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ارزیابی و ارزش‌گذاری اقتصادی خدمات اکوسیستمی پیشگیری از فرسایش خاک و تثبیت رسوب در تالاب بین‌المللی حرای خورخوران Assessment and Economic Valuation of Ecosystem Services for Soil Erosion Prevention and Sediment Retention in the Khorkhoran International Mangrove Wetland 1 1 اکوسیستم‌های حرا نقش مهمی در کاهش فرسایش خاک و تثبیت رسوب دارند. این پژوهش به ارزیابی و ارزش‌گذاری اقتصادی خدمات اکوسیستمی پیشگیری از فرسایش خاک و انتقال رسوب در تالاب بین‌المللی حرای خورخوران در استان هرمزگان پرداخته است. مدل SDR از مجموعه InVEST با استفاده از معادله RUSLE برای شبیه‌سازی توزیع مکانی فرسایش، انباشت و تثبیت رسوبات به کار گرفته شد. نتایج مدل‌سازی نشان داد که میزان فرسایش سالیانه در محدوده مورد مطالعه بین ۰ تا ۳۵۰ تن در هکتار متغیر است، درحالی‌که در صورت فقدان پوشش گیاهی، این مقدار ممکن است تا ۴۳۰ تن در هکتار افزایش یابد. یافته‌ها نشان داد که پوشش گیاهی سالانه از فرسایش 42112 تن خاک و انتقال 48937 تن رسوب به مجاری آبی جلوگیری می‌کند. ارزش اقتصادی این خدمات به ترتیب 43/37 میلیارد تومان برای پیشگیری از فرسایش خاک و 196/6 میلیارد تومان برای پیشگیری از انتقال رسوب برآورد شد که مجموعاً حدود ۲۴۰ میلیارد تومان است. این نتایج اهمیت حفظ پوشش گیاهی در مدیریت منابع طبیعی را برجسته کرده و مدل SDR را به عنوان ابزاری مؤثر در برآورد کمی و نقشه‌سازی توزیع خدمات اکوسیستمی مورد نظر معرفی می‌کند. 2 Mangrove ecosystems play a crucial role in reducing soil erosion and retaining sediment. This study evaluates and economically values the ecosystem services related to prevention of soil erosion and sediment transfer in the Khorkhoran International Mangrove Wetland in Hormozgan province. The SDR model (from the InVEST suite) and the RUSLE equation was applied to simulate the spatial distribution of erosion, sediment accumulation, and retention. The modeling results showed that annual soil loss in the study area ranged from 0 to 350 tons per hectare, with the potential to increase up to 430 tons per hectare in the absence of vegetation cover. The findings indicated that vegetation prevents approximately 42,112 tons of soil erosion and 48,937 tons of sediment transfer to water bodies annually. The economic value of these services was estimated at 43.37 billion IRR for soil erosion prevention and 196.6 billion IRR for sediment transfer prevention, totaling approximately 240 billion IRR annually. These results emphasize the importance of preserving vegetation cover in natural resource management and suggest the SDR model as an effective tool for quantitative assessment and mapping distribution of the ecosystem services in question. 1 20 2025/04/17 1404/1/28 2025/06/15 1404/3/25 اردوان زرندیان A. Zarandian گروه پژوهشی ارزیابی و مخاطرات محیط زیستی، پژوهشکده محیطزیست و توسعه پایدار، سازمان حفاظت محیطزیست، تهران، ایران. ایران azarandian@gmail.com مجید رمضانی مهریان M. Ramezani Mehrian گروه مطالعات محیطی، پژوهشکده تحقیق و توسعه علوم انسانی (سمت)، تهران، ایران. ایران mehrian@samt.ac.ir فاطمه محمدیاری F. Mohammadyari گروه مهندسی محیط زیست، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران. ایران mohammadyari.f@sku.ac.ir رویا موسی زاده R. Mousazadeh گروه پژوهشی اقتصاد محیطزیست، پژوهشکده محیطزیست و توسعه پایدار، سازمان حفاظت محیطزیست، تهران، ایران. ایران roya.mousazadeh@gmail.com نسرین عزیزی N. Azizi گروه زیستگاه‌های آبی، دفتر حفاظت از زیست‌بوم‌ها و سواحل دریایی، سازمان حفاظت محیط‌زیست، تهران، ایران. ایران nazizi@doe.ir Modeling RUSLE equation Service mapping Soil conservation InVEST SDR model Cost replacement method مدل‌سازی معادله RUSLE نقشه‌سازی خدمات نگهداشت خاک مدل InVEST SDR روش هزینه جایگزینی 1. Abeysingha, N. S., Padminda, H. B. G. D. M., Amarasekara, T., Ray, R. L. and Samarathunga, D. K. 2025. The use of InVEST-SDR model to evaluate soil erosion and sedimentation in the closer catchment of a proposed tropical reservoir in Sri Lanka. International Journal of Sediment Research 40: 253–268.##2. Arnold, J. G., Srinivasan, R., Muttiah, R. S. and Williams, J. R. 1998. Large area hydrologic modeling and assessment part I: model development. Journal of the American Water Resources Association 34: 73–89.##3. Bhattarai, R. and Dutta, D. 2007. Estimation of soil erosion and sediment yield using GIS at catchment scale. Water Resources Management 21(10): 1635–1647.##4. Borselli, L., Cassi, P. and Torri, D. 2008. Prolegomena to sediment and flow connectivity in the landscape: A GIS and field numerical assessment. Catena 75(3): 268–277.##5. Chen, W., Li, J., Yang, X., Zhao, Y. and Han, Y. 2023. Spatiotemporal changes in soil erosion and sediment retention in the Qinghai Lake Basin. Science of the Total Environment 894: 164842.##6. Cinco-Castro, D., Flores-Verdugo, F., Herrera-Silveira, J. A. and Flores de Santiago, F. 2022. Organic carbon accumulation in mangrove soils: role in climate change mitigation. Ecological Indicators 139: 108904.##7. Costanza, R. 2020. The value of ecosystem services: A new approach to economic valuation. Ecological Economics 169: 106554.##8. Dashtbozorgi, F., Hedayatiaghmashhadi, A., Dashtbozorgi, A., Ruiz–Agudelo, C. A., Fürst, C., Cirella, G.T. and Naderi, M. 2023. Ecosystem services valuation using InVEST modeling: Case from southern Iranian mangrove forests. Regional Studies in Marine Science, 60: 102813.##9. Debie, A. and Awoke, A. 2024. Modeling of soil erosion using InVEST SDR model in upper Blue Nile basin, Ethiopia. Modeling Earth Systems and Environment 10: 311–325.##10. Desmet, P. J. J. and Govers, G. 1996. A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. Journal of Soil and Water Conservation 51(5): 427–433.##11. Douglas-Mankin, K. R., Srinivasan, R. and Arnold, J. G. 2010. Soil and Water Assessment Tool (SWAT) model: current developments and applications. Transactions of the ASABE 53(5): 1423–1431.##12. Flanagan, D. C., Gilley, J. E. and Franti, T. G. 2007. Water Erosion Prediction Project (WEPP): Development history, model capabilities, and future enhancements. Transactions of the ASABE 50: 1603–1612.##13. Gashaw, T., Tulu, T., Argaw, M. and Worqlul, A. 2021. Evaluation of soil erosion risk and sediment yield in the Koga watershed, upper Blue Nile basin. Environmental Earth Sciences 80(6): 1–14.##14. Gijsman, R. K., van der Werf, G. R. and Verburg, P. H. 2024. Coastal ecosystems and sea-level rise: Sediment retention capacity of mangroves. Nature Climate Change 14: 215–222.##15. Hiederer, R. 2013. Mapping soil properties for Europe–spatial representation of soil database attributes. Available online at http://publications.jrc.ec.europa.eu. Accessed 11 September 2025.##16. Jafari Takhtinajad, R., Abbaspour, M. and Rajabifard, A. 2019. Impact of land use changes on sediment retention service. Environmental Monitoring and Assessment 191(2): 77.##17. Jian, X., Wang, L. and Zhao, Q. 2024. Economic valuation of sediment retention ecosystem services in river basins. Journal of Environmental Management 341: 117041.##18. Kabolizadeh, M., Rangzan, K. and Mohammadi, S., 2023. Estimation of soil erodibility (k-factor) in Iran using SoilGrids and HWSD spatial databases. Watershed Management Research, 36(1): 13-33. (In Persian).##19. Karunaratne, S. B., Jayasinghe, R. P., Dissanayake, H. 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Modelling sediment retention services and soil erosion changes in Portugal: A spatio-temporal approach. ISPRS International Journal of Geo-Information, 10(4): 262.##26. Meraj, G., Farooq, M., Singh, S. K., Islam, M. N. and Kanga, S. 2022. Modeling the sediment retention and ecosystem provisioning services in the Kashmir valley, India, Western Himalayas. Modeling Earth Systems and Environment 8: 3859–3884.##27. Morgan, R. P. C, 2009. Soil erosion and conservation. John Wiley & Sons.‌ Chichester, UK.##28. Nachtergaele, F., van Velthuizen, H., Verelst, L., Batjes, N., Dijkshoorn, K., van Engelen, V., Fischer, G., Jones, A., Montanarella, L. and Petri, M. 2010. Harmonized world soil database. Proceedings of the 19th World Congress of Soil Science, 34–37.##29. Ougougdal, H. A., Khebiza, M. Y., Messouli, M. and Bounoua, L. 2020. Delineation of vulnerable areas to water erosion in a mountain region using SDR-InVEST model: A case study of the Ourika watershed, Morocco. 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برآورد هزینه - فایده آزادراه بندرعباس- کرمان با استفاده از ارزش مکانی خدمات اکوسیستم Estimation of the Cost-Benefit of the Bandar Abbas-Kerman Freeway Using the Spatial Value of Ecosystem Services 1 1 مداخله در طبیعت از طریق اجرای طرح‌های مختلف توسعه به منظور رفع نیازهای روزافزون جمعیت اجتناب‌ناپذیر است. هر گونه مداخله‌ دارای پیامدهای منفی و مثبت بر خدمات اکوسیستم است. از این رو، در دهه های اخیر ارزیابی اثرات محیط زیستی در سراسر جهان مورد توجه قرار گرفته است. ارزش‌گذاری خدمات اکوسیستم می‌تواند به‌عنوان رویکردی مکمل در تحلیل اثرات محیط‌زیستی، نقش مؤثری در تصمیم‌گیری‌های توسعه‌ای ایفا کند. با این حال، در ارزیابی پروژه‌های عمرانی این موضوع کمتر مدنظر قرار گرفته است. از این‌رو، پژوهش حاضر با تلفیق رویکردهای ارزیابی اثرات محیط‌زیستی و ارزش‌های اقتصادی خدمات اکوسیستم، به برآورد هزینه-فایده احداث آزادراه بندرعباس-کرمان پرداخته است. خدمات اکوسیستمی مورد نظر شامل نگهداشت رسوب، تولید آب، کیفیت زیستگاه، ترسیب کربن، تولید غذا، گردشگری، کیفیت رویشگاه، تولید علوفه و تولید اکسیژن است. نتایج مطالعه با بروزرسانی بر اساس نرخ تورم سالیانه نشان داد که در فاز ساختمانی و بهره‌برداری منتهی به سال 1403 به¬ترتیب 161,842,750,785 و 332,307,472,031 تومان خسارت (هزینه) بر اثر اجرای طرح بر خدمات اکوسیستم تحمیل شده است. همین طور فایده در مرحله ساختمانی در هیچ یک از خدمات اکوسیستمی رخ نداده و در فاز بهره‌برداری فایده‌ای معادل 366,024,608,960 تومان به همراه داشته است و بنابراین خسارت خالص از اجرای این طرح 128,125,613,856 تومان برآورد گردید. 2 Intervention in nature through the implementation of various development projects to meet the growing needs of the population is inevitable. Any intervention has both negative and positive consequences for ecosystem services; therefore, environmental impact assessment has been a focus of attention worldwide for decades. Ecosystem services valuation can serve as a complementary approach in environmental impact assessments, playing a significant role in development-related decision-making. Though, this has been ignored in the evaluation of infrastructure projects. The present study integrates environmental impact assessment approaches with the economic valuation of ecosystem services to conduct a cost-benefit analysis of the Bandar Abbas–Kerman freeway construction project. The ecosystem services considered included sediment retention, water production, habitat quality, carbon sequestration, food production, tourism, vegetation quality, forage, and oxygen production. The results (updated based on the annual inflation rate) showed that the project imposed damages (costs) of 161,842,750,785 tomans and 332,307,472,031 tomans on ecosystem services in the construction and operation phases, respectively. Similarly, no benefit observed in ecosystem services during the construction phase, while the operation phase resulted in a benefit equivalent to 366,024,608,960 tomans. Consequently, the net damage from the implementation of this project was estimated to be 128,125,613,856 tomans. 21 40 2025/04/172025/05/8 1404/2/18 2025/06/152025/07/29 1404/5/7 ملیحه عرفانی M. Erfani دانشکده منابع طبیعی، دانشگاه زابل، زابل، ایران.‌ ایران maliheerfani@uoz.ac.ir فاطمه جهانی شکیب F. Jahanishakib دانشکده منابع طبیعی و محیط زیست، دانشگاه بیرجند، بیرجند، ایران. ایران Jahanishakib@birjand.ac.ir Environmental Impact Assessment (EIA) Damage estimate Economic evaluation Ecosystem services مدل‌سازی ارزیابی اثرات توسعه برآورد خسارت ارزشگذاری اقتصادی خدمات اکوسیستم 1. Acharya, R. P., Maraseni, T. and Cockfield, G. 2019. Global trend of forest ecosystem services valuation–An analysis of publications. Ecosystem Services, 39: 100979. ##2. Ali, S. S. and Batool, A. 2024. Integration of strategic environmental assessment (SEA) in motorway/highway planning and construction: A case study for sustainable infrastructure development in Pakistan. NUST Journal of Natural Sciences, 9(4).##3. Caro-Gonzalez, A. L., Nita, A., Toro, J. and Zamorano, M. 2023. From procedural to transformative: a review of the evolution of effectiveness in EIA. Environmental Impact Assessment Review, 103: 107256. ##4. Elzam Consulting Engineers. 2017. Environmental impact assessment study of the Bandar Abbas–Kerman freeway. Department of Environment of Iran, Kerman. (In Persian)##5. Erfani, M., Ardakani, T. and Jahanishakib, F. 2024. Spatial evaluation of the habitats of rangeland medicinal plants and‎ revealing hot spots of plant‎ habitat quality in Kerman province‎. Journal of Range and Watershed Management, 77(4): 491-505. (In Persian)##6. Erfani, M., Ardakani, T., Joorabian Shoushtari, Sh. and Jahani, F. 2021. Economic valuation report of environmental basic resources in Kerman Province. Department of Environment of Iran, Kerman. (In Persian)##7. Erfani, M., Jahanishakib, F., Ardakani, T. 2023. Habitat quality assessment using InVEST model and its valuation through ‎cost compensation method‎ in ‎Kerman ‎province. Iranian Journal of Applied Ecology, 12(2): 11-23 (In Persian)##8. Erfani, M. and Jahanishakib, F. 2024. Economic valuation of atmospheric gas regulation services through spatial modeling ‎in Kerman province, Iran. ECOPERSIA, 12(4): 391-404.##9. Erfani, M., Joorabian shooshtari, S., Ardakani, T. and Jahanishakib, F. 2023. Spatial gradient modeling of water yield service using InVEST in northern sub-basins of ‎Kerman province, Water and Irrigation Management, 13(1): 63-81. (In Persian)##10. Forbes, V.E. and Calow, P. 2013. Use of the ecosystem services concept in ecological risk assessment of chemicals. Integrated Environmental Assessment and Management, 9(2): 269-275.##11. Gaylard, S., Colella, R., Nelson, M., Lavery, P. and Waycott, M. 2025. Incorporating ecosystem service assessments into development planning − impact from a dredging project in South Australia on seagrass. Ecosystem Services 66: 101738. ##12. Ghobadi, M., Ahmadipari, M. and Pazoki, M. 2020. Assessment of disposal scenarios for solid waste management using fuzzy rapid impact assessment matrix; a case study of Khorramabad Industrial Estate. Pollution 6(3): 531-541.##13. Gong, J., Xu, C. X., Yan, L. L. and Guo, Q. H. 2019. A critical review of progresses and perspectives on ecosystem services from 1997 to 2018. Ying Yong Sheng tai xue bao. The Journal of Applied Ecology, 30(10): 3265-3276.##14. Guo, C., Han, B., Shu, C., Ding, S. and Wang, H. 2023. Research on CLUFS and Its application in rapid prediction of the impact of regulating services value in construction projects. Land, 12(11): 2041. ##15. Kim, C., Butt, A A., Harvey, J. T. and Ostovar, M. 2024. Environmental impacts from traffic on highway construction work zones: Framework and simulations. International Journal of Sustainable Transportation, 18(8): 680-694.##16. Kumi, S., Addo-Fordjour, P. and Fei-Baffoe, B. 2023. Mining-induced changes in ecosystem services value and implications of their economic and relational cost in a mining landscape, Ghana. Heliyon, 9(10): e21156.##17. Landsberg, F., Stickler, M., Henninger, N. and Treweek, J. 2013. Weaving ecosystem services into impact assessment. World Resources Institute, Washington, DC.##18. Mangi, S. C. 2013. The impact of offshore wind farms on marine ecosystems: a review taking an ecosystem services perspective. Proceedings of the IEEE, 101(4): 999-1009.##19. Mobareghee, N. and Barghjelveh, S. 2012. Feasibility of combining two issues “Environmental Impact Assessment” and "Ecosystem Services Valuation” in Iran. Environmental Researches, 2(3): 49-64. (In Persian)##20. Neposhyvailenko, N., Omelych, I. and Dziuba, N. 2024. Assessment of environmental impact of road construction based on results of remote sensing monitoring. Agrology, 7(2): 54-60.##21. Nita, A., Hossu, C. A., Mitincu, C. G. and Iojă, I. C. 2022. A review of the quality of environmental impact statements with a focus on urban projects from Romania. Ecological Informatics, 70: 101723. ##22. Pinheiro, M. D. 2025. Environmental Impact Assessment—Exploring New Frontiers. Environments 12(1): 8.##23. Polasky, S., Tallis, H. and Reyers, B. 2015. Setting the bar: Standards for ecosystem services. Proceedings of the National Academy of Sciences, 112(24): 7356-7361.##24. Qin, X., Wang, Y., Cui, S., Liu, S., Liu, S. and Wangari, V.W. 2023. Post-assessment of the eco-environmental impact of highway construction–A case study of Changbai Mountain Ring Road. Environmental Impact Assessment Review, 98: 106963.##25. Radzuan, H. S. M. and Martin, J. 2024. An evaluation of air quality impact prediction performance undertaken as part of environmental impact assessment (EIA) in India. Heliyon, 10(11): e31263. ##26. Sarraf, M., Owaygen, M., Ruta, G. and Croitoru, L. 2005. Islamic Republic of Iran: Cost assessment of environmental degradation, rural development, water and environment department. Middle East and North African Region. (World Bank, Report No. 32043-IR). Available online at: https://documents1.worldbank.org. Accessed 5 May 2025.##27. Schernewski, G., Jekat, M., Kösters, F., Neumann, T., Steffen, S. and von Thenen, M. 2024. Ecosystem services supporting environmental impact assessments (EIAs): Assessments of navigation waterways deepening based on data, experts, and a 3D ecosystem model. Land, 13(10): 1653. ##28. Shirinkalam, P., Salehi, E. and Jajarmi, H. I. 2025. A practical approach to environmental and social impact assessment of highway construction. Environmental Engineering & Management Journal (EEMJ), 24(1): 79. ##29. Singh, G. G., Lerner, J., Mach, M., Murray, C. C., Ranieri, B., St‐Laurent, G. P., Wong, J., Guimaraes, A., Yunda‐Guarin, G., Satterfield, T. and Chan, K. M. 2020. Scientific shortcomings in environmental impact statements internationally. People and Nature, 2(2): 369-379. ##30. Teixeira, R. F. 2014. Integrating biodiversity and ecosystem services in life cycle assessment: methodological proposals for new challenges. Chemical engineering transactions, 42: 127-132.##31. Tinch, R., Beaumont, N., Sunderland, T., Ozdemiroglu, E., Barton, D., Bowe, C., Börger, T., Burgess, P. J., Cooper, C. N., Faccioli, M., Failler, P., Gkolemi, I., Kumar, R., Longo, A., McVittie, A., Morris, J., Park, J., Ravenscroft, N., Schaafsma, M., Vause, J. and Ziv, G. 2019. Economic valuation of ecosystem goods and services: a review for decision makers. Journal of Environmental Economics and Policy, 8(3): 359–378. ##32. Van der Biest, K., Staes, J., Prigge, L., Schellekens, T., Bonte, D., D’hondt, B., Ysebaert, T., Vanagt, T. and Meire, P. 2023. Integrating ecosystem services into impact assessments: a process-based approach applied to the Belgian coastal zone. Sustainability, 15(21):15506.##33. Wen, M., Zhang, L., Wan, H., Shi, P., Lu, L., Zhao, Z., Zhang, Z. and Wu, J. 2025. Analysis of roadside land use changes and landscape ecological risk assessment based on GF-1: A case study of the linghua expressway. Remote Sensing, 17(2): 211.## ##

ارزیابی مطلوبیت و ارتباط زیستگاهی پلنگ ایرانی در مجموعه حفاظت شده دنا Assessing Habitat Suitability and Connectivity for the Persian Leopard in the Dena Conservation Complex 1 1 تخریب زیستگاه، کاهش طعمه و تعارضات با انسان مهمترین عوامل تهدید پلنگ ایرانی هستند. حفاظت بلند مدت این گونه نیازمند آگاهی از الگوی پراکنش و کریدورهای زیستگاهی گونه است. پژوهش حاضر با هدف بررسی مطلوبیت و ارزیابی کریدورهای پلنگ ایرانی در مجموعه حفاظتی دنا و مناطق پیرامونی به عنوان یکی از زیستگاه های اصلی پلنگ ایرانی در ایران انجام شد. مدلسازی مطلوبیت زیستگاه بر اساس ترکیب پنج مدل در یک مدل اجماعی و بر اساس بسته Biomod2 در نرم افزار R انجام گرفت. از نقشه مطلوبیت زیستگاه برای شناسایی لکه-های زیستگاهی مطلوب و مدلسازی کریدورهای بین آنها بر اساس رویکرد تئوری مدار الکتریکی استفاده شد. تمامی مدل ها از عملکرد مناسبی برای شناسایی زیستگاه های مطلوب برخوردار بودند. متغیرهای حضور طعمه، فاصله از کاربری مسکونی، تراکم کاربری جنگل، ارتفاع و فاصله از جاده به ترتیب بیشترین تاثیر را بر پراکنش پلنگ داشتند. 8/16 درصد از منطقه مطالعاتی دارای مطلوبیت بالا برای پلنگ بوده و این میزان در مناطق تحت حفاظت 20/9 درصد می‌باشد. ارزیابی کریدورها حاکی از وجود جریان قوی درون مناطق تحت حفاظت بوده اما در مناطق پیرامونی جریان ضعیف تر است. بر این اساس، ارتقای سطح حفاظتی خصوصا در مناطق پیرامونی در حفاظت بلند مدت گونه در منطقه ضروری است. 2 Habitat destruction, prey depletion, and human-wildlife conflicts pose significant threats to the Persian leopard. Ensuring long-term conservation of this species requires a comprehensive understanding of its distribution patterns and habitat connectivity. This study assessed the habitat suitability and examined the connectivity of ecological corridors for the Persian leopard within the Dena Conservation Complex and adjacent regions, a key stronghold for the species in Iran. Habitat suitability modeling was conducted using an ensemble approach that combined five models within the Biomod2 package in R software. The habitat suitability map facilitated the identification of key habitat patches and the modeling of corridors between them using the electrical circuit theory approach. All models demonstrated strong performance in identifying suitable habitats. The most influential factors affecting leopard distribution were prey presence, proximity to residential areas, forest density, altitude, and distance from roads. The results showed that 16.8% of the entire area assessed has high level of habitat suitability, increases to 20.9% in areas that are designated as protected zones. Corridor analysis revealed strong connectivity within protected zones but weaker connections in peripheral regions. Therefore, strengthening conservation efforts, especially in peripheral habitats, is crucial to ensuring the long-term conservation of the species across the region. 41 54 2025/04/172025/05/82025/06/10 1404/3/20 2025/06/152025/07/292025/07/29 1404/5/7 محمد توکلی M. Tavakoli دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران. ایران mohammadtavakoli5388@gmail.com محسن احمدی M. Ahmadi گروه محیط زیست دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران. ایران mahmadi@iut.ac.ir منصوره ملکیان M. Malekian گروه محیط زیست دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران. ایران mmalekian@iut.ac.ir علیرضا محمدی A.R. Mohammadi گروه علوم و مهندسی محیط زیست، دانشکده منابع طبیعی، دانشگاه جیرفت، جیرفت، ایران. ایران armohammadi1989@gmail.com Persian leopard Habitat suitability modeling Corridor assessment Core area. پلنگ ایرانی مدلسازی مطلوبیت زیستگاه ارزیابی کریدور زیستگاه‌های هسته 1. Ahmadi, M., Nezami, B., Jowkar, H., Hemami, M. R., Fadakar, D., Malakoutikhah, S. and Ostrowski, S., 2017. Combining landscape suitability and habitat connectivity to conserve the last surviving population of cheetah in Asia. Diversity and Distributions 23: 592–603.##2. Ahmadi. M., Kaboli, M., Hemami, M.R. and Shabani, F., 2023. MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models. Ecology and Evolution 13: 10.1002/ece3.9827.##3. Alemohammad, S., Yavari, A. R., Malek-Mohammadi, B., Salehi, E. and Amiri, M. J., 2022. Landscape conservation and protected areas (case of Dena, Iran). Environmental Monitoring and Assessment 194(2): 54.##4. Allouche, O., Tsoar, A. and Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, Kappa and the True Skill Statistic (TSS). Journal of Applied Ecology 43: 1223-1232.##5. Araújo, M. B. and Guisan, A., 2006. Five (or so) challenges for species distribution modelling. Journal of Biogeography 33: 1677–1688.##6. Araújo, M. B. and New, M., 2007. Ensemble forecasting of species distributions. Trends in Ecology & Evolution 22: 42-47.##7. Bennett, A. and Saunders, D., 2011. Habitat fragmentation and landscape change. pp. 88-106. In: Sodhi, N.S. and Ehrlich, P.R., (Ed.), Conservation Biology for All. Oxford University Press, USA.##8. Bleyhl, B., Gerngross, P., Askerov, E., Farhadinia, M., Ghoddousi, A., Heidelberg, A., Khorozyan, I., Karen, M., Mengüllüoğlu, D. and Ostrowski, S., 2022. Mapping the distribution and habitat of Persian leopard across its historical range. Cat News 15: 9-18.##9. Di Minin, E., Slotow, R., Hunter, L. T., Pouzols, F. M., Toivonen, T., Verburg, P. H., Leader-Williams, N., Petracca, L. and Moilanen, A., 2016. Global priorities for national carnivore conservation under land use change. Scientific Reports 6: 1-9.##10. Dormann, C., Mcpherson, J., Araújo, M., Bivand, R., Bolliger, J., Carl, G., Davies, R., Hirzel, A., Jetz, W. and Kissling, W., 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: A Review. Ecography 30: 609-628.##11. Elliot, N., Cushman, S., Macdonald, D. and Loveridge, A., 2014. The devil is in the dispersers: Predictions of landscape connectivity change with demography. Journal of Applied Ecology 51(5): 1-10.##12. Farhadinia, M., Ahmadi, M., Sharbafi, E., Khosravi, S., Alinezhad, H. and Macdonald, D., 2015. Leveraging trans-boundary conservation partnerships: Persistence of Persian leopard (Panthera pardus saxicolor) in the Iranian Caucasus. Biological Conservation 191: 770-778.##13. Farhadinia, M. S., Gholikhani, N., Behnoud, P., Hobeali, K., Taktehrani, A., Hosseini-Zavarei, F., Eslami, M. and Hunter, L.T., 2016. Wandering the barren deserts of Iran: illuminating high mobility of the Asiatic cheetah with sparse data. Journal of Arid Environments 134: 145–149##14. Farhadinia, M., Soofi, M., Rosen, T., Moghadas, P., Hobeali, K., Behnoud, P., Amanov, A., Dieterich, T., Hojamuradov, H. and Hudaikuliev, N., 2022. Status of Persian leopards in northern Iran and Central Asia. Cat News 15: 29-35.##15. Ghoddousi, A., Bleyhl, B., Askerov, E., Heidelberg, A., Kabir, M., Lortkipanidze, B., Karen, M., Mengüllüoğlu, D., Moheb, Z. and Mousavi, M., 2022. A range-wide monitoring framework for the Persian leopard and its prey. Cat News 15: 61-66.##16. Ghoddousi, A. & Khorozyan, I., 2025. Panthera pardus ssp. tulliana (amended version of 2024 assessment). The IUCN Red List of Threatened Species 2025: e.T15961A274967670. https://dx.doi.org/10.2305/IUCN.UK.20251.RLTS.T15961A274967670.en. Accessed on 07 July 2025.##17. Hanski, I. and Ovaskainen, O., 2003. Metapopulation theory for fragmented landscapes. Theoretical Population Biology 64(1): 119–127.##18. Hemami, M. R., Esmaeili, S., Brito, J. C., Ahmadi, M., Omidi, M. and Martínez-Freiría, F., 2018. Using ecological models to explore niche partitioning within a guild of desert felids. Hystrix, the Italian Journal of Mammalogy 29: 216–222.##19. Kaboodvandpour, S., Almasieh, K. and Zamani, N., 2021. Habitat suitability and connectivity implications for the conservation of the Persian leopard along the Iran–Iraq border. Ecology and Evolution 11: 13464-13474.##20. Kaszta, Ż., Cushman, S. and Macdonald, D., 2020. Prioritizing habitat core areas and corridors for a large carnivore across its range: Range-wide connectivity prioritization. Animal Conservation 23(5): 607-616.##21. Kheibari, V. and Armin, M., 2020. Assessing the ecotourism potential of the Dena Protected Area landscape using a quantitative diversity method. Journal of Natural Environment 73(4): 745-758. (in Persian) ##22. Khosravi, R., Hemami, M. R., Malakoutikhah, S., Ashrafzadeh, M. R. and Cushman, S. A., 2021. Prey availability modulates predicted range contraction of two large felids in response to changing climate. Biological Conservation 255: 109018.##23. McRae, B., Schumaker, N. H., Mckane, R. B., Busing, R. T., Solomon, A. M. and Burdick, C. A., 2008. A multi-model framework for simulating wildlife population response to land-use and climate change. Ecological Modelling 219(1): 77-91.##24. Mohammadi, A., Almasieh, K., Clevenger, A. P., Fatemizadeh, F., Rezaei, A., Jowkar, H. and Kaboli, M., 2018. Road expansion: A challenge to conservation of mammals, with particular emphasis on the endangered Asiatic cheetah in Iran. Journal for Nature Conservation 43: 8–18.##25. Mohammadi, A., Almasieh, K., Nayeri, D., Adibi, M.A. and Wan, H.Y., 2022. Comparison of habitat suitability and connectivity modelling for three carnivores of conservation concern in an Iranian montane landscape. Landscape Ecology 37(2): 411-430.##26. Noroozi, J., Talebi, A., Suen, M. and Schneeweiss, G., 2024. Plant biogeography, endemism and vegetation types of Dena Mts, Zagros, West Iran. Vegetation Classification and Survey 5: 185-202.##27. Omidi, M., Kaboli, M. and Karami, M., 2009. Persian leopard (Panthera pardus saxicolor) habitat suitability modelling by method Ecological Niche Factor Analysis (ENFA) in Kollah ghazi national park, Isfahan province. Environmental Science and Technology 12(1): 137-148. (in Persian)##28. Parchizadeh, J. and Adibi, M. A., 2019. Distribution and human‐caused mortality of Persian leopards Panthera pardus saxicolor in Iran, based on unpublished data and Farsi gray literature. Ecology and Evolution 9: 1-7.##29. Rohi, H., Tahsini, H., Soleiman Mahini, A. and Rezai, H., 2017. Persian leopard (Panthera pardus saxicolor) habitat suitability modelling by method Ecological Niche Factor Analysis (ENFA)in Kosh yeylagh wildlife refuge. Journal of Animal Environment 10(4): 13-22. (in Persian)##30. Rosen, T. and Mengüllüoğlu, D., 2022. Biology, ecology and taxonomy of the Persian leopard. Cat News 15: 6-8.##31. Shahsavarzadeh, R., Hemami, M. R., Farhadinia, M., Fakheran, S. and Ahmadi, M., 2023. Spatially heterogeneous habitat use across distinct biogeographic regions in a wide-ranging predator, the Persian leopard. Biodiversity and Conservation 32(4): 2037-2053.##32. Salmerón, R.; Garcia, C. and Pérez, J., 2024. A redefined variance inflation factor: Overcoming the limitations of the variance inflation factor. Computational Economics 65: 337-363. ##33. Shoaei, A., gholipour, M., Rezaei, H. and Yarmohammadi, B.T., 2016. Persian leopard (Panthera pardus saxicolor) habitat suitability assesment by using MaxEnt in Tandoreh National Park in summer and autumn seasons. Animal Environment 9(1): 21-30. (in Persian)##34. Tavakoli, M., 2024. Assessing Land use change impacts on Persian leopard’s (Panthera pardus tulliana) habitat suitability and connectivity and its connection with human-leopard conflicts in Dena Nation Park. MSc Thesis. Isfahan University of Technology. Isfahan, Iran.##35. Thuiller, W., Lafourcade, B., Engler, R. and Araújo, M., 2009. Biomod: A platform for ensemble forecasting of species distributions. Ecography 32: 369-373.##36. Walpole, A., Bowman, J., Murray, D. and Wilson, P., 2012. Functional connectivity of lynx at their southern range periphery in Ontario, Canada. Landscape Ecology 27: 761-773.##37. Zeberlou, A. R., Salehi, A. and Farzin, M., 2021. Assessment of landscape degradation in the Dena Protected Area between the years 1388-1398. Natural Environment 74(4): 677-693. (in Persian)## ##

بررسی عوامل بوم‌شناختی مؤثر بر توزیع جغرافیایی بیماری لیشمانیوز پوستی در استان فارس Assessment of Ecological Factors Impacting the Distribution of Cutaneous Leishmaniasis in Fars Province. 1 1 بیماری لیشمانیوز پوستی به عنوان یکی از مهم‌ترین بیماری‌های مشترک بین انسان و حیوان، تهدیدی جدی برای سلامت انسان است. عامل این بیماری انگل خونی بسیار ریز به نام لیشمانیوز است که از طریق ناقلین خاص (پشه خاکی) منتقل میشود و می‌تواند سبب بروز عفونت در میزبانهای مهره‌دار (مخازن: انسان یا حیوان) گردد. با توجه به شیوع بالای لیشمانیوز پوستی در استان فارس، مدلسازی آشیان بوم‌شناختی ناقل و مخازن اصلی با هدف شناسایی مناطق بالقوه در معرض خطر این بیماری با استفاده از الگوریتم MaxEnt انجام شد. تحلیل همپوشانی آشیان بوم شناختی گونه های هدف با استفاده از شاخص Schoener's D ارزیابی شد. نتایج نشان داد که 8% از کل سطح استان فارس به عنوان مناطق با مطلوبیت بسیار بالا (مطلوبیت بیش از 70%) برای گونه‌های ناقل و مخازن است. بخش‌های مرکزی استان به عنوان پرخطرترین نواحی این بیماری شناخته شد. گستره زیستگاهی ناقل نسبت به مخازن وسیع‌تر بوده و همپوشانی زیستگاهی ناقل با مخازن، زیاد ارزیابی شد. یافته‌های این پژوهش می‌تواند تصویر روشن‌تری از کانون‌های اولویت‌دار حفاظتی در مواجه با این بیماری در ابعاد یک سیستم بهداشتی برای سیاستگذاران سلامت فراهم آورد. 2 Cutaneous leishmaniasis is a significant zoonotic disease posing a serious threat to human health. Caused by the protozoan parasite Leishmania, it is transmitted by specific vectors (sand flies) and can cause infection in vertebrate hosts (reservoirs: humans or animals). Due to the high prevalence of the disease in Fars province, the ecological niches of the main vectors and reservoirs were modeled using the MaxEnt algorithm to identify potential high-risk areas. Niche overlap among target species was analyzed using Schoener's D index. Results indicated that 8% of the total area of the province is highly suitable (>70% suitability) for the vector and reservoir species. The central parts of the province were identified as the highest-risk regions. The vector's habitat range was wider than that of the reservoirs, and a significant habitat overlap was observed between them. These findings provide a clearer picture of priority conservation hotspots for health system policymakers in managing this disease. 55 68 2025/04/172025/05/82025/06/102025/05/19 1404/2/29 2025/06/152025/07/292025/07/292025/08/12 1404/5/21 فرزین شهریاری F. Shahriari علوم و مهندسی محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران. ایران farzin.shahriari@gmail.com محمود رضا همامی M.R Hemami دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران. ایران mrhemami@gmail.com کوروش عزیزی K. Azizi دانشکده بهداشت، دانشگاه علوم پزشکی شیراز، فارس، ایران. ایران azizik@sums.ac.ir احمد علی حنفی بجد A.A Hanafi-Bojd بیولوژی و کنترل ناقلین بیماری‌ها، دانشکده بهداشت، دانشگاه علوم پزشکی تهران، تهران، ایران. ایران aahanafibojd@tums.ac.ir زهرا نیک نداف Z. Niknaddaf علوم و مهندسی محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران. ایران zahraniknadaf@na.iut.ac.ir Habitat suitability modeling Fuzzy logic Sandflies Rodents Schoener’s D index Zoonotic diseases. مدل سازی مطلوبیت زیستگاه پشه خاکی جوندگان شاخص همپوشی آشیان شانر بیماریهای زئونوز 1. Alimohammadi, A. and Mozafari, Z. 2013. The role of time intervals in modeling the relationship between the distribution of cutaneous leishmaniasis and environmental variables using remote sensing and GIS techniques. In: Proceedings of the Second Specialized Seminar on Applications of Geoinformatics Sciences and Technology in Health System (Geoinformatics in Service of Healthy Environment); Tehran, Iran. (In Persian).##2. Alvar, J., Velez, I. D., Bern, C., Herrero, M., Desjeux, P., Cano, J., Jannin, J. and Boer, M. D. 2012. WHO Leishmaniasis Control Team. Leishmaniasis worldwide and global estimates of its incidence. PLoS ONE 7: e35671.##3. Badirzadeh, A., Mohebali, M., Asadgol, Z., Soong, L., Zeinali, M., Mokhayeri, Y., Shirzadi, M.R. & Spotin, A. 2017. The burden of leishmaniasis in Iran, acquired from the global burden of disease during 1990–2010. Asian Pacific Journal of Tropical Disease. 7(9):513–518.##4. Bakhshizadeh, F. 2011. The role of climate in the prevalence of cutaneous leishmaniasis. 1st ed. Jahad Daneshgahi Publications, Tehran, Iran. (In Persian).##5. Baneth, G., Petersen, C., Solano-Gallego, L. and Sykes, J. E. 2022. Leishmaniosis. pp. 1179–1202. In: Sykes, J. E. (Ed.), Greene’s Infectious Diseases of the Dog and Cat. 5th ed. W.B. Saunders. St. Louis, Missouri.##6. Bamorovat, M., Sharifi, I., Aflatoonian, M., Salarkia, E., Afshari, S., Pourkhosravani, M., Karamoozian, A., Khosravi, A., Aflatoonian, B., Sharifi, F., Divsalar, H., Amiri, B. and Shirzadi, M.R. 2024. A prospective longitudinal study on the elimination trend of rural cutaneous leishmaniasis in southeastern Iran: climate change, population displacement, and agricultural transition from 1991 to 2021. Science of the Total Environment 913: 169684.##7. Bozorg-Omid, F., Kafash, A., Jafari, R., Akhavan, A.A., Rahimi, M., Foroushani, A.R., Youssefi, F., Shirzadi, M.R., Taghizadeh, A.O. and Hanafi-Bojd, A.A. 2023. Predicting current and future high-risk areas for vectors and reservoirs of cutaneous leishmaniasis in Iran. Scientific Reports 13(1): 11546.##8. Bozorg-Omid, F., Youssefi, F., Hassanpour, G., Foroushani, A.R., Rahimi, M., Shirzadi, M.R., Jafari, R. and Hanafi-Bojd, A.A. 2025. Predicting the Effect of Temperature Changes on Phlebotomus papatasi Activity, as the main vector of Zoonotic Cutaneous Leishmaniasis in Iran. Transboundary and Emerging Diseases 2025(1): 9518371.##9. 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Relationship between climatic factors and the prevalence of cutaneous leishmaniasis in Larestan City. Journal of Military Medicine 16(2): 99–104. (In Persian).##13. Fakhari, A. Akbarpour, M. 2011. Study of cutaneous leishmaniasis in an endemic focus using GIS and RS in Iran. In: Proceedings of the First Congress on Application of Information Technology in Health; 2011 Oct 19–20; Sari, Iran. (In Persian).##14. Ferreira, A. L., Sessa, P. A., Varejão, J. B. and Falqueto, A. 2001. Distribution of sand flies (Diptera: Psychodidae) at different altitudes in an endemic region of American cutaneous leishmaniasis in the State of Espírito Santo, Brazil. Memórias do Instituto Oswaldo Cruz 96: 1061–1067.##15. Firouraghi, N., Mohammadi, A., Hamer, D.H., Bergquist, R., Mostafavi, S.M., Shamsoddini, A., Raouf-Rahmati, A., Fakhar, M., Moghaddas, E. and Kiani, B. 2022. Spatio-temporal visualization of cutaneous leishmaniasis in an endemic urban area in Iran. Acta Tropica 225: 106181.##16. 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تحلیل مقایسه‌ای روابط خویشاوندی در جمعیت‌های تحت اسارت با استفاده از نشانگرهای ریزماهواره، مطالعه موردی: گوسفند وحشی در مرکز تکثیر در اسارت میانکتل Comparative analysis of Kinship in Captive Populations Using Microsatellite Markers: A Case Study of Wild Sheep at the Miankotal Captive Breeding Site 1 1 اگرچه روش‌های مختلفی برای تعیین روابط خویشاوندی وجود دارد، اما در مواردی نتایج این روش‌ها با یکدیگر منطبق نیست. ازاین‌رو، استفاده هم‌زمان از چند رویکرد تحلیلی می‌تواند به افزایش اعتبار نتایج کمک کند. در این پژوهش، از 20 نمونه خون گوسفند وحشی (Ovis gmelini) در مرکز تکثیر در اسارت میانکتل (فارس) برای مقایسه‌ مدل های تحلیل روابط خویشاوندی بر مبنای داده‌های ریزماهواره استفاده شد. با استفاده از شبیه سازی، قدرت هر جایگاه ژنی در تفکیک روابط خویشاوندی ارزیابی شد. این روابط با استفاده از نرم‌افزارهای COLONY، CERVUS،  ML-Relate و شاخص های خویشاوندی بررسی شد. جایگاه های OarFCB304 و McM527 به ترتیب بیشترین و کمترین میزان اطلاعات را در تعیین روابط نشان دادند. علیرغم انطباق بالا بین روش های مختلف، تفاوت‌هایی نیز مشاهده شد. نرم افزار CERVUS نشان داد که سه جفت رابطه والد-فرزندی با سطح اطمینان بالا وجود دارد، اگرچه گروه بندی این نمونه ها با استفاده از نرم افزارهای ML-Relate و COLONY عمدتا نشان‌دهنده رابطه خواهر-برادری بود ، محتمل‌ترین الگوی خویشاوندی در جمعیت رابطه خواهر-برادر ناتنی بود، اما ضروری است با اقدامات مدیریتی همچون اضافه نمودن افراد جدید به جمعیت، احتمال درون آمیزی را کاهش داد. این مطالعه نشان داد که استفاده تلفیقی از شاخص های خویشاوندی، تحلیل های والدینی و اطلاعات زیستی می‌تواند تصویری دقیق‌تر از ساختار خویشاوندی ارائه دهد. 2 While numerous methods exist for kinship determination, their results frequently show incomplete concordance. To improve accuracy and reliability, we employed a multi-analytical approach in this study. Using blood samples from 20 wild sheep (Ovis gmelini) at the Miankotal Captive Breeding Site (Fars Province, Iran), we conducted a comparative analysis of kinship inference models based on nuclear microsatellite data. Initial simulations assessed each locus's discriminatory power for kinship determination. Kinship relationships were evaluated using multiple parentage analysis software (COLONY, CERVUS, and ML-Relate) and relatedness indices. The OarFCB304 and McM527 ranked as the highest and lowest in terms of information content for relatedness estimation. Although we observed agreement between methods, some discrepancies emerged. CERVUS identified three parent–offspring pairs with high confidence, whereas classification of these samples using ML-Relate and COLONY mainly indicated sibling relationships. Despite the fact the most likely kinship pattern in the population was half-sibling relationships, it is essential to reduce the likelihood of inbreeding through management actions such as introducing new individuals into the population. This study demonstrates that an integrative analytical approach incorporating kinship indices, parentage analyses, and biological information yields a more precise understanding of population kinship structure. 69 84 2025/04/172025/05/82025/06/102025/05/192025/06/16 1404/3/26 2025/06/152025/07/292025/07/292025/08/122025/08/17 1404/5/26 رسول خسروی R. Khosravi بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران. ایران r-khosravi@shirazu.ac.ir ملیکا روح بخش M. Roohbakhsh بخش مهندسی منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران. ایران roohbakhshmelika22@gmail.com رویا آداودی R. Adavoudi دانشکده زیست شناسی، دانشگاه گدانسک، گدانسک، لهستان. ایران roya.adavoudi@gmail.com محمد کابلی M. 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ارزش گذاری اقتصادی مکان مند کیفیت زیستگاه بر اساس مدل InVEST در استان خوزستان Spatial Economic Valuation of Habitat Quality Based on the InVEST Model in Khuzestan Province 1 1 خدمات اکوسیستم شاخصی مهم برای اندازه گیری ارزش حفاظت از اکوسیستم های طبیعی هستند که به‌ عنوان شاخص‌های مرجع، نقش مهمی در تصمیم‌گیری مدیریتی مناطق حفاظت شده ایفا می‌کنند. هدف این مطالعه پاسخ به نبود بررسی جامع در رابطه با استفاده از مفهوم خدمات اکوسیستم در کارایی اکوسیستم ها است. بدین منظور مدل سازی خدمت اکوسیستمی کیفیت زیستگاه در استان خوزستان به عنوان اکوسیستم شهری و در مناطق حفاظت شده آن به عنوان اکوسیستم های طبیعی با مدل InVEST انجام شد. سپس ارزش اقتصادی کیفیت زیستگاه هر دو اکوسیستم طبیعی- انسانی با کاربرد داده های اقتصادی ملی و بین المللی و مبتنی بر روش انتقال منفعت بر اساس خروجی نقشه مدل سازی تعیین شد. بر اساس نتایج مدلسازی، مناسب ترین زیستگاه ها به دلیل بارش مطلوب، پوشش گیاهی متنوع و فاصله تا منابع تهدید در بخش هایی از شمال، شمال شرق، شرق و تکه هایی از جنوب و مرکز استان مشاهده می شود. بر اساس نتایج ارزش اقتصادی سالانه کیفیت زیستگاه و تنوع زیستی در کل استان، 62/02تریلیون ریال (6 همت) و در مناطق تحت حفاظت 6440 میلیارد ریال در سال است. نتایج به دست آمده از این مطالعه می تواند ابزاری برای مدیریت خردمندانه مناطق تحت مدیریت و بازنگری مرزهای این مناطق در آینده باشد. 2 Ecosystem services (ES) are essential indicators for assessing the value of natural ecosystem conservation and play a critical role in implementinig management decisions within protected areas. This study aims to fill the existing gap in evaluating ecosystem performance through the application of the ES framework. The InVEST model was used to map and assess habitat quality (HQ) as an ecosystem service across Khuzestan Province. The analysis covered both urban-human-modified ecosystems and natural ecosystems within designated protected areas. The economic value of habitat quality in both ecosystem types was estimated using national and international datasets and applying the benefit transfer method based on spatial modeling outputs. Based on the modeling results, optimal habitats—defined by favorable rainfall, diverse vegetation, and remoteness from threats such as development and fragmentation—are mainly concentrated in the north, northeast, and east, with scattered patches in the south and center  of the province. The annual economic value of habitat quality and biodiversity is estimated at 62.02 trillion Rials for the entire province, and 6.44 trillion Rials within the protected areas. 85 100 2025/04/172025/05/82025/06/102025/05/192025/06/162025/06/7 1404/3/17 2025/06/152025/07/292025/07/292025/08/122025/08/172025/09/3 1404/6/12 فاطمه محمدیاری F. Mohammadyari گروه مهندسی محیط زیست، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران. ایران mohammadyari.f@sku.ac.ir رویا موسی زاده R. Mousazadeh گروه پژوهشی اقتصاد محیط زیست، پژوهشکده محیط زیست و توسعه پایدار، سازمان حفاظت محیط زیست، تهران، ایران. ایران Roya.mousazadeh@gmail.com مجید رمضانی مهریان M. Ramezani Mehrian گروه مطالعات محیطی، پژوهشکده تحقیق و توسعه علوم انسانی (سمت)، تهران، ایران. ایران mehrian@samt.ac.ir اردوان زرندیان A. 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