methods. International Journal of Engineering and Technology 3:530-539.##4. Chen, A., M. Wu, S. Wu, X. Sui, J. Wen, P. Wang, L. Cheng, G. Lanza, C. Liu and W. Jia. 2019. Bridging gaps between environmental flows theory and practices in China. Water Science and Engineering, 12(4): 284- 292.##5. Conservation of Iranian Wetlands Project (CIWP). 2014. Manual for determining the water requirement of wetlands. Nashre Talaee Publication, Tehran, p 187. (In Persian)##6. Department of Natural Resources. 2018. Environmental flow requirement of Zanadehrud River and GavKhooni International Wetland for sustainable ecological functions. Isfahan University of Technology, Isfahan Provincial Directory of Environmental Protection, P 756. (In Persian)##7. Dolatpour, A. 2013. Relationship between habitat suitability and biological parameters of Siahmahi trutta in (Capoeta damascina) in the Kordan River. MSc Thesis, Faculty of Natural Resources, University of Tehran, Tehran, Iran. (In Persian)##8. Fatemi, Y., M. Amoyi and H. Mousavi Sabet. 2019. Updated checklist and geographical distribution of fishes in Kohgiluyeh and Boyer-Ahmad Province. Iranian Scientific Fisheries Journal 28 (5): 111-119. (In Persian)##9. Golchin Manshadi, A. R., A. Kiamarsi and M. Tarhomi. 2019. Identification and survey on frequency of Beshar river’s fish, Kohgiluyeh and Boyer-Ahmad province. Journal of Experimental Animal Biology 8 (8): 67-75. (In Persian)##10. Jahanbakhsh, A., R. Rezaei and M. Khezri. 2012. Investigation of physical and chemical pollution of effluent of Yasuj wastewater treatment plant on Beshar river and determination of its self-purification capacity by Streeter-phelps method. MSc Thesis, Yasuj Azad University, Yasuj, Iran. (In Persian)##11. Karimi, S., M. Salarijazi, K. Ghorbani and M. Heydari. 2021. Comparative assessment of environmental flow using hydrological methods of b low flow indexes, Smakhtin, Tennant and flow duration curve. Acta Geophysica 69: 285-293.##12. Keivany, Y., M. Nasri, K. Abbasi and A. Abdoli. 2015. Atlas of Inland Waters Fishes of Iran, Environmental Protection Organization, p 218. (In Persian)##13. Kim, S. K. and S. U. Choi. 2019. Comparison of environmental flows from a habitat suitability perspective: A case study in the Naeseong‐cheon Stream in Korea. Ecohydrology 12(6): 1-10.##14. [14] Lacroix, K., E. Tapia and A. Springer. 2017. Environmental flows in the desert rivers of the United States and Mexico: Synthesis of available data and gap analysis. Journal of Arid Environments 140: 67-78.##15. Li, O., W. Wan and J. Zhao. 2018. Optimizing environmental flow operations based on explicit quantification of IHA parameters. Journal of Hydrology 563: 510-522.##16. Mahdavi, M. 2016. Applied Hydrology. University of Tehran, Tehran. (In Persian)##17. Mohamadiyani, V. 2016. Reproductive biology and growth of large scales Siahmahi (Capoeta aculeate) in Gizehrud River, Nurabad, Lorestan province. MSc Thesis, Faculty of Natural Resources, Isfahan University of Technology, Isfahan, Iran. (In Persian)##18. Maramazi, M., M. Zakeri, M. T. Ronaq, B. Kochanian and M. Haghi. 2014. Diet and feeding indices of small scale sardeh fish (Capoeta damascina) in Sezar River (Lorestan province). Journal of Animal Research 3 (3): 405-416. (In Persian)##19. Munz, J.T. and C.L. Higgins. 2013. The influence of discharge, photoperiod, and temperature on the reproductive ecology of cyprinids in the Paluxy River, Texas. Aquatic Ecology 47: 67–74.##20. Naderi, M. H., M. Zakerinia and M. Salarijazi. 2018. Application of the PHABSIM model in explaining the ecological regime of the river in order to estimate the environmental flow and compare with hydrological methods (Case study: Gharasoo River). Eco Hydrology 5 (3): 941-955. (In Persian)##21. Naderi, M. H., N. Arab, A. Jahandideh, M. Salarijazi and F. Arab. 2020. Estimation of optimal release flow rRange from Jamishan Dam considering the optimal instream ecological water demand for conservation the habitat potential of the Dinavar River. Journal of Water and Soil, 35 (2): 203-2225. (In Persian)##22. Nick Ghalb Ashuri, S. and H. Nick Ghalb Ashuri. 2016. Calculation of environmental water content of Kazemrud River using habitat simulation method. The Second International Conference on New Research Findings in Civil Engineering, Architecture and Urban Management, Iran, p 14. (In Persian)##23. Nikghalb, S., A. Shokoohi, V. P. Singh and R. Yu. 2016. Ecological regime versus minimum environmental flow: Comparison of results for a river in a semi Mediterranean region. Water Resour Manage 30: 4969–4984##24. Panahi, Q., S. R. Theologian and A. R. Farid Hassani. 2018. Evaluation of methods for estimating environmental flow in rivers. Journal of Water and Sustainable Development 4 (1): 73 -80. (In Persian)##25. Peng, L. and L. Sun. 2016. Minimum instream flow requirement for the water reduction section of diversion-type hydropower station: a case study of the Zagunao River, China. Environmental Earth Science 75: 2-8.##26. Poursalehan, J., M. Sedghi Asl and M. Parvizi. 2013. Using the wetted environment method to estimate the minimum environmental flow of Bashar River. Irrigation Science and Engineering 1: 118-107.##27. Roshan Qiyas, M.; M. Sedqi Asl and M. Parvizi. 2014. Assessment of environmental flow of the Beshar River by three methods, Tenant, Flow Duration Curve and Esmakhtin. National Conference on New Findings in Civil Engineering, Islamic Azad University of Yasouj, 632-640. (In Persian)##28. Shokouhi, A. and Y. Hong. 2011. Determining the minimum rcological water requirements in perennial rives using morphological parameters. Journal Environmental Studies 58: 117-128. (In Persian)##29. Tegos, M., I. Nalbantis and A. Tegos. 2017. Environmental flow assessment through integrated approaches. European Water 60: 167-173.##30. Tennant, D. L. 1976. Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries 1: 6–10.##31. Vice President for Strategic Planning and Oversight. 2011. Guide to determining the water requirements of aquatic ecosystems. Vice President for Strategic Planning and Oversight, Issue No. 557. (In Persian)##32. Volchek, A., I. Kirvel and N. Sheshko. 2018. Environmental flow assessment for the Yaselda River in its Selets reservoir section. Ecohydrology and Hydrobiology 19 (1): 109-118.##33. Zamani Faradonbe, M., S. Eagderi and H. Poorbagher. 2014. Study of habitat suitability index of Kura barbel (Barbus cyri Filippi, 1865) in Taleghan River (Sefidrud River basin: Alborz Province). Journal of Applied Ichthyological Research 2 (2): 41-53. (In Persian)##34. Zhang, X. R., D. R. Zhang and Y. Ding. 2021. An environmental flow method applied in small and medium-sized mountainous rivers. Water Science and Engineering 14 (4): 323-329.## ##

تهیه نقشه طبیعی بودن شهرستان فریدونشهر جهت توسعه اکوتوریسم با استفاده از عملگر میانگین وزنی مرتب‌شده Naturalness Mapping of Fereydounshahr County with Respect to Ecotourism, Using Ordered Weighted Averaging Operator 1 1 اهمیت بعد اقتصادی گردشگری، معمولاً بسیاری از مقاصد، بخصوص طبیعی، را در معرض تهدید قرار می¬دهد. شهرستان فریدونشهر در استان اصفهان، با چشم¬اندازهای کوهستانی دست نخورده و جاذبه¬های طبیعی متنوع، از مزیت¬های نسبی مناسبی برای جذب گردشگر برخوردار است. بکر بودن بسیاری از مناطق آن، ضرورت ورود گردشگر را به طور اصولی و متناسب با درجه طبیعی آن نشان می¬دهد. در همین راستا، هدف این تحقیق، طبقه¬بندی شهرستان فریدونشهر بر اساس طبیعی بودن است. برای این هدف، در ابتدا نه شاخص موثر با استفاده از روش فرایند سلسله مراتبی وزن¬دهی شدند و بعد از رویهم گذاری لایه¬های رقومی معیارها در محیط نرم افزار ایدریسی، نقشه نهایی در پنج طبقه (از بسیار طبیعی تا توسعه یافته) و بر اساس عملگر میانگین وزنی و تحت شش سناریوی مختلف بدست آمد. نتایج نشان داد که نواحی توسعه یافته در سناریوی سطح ریسک پایین و بدون جبران و نواحی بکر در سناریوی سطح بالای ریسک و بدون جبران، بیشترین مساحت را به خود اختصاص داده¬اند و با حرکت از ریسک¬گریزی به سمت ریسک¬پذیری، وسعت مناطق طبیعی و نسبتا طبیعی افزایش پیدا می¬کند. نتایج نشان داد که قسمت اعظم مناطق بکر شهرستان فریدونشهر در بخش¬های غربی شهرستان به دلیل دوری از جاده¬های اصلی، حضور بالای حیات وحش و وجود شیب¬های تند متمرکز شده است. 2 The importance of the economic aspect of tourism usually overshadow many natural tourism destinations. Fereydounshahr county in Isfahan province, with pristine mountainous landscapes and diverse natural features, has high potential to attract many tourists. However, pristineness over much of its area suggests limiting the public access based on the degree of naturalness. Hence, the aim of this research was to classify Fereydounshahr county based on naturalness. Accordingly, nine effective indicators were weighted, using Analytical Hierarchy Process (AHP). Idrisi software was then employed to overlay the digital layers of criteria and produce a naturalness map, that consisted of five classes (from very natural to developed), based on Ordered Weighted Averaging (OWA) approach under six different scenarios. Results showed that developed areas under the AND scenario and the natural areas under the OR scenario have the largest range. The extent of desirable areas, including natural and relatively natural, increased when moving from risk aversion to risk taking scenarios. Based on the results, most of the natural areas were concentrated in western parts of the region, due to the long distances from the main roads, high presence of wildlife, and the presence of steep slopes. 21 35 2022/09/162022/08/29 1401/6/7 2022/11/302022/12/12 1401/9/21 فاطمه خزاعی فدافن F. Khazaee Fadafan دانشگاه صنعتی اصفهان ایران f.khazaee@na.iut.ac.ir علیرضا سفیانیان A. R. Soffianian دانشگاه صنعتی اصفهان ایران soffianian@iut.ac.ir سعید پورمنافی S. Pourmanafi دانشگاه صنعتی اصفهان ایران spourmanafi@iut.ac.ir مارک مورگان M. Morgan دانشگاه میزوری آمریکا آمریکا markmorgan@missouri.edu Naturalness Ehcotourism Analytical Hierarchy Process (AHP) Ordered weighted averaging approach Fereydounshahr طبیعی بودن اکوتوریسم فرایند تحلیل سلسله مراتبی عملگر میانگین وزنی فریدونشهر 1. Ahmadizadeh, S., Z. Karimzadeh and A. Ashrafi. 2016. Capability evaluation of ecotourism in Birjand county based on scenario design and Fuzzy_OWA algorithm. Environmental Researches 7(13): 31-46. (In Persian).##2. Blamey, R.K. 1997. Ecotourism: The search for an operational definition. Journal of sustainable tourism 5 (2): 109–130.##3. Boyd, S.W., R.W. Butler, W. Haider, and A. Perera. 1994. Identifying areas for ecotourism in Northern Ontario: application of a geographical information system methodology. Journal of Applied Recreation Research 19 (1): 41–66.##4. Brabyn, L. 2005. Solutions for characterising natural landscapes in New Zealand using geographical information systems. Journal of environmental management 76 (1): 23–34.##5. Bunruamkaew, K. and Y. Murayam. 2011. Site suitability evaluation for ecotourism using GIS & AHP: A case study of Surat Thani province, Thailand. Procedia-Social and Behavioral Sciences 21: 269–278.##6. Deng, J., B. King and T. Bauer. 2002. Evaluating natural attractions for tourism. Annals of tourism research 29 (2): 422–438.##7. Dhami, I., J. Deng, R.C. Burns and C. Pierskalla. 2014. Identifying and mapping forest-based ecotourism areas in West Virginia–incorporating visitors’ preferences. Tourism Management 42: 165–176.##8. Dhami, I., J. Deng, M. Strager and J. Conley. 2017. Suitability-sensitivity analysis of nature-based tourism using geographic information systems and analytic hierarchy process. Journal of Ecotourism 16 (1): 41–68. ##9. Department of Environmental Protection Agency. 2016. Fereydounshahr plant and animal species. Available online at: http://isfahan-doe.ir/. Accessed 12 April 2019.##10. Ferrari, C., G. Pezzi, L. Diani and M. Corazza. 2008. Evaluating landscape quality with vegetation naturalness maps: An index and some inferences. Applied Vegetation Science 11 (2): 243–250.##11. Gigović, L., D. Pamučar, D. Lukić and S. Marković. 2016a. GIS-fuzzy dematel MCDA model for the evaluation of the sites for ecotourism development: A case study of “Dunavski ključ” region, Serbia. Land Use Policy 58: 348–365.##12. 12.Gorsevski, P. V., K.R. Donevska and J.P. Mitrovski Frizado. 2012. Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average. Waste management 32(2):287–96.##13. Hajizadeh, F., M. Poshidehro and E. Yousefi. 2020. Scenario-based capability evaluation of ecotourism development–an integrated approach based on WLC, and FUZZY–OWA methods. Asia Pacific Journal of Tourism Research 25(6):637–50.##14. Heydarzadeh, H., J. Balist and A. Malek Mohammadi. 2017. Ecotourism potential evaluation and zoning modelling by Fuzzy Logic, FAHP and TOPSIS (Case Study: the SHAHROOD County). Environmental Researches 8(15): 17-30.##15. Hoang, H.T.T., Q.H. Truong, A.T. Nguyen and L. Hens. 2018. Multicriteria evaluation of tourism potential in the central highlands of Vietnam: combining Geographic Information System (GIS), Analytic Hierarchy Process (AHP) and Principal Component Analysis (PCA). Sustainability 10 (9): 3097.##16. Hunt, C.A., W.H. Durham, L. Driscoll and M. Honey. 2015. Can ecotourism deliver real economic, social, and environmental benefits? A study of the Osa Peninsula, Costa Rica. Journal of Sustainable Tourism 23 (3): 339–357.##17. Jafari, Z., A. Mikaeali-Tabrizy, M. Mohammadzadeh and O. Abdi. 2012. Evaluation of Ecotourism Competence in Golestan National Park through Weighted Linear Combination Method. Journal of renewable natural resources research 2(4): 25-37. ##18. Khazaee Fadafan, F., A. Danehkar and S. Pourebrahim. 2018. Developing a non-compensatory approach to identify suitable zones for intensive tourism in an environmentally sensitive landscape. Ecological Indicators 87: 152–166.##19. Kuiters, A.T., M. Van Eupen, S. Carver, M. Fisher, Z. Kun and V. Vancura. 2013. Wilderness register and indicator for Europe final report (EEA Contract No: 07.0307/2011/610387/SER/B. 3).##20. Lola, M.S., M.F. Hussin, I.M. Yusoff, M.N.A. Ramlee, S.H. Isa, A.A. Kamil, N.Z.A. Khadar and M.T. Abdullah. 2017. A system dynamic model for sustainable ecotourism in Tasik Kenyir, Terengganu, Malaysia. Preprints 1–13. ##21. Malczewski, J. 1999. GIS and multicriteria decision analysis. John Wiley & Sons, New York.##22. Malczewski J. 2006. Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. International journal of applied earth observation and geoinformation 8(4):270–7.##23. Malczewski J. 2006. Integrating multicriteria analysis and geographic information systems: the ordered weighted averaging (OWA) approach. International Journal of Environmental Technology and Management 6 (1–2):7–19.##24. Măntoiu, D.Ş., , M.C. Nistorescu, I.C. Şandric, I.C. Mirea, A. Hăgătiş and E. Stanciu. 2016. Wilderness areas in Romania: a case study on the South Western Carpathians. In: Mapping wilderness, pp. 145–156. Springer. Germany.##25. Ólafsdóttir, R. and Runnström, M.C. 2011. How wild is Iceland? Wilderness quality with respect to nature-based tourism. Tourism Geographies 13 (2): 280–298.##26. Ólafsdóttir, R.; Sæþórsdóttir, A.D. and Runnström, M. 2016. Purism scale approach for wilderness mapping in Iceland. In: Mapping Wilderness, pp. 157–176. Springer. Germany.##27. Plutzar, C., K. Enzenhofer, F. Hoser, M. Zika and B. Kohler. 2016. Is there something wild in Austria? In: Mapping wilderness, pp. 177–189. Springer. Germany##28. Rahnama, M., H. Aquajani and M. Fattahi. 2012. Integrating multi-criteria evaluation techniques with geographic information Systems for landfill site selection: A case study using ordered weighted average in Mashhad. Journal of Geography and Environmental Hazards 1(3): 87-106. ##29. Saadatfar, A. and H. Faramarzi. 2018. Optimum ecotourism site selection in Kojur basin of Mazandaran province using ordered weighted average (OWA) and Geographic information system (GIS). Journal of RS and GIS for Natural Resources 9(2): 108-120. (In Persian).##30. Salman Mahini, A., B. Riazi, B. Naeimi, S. Babaei kafaei and A. Javadi Larijani. 2009. Evaluating the potential of nature tourism in Behshahr based on the multi-criteria evaluation method using GIS. Journal of Environmental Science and Technology 11(1): 187-198. (In Persian).##31. Management and Planning Organization of Isfahan Province. 2017.Statistical Yearbook of Isfahan Province, Isfahan.##32. Strickland-Munro, J. and S. Moore. 2013. Indigenous involvement and benefits from tourism in protected areas: A study of Purnululu National Park and Warmun Community, Australia. Journal of Sustainable Tourism 21 (1): 26–41.##33. Yager, R. 1988. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on systems, Man, and Cybernetics 18(1):183–90.##34. Zebardast, A. 2002. Application of hierarchical analysis process in urban and regional planning. Honarhaye-Ziba journal 10: 13-21. (In Persian).## ##

مدل‌سازی تخریب جنگل‌های هیرکانی با استفاده از روش رگرسیون لجستیک (مطالعه موردی: جنگل‌های شن‌رود گیلان) Modeling the Degradation of Hyrcanian Forests Using Logestic Regression Method (Case Study: Shenrood Forests, Guilan) 1 1 لازمه هر گونه مدیریت و برنامه¬ریزی اصولی برای کاهش اثرات تخریب جنگل، داشتن اطلاعات دقیق کمی و کیفی از وضعیت توده-های جنگلی است. هدف از این پژوهش، مدل¬سازی تخریب جنگل¬های هیرکانی تحت تأثیر متغیرهای تعداد و حجم در هکتار درختان با استفاده از رگرسیون لجستیک بود. به¬منظور انجام این پژوهش 252 قطعه نمونه دایره¬ای شکل 10 آری اندازه¬گیری شد. در هر قطعه علاوه بر گونه، قطر برابرسینه، ارتفاع درختان، تعداد در هکتار و حجم، وجود یا عدم وجود تخریب نیز یادداشت شد. برای مدل¬سازی تخریب جنگل از رگرسیون لجستیک و برای ارزیابی مدل رگرسیون لجستیک از آزمون¬های "Omnibus"، لگاریتم درست¬نمایی و ضریب تعیین پزودو (کاکس-نل و نیجل¬کرک) استفاده شد.  بر اساس نتایج، میانگین تعداد در هکتار و حجم درختان به¬ترتیب برابر 136/8 اصله و 242/9 مترمکعب در هکتار به¬دست آمد. همچنین نتایج نشان داد 46/82 درصد منطقه مورد مطالعه دچار تخریب شده است. آزمون همبستگی نشان داد، بین متغیرهای کمّی بررسی شده با متغیر تخریب جنگل رابطه منفی و معنی¬دار وجود دارد. متغیرهای مستقل تعداد در هکتار و حجم درختان توانسته¬اند 61/6 تا 82/3 درصد از واریانس متغیر وابسته (تخریب جنگل) را برآورد کنند. بررسی متغیرهای ورودی به مدل رگرسیونی نشان داد اثر متغیرهای تعداد در هکتار و حجم درختان بر میزان تخریب جنگل معنی¬دار بوده و امکان پیش¬بینی تغییرات متغیر وابسته یعنی وجود یا عدم وجود تخریب جنگل را دارند. 2 Having accurate quantitative and qualitative information about the state of forest stands, is necessary for any basic management and planning, to reduce the effects of forest degradation. The current study aimed to model the destruction of Hyrcanian forests under the effects of density and volume (per hectare) variables, using logistic regression. In total, 252 plots of 1000 m2 area were measured. In each sample plot, species name, Diameter at Breast Height (DBH), height, density, volume and the presence or absence of forest degradation were measured and recorded. To model forest degradation, logistic regression model was utilized and Omnibus test, log-likelihood and pseudo r-square (Cox&Snell and Nagelkerke) coefficients were used to evaluate the  model. Results showed that the mean of density and volume of trees were 136.8 tree and 239.9 m3/ha, respectively. In addition, the results indicated that 46.82% of the study area was degraded. The results of correlation test showed that there was a srtong negative correlation between quantitative variables and the forest degradation. The independent variables of density and volume of trees were respectively explained 61.6 to 82.3% of the variance of the dependent variable (forest degradation). Among the input variables of the regression model, the effects of density and volume were significant on the forest degradation and it was possible to predict the changes of dependent variables (presence or absence of forest degradation). 37 46 2022/09/162022/08/292022/06/7 1401/3/17 2022/11/302022/12/122022/12/24 1401/10/3 حسن پورربابایی H. Pourbabaei دانشگاه گیلان ایران HPourbabaei@gmail.com احمد پوررستم A. Poorrostam دانشگاه گیلان ایران poorrostamahmad2022@gmail.com علی صالحی A. Salehi دانشگاه گیلان ایران aSalehi@guilan.ac.ir Degradation modeling Logestic regression Structure changes Shenrood forests of Guilan مدل‌سازی تخریب رگرسیون لجستیک تغییرات ساختار جنگل‌های شن‌رود گیلان 1. Agramont, A.R., S.F. Maass, G. Bernal, J.I. Hernández, & T. Fredericksen, 2012. Effect of human disturbance on the structure and regeneration of forests in the Nevado de Toluca National Park, Mexico. Journal of Forestry Research, 23(1): 39−44. ##2. Amini, M. 2009. Deforestation modeling and investigation on relate physiographic and human factors using satellite images and GIS (Case study: Armerdeh forests of Baneh). Iranian Journal of Forest and Popular Research, 16(3): 431-443. (In Persian)##3. Booklet of revised forestry plan series 7 Shenroud. 2004. Department of natural resources and watershed Siahkal, 347p. (In Persian)##4. Esther, V., K. Martha, T. Harrison, & O. Lenard, 2014. The impacts of human activities on treespecies richness and diversity in kakmaega forest, Western Kenya. International Journal of Biodiversity and Conservation, 6(6): 428- 435.##5. Heydari, M., H. Poorbabaei, O. Esmailzadeh, A. Salehi, & J. Eshaghi Rad, 2015. Indicator plant species in monitoring forest soil conditions using logistic regression model in Zagros Oak (Quercus brantii var. persica) forest ecosystems, Ilam city. Journal of Plant Research, 27(5): 811-828. (In Persian)##6. Hosmer, D.W., S. Lemeshow, & R.X. Sturdivant, 2013. Applied Logistic Regression. Third Edition. New Jersey: John Wiley & Sons, 528 p.##7. Keenan, J.R., G.R. Reams, F. Achard, V.J. De Freitas, A. Grainger, & E. Lindquist, 2015. Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. Forest Ecology and Management, 352: 9-20.##8. Leites, L.P., A.P. Robinson, & N.L. Crookston, 2009. Accuracy and equivalence testing of crown ratio models and assessment of their impact on diameter growth and basal area increment predictions of two variants of the forest vegetation simulator. Canadian Journal of Forest Research, 39(3): 655-665.##9. Masrouri, E., Sh. Shataei, M.H. Moayeri, J. Soosani, & R. Bagheri, 2015. Modeling of forest degradation extend using physiographic and socio-economic variables (case study: a part of kaka-reza district in Khoram-Abad). Ecology of Iranian Forests, 3(5): 20-30. (In Persian)##10. Mirzaei, M., A.E. Bonyad, H. Pourbabaei, & M. Mohebi Bijarpasi, 2016. Frequencies evaluation in estimation of tree species diversity in degraded forests (case study: Kouhmian forests, Azadshahr, Golestan province). Journal of Forest and Wood Products, 68(4): 971-979. (In Persian)##11. Mirzaei, M., A.E. Bonyad, R. Akhavan, & R. Naghdi, 2019. Decline modelling of Quercus brantii under effects of physiographic factors in Dalab forests of Ilam. Journal of Forest Research and Development, 5(2): 329-342. (In Persian)##12. Pampel, F.C. 2000. Logistic regression: A primer. Sage University Papers Series on Quantitative Applications in the Social Sciences, 07-132. Thousand Oaks, CA, Sage Publications.##13. Pirbavaghar, M. 2015. Deforestation modelling using logistic regression and GIS. Forest Science, 61(5): 193-199.##14. Rauduskoski, A. 20014. Human disturbance on polylepis mountain forests in peruvan Andes. Department of Biological and Environmental Science, University of Jyvaskyla, 328 p.##15. Rezvani, M. & F. Hashemzadeh, 2013. Investigating the effective factors on forest degradation and impact of moving out livestock from district 14 of the northern forests of Iran: an environmental and economic perspective (Fuman). Journal of Wood & Forest Science and Technology, 20(3): 125-138. (In Persian)##16. Temesgen, H., V. Lemay, & S.J. Mitchell, 2005. Tree crown ratio models for multi-species and multi-layered stands of southeastern British Columbia. The Forestry Chronicle, 81(1): 133–141.##17. Tohidy, M., J. Jalali, F. Yazdian, M.N. Adel, R. Jiroudnezhad, M.R. Azarnoosh, & J.S. Kuhestani, 2019. Effects of livestock and forest dweller exclusion on natural regeneration in Abbas-Abad forest, Mazandaran province. Journal of Human & Environment, 47(4): 139-158. (In Persian)##18. Top, N., N. Mizoue, S. Ito, & S. Kai, 2009. Effects of population density on forest structure and species richness and diversity of trees in Knmpong Thom Province, Cambodia. Biodiversity Conservation, 18: 717- 738.##19. Vu, Q.M., Q.B. Le, E. Frossard, P.L.G. Vlek, 2014. Socio-economic and biophysical determinants of land degradation in Vietnam: An integrated causal analysis at the national level. Land Use Policy, 36: 605-617.##20. Waisse, A., F.J. Sterck, D. Teketay, & F. Bongers, 2009. Effects of livestock exclusion on tree regeneration in Chuch forest of Ethiopia. Forest Ecology and Management, 257: 765-772.## ##

ارزیابی روند تغییرات دمای سطح زمین و تجزیه و تحلیل همبستگی مکانی با عناصر ساختاری سرزمین در حوزه آبخیز رشت، استان گیلان Assessment of Land Surface Temperature Trend and Its Spatial Correlation with Landscape Structural Elements in Rasht Watershed, Guilan Province 1 1 داده‌های سنجش از دور نقش مهمی در برنامه‌ریزی و پایش محیط زیست دارند. هدف از این پژوهش، بررسی دمای سطح زمین  (Land Surface Temprature, LST) و تاثیر عوامل محیطی بر میزان دمای سطح زمین و شناسایی الگوهای زمانی – مکانی و تعیین لکه‌های داغ در بازه زمانی ۲۰۱۳ تا ۲۰۱۹ با استفاده از تصاویر لندست ۸ است. در این تحقیق تاثیر شاخص‌های طیفی (Normalized Difference Build up Index, NDBI)، (Normalized Difference Vegetation Index, NDVI) و (Normalized Difference Water Index, NDWI) بر LST مورد بررسی قرار گرفت. نتایج نشان داد که کمترین میانگین دمایی در سال ۲۰۱۹ و حداکثر آن مربوط به سال ۲۰۱۷ است. نتایج حاصل از خروجی‌های همبستگی شاخص موران نیز نشان داد که بزرگترین الگوی خوشه‌ای دمای حداکثر با مقدار 0/85 در سال ٢٠١٩ اتفاق افتاده و بیشترین همبستگی بین LST و NDBI در سال ٢٠١٥ با R = 0.76، بیشترین همبستگی بین LST و NDVI در سال ٢٠١٥ با R = -0.56 و بیشترین همبستگی بین LST و NDWI در سال ٢٠١٣ با R = -0.53 است. آبخیز رشت، در استان گیلان تحت تاثیر عوامل انسانی و تغییرات کاربری قرار دارد. بنابراین پیشنهاد می‌شود افزایش پوشش گیاهی در سطح شهرها و بام مناطق شهری، کاهش تغییر کاربری مرتع به کشاورزی و کاهش تخریب جنگل در الویت قرار بگیرند. 2 Remote sensing data play an important role in environmental planning and monitoring. The current study aimed to investigate the land surface temperature (LST) and the effect of environmental factors on the LST, to identify the temporal-spatial patterns and determine the hot spots in the period of 2013 to 2019, using Landsat 8 images. The effect of spectral indices: Normalized Difference Build-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on the surface temperature was investigated. Results indicated that the lowest average temperature has occurred in 2019 and the highest LST was in the 2017. The results of Moran's index correlation also showed that the most clustering pattern of LST, with the Moran value of 0.85 was obtained in 2019, the highest correlation between LST and NDBI, with the R value of 0.76 in the 2015, the highest correlation between LST and NDVI in the 2015 (R = -0.56), and the highest correlation between LST and NDWI in 2013 (R = -0.53). Rasht watershed in Guilan province is affected by human factors and land use changes. Therefore, it is recommended to increase the vegetation cover in urban areas, reduce the change of pasture to agricultural area, and reduce forest destruction. 47 59 2022/09/162022/08/292022/06/72022/03/17 1400/12/26 2022/11/302022/12/122022/12/242023/01/1 1401/10/11 نیوشا دیوسالار N. Divsalar دانشکده منابع طبیعی صومعه سرا؛ دانشگاه گیلان ایران www.newsha.divsalar@gmail.com محمود هاشمی M. Hashemi دانشکده منابع طبیعی صومعه سرا ایران hashemeesm@gmail.com سجاد کربلای صالح S. Karbalay Saleh دانشگاه کشاورزی و منابع طبیعی گرگان ایران sajjad7374@gmail.com Land surface temperature Hot spots Moran statistical test Thermal remote sensing Rasht watershed دمای سطح زمین لکه‌های داغ موران داده‌های سنجش از دور رشت 1. Aliabadi, K. and A. Dadashi-roudbari. 2015. Investigation of changes in spatial autocorrelation patterns of Iran's maximum temperature. Geographical Studies of Arid Areas, 6 (21): 86-104. (In Persian)##2. Baloloy, A., J. A. Cruz, A. C. Blanco, N. V. Lubrica, C. J. Valdez and E. P. Cajucom. 2019. Spatiotemporal multi-satellite biophysical data analysis of the effect of urbanization on land surface and air temperature in BAGUIO city, Philippines. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , XLII-4/W19: 47–54. ##3. Caldas de Castro, M. and B. H. Singer. 2006. Controlling the false discovery rate: a new application to account for multiple and dependent tests in local statistics of spatial association. Geographical Analysis, 38(2): 180-208.##4. Chatterjee, R. S., N. Singh, S. Thapa, D. Sharma and D. Kumar. 2017. Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. International journal of applied earth observation and geoinformation, 58: 264-277.##5. Deng, Y., S. Wang, X. Bai, Y. Tian, L. Wu, J. Xiao and Q. Qian. 2018. Relationship among land surface temperature and LUCC, NDVI in typical karst area. Scientific reports, 8(1):, 1-12.##6. Galdies, C. and H. S. Lau. 2020. Urban heat island effect, extreme temperatures and climate change: a case study of Hong Kong SAR. pp. 369-88, In: W. Leal Filho (ed), Climate Change, Hazards and Adaptation Options: handling the impacts of a changing climate, Springer, Cham.##7. Getis, A. and J. K. Ord. 1992. The analysis of spatial association by use of distance statistics. Geographical analysis, 24(3):189-206.##8. Guha, S. and H. Govil. 2021. An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability, 23(2): 1944-1963.‌##9. Guha, S., H. Govil, and M. Besoya. 2020. An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data. Geomatics, Natural Hazards and Risk, 11(1): 1319-1345.‌##10. Guha, S., H. Govil, A. Dey and N. Gill. 2018. Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy. European Journal of Remote Sensing, 51(1): 667-678.##11. Hashemi, S. M., M. Dinarvandi and S. K. Alivipanah. 2013. Assessment of configuration of LST in urban landscape using thermal remote sensing. Journal of Environmental study, 39(1): 81-92. (In Persian)##12. Ihlen, V. and K. Zanter. 2019. Landsat 8 (L8) Data Users Handbook, US Geological Survey, USA.##13. Illian, J., A. Penttinen, H. Stoyan and D. Stoyan. 2008. Statistical analysis and modelling of spatial point patterns. John Wiley and Sons, Chichester.##14. Jamei, Y., P. Rajagopalan and Q. C. Sun. 2019. Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Science of the total environment, 659: 1335-1351.##15. Jothimani, M., J. Gunalan, R. Duraisamy and A. Abebe. 2021. Study the relationship between LULC, LST, NDVI, NDWI and NDBI in Greater Arba Minch area, Rift Valley. Atlantis Highlights in Computer Sciences, 4: 183-193.‌##16. Karami, M., A. Dadashi-Roudbari and M. Asadi. 2016. Investigating the spatial variation of heat Island of Tehran. International Journal of Scientific and Research Publications, 6 (2): 22-31.##17. Levine, N. 1996. spatial statistics and GIS: software tools to quantify spatial patterns. Journal of the American Planning Association, 62(3): 381-391.##18. Li, B., D. Chen, S. Wu, S. Zhou, T. Wang and H. Chen. 2016. Spatio-temporal assessment of urbanization impacts on ecosystem services: case study of Nanjing City, China. Ecological Indicators, 71: 416-427.##19. El Garouani, M. Amyay, A. Lahrach and H. J. Oulidi. Land surface temperature in response to land use/cover change based on remote sensing data and GIS techniques: Application to Saïss Plain, Morocco. Journal of Ecological Engineering, 22(7): 100-12.##20. McFeeters, S. K. 1996. The use of NDWI in the delineation of open water features. International journal of remote sensing, 17(7): 1425-1432.##21. Mitchell, A. 2005. The ESRI guide to GIS analysis, volume 2: spatial measurements and statistics. ESRI, Redlands.##22. Naserikia, M., E. Asadi Shamsabadi, M. Rafieian and W. Leal Filho. 2019. The urban heat island in an urban context: A case study of Mashhad, Iran. International journal of environmental research and public health, 16(3): 313.##23. Phan, T., M. Kappas, and T. Tran. 2018. Land surface temperature variation due to changes in elevation in northwest Vietnam. Climate, 6(2): 28.##24. Rouse, J. W., R. H. Haas, J. A. Schell and D. W. Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351(1): 309.##25. Sannigrahi, S., S. Bhatt, S. Rahmat, B. Uniyal, S. Banerjee, S. Chakraborti and A. Bhatt. 2018. Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heating. Urban climate, 24: 803-819.##26. Santamouris, M. 2015. Regulating the damaged thermostat of the cities: status, impacts and mitigation challenges. Energy and Buildings, 91, 43-56.##27. Shi, Y. and Y. Zhang. 2018. Remote sensing retrieval of urban land surface temperature in hot-humid region. Urban climate, 24: 299-310.##28. Sun, Y. and S. Zhao. 2018. Spatiotemporal dynamics of urban expansion in 13 cities across the Jing-Jin-Ji urban agglomeration from 1978 to 2015. Ecological Indicators, 87: 302-313.##29. Tran, D. X., F. Pla, P. Latorre-Carmona, S. W. Myint, M. Caetano and H. V. Kieu. 2017. Characterizing the relationship between land use land cover change and land surface temperature. Journal of Photogrammetry and Remote Sensing, 124: 119-132.##30. Waagepetersen R. and T. Schweder. 2006. Likelihood-based inference for clustered line transect data. 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مقایسۀ خصوصیات پوشش گیاهی تحت سه تیمار مدیریت (چرای شدید، قرق و قرق با عملیات اصلاحی) در مراتع ییلاقی کیاسر چهاردانگه ساری Comparison of Vegetation Characteristics under Three Management Treatments (Heavy Grazing, Exclusure and Exclusure with Restoration Practices) in Kiasar Chardange Rangeland of Sari 1 1 انتخاب استراتژی¬های مدیریت بر روی خصوصیات پوشش گیاهی و تنوع زیستی گیاهان اثرگذارند. در این تحقیق اثرات سه تیمار مدیریت شامل چرای مفرط، قرق و قرق همراه عملیات احیا بر روی خصوصیات گیاهی شامل پوشش تاجی، ترکیب و تنوع در مراتع ییلاقی کیاسر ساری مطالعه شد. در هر تیمار مدیریت، سه ترانسکت تصادفی 50 متری در مرتع مستقر و در  پنج پلات تصادفی یک مترمربعی آشیان¬شده در ترانسکت¬ها، معیارهای درصد پوشش تاجی، ترکیب درصد کلاس¬های خوش¬خوراکی ثبت گردید و شاخص¬های تنوع، یکنواختی و غنا محاسبه شد. برای مقایسۀ اثرات تیمارهای مختلف بر روی خصوصیات پوشش گیاهی و شاخص¬های تنوع از تحلیل واریانس با زیرنمونه برابراستفاده شده برای تفسیر الگوی پراکنش ترکیب گیاهی در ارتباط با تیمارهای مختلف مدیریت، مقیاس-بندی چندبعدی غیرمتریک به¬کار گرفته شد. نتایج نشان داد ‌که تمامی شاخص‌های تنوع، یکنواختی و غنا در سه تیمار مدیریت دارای اختلاف معنی¬دارند (p< 0.05). بیشترین مقادیر شاخص‌های تنوع سیمپسون (0/74)، تنوع شانون- وینر (2/12)، شاخص‎‌های غنای مارگالوف (1/44)، منهنیک (1/04)، تنوع آلفا (6/13) و تنوع بتا (20/87) به تیمار قرق با عملیات اصلاحی و کمترین آن‌ها به¬تیمار چرا اختصاص دارد. بیشترین مقادیر شاخص‌های یکنواختی کامارگو (0/77) و اسمیت- ویلسون (0/87) به تیمار قرق و کمترین آن به تیمار قرق با عملیات اصلاحی تعلق دارد. یافته¬های به¬دست آمده می‌تواند نقشه راهی جهت تصمیم‌گیری‌های آتی در مناطق مشابه باشد. برای احیای مراتع و بهبود تنوع زیستی، قرق با سایر اقدامات مدیریتی پیشنهاد می‌شود. 2 Choosing management strategies are influencing the vegetation characteristics and plant diversity in rangelands. We have studied the effects of three management treatments including overgrazing, exclusure, and exclusure with restoration operations on vegetation characteristics of plant cover, composition, and diversity in Kiasar Chardange summer rangelands. In each management treatment, three random transects with the length of 50 m were established in the pasture and in five random plots of one square meter nested in the transects, the percentage of canopy cover, the composition of three palatability classes were recorded and the indices of plant diversity, plant evenness, species richness were calculated. For comparing the effects of different treatments on vegetation characteristics and diversity indices, an analysis of variance with equal subsamples was performed. Nonmetric multidimensional scaling was used to interpret the scattered patterns of plant compositions in relation to different management treatments. The results showed that all diversity, evenness, and richness indices were significantly different in three management treatments (p<0.05) The exclusure with restoration treatment had the highest indices of Simpson diversity (0.74), Shannon-Weiner diversity (2.12), Margalov richness (1.44), Mehnick (1.04), alpha diversity (6.13), and beta diversity (20.87). In contrast, the overgrazed site had the lowest values. The Camargo (0.77) and Smith-Wilson (0.87) evenness indices had the highest values for exclusure and the lowest values for exclusure with restoration treatments. Our findings could be a road map for future planning in similar sites. For rehabilitation of rangelands and improving plant diversity, restoration treatments in exclusures are recommended. 61 76 2022/09/162022/08/292022/06/72022/03/172022/10/16 1401/7/24 2022/11/302022/12/122022/12/242023/01/12023/01/23 1401/11/3 محمد قلیپور سلوشی M. Gholipour soloshi دانشگاه تربیت مدرس ایران mohammad.m.gh.p@gmail.com قاسمعلی دیانتی تیلکی Gh. A. Dianati Tilaki دانشگاه تربیت مدرس ایران dianatig@modares.ac.ir مهدی عابدی M. Abedi دانشگاه تربیت مدرس ایران abedimail@gmail.com Sowing Alpha diversity Species richness Palatability Restoration plan Species diversity Beta diversity Evenness بذرکاری تنوع آلفا تنوع بتا تنوع گونه‌ای خوشخوراکی طرح احیا غنا گونه‌ای یکنواختی 1. Aghasi, M. J., M. A. Bahmaniar and M. Akbarzadeh. 2006. Comparison of the effects of exclusion and water spreading on vegetation and soil parameters in Kyasar rangelands, Mazandaran province. Journal of Agricultural Sciences and Natural Resources,13(4):73-84. (In Persian)##2. Ahmadi, R. 2018. Effect of different intensities grazing livestock on certain quantitative and qualitative indicators of plant (Case Study: Choghakadou r- angeland in the Kermanshah province). Journal of Plant Ecosystem Conservation, 5(11):177-90. (In Persian)##3. Ahmadkhani, R., M. Moameri and S. 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ارزیابی تنوع ژنتیکی کلونی‌های قوی فریادکش (Cygnus cygnus, Linnaeus, 1758) در ایران Evaluating Genetic Diversity of Whooper Swan (Cygnus cygnus, Linnaeus, 1758) Colonies in Iran 1 1 قو فریادکش دارای دو جمعیت قدیمی و جدید است که جمعیت جدید از سال 1386 گزارش شد. هدف از این مطالعه بررسی ساختار ژنتیکی این گونه و مقایسه اندوخته ژنی آن‌ها به‌ویژه جمعیت جدید است. بدین منظور 110 نمونه از ایران و چهار کشور اروپایی در شش جایگاه ریزماهواره بررسی شد. علاوه بر نمونه‌های جمعیت نوظهور در تالاب بین‌المللی فریدونکنار و جمعیت قدیمی گیلان، نمونه‌هایی از کشورهای ایسلند، سوئد، فنلاند و لهستان نیز مطالعه شد. متوسط هتروزیگوسیتی مشاهده شده از 0/598 تا 1 و متوسط هتروزیگوستی مورد انتظار از 0/661 تا 0/950 قرار دارد. ایسلند در منتهی‌الیه غرب پالئارکتیک با مقدار 1/00 از بالاترین هتروزیگوسیتی و ایران در شرقی‌ترین مکان این مطالعه با 12/72 از بیشترین فراوانی آلل موثر برخوردار بود. همچنین جمعیت ایران بجز یک جایگاه در باقی موارد انحراف از تعادل هاردی-واینبرگ را نشان داد. بر اساس نتایج برنامه STRUCTURE 2.3.4  ساختار ژنتیکی جمعیت قو‌های ایران به دلیل پراکندگی بالای کلونی‌های زادآوری در عرض‌های شمالی دارای بیشترین جریان ژنی بود. لذا قوهای مهاجر پاییزه و زمستانه در فریدونکنار از دو جمعیت و قوهای گیلان نیز از یک جمعیت مجزا برخوردارند. در مجموع غنای بالای آللی در جمعیت جدید بیانگر ارزش بالای حفاظتی برای کلونی زمستان‌‌گذران فریدونکنار است. 2 Whooper swans have two old and new populations, the new population has reported since 2007. The current study aimed to investigate the genetic structure of this species and compare their gene pool, especially the new population. For this purpose, 110 samples from Iran and four European countries were analyzed, using six microsatelite loci. In addition to the samples of the emerging population in Fereydoonkenar International Wetland and the old population of Gilan, samples from Iceland, Sweden, Finland and Poland were also examined. The observed average heterozygosity ranged from 0.598 to 1.0 and the expected heterozygosity ranged from 0.661 to 0.950. Iceland in the extreme west of Palearctic had the highest observed heterozygosity of 1.0 and Iran in the easternmost part of the study area had the highest effective allele frequency (12.72). Also, the population of Iran showed deviation from Hardy-Weinberg equilibrium, except for one loci. Based on the STRUCTURE 2.3.4 results, the Iranian swan population had the highest gene flow due to the high dispersion of breeding colonies in northern latitudes. Therefore, the autumn and winter migrant swans have two populations in Fereydunkanar and one separate population in Guilan. Generally, the high allelic richness in the new population indicates a high conservation value of  the Fereydoonkenar wintering colony. 77 92 2022/09/162022/08/292022/06/72022/03/172022/10/162022/11/4 1401/8/13 2022/11/302022/12/122022/12/242023/01/12023/01/232023/01/28 1401/11/8 جواد دلپسند J. Delpassand تربیت مدرس ایران javad_dellpasssand@yahoo.com سید محمود قاسمپوری S. M. Ghasempouri تربیت مدرس ایران ghasempm@modares.ac.ir Population genetics Microsatellite marker Genetic bottleneck Gene flow ژنتیک جمعیت نشانگر ریزماهواره تنگنای ژنتیکی جریان ژنی 1. Airey, A.F. 1955. Whooper swans in southern lakeland. Bird Study. 2(3):143-150.##2. Boyd, H. and S.K. Eltringham. 1962. The whooper swan in Great Britain. 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