الگوهای انتشار و اندمیسم جنس (Onosma L. (Boraginaceae در البرز مرکزی Distribution Patterns and Endemism of the genus Onosma L. (Boraginaceae) in Central Alborz 1 1 ارزیابی و تهیه بانک اطلاعات تنوع و انتشار یکی از مهمترین اقدامات مدیریتی در حفاظت از تنوع زیستی محسوب می¬شود. ایران به عنوان یکی از مراکز مهم تنوع و اندمیسم جنس .Onosma L در جهان محسوب می¬گردد. به دلیل فقدان داده¬های بوم¬شناختی و جغرافیای گیاهی در مورد گونه¬ها و زیستگاههای این جنس در منطقه البرز مرکزی، جمعیت¬های گونه¬های مختلف این جنس بر اساس مطالعات میدانی و آنالیزهای مبتنی بر سامانه اطلاعات جغرافیایی (GIS) مورد مطالعه قرار گرفتند. نمونه‌برداری به صورت پیمایش از نقاط گزارش شده و نیز از سایر بخش¬های منطقه مورد نظر انجام شد. به¬علاوه جهت آنالیزهای فیزیکوشیمیایی خاک (pH، کربن آلی، درصد پتاسیم، فسفر، نیتروژن و بافت خاک)، نمونه خاک هر زیستگاه از سطح تا عمق نفوذ ریشه گیاه جمع¬آوری شد. نتایج این مطالعه نشان داد که دامنه ارتفاعی گونه¬های مورد مطالعه 3060-1257 متر از سطح دریا می¬باشد که اعضای زیربخش Haplotricha در ارتفاع 3060-1257 متر و زیربخش Heterotricha در ارتفاع 2880-1589 متر پراکنده شده¬اند. اغلب گونه¬های مورد مطالعه در رویشگاه¬هایی با اقلیم نیمه¬خشک و مدیترانه¬ای و تعدادی نیز در مواردی محدود در رویشگاه¬هایی با اقلیم نیمه¬مرطوب پراکنش یافته¬اند. به‌طورکلی بیشتر گونه¬های مورد مطالعه در رویشگاه¬هایی با میانگین بارندگی سالانه حدود 500-250 میلی¬¬متر پراکنده شده¬اند. دامنه ارتفاعی انتشار گونه O. dichroantha در بین گونه¬¬های مورد بررسی از گستره بالاتری (3060-1257متر) برخوردار بوده و در رویشگاه¬هایی با بستر رسوبی، رسوبی-آتشفشانی، آذرین و به ندرت نهشته¬های کواترنری پراکنش یافته است. اسیدیته خاک در رویشگاه¬های این گونه بین 7/13 (O. gaubae) تا 8 (O. dichroantha) است و گونه¬های مورد مطالعه بیشتر در خاک¬هایی با بافت شنی رسی لومی یافت می¬شوند. این در حالی است که کمتر از 50 درصد از جمعیت¬های گونه¬های مختلف Onosma در منطقه مورد مطالعه در محدوده مناطق حفاظت شده قرار دارند. به علاوه گونه O. gaubae به دلیل اندازه و تعداد جمعیت بسیار پایین و دامنه پراکنش جغرافیایی در گروه نادر طبقه-بندی می¬شود که در اولویت حفاظت قرار می¬گیرد. نتایج این مطالعه الگوهای انتشار این جنس را در منطقه البرز مرکزی براساس فاکتورهای بوم¬شناختی زیستگاه تحلیل نموده است. به¬علاوه این یافته¬ها به¬عنوان گامی مهم در راستای تحلیل الگوهای انتشار این جنس در ایران و منطقه جنوب غرب آسیا کاربرد دارد. 2 The evaluation as well as data banking of distribution patterns are considered as the most important management action for the conservation of biodiversity. Iran is considered as one of the most important of diversity centers of Onosma L. and includes a high rate of endemism. Due to the lack of adequate data on conservation and distribution patterns of the genus at local scale, the current study was conducted in Central Alborz, as an important diversity and endemism centers in Iran, based on field and herbarium assessments, using Geographic Information System. Sampling was performed as a survey of reported points as well as other parts of the region. For physico-chemical analysis of soils, pH, nitrogen (N), phosphorus (P), potassium (K), organic carbon (C) and soil texture of each habitat were measured from the surface to the depth of plant roots. Results indicated that studied species are mainly distributed at the elevation range of 1257 to 3060 m.a.s.l. Members of Haplotricha subdivision are scattered at an altitude of 307 to1257 m and Heterotricha subdivision at an altitude of 2880-1589 m. Dominantly, the studied species are distributed in semi-arid and Mediterranean climates. However, some species are distributed in semi-humid climate. Generally, most of the species are distributed in habitats with an average annual rainfall of about 250 to 500 mm, on sedimentary, sedimentary-volcanic, igneous and rarely on quaternary deposits. Soils` pH ranged between 7.13 (O. gaubae) and 8 (O. dichroantha). Studied species are often on loamy clay sandy soils. Less than 50% of the populations of various Onosma species, across the study area, are located in protected areas. In addition, O. gaubae is classified as a rare taxon with a priority for conservation, due to its very small population size and geographical range. The current study has also analyzed the distribution patterns of Onosma in Central Alborz region, based on geo-botanical factors. 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پیش‌بینی پراکنش سیاه‌گوش اوراسیایی(Lynx lynx) با استفاده از رویکرد مدل‌سازی ترکیبی: مطالعه موردی منطقه خرقان زرندیه ساوه، استان مرکزی Predicting distribution of Eurasian Lynx (Lynx lynx) using an ensemble modeling approach: A Case Study: Saveh Zarandieh Kharaghan Area, Markazi Province 1 1 آگاهی از پراکنش زیستگاه‌های مطلوب حیات وحش جهت جلوگیری از تخریب و نابودی زیستگاه و انقراض آنها و همچنین برای مدیریت و حفاظت از آنها ضروری است. سیاه¬گوش اوراسیایی از جمله گربه‌سانان با پراکنش وسیع در آسیا است. در این مطالعه، با به-کارگیری رویکرد مدل¬¬سازی ترکیبی و استفاده از 10 متغیر پیش¬بینی¬کننده، مطلوبیت زیستگاه سیاه¬گوش اوراسیایی در منطقه خرقان زرندیه ساوه در استان مرکزی مدل¬سازی شد. با توجه به مقدار بالای شاخص AUC (بیشتر از 0/9) برای مدل¬های حداکثر آنتروپی، جنگل تصادفی، درخت رگرسیون ارتقا یافته و مدل خطی تعمیم¬یافته، این مدل¬ها برای تهیه مدل ترکیبی نهایی پراکنش گونه هدف در نظر گرفته شدند. نتایج به¬دست آمده از مدل ترکیبی نشان داد که 53/22% وسعت منطقه خرقان از نظر شرایط زیستگاهی برای این گونه مطلوب است. فاکتورهای فاصله از مناطق مسکونی، فاصله از رودخانه، و دسترسی به طعمه به¬عنوان مهم¬ترین متغیرهای تأثیرگذار بر سیاه¬گوش اوراسیایی مشخص شدند. این پژوهش نشان می¬دهد زیستگاه¬های سیاه¬گوش به حفاظت بیشتری نیاز دارند. 2 Adequate knowledge about suitable habitats for wildlife is essential to prevent habitat destruction and extinction of species and for their conservation and management. The Eurasian lynx is one of the mostly distributed cats in Asia. In this study, we applied an ensemble habitat suitability modeling approach, using ten predictor variables to model Eurasian Lynx’s habitat suitability in Saveh Zarandieh Kharaghan Area, Markazi Province. Due to the high value of the area under the curve index (AUC > 0.9) for Maximum Entropy, Random Forest, Boosted Regression Tree, and Generalized Linear Model, these models were used to produce the final distribution model of the target species. Based on the ensemble model, 53.22% of Saveh Zarandieh Kharaghan Area were identified as suitable for this species. According to the results, distance to residential areas, distance to river and availability of prey were identified as the most important variables, affecting the distribution of Eurasian Lynx. This study indicated that the Eurasian Lynx habitats need more protection. 19 30 2020/03/112020/06/30 1399/4/10 2020/07/142020/07/18 1399/4/28 امیر انصاری A. 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بررسی تنوع زیستی گونه‌های گیاهی تیره شب‌بو در امتداد بزرگراه خرم‌آباد-اندیمشک Investigating Species Diversity of Family Brassicaceae along Khorramabad-Andimeshk Highway 1 1 برای دستیابی به توسعه پایدار و همچنین حفاظت از اکوسیستم‌های طبیعی و تنوع زیستی آن‌ها لازم است عوامل بوم¬شناختی و تأثیر آن‌ها بر تنوع گونه‌های گیاهی مورد مطالعه قرار گیرد. به‌منظور مطالعه تنوع زیستی تیره شب‌بو، بزرگراه خرم‌آباد-اندیمشک انتخاب شد. طول این بزرگراه 100 کیلومتر و ارتفاع آن از سطح دریا 420 تا 1740 متر است. دامنه ارتفاعی به سه طبقه تقسیم شد. نمونه‌برداری در شش بازه زمانی یک‌ماهه و در هر یک از این طبقات به‌صورت جداگانه انجام شد. در هر ماه 300 پلات 4 مترمربعی در فواصل 5 کیلومتری و بر اساس روش مساحت حداقل برداشت گردید. داده‌های هر طبقه، جهت تعیین شاخص‌های غنا و تنوع گونه‌ای، با آنالیز واریانس یک‌طرفه مورد تجزیه و تحلیل قرار گرفت. در مجموع 46 گونه متعلق به 33 سرده شناسایی شد. پوﺷﺶ ﮔﻴﺎﻫﻲ تیره شب‌بوی ﻣﻨﻄﻘـﻪ ﺑﻴﺸـﺘﺮ از ﮔﻴﺎﻫﺎن یک‌ساله (41 گونه) ﺗﺸﻜﻴﻞ شده که از ویژگی¬های ﻣﻨﺎﻃﻖ خشک و نیمه¬خشک است. نتایج نشان داد که ارتفاع از سطح دریا بر تنوع گونه‌ای تأثیری نداشته اما بر غنا تأثیر معنی‌داری داشته است. همچنین نتایج نشان داد زمان رویش بر تنوع و غنای گونه‌ای تأثیر معنی‌داری داشته است. بیشترین میزان غنا در طبقه ارتفاعات پایین‌تر و بیشترین تنوع و غنا در ماه‌های اسفند و اردیبهشت است. 2 To achieve sustainable development as well as the conservation of natural ecosystems and their biodiversity, it is necessary to study ecological foctors and their impacts on plant species diversity. The Khorramabad-Andimeshk highway was selected in the current study to investigate the diversity of the family Brassicaceae. The length of this highway is 100 km and its height ranged from 420 to 1740 m. The altitudinal range was divided into three classes. Sampling was performed separately at each of these classes in six time periods of one month interval. In each month, 300 plots (4 m2) were conducted with a distance interval of 5 km, based on the minimal area method. Data of each class were analyzed, using one-way analysis of variance (ANOVA) to determine the indices of species richness and diversity. A total of 46 species, belonging to 33 genera were identified. The majority of Brassicaceae species, across the study area, were annual plant species (41 species), showing the characteristic of arid and semi-arid environments. Results showed that elevation has no effect on species diversity but it has a significant effect on species richness. In addition, growth time has significant effects on species diversity and richness. The highest level of species richness was observed in the lower altitudes and the highest indices of species diversity and richness were recorded in March and May. 31 44 2020/03/112020/06/302019/12/10 1398/9/19 2020/07/142020/07/182020/07/29 1399/5/8 محمد مهدی دهشیری M. M. Dehshiri دانشگاه آزاد اسلامی ایران dehshiri2005@yahoo.com سپیده ساجدی S. Sajedi مؤسسه تحقیقات گیاهپزشکی کشور ایران s_sajedi10@yahoo.com یونس عصری Y. Asri مؤسسه تحقیقات جنگل‌ها و مراتع کشور ایران asriyounes@yahoo.com Vegetation Life form Flora Lorestan Arid and semi-arid areas Phytogeographical regions پوشش گیاهی شکل زیستی فلور لرستان مناطق خشک و نیمه‌خشک نواحی فیتوجغرافیایی 1. Amiri, S., R. Erfanzadeh and Y. Esmaeilpour. 2014. Study on the variation of plant species diversity components along elevation gradient using additive partitioning (case study: protected area of Geno, Hormozgan province). 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Journal of Plant Researches (Iranian Journal of Biology) 28: 1-11. (In Farsi). ##6. Cain, S. A. 1938. The species-area curve. American Midland Naturalist 19: 573-580.##7. Davis, P. H. 1965. Cruciferae. vol. 1. pp. 248-495, In: P. H. Davis (ed.), Flora of Turkey and the East Aegean Islands. Edinburgh University Press, Edinburgh.##8. Dehshiri, M. M. 2016. Floristic study of Khargushan Mountain, Lorestan province. Taxonomy and Biosystematics 8: 53-68. (In Farsi).##9. Dehshiri, M. M. 2020. A review on the traditional applications of medicinal plants of the Brassicaceae in Iran. Journal of Plant Researches (Iranian Journal of Biology) (In press). (In Farsi).##10. Dehshiri, M. M. 2020. Study on the plant species diversity along an altitudinal gradient in Nova Mountain, Kermanshah Province of Iran. Journal of Applied Biology 33: 62-74. (In Farsi).##11. Ejtehadi, H., A. Sepehri and H. R. Akkafi. 2009. Methods of Measuring Biodiversity. Ferdowsi University Publications, Mashhad. (In Farsi). ##12. Eslami Farouji, A. and H. Khodayari. 2014. The relationship between biodiversity and soil texture properties in Lorestan province (Kaka Reza region as a case study). In: Proceeding of 2nd National Conference on Environment, Energy and Biodefense in Iran. Tehran, Iran. (In Farsi).##13. Eslami Farouji, A. and H. Khodayari. 2016. Evaluation of vegetation types in the West Zagros (Beiranshahr region as a case study), in Lorestan Province, Iran. Biodiversitas 17: 1-10. (In Farsi).##14. Fadaei, F. 2014. Species diversity on the family Brassicaceae in habitats of the Taftan Mountain, Sistan and Balouchestan province. In: Proceeding of 1st National Congress on Biology and Natural Sciences in Iran, Tehran, Iran. (In Farsi).##15. Hamzeh'ee, B., M. Khanhasani, Y. Khodakarami and M. Nemati Peykani. 2008. Floristic and phytosociological study of Chaharzebar forests in Kermanshah. Iranian Journal of Forest and Poplar Research 16: 211-229. (In Farsi).##16. Hedge, I. C. 1968. Cruciferae. vol. 57. 372 pp. In: K. H. Rechinger (ed.), Flora Iranica. Akademische Druck-U Verlagsanstalt, Graz. ##17. Hedge, I. C. and J. L. Lamond. 1980. Brassicaceae. vol. 4. pp. 827-1085, In: C. C. Townsend and E. Guest (eds.), Flora of Iraq. Ministry of Agriculture and Agrarian Reform, Baghdad. ##18. Hosseinzadeh, R., J. Soosani, V. Alijani, S. Khosravi and H. Karimikia. 2016. Diversity of woody plant species and their relationship to physiographic factors in central Zagros forests (case study: Perc forest, Khorramabad, Iran). Journal of Forest Research 27: 1137-1141. (In Farsi).##19. Jalili, A. and Z. Jamzad. 1999. Red Data Book of Iran, a Preliminary Survey of Endemic, Rare and Endangered Plant Species in Iran. Research Institute of Forests and Rangelands, Tehran. ##20. Jalilian, N., A. Sheikhi and M. M. Dehshiri. 2014. A floristic study in Bahar-ab Kuh area in Zagros Mountains (in the border of Kermanshah and Ilam provinces, Iran). Taxonomy and Biosystematics 6: 65-76. (In Farsi).##21. Kazemnezhad, F., A. R. Hasanpour Lima, K. Haghverdi and F. Asadollahi. 2012. Plant biodiversity in the altitude gradient of forest north Iran (case study: watershed number 45 Golband, Nowshahr). Natural Ecosystems of Iran 2: 1-12. (In Farsi).##22. Khodayari, H. and S. Noroozi. 2015. Study of biodiversity of plant species on the family Brassicaceae in Lorestan province. In: Proceeding of 3rd National Conference on the Environment and Agricultural Research in Iran, Hamedan, Iran. (In Farsi).##23. Léonard, J. 1989. Considérations phytogéographiques sur les phytochories irano-touranienne, saharo-sindienne et de la Somalie-pays Masai. vol. 9. 123 pp. In: J. Léonard (ed.), Contribution à l'étude de la flore et de la végétation des deserts d'Iran: Dasht-e-Kavir, Dasht-e-Lut, Jaz Murian. Jardin botanique national de Belgique, Meise. ##24. Mehrnia, M. and P. Ramak. 2014. Floristic investigation of Noujian Watershed (Lorestan province). 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پیش‌بینی پراکنش بالقوه گونه زالزالک (Crataegus pontica C.Koch) با استفاده از رویکرد مدل‌سازی ترکیبی در استان لرستان Predicting the Potential Habitat Distribution of Crataegus Pontica C. Koch, Using a Combined Modeling Approach in Lorestan Province 1 1 تخریب رویشگاه از مهم¬ترین دلایل انقراض گونه¬های گیاهی است. به¬منظور شناسایی رویشگاه¬های بالقوه یک گونه می¬توان از تکنیک-های مدل¬سازی استفاده نمود. بنابراین هدف مطالعه حاضر شناسایی مناطق مطلوب گونه زالزالک در استان لرستان به کمک مدل¬سازی است. بدین¬منظور داده¬های حضور گونه و 23 متغیر محیطی در سطح استان لرستان جمع¬آوری شد. با استفاده از روابط همبستگی تعداد متغیرهای محیطی به 9 متغیر مؤثر کاهش یافت و از 5 روش حداکثر آنتروپی، خطی تعمیم¬یافته، مدل افزایشی تعمیم¬یافته، قطعات رگرسیون تطبیقی چندمتغیره و رگرسیون تقویت¬شده برای مدل¬سازی رویشگاه زالزالک بهره گرفته شد و نتایج مدل¬ها با ضرایب ROC، Kappa و TSS ارزیابی گردید. مطابق نتایج، ضرایب ROC و Kappa برای تمام مدل¬ها عملکرد عالی، ضریب TSS برای مدل¬های GBM و MAXENT عملکرد عالی، مدل¬های MARS و GAM عملکرد خوب و مدل GLM عملکرد متوسط را نشان دادند. بر اساس مدل ترکیبی، 40 درصد از اراضی استان به¬عنوان رویشگاه مطلوب و 60 درصد به¬عنوان رویشگاه نامطلوب برای گونه زالزالک شناسایی شد. از میان متغیرهای مختلف محیطی، تأثیرگذارترین آنها بر گونه مذکور به¬ترتیب بارش در سه¬ماهه سرد سال، بارش سالانه و ارتفاع از سطح دریا بود و احتمال حضور گونه در شرایط بارندگی سه ماه سرد سال، دامنه بارش 220-160 میلی¬متر و بازه ارتفاعی 1850-1300 متر به اوج خود رسید. با توجه به میزان صحت بالای نقشه رویشگاه مطلوب زالزالک می¬توان از آن به¬عنوان ابزار مناسبی در راستای احیای مناطق تخریب¬یافته و حفاظت از زیستگاه¬های موجود استفاده نمود. 2 Habitat degradation is one the important reasons of plant species extinction. Modeling techniques are widely used for identifying the potential habitats of different plant species. Thus, the purpose of current study was to determine potential habitats of Zalzalak in Lorestan Province. Species presence data and 23 environmental variables were collected in Lorestan Province. Correlation analysis was then applied to reduce the environmental variables into nine effective ones and potential habitat of the species was determined using five models including Maximum Entropy (MAXENT),Generalized Linear Models (GLM), Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS) and Generalized Boosting Model (GBM). Models were evaluated with receiver operating characteristic (ROC) plots, true skill statistic (TSS) and Kappa coefficients. Results showed that ROC and Kappa coefficients were excellent for all models and TSS values were excellent for GBM and MAXENT, good for MARS and GAM and medium for GLM model. According to the combined model, 40% of the province was classified as suitable and 60% as unsuitable. The precipitation of the three coldest months of the year, annual rainfall and elevation were the most effective environmental variables in Zalzalak habitat mapping. The maximum presence of the species occurred at the rainfall and elevation range of 160–220 mm and 1300–1850 m, respectively. Due to the high accuracy of the Zalzalak suitable habitat map, it can be used by related organizations, as an appropriate tool for reclamation of degraded regions and conservation of current habitats. 45 59 2020/03/112020/06/302019/12/102019/12/28 1398/10/7 2020/07/142020/07/182020/07/292020/08/5 1399/5/15 رضا جعفری R. jafari دانشگاه صنعتی اصفهان ایران reza.jafari@iut.ac.ir قهرمان رفیعی مو Gh. rafiee mo دانشگاه صنعتی اصفهان ایران g.rafiee@na.iut.ac.ir سید حمید متین خواه S. H. matinkhah دانشگاه صنعتی اصفهان ایران Matinkhah@iut.ac.ir مصطفی ترکش اصفهانی M. tarkesh isfahani دانشگاه صنعتی اصفهان ایران m_tarkesh@iut.ac.ir حمیدرضا کریم زاده H. R. karimzadeh دانشگاه صنعتی اصفهان ایران karimzadeh@iut.ac.ir زهرا جعفری Z. jafari دانشگاه صنعتی اصفهان ایران zahra.jafari@na.iut.ac.ir Habitat modeling Environmental variables NDVI Combined model Crataegus Pontica مدل‌سازی رویشگاه عوامل محیطی NDVI مدل ترکیبی Crataegus pontica 1. Aertsen, W., V. Kint, J. V. Orshoven, K. Özkan and B. Muys. 2010. Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Journal of Ecological modelling 221(8): 1119-1130.##2. Aliyari, H. 2008. Natural Geography Textbook of Lorestan Province. School Textbook Press, Tehran. (In Farsi).##3. Allouche, O., A. Tsoar and R. Kadmon. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43(6): 1223-1232.##4. Araujo, B. M. and M. New. 2006. Ensemble forecasting of species distributions. Trends in Ecology and Evolution 22: 42-47.##5. Bombi, P. 2010. Toward a new instrument for identifying the Italian hotspots of biodiversity: A case study of the amphibians and reptiles of Sicily. Italian Journal of Zoology 77(4): 453-459.##6. Braun, W. J. and D. J. Murdoch. 2007. A First course in statistical programming with R. Cambridge University Press, Cambridge.##7. Brown, J. L. and A. D. Yoder. 2015. Shifting ranges and conservation challenges for lemurs in the face of climate change. Journal of Ecology and Evolution 5(6): 1131-1142.##8. Elith, J. and J. R. Leathwick. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics 40: 667-697.##9. Flessner, B., M. C. Henry and J. Green. 2017. 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Modeling geographic distribution of Artemisia sieberi and Artemisia aucheri using presence-only modelling methods (MAXENT & ENFA). Journal of Renewable Natural Resources Research 1(1): 62-89. (In Farsi).##24. Khalyani, A. H. and A. L. Mayer. 2013. Spatial and temporal deforestation dynamics of Zagros forests (Iran) from 1972 to 2009. Journal of Landscape and Urban Planning 117: 1-12.‌##25. Kokubun, T. J., B. Harborne, J. Eagles and P. G. Waterman. 1995. Dibenzofuran phytoalexins from the sapwood tissue of Photinia pyracantha and Crataegus species. Journal of Phytochemistry 39(5): 1033-1037.##26. Landis, J. R. and G. G. Koch. 1977. The measurement of observer agreement for categorical data. Journal of biometrics 114:159-174.##27. Leibold, M. A. and M. A. McPeek. 2006. Coexistence of the niche and neutral perspectives in community ecology. Journal of Ecology 87(6): 1399-1410.##28. Mahdavi, A., V. Mirzaiezadeh, M. Niknejad and O. Karami. 2015. Assesment and prediction of oak trees decline using logistic regression model (Case study: Bivareh forests, Malekshahi-Ilam). Iranian Journal of Forest and Range Protection Research 1: 20-32. (In Farsi).##29. McCullagh, P. and J. A. Nelder. 1989. Generalized Linear Models. Chapman and Hall, London.##30. Mohammadi, A., S. J. Alavi and Q. M. Hosseini. 1396. Modeling the utility of habitat of Ulmus glabra species in Kheirud Noshahr forest. Journal of Wood and Forest Science and Technology Research 24 (3): 67-80.##31. Mozaffarian, V. 2015. Trees and shrubs of Iran. Farhang Moaser Publications, Tehran. (In Farsi).##32. Naimi, B. and M. B. Araujo. 2016. SDM: a reproducible and extensible R platform for species distribution modelling. Journal of Ecography 39: 368-375.##33. Piedallu, C., J. C. Gégout, F. Lebourgeois and I. Seynave, 2016. Soil aeration water deficit nitrogen availability acidity and temperature all contribute to shaping tree species distribution in temperate forests. 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پراکنش ارتفاع و سطح تاج درختچه‌زارهای زردکیش (.Cionura erecta L) در استان چهارمحال و بختیاری با استفاده از توابع توزیع احتمال Height and Crown Area Distribution of Cionura erecta Shrub lands in chaharmahal and Bakhtiari Province, Using Probability Distribution Functions 1 1 امروزه اهمیت توابع توزیع احتمال در مطالعات منابع طبیعی به¬دلیل نقش مؤثری که در شناخت بهتر ساختار پوشش گیاهی و ارائه مدل-های مفهومی از وضعیت شاخص¬های کمی گونه¬های گیاهی دارند، رو به افزایش است. مطالعه حاضر به¬منظور مدل¬سازی پراکنش ارتفاع و سطح تاج درختچه زردکیش (. Cionura erecta L) با استفاده از توابع توزیع احتمال در ذخیره¬گاه جنگلی چهارطاق در استان چهارمحال و بختیاری انجام شد. برداشت داده¬های پژوهش شامل ارتفاع درختچه و دو قطر بزرگ و کوچک تاج گونه مورد مطالعه توسط متر نواری در 35 قطعه نمونه هزار متر مربعی انجام شد. با در نظر گرفتن شکل تاج گونه مورد مطالعه به¬صورت دایره، مساحت تاج گونه محاسبه شد. از توابع توزیع احتمال نرمال، لوگ¬نرمال، بتا، گاما، وایبول و اکسپوننشیال برای مدل¬سازی ارتفاع و سطح تاج پوشش گونه مورد مطالعه استفاده شد و نیکویی برازش توابع مورد استفاده با آزمون¬های کلموگروف-اسمیرنف و اندرسون-دارلینگ سنجیده شد. بر اساس نتایج به¬دست آمده، شکل منحنی پراکنش گونه در طبقات ارتفاعی، زنگوله¬ای¬شکل و چوله به راست و در مورد تاج پوشش کاهشی بود. توابع توزیع بتا و گاما مناسب¬ترین توابع برای برازش با منحنی ارتفاع و توابع لوگ¬نرمال و وایبول مناسب¬ترین توابع برای مدل¬سازی سطح تاج¬پوشش گونه مورد مطالعه شناخته شدند. پژوهش حاضر نشان¬دهنده فاصله¬گرفتن خصوصیات کمی گونه مورد بررسی از حالت نرمال تحت تأثیر عوامل انسانی است. 2 Importance of probability distribution functions in natural resource studies is increasing due to their effective roles in better understanding of vegetation structure and providing conceptual models of quantitative indices of plant species. The present study was performed to model the distribution of height and canopy area of Cionura erecta L. shrub, using probability distribution functions in Chahar-Tagh forest reserve in Chaharmahal and Bakhtiari Province. Height and crown cover (the largest and smallest diameters) of the studied species were measured by a strip meter in 35 sample plots of 10r area. The area of canopy cover was calculated by considering the crown shape of the studied species as a circle. Normal, Log-normal, Beta, Gamma, Weibull and Exponential probability distribution functions were used to model the height and canopy cover of the studied species and their goodness of fit were examined by Kolmogorov–Smirnov and Anderson Darling tests. Results showed bell-shaped distributions skewed to the right for height classes and in the case of the canopy cover it was decreasing. Beta and Gamma distribution functions were identified as the most appropriate functions for fitting to the height curve and Log-normal and Weibull functions were the most suitable ones for modeling the canopy cover. The present study showed that the quantitative characters of the studied species are far from a normal distribution under the influence of human activities. 61 71 2020/03/112020/06/302019/12/102019/12/282020/02/28 1398/12/9 2020/07/142020/07/182020/07/292020/08/52020/08/19 1399/5/29 ابوذر حیدری صفری کوچی A. heidari safari kouchi دانشگاه گیلان ایران heidariabouzar@gmail.com کامبیز طاهری آبکنار K. taheri abkenar دانشگاه گیلان ایران kmbiztaheriabkenar@gmail.com فرشته مرادیان فرد F. moradian fard دانشگاه گیلان ایران f_moradianfard@yahoo.com یعقوب ایران منش Y. iran manesh مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان چهارمحال و بختیاری ایران y_iranmanesh@yahoo.com Anderson-Darling Chahartagh Gamma goodness of fit Zagros اندرسون-دارلینگ چهارطاق زاگرس گاما نیکویی برازش 1. Heidari Safari Kouchi, A., F. Moradian Fard, A. Eskandari and T. Rostami Shahraji. 2015. Investigation of some quantitative and qualitative characteristics of Persian Oak (Quercus brantii Lindl) in Bazoft forests of ChaharMahal and Bakhtiari Province. Zagros Forests Research 2(1): 75-91. (In Farsi).##2. Heydari, M., A. Mahdavi and A. Modaberi. 2017. The most appropriate statistical distribution for studding effect of land use changes on distribution of Oak trees (Quercus brantii) canopy in Zagros forests of Ilam Province. Journal of Forest Research and Development 2(4): 353-366. (In Farsi).##3. Jahanbazi Gojani, H., M. Talebi, M. Khoshnevis, A. Ebrahimi, B. Hamzeh, V. Mozaffarian and A. S. Emami. 2000. Investigating the effect of 12 years on the development of vegetation, soil improvement and regeneration of forest species in Chaharmahal and Bakhtiari province, Final report of the research project. Research Institute of Forests and Rangelands, Tehran. (In Farsi).##4. Johnson, E. W. 2010. Forest sampling desk reference. CRC Press LLC, Florida.##5. Khoshnevis, M., M. Matinizadeh, A. Shirvani and M. Teimouri. 2017. Iranian long-lived Junipers. Iran Nature 2(5): 20-35. (In Farsi).##6. Mateus, A. and M. Tome. 2011. Modeling the diameter distribution of eucalyptus plantations with Johnson’s SB probability density function: parameters recovery from a compatible system of equations to predict stand variables. Annals of Forest Science 68(2): 325-335.##7. Milios, E., P. Smiris, E. Pipinis and P. Petrou. 2009. The growth ecology of Juniperus excelsa Bieb. Trees in the central part of the Nestos valley (NE Greece) in the context of anthropogenic disturbances. Journal of Biological Research-Thessaloniki 11:‌ 83-94.##8. Mirzaei, M., A. E. Bonyad, M. Mohebi Bijarpas. 2014. Application of probability distributions in order to fit canopy classes of Quercus brantii trees, Case Study: Dalab forests of Ilam. Journal of Forest Sustainable Development 1(2): 195-203. (In Farsi).##9. Modaberi, A., J. Soosani and Sh. Khosravi. 2015. Effect of the decline on changes in the statistical distribution of hight breast diameter in the central Zagros forests (Case study: Lorestan-Ilam). Journal of Zagros Forests Researches 2(1): 105-117 . (In Farsi). ##10. Mohammadalizadeh, K., M. Zobeiri, M. Namiranian, A. Hourfar and M. Mohajer. 2010. Fitness of distribution of diameter at breast height by using some statistic distribution models. Iranian journal of forest and poplar research 17(1): 116-124. (In Farsi).##11. Mohammadalizadeh, K., M. Namiranian, M. Zobeiri, A. Hoorfar and M. Marvie-Mohadjer. 2013. Modeling of frequency distribution of tree's height in uneven-aged stands (Case study: Kheyroud forest). Forest and Poplar research of Iran 66(2): 155-165. (In Farsi).##12. Moradianfard Junaghani, F., K. Taheri-Abkenar and Y. Iranmanesh. 2015. Effects of physiographic factors and some physical and chemical soil properties on distribution of Marsdenia erecta (L.) R. Br. ex DC. in ChaharMahal and Bakhtiari province. Iranian Journal of Forest and Poplar Research 23(4): 757-768. (In Farsi).##13. Moradianfard Junaghani, F., K. Taheri-Abkenar, A. Heidari Safari Kouchi and Y. Iranmanesh. 2018. Identification and introduction of chemical compounds in essential oil of Cionura Erecta shrub species (Case study: Chahartagh forest reserve- ChaharMahal and Bakhtiari Province). Ecology of Iranian Forest 6(12): 62-71. (In Farsi).##14. Moradianfard Junaghani, F., Y. Iranmanesh, A. Heidari safari kouchi and A. Eskandari. 2015. Introduction of Cionura erecta species for planting in dry areas. In: Proceeding of first National Conference of Low Water Green Space. Kashan, Iran, pp. 1-5. (In Farsi).##15. Mozafari, A. 2014. Investigation of the chemical structure of the effect of larvae and tuberculosis of Cionura erecta on Anophelespothene in laboratory conditions, MSc thesis. University of Tehran, Tehran. (In Farsi).##16. Sheykholeslami, A., Kh. KiaPasha and A. Kia-Lashaki. 2011. A study of tree distribution in diameter classes in natural forests of Iran. Annals of Biological Research 2(5): 283-290. (In Farsi).##17. Siipilehto, J. 2006. Height distribution of Scots pine sapling stands affected by retained tree and edge stand competition. Silva Fennica 40(3): 473-483.##18. Sohrabi, H. and M. J. Taheri-Sarteshnizi. 2012. Fitting probability distribution functions for modeling diameter distribution of oak species in pollarded northern Zagros forests (case study: Armardeh-Baneh). Iranian Journal of Forest 4(4): 333-343. (In Farsi).##19. Zheng, G. Q., P. M. Kenney and L. K. T. Lam. 1992. Sesquiterpenes from clove (Eugenia caryophyllata). Journal of Natural Products 55: 999-1003.##20. Zheng, L. F. and X. N. Zhou. 2010. Diameter distribution of trees in natural stands managed on polycyclic cutting system. Forestry Studies in China 12(1): 21-25.## ##

کاربرد شبکه عصبی مصنوعی در تحلیل فرایند تغییر سیمای سرزمین در حوزه قره‌سو، استان گلستان Application of Artificial Neural Network in Landscape Change Process in Gharesou Watershed, Golestan Province 1 1 تغییر کاربری یقیناً مهم¬ترین عاملی است که حفاظت از اکوسیستم¬های طبیعی را تحت تأثیر قرار می¬دهد و نتیجه آن تبدیل اراضی طبیـعی از جمـله جنگل و مـرتع به کاربری¬های کشاورزی، صنعتی و شهری است. با وجود مطالعات متعدد برای بررسی الگوی سیمای سرزمین در اثر تغییر کاربری زمین، نوع فرایند تغییر در کمتر مطالعه¬ای در کشور بررسی شده است. در این مطالعه، فرایند تغییرات سیمای سرزمین با استفاده از مدل شبکه عصبی مصنوعی و 10 متغیر شیب، فاصله از مناطق مسکونی، پهنه¬های آبی، جاده، مرز جنگل، مرتع و زمین¬های کشاورزی، جهت، تعداد طبقات جنگلی و ارتفاع بررسی گردید. بر اساس آماره V کرامر متغیرهای جهت و فاصله از پهنه¬های آبی حذف شدند. با استفاده از نقشه پتانسیل تغییرات، لایه کاربری زمین برای سال 1410 پیش¬بینی شد. میزان اثرگذاری متغیرها در مدل با ثابت نگه¬داشتن مرحله¬ای یک متغیر، بررسی شد و فاصله از مراتع و مناطق مسکونی به¬عنوان مؤثرترین متغیر شناسایی شد. مقدار ضریب کاپا برای ارزیابی دقت مدل¬سازی با استفاده از مدل شبکه عصبی مصنوعی به¬میزان 0/82 برآورد شد. برای مقایسه نتایج مدل¬سازی از تحلیل فرایند تغییرات سیمای سرزمین استفاده شد. الگوی کلی تغییرات کاربری در منطقه نشان داد که فرایند تغییر کاربری¬های مرتبط با انسان (مناطق انسان¬ساخت و کشاورزی) به¬صورت ایجاد لکه¬های جدید و تجمیع آنها بوده و برای کاربری¬های طبیعی (مرتع و جنگل) به¬صورت حذف و ازهم¬گسیختگی است. معرفی رویکرد "فرایند تغییر سیمای سرزمین" در این مطالعه، با ساده¬تر نمودن این تحلیل، درک کلی و جامع¬تری از تغییرات ساختار سیمای سرزمین برای هر طبقه کاربری فراهم می¬کند. 2 Land use change is certainly the most important factor that affects the conservation of natural ecosystems, resulting the conversion of natural lands such as forests and pastures into agricultural, industrial and urban areas. Despite numerous studies investigating landscape patterns due to land use change, the driving forces of landscape change has been less studied in Iran. In this study, Artificial Neural Network (ANN) method was used to investigate the process of landscape change using ten variables including slope, distance from built-up areas, water bodies, road, forest edge, rangland and agriculture, number of forest classes and elevation. Aspect and distance from water bodies variables were removed based on the Cramer’s V statistic. Using transition potential maps, land cover distribution patterns for the year 2032 were created. Also, the relative effects of the 10 predictor variables were evaluated through the sensitivity of the model by forcing a single independent variable to be constant. Distance from rangeland and distance from built-up areas were the most influential variables on land use change. Kappa coefficient was used to assess the accuracy of the modeling approach. Kappa value for ANN was 0.82. We also used landscape analysis to compare modeling results through landscape change process. The general pattern of land use change in Gharesoo Watershed showed that the landscape change process related to human (built-up areas and agricultural lands) was in the form of creation and aggregation and the category of change for natural uses (rangeland and forestland) was in the form of loss and fragmentation. Introducing "Landscape Change Process" approach in this study provides a comprehensive understanding of changes in the landscape configuration for each land use class by simplifying the analysis. 73 87 2020/03/112020/06/302019/12/102019/12/282020/02/282020/02/9 1398/11/20 2020/07/142020/07/182020/07/292020/08/52020/08/192020/08/19 1399/5/29 حمیدرضا کامیاب H. R. Kamyab دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران hrkamyab@gau.ac.ir سیروس علی پور ورکی Siroos Alipour Varaki دانشگاه علوم کشاورزی و منابع طبیعی گرگان ایران siroosalipour1@gmail.com Land use change modeling Landscape change process Artificial Neural Network Gharesou Watershed مدل‌سازی تغییر کاربری زمین شبکه عصبی مصنوعی فرایند تغییر سیمای سرزمین حوزه قره‌سو 1. Aarthi, A. D. and L. Gnanappazham. 2018. Urban growth prediction using neural network coupled agents-based Cellular Automata model for Sriperumbudur Taluk, Tamil Nadu, and India. The Egyptian Journal of Remote Sensing and Space Science 21(3): 353-362.‌##2. Aitkenhead, M. and I. Aalders. 2009. Predicting land cover using GIS, Bayesian and evolutionary algorithm methods. Journal of Environmental Management 90: 236-250.##3. Bogaert, J., P. Van Hecke, D. S. V. Eysenrode and I. Impens. 2000. Landscape fragmentation assessment using a single measure. Wildlife Society Bulletin 28: 875-881.##4. Boongaling, C. G. K., D. V. Faustino-Eslava and F. P. Lansigan. 2018. Modeling land use change impacts on hydrology and the use of landscape metrics as tools for watershed management: The case of an ungauged catchment in the Philippines. Land use policy 72: 116-128.##5. Clarke, K. C. 2018. A short presentation of SLEUTH. pp. 521-525, In: M. T. Camacho Olmedo, M. Paegelow, J. F. Mas, F. Escobar (eds.), Geomatic approaches for modeling land change scenarios. Springer, Cham.##6. Deo, R. C. and M. Şahin. 2015. Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydro meteorological parameters and climate indices in eastern Australia. Atmospheric research 161: 65-81.##7. Eastman, J. R. 2015. TerrSet manual. Clark University, Worcester, Massachusetts.##8. Forman, R. and M. Godron. 1986. Landscape Ecology. John Wiley & Sons, New York.##9. Gholamalifard, M., Sh. Joorabian Shooshtari, S. H. Hosseini Kahnuj and M. Mirzaei. 2012. 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