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مدل‌سازی پراکنش تون چشم‌درشت (Thunnus obesus Lowe, 1839) در اقیانوس هند با استفاده از متغیرهای محیطی حاصل از تصاویر ماهواره‌ای Distribution Modeling of Bigeye Tuna (Thunnus obesus Lowe, 1839), Using Satellite Derived Environmental Variables in Indian Ocean 1 1 اطلاع از تأثیر محیط بر پراکنش آبزیان اقتصادی قدمی بنیادی در مدیریت اکوسیستم¬¬محور شیلات و در نهایت به¬عنوان یک رویکرد استاندارد در سیاست¬های مدیریتی به¬ حساب می¬آید. تون چشم¬¬درشت (Thunnus obesus) از مهم‌ترین آبزیان در حال برداشت در اقیانوس هند است. مطالعه حاضر به بررسی ارتباط متغیرهای تأثیرگذار بر روی میزان صید و پراکنش تون چشم¬درشت صید شده توسط پرساینرهای ایرانی در اقیانوس هند با به‌کارگیری مدل جمعی تعمیم‌یافته (GAM) و حداکثر آنتروپی (MaxEnt) و متغیرهای حاصل از تصاویر ماهواره‌ای پرداخته است. نتایج بیانگر تأثیر متغیرهای زمانی و مکانی همراه با متغیرهای انرژی جنبشی ادی، ارتفاع سطح دریا، عمق لایه هم¬دمایی 20 درجه سانتی¬گراد و دمای سطحی آب است. بیشترین مطلوبیت زیستگاهی حاصل از مدل حداکثر آنتروپی در عرض‌های 0 تا 5 درجه شمالی و جنوبی و به‌ویژه در ناحیه غربی اقیانوس هند و طول 45 تا 70 درجه شرقی مشاهده شد. تحقیق حاضر با به¬کارگیری تصاویر ماهواره¬ای و تعیین مهم¬ترین فاکتورهای محیطی تأثیرگذار بر مناطق با مطلوبیت زیستگاهی بالا می‌تواند با افزایش کارایی صید به ناوگان پرساینرهای ایرانی در یافتن محل تجمع این گونه و در نهایت به مدیران شیلاتی کشور برای اجرای مدیریت اکوسیستم¬محور شیلاتی در آب‌های مورد بهره‌برداری از ذخایر مشترک اقیانوس هند کمک کند. 2 Understanding effects of environment on the distribution of economic fish is a fundamental step in the ecosystem-based management and ultimately a standard approach in management policies. Bigeye tuna (Thunnus obesus) is one of the most important aquatic species harvested in the Indian Ocean. The present study investigated the association of different variables effecting the rate of catch and distribution of bigeye tune, using generalized additive model (GAM) and maximum entropy (MaxEnt) and satellite derived environmental variables in the Indian Ocean. Results highlighted the importance of temporal and spatial variables along with the eddy kinetic energy, sea level height, depth of 20°C isotherm and sea surface temperature on the distribution of the species. The most suitable habitat predicted by MaxEnt model was observed around the latitudes of 0 to 5 degrees of north and south, mainly in the western part of the Indian Ocean and longitude of 45 to 70 degrees east. Using satellite data, the present study determinied the important factors and suitable habitats for the species, which can be useful for Iranian fisheries managers to increase the fishing efficiency and implementing of ecosystem-based fisheries management in the shared exploited stocks of the Indian Ocean. 1 14 2020/09/20 1399/6/30 2020/11/10 1399/8/20 علی حقی وایقان A. Haghi Vayghan دانشگاه ارومیه ایران a.haghi@urmia.ac.ir Bigeye tuna distribution habitat modeling ecosystem management تون چشم‌درشت پراکنش مدل‌سازی زیستگاه مدیریت اکوسیستم 1. Arrizabalaga, H., J. G. Pereira, F. Royer, B. Galuardi, N. Goni, I. Artetxe, I. Arregi and M. Lutcavage. 2008. Bigeye tuna (Thunnus obesus) vertical movements in the Azores Islands determined with pop-up satellite archival tags. Fisheries Oceanography 17 (2): 74-83.##2. Austin, M. 2007. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling 200: 1-19.##3. Bertrand, A., F. X. Bard and E. Josse. 2002. Tuna food habits related to the micronekton distribution in French Polynesia. Marine Biology 140 (5): 1023-1037.##4. Brill, R. W., K. A. Bigelow, M. K. Musyl, K. A. Fritsches and E. J. Warrant. 2005. Bigeye tuna (Thunnus obesus) behavior and physiology and their relevance to stock assessments and fishery biology. Collective Volumes of Scientific Papers 57 (2): 142-161.##5. Brodziak, J. and J. Link. 2002. Ecosystem-based fishery management: what is it and how can we do it? Bulletin of Marine Science 70 (2): 589-611.##6. Cai, L. N., L. L. Xu, D. L. Tang, W. Z. Shao, Y. Liu, J. C. Zuo and Q. Y. Ji. 2020. The effects of ocean temperature gradients on bigeye tuna (Thunnus obesus) distribution in the equatorial eastern Pacific Ocean. Advances in Space Research 65 (12): 2749-2760.##7. Chiang, H. C., C. C. Hsu, G. C. C. Wu, S. K. Chang and H. Y. Yang. 2008. Population structure of bigeye tuna (Thunnus obesus) in the Indian Ocean inferred from mitochondrial DNA. Fisheries Research 90 (1–3): 305-312.##8. Dagorn, L., P. Bach and E. Josse. 2000. Movement patterns of large bigeye tuna (Thunnus obesus) in the open ocean, determined using ultrasonic telemetry. Marine Biology 136 (2): 361-371.##9. Darvishi, M., S. Y. Paighambari, A. R. Ghorbani and F. Kaymaram. 2018. Population assessment and yield per recruit of long tail tuna (Thunnus tonggol) in Northern of the Persian Gulf and Oman Sea (Iran, Hormozgan Province). Iranian Journal of Fisheries Sciences 17 (4): 776-789.##10. Dufour, F., H. Arrizabalaga, X. Irigoien and J. Santiago. 2010. Climate impacts on albacore and bluefin tunas migrations phenology and spatial distribution. Progress in Oceanography 86 (1): 283-290.##11. Elith, J., S. J. Phillips, T. Hastie, M. Dudík, Y. E. Chee and C. J. Yates. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17 (1): 43-57.##12. Erauskin Extramiana, M., H. Arrizabalaga, A. J. Hobday, A. Cabré, L. Ibaibarriaga, I. Arregui, H. Murua and G. Chust. 2019. Large-scale distribution of tuna species in a warming ocean. Global Change Biology 25 (6): 2043-2060.##13. Evans, K., A. Langley, N. P. Clear, P. Williams, T. Patterson, J. Sibert, J. Hampton and J. S. Gunn. 2008. Behaviour and habitat preferences of bigeye tuna (Thunnus obesus) and their influence on longline fishery catches in the western Coral Sea. Canadian Journal of Fisheries and Aquatic Sciences 65 (11): 2427-2443.##14. Haghi Vayghan, A., H. Poorbagher, H. Taheri Shahraiyni, H. Fazli and H. Nasrollahzadeh Saravi. 2013. Suitability indices and habitat suitability index model of Caspian kutum (Rutilus frisii kutum) in the southern Caspian Sea. Aquatic Ecology 47 (4): 441-451.##15. Haghi Vayghan, A., H. Fazli, R. Ghorbani, M. A. Lee and H. N. Saravi. 2015. Temporal habitat suitability modeling of Caspian shad (Alosa spp.) in the southern Caspian Sea. Journal of Limnology 75 (1): 210-223.##16. Haghi Vayghan, A., R. Zarkami, R. Sadeghi and H. Fazli. 2016. Modeling habitat preferences of Caspian kutum, Rutilus frisii kutum (Kamensky, 1901)(Actinopterygii, Cypriniformes) in the Caspian Sea. Hydrobiologia 766 (1): 103-119.##17. Haghi Vayghan, A., R. Ghorbani, S. Y. Peyghambari, M. A. Lee, D. M. Kaplan and B. A. Block. 2017. Relationship between yellowfin tuna (Thunnus albacares) distribution caught by Iranian purse seiners and environmental variables in the Indian Ocean. Iranian Scientific Fisheries Journal 26 (1): 67-82. ##18. Haghi Vayghan, A., R. Ghorbani, Y. Peighambari, M. A. Lee, D. M. Kaplan and B. A. Block. 2018. Association between Skipjack (Katsuwonus pelamis) distribution caught by Iranian purse seiners and environmental variables in the Indian Ocean. Journal of Applied Ichthyological Research 6 (1): 1-20.##19. Haghi Vayghan, A., M. A. Lee, J. S. Weng, S. Mondal, C. T. Lin and Y. C. Wang. 2020. Multisatellite-based feeding habitat suitability modeling of Albacore Tuna in the Southern Atlantic Ocean. Remote Sensing 12 (16): 2515.##20. Hastie, T. and R. Tibshirani. 1990. Generalized additive models. Chapman and Hall, London.##21. Hilborn, R. 2011. Future directions in ecosystem based fisheries management: A personal perspective. Fisheries Research 108: 235-239.##22. Lam, C. H., B. Galuardi and M. E. Lutcavage. 2014. Movements and oceanographic associations of bigeye tuna (Thunnus obesus ) in the Northwest Atlantic. Canadian Journal of Fisheries and Aquatic Sciences 71 (10): 1529-1543.##23. Lan, K. W., M. A. Lee, C. P. Chou and A. H. Vayghan. 2018. Association between the interannual variation in the oceanic environment and catch rates of bigeye tuna (Thunnus obesus ) in the Atlantic Ocean. Fisheries Oceanography 27 (5): 395-407.##24. Lan, K. W., T. Nishida, M. A. Lee, H. J. Lu, H. W. Huang, S. K. Chang and Y. C. Lan. 2012. Influence of the marine environment variability on the yellowfin tuna (Thunnus albacares) catch rate by the Taiwanese longline fishery in the Arabian Sea, with special reference to the high catch in 2004. Journal of Marine Science and Technology 20 (5): 514-524.##25. Lan, K. W., M. A. Lee, H. J. Lu, W. J. Shieh, W. K. Lin and S. C. Kao. 2011. Ocean variations associated with fishing conditions for yellowfin tuna (Thunnus albacares) in the equatorial Atlantic Ocean. ICES Journal of Marine Science: Journal Du Conseil 68 (6): 1063-1071.##26. Lan, K. W., T. Shimada, M. A. Lee, N. J. Su and Y. Chang. 2017. Using remote-sensing environmental and fishery data to map potential Yellowfin Tuna habitats in the Tropical Pacific Ocean. Remote Sensing 9 (5): 444.##27. Last, P. R., W. T. White, D. C. Gledhill, A. J. Hobday, R. Brown, G. J. Edgar and G. Pecl. 2011. Long-term shifts in abundance and distribution of a temperate fish fauna: a response to climate change and fishing practices. Global Ecology and Biogeography 20 (1): 58-72.##28. Lee, P. F., I. C. Chen and W. N. Tzeng. 2005. Spatial and temporal distribution patterns of bigeye tuna (Thunnus obesus) in the Indian Ocean. ZOOLOGICAL STUDIES-TAIPEI 44 (2): 260.##29. Lee, M. A., A. H. Vayghan, D. C. Liu and W. C. Yang. 2017. Potential and prospective seasonal distribution of hotspot habitat of albacore tuna (thunnus alalunga) in the South Indian Ocean using the satellite data. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth, Texas, USA. pp. 5747-5750.##30. Lee, M. A., J. S. Weng, K. W. Lan, A. H. Vayghan, Y. C. Wang and J. W. Chan. 2020. Empirical habitat suitability model for immature albacore tuna in the North Pacific Ocean obtained using multisatellite remote sensing data. International Journal of Remote Sensing 41 (15): 5819-5837. ##31. Liming, S., X. Liuxiong and C. Xinjun. 2004. 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ارائه روش نوین اولویت‌بندی راهبردهای مقابله با بیابان‌زایی بر اساس تصمیم‌گیری چند‌معیاره Introducing a New Approach for Prioritizing Combating Desertification Strategies Based on Multi- Attribute Decision Making 1 1 پرداختن به مسئله بیابان¬¬زایی به¬خاطر ماهیت چندمعیاری آن، توسعه روز افزون، گسترده و بلندمدت بودن و تأثیر هم¬زمان بر منابع سرزمینی و جمعیت¬های انسانی، به¬منظور دستیابی به توسعه پایدار ضروریست. از این¬رو لازم است به¬منظور بهره¬برداری بهینه از امکانات و سرمایه¬های محدود اختصاص¬یافته به این امر، راهبردهای مقابله با بیابان¬زایی با توجه به معیارهای مختلف مورد ارزیابی قرار گیرد تا ضمن دستیابی به نتایج بهتر، از هدررفت سرمایه¬¬های ملی جلوگیری شود. بنابراین به¬منظور رتبه¬بندی راهکارهای مقابله با بیابان¬زایی، در چارچوب مدل¬های تصمیم¬گیری چندشاخصه، از روش دیمتل استفاده شد. ابتدا در چارچوب روش تصمیم¬¬گیری چند شاخصه و با استفاده از مدل دلفی، معیارها و راهبردهای مهم و اولویت¬دار شناسایی شده و سپس اولویت نهایی راهبردها با استفاده از روش دیمتل مورد ارزیابی قرار گرفت. بر مبنای نتایج به¬دست آمده، راهبردهای توسعه و احیاء پوشش گیاهی (23A)، تغییر الگوی آبیاری و اجرای روش¬های کم¬آب¬خواه (33A) و کنترل چرای دام (20A) به¬ترتیب به¬عنوان مهم¬ترین راهبردهای مقابله با بیابان¬زایی در منطقه تشخیص داده شدند. بنابراین پیشنهاد شد در طرح¬های کنترل و کاهش اثرات بیابان¬زایی و احیاء اراضی تخریب¬¬یافته، نتایج و رتبه¬بندی به¬دست-آمده مورد توجه قرار گیرد. 2 Addressing desertification, due to its multi-criteria nature, increasing development, extensive and long-term impacts on natural resources and human populations, is necessary to achieve sustainable development. Therefore, for optimal utilization of facilities and limited funds allocated to this issue, evaluation of current strategies, based on different criteria is essential to avoid wasting national funds, while achieving better results. The current study used Decision Making Trial and Evaluation Laboratory (DEMATEL) to rank desertification strategies in the context of Multi-Attribute Decision-Making (MADM) models. First, important and high-priority criteria and strategies were identified within the framework of MADM, using the Delphi method. Then, the final priority of strategies was determined using the DEMATEL method. Based on the obtained results, vegetation cover development and reclamation (A23), change of irrigation patterns and implementation of low water demand methods (A33), and livestock grazing control (A20) were recognized as the most important combating desertification strategies in the region. Therefore, it is suggested to consider the obtained results and ranking in control plans to reduce the effects of desertification and reclamation of destructed lands. 15 33 2020/09/202020/05/11 1399/2/22 2020/11/102020/11/25 1399/9/5 محمد حسن صادقی روش M. H. Sadeghi Ravesh دانشگاه آزاد واحد تاکستان ایران m.sadeghiravesh@tiau.ac.ir Combating Desertification DEMETAL Method Multi-Attribute Decision Making (MADM) Methods Pairwise Comparisons مقابله با بیابان‌زایی روش‌های تصمیم‌گیری چندمعیاره مدل دیمتل مقایسه زوجی 1. Ali, T. A. 1998. Extent, severity and causative factors of land degradation in the Sudan. Journal of Arid Environment 38: 397-409.##2. Asgharpur, M. J. 2015. Group decision and game theory, operations research approach, 3rd edition. Tehran University, Tehran. (In Farsi)##3. Azar, A. and A. Rajabzadeh. 2018. Applied decision making with an approach of Multi-Attribute Decision Making (MADM), 6th edition. Negah Danesh, Tehran. (In Farsi)##4. Bowyer, C., S. Withana, I. Fenn, S. Bassi, M. Lewis, T. Cooper, P. Benito and S. Mudgal. 2009. Land degradation and desertification. European Parliament, Policy Department A, Economic and Scientific Policy, Brussels, Belgium.##5. Briassoulis, H. 2019. Combating land degradation and desertification: the land-use planning quandary. Land 8(27): 1-26.##6. Chauhan, A., A. Singh and S. Jharkharia. 2018. An ISM and DEMATEL method approach for the Analysis of barriers of waste recycling in India. Journal of the Air & Waste Management Association 68(2): 100-110. ##7. Cheshmberah, M., A. Naderizadeh, A. Shafaghat and M. Karimi Nokabadi. 2020. An integrated process model for root cause failure analysis based on reality charting, FMEA and DEMATEL. International Journal of Data and Network Science 4(2): 225-236.##8. Ding, X. F. and H. C. Liu. 2018. A 2-dimension uncertain linguistic DEMATEL method for identifying critical success factors in emergency management. Applied Soft Computing 71: 386-395.##9. Do, T. H. N. and W. Shih. 2016. Destination decision-making process based on a hybrid MCDM model combining DEMATEL and ANP: the case of Vietnam as a destination. Modern Economy 7: 966-983. http://dx.doi.org/10.4236/me.2016.79099.##10. Dou, Y. and J. Sarkis. 2013. A multiple stakeholder perspective on barriers to implementing China roHS regulations. Resources Conservation and Recycling 81: 92-104. ##11. Falatoonitoosi, E., Z. Leman, S. Sorooshian and M. Salimi. 2013. Modeling for green supply chain evaluation. Mathematical Problems in Engineering 2013: 1-9. https://doi.org/10.1155/2013/201208.##12. Falatoonitoosi, E., A. Shamsuddin and S. Shahryar. 2014, Expanded DEMATEL for determining cause and effect group in bidirectional relations. The Scientific World Journal 2014: 1-7. .http://dx.doi.org/10.1155/2014/103846.##13. Fontela, E. and A. Gabus. 1976. The DEMATEL pilot survey. Futures 8(4): 379-389.##14. Food and Agriculture Organization (FAO). 1979. A provisional methodology for soil degradation assessment. 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پیش‌بینی اثر تغییر اقلیم بر پراکنش گونه‌های خویشاوندان وحشی خانواده سیب‌زمینی (Solanaceae) در ایران با تأکید بر امنیت غذایی Predicting the Effect of Climate Change on the Distribution of Wild Relatives of the Potato Family (Solanaceae) in Iran with Emphasis on Food Security 1 1 خانواده سیب‌زمینی با 49 گونه از 10 جنس یکی از خانواده‌های مهم غذایی، اقتصادی، دارویی و زینتی به‌ حساب می‌آید که شش جنس آن در گروه خویشاوندان وحشی طبقه‌بندی شده‌اند. پیش‌بینی اثر تغییر اقلیم بر پراکنش گونه‌های گیاهی، امری مهم در راستای مدیریت و حفاظت آن‌ها محسوب می‌شود. در این مطالعه، بررسی تأثیرات تغییر اقلیم بر خانواده سیب‌زمینی با رویکرد حفاظتی مورد ارزیابی قرارگرفته و با استفاده از ابزار مدل‌سازی پراکنش گونه‌ها، پراکنش مکانی آینده آن¬ها توسط مدل بیشینه آنتروپی (MaxEnt) در سناریوهای مختلف خوش‌بینانه (2.6RCP) و بدبینانه (8.5RCP) برای سال‌های 2050 و 2080 در محیط نرم‌افزاری R پیش‌بینی شد. بر اساس نتایج، عملکرد مدل‌ بر اساس شاخص AUC در گونه‌های مختلف، خوب یا عالی (0/8≥) بوده و این نشان می‌دهد مدل¬سازی پراکنش گونه‌ها با اطمینان آماری بالایی انجام شد. به¬علاوه، پراکنش همه گونه‌ها در برابر تغییر اقلیم در سناریوهای مختلف آینده هم به-صورت کاهش در زیستگاه‌های اصلی فعلی و هم افزایش در زیستگاه‌های جدید پیش‌بینی شده است، اما نسبت این افزایش و کاهش در گونه‌های مختلف متفاوت است، به¬طوری¬که در گونه‌های Atropa acuminate،Solanum surratense ، Datura stramonium،Hyoscyamus niger، Hyoscyamus reticulatus، Physalis alkekengi و Solanum dulcamara نسبت کاهش بیشتر از افزایش است یعنی دامنه تغییرات آنها منفی بوده و تغییر اقلیم، بیشتر سبب حذف آنها خواهد شد. در مقابل، در گونه‌های Datura innoxia، Physalis divaricate،Solanum alatum و Withania somnifera نسبت افزایش بیشتر از کاهش است، یعنی دامنه تغییرات آنها مثبت بوده و تغییر اقلیم، بیشتر سبب گسترش آنها خواهد شد. 2 The Solanaceae family with 49 species of 10 genera is one of the important nutritional, economical, medicinal and ornamental families in which six genera of them are classified in the group of wild relatives. Predicting the effect of climate change on the distribution of plant species is important for their management and conservation. In this study, the effects of climate change on this family was investigated with a conservation approach, using Species Distribution Modeling (SDM). Future spatial distribution of this family was predicted by MaxEnt model under optimistic (RCP2.6) and pessimistic (RCP8.5) scenarios for the year 2050 and 2080 in R software. In general, the performance of the model for different species was good or excellent based on the area under the curve index (AUC ≥ 0.8), showing high statistical reliability of the distribution modeling. Predicting the distribution of all species in the face of climate change and under various future scenarios showed both decrease in the current original habitats and increase in new habitats. However, the ratio of the increase and decrease was different in various species. In Atropa acuminate, Solanum surratense, Datura stramonium, Hyoscyamus niger, Hyoscyamus reticulatus, Physalis alkekengi and Solanum dulcamara the ratio of decrease was more than increase, implying the negative effects of climate change on these species and may cause species extinction. However, the ratio of increase was more than decrease in Datura innoxia,Physalis divaricate,Solanum alatum and Withania somnifera, showing a positive effect and climate change may  cause species expansion. 35 55 2020/09/202020/05/112020/11/10 1399/8/20 2020/11/102020/11/252021/01/18 1399/10/29 صدف صیادی S. Sayadi دانشگاه شهیدبهشتی ایران sadaf_sayadi@yahoo.com احمدرضا محرابیان A. R. Mehrabian دانشگاه شهیدبهشتی ایران A_mehrabian@sbu.ac.ir حسین مصطفوی H. Mostafavi دانشگاه شهیدبهشتی ایران hmostafaviw@gmail.com Climate change Wild relative Species distribution modeling Conservation تغییر اقلیم خویشاوند وحشی مدل‌سازی پراکنش گونه حفاظت 1. Abdelaal, T., L. Michielsen, D. Cats, D. Hoogduin, H. Mei, M. J. 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پیش‌بینی مساحت روشنه‌های تاجی قبل از اجرای شیوه تک‌گزینی یا گروه‌گزینی در جنگل‌های طبیعی آمیخته راش شمال کشور (پژوهش موردی: قطعه شاهد سری 3 طرح جنگل‌داری گلندرود نور) Predicting Canopy Gap Size Before Applying Single- or Group Selection Methods in the Hyrcanian Natural Mixed-Beech Forests (Case Study: Control Plot of Series 3 of Glandroud Forests) 1 1 با توجه به اینکه در برنامه‌ریزی‌های مربوط به عملیات نشانه‌گذاری جنگل، طراحی فضای سطح روشنه تاجی امکان‌پذیر نبوده و نشانه-گذار فقط می‌تواند بر مبنای تخمین مساحت روشنه گسترش‌یافته‌ای که از قطع درخت حاصل می‌شود اقدام به اجرای عملیات نماید، از این‌رو با ارائه مدل‌های محاسباتی دقیق می‌توان در برآورد نزدیک¬به¬واقعیت مساحت روشنه‌های تاجی قبل از اجرای شیوه‌های گزینشی در جنگل‌های هیرکانی اقدام نمود. بر اساس آماربرداری صددرصد در قطعه شاهد جنگل‌های آمیخته گلندرود برای اندازه‌گیری مساحت روشنه‌های تاجی و گسترش‌یافته با استفاده از روش شعاعی و تفکیک رده‌های مساحتی، محاسبه شاخص‌های تنوع گونه‌ای درختان حاشیه روشنه‌ها و ثبت ویژگی‌های فیزیوگرافی با استفاده از تحلیل‌های خطی و غیرخطی رگرسیون، مدل‌سازی مساحت روشنه تاجی در قالب متغیر پاسخ اجرا شد. نتایج پژوهش حاضر، مجموع مساحت رویشگاه مورد پژوهش توسط روشنه‌های گسترش‌یافته با مساحت 2/7 هکتار را تقریباً یک هکتار بیشتر از مجموع مساحت روشنه‌های تاجی (با مساحت 1/6 هکتار) نشان داد. تحلیل‌های مدل‌سازی رگرسیون با استفاده از روش‌های تبدیل لگاریتمی و بازتبدیل نمایی نشان داد که مدل خطی چندگانه حاصل از تبدیل لگاریتمی تابع توانی مشتمل بر مساحت روشنه‌های گسترش‌یافته و شاخص‌های تنوع گونه‌ای هم¬بسته با درختان حاشیه روشنه‌ها شامل تنوع شانون‌وینر و غلبه گونه‌ای، دارای اعتبار محاسباتی و دقت برآوردی قابل قبول بوده است. 2 During the marking operations planning in forest ecosystems, designing the canopy gap size (CGS) is not clearly possible and a marker can only be capable of calculating the expanded gap size (EGS) that will be created by cutting and felling trees. Therefore, the main goal of the current study was to provide a solution to this problem on the basis of accurate prediction for CGS, before applying selection methods, through developing regression models for predicting the CGS in Glandroud forests. On the basis of full inventory for measuring the area of canopy and expanded gaps using radial technique, calculating the species diversity indices and observations of physiographic units, the regression models were developed for estimating the CGS as the response in the research. The results showed that the site fraction occupied by expanded gaps has one hectare more than the fraction occupied by the canopy gaps. Furthermore, the results of correlation tests showed that the CGS had significant relationship with the Shannon diversity and species dominance indices. 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ارزیابی تنوع گونه‌ای و غنای پوشش گیاهی در دو کانون گرد و غبار استان خوزستان Assessment of Species Diversity and Vegetation Richness Indices in Two Dust Centers of Khuzestan Province 1 1 تالاب‌های منصوریه و شریفیه به دلایل متعدد طی سال‌های اخیر خشک شده و به کانون گرد و غبار در استان خوزستان تبدیل شده¬اند. این پژوهش در تالاب‌های شریفیه و منصوریه با هدف اصلی مطالعه پوشش گیاهی از جنبه تنوع و غنای گونه‌ای این مناطق تالابی در زمان پخش آب و آب¬گیری و ارزیابی روند تغییرات آن پس از پخش آب و نیز آب¬گیری در اثر باران‌های سال‌های اخیر انجام شده است. برای ارزیابی تغییرات پوشش گیاهی با پیمایش صحرایی، تعداد 5 ترانسکت دائمی 100 متری با فواصل 50 متری در محل پخش آب به¬صورت تصادفی سیستماتیک مستقر شد و در مجموع از تعداد 30 پلات ثابت، داده‌ها در 6 زمان مختلف در سال‌های 1396 تا 1398 برداشت شد. با استفاده از عامل درصد پوشش، شاخص‌های غنا، یکنواختی و تنوع گونه‌ای اندازه‌گیری شد. نتایج نشان داد تفاوت معنی‌داری در زمان پخش آب و زمان بدون آب¬رسانی در تالاب منصوریه وجود دارد. شاخص‌های تنوع سیمپسون و شانون نشان داد پس از پخش آب به¬وسیله کانال شهید پورشریفی و وقوع بارندگی‌ها، میزان تنوع به¬تدریج افزایش یافته است. همچنین در تالاب شریفیه نیز شاخص‌های تنوع شانون و سیمپسون در پی افزایش غنا و کاهش غالبیت از 2 گونه غالب به 20 گونه متنوع، افزایش یافتند به-نحوی¬که بین درصد پوشش و شاخص‌های تنوع و غنا در سال‌های متوالی 1396 و 1397 تفاوت معنی‌داری مشاهده شد. در مجموع در عرصه‌های پخش آب، افزایش پوشش گیاهی بومی مشاهده شد که سبب جلوگیری از ایجاد گرد و غبار می‌شود. 2 Mansourieh and Sharifieh are two recently dried wetlands in Khuzestan province, that have become important dust sources. This study was carried out in Sharifieh and Mansourieh wetlands to assess the vegetation condition and trend, following the implementation of water spreading and artificial recharge in recent years. To evaluate changes in vegetation, five transects of 100 meters, 50 meters apart, were established in the water spreading site in a random systematic manner, and data was collected from 30 permanent plots for six times between 2017-2019. Species richness and diversity indices were measured using the coverage percentage factor. The results obtained in Mansourieh showed a significant difference between the water spreading site and the non-irrigated site. Simpson and Shannon indices showed a gradual increase in species diversity following the implementation of flood spreading by means of Shahid Poursharifi Channel. Likewise, in Sharifieh wetland, Shannon and Simpson diversity indices improved in response to the enhancement of species richness and decreasing dominance (from two dominant species to over 20 species). In Sharifieh, we obtained a significant difference between the years 2017 and 2018 in terms of diversity indices. In general, the flood spreading areas will experience an improvement in native vegetation, which will be translated into reduced dusts storm frequency and severity.  73 87 2020/09/202020/05/112020/11/102020/09/242020/07/23 1399/5/2 2020/11/102020/11/252021/01/182021/01/302021/02/13 1399/11/25 مهری دیناروند M. Dinarvand مرکز تحقیقات کشاورزی و منابع طبیعی خوزستان ایران mehri.dinarvand@gmail.com محمد فیاض M. Fayaz: موسسه تحقیقات جنگلها و مراتع کشور ایران fayaz1335@gmail.com کوروش بهنام فر K. Behnamfar: مرکز تحقیقات کشاورزی و منابع طبیعی خوزستان ایران ko_behnamfar@yahoo.com فرهاد خاکساریان F. Khaksarian موسسه تحقیقات جنگلها و مراتع کشور ایران fsong52@gmail.com بنفشه یثربی B. 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Junk. 2001. Distribution of woody plant communities along the flood gradient in the Pantanal of Pocone, Mato Grosso, Brazil. International Journal of Ecological Environment Science 27: 63-70.##5. Dargahian, F., S. Teimori, S. Lotfinasbasl and S. Razavizadeh. 2019. Land use changes in the Mansouriyeh wetland and its relation with the occurrence of drought and dust formation in the Ahvaz metropolis. Watershed Management Research 32(4): 94-104. (In Farsi)##6. Davis, P. H. 1967-1982. Flora of Turkey and the East Aegean Islands, Volumes 1-8. University of Edinburgh, Edinburgh.##7. Derakhshi, M., M. Eskandari Torbaghan and A. Nejad Mohamad Namaghi. 2016. Use of flood water to improve the characteristics of soil and vegetation (case study: Jahan-Abad basin of Torbat-e-Jam). Water Harvesting and Watershed Management Congress. Mashhad, Iran.17 Feb. 2016. (In Farsi)##8. Dinarvand, M., H. Ejtehadi, M. Jankju and B. Andarzian. 2015. Study of floristics, life form and chorology of plants in Shimbar protected area (Khuzestan Province). The Iranian Journal of Biology 7(23): 1-14. (In Farsi)##9. Dinarvand, M., H. Ejtehadi, M. Farzam and S. B. Andarzian. 2016. A survey on the impacts of environmental factors on biodiversity, and modeling the effects of climate change on certain species distribution in Shimbar protected area, Khuzestan Province, SW Iran. PhD thesis, Ferdowsi University of Mashhad, Mashhad. (In Farsi)##10. Dinarvand, M. and Z. Jamzad. 2016. Final report of Recognition plant specimens of Khuzestan Province herbarium. Khuzestan Agricultural and Natural Resources Research and Education Center, Ahvaz. (In Farsi)##11. Dinarvand, M., H. Keneshloo and M. Fayaz. 2018. Vegetation of dusty place in Khuzestan Province. Iran Nature 3(3): 32-42. (In Farsi)##12. Dinarvand. M. and Z. Jamzad. 2020. Plant diversity of Khuzestan and dust sources in the southwest of Iran, with a checklist of vascular plants. Phytotaxa 434(3): 219-254.##13. Ejtehadi, H., A. Sepehry, and H. R. Akafi. 2008. Methods of measuring biodiversity. Ferdowsi University of Mashhad, Mashhad. (In Farsi)##14. Ghasemi, A. and H. Hydari. 2009. Assessment of the effects of flood spreading on soil properties and vegetative characteristics of Nubk, Common Mesquite and Gum arabic in Tangestan, Bushehr Province. Journal of Wood & Forest Science and Technology 16(4): 59-72. (In Farsi)##15. Heidarian, P., A. Azhdari, M. Joudaki, J. Darvishi Khatooni and S. Fathtabar Firoozjaei. 2018. Integrating remote sensing, GIS and sedimentology techniques for identifying dust storm sources: a case study in Khuzestan, Iran. Journal of the Indian Society of remote sensing 46: 1116-1124. http://doi.org/10.1007/s12524-018-0774-2 ##16. Hejcmanova, P. and M. Hejcman. 2006. A canonical correspondence analysis (CCA) of the vegetation-environment relationships in Sudanese Savannah, Senegal, South Africa. Journal of Botany 72: 256-262.##17. 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ارزیابی اثر برخی عوامل فیزیوگرافیک بر مدل‌های رویشی گونه ممرز (.Carpinus betulus L) در جنگل ارسباران Evaluating the Effect of Some Physiographic Factors on Growth Models of Hornbeam (Carpinus betulus L.) in Arasbaran Forest 1 1 هدف پژوهش حاضر بررسی تغییرات مدل‌های رگرسیونی رویشی گونه ممرز (.Carpinus betulus L) به‌عنوان فراوان‌ترین گونه در جنگل ارسباران در ارتفاع و جهت‌های مختلف دامنه بود. نمونه‌برداری انتخابی در دامنه ارتفاعی 1200 تا 1500 متر با آماربرداری در قطعه نمونه‌های یک¬هکتاری شامل قطر برابر سینه، ارتفاع کل و قطر تاج انجام شد. سپس با محاسبات رگرسیون غیرخطی، بهترین مدل‌ها برای بررسی ارتباط بین قطر (متغیر مستقل) و ارتفاع، سطح مقطع و مساحت تاج (متغیرهای وابسته) در هر ارتفاع و جهت دامنه بر اساس حداکثر ضریب همبستگی، ضریب تعیین، حداقل خطای استاندارد و ضریب آکائیک برازش شدند. همچنین آزمون همبستگی پیرسون به‌منظور بررسی رابطه بین متغیرهای وابسته و مستقل و آنالیز واریانس با استفاده از آزمون توکی برای بررسی معنی‌داری روابط بین متغیرهای مورد مطالعه و عوامل محیطی انجام شد. نتایج نشان داد همبستگی مثبت و معنی‌داری بین قطر برابر سینه با ارتفاع و سطح مقطع درختان و همبستگی منفی معنی‌داری بین قطر برابر سینه و مساحت تاج درختان وجود داشت که شدت این همبستگی بین قطر برابر سینه و سطح مقطع بسیار قوی بود (0/994=r). ارتفاع و مساحت تاج درختان در جهت‌های مختلف دامنه و ارتفاع‌های مختلف، اختلاف معنی‌داری نشان دادند، اما قطر برابر سینه و سطح مقطع درختان نسبت به تغییرات ارتفاع و جهت دامنه تفاوت معنی‌داری نداشتند. مدل‌های نمایی به‌علاوه خطی، منطقی، Heat capacity، سینوسی، نمایی 3، گاوسی، لگاریتم طبیعی و درجه دوم معکوس مهم‌ترین مدل‌ها بودند. گونه ممرز در جهت‌ها و ارتفاع‌های مختلف از مدل‌های رویشی متفاوتی پیروی می‌کرد و تنها در جهت شمال شرقی، مدل رویشی یکسانی بر حسب قطر-سطح مقطع (مدل گویا) در سه ارتفاع مورد بررسی نشان داده شد. مدل‌های رگرسیونی غیرخطی برای نشان¬دادن رابطه قطر با مشخصه‌های سطح مقطع و تاج نسبت به ارتفاع درخت، بهتر عمل کردند. پیشنهاد می‌شود مطالعات مدل‌سازی برای سایر گونه‌ها و عوامل محیطی تکرار شود تا با کسب نتایج تکمیلی، امکان ارائه دستورالعمل‌های مدیریتی دقیق فراهم شود. 2 The current study aimed to investigate changes in growth models of hornbeam (Carpinus betulus), as the most abundant species in Arasbaran forest, at different altitude ranges and aspects. Sampling was performed in one-hectare plots at the altitude range of 1200 to 1500 meters and hornbeam trees were measured on the diameter at the breast height (DBH), total height and the diameter of crown canopy. Data analysis was carried out by Nonlinear Regression Models and the best models were fitted based on maximum correlation coefficient, coefficient of determination, minimum standard error and Akaike coefficient for the relationship between diameter (independent variables), and height, basal area and the crown canopy (dependent variable). Results showed that there was a positive correlation between DBH, height, and basal area of trees (r = 0.994) but DBH and crown canopy had a negative correlation. In addition, there was a significant difference in height and crown canopy of the trees across altitudes and aspects. The results of nonlinear models revealed that Exponential Linear Model, Rational Model, Heat Capacity, Sinusoidal, Exponential Association 3, Gaussian, Natural Logarithm and Reciprocal Quadratic YD were the most important models. The hornbeam species follows different models at different altitudes and aspects (except in the northeast aspect which only showed a Rational Model at different altitudes). Nonlinear Regression Models performed reasonable in showing the relationship between tree characteristics including diameter-basal area and diameter-canopy cover. It is recommended to use such models for other species in the region with environmental factors to provide complementary results for more accurate management guidelines. 89 105 2020/09/202020/05/112020/11/102020/09/242020/07/232020/12/3 1399/9/13 2020/11/102020/11/252021/01/182021/01/302021/02/132021/03/3 1399/12/13 رؤیا عابدی R. 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