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ارزیابی ظرفیت برد گردشگری طبیعت و اقلیم آسایش گردشگری در مناطق حفاظت شده (مطالعه موردی: منطقه حفاظت شده اشترانکوه) Assessing the Nature Tourism Carrying Capacity ‎and Tourism Comfort Climate in Protected Areas (Case Study: Oshtorankuh Protected Area) 1 1 توسعه بدون برنامه‌ریزی گردشگری منجر به تهدید اکوسیستم‌های طبیعی و افزایش ناپایداری‌ در مناطق ‏تحت حفاظت شده است. در مطالعه حاضر، با هدف اتخاذ تصمیمات مدیریتی برای کنترل تعداد گردشگران به برآورد ظرفیت برد گردشگری طبیعت در منطقه حفاظت شده اشترانکوه مطابق با چارچوب دستورالعمل ‏اتحادیه جهانی حفاظت از طبیعت و منابع طبیعی ‏در سه سطح فیزیکی، واقعی و مؤثر اقدام شد. مطابق نتایج، ظرفیت برد فیزیکی سالانه معادل با ‏‏6,742,163‏‎ ‎نفر گردشگر است. ظرفیت برد واقعی با توجه به محدودیت‌های ‏اقلیمی و عدم امکان فعالیت‌های گردشگری در 4 ماه از سال (اردیبهشت، خرداد، تیر و مرداد)، ‏معادل با ‏1,334,075‏‎ ‎نفر برآورد شد که گردشگران در ‏ماه‌هایی با شرایط اقلیمی مطلوب می‌توانند به بازدید از منطقه بپردازند. ظرفیت برد مؤثر در منطقه با توجه به وسعت و تعداد محیط‌بانان برای مدیریت و کنترل، برابر با ‏359,525‏ نفر در سال است. ازاین رو، برآورد ظرفیت برد در مناطق حفاظت شده، موجب استفاده متناسب و متوازن انسان از سرزمین و کنترل تعداد گردشگران در این مناطق می‌شود. همچنین، مدیران و تصمیم‌گیران می‌‌توانند با کنترل تعداد گردشگران برای حفاظت در این منطقه اقدام کنند، به‌طوری که ضمن بهره‌برداری از قابلیت‌های گردشگری آن، حداقل تخریب و آسیب به این ذخایر ارزشمند طبیعی وارد شود.. 2 Unplanned tourism development threatens natural ecosystems and increases instability within protected areas. This study aimed to inform management decisions by estimating the tourism carrying capacity of Oshtorankuh Protected Area, following the framework of the International Union for Conservation of Nature (IUCN) at three levels: physical, real, and effective. The annual physical capacity was estimated at 6,742,163 tourists. Considering climatic constraints and the unavailability of tourism activities during four months (May–August), the real carrying capacity was estimated at 1,334,075 visitors, representing those who can visit during favorable months. The effective capacity, accounting for area size and ranger staffing for management and control, is approximately 359,525 visitors per year. These estimates help promote balanced human use of the land and enable managers to implement measures that protect the area. By controlling tourist numbers, it is possible to maximize tourism potential while minimizing environmental degradation, ensuring the preservation of these invaluable natural resources for future generations. 1 19 2025/08/4 1404/5/13 2025/12/27 1404/10/6 پروانه سبحانی Parvaneh ‎ Sobhani ‎ گروه محیط زیست، دانشگاه لرستان، دانشکده منابع طبیعی، خرم آباد، ایران. ایران sobhani.pa@lu.ac.ir امین سپهوند Amin Sepahvand گروه محیط زیست، دانشگاه لرستان، دانشکده منابع طبیعی، خرم آباد، ایران. ایران Sepahvandamin867@gmail.com Carrying capacity Tourism comfort climate Protected areas Sustainable nature tourism development Oshtorankuh protected area ظرفیت برد اقلیم آسایش گردشگری مناطق حفاظت شده توسعه طبیعت‌گردی پایدار منطقه حفاظت شده اشترانکوه 1. Abedi, T., Kazemi-Rad, L., and Abedi, R., 2021. Determining the tourism calendar using the tourism climate index (TCI) and the holiday climate index (HCI) (Case study: Astara, Gilan province). Social Studies of Tourism, 19 (10): 251-276. (In Persian)##2. Adu-Ampong, E. A., 2018. Tourism and national economic development planning in Ghana, ‎‎1964-2014. International Development Planning Review, 40(1): 75-95.‎##3. Ahmadi, A., Danehkar, A., Shabani, A. A., and Sobhani, P., 2024. Estimating the tourism climate of the mangrove forests of Nayband Bay using TCI and PET indices. Scientific-Research Quarterly of Geographical Data (SEPEHR), 33: 127-142. (In Persian)##4. Alizadeh, M., Aroji, H., Molaei Gholichi, M., and Jafari, R., 2011. Determining the tourism carrying capacity in Solqan Cave for the purpose of protecting natural resources. Proceedings of the Third Geography Student Conference. Tehran. 220 pp. (In Persian)##5. Arnberger, A., Budruk, M., Schneider, I. E., and Stanis, S. A. W., 2022. Predicting place attachment among walkers in the urban context: The role of dogs, motivations, satisfaction, past experience and setting development. Urban Forestry & Urban Greening, 70: 127531.##6. Babí-Almenar, J., Elliot, T., Rugani, B., Philippe, B., Navarrete Gutierrez, T., Sonnemann, G., and ‎Geneletti, D. (2021). Nexus between nature-based solutions‌,‌‎ ecosystem services and urban ‎challenges. Land Use Policy, 100: 104898.‎##7. ‎Bakhtiari, B., Bakhtiari, A., and Afzali Gorouh, Z., 2018. Investigation of climate change ‎impacts on tourism climate comfort in Iran. Global NEST Journal, 20 (2): 291-303.‎##8. Blanco-Cerradelo, L., Diéguez-Castrillón, M. I., Fraiz-Brea, J. A., and Gueimonde-Canto, A., 2022. Protected areas and tourism resources: Toward sustainable management. Land, 11(11): 2059.##9. Ceballos-Lascuráin, H. (1996). Tourism, Ecotourism and Protected Areas: The State of Nature-based Tourism Around the World and Guidelines for Its Development. IUCN Publications, Cambridge.##10. Danehkar, A., and Biglar Fadafan, M., 2018. Determining the Level of Occupation for The Calculation of The Tourism Carrying Capacity Range. Journal of Sonboleh, 260: 98-101. (In Persian)##11. Danehkar, A., and Mahmoudi, B., 2013. Nature Tourism: Development and Design Criteria. Tehran Academic Jihad Organization, 1-296. (In Persian)##12. Darvish, M., and Shokouei, M., 2005. Integrated report and development of the management plan for the Oshtorankoh Protected Area. Studies and preparation of the management plan for the Oshtorankoh Protected Area. Environmental Protection Organization, Consulting Engineers Tekm, 15: 1-240. (In Persian)##13. Ghanbari Nasab, A., 2009. Ecological footprint analysis of second home tourism in rural areas. Master's thesis in Geography and Rural Planning. University of Tehran. Department of Human Geography, 1-111. (In Persian)##14. Hatefrabiee, Z., Danehkar, A., Kaboli, M., and Sobhani, P., 2024. Determining the Tourism Comfort Climate of the Mangrove Forests of Nayband Bay Based on Baker & Terjong Indices. Journal of Meteorogical Organization, 124(48): 50-65. (In Persian)##15. IUCN., 2003. Guidelines for Protected Area Management Categories. IUCN, Gland and Cambridge.##16. Liu, Q., Lin, L., Deng, H., Zheng, Y., and Hu, Z., 2023. The index of clothing for assessing tourism climate comfort: Development and application. Frontiers in Environmental Science, 10: 992503.##17. Margaryan, L., 2018. Nature as a commercial setting: the case of nature-based tourism ‎providers in Sweden. Curr. Issues Tour, 21: 1893-1911.‎##18. Marsiglio, S., 2018. On the Carrying Capacity and the Optimal Number of Visitors in Tourism ‎Destinations. Tourism Economics, 23(3): 632-646.‎##19. Metin, T. C., 2019. Nature-based tourism, nature-based tourism destinations’ attributes and nature-based tourists’ motivations. Travel motivations: A systematic analysis of travel motivations in different tourism context, 7: 174-200.##20. ‎Mieczkowski, Z., 1985. "The tourism climatic index: a method of evaluating world ‎climates for tourism." Canadian Geographer/Le Géographe Canadien, 29(3): 220-233.‎##21. Mileusnić Skrtic, M., Tisma, S., and Grgurevic, D., 2024. Conservation under siege: The intersection of tourism and environmental threats in Croatian protected areas. Land, 13(12): 2114.##22. National Meteorological Organization‎., 2024. Annual Climate Report (https://data.irimo.ir). (In Persian)##23. Pásková, M., Wall, G., Zejda, D., and Zelenka, J., 2021. Tourism carrying capacity reconceptualization: Modelling and management of destinations. Journal of Destination Marketing & Management, 21: 100638.##24. Pulido-Fernández, J. I., Carrillo-Hidalgo, I., López-Sánchez, Y., and Casado-Montilla, J., 2025. Assessing social carrying capacity of tourists in protected natural areas. Current Issues in Tourism, 28(7): 1062-1078.##25. Rahimi, E., and Barghjelveh, S., 2020. Zoning of Oshtrankou Protected Area Using Multi-criteria Evaluation and K-MEANS Clustering Algorithm. Environmental Researches, 11(21): 43-60. (In Persian)##26. Rocha, C. H. B., Fontoura, L. M., Vale, W. B. D., Castro, L. F. D. P., da Silva, A. L. F., Prado, T. D. O., & da Silveira, F. J. (2021). Carrying capacity and impact indicators: analysis and suggestions for sustainable tourism in protected areas–Brazil. World Leisure Journal, 63(1): 73-97.##27. Salemi, M., Jozi, S. A., Malmasi, S., and Rezaian, S., 2019. Assessing the Carrying Capacity of Nature Tourism Development (Case Study of South Karkheh Protected Area). Geography (Regional Planning), 9 (2): 53-63. (In Persian)##28. Sánchez-Rivero, M., de la Cruz Sánchez-Domínguez, J., and Rodríguez-Rangel, M. C., 2022. Estimating the probability of visiting a protected natural space and its conditioning factors: The case of the Monfragüe Biosphere Reserve (Spain). Land, 11(7): 1032.##29. Santos, P. L., and Brilha, J., 2023. A review on tourism carrying capacity assessment and a proposal for its application on geological sites. Geoheritage, 15(2): 47.##30. Sheikh, A., Jafari, A., Yarali, N. A., and Sotoudeh, A., 2013. Assessing the tourism carrying capacity of Qaysari protected area in Chaharmahal and Bakhtiari province. Applied Ecology, 2 (5), 63-51. (In Persian)##31. Shi, C., He, Y., and Li, H., 2023. How does ecological poverty alleviation contribute to improving residents' sustainable livelihoods? Evidence from Zhejiang Province, China. Sustainable Production and Consumption, 41: 418-430.##32. Shorche, M., and Farhoudi, R. A., 2007. Analyzing the Carrying Capacity of Anahita Temple in Kangavar City. Thesis in Geography and Urban Planning. University of Tehran. Department of Human Geography, 1-89. (In Persian)##33. Simon, F., Naranjavana, Y., and Marques, D. (2004). Carrying capacity in the tourism industry: a ‎case study of Hengistbury Head). Tourism Management, 25: 275-283.‎##34. Skiniti, G., Lilli, M., Skarakis, N., Tournaki, S., Nikolaidis, N., and Tsoutsos, T., 2024. A holistic approach for tourism carrying capacity estimation in sensitive ecological areas. Environment, Development and Sustainability, 26(12), 31971-31995.##35. Sobhani, P., and Esmaeilzadeh, H., 2020. Impact of climate change on tourism in protected areas (Study area: Alvand No-hunting area). Quarterly Journal of Geography and Urban- Regional Studies, 10(37): 65-90. (In Persian)##36. Sobhani, P., Esmaeilzadeh, H., Sadeghi, S. M.M., and Marcu, M.V., 2022. Estimation of ‎Ecotourism Carrying Capacity for Sustainable Development of Protected Areas in Iran. International Journal of Environmental Research and Public Health, 19: 1059.‎##37. Sobhani, P., Esmaeilzadeh, H., Wolf, I. D., Deljouei, A., Marcu, M. V., and Sadeghi, S. M. M., 2023. Evaluating the ecological security of ecotourism in protected area based on the DPSIR model. Ecological Indicators, 155: 110957.##38. Sobhani, P., and Danehkar, A., 2023. Estimation of nature tourism carrying capacity in the mangrove forests of Khamir and Qeshm. Iranian Journal of Forest, 15(3): 377-392. (In Persian)##39. Sultana, F., and Paul, A. K., 2023. Tourism Climate Index (TCI) for Assessing the Favourable Period for Tourism Recreation Activities with the Application of Geospatial Techniques. In Crisis on the Coast and Hinterland: Assessing India’s East Coast with Geomorphological, Environmental and Remote Sensing and GIS Approaches (383-392).Springer Nature, Switzerland.##40. Tabibiyan, S., 2024. Investigating the ecological attractions of protected areas around Tehran with emphasis on tourism carrying capacity, case study: Varjin. Urban Ecology Research, 15 (35): 61-82. (In Persian)##41. TIES., 2023. "Ecotourism and Sustainable Tourism: Defining a Path Forward." The International ‎Ecotourism Society (TIES), 2023, https://www.ecotourism.org/ecotourism-and-sustainable-‎tourism-defining-a-path-forward.‎##42. Tyrvainen, L., Silvennoinen, H., and Hallikainen, V., 2017. Effect of the season and forest ‎management on the visual quality of the nature-based tourism environment: a case from Finnish ‎Lapland. Scand. Journal of Forest Research, 32: 349–359.‎##43. Wassie, S. B., 2020. Natural resource degradation tendencies in Ethiopia: a review. Environmental Systems Research: 9(1): 1-29.##44. Xiao, Y., Tang, X., Wang, J., Huang, H., and Liu, L., 2022. Assessment of coordinated development between tourism development and resource environment carrying capacity: A case study of Yangtze River economic Belt in China. Ecological Indicators, 141: 109125.##45. Zekan, B., Weismayer, C., Gunter, U., Schuh, B., and Sedlacek, S., 2022. Regional sustainability and tourism carrying capacities. Journal of Cleaner Production: 339, 130624.##46. Zhang, X., Zhong, L., and Yu, H., 2022. Sustainability assessment of tourism in protected areas: A relational perspective. Global Ecology and Conservation, 35: e02074.## ##

مقایسه عملکرد روش‌های اصلاح اریب مکانی داده‌های حضور در بهبود پیش‌بینی مدل‌های پراکنش Comparison of Spatial Bias Correction Methods for Presence Data in Improving Species Distribution Model Predictions 1 1 اریب مکانی در داده های وقوع پستانداران به دلیل نمونه‌برداری غیریکنواخت، یکی از مهم‌ترین چالش‌های مدل های پراکنش است. از این رو، ارزیابی اریب نقاط حضور، پیش‌نیاز بهبود دقت مدل‌های پراکنش است. در پژوهش حاضر، مجموعه‌ای از روش‌های متداول و نوین اصلاح اریب مکانی بر روی نقاط حضور دو علفخوار شاخص کشور شامل کل و بز (Capra aegagrus) و گوسفند وحشی(Ovis gmelini/O.vignei) به‌کار گرفته شد و با اعمال رویکردهای مختلف اصلاح اریب، تأثیر ناهمگونی تلاش نمونه‌برداری بر عملکرد مدل‌ها ارزیابی گردید. کارایی هر یک از این روش‌های پیشنهادی با استفاده از شبیه سازی نقاط حضور برای مجموعه ای از گونه های فرضی نیز ارزیابی و مقایسه شد. اگرچه تمامی روش‌های استفاده شده عملکرد مناسبی در پیش‌بینی پراکنش گونه‌ها داشتند (AUC > 0.75)، اما بر اساس شاخص‌‌های تشابه، رویکرد تلفیقی گونه های هدف به‌عنوان مبنایی برای انتخاب نقاط پس زمینه و فیلتر نقاط حضور در یک فضای جغرافیایی در مقایسه با سایر روش‌ها عملکرد بهتری نشان داد. نتایج نشان داد که اصلاح اریب مکانی در داده‌های حضور علفخواران، نقش اساسی در بهبود دقت مدل‌های پراکنش دارد و به‌طور مؤثری اثر ناهمگونی تلاش نمونه‌برداری را کاهش داد. رویکردهای پیشنهادی می تواند الگویی جهت بهبود دقت مدل های پراکنش سایر گونه های جانوری باشد.  2 Spatial bias in mammal occurrence data due to uneven sampling represents a major challenge for species distribution models. Therefore, assessing bias in presence data is a prerequisite for improving the accuracy of models. At the present study, a range of commonly used and novel methods for correcting spatial bias was applied to the presence data of two herbivores, the wild goat (Capra aegagrus) and the wild sheep (Ovis gmelini / O. vignei) and by implementing different bias-correction approaches, the effect of heterogeneous sampling effort on model performance was evaluated. The effectiveness of each method was further assessed using simulated presence records generated for a set of virtual species. While all methods showed high performance in prediction the spatial range of the species (AUC > 0.75), similarity indices indicated that combination of target-group approach, used as a basis for selecting backgrounds, and filtering presence data within a geographic space performed better than the other methods. The findings demonstrated that correcting spatial bias in presence data plays a fundamental role in improving the accuracy of distribution models and effectively reduced the impact of uneven sampling effort. The proposed approaches provide a useful framework for improving distribution modelling of other species. 21 36 2025/08/42026/01/15 1404/10/25 2025/12/272026/04/18 1405/1/29 رسول خسروی Rasoul Khosravi بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران. ایران r-khosravi@shirazu.ac.ir حسین رحیمی نژاد Hossein Rahimi-Nezhad بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران. ایران hosseinr9947@gmail.com قصیده نیک آئین Ghaside Nikaeen بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران. ایران nikaeenghaside@gmail.com سید رشید فلاح شمسی Seyed Rashid Fallah Shamsi بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران. ایران fallahsh@shirazu.ac.ir Spatial bias Sampling effort Accessibility index Virtual species MaxEnt model اریب مکانی تلاش نمونه برداری شاخص دسترسی گونه های فرضی مدل مکسنت 1.Amininasab, S.M., Zamani, N., Taleshi, H. and Xu, C.C., 2023. Ensemble modelling the distribution and habitat suitability of wild goat Capra aegagrus in southwestern Iran. Biodiversity, 24(3): 124-136.##2. Amiri, M.S., Yazdi, M.E.T. and Rahnama, M., 2021. Medicinal plants and phytotherapy in Iran: Glorious history, current status and future prospects. Plant Science Today, 8(1): 95-111.##3. Barber, R.A., Ball, S.G., Morris, R.K.A. and Gilbert, F., 2022. Target-group backgrounds prove effective at correcting sampling bias in Maxent models. Diversity and Distributions, 28: 128–141.##4.Barbet‐Massin, M., Jiguet, F., Albert, C.H. Thuiller, W., 2012. Selecting pseudo‐absences for species distribution models: How, where and how many?. Methods in Ecology and Evolution, 3(2): 327-338##5. Bashari, H. and Hemami, M.R., 2013. A predictive diagnostic model for wild sheep (Ovis orientalis) habitat suitability in Iran. Journal for Nature Conservation, 21(5): 319-325.##6. Blair, M.E., Le, M.D. and Xu, M., 2022. Species distribution modeling to inform transboundary species conservation and management under climate change: Promise and pitfalls. Frontiers of Biogeography, 14(1): e54662##7. Boria, R.A., Olson, L.E., Goodman, S.M. and Anderson, R. P., 2014. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275: 73-77##8. Bracho-Estévanez, C.A., Arenas-Castro, S., González-Varo, J.P. and González-Moreno, P., 2024. Spatially explicit metrics improve the evaluation of species distribution models facing sampling biases. Ecological Informatics, 84: 102916.##9. Chauvier, Y., Zimmermann, N.E., Poggiato, G., Bystrova, D., Brun, P. and Thuiller, W., 2021. Novel methods to correct for observer and sampling bias in presence‐only species distribution models. Global Ecology and Biogeography, 30(11): 2312-2325.##10. De Groot, M., Kozamernik, E., Kermavnar, J., Kolšek, M., Marinšek, A., Nève Repe, A. and Kutnar, L., 2024. Importance of habitat context in modelling risk maps for two established invasive alien plant species: the case of Ailanthus altissima and Phytolacca americana in Slovenia (Europe). Plants, 13(6): 883-890.##11. Dubos, N., Préau, C., Lenormand, M., Papuga, G., Monsarrat, S., Denelle, P. and Luque, S., 2022. Assessing the effect of sample bias correction in species distribution models. Ecological Indicators, 145: 109487.##12. Elith, J., Phillips, S.J., Hastie, T., Dudı ´k, M., Chee, Y. E. and Yates, C.J., 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17: 43–57##13.Evans, J.S., 2020 spatialEco: R package version 1.3-1.Retrieved from https://cran.r-project.org/packa ge=spati alEco##14.Eyre, A.C., Briscoe, N.J., Harley, D.K., Lumsden, L.F., McComb, L.B. and Lentini, P.E., 2022. Using species distribution models and decision tools to direct surveys and identify potential translocation sites for a critically endangered species. Diversity and Distributions, 28(4): 700-711.##15. Fourcade, Y., Engler, J.O., Rodder, D. and Secondi, J., 2014. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias Bias. PLoS ONE, 9(5): e97122.##16.Freeman, M.S., Dick, J.T. and Reid, N., 2022. Dealing with non-equilibrium bias and survey effort in presence-only invasive Species Distribution Models (iSDM); predicting the range of muntjac deer in Britain and Ireland. Ecological Informatics, 69: 101683.##17.Gaul, W., Sadykova, D., White, H.J., Leon-Sanchez, L., Caplat, P., Emmerson, M.C. and Yearsley, J. M., 2020. Data quantity is more important than its spatial bias for predictive species distribution modelling. PeerJ, 8: e10411##18.Ghoddousi, A., Soofi, M., Hamidi, A. K., Ashayeri, S., Egli, L., Ghoddousi, Speicher, J., Khorozyan, I., Kiabi, B. and Waltert, M., 2019. The decline of ungulate populations in Iranian protected areas calls for urgent action against poaching. Oryx, 53(1): 151-158.##19. González‐Trujillo, J.D., Naimi, B., Assis, J. and Araújo, M.B., 2024. Reshuffling of Azorean coastal marine biodiversity amid climate change. Journal of Biogeography, 51(12): 2546-2555.##20. Guisan, A., Thuiller, W. and Zimmermann, N. E., 2017. Habitat Suitability and Distribution Models: with Applications in R. Cambridge University Press.##21.Hastie, T. and Tibshirani, R., 1986. Generalized additive models. Statistical Science, 1(3): 297-310. ##22. Hijmans, R.J., Phillips, S., Leathwick, J., Elith, J. and Hijmans, M.R.J., 2017. Package ‘dismo’. Circles, 9(1): 1-68.##23. Khosravi, M., Chamani, A. and Mirzaei, R., 2021. Species distribution models unveil niche partitioning in bovid guilds of southwestern Asia. Annales Zoologici Fennici, 58(1-3): 75-86.##24. Komori, O., Eguchi, S., Saigusa, Y., Kusumoto, B. and Kubota, Y., 2020. Sampling bias correction in species distribution models by quasi-linear Poisson point process. Ecological Informatics, 55: 101015.##25. Kuemmerle, T., Bluhm, H., Ghoddousi, A., Arakelyan, M., Askerov, E., Bleyhl, B. and Zazanashvili, N., 2020. Identifying priority areas for restoring mountain ungulates in the Caucasus ecoregion. Conservation Science and Practice, 2(11): e276.##26. Louppe, V., Leroy, B., Herrel, A. and Veron, G., 2020. The globally invasive small indian mongoose Urva auropunctata is likely to spread with climate change. Scientific Reports, 10: 1–11.##27. Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. and Hornik, K., 2021. Cluster Analysis Basics and Extensions. R package version 2.1.2##28. Mccarthy, K.P., Fletcher Jr, R.J., Rota, C.T. and Hutto, R. L., 2012. Predicting species distributions from samples collected along roadsides. Conservation Biology, 26(1): 68-77.##29. Mejía-Jurado, E., Echeverry-Cárdenas, E. and Aguirre-Obando, O.A., 2024. Potential current and future distribution for Aedes aegypti and Aedes albopictus in Colombia: important disease vectors. Biological Invasions, 26(7): 2119-2137.##30. Miranda, E.B., Menezes, J.F.S., Farias, C.C., Munn, C. and Peres, C.A., 2019. Species distribution modeling reveals strongholds and potential reintroduction areas for the world’s largest eagle. PloS one, 14(5): e0216323.##31. Moudrý, V., Bazzichetto, M., Remelgado, R., Devillers, R., Lenoir, J., Mateo, R.G. and Šímová, P., 2024. Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter. Ecography, 2024(12): e07294.##32.Noack, S., Knobloch, A., Etzold, S.H., Barth, A. and Kallmeier, E., 2014. Spatial predictive mapping using artificial neural networks. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Volume XL-2, 2014 ISPRS Technical Commission II Symposium, 6 – 8 October 2014, Toronto, Canada##33. Noori, S., Zahiri, R., Yusefi, G. H., Rajabizadeh, M., Hawlitschek, O., Rakhshani, E. and Rajaei, H., 2024. Patterns of zoological diversity in Iran—a review. Diversity, 16(10): 621-631.##34.Oliveira, U., Soares‐Filho, B. and Nunes, F., 2024. Controlling the effects of sampling bias in biodiversity models. Journal of Biogeography, 51(9): 1755-1766.##35.Phillips, S.J., Dudik, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J. and Ferrier, S., 2009. Sample selection bias and presence-only distribution model: Implications for background and pseudo-absence data. Ecological Society of America, 19: 181–197##36. Ramampiandra, E.C., Scheidegger, A., Wydler, J. and Schuwirth, N., 2023. A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation. Ecological Modelling, 481: 110353.##37. Rathore, M.K. and Sharma, L.K., 2023. Efficacy of species distribution models (SDMs) for ecological realms to ascertain biological conservation and practices. Biodiversity and Conservation, 32(10): 3053-3087.##38. Sanderson, E. W., 2002. The human footprint and the last of the wild. Bioscience, 52: 891–904.##39. Sorbe, F., Gränzig, T. and Förster, M., 2023. Evaluating sampling bias correction methods for invasive species distribution modeling in Maxent. Ecological Informatics, 76: 102124.##40.Steen, B., Broennimann, O., Maiorano, L. and Guisan, A., 2024. How sensitive are species distribution models to different background point selection strategies? A test with species at various equilibrium levels. Ecological Modelling, 493: 110754.##41.Steen, V.A., Tingley, M.W., Paton, P.W. and Elphick, C.S., 2021. Spatial thinning and class balancing: Key choices lead to variation in the performance of species distribution models with citizen science data. Methods in Ecology and Evolution, 12(2): 216-226.##42.Stockwell, D., 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13(2): 143-158.##43.Ten Caten, C. and Dallas, T., 2023. Thinning occurrence points does not improve species distribution model performance. Ecosphere, 14(12): e4703##44.Warren, D.L., Matzke, N., Cardillo, M., Baumgartner, J., Beaumont, L., Huron, N. and Dinnage, R. (2019). ENMTools (Software Package). Retrieved from https://github.com/danlw arren/ ENMTools 2025-07-22##45. Wüest, R.O., Zimmermann, N.E., Zurell, D., Alexander, J.M., Fritz, S.A., Hof, C., Kreft, H., Normand, S., Cabral, J. S., Szekely, E., Thuiller, W., Wikelski, M. and Karger, D.N., 2020. Macroecology in the age of Big Data—Where to go from here. Journal of Biogeography, 47: 1–12. ##46. Zhang, F.G., Zhang, S., Wu, K., Zhao, R., Zhao, G. and Wang, Y., 2024. Potential habitat areas and priority protected areas of Tilia amurensis Rupr in China under the context of climate change. Frontiers in Plant Science, 15: 1365264.## ##

پاسخ جوامع پرندگان و حشرات به روشنه‌های طبیعی و مصنوعی (مطالعه موردی: جنگل شصت کلاته) Responses of Birds and Insects Communities to Natural and Artificial Forest Gaps (Case Study: Shastkalateh Forest, Gorgan, Iran) 1 1 روشنه‌های جنگلی به عنوان یکی از مهم‌ترین آشفتگی‌های اکولوژیک، بر پوشش گیاهی، حشرات و پرندگان اثر می‌گذارند. این پژوهش با هدف مقایسه تراکم و تنوع پرندگان در سه تیمار تاج‌بسته (شاهد)، روشنه طبیعی و روشنه مصنوعی در جنگل حفاظت‌شده شصت‌کلاته انجام شد. در ۷۰ نقطه به شعاع ۲۵ متر، نمونه‌برداری از پرندگان، حشرات (با استفاده از تله پنجره‌ای و گودالی) و متغیرهای زیستگاهی به روش نقطه‌ای و فاصله‌ای صورت گرفت. نتایج تحلیل افزونگی نشان داد که بیشترین همبستگی مثبت پرندگان با متغیرهای زیستگاهی و حشرات، مربوط به روشنه‌های طبیعی و تاج‌بسته است. روشنه‌های طبیعی بیشترین فراوانی پرندگان را داشتند که به دلیل ورود نور، پیچیدگی ساختاری و پویایی بالای آن‌هاست. در مقابل، روشنه‌های مصنوعی با کاهش پیچیدگی ساختاری و ناپایداری منابع غذایی، تأثیر منفی بر پرندگان زادآور گذاشتند. بر اساس نمونه‌گیری فاصله‌ای، گونه سینه‌سرخ اروپایی (Erithacus rubecula) در هر سه تیمار بیشترین تراکم را داشت. تحلیل آنوسیم (ANOSIM) اختلاف معنی‌داری در ترکیب گونه‌ای پرندگان میان تیمارها نشان داد (0/001=p ). به طور کلی، روشنه‌های طبیعی نسبت به شاهد و روشنه‌های مصنوعی از تراکم و تنوع پرندگان و تنوع حشرات بالاتری برخوردار بودند. 2 Forest gaps represent significant ecological disturbances that can affect vegetation structure, insect populations, and avian communities. This study aimed to evaluate the density and diversity of bird species across three treatment types: closed canopy (control), natural gaps, and artificial gaps, within the protected Shast-Kalateh forest. Bird and insect sampling was conducted at 70 points, each with a 25-meter radius, utilizing point count and distance sampling methodologies. Redundancy analysis indicated that the strongest positive correlations between bird populations and habitat variables, as well as insect abundance, were found in natural gaps and closed canopy areas. Natural gaps exhibited the highest avian abundance, likely attributed to increased light availability, enhanced structural complexity, and greater ecological dynamism. Conversely, artificial gaps, which displayed reduced structural complexity and unstable food resources, negatively impacted breeding bird populations. Distance sampling revealed that the European Robin (Erithacus rubecula) had the highest density across all treatments. ANOSIM analysis demonstrated significant differences in bird species composition among the treatments (p = 0.001). Overall, natural gaps fostered higher bird density, greater avian diversity, and increased insect diversity compared to both closed canopy areas and artificial gaps. 37 53 2025/08/42026/01/152026/02/2 1404/11/13 2025/12/272026/04/182026/04/21 1405/2/1 ملیحه بروغنی Malihe Boroughani گروه محیط زیست، دانشکده شیلات و محیط زیست، دانشگاه علوم کشاورزی و منابع طبیعی گرگان.، گرگان، ایران. ایران malihe.boroughani_s00@gau.ac.ir حسین وارسته مرادی Hossein Varasteh Moradi گروه محیط زیست، دانشکده شیلات و محیط زیست، دانشگاه علوم کشاورزی و منابع طبیعی گرگان.، گرگان، ایران. ایران hvarasteh2009@yahoo.com احمد ندیمی Ahmad Nadim گروه گیاهپزشکی، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان.، گرگان، ایران. ایران nadimi@gau.ac.ir Forest gap ecology Gap biodiversity Bird density Insect abundance estimation Shast-Kalateh forest. بوم‌شناسی روشنه های جنگلی تنوع زیستی روشنه ها تراکم پرندگان برآورد فراوانی حشرات جنگل شصت کلاته 1. Abari, V., and Kambiz, M., 2016. The relationship between canopy gaps resulting from single-tree selection method implementation and humus layer thickness in a beech forest (Case study: Alandan beech forest, Sari). Iranian Journal of Forest and Poplar Research, 24(3): 541. (In Persian).##2. Achury, R., Staab, M., Blüthgen, N. and Weisser, W.W., 2023. Forest gaps increase true bug diversity by recruiting open land species. Oecologia, 202(2): 299-312.##3. Asbeck, T., Basile, M., Stitt, J., Bauhus, J., Storch, I. and Vierling, K.T., 2020. Tree-related microhabitats are similar in mountain forests of Europe and North America and their occurrence may be explained by tree functional groups. Trees, 34: 1453-1466. ##4. Augusto, L. and Boča, A., 2022. Tree functional traits, forest biomass, and tree species diversity interact with site properties to drive forest soil carbon. Nature Communications, 13(1): 1097.##5. Basile, M., Asbeck, T., Jonker, M., Knuff, A.K., Bauhus, J., Braunisch, V., Mikusiński, G. and Storch, I., 2020. What do tree-related microhabitats tell us about the abundance of forest-dwelling bats, birds, and insects? Journal of Environmental Management, 264:110401. ##6. Benedetti, Y., Morelli, F., Munafo, M., Assennato, F., Strollo, A. and Santolini, R., 2020. Spatial associations among avian diversity, regulating and provisioning ecosystem services in Italy. Ecological Indicators, 108: 105742. ##7. Bradfer-Lawrence, T., Bunnefeld, N., Gardner, N., Willis, S.G. and Dent, D.H., 2020. Rapid assessment of avian species richness and abundance using acoustic indices. Ecological Indicators, 115: 106400. ##8. Bröcher, M., Ebeling, A., Bassi, L., Medina‐van Berkum, P., van Dam, N.M., Eisenhauer, N., Madaj, A.M., Unsicker, S., Weigelt, A. and Meyer, S.T., 2025. Plant–herbivore interactions depend on plant richness, plant and soil history. Functional Ecology, 39(12):3672-3687. ##9. Bujoczek, L., Bujoczek, M. and Zięba, S., 2021. Distribution of deadwood and other forest structural indicators relevant for bird conservation in Natura 2000 special protection areas in Poland. Scientific Reports, 11(1): 14937. ##10. Campanaro, A. and Parisi, F., 2021. Open datasets wanted for tracking the insect decline: let’s start from saproxylic beetles. Biodiversity Data Journal, 9: e72741. ##11. Castelletta, M., Thiollay, J. M. and Sodhi, N.S., 2005. The effects of extreme forest fragmentation on the bird community of Singapor Island. Biological conservation. 121: 135-155. ##12. Dufour-Pelletier, S., A. Tremblay, J., Hébert, C., Lachat, T. and Ibarzabal, J., 2020. Testing the effect of snag and cavity supply on deadwood-associated species in a managed boreal forest. Forests, 11(4): 424.##13. Feldmann, E., Drößler, L., Hauck, M., Kucbel, S., Pichler, V. and Leuschner, C., 2018. Canopy gap dynamics and tree understory release in a virgin beech forest, Slovakian Carpathians. Forest Ecology and Management, 415: 38-46. ##14. Graser, A., Frank, C., Kunz, F., Schuldt, A., Senf, C., Sudfeldt, C., Trautmann, S. and Kamp, J., 2025. Increase in disturbance-induced canopy gaps leads to reorganization of Central European bird communities. Basic and Applied Ecology, 83: 88-97. ##15. Guo, Y., Zhao, P. and Yue, M., 2019. Canopy disturbance and gap partitioning promote the persistence of a pioneer tree population in a near‐climax temperate forest of the Qinling Mountains, China. Ecology and Evolution, 9(13): 7676-7687. ##16. Hanle, J., Duguid, M.C. and Ashton, M.S., 2020. Legacy forest structure increases bird diversity and abundance in aging young forests. Ecology and evolution, 10(3): 1193-1208. ##17. Henneberg, B., Feldhaar, H., Förtsch, S., Schauer, B. and Obermaier, E., 2025. Threatened saproxylic beetle species in tree hollows react more sensitively to surrounding landscape composition in central European managed forests than total species richness. Biodiversity and Conservation, 1-21. ##18. Jörg, M., Simon, T., Roland, B., Khosro, S.T., Barimani, H.V., Sebastian, S., Ulyshen, M.D. and Gossner, M.M., 2016. Protecting the forests while allowing removal of damaged trees may imperil saproxylic insect biodiversity in the Hyrcanian beech forests of Iran. Conservation letters, 9(2): 106-113. ##19. Junggebauer, A., Gericke, N.M., Krakau, L.K., Bluhm, S.L., Maraun, M., Pollierer, M.M. and Scheu, S., 2024. Effects of forest gap formation and deadwood enrichment on oribatid mites (Acari: Oribatida) vary between regions. Forest Ecology and Management, 565: 122015. ##20. Kärvemo, S., Rogell, B. and Schroeder, M., 2014. Dynamics of spruce bark beetle infestation spots: Importance of local population size and landscape characteristics after a storm disturbance. Forest ecology and management, 334: 232-240. ##21. Kern, C.C., Montgomery, R.A., Reich, P.B. and Strong, T.F., 2013. Canopy gap size influences niche partitioning of the ground-layer plant community in a northern temperate forest. Journal of Plant Ecology, 6(1): 101-112. ##22. Krüger, K., Senf, C., Jucker, T., Pflugmacher, D. and Seidl, R., 2024. Gap expansion is the dominant driver of canopy openings in a temperate mountain forest landscape. Journal of Ecology, 112(7): 1501-1515. ##23. Larrieu, L., Paillet, Y., winter, S., Bütler, R., Kraus, D., Krumm, F., Lachat, T., Michel, A.K., Regnery, B. and Vandekerkhove, K., 2018. Tree related microhabitats in temperate and Mediterranean European forests: A hierarchical typology for inventory standardization. Ecological Indicators, 84: 194-207.##24. Leidinger, J., Seibold, S., Weisser, W.W., Lange, M., Schall, P., Türke, M. and Gossner, M.M., 2019. Effects of forest management on herbivorous insects in temperate Europe. Forest ecology and management, 437: 232-245. ##25. Lekoveckaitė, A., Podėnienė, V. and Ferenca, R., 2023. Beetles (Coleoptera) in deciduous dead wood tree species trunks in Lithuania. Biodiversity Data Journal, 11: e106132. ##26. Lewandowski, P., Przepióra, F. and Ciach, M., 2021. Single dead trees matter: Small-scale canopy gaps increase the species richness, diversity and abundance of birds breeding in a temperate deciduous forest. Forest Ecology and Management, 481: 118693. ##27. Lima, J. and Guilherme, E., 2021. Birds associated with treefall gaps in a lowland forest in southwestern Brazilian Amazonia. Acta Amazonica, 51: 42-51. ##28. Moaiery, M. H. and Mohammad Alipour Malekshah, A.A., 2009. Review of the Second Series of Forestry Plan by Dr. Bahramnia. College of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Faculty of Forest Sciences, 128 p. (In Persian). ##29. Oeser, J., Heurich, M., Senf, C., Pflugmacher, D., Belotti, E. and Kuemmerle, T., 2020. Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat. Remote Sensing in Ecology and Conservation, 6(1): 52-69. ##30. Perreault, L., Forrester, J.A., Lindner, D.L., Jusino, M.A., Fraver, S., Banik, M.T. and Mladenoff, D.J., 2023. Linking wood-decay fungal communities to decay rates: Using a long-term experimental manipulation of deadwood and canopy gaps. Fungal Ecology, 62: 101220. ##31. Pollock, H.S., Jones, T.M., Tarwater, C.E., Nishikawa, E.T. and Brawn, J.D., 2020. Rapid colonization and turnover of birds in a tropical forest treefall gap. Journal of Field Ornithology, 91(2): 107-117. ##32. Przepióra, F., Loch, J. and Ciach, M., 2020. Bark beetle infestation spots as biodiversity hotspots: Canopy gaps resulting from insect outbreaks enhance the species richness, diversity and abundance of birds breeding in coniferous forests. Forest ecology and management, 473: 118280. ##33. Remeš, V., Remešová, E., Friedman, N.R., Matysioková, B. and Rubáčová, L., 2021. Functional diversity of avian communities increases with canopy height: From individual behavior to continental‐scale patterns. Ecology and Evolution, 11(17): 11839-11851.##34. Rothacher, J., Seidl, R., Thom, D., Kortmann, M., Chao, A., Chiu, C.H., Heibl, C., Hothorn, T., Mitesser, O., Mori, A.S. and Morinière, J., 2025. The impact of tree mortality and post‐disturbance management on insect diversity in temperate forests: Insights from a replicated experiment. Journal of Applied Ecology, 62(8): 1878-1888.##35. Sandström, U.G., Angelstam, P. and Mikusiński, G., 2006. Ecological diversity of birds in relation to the structure of urban green space. Landscape and urban planning, 77(1-2): 39-53. ##36. Schillé, L., Valdés‐Correcher, E., Archaux, F., Bălăcenoiu, F., Bjørn, M.C., Bogdziewicz, M., Boivin, T., Branco, M., Damestoy, T., de Groot, M. and Dobrosavljević, J., 2024. Decomposing drivers in avian insectivory: Large‐scale effects of climate, habitat and bird diversity. Journal of Biogeography, 51(6): 1079-1094. ##37. Seibold, S., Hagge, J., Müller, J., Gruppe, A., Brandl, R., Bässler, C. and Thorn, S., 2018. Experiments with dead wood reveal the importance of dead branches in the canopy for saproxylic beetle conservation. Forest Ecology and Management, 409: 564-570. ##38. Seibold, S., Rammer, W., Hothorn, T., Seidl, R., Ulyshen, M.D., Lorz, J., Cadotte, M.W., Lindenmayer, D.B., Adhikari, Y.P., Aragón, R. and Bae, S., 2021. The contribution of insects to global forest deadwood decomposition. Nature, 597(7874): 77-81. ##39. Shabani, S. 2008. The relationship between the area of regeneration gaps with physiographic factors and vegetation cover in the Lalis-Noshahr forest region. Master’s Thesis, Tarbiat Modares University, 80 pp. (In Persian).##40. Siri, S., Ponpithuk, Y., Safoowong, M., Marod, D., de la Fuente, A., Williams, S.E. and Duengkae, P., 2025. The effect of forest gap dynamics on tropical rainforest birds. Ecologica Montenegrina, 88: 164-185.##41. Spiller, K.J. and Dettmers, R., 2019. Evidence for multiple drivers of aerial insectivore declines in North America. The Condor, 121(2): duz010. ##42. Storch, F., Boch, S., Gossner, M.M., Feldhaar, H., Ammer, C., Schall, P., Polle, A., Kroiher, F., Müller, J. and Bauhus, J., 2023. Linking structure and species richness to support forest biodiversity monitoring at large scales. Annals of Forest Science, 80(1): 3. ##43. Szefer, P., Molem, K., Sau, A and Novotny, V., 2022. Weak effects of birds, bats, and ants on their arthropod prey on pioneering tropical forest gap vegetation. Ecology, 103(6): e3690. ##44. Thom, D., Sommerfeld, A., Sebald, J., Hagge, J., Müller, J. and Seidl, R., 2020. Effects of disturbance patterns and deadwood on the microclimate in European beech forests. Agricultural and Forest Meteorology, 291: 108066. ##45. Thorn, S., Seibold, S., Leverkus, A.B., Michler, T., Müller, J., Noss, R.F., Stork, N., Vogel, S. and Lindenmayer, D.B., 2020. The living dead: acknowledging life after tree death to stop forest degradation. Frontiers in Ecology and the Environment, 18(9): 505-512. ##46.Tian, A., Halik, U., Fu, W., Sawirdin, S., Cheng, S and Lei, J., 2023. Research history of forest gap as small-scale disturbances in forest ecosystems. Forests, 15(1): 21. ##47. Ulyshen, M., Urban‐Mead, K.R., Dorey, J.B. and Rivers, J.W., 2023. Forests are critically important to global pollinator diversity and enhance pollination in adjacent crops. Biological Reviews, 98(4): 1118-1141.##48. Valencia-Cuevas, L. and Tovar-Sánchez, E., 2015. Oak canopy arthropod communities: which factors shape its structure? Revista chilena de historia natural, 88(1): 1-22. ##49. Varasteh Moradi, H., 2011. Evaluation of the effects of the Tehran- Mashhad Asian highway on the society of birds In Golestan National Park. Environmental Research, 2(3): 21-34. (In Persian).##50. Watson, J.E., Whittaker, M.R.J and Dawson, T.P., 2004. Habitat structure and proximity to forest edge affect the abundance and distribution of forest-dependent bird in tropical forests of south-eastern Madagascar. Biological##Conservation 120: 311-327. ##51. Wildermuth, B., Penanhoat, A., Sennhenn-Reulen, H., Matevski, D., Drescher, J., Aubry-Kientz, M., Seidel, D. and Schuldt, A., 2024. Canopy structure influences arthropod communities within and beyond tree identity effects: Insights from combining LiDAR data, insecticidal fogging and machine learning regression modelling. Ecological Indicators, 160: 111901.##52. Winter, M.B., Ammer, C., Baier, R., Donato, D.C., Seibold, S. and Müller, J., 2015. Multi-taxon alpha diversity following bark beetle disturbance: evaluating multi-decade persistence of a diverse early-seral phase. Forest Ecology and Management, 338: 32-45. ##53. Wu, Y., Li, H., Liang, X., Jiang, M., He, S. and He, Y., 2025. Mechanisms behind the Soil Organic Carbon Response to Temperature Elevations. Agriculture, 15(11): 1118. ##54. Xu, J., Lie, G. and Xue, L., 2016. Effects of gap size on diversity of soil fauna in a Cunninghamia lanceolata stand damaged by an ice storm in southern China. Journal of forestry research, 27: 1427-1434.## ##

تنوع و ساختار ژنتیکی آهوی گواتردار (Gazella subgutturosa) در استان فارس با استفاده از نشانگر ریزماهواره Genetic Diversity and Population Structure of the Goitered Gazelle (Gazella subgutturosa) in Fars Province Using Microsatellite Marker 1 1 کاهش جمعیت می‌تواند منجر به افت تنوع ژنتیکی، افزایش هم‌خونی و کاهش توان سازگاری، در برابر تغییرات محیطی شود. بنابراین، پایش شاخص‌های ژنتیکی برای ارزیابی پایداری جمعیت‌ها ضروری است. در مطالعه حاضر، تنوع و ساختار ژنتیکی آهوی گواتردار (Gazella subgutturosa) در استان فارس (پارک ملی بمو و منطقه شکار ممنوع بصیران) با استفاده از ۴۵ نمونه سرگین و 15 نشانگر ریزماهواره ارزیابی شد. شاخص‌های تنوع ژنتیکی، درون‌آمیزی، تمایز و ساختاربندی ژنتیکی و گردنه بطری برآورد گردید. بر اساس نتایج به‌دست‌آمده مقدار هتروزیگوسیتی مشاهده شده بین 0/95 - 0/34 برآورد گردید که نشان‌دهنده سطح مناسبی از تنوع ژنتیکی در هر دو منطقه است. همچنین شاخص تمایز ژنتیکی 0/056 محاسبه شد که وجود تمایز بالا بین دو جمعیت را آشکار می‌سازد که می‌تواند ناشی از محدودیت جریان ژنی، جدایی جغرافیایی، و یا تأثیر مقاومت محیطی باشد. نشانه‌ای از وقوع پدیده گردنه بطری در گذشته نزدیک در هیچ یک از دو جمعیت مشاهده نشد. اگرچه وضعیت تنوع ژنتیکی مطلوب ارزیابی شد، اما تمایز ژنتیکی مشاهده‌شده ضرورت توجه به مدیریت جداگانه هر جمعیت و در صورت لزوم، تقویت ارتباط زیستگاهی میان جمعیت‌ها را برجسته می‌کند. این نتایج می‌توانند مبنایی علمی برای تدوین راهبردهای حفاظتی جمعیت‌های آهوی گواتردار را فراهم آورند. 2 Decline in population size may lead to decrease in genetic diversity, increase in inbreeding rate and also decrease in adaptation ability of populations against environmental changes. Therefore, monitoring genetic indices is one of the main tools for understanding the stable situation of species. In this study, the genetic diversity and population structure of the Goitered gazelle (Gazella subgutturosa) in Fars province (Bamou National Park and Basiran No-Hunting Area) were evaluated using 45 scat samples and 15 microsatellites. Indices of genetic variation, inbreeding, structuring, and bottleneck were estimated. Baesed on the results, the observed heterozygosity ranged from 0.34 to 0.95, indicating a relatively high level of genetic diversity in both populations. Additionally, the genetic differentiation index (FST) was estimated at 0.056, revealing a significant level of genetic differentiation between the two regions. This pattern may be resulted from gene flow limitation, geographic distance, and landscape resistance. There was no sign of bottleneck in the populations. Although moderate genetic variation was found, the presence of genetic structuring highlights the importance of improving habitat connectivity and considering the concept of management units in future efforts. These results can provide scientific bases for making conservation strategies and sustainable management of Iranian Goitered gazelles. 55 68 2025/08/42026/01/152026/02/22026/03/7 1404/12/16 2025/12/272026/04/182026/04/212026/05/3 1405/2/13 علی فرزام Ali Farzam گروه محیط زیست، دانشکده منابع طبیعی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، البرز، ایران. ایران alii.farzam@yahoo.com رویا آداودی Roya Adavoudi دکتری ژنتیک تکاملی، دانشکده زیست شناسی، دانشگاه گدانسک، گدانسک، لهستان. لهستان roya.adavoudi@gmail.com رسول خسروی Rasoul Khosravi بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران. ایران r-khosravi@shirazu.ac.ir شروین مقصودلو Shervin Maghsoudloo گروه محیط زیست، دانشکده منابع طبیعی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، البرز، ایران. ایران shervin.maghsoul@ut.ac.ir محمد کابلی Mohammad Kaboli استاد گروه محیط زیست، دانشکده منابع طبیعی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، البرز، ایران. mkaboli@ut.ac.ir Goitered gazelles Genetic diversity and structure Microsatellites Non-invasive sampling آهوی گواتردار تنوع و ساختار ژنتیکی نشانگرهای ریزماهواره نمونه‌برداری غیرتهاجمی 1. Anderson, E.C. and Thompson, E.A., 2002. A model-based method for identifying species hybrids using multilocus genetic data. Genetics, 160: 1217-1229.##2. Asadollahi, M., Ahmadzadeh, F. and Keyghobadi, N., 2024. Unraveling goitered gazelle (Gazella subgutturosa) diversification: insights from phylogeography and species distribution modeling. Contributions to Zoology, 93(3): 201-228.##3. Beja-Pereira, A., Oliveira, R., Alves, P.C., Schwartz, M.K. and Luikart, G., 2009. Advancing ecological understandings through technological transformations in noninvasive genetics. Molecular Ecology Resources, 9: 1279–1301.##4. Chapuis, M.P. and Estoup, A., 2007. Microsatellite null alleles and estimation of population differentiation. Molecular Biology and Evolution, 24: 621–631.## 5. Crooks, K.R., Burdett, C.L., Theobald, D.M., King, S.R., Di Marco, M., Rondinini, C. and Boitani, L., 2017. Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals. Proceedings of the National Academy of Sciences, 114(29): 7635-7640.##6. Dempster, A.P., Laird, N.M. and Rubin, D.B., 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society,Series B (Methodological), 39: 1-22.## 7. DeWoody, J.A., Harder, A.M., Mathur, S. and Willoughby, J. R., 2021. The long‐standing significance of genetic diversity in conservation. Molecular Ecology, 30(17): 4147-4154.##8. Di Stasio, L., 2001. Panels of markers for parentage verification tested at the 2001/02 ISAG Comparison test, ISAG Standing Committee on “Applied Genetics in Sheep and Goats” ISAG Standing Committee.##9. Ellegren, H. and Galtier, N., 2016. Determinants of genetic diversity. Nature Reviews Genetics, 17(7): 422-433.## 10. England, P.R., Luikart, G. and Waples, R.S., 2010. Early detection of population fragmentation using linkage disequilibrium estimation of effective population size. Conservation Genetics, 11(6): 2425-2430.##11. Fadakar, D., Bärmann, E.V., Lerp, H., Mirzakhah, M., Naseri Nasari, M. and Rezaei, H. R., 2020. Diversification and subspecies patterning of the goitered gazelle (Gazella subgutturosa) in Iran. Ecology and Evolution, 10(12): 5877-5891.##12. Falconer, D. S., 1996. Introduction To Quantitative Genetics. Pearson Education India.##13. Frankham, R., 2006. Genetics and landscape connectivity. Conservation Biology Series-Cambridge, 14: 72.##14. Goudet, J., 1995. FSTAT (Version 1.2): a computer program to calculate F-statistics. Heredity, 86: 485–486.##15. Groves, C. and Grubb, P., 2011. Ungulate taxonomy.##16. Guo, S.W. and Thompson, E.A., 1992. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics, 48: 361-372.##17. Hasselgren, M., Dussex, N., von Seth, J., Angerbjörn, A., Dalén, L. and Norén, K., 2024. Strongly deleterious mutations influence reproductive output and longevity in an endangered population. Nature Communications, 15(1): 8378.##18. Hemami, M.R., Khosravi, R., Groves, C. and Ahmadi, M. 2020. Morphological diversity and ecological niche divergence in goitered and sand gazelles. Ecology and Evolution, 10(20): 11535-11548.##19. Hemami, M.R. and Groves C.P., 2001. Global Antelope Survey and Regional Action Plans: Iran. In: Mallon D.P. and Kingswood, S.C. (eds.), Antelopes: Part 4. North Africa, the Middle East and Asia. IUCN Gland, Switzerland and Cambridge, UK: 114-118.##20. Kappes, S.M., Keele, J.W. and Stone, R.T., 1997. A second-generation map of the bovine genome. Genome Research, 7: 235–249## 21. Kardos, M., Armstrong, E.E., Fitzpatrick, S.W., Hauser, S., Hedrick, P.W., Miller, J.M. and Funk, W.C., 2021. The crucial role of genome-wide genetic variation in conservation. Proceedings of the National Academy of Sciences, 118(48): e2104642118.##22. Khosravi, R., Hemami, M.R., Malekian, M., Silva, T.L., Rezaei, H.R. and Brito, J.C., 2018. Effect of landscape features on genetic structure of the goitered gazelle (Gazella subgutturosa) in Central Iran. Conservation Genetics, 19(2): 323-336.##23. Kumar, S., Dixit, S.P. and Verma, N.K., 2009 Genetic diversity analysis of the Gohilwari breed of Indian goat (Capra hircus) using microsatellite markers. American Journal of Animal Veterinary Sciences, 4: 49–57##24. Mahmoudi, D., Khosravi, R., Kaboli, M. and Adavoudi, R., 2024. Genetic variation and population structure of wild sheep in Bamou National Park in Fars province using microsatellite markers. Iranian Journal of Applied Ecology, 12(4): 45-56. (In Persian)##25. Naghavi, M.R., Ghariazi, B. and Hosseini Salekdeh, Gh., 2007. Molecular Markers. University of Tehran Press, Tehran (In Persian).## 26. Pinto, A. V., Hansson, B., Patramanis, I., Morales, H.E. and van Oosterhout, C., 2024. The impact of habitat loss and population fragmentation on genomic erosion. Conservation Genetics, 25(1): 49-57.## 27. Pinto. P.V., Lopes, S. and Mourão, S., 2015. First estimates of genetic diversity for the highly endangered giant sable antelope, using a set of 57 microsatellites. European Journal of Wildlife Resource, 61: 313–317.##28. Pritchard, J.K., Stephens, M. and Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics, 155: 945–959.## 29. Randi, E. and Lucchini, V., 2002. Detecting rare introgression of domestic dog genes into wild wolf (Canis lupus) population by Bayesian admixture analyses of microsatellite variation. Conservation Genetics, 3: 31-45.##30. Raymond, M. and Rousset, F., 1995. An exact test for population differentiation. Evolution, 49: 1283-1286.##31. Rice, W.R., 1989. Analyzing tables of statistical tests. Evolution, 43: 223–225.##32. Rocha, E.C., Brito, D., Silva, J., Bernardo, P.V.D.S. and Juen, L., 2018. Effects of habitat fragmentation on the persistence of medium and large mammal species in the Brazilian Savanna of Goiás State. Biota Neotropica, 18: e20170483.##33. Shi, L., Yang, X., Cha, M., Lyu, T., Wang, L., Zhou, S. and Zhang, H., 2023. Genetic diversity and structure of Mongolian gazelle (Procapra gutturosa) populations in fragmented habitats. BMC Genomics, 24(1): 507.##34. Taberlet, P., Waits, L.P. and Luikart, G., 1999. Noninvasive genetic sampling: look before you leap. Trends in Ecology and Evolution, 14: 323–327.##35. Toro, M.A. and Caballero, A., 2005. Characterization and conservation of genetic diversity in subdivided populations. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1459): 1367-1378.##36. Turnock, M.F., Teisberg, J.E., Kasworm, W.F., Falcy, M.R., Proctor, M.F. and Waits, L.P., 2025. Gene flow prevents genetic diversity loss despite small effective population size in fragmented grizzly bear (Ursus arctos) populations. Conservation Genetics, 26(2): 279-291.##37. Vaiman, D., Mercier, D. and Moazami-Goudarzi, K., 1994. A set of cattle microsatellites: characterization, synteny mapping, and polymorphism. Mammal Genome, 5: 288–297.##38. Van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M. and Shipley, P., 2004. MICROCHECKER: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Research, 4: 535–538.##39. Weir, B.S. and Cockerham, C.C., 1984. Estimating F-statistics for the analysis of population structure. Evolution, 38: 1358-1370.## 40 Willi, Y., Van Buskirk, J. and Hoffmann, A.A., 2006. Limits to the adaptive potential of small populations. Annual Review of Ecology, Evolution, and Systematics, 37(1): 433-458.## ##

پهنه‌بندی خطر پراکنش پروانه جوانه‌خوار بلوط (.Tortrix viridana L) در جنگل‌های سروآباد استان کردستان، ایران Hazard zonation of the Green Oak Tortrix (Tortrix viridana L.) in the forests of Sarvabad, Kurdistan Province, Iran 1 1 پروانه جوانه‌خوار بلوط از آفات مهم جنگل‌های زاگرس است که با تغذیه از برگ و جوانه، موجب ضعف شدید درختان می‌شود. با وجود تلاش برای مهار این آفت، اطلاعات دقیق درباره پراکنش آفت و مناطق بحرانی محدود است. این پژوهش با هدف مدل‌سازی احتمال حضور آفت و پهنه‌بندی خطر آن در جنگل‌های سروآباد با استفاده از رگرسیون لجستیک انجام شد. بدین منظور ۶۱ نقطه آلوده و ۵۷ نقطه غیرآلوده به‌صورت تصادفی نمونه‌برداری شد. ویژگی‌های مکانی شامل ارتفاع از سطح دریا، جهت، شیب و فاصله از اراضی کشاورزی، مناطق مسکونی، جاده‌ها و رودخانه‌ها در محیط GIS استخراج و به‌عنوان متغیرهای پیش‌بینی‌کننده وارد مدل شدند. نتایج نشان داد ارتفاع، شیب، فاصله از مناطق مسکونی و فاصله از جاده اثر منفی و معنی‌داری بر احتمال حضور آفت دارند. شاخص‌های برازش مدل  ضریب تعیین نک‌جرکی (۰٫۵۰۲)، آزمون هازمر-لمشاو (۰٫۷۰۷ = p-value)، سطح زیر منحنی راک (۰٫۸۶۴) و صحت کلی طبقه‌بندی (۷۵٫۴ درصد) بود. بر اساس نقشه پهنه‌بندی، ۲۲ درصد از جنگل‌های سروآباد در زون پرخطر، 11/2 درصد در زون متوسط و بقیه در زون کم‌خطر قرار گرفتند. بر پایه یافته‌ها، عوامل شناسایی‌شده نقش مهمی در تبیین الگوی حضور آفت در جنگل‌های سروآباد دارند و به‌کارگیری نتایج می‌تواند کارایی برنامه‌های مدیریتی و اقدامات کنترلی را افزایش دهد.  2 The green oak tortrix (Tortrix viridana) is a significant pest affecting the Zagros forests, where larval feeding on leaves and buds severely weakens oak trees. Despite ongoing control efforts, comprehensive data on its distribution and critical risk areas remain scarce. This study aimed to model the probability of pest occurrence and delineate hazard zones in the Sarvabad forests using logistic regression. We randomly sampled 61 infested and 57 non-infested points. Spatial variables, including elevation, aspect, slope, and distances to agricultural lands, residential areas, main roads, and rivers, were extracted in a GIS environment and utilized as predictors. Our results indicated that elevation, slope, distance from residential areas, and distance from roads significantly negatively influenced the probability of pest presence. Model fit indices (Nagelkerke R² = 0.502, Hosmer–Lemeshow, AUC = 0.864, and an overall classification accuracy of 75.4%) demonstrated good predictive performance. The hazard zonation map revealed that 22% of the Sarvabad forests were classified as high risk, 11.2% as moderate risk, and the remainder as low risk. These findings highlight the influential factors that explain the spatial distribution of the pest and suggest that applying these results can improve management strategies and control measures. 69 84 2025/08/42026/01/152026/02/22026/03/72026/01/29 1404/11/9 2025/12/272026/04/182026/04/212026/05/32026/05/9 1405/2/19 احمد ولی پور Ahmad Valipour گروه جنگلداری و مرکز پژوهش و توسعه جنگلداری زاگرس شمالی دکتر هدایت غضنفری، دانشگاه کردستان، سنندج، ایران. ایران ahmadvalipour@uok.ac.ir مهتاب پیرباوقار Mahtab Pir Bavaghar گروه جنگلداری و مرکز پژوهش و توسعه جنگلداری زاگرس شمالی دکتر هدایت غضنفری، دانشگاه کردستان، سنندج، ایران. ایران m.bavaghar@uok.ac.ir عبدالله نادری Abdollah Naderi دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران. ایران abdnaderi62@gmail.com green oak leaf-roller moth logistic regression Zagros forests environmental and anthropogenic factors GIS distribution modeling پروانه جوانه‌خوار بلوط رگرسیون لجستیک جنگل‌های زاگرس عوامل محیطی و انسانی GIS مدل‌سازی پراکنش 1. Talebi, K.S., Sajedi, T. and Pourhashemi, M., 2014. Forests of Iran: A Treasure from the Past, a Hope for the Future. Springer.##2. Askary, H., Tabrizian, M., Zargaran, M.R., Alemansoor, H., Ghazi, M.M., Barimani, M.H. and Ajam Hassani, M., 2009. Evaluation of trap shape and pheromone dispensers in capturing male Tortrix viridana (Lep.: Tortricidae). Applied Entomology and Phytopathology, 77(1): 33–50. (In Persian)##3. Ghobari, H., Goldansaz, S.H., Askari, H., Ashouri, A., Kharazi-Pakdel, A. and Bihamta, M.R., 2007. Investigation of presence, distribution and flight period of oak leaf roller moth, Tortrix viridana using pheromone traps in Kurdistan province. Journal of Entomological Society of Iran, 27(1): 47–59. (In Persian)##4. Zargaran, M.R., Mousavi Mirkala, S.R., Banj Shafiei, A. and Ramezani Kakroudi, E., 2015. Survey on biology of Tortrix viridana L. in laboratory and field conditions and its distribution in West-Azerbaijan. Forest Research and Development, 1(1): 31–42. (In Persian)##5. Flower, C.E. and Gonzalez-Meler, M.A., 2015. Responses of temperate forest productivity to insect and pathogen disturbances. Annual Review of Plant Biology, 66: 547–569. ##6. Munro, H.L., Montes, C.R., Gandhi, K.J.K. and Poisson, M.A., 2022. A comparison of presence-only analytical techniques and their application in forest pest modelling. Ecological Informatics, 68: 101525.##7. Rusch, A., Valantin-Morison, M., Roger-Estrade, J. and Sarthou, J.P., 2012. Using landscape indicators to predict high pest infestations and successful natural pest control at the regional scale. Landscape and Urban Planning, 105(1–2): 62–73.##8. Alijanpour, A., Zargaran, M.R. and Motallebi, R., 2016. Survey on nutritional indices of green oak leaf roller (Tortrix viridana L.) in grouping and individual nutrition methods. Forest Research and Development, 1(3):181–193. (In Persian)##9. Alehosseini, S.A., Saadati, S.H. and Zarghani, H.H., 2013. Study of population dynamics of oak tortrix moth (Tortrix viridana) and its natural enemies in Fars province. Plant Protection Journal, 5:1–12. (In Persian)##10. Valipour, A., Plieninger, T., Shakeri, Z., Ghazanfari, H., Namiranian, M. and Lexer, M.J., 2014. Traditional silvopastoral management and its effects on forest stand structure in northern Zagros, Iran. Forest Ecology and Management, 327: 221–230.##11. Seifi, S., Madadi, H., Ghobari, H. and Pir-Bavaghar, M., 2023. Studying the effective factors of spatial distribution of Tortrix viridana L. in Mariwan oak forests. Iranian Journal of Forest and Range Protection Research, 21(2): 337–349. (In Persian)##12. Rubtsov, V.V. and Utkina, I.A., 2003. Interrelations of green oak leaf roller population and common oak: results of 30-year monitoring and mathematical modeling. In: McManus, M.L. and Liebhold, A.M. (Eds.), Proceedings: Ecology, Survey and Management of Forest Insects, 1–5 September 2002, Krakow, Poland. Gen. Tech. Rep. NE-311. USDA Forest Service: 90–97.##13. Schroeder, H. and Degen, B., 2008. Spatial genetic structure in populations of the green oak leaf roller, Tortrix viridana L. European Journal of Forest Research, 127(6): 447–453.##14. Montgomery, D.C. and Runger, G.C., 2011. Applied Statistics and Probability for Engineers. 5th ed. Hoboken: John Wiley & Sons.##15. Ghanbari, F., Shataee Joybari, S., Azim Mohseni, M. and Habashi, H., 2011. Application of topography and logistic regression in forest type spatial prediction. Iranian Journal of Forest and Poplar Research, 19(1): 27–41. (In Persian)##16. Jafarian, Z., Arzani, H., Jafari, M., Zahedi, Gh. and Azarnivand, H., 2012. Mapping spatial prediction of plant species using logistic regression (Case study: Rineh Rangeland, Damavand Mountain). Physical Geography Research, 44(1):1–18. (In Persian)##17. Modares Gorji, H., Pir Bavaghar, M. and Ghahramany, L., 2014. Modeling distribution of forest types of Armardeh forests (Baneh) using logistic regression method. Forest and Poplar Research, 21(4): 629–642. (In Persian)##18. Khaledi, S., Derafshi, K., Mehrjunejad, A., Gharachahi, S. and Khaledi, S., 2012. Assessment of landslide effective factors and zonation using logistic regression in GIS environment: Taleghan Watershed case study. Journal of Geography and Environmental Hazards, 1(1): 65–82. (In Persian)##19. Mohammadi, F., Pir Bavaghar, M. and Shabanian, N., 2014. Application of artificial neural network for forest fire risk mapping based on physiographic, human and climatic factors in Sarvabad, Kurdistan Province. Forest and Poplar Research, 11(2): 97–107. (In Persian)##20. Mladenoff, D.J., Sickley, T.A. and Wydeven, A.P., 1999. Predicting grey wolf landscape colonization: logistic regression models vs. new field data. Ecological Applications, 9: 37–44.##21. Arekhi, S., Mahmoudian, A. and Emadaddian, S., 2022. Forest degradation using GIS and logistic regression (Case study: Forests of Sardasht). Journal of Geography and Environmental Hazards, 10(4): 69–92. (In Persian)##22. Zhou, H., Xu, Z., Chen, Y., Yan, Y., Zhang, S., Lin, X., Cui, D. and Yang, J., 2025. The combined multilayer perceptron and logistic regression (MLP-LR) method better predicted the spread of Hyphantria cunea. Journal of Economic Entomology, 118(3): 1156–1173.##23. Beers, T.W., Press, P.E. and Wensel, L.C., 1996. Aspect transformation in site productivity research. Journal of Forestry, 64: 691–692.##24. StatSoft Inc., 2013. STATISTICA Formula Guide: Logistic Regression (Version 1.1). Available at: https://www.statsoft.com##25. Pontius Jr., R.G. and Schneider, L.C., 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems and Environment, 85: 239–248.##26. Yesilnacar, E. and Topal, T., 2005. Landslide susceptibility mapping: A comparison of logistic regression and neural networks in a medium-scale study, Hendek region (Turkey). Engineering Geology, 79: 251–266.##27. Mirzaei Zadeh, V., Mahdavi, A., Karmshahi, A. and Jaefarzadeh, A.A., 2016. Investigation of the spatial pattern of forest cover changes using logistic regression in Malekshahi. Journal of Wood & Forest Science and Technology, 23(3): 45–68. (In Persian)##28. Raffa, K.F., Aukema, B.H., Bentz, B.J., Carroll, A.L., Hicke, J.A., Turner, M.G. and Romme, W.H., 2008. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: the dynamics of bark beetle eruptions. BioScience, 58(6): 501–517.##29. Liebhold, A.M., 2012. Forest pest management in a changing world. International Journal of Pest Management, 58(3): 289–295.##30. Kocacinar, F., Kezik, U. and Eroglu, M., 2014. Larval development and behavior of oak leaf roller depending on Brant’s oak phenology. In: Proceedings of the Turkey II Forest Entomology and Pathology Symposium, Antalya, Turkey.## ##

اثر سنجه های سیمای سرزمین بر فراوانی و غنای پرندگان ساحلی در استان هرمزگان The Effect of Landscape Metrics on the Abundance and Species Richness of shorebirds in Hormozgan Province 1 1 اکوسیستم‌های ساحلی از زیستگاه‌های کلیدی پرندگان مهاجر، به‌ویژه در مناطقی هستند که نواحی جزرومدی با جنگل‌های مانگرو همپوشانی دارند. این زیستگاه‌ها شرایط مناسبی برای استقرار و تغذیه پرندگان ساحلی فراهم می‌کنند. با این حال، نقش سنجه‌های سیمای سرزمین در تبیین الگوهای غنا و فراوانی این پرندگان هنوز به‌طور کامل شناخته نشده است. در این پژوهش، با استفاده از داده‌های سرشماری پرندگان ساحلی و تصاویر ماهواره‌ای Sentinel-2، اثر سنجه‌های سیمای سرزمین مرتبط با جنگل‌های مانگرو و نواحی جزرومدی بر غنا و فراوانی پرندگان ساحلی در ۲۳ سایت استان هرمزگان طی سال‌های ۱۴۰۱ و ۱۴۰۲ بررسی شد. پس از استخراج نقشه‌های مانگرو و پهنه‌های جزرومدی، سنجه‌های سیمای سرزمین محاسبه و روابط آن‌ها با جوامع پرندگان با استفاده از مدل‌های ترکیبی خطی تعمیم‌یافته بیزین تحلیل شد. نتایج نشان داد میانگین فراوانی پرندگان در نواحی جزرومدی دارای مانگرو به‌طور معنی‌داری بیشتر از سایت‌های فاقد مانگرو بود (۰۵/۰>p) و این مناطق از نظر ترکیب جوامع پرندگان نیز متمایز بودند. طول نواحی جزرومدی و تراکم حاشیه‌ای لکه‌های مانگرو اثر مثبت و معنی‌داری بر فراوانی و غنای پرندگان داشت، در حالی که اندازه لکه‌های مانگرو تأثیر معنی‌داری نشان نداد. این یافته‌ها اهمیت ساختار و آرایش فضایی زیستگاه‌ها را در حفاظت از پرندگان ساحلی خلیج فارس نشان می‌دهد. 2 Coastal ecosystems are key habitats for migratory birds, particularly where intertidal zones overlap with mangrove forests, providing suitable conditions for shorebird roosting and foraging. However, the role of landscape metrics in explaining patterns of shorebird richness and abundance in these habitats remains poorly understood. In this study, shorebird census data and Sentinel-2 satellite imagery were used to investigate the effects of landscape metrics associated with mangrove forests and intertidal zones on shorebird richness and abundance across 23 sites in Hormozgan Province during 2022–2023. Following the extraction of mangrove and intertidal habitat maps, landscape metrics were calculated and their relationships with bird communities were analyzed using Bayesian generalized linear mixed models. Results showed that mean shorebird abundance was significantly higher in intertidal areas containing mangroves than in sites without mangroves (p < 0.05), and these areas also exhibited distinct bird community compositions. Intertidal zone length and mangrove edge density had significant positive effects on bird abundance and richness, whereas mangrove patch size showed no significant effect. These findings highlight the importance of habitat structure and spatial configuration for shorebird conservation in the Persian Gulf. 85 100 2025/08/42026/01/152026/02/22026/03/72026/01/292026/02/4 1404/11/15 2025/12/272026/04/182026/04/212026/05/32026/05/92026/05/13 1405/2/23 شیرکو شکری Shirko Shokri دانشجوی دکتری علوم و مهندسی محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، ایران. ایران s.shokri@na.iut.ac.ir محمودرضا همامی Mahmoud-Reza Hemami گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، ایران. ایران mrhemami@iut.ac.ir ایمان ابراهیمی Iman Ebrahimi عضو انجمن حفاظت از پرندگان آوای بوم ، ایران. ایران i.ebrahimi1212@gmail.com میثم قاسمی Maysam Ghasemi معاون محیط طبیعی اداره محیط زیست استان هرمزگان، ایران. ایران maysamghasemi@yahoo.com فاطمه کاظمی Fatemeh Kazemi عضو انجمن حفاظت از پرندگان آوای بوم ، ایران. ایران fateme.kazemi.r@gmail.com فهیمه گودرزی Fahimeh Ghoudarzi رییس اداره حیات وحش اداره محیط زیست استان هرمزگان، ، ایران. ایران Fg1360@gmail.com سعید پورمنافی Saeed Pourmanafi گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، ایران. ایران spourmanafi@iut.ac.ir محسن احمدی Mohsen Ahmadi گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، ایران. ایران mahmadi@iut.ac.ir Mangrove ecosystems patch size tidal flats Bayesian model. اکوسیتم های مانگرو اندازه لکه نواحی جزر و مدی مدل بیزین 1. Aloysius, N., Madhushanka, S. and Chandrika, C., 2023. Avifaunal Diversity and Abundance in the Proposed Sarasalai Mangrove Reserve, Jaffna, Sri Lanka. Birds, 4(1): 103-116.##2. Armitage, A.R., Weaver, C.A., Whitt, A.A. and Pennings, S.C., 2021. Effects of mangrove encroachment on tidal wetland plant, nekton, and bird communities in the Western Gulf of Mexico. Estuarine, Coastal and Shelf Science: 248: 106767.##3. Baek, S. and Kim, W., 2025. Review on hyperspectral remote sensing of tidal zones. Ocean Science Journal, 60(1): 3.##4. Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C. and Silliman, B.R., 2011. The value of estuarine and coastal ecosystem services. Ecological Monographs, 81(2): 169-193.##5. Beatley, T., 1991. Protecting biodiversity in coastal environments: introduction and overview. Coastal Management, 19(1): 1-19.##6. Bihamta Toosi, N., Soffianian, A. and Fakheran, S., 2025. The Application of Landscape Ecology in Monitoring Changes in the Spatial Patterns of Mangrove Ecosystems (Case Study: Qeshm Mangrove Forests). Ecology of Iranian Forest, 13(2): 91-101. ##7. Buelow, C. and Sheaves, M., 2015. A birds-eye view of biological connectivity in mangrove systems. Estuarine, Coastal and Shelf Science, 152: 33-43.##8. Buelow, C.A., Baker, R., Reside, A.E. and Sheaves, M., 2017. Spatial dynamics of coastal forest bird assemblages: the influence of landscape context, forest type, and structural connectivity. Landscape Ecology, 32(3): 547-561.##9. Bürkner, P.-C., and Vuorre, M., 2019. Ordinal regression models in psychology: A tutorial. Advances in Methods and Practices in Psychological Science, 2(1): 77–101. ##10. Bürkner, P.-C., 2017. brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1), 1–28. ##11. Cai, S., Mu, T., Peng, H.B., Ma, Z. and Wilcove, D.S., 2024. Importance of habitat heterogeneity in tidal flats to the conservation of migratory shorebirds. Conservation Biology, 38(1): 14153.##12. Chen, C., Zhang, C., Tian, B., Wu, W. and Zhou, Y., 2023. Tide2Topo: A new method for mapping intertidal topography accurately in complex estuaries and bays with time-series Sentinel-2 images. ISPRS Journal of Photogrammetry and Remote Sensing, 200: 55-72.##13. Clarke, K.R., 1993. Non‐parametric multivariate analyses of changes in community structure. Australian journal of ecology, 18(1), 117-143.##14. Diskin, M.S. and Smee, D.L., 2017. Effects of black mangrove Avicennia germinans expansion on salt marsh nekton assemblages before and after a flood. Hydrobiologia, 803(1): 283-294.##15. Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D., and Lautenbach, S., 2013. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1): 27–46. ##16. Fitton, J.M., Rennie, A.F., Hansom, J.D. and Muir, F.M., 2021. Remotely sensed mapping of the intertidal zone: A Sentinel-2 and Google Earth Engine methodology. Remote Sensing Applications: Society and Environment, 22: 100499.##17. R., Danehkar, A., Alambeigi, A., Alizadeh Shabani, A. and Sobhani, P., 2025. Comparison and prioritization of environmental threats in natural habitats of mangrove forests of Iran. Journal of Natural Environmental Hazards, 14(44), 37-62.##18. Ghasemi, S., Mola-Hoveizeh, N., Zakaria, M., Ismail, A. and Tayefeh, F.H., 2012. Relative abundance and diversity of waterbirds in a Persian Gulf mangrove forest, Iran. Tropical Zoology, 25(1): 39-53.##19. Ghasemi, S., Zakaria, M., Abdul-Hamid, H., Yusof, E., Danehkar, A. and Rajpar, M.N., 2010. A review of mangrove value and conservation strategy by local communities in Hormozgan province, Iran. Journal of American Science, 6(10): 29-338.##20. Gimenez, O., Royle, A., Kéry, M., and Nater, C., 2024. Ten quick tips to get you started with Bayesian statistics. PLOS Computational Biology, 20(2), 1011876. ##21. Goldberg, L., Lagomasino, D., Thomas, N. and Fatoyinbo, T., 2020. Global declines in human‐driven mangrove loss. Global Change Biology, 26(10): 5844-5855.##22. Graells, G., Celis-Diez, J.L., Corcoran, D. and Gelcich, S., 2022. Bird communities in coastal areas. effects of anthropogenic influences and distance from the coast. Frontiers in Ecology and Evolution, 10, 807280.##23. Hesselbarth, M.H., Sciaini, M., With, K.A., Wiegand, K. and Nowosad, J., 2019. landscapemetrics: an open‐source R tool to calculate landscape metrics. Ecography, 42(10): 1648-1657.##24. Holm, K.J. and Burger, A.E., 2002. Foraging behavior and resource partitioning by diving birds during winter in areas of strong tidal currents. Waterbirds, 25(3): 312-325.##25. Jia, M., Wang, Z., Mao, D., Ren, C., Wang, C. and Wang, Y., 2021. Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine. Remote Sensing of Environment, 255: 112285. ##26. Ju, C., Fu, D., Lyne, V., Xiao, H., Su, F. and Yu, H., 2025. Global declines in mangrove area and carbon‐stock from 1985 to 2020. Geophysical Research Letters, 52(8): e2025GL115303.##27. Kéry, M., and Royle, A., 2021. Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS. Volume 2: Dynamic and Advanced Models. Academic Press, Elsevier, the Netherlands.##28. Kéry, M., and Royle, J. A., 2016. Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS. Volume 1: Prelude and Static Models. Academic Press, Elsevier, the Netherlands.##29. Kéry, M., and Schaub, M., 2011. Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective. Academic Press, The Netherlands.##30. Kirby, J.S., Stattersfield, A.J., Butchart, S.H., Evans, M.I., Grimmett, R.F., Jones, V.R., O'Sullivan, J., Tucker, G.M. and Newton, I., 2008. Key conservation issues for migratory land-and waterbird species on the world's major flyways. Bird Conservation International, 18(S1): 49-S73.##31. Marley, G., Lawrence, A.J., Phillip, D.A. and Hayden, B., 2019. Mangrove and mudflat food webs are segregated across four trophic levels, yet connected by highly mobile top predators. Marine Ecology Progress Series, 632: 13-25.##32. Mohd-Azlan, J. and Lawes, M.J., 2011. The effect of the surrounding landscape matrix on mangrove bird community assembly in north Australia. Biological Conservation, 144(9): 2134-2141.##33. Mohd-Azlan, J., Noske, R.A. and Lawes, M.J., 2012. Avian species-assemblage structure and indicator bird species of mangroves in the Australian monsoon tropics. Emu-Austral Ornithology, 112(4): 287-297.##34. Mohd-Azlan, J., Noske, R.A. and Lawes, M.J., 2015. The role of habitat heterogeneity in structuring mangrove bird assemblages. Diversity, 7(2): 118-136.##35. Mohd-Taib, F.S., Mohd-Saleh, W., Asyikha, R., Mansor, M.S., Ahmad-Mustapha, M., Mustafa-Bakray, N.A., Mod-Husin, S., Md-Shukor, A., Amat-Darbis, N.D. and Sulaiman, N., 2020. Effects of anthropogenic disturbance on the species assemblages of birds in the back mangrove forests. Wetlands Ecology and Management, 28(3): 479-494.##36. Mulyani, Y.A., 2004. Reproductive ecology of tropical mangrove-dwelling warblers: the roles of nest predation, brood parasitism and food limitation. Charles Darwin University (Australia).##37. Murray, N.J., Phinn, S.R., DeWitt, M., Ferrari, R., Johnston, R., Lyons, M.B., Clinton, N., Thau, D. and Fuller, R.A., 2019. The global distribution and trajectory of tidal flats. Nature, 565(7738): 222-225.##38. Nixon, S.W., Oviatt, C.A., Frithsen, J. and Sullivan, B., 1986. Nutrients and the productivity of estuarine and coastal marine ecosystems. Journal of the Limnological Society of Southern Africa, 12(1-2): 43-71.##39. Nourani, E., Kaboli, M. and Collen, B.E.N., 2015. An assessment of threats to Anatidae in Iran. Bird Conservation International, 25(2): 242-257.##40. Oksanen, J., 2015. Vegan: community ecology package. R package version, 2, p.3.##41. Raman, T.S., 2006. Effects of habitat structure and adjacent habitats on birds in tropical rainforest fragments and shaded plantations in the Western Ghats, India. Biodiversity and Conservation, 15(4): 1577-1607.##42. Ramli, R. and Norazlimi, N.A., 2016. Effects of tidal states and time of day on the abundance and behavior of shorebirds utilizing tropical intertidal environment. Journal of Animal and Plant Sciences, 26(4): 1164-1171.##43. Rashvand, S. and Sadeghi, S.M., 2013. Distribution, characteristics and economic importance of mangrove forests in Iran. In Mangrove ecosystems of Asia: Status, challenges and management strategies. New York, NY: Springer New York. 95-126##44. Rodrigues-Filho, J.L., Macêdo, R.L., Sarmento, H., Pimenta, V.R., Alonso, C., Teixeira, C.R., Pagliosa, P.R., Netto, S.A., Santos, N.C., Daura-Jorge, F.G. and Rocha, O., 2023. From ecological functions to ecosystem services: linking coastal lagoons biodiversity with human well-being. Hydrobiologia, 850(12): 2611-2653.##45. Santos, C.D., Catry, T., Dias, M.P. and Granadeiro, J.P., 2023. Global changes in coastal wetlands of importance for non-breeding shorebirds. Science of the Total Environment, 858: 159707. ##46. Schummer, M. L., Kaminski, R. M., Raedeke, A. H., and Graber, D. A., 2010. Weather-related indices of autumn–winter dabbling duck abundance in middle North America. Journal of Wildlife Management, 74(1): 94-101. ##47. Smee, D.L., Sanchez, J.A., Diskin, M. and Trettin, C., 2017. Mangrove expansion into salt marshes alters associated faunal communities. Estuarine, Coastal and Shelf Science, 187: 306-313.##48. Soofi, M., Qashqaei, A. T., Trei, J.-N., Shokri, S., Selyari, J., Ghasemi, B., Sepahvand, P., Egli, L., Nezami, B., Zamani, N., Yusefi, G. H., Kiabi, B. H., Balkenhol, N., Royle, A., Pavey, C. R., Redpath, S. M., and Waltert, M. , 2022. A novel application of hierarchical modelling to decouple sampling artifacts from socio-ecological effects on poaching intensity. Biological Conservation, 267: 109488. ##49. Tian, P., Zhang, F., Zhang, H., Wang, L., Zeng, H., Liu, Y. and Li, J., 2025. Dynamics of coastal wetlands and their impacts on migratory bird habitats in China-Southeast Asia-South Asia. Ocean and Coastal Management, 270: 107883.##50. Toosi, N.B., Soffianian, A.R., Fakheran, S., Pourmanafi, S., Ginzler, C. and Waser, L.T., 2019. Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, 19: 00662.##51. Tran, T.V., Reef, R. and Zhu, X., 2022. A review of spectral indices for mangrove remote sensing. Remote Sensing, 14(19): 4868.##52. Vehtari, A., Gelman, A., and Gabry, J., 2017. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27(5): 1413–1432. ##53. Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., and Bürkner, P.-C. 2021. Rank-normalization, folding, and localization: An improved Rˆ for assessing convergence of MCMC (with discussion). Bayesian Analysis, 16(2): 667-718. ##54. Watson, J.E., Whittaker, R.J. and Dawson, T.P., 2004. Habitat structure and proximity to forest edge affect the abundance and distribution of forest-dependent birds in tropical coastal forests of southeastern Madagascar. Biological Conservation, 120(3): 311-327.##55. Zahed, M.A., Rouhani, F., Mohajeri, S., Bateni, F. and Mohajeri, L., 2010. An overview of Iranian mangrove ecosystems, northern part of the Persian Gulf and Oman Sea. Acta Ecologica Sinica, 30(4): 240-244.##56. Zar, J.H., 2010. Biostatistical analysis pearson prentice-hall. Upper Saddle River, NJ.## ##