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بررسی لکه‌های زیستگاهی خرس سیاه بلوچی (Ursus thibetanus gedrosianus) با استفاده از سنجه های سیمای سرزمین (مطالعه موردی: مناطق بحر آسمان و زریاب استان کرمان) Investigating the Habitat Patches of the Baluchistan Black Bear (Ursus thibetanus gedrosianus), Using Landscape Metrics (Case Study: Bahr Asman and Zaryab Areas, Kerman Province) 1 1 تحلیل زیستگاه با استفاده از سنجه‌های سیمای سرزمین می‌تواند در راستای مدیریت بهتر زیستگاه کارآمد باشد. خرس سیاه بلوچی به‌عنوان زیرگونه درخطر انقراض در مناطق بحر آسمان و زریاب استان کرمان پراکندگی دارد. هدف از این مطالعه مدل‌سازی پراکنش گونه و ارزیابی کیفیت لکه‌های زیستگاهی با استفاده از سنجه‌های سیمای سرزمین است. مدل‌سازی پراکنش با استفاده از رویکرد اجماع (Ensemble)، حاصل از تلفیق مدل‌های فقط حضور و حضور/ شبه عدم حضور در نرم‌افزار ModEco انجام گرفت. روی مدل اجماع از حد آستانه (True Skill Statistics, TSS) استفاده شد و لکه‌های زیستگاهی با استفاده از سنجه‌های سیمای سرزمین تحلیل شدند. نتایج این مطالعه نشان داد که گستره پراکنش، فراتر از مرز مناطق حفاظت‌شده موردمطالعه است و متغیرهای مرتبط با رطوبت بیشترین تأثیر را بر روی حضور گونه داشتند. بر پایه تحلیل سنجه‌ها، لکه‌های زیستگاهی در پناهگاه حیات‌وحش زریاب از پیوستگی بالاتر و حاشیه کمتری نسبت به منطقه حفاظت‌شده بحر آسمان برخوردار هستند. در منطقه حفاظت‌شده بحر آسمان تعداد لکه‌های زیستگاهی بیشتر بوده و عدم پیوستگی لکه‌ها منجر به شکل‌گیری حاشیه شده است. نتایج این مطالعه می‌تواند در راستای مدیریت لکه‌های زیستگاهی این‌گونه درخطر انقراض مورد استفاده قرار گیرد. 2 Habitat analysis using landscape metrics can be efficient in better management of habitat. As a critically endangered subspecies, the Baluchistan black bear is scattered in the Bahr Asman and Zaryab areas in Kerman province. The purpose of this study was to model the distribution of the sub-species and evaluate the quality of its habitat patches, using landscape metrics. Distribution modeling was conducted using an ensemble approach, resulting from the combination of presence-only and presence/pseudo-absence data in ModEco software. True Skill Statistic (TSS) threshold was applied to the ensemble model and the habitat patches were analyzed, using landscape metrics. The results showed that the distribution rage is beyond the border of the studied protected areas and the variables related to humidity had the greatest effect on the presence of the species. Based on the metrics analysis, habitat patches in the Zaryab wildlife refugee have higher connectivity and less margin than Bahr Asman protected area. In Bahr Asman protected area, the number of habitat patches is greater than the Zaryab wildlife refugee and the lack of continuity among the patches has led to the edge formation. The results of this study can be used for the habitat management of this critically endangered subspecies   1 17 2022/06/7 1401/3/17 2022/09/24 1401/7/2 حسین روح الامینی نژاد H. Ruholamininejad دانشگاه اردکان ایران hoseen.roholamini@gmail.com مریم مروتی M Morovati دانشگاه اردکان ایران mymorovati@ardakan.ac.ir پیمان کرمی P. Karami دانشگاه ملایر ایران peymankarami1988@gmail.com Black bear Ensemble model Habitat patches Landscape Kerman province خرس سیاه مدل اجماع لکه‌های زیستگاهی سیمای سرزمین استان کرمان Ahmadi, M., B. Nezami Balouchi, H. Jowkar, M.R. Hemami, D. Fadakar, S. Malakouti‐Khah and S. Ostrowski. 2017. Combining landscape suitability and habitat connectivity to conserve the last surviving population of cheetah in Asia. Diversity and Distributions, 23(6): 592-603.##2. Ali, A., Z. Zhou, M. Waseem, M.F. Khan, I. Ali, M. Asad and A.T. Qashqaei. 2017. An assessment of food habits and altitudinal distribution of the Asiatic black bear (Ursus thibetanus) in the western Himalayas, Pakistan. Journal of Natural History, 51(11-12): 689-701.##3. Almasieh, K. and M. Kaboli. 2019. Assessment of landscape connectivity and prediction of migration corridors for the Baluchistan Black Bear (Ursus thibetanus gedrosianus Blanford, 1877) in the southeastern habitats, Iran. Iranian Journal of Applied Ecology, 8(1): 33-45. (In Persian)##4. Almasieh, K., M. Kaboli and P. Beier. 2016. Identifying habitat cores and corridors for the Iranian black bear in Iran. Ursus, 27(1): 18-30.##5. Almasieh, K., M. Kaboli, F. Rasouli Nasab, T. Ghadirian, H. Fahimi and E. Abtin. 2017. Identifying habitat blocks and corridors for the Iranian black bear (Ursus thibetanus gedrosianus) in Hormozgan province. Journal of Animal Environment, 9(1): 31-38. (In Persian)##6. Ansari, A. 2020. Predicting distribution of Eurasian lynx (Lynx lynx) using an ensemble modeling approach: a case study: Saveh Zarandieh Kharaghan area, Markazi province. Iranian Journal of Applied Ecology, 9(2): 19-30. (In Persian)##7. Bista, R. and A. Aryal. 2013. Status of the Asiatic black bear Ursus thibetanus in the southeastern region of the Annapurna conservation area, Nepal. Zoology and Ecology, 23(1): 83-87.##8. Capinha, C. and P. Anastácio. 2011. Assessing the environmental requirements of invaders using ensembles of distribution models. Diversity and Distributions, 17(1): 13-24.##9. Chefaoui, R.M. and J.M. Lobo. 2008. Assessing the effects of pseudo-absences on predictive distribution model performance. EcologicalModelling, 210(4): 478-486.##10. Chemaneh far, S. 2014. The role of habitat quality in the natural regulation of the black bear population. 2th National Conference on Environment, Energy and Bio-defense. Tehran, Iran. Volume 1, 1-7. (In Persian)##11. Darvishi, A., S. Fakheran A. Soffianian and M. Ghorbani. 2014. Quantifying landscape spatial pattern changes in the Caucasian black grouse (Tetrao mlokosiewiczi) habitat in Arasbaran biosphere reserve. Iranian Journal of Applied Ecology, 2(5): 27-38. (In Persian)##12. Doko, T., H. Fukui, A. Kooiman, A.G. Toxopeus, T. Ichinose, W. Chen and A.K. Skidmore. 2011. Identifying habitat patches and potential ecological corridors for remnant Asiatic black bear (Ursus thibetanus japonicus) populations in Japan. Ecological Modelling, 222(3): 748-761.##13. Engler, R., A. Guisan and L. Rechsteiner. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo‐absence data. Journal of Applied Ecology, 41(2): 263-274.##14. Escobar, L.E., M.N. Awan and H. Qiao. 2015. Anthropogenic disturbance and habitat loss for the red-listed Asiatic black bear (Ursus thibetanus): using ecological niche modeling and nighttime light satellite imagery. Biological Conservation, 191: 400-407.##15. Fahimi, H., A.T. Qashqaei, M. Chalani, Z. Asadi, S. Broomand, N. Ahmadi and G.H. Yusefi. 2018. Evidence of seed germination in scats of the Asiatic black bear Ursus thibetanus in Iran (Mammalia: Carnivora). Zoology in the Middle East, 64(2): 182-184.##16. Fahimi, H., G.H. Yusefi, S.M. Madjdzadeh, A.A. Damangir, M.E. Sehhatisabet and L. Khalatbari. 2011. Camera traps reveal use of caves by Asiatic black bears (Ursus thibetanus gedrosianus) (Mammalia: Ursidae) in southeastern Iran. Journal of Natural History, 45(37-38): 2363-2373.##17. Garshelis, D. and R. Steinmetz. 2020. Ursus thibetanus (amended version of 2016 assessment). The IUCN red list of threatened species, 2020: e.T22824A166528664##18. Ghadirian, O., M.R. Hemami, A. Soffianian, M. Malekian, S. Pourmanafi and M. Amiri. 2019. The prediction of Persian squirrel distribution using a combined modeling approach in the forest landscapes of Luristan province. Iranian Journal of Applied Ecology, 8(1): 47-58. (In Persian)##19.Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau and R. Moore. 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202: 18-27.##20. Guo, Q. and Y. Liu. 2010. ModEco: an integrated software package for ecological niche modeling. Ecography, 33: 637-642##21. Hindmarch, S., J.E. Elliott, S. Mccann and P. Levesque. 2017. Habitat use by barn owls across a rural to urban gradient and an assessment of stressors including, habitat loss, rodenticide exposure and road mortality. Landscape and Urban Planning, 164: 132-143.##22. Jafari, A., R. Mirzaei and R. Zamani-Ahmadmahmoodi. 2016. Species distribution modeling of wild sheep based on improving bias of occurrence records and selecting appropriate environmental predictors using MaxEnt. Iranian Journal of Applied Ecology, 5 (15), 39-49. (In Persian)##23. Kamyab, H.R. and A.R. Salman Mahini. 2014. Tempo-spatial patterns of landscape changes and urban development (Case study: Gorgan). Journal of RS and GIS for Natural Resources, 5(2), 15-24. (In Persian)##24. Karami, P. 2021. Identifying and analyzing distribution of habitat's hotspots of salient vertebrates from landscape perspective in Kermanshah province. Ph.D Thesis of Environmental Science, Faculty of Natural Resources and Environment, Malayer University, 429 p. (In Persian)##25. Karami, P. and K. Shayesteh. 2018. Investigation of ecological niche of wild sheep (Ovis oriantalis) in protected areas of Lashgardar-Golparbad, Alvand-Chalkhatoon-Rasvand and Polangab. Journal of Animal Environment, 10(4), 65-74. (In Persian)##26. Karami, P. and S. Tavakoli. 2022. Identification and analysis of areas prone to conflict with wild boar (Sus scrofa) in the vineyards of Malayer county, western Iran. Ecological Modelling, 471, 110039.##27. Karami, P., Y. Esmaeilpour and M. Sharifi. 2017. Investigating the effects of livestock entry on ecological niche changes of the Persian gazelle (Gazella subgutturosa subgutturosa) (Case study: Qaraviz no hunting area, Kermanshah). Journal of Environmental Science and Technology, 19(5), 507-521. (In Persian)##28. Khalili, F., M. Malekian, N. Rojae and M.R. Hemami. 2017. Habitat evaluation of persian squirrel (Sqiurus anumalus) in servak forested area in Kohgiluyeh & Boyer Ahmad province. Iranian Journal of Applied Ecology, 5(18), 15-25. (In Persian)##29. Khosravian, M., A. Antezari, M. Karami and M. Abrahimi. 2016.Modeling and identification of areas susceptible to landslides affecting factors of using the Shannon entropy model (case study: watershed time Nishapur). Journal of Geographical Sciences, 12 (25), 15- 32. (In Persian)##30. Khosravian, M., A. Entezari, A. Rahmani and M. Baaghide. 2018. Monitoring the disturbance of lake district water level changes using remote sensing indices. Hydrogeomorphology, 4 (13), 99- 120. (In Persian)##31. Liley, S. G. and R. N. Walker. 2015. Extreme movement by an American black bear in New Mexico and Colorado. Ursus, 26 (1), 1- 6.##32. Liu, C., G. Newell and M. White. 2019. The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo‐absences or background sites. Ecography, 42 (3), 535- 548.##33. Liu, F., W. McShea and D. Garshelis. 2011. Human-wildlife conflicts influence attitudes but not necessarily behaviors: factors driving the poaching of bears in China. Biological Conservation, 134, 538- 547.##34. Mahmoodi, S., A. Alizadeh Shabani, M. Zeinalabedini, S. Ashrafi and O. Khalilipour. 2020. Determining suitable habitats for roe deer (capreolus capreolus) in north of iran using ensemble approach. Journal of Animal Environment, 12(4),17-24. (In Persian)##35. Mairota, P., B. Cafarelli, L. Boccaccio, V. Leronni, R. Labadessa, V. Kosmidou and H. Nagendra. 2013. Using landscape structure to develop quantitative baselines for protected area monitoring. Ecological Indicators, 33, 82-95.##36. Matutini, F., J. Baudry, M.J. Fortin, G. Pain and J. Pithon. 2021. Integrating multiple and fine scale predictors of habitat selection and landscape resistance for Amphibian distribution modelling at large scale. Research Square, 1-28.##37. McGarigal, K., S.A. Cushman, M.C. Neel and E. Ene. 2002. FRAGSTATS: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: www. umass.edu/landeco/research/fragstats/fragstats##38. Mohseninejad, H. and P. Karami. 2020. Quantification of the Distribution and Changing Trend of the Ecological Niche of Brown Bear (Ursus arctos Linnaeus, 1758) in Ilam province. Journal of Animal Environment, 12(1), 1-8. (In Persian)##39. Moisen, G. G. and T. S. Frescino. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157, 209- 225.##40. Mokhtari, S., S.F.H. Soltani Fard and A. Yavari. 2010. Consideration of the changing and self-organizing trend in Hur-Al-Azim wetland by using image processing to refer landscape ecology approach, Khuzestan, Iran. Physical Geography Research Quarterly, 41(70). (In Persian)##41. Moosavi, S.Z. and M.M. Mirsanjari. 2020. Study of relationship between threshold and habitat patches of brown bears (Ursus arctos Linnaeus, 1758) in Sephid-Koh protected area by using land scape approach. Journal of Animal Environment, 12(4), 33-40. (In Persian)##42. Morovati, M., P. Karami and F. Bahadori Amjas. 2020. Accessing habitat suitability and connectivity for the westernmost population of Asian black bear (Ursus thibetanus gedrosianus, Blanford, 1877) based on climate changes scenarios in Iran. PLoS ONE, 15 (11), 1-22.##43. Nasiri, V. and M. Ghorbani. 2020. An approach for quantifying the spatial disturbance variations based on landscape metrics (case study: Arasbaran protected area during 1990-2014). Journal of Natural Environment, 73(2), 397-409. (In Persian)##44. Nezami, B., F. Ataei, A. Kafash, A. Alizadeh Shaabani, R. Eshaghi and R. Naeimaei. 2018. Key source area to conserve brown bear Ursus arctos Linnaeus, 1758 in Alborz Mountain. Experimental Animal Biology, 6(3), 127-141. (In Persian)##45. Noori, S., K. Nourijelyani, K. Mohammad, M. H. Niknam, M. Mahmoudi, L. Andonian and A. Akaberi. 2012. Random forests analysis: a modern statistical method for screening in high-dimensional studies and its application in a population-based genetic association study. JNKUMS, 3(5), 93-101. (In Persian)##46. Norouzi, H., A. A. Nadiri, A. Asghari Mogaddam and M. Gharekhani. 2017. Prediction of transmissivity of malikan plain aquifer using Random Forest method. Water and Soil Science, 27(2), 61-75. (In Persian)##47. Rahdari, V. 2020. Black bear habitat suitability modeling in a part of Sistan and Balouchestan province using multicriteria bevaluation method. Journal of Natural Environment, 73(3), 485-499. (In Persian)##48. Report of Natural Environment, Kerman Environment Department, Management of protected areas, 2020. (In Persian)##49. Ripple, W. J., J. A. Estes, R. L. Beschta, C. C. Wilmers, E. G. Ritchie and M. Hebblewhite. et al. 2014. Status and ecological effects of the world`s largest carnivores. Science, 343, 124-148.##50. Rouhi, H., A. Salman Mahini and H.R. Rezaei. 2019. Modeling potential corridors for the Persian leopard (Pantera pardus saxicolor) between the two habitats of Khoshyeilagh wildlife refuge and Golestan national park based on the least-cost path analysis. Journal of Environmental Science and Technology, 21(3), 195-209. (In Persian)##51. Jalali, S., M. Samadi, M. Samadi Gheshlaghchaee and A. Kornejadi. 2016. Investigation of morphometric indexes with GIS in Chel-Chay watershed, Golestan province. Geospatial Engineering Journal, 7(4), 37-48. (In Persian)##52. Senay, S. D., S. P. Worner and T. Ikeda. 2013. Novel three-step pseudo-absence selection technique for improved species distribution modelling. PloS One, 8(8), p.e71218.##53. Shahnaseri, G., M.R. Hemami, R. Khosravi, S. Malakoutikhah, M. Omidi and S.A. Cushman. 2019. Contrasting use of habitat, landscape elements, and corridors by grey wolf and golden jackal in central Iran. Landscape Ecology, 34(6), 1263-1277.##54. Smeraldo, S., L. Bosso. M. Fraissinet, L. Bordignon, M. Brunelli and L. Ancillotto. et al. 2020. Modelling risks posed by wind turbines and power lines to soaring birds: the black stork (Ciconia nigra) in Italy as a case study. Biodiversity and Conservation, 2020,1-18.##55. Stockwell, D.R. and A.T. Peterson. 2002. Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148(1), 1-13.##56. Studies on developing action plan for black bear conflict management in Kerman province. 2020. General Department of Environmental Protection of Kerman Province, 184 p. (In Persian)## ##

پیش‌بینی تغییرات تولید خالص اولیه در مناطق مختلف رویشی ایران در دوره زمانی 2000 تا 2016 با استفاده از مدل‌های سری زمانی Prediction of Net Primary Production Changes in Different Phytogeographical Regions of Iran from 2000 to 2016, Using Time Series Models 1 1 پوشش گیاهی از اجزاء مهم اکوسیستم‌های خاکی است که به صورت فصلی تغییر می‌کند و پارامترسازی دقیق پویایی پوشش-گیاهی با توسعه الگوهای دوره‌ای آن می‌تواند درک ما را از تعاملات پوشش گیاهی-اقلیمی تقویت کند. پژوهش حاضر با هدف بررسی و مدلسازی تغییرات تولید خالص اولیه در طول زمان در برخی از مناطق رویشی کشور شامل مناطق خزری، بلوچی، نیمه بیابانی، استپی معتدل، نیمه استپی گرم و جنگل‌های خشک و مقایسه رفتار تصادفی این مناطق با یکدیگر انجام گرفت. در پژوهش حاضر جهت بررسی تغییرات تولید خالص اولیه از تولیدات تصاویر سنجنده مودیس با کد MOD17A2 استفاده گردید. مدلسازی با استفاده از مدل سری زمانی (Seasonal Auto Regressive Intergrated Moving Average , SARIMA)  انجام گردید. بررسی توابع (Autocorrelation Function, AFC) و (Partial Autocorrelation Function, PACF) در مناطق مورد مطالعه نشان داد که مدل‌های سری‌ زمانی این مناطق، ایستا با ویژگی فصلی بودن در دوره های 12 ماهه بودند. پوشش گیاهی در منطقه خزری پایدارتر بود که نشان دهنده یک محیط پایدار با کمترین انحراف در تغییرات آب، نور و مواد غذایی می باشد. همچنین مشخص گردید بیشتر مناطق رویشی ایران را می‌توان با SARIMA مدلسازی و تغییرات آن را تا حد قابل اطمینانی پیش‌بینی کرد. مدل‌های برآورد شده برای مناطق خزری 0/83 = (Mean Relative Absolute Erorr, MRAE )،R2  =0/87 و 0/12 = (Root-Mean-Square Erorr, RMSE) و نیمه بیابانی با (0/12=RMSE 0/95=R2, 0/048=MARE) نسبت به سایر مناطق مدل های مناسب تری بودند.        2 Vegetation cover is an important component of terrestrial ecosystems that changes seasonally. Accurate parameterization of vegetation cover dynamics through developing indicators of periodic patterns can assist our understanding of vegetation-climate interactions. The current study was conducted to investigate and model vegetation changes in some phytogeographical regions of Iran including, Khazari, Baluchi, semi-desert, temperate steppe, warm semi-steppe and arid forest and to compare their stochastic behavior. To study the vegetation changes the net primary production (NPP) was used, based on the products of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (MOD17A2 series). Seasonal Auto Regressive Integrated Moving Average (SARIMA) time series model was used for modeling NPP. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) of time series showed that these areas were static with seasonality in 12-month periods. It also showed that the vegetation in Khazari region was more stable, which indicates a stable environmental condition with the least deviation in water, light and nutrients. We also found that most of the vegetative regions of Iran can be modeled with SARIMA and its changes can be reliably predicted. Estimated models for Khazari (Root-Mean-Square Error, (RMSE) = 0.12, R2 = 0.87, Mean Relative Absolute Error (MARE) = 0.083) and semi-desert (RMSE = 0.12, R2 = 0.95, MARE = 0.048) were more suitable models than other regions.   19 35 2022/06/72022/06/14 1401/3/24 2022/09/242022/09/28 1401/7/6 فهیمه صیدزاده F. Sayedzadeh دانشگاه صنعتی اصفهان ایران F.sayedzadeH@na.iut.ac.ir سعید سلطانی S. Soltani دانشگاه صنعتی اصفهان ایران ssoltani@iut.ac.ir رضا مدرس R. Modarres دانشگاه صنعتی اصفهان ایران reza.modarres@iut.ac.ir The autocorrelation function Partial autocorrelation function Box-Jenkins model Seasonality Seasonal autoregressive integrated moving average (SARIMA) توابع خود همبستگی و خودهمبستگی جزئی مدلسازی باکس و جنکینز تغییرات فصلی مدل فصلی خودهمبسته‌ی میانگین-متحرک 1. Aggarwal, A., M. Alshehri, M. Kumar, O. Alfarraj, P. Sharma and K. R. Pardasani. 2020. Landslide data analysis using various time-series forecasting models. Computers and Electrical Engineering, 88:106858. ##2. Dawson, C.W., Abrahart, R.J. and L.M. See. 2007. HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environmental Modelling and Software, 22 (7): 1034–1052.##3. Dyah R. P. and H. T. Bambang. 2012. Seasonal Pattern of Vegetative Cover from NDVI TimeSeries. Pp. 254-268, In: P. Sudarshana (Ed.), Tropical Forests, InTech, Krautzeka. ##4. Fernández-Manso. A., C. Quintano and O. Fernández-Manso. 2011. Forecast of NDVI in coniferous areas using temporal ARIMA analysis and climatic data at a regional scale, International Journal of Remote Sensing, 32:6, 1595-1617.##5. Guan. K., D. Medvigy, E.F. Wood, K.K. Caylor, S. Li, and, S.J. Jeong. 2014. Deriving vegetation phenological time and trajectory information over africa using seviri daily LAI. IEEE Trans. Geoscience and Remote Sensing, 52: 1113–1130.##6. Han, P., P. X. Wang, S. Y. Zhang and D. H. Zhu. 2010. Drought forecasting based on the remote sensing data using ARIMA models. Mathematical and Computer Modelling, 51: 1398–1403. ##7. Jiang, B., Liang. S., Wang., J and Z. Xiao. 2010. Modeling MODIS LAI time series using three statistical methods. Remote Sensing of Environment, 114: 1432–1444.##8. Modarres, R. and da Silva, V.D.P.R. 2007 Rainfall trends in arid and semi-arid regions of Iran. Journal of Arid Environments,70(2): 344-355.(In Persian)##9. Mutti, P. R., P. S. Lúcio, V. Dubreuil and B. G. Bezerra. 2020. NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots. International Journal of Remote Sensing, 41: 2759-2788.##10. Pabot, H. 1967. Report to Government of Iran: Pasture development and range improvement through botanical and ecological studies. UNDP/FAO, Rome. ##11. Patel, N. R., Dadhwal, V. K., Saha, S. K., Garg, A. and Sharma, N. 2010. Evaluation of MODIS data potential to infer water stress for wheat NPP estimation, Tropical Ecology, 51(1): 93-105. ##12. Recuero. L., J. Litago, J. E. Pinzón., M. Huesca., M. C. Moyano and A. Palacios-Orueta. 2019. Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series, Remote Sensing, 11(21), 2497.##13. Said. O. M. 2022. Forecasting vegetation condition using remote sensing time series data. PhD Thesis. Graduate School of Applied Informatics University of Hyogo. Hyogo. Japan.##14. Wang, H., He, B., Zhang, Y., Huang, L., Chen, Z. and Liu, J. 2018. Response of ecosystem productivity to dry/wet conditions indicated by different drought indices. Science of the Total Environment, 612: 347–357.##15. Wei. W. W.S.2019. Time Series Analysis: Univariate and Multivariate Methods. Pearson Education. Boston.##16. Zhao. M.S. and Running. S.W.2010. Drought-induced reduction in global terrestrial net primary production from 2000 to 2009. Science, 329: 940–943.## ##

مدلسازی آشیان بوم‌شناختی افعی‌های کوهستانی تبار راده‌ای (Raddei) در ایران، قفقاز و شرق ترکیه Ecological Niche Modeling of Mountain Vipers from the Raddei Clade in Iran, Caucasus and Eastern Turkey 1 1 افعی‌های کوهستانی جنس مونتی‌ویپرا (Montivipera) به‌طور کلی، و گونه‌های تبار Raddei به‌طور خاص، نمونه‌های ویژه‌ای از بوم‌زادی جدید (Neo-endemism) در فلات ایران، آناتولی و قفقاز هستند. با توجه به چالش‌های پیش روی حفاظت از گونه‌های مذکور، تعیین زیستگاه‌های مطلوب آنها، به منظور اتخاذ رویکردهای حفاظتی مناسب، ضروری است. به این منظور آشیان بوم شناختی هر گونه بر اساس چهار الگوریتم مدل تعمیم یافته خطی، مدل تعمیم یافته ارتقایی، جنگل تصادفی و بیشینه بی‌نظمی مدلسازی و در قالب یک مدل اجماعی بررسی شد. همچنین، با استفاده از یک رویکرد تجزیه به مؤلفه‌های اصلی، جدایی آشیان بوم‌شناختی گونه‌ها مورد بررسی قرار گرفت. نتایج نشان داد که مدل پراکنش گونه‌ها با (Area Under the Curve, AUC) و (True Skills Statistics, TSS) بالاتر از 0/9 عملکرد پیش‌بینی بالایی دارند. گونه‌های تبار Raddei الگوهای متفاوتی از اشغال آشیان بوم‌شناختی را نشان داده و بیشترین تمایز در گرادیان متغیرهای تغییرات فصلی درجه حرارت، بارش سالانه و تنوع ناهمواری‌ها دیده شد. دو گونه افعی لطیفی و افعی کوهرنگی، با وجود فاصله جغرافیایی زیاد، بیشترین میزان مشابهت و هم‌پوشی آشیان بوم‌شناختی را نشان دادند. با توجه به پراکنش و انعطاف‌پذیری تکاملی محدود افعی‌های کوهستانی در مناطق کوهستانی البرز و زاگرس، طرح‌ریزی اقدامات مدیریتی جهت کاهش عوامل تهدید کننده بقای طولانی مدت این گونه‌ها امری ضروری است. 2 Mountain vipers of the genus Montivipera, generally, and the species of the Raddei clade, specifically, are interesting examples of species neo-endemism in Iran, Anatolia, and the Caucasus. Given the critical conservation status of these species, it is necessary to identify their suitable habitats for prioritizing conservation measures. We modeled ecological niche of each species based on four species distribution models, including generalized linear models, generalized boosting models, random forest, and maximum entropy and combined them in an ensemble model. Also, using a new principal component analysis (PCA-env), the ecological niche divergence of the species was investigated. Results indicated that all models with AUC and TSS > 0.9 had excellent predictive performance. The species of the Raddei clade showed different patterns of ecological niche occupation and the greatest differentiation was seen in the gradient of temperature seasonality, annual precipitation, and topographical ruggedness. M. latifii and M. kuhrangica, despite the great geographical distance, revealed the highest degree of niche overlap and niche similarity. Due to the restricted distribution and limited evolutionary adaptability of mountain vipers in the mountainous regions of Alborz and Zagros, it is imperative to plan conservation measures to reduce the factors that threaten the long-term survival of these species.   37 49 2022/06/72022/06/142022/07/20 1401/4/29 2022/09/242022/09/282022/10/15 1401/7/23 محسن احمدی M. Ahmadi دانشگاه صنعتی اصفهان ایران mahmadi@iut.ac.ir محمودرضا همامی M. R. Hemami دانشگاه صنعتی اصفهان ایران mrhemami@iut.ac.ir محمد کابلی M. Kaboli دانشگاه تهران ایران mkaboli@ut.ac.ir منصوره ملکیان M. Malekian دانشگاه صنعتی اصفهان ایران mmalekian@iut.ac.ir Habitat suitability modeling Niche conservatism Evolutionary flexibility Conservation planning مدل‌سازی مطلوبیت زیستگاه نگهداشت آشیان بوم‌شناختی انعطاف‌پذیری تکاملی طرح‌ریزی حفاظت 1. Ahmadi, M., M. Naderi, M. Kaboli, M. Nazarizadeh, M. Karami and S. M. Beitollahi. 2018. Evolutionary applications of phylogenetically-informed ecological niche modelling (ENM) to explore cryptic diversification over cryptic refugia. Molecular Phylogenetics and Evolution. 127, 712-722.##2. Ahmadi, M., M.R. Hemami, M. Kaboli, M. Malekian and N. E. Zimmermann. 2019. Extinction risks of a Mediterranean neo-endemism complex of mountain vipers triggered by climate change. Scientific Reports 9, 1-12.##3. Ahmadi, M., M.-R. Hemami, M. Kaboli, M. Nazarizadeh, M. Malekian, R. Behrooz, P. Geniez, J. Alroy and N.E. Zimmermann. 2021. The legacy of Eastern Mediterranean mountain uplifts: rapid disparity of phylogenetic niche conservatism and divergence in mountain vipers. BMC Ecology and Evolution 21, 1-13.##4. Ahmadzadeh, F., M. Flecks, M. A. Carretero, W. Böhme, F. Ihlow, P. Kapli, A. Miraldo and D. Rödder. 2016. Separate histories in both sides of the Mediterranean: phylogeny and niche evolution of ocellated lizards. Journal of Biogeography 43(6), 1242–1253. ##5. Almasieh, K., S. M. Mirghazanfari and S. Mahmoodi. 2019. Biodiversity hotspots for modeled habitat patches and corridors of species richness and threatened species of reptiles in central Iran. European Journal of Wildlife Research, 65, 92.##6. Araújo, M. B. and M. New. 2007. Ensemble forecasting of species distributions. Trends in Ecology & Evolution 22(1), 42-47. ##7. Behrooz, R., M. Kaboli, V. Arnal, M. Nazarizadeh, A. Asadi, A. Salmanian, M. Ahmadi and C. Montgelard. 2018. Conservation below the species level: suitable evolutionarily significant units among mountain vipers (the Montivipera raddei complex) in Iran. Journal of Heredity 109(4), 416-425. ##8. Bivand, R., T. Keitt, B. Rowlingson and E. Pebesma. 2014. rgdal: Bindings for the geospatial data abstraction library. R package version 0.8-16. ##9. Broennimann, O., M. C. Fitzpatrick, P. B. Pearman, B. Petitpierre, L.,Pellissier, N. G. Yoccoz, W. Thuiller, M. J. Fortin, C. Randin, and N. E. Zimmermann, C. H. Graham and A. Guisan. 2012. Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecology and Biogeography 21(4), 481-497. ##10. Collen, B., S. T. Turvey, C. Waterman, H. M. Meredith, T. S. Kuhn, J. E. Baillie and N. J. Isaac. 2011. Investing in evolutionary history: implementing a phylogenetic approach for mammal conservation. Philosophical Transactions of the Royal Society of London B: Biological Sciences 366(1578), 2611-2622. ##11. Di Cola, V., O. Broennimann, B. Petitpierre, F. T. Breiner, M. d'Amen, C. Randin, R. Engler, J. Pottier, D. Pio, A. Dubuis and L. Pellissier. 2017. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography, 40(6), 774-787.##12. Djamali, M., S. Brewer, S. W. Breckle and S. T. Jackson. 2012. Climatic determinism in phytogeographic regionalization: a test from the Irano-Turanian region, SW and Central Asia. Flora-Morphology, Distribution, Functional Ecology of Plants 207(4), 237-249. ##13. Faith, D. P. 2002. Quantifying biodiversity: a phylogenetic perspective. Conservation Biology 16(1), 248-252. ##14. Franklin, J.2010. Mapping species distributions: spatial inference and prediction. Cambridge University Press.##15. Fujita, M. K., A. D. Leaché, F. T. Burbrink, J. A. McGuire and C. Moritz. 2012. Coalescent-based species delimitation in an integrative taxonomy. Trends in Ecology & Evolution 27(9), 480-488. ##16. Graham, C. H., S. R. Ron, J. C. Santos, C. J. Schneider and C. Moritz. 2004. Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution 58(8), 1781-1793. ##17. Guisan, A., T. C. Edwards Jr. and T. Hastie. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157(2-3), 89-100. ##18. Guisan, A., W. Thuiller and N. E. Zimmermann. 2017. Habitat Suitability and Distribution Models: With Applications in R. Cambridge University Press, p 462.##19. Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25(15), 1965-1978. ##20. Holt, R. D. 2009. Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives. Proceedings of the National Academy of Sciences 106(Supplement 2), 19659-19665. ##21. Ihlow, F., J. Dambach, J. O. Engler, M. Flecks, T. Hartmann, S. Nekum, H. Rajaei and D. Rödder. 2012. On the brink of extinction? How climate change may affect global chelonian species richness and distribution. Global Change Biology 18(5), 1520-1530. ##22. Jetz, W., J. M. McPherson and R. P. Guralnick. 2012. Integrating biodiversity distribution knowledge: toward a global map of life. Trends in Ecology & Evolution 27(3), 151-159. ##23. Jones, G. 1997. Acoustic signals and speciation: the roles of natural and sexual selection in the evolution of cryptic species. Advances in the Study of Behaviour 26, 317-354. ##24. Kramer-Schadt, S., J. Niedballa, J. D. Pilgrim, B. Schröder, J. Lindenborn, V. Reinfelder, M. Stillfried, I. Heckmann, A. K. Scharf, D. M. Augeri, S. M. Cheyne, A. J. Hearn, J. Ross, D. W. Macdonald, J. Mathai, J. Eaton, A. J. Marshall, G. Semiadi, R. Rustam, H. Bernard, R. Alfred, H. Samejima, J. W. Duckworth, C. Breitenmoser-Wuersten, J. L. Belant, H. Hofer and A. Wilting. 2013. The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions 19(11), 1366–1379. ##25. Lavergne, S., M. E. Evans, I. J. Burfield, F. Jiguet and W. Thuiller. 2013. Are species' responses to global change predicted by past niche evolution? Philosophical Transactions of the Royal Society B: Biological Sciences 368(1610), 2012009.##26. Merow, C., M. J. Smith, T. C. Edwards, A. Guisan, S. M. McMahon, S. Normand, W. Thuiller, R. O. Wüest, N. E. Zimmermann and J. Elith. 2014. What do we gain from simplicity versus complexity in species distribution models? Ecography 37(12), 1267-1281. ##27. Naimi, B. 2015. usdm: Uncertainty analysis for species distribution models. R package version, 1.1-15. ##28. Rajabizadeh, M., G. Nilson and H. G. Kami. 2011. A new species of mountain viper (Ophidia: Viperidae) from the Central Zagros Mountains, Iran. Russian Journal of Herpetology 18(3), 235-240. ##29. Rajabizadeh, M., D. Adriaens, M. Kaboli, J. Sarafraz and M. Ahmadi. 2015. Dorsal colour pattern variation in Eurasian mountain vipers (genus Montivipera): A trade-off between thermoregulation and crypsis. Zoologischer Anzeiger-A Journal of Comparative Zoology 257, 1-9. ##30. Rajaei, H., D. Rödder, A. M. Weigand, J. Dambach, M. J. Raupach and J. W. Wägele. 2013. Quaternary refugia in southwestern Iran: insights from two sympatric moth species (Insecta, Lepidoptera). Organisms Diversity & Evolution 13(3), 409-423. ##31. Santos, H., J. Juste, C. Ibáñez, J. M. Palmeirim, R. Godinho, F. Amorim, P. Alves, H. Costa, O. Paz and G. Pérez‐Suarez. 2014. Influences of ecology and biogeography on shaping the distributions of cryptic species: three bat tales in Iberia. Biological Journal of the Linnean Society 112(1), 150-162. ##32. Schluter, D. 2009. Evidence for ecological speciation and its alternative. Science 323(5915), 737-741. ##33. Schluter, D. and G. L. Conte. 2009. Genetics and ecological speciation. Proceedings of the National Academy of Sciences 106(Supplement 1), 9955-9962. ##34. Sexton, J. P., J. Montiel, J. E. Shay, M. R. Stephens and R. A. Slatyer. 2017. Evolution of ecological niche breadth. Annual Review of Ecology, Evolution, and Systematics 48, 183-206. ##35. Soberón, J. 2007. Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters 10(12), 1115-1123. ##36. Stümpel, N. and U. Joger. 2009. Recent advances in phylogeny and taxonomy of Near and Middle Eastern vipers–an update. ZooKeys 31, 179. ##37. Stümpel, N., M. Rajabizadeh, A. Avcı, W. Wüster and U. Joger. 2016. Phylogeny and diversification of mountain vipers (Montivipera, Nilson et al., 2001) triggered by multiple Plio–Pleistocene refugia and high-mountain topography in the Near and Middle East. Molecular Phylogenetics and Evolution 101, 336-351. ##38. Thuiller, W., B. Lafourcade, R. Engler and M. B. Araújo. 2009. BIOMOD–a platform for ensemble forecasting of species distributions. Ecography 32(3), 369-373. ##39. Venter, O., E. Sanderson, A. Magrach, J. Allan, J. Beher, K. Jones and B.Fekete. 2018. Last of the wild project, version 3 (LWP-3): 2009 human footprint, 2018 release. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC), 10, H46T40JQ44. ##40. Warren, D. L., R. E. Glor and M. Turelli. 2008. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62(11), 2868-2883.##41. Wiens, J. J. and C. H. Graham. 2005. Niche conservatism: integrating evolution, ecology, and conservation biology. Annual Review of Ecology, Evolution, and Systematics, 519-539.##42. Wiens, J. J., D. D. Ackerly, A. P. Allen, B. L. Anacker, L. B. Buckley, H. V. Cornell, E. I. Damschen, J. T. Davies, J. A. Grytnes and S. P. Harrison. 2010. Niche conservatism as an emerging principle in ecology and conservation biology. Ecology Letters 13(10), 1310-1324. ##43. Yousefi, M., M. Ahmadi, E. Nourani, R. Behrooz, M. Rajabizadeh, P. Geniez and M. Kaboli. 2015. Upward altitudinal shifts in habitat suitability of mountain vipers since the last glacial maximum. PLoS ONE 10(9), e0138087.## ##

ارزیابی تغییرات کاربری اراضی در مناطق با ریسک بالای تعارض خرس قهوه‌ای در استان فارس Assessing Land Use Changes in Areas with High Risk of Human-Brown Bear Conflict in Fars Province 1 1 ارزیابی تغییرات صورت گرفته در زیستگاه گامی مهم در اولویت بندی مکانی برنامه های کاهش تعارض انسان و حیات وحش است. پژوهش حاضر با هدف ارزیابی تغییرات کاربری اراضی در محدوده مناطق با ریسک بالای تعارض خرس قهوه ای در استان فارس اجرا شد. در گام نخست، با استفاده از نقاط وقوع تعارض، متغیرهای پیش بینی کننده و رویکرد اجماع مدل های کوچک، نقشه مناطق با ریسک بالای تعارض تهیه شد. در گام دوم، با استفاده از روش‌ های سنجش از دور، روند تغییرات کاربری اراضی در بازه زمانی 30 ساله در محدوده مناطق با ریسک بالا ارزیابی شد. نتایج مدل سازی ریسک نشان داد که فاصله از مناطق روستایی، کریدورهای مهاجرتی و تراکم لکه های جنگلی مهم ترین متغیرها در احتمال وقوع ریسک تعارض خرس است. به ترتیب 3/75 و 6/91 درصد از منطقه در طبقات با خطر بالا و متوسط قرار گرفت. ارزیابی تغییرات کاربری نشان داد در یک بازه زمانی 30 ساله سطح باغات و اراضی کشاورزی از 12167 به 52662 هکتار افزایش یافته است. چنین تغییری می تواند احتمال تعارض خرس با جوامع بومی را افزایش دهد. بر اساس نتایج این پژوهش طرح ریزی برنامه ریزی های بین سازمانی به منظور جلوگیری از روند تخریب عرصه های طبیعی جهت کاهش تعارضات انسان و حیات وحش امری ضروری است. 2 Assessing habitat changes is an important step in the spatial prioritization of management efforts, aimed at reducing conflicts. We assessed landuse/cover change in areas with high risk of human-bear conflict in Fars province. In the first step, we predicted the conflict hotspots, using bear damage incidents, a suit of predictors, and the Ensembles of Small Models (ESMs) approach. In the second step, we assessed the trend of landuse/cove changes in a 30-years period in the areas with medium to high risk of conflict, using remote sensing techniques. Results of conflict risk modeling showed that proportion of suitable habitats, distance to village, density of forest patches, and corridor bottlenecks were the main predictors, contributing to bear damaging risk. A total of 3.75 and 6.91% of the landscape were identified as the areas with high and medium risk, respectively. Assessment of landuse/cove changes showed that in a period of 30 years, the extent of croplands and orchards has increased from 12,167 to 52,662 hectares. Such a substanitial landuse/cover changes can increase the risk of bear damages. The obtained results emphasize that inter-organizational planning is an emergency effort in mitigating human-bear conflicts. 51 64 2022/06/72022/06/142022/07/202022/09/6 1401/6/15 2022/09/242022/09/282022/10/152022/10/23 1401/8/1 رسول خسروی R. Khosravi دانشگاه شیراز ایران r-khosravi@shirazu.ac.ir حمیدرضا پورقاسمی H. R. Pourghasemi دانشگاه شیراز ایران hm_porghasemi@yahoo.com یلدا موثقی Y. Moveseghi دانشگاه شیراز ایران y.movaseghi76@gmail.com Human-brown bear conflict Landuse changes Risk modeling Conflict hotspots تعارض خرس قهوه‌ای-انسان تغییرات کاربری اراضی مدل‌سازی ریسک نقاط داغ تعارض 1. Acharya, K. P., P. K. Paudel, S. R. Jnawali, P. R. Neupane and M. Koehl. 2017. Can forest fragmentation and configuration work as indicators of human–wildlife conflict? Evidences from human death and injury by wildlife attacks in nepal. Ecological Indicators 80, 7-83.##2. Ashrafzadeh, M. R., R. Khosravi, M. A. Adibi, A. Taktehrani, H. Y. Wan and S. A. Cushman. 2020. A multi-scale, multi-species approach for assessing effectiveness of habitat and connectivity conservation for endangered felids. Biological Conservation 24, 108523.##3. Behdarvand, N., M. Kaboli, M. Ahmadi, E. Nourani, A. S. Mahini and M. A. Aghbolaghi. 2014. Spatial risk model and mitigation implications for wolf–human conflict in a highly modified agroecosystem in western Iran. Biological Conservation 177, 15-164.##4. Billah, M. M., M. Rahman, J. Abedin and H. Akter. 2021. Land cover change and its impact on human–elephant conflict: a case from Fashiakhali forest reserve in Bangladesh. SN Applied Sciences 3, 1-17.##5. Bleyhl, B., M. Baumann, P. Griffiths, A. Heidelberg, K. Manvelyan, V. C. Radeloff, N. Zazanashvili and T. Kuemmerle. 2017. Assessing landscape connectivity for large mammals in the caucasus using landsat 8 seasonal image composites. Remote Sensing of Environment 193, 19-203.##6. Breiner, F. T., M. P. Nobis, A. Bergamini and A. Guisan. 2018. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods in Ecology and Evolution 9, 80-808.##7. Broekhuis, F., S. A. Cushman and N. B. Elliot. 2017. Identification of human–carnivore conflict hotspots to prioritize mitigation efforts. Ecology and Evolution 7, 1063-10639.##8. Broennimann, O., V. Di Cola and A. Guisan. 2016. Ecospat: spatial ecology miscellaneous methods. R package v. 2.1. 1.##9. Dai, Y., C. E. Hacker, Y. Zhang, W. Li, J. Li, Y. Zhang, G. Bona, H. Liu, Y. Li and Y. Xue. 2019. Identifying the risk regions of house break‐ins caused by tibetan brown bears (Ursus arctos pruinosus) in the sanjiangyuan region, china. Ecology and Evolution 9, 13979.##10. Dewan, A. M and Y. Yamaguchi. 2009. Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Applied Geography 10, 390–401. ##11. Dickman, A. J. 2009. Key determinants of conflict between people and wildlife, particularly large carnivores, around ruaha national park, Tanzania. Ph.D. Thesis, University College London.##12. Dickman, A. J. 2010. Complexities of conflict: the importance of considering social factors for effectively resolving human–wildlife conflict. Animal Conservation 13, 458-466.##13. Ghoddousi, A., B. Bleyhl, C. Sichau, D. Ashayeri, P. Moghadas, P. Sepahvand, A. K. Hamidi, M. Soofi and T. Kuemmerle. 2020. Mapping connectivity and conflict risk to identify safe corridors for the Persian leopard. Landscape Ecology 35, 1809-1825.##14. Gordon, I. J. 2009. What is the future for wild, large herbivores in human-modified agricultural landscapes? Wildlife Biology 15, 1-9.##15. Harich, F. K. 2017. Conflicts of human land-use and conservation areas: the case of Asian elephants in rubber-dominated landscapes of Southeast Asia. Ph.D. Thesis, University of Hohenheim Agrarwissenscha, Hohenheim, Germany.##16. Hijmans, R. J., S. Phillips, J. Leathwick, J. Elith and M. R. J. Hijmans. 2017. Package ‘dismo’. Circles 9, 1.##17. Hijmans, R and J. van Etten. 2011. raster: Geographic analysis and modeling with raster data R package version 1.9-33. https://CRAN.R-proje ct.org/package=raster##18. IRG (International Resource Group). 2012. Integrated protected area co-management (IPAC), State of Bangladesh’s Forest Protected Areas 2010. http:// nisho rgo. org/ wp- conte nt/ uploa ds/ 2017/ 02/5- 43- NN- SOPA_ USAID. pdf. Accessed 01 Dec 2020##19. Khosravi, R., M. R., Hemami, S. Malakoutikhah, M. R. Ashrafzadeh and S. A. Cushman. 2021. Prey availability modulates predicted range contraction of two large felids in response to changing climate. Biological Conservation 255, 109018.##20. Khosravi, R and M. R. Sadeghi. 2021. An assessment of feeding ecology of the brown bear in areas with high risk of conflict in Fars province. Journal of Animal Environment. Accepted for publication. (In Farsi)##21. Khosravi, R., H. Y. Wan, M. R. Sadeghi and S. A. Cushman. 2022. Identifying human–brown bear conflict hotspots for prioritizing critical habitat and corridor conservation in southwestern Iran. Animal Conservation.##22. 22.Lopez, E., G. Bocco, M. Mendoza and E. Duhau. 2001. Predicting land cover and land use change in the urban fringe a case in Morelia city, Mexico. Landscape Urban Planning 55, 271–285. ##23. Maiorano, L., L. Chiaverini, M. Falco and P. Ciucci. 2019. Combining multi-state species distribution models, mortality estimates, and landscape connectivity to model potential species distribution for endangered species in human dominated landscapes. Biological Conservation 237, 1-29.##24. McGarigal, K., S. A. Cushman and E. Ene. 2012. Spatial pattern analysis program for categorical and continuous maps. Computer software program, University of Massachusetts, Amherst. FRAGSTATS v4. Available at http://wwwumassedu/landeco/research/fragstats/fragstatshtml.##25. Miller, J. R. 2015. Mapping attack hotspots to mitigate human–carnivore conflict: Approaches and applications of spatial predation risk modeling. Biodiversity and Conservation 24, 2887.##26. Mohammadi, A and K. Almasieh. 2022. Human-brown bear conflict in the southernmost part of its distribution in Iran (Roshan Kooh no-hunting area, Fars province). Journal of Natural Environment 74, 539-549. (In Farsi)##27. Mohammady, M., H. R. Morady, H. Zeinivand and A. J. A. M. Temme. 2015. A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. International Journal of Environmental Science and Technology 12, 1515-1526.##28. Nad, C., R. Roy and T. B. Roy. 2022. Human elephant conflict in changing land-use land-cover scenario in and adjoining region of Buxa tiger reserve, India. Environmental Challenges 7, 100384.##29. Nandy, S., S. P. S. Kushwaha and S. Mukhopadhyay. 2007. Monitoring the Chilla–Motichur wildlife corridor using geospatial tools. Journal of Natural Conservation 15, 237–244##30. Noe, C. 2003. The dynamics of land use changes and their impacts on the wildlife corridor between Mt. Kilimanjaro and Amboseli national park, Tanzania. LUCID project. International Livestock Research Institute. Nairobi, Kenya, p 31.##31. Ntukey, L. T., L. K. Munishi, E. Kohi and A. C. Treydte. 2022. Landuse/cover change reduces elephant habitat suitability in the Wami Mbiki–Saadani wildlife corridor, Tanzania. Land 11, 307-316.##32. Pettigrew, M., Y. Xie, A. Kang, M. Rao, J. Goodrich, T. Liu and J. Berger. 2012. Human–carnivore conflict in China: a review of current approaches with recommendations for improved management. Integrative Zoology 7, 21-217.##33. Phillips, S. J., R. P. Anderson and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 23-239.##34. Pourghasemi, H., M. Mohammady, H. Noor and S. F. Afzali. 2022. Land use change simulation using CLUE-s model in the watershed of Doroodzan dam. Iranian Journal of Watershed Management Science and Engineering 16, 23-31. (In Farsi)##35. Rahman, M. M. 2013. Temporal change detection of vegetation coverage in Patuakhali Coastal Area of Bangladesh using GIS & remotely sensed data. International Journal of Geomatics and Geosciences 4, 36–46##36. Rostro-García, S., L. Tharchen, L. Abade, C. Astaras, S. A. Cushman D. W. Macdonald. 2016. Scale dependence of felid predation risk: identifying predictors of livestock kills by tiger and leopard in Bhutan. Landscape Ecology 31, 1277–1298.##37. Sacks, B. N., K. M. Blejwas and M. M. Jaeger. 1999. Relative vulnerability of coyotes to removal methods on a northern California ranch. The Journal of Wildlife Mmanagement 63, 939-949.##38. Serra P., X. Pons and D. Sauri. 2008. Land-cover and land-use change in a Mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Applied Geography 28,189–209##39. Sharma, P., N. Chettri, K. Uddin, K. Wangchuk, R. Joshi, T. Tandin, A. Pandey, K. S. Gaira, K. Basnet and S. Wangdi. 2020. Mapping human‒wildlife conflict hotspots in a transboundary landscape, eastern Himalaya. Global Ecology and Conservation 24, e01284.##40. Sillero-Zubiri, C and D. Switzer. 2004. Management of wild canids in human-dominated landscapes. Canids: foxes, wolves, jackals and dogs: Status Survey and Conservation Action Plan. IUCN Canid Specialist Group, Gland, Switzerland and Cambridge, UK. pp. 257-26##41. Singh, S. K., P. K. Srivastava, S. Szabó, G. P. Petropoulos, M. Gupta and T. Islam. 2017. Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth observation data-sets. Geocarto International 32, 113-127.##42. Thuiller, W., B. Lafourcade, R. Engler and M. B. Araújo. 2009. Biomod–a platform for ensemble forecasting of species distributions. Ecography 32, 369-379.##43. Treves, A., L. Naughton‐Treves and V. Shelley. 2013. Longitudinal analysis of attitudes toward wolves. Conservation Biology 27, 315-320.##44. Treves, A., R. B. Wallace, L. Naughton-Treves and A. Morales. 2006. Co-managing human–wildlife conflicts: a review. Human Dimensions of Wildlife 11, 383-390.##45. van Bommel, J. K., M. Badry, A. T. Ford, T. Golumbia and A. C. Burton. 2020. Predicting human-carnivore conflict at the urban-wildland interface. Global Ecology and Conservation 24, e01322.##46. Woodroffe, R and J. R. Ginsberg. 1998. Edge effects and the extinction of populations inside protected areas. Science 280, 2126.-2137.##47. Wynn-Grant, R, J. R. Ginsberg, C. W. Lackey, E. J. Sterling and J. P. Beckmann. 2018. Risky business: modeling mortality risk near the urban-wildland interface for a large carnivore. Global Ecology and Conservation 16, e00443.##48. Zarzo-Arias, A., V. Penteriani, M. Delgado, P. Peón, P. Torre, R. García-González, M. C. Mateo-Sánchez, P, Vázquez García and F. Dalerum. 2019. Identifying potential areas of expansion for the endangered brown bear (Ursus arctos) population in the cantabrian mountains (nw Spain). PloS One 14, e0209972.## ##

تغییرات مکانی-زمانی اختلال پوشش گیاهی طبیعی در حوضه اهل ایمان، استان اردبیل Spatio-Temporal Changes of Natural Vegetation Disturbance in the Ahle Iman Watershed, Ardabil Province 1 1 تحقیق حاضر با هدف ارزیابی تغییرات مکانی-زمانی وضعیت اختلال در پوشش گیاهی طبیعی حوضه اهل ایمان واقع در استان اردبیل برنامه‌ریزی شده است. بدین منظور، ابتدا نقشه کاربری اراضی سه سال 2000، 2010 و 2020 از تصاویر ماهواره لندست استخراج شد. سپس هفت سنجه سیمای سرزمین (تراکم لکه، تراکم حاشیه، شاخص تکه‌شدگی، فاصله اقلیدسی نزدیک‌ترین همسایه، شاخص سرایت، نسبت محیط به مساحت و غنای لکه)، شاخص پوشش گیاهی تفاضلی نرمال شده (Normalized Difference Vegetation Index, NDVI) و تراکم جاده محاسبه شد. در نهایت شاخص اختلال (Disturbance Index, DI) از طریق مجموع حاصل‌ضرب مقادیر معیارها در وزن آن‌ها در 11 زیرحوضه محاسبه شد. شاخص اختلال (DI) در پنج طبقه خیلی کم (87–0)، کم (163–88)، متوسط (239–164)، زیاد (315-240) و خیلی زیاد (316<) طبقه‌بندی شد. بر اساس نتایج مشخص شد که از نظر کلی حوضه در سال‌های 2000، 2010 و 2020 به‌ترتیب دارای DI برابر با 77/177، 17/95 و 07/135 بوده که ‌به‌ترتیب نشان‌دهنده وضعیت اختلال متوسط، کم و کم است. همان‌گونه که مشاهده می‌شود بین سه سال مورد بررسی، از نظر وضعیت اختلال تفاوت معنی‌داری وجود ندارد. هم‌چنین، به‌طور کلی در بخش‌های شرق و شمال حوضه وضعیت بدتری از اختلال نسبت به بخش‌های جنوبی و مرکزی مشاهده شد.  2 The present study aimed to assess the spatio-temporal changes in the natural vegetation cover of Ahl Iman watershed, Ardabil province. For this purpose, land use maps of the three years (2000, 2010, and 2020) were extracted from Landsat satellite images. Then, seven landscape metrics (patch density, edge density, patch richness, splitting index, contagion index, Euclidean nearest neighbor distance, and mean perimeter–area ratio), Normalized Differential Vegetation Index (NDVI), and road density were calculated. Finally, the Disturbance Index (DI) was calculated by the sum of the values of the criteria multiplied by their weight in the 11 sub-watersheds. The disturbance index (DI) was classified into very low (0-87), low (88-163), medium (164-239), high (240-315), and very high (>316) categories. Results showed DI of 177.77, 95.17, and 135.07 for the three studied years (2000, 2010, and 2020), representing a moderate, low, and low disturbance respectively. There was no significant difference between the three years in terms of disturbance. Further, the eastern and northern parts had higher disturbance indices compared to the southern and central parts of the watershed. 65 82 2022/06/72022/06/142022/07/202022/09/62022/03/24 1401/1/4 2022/09/242022/09/282022/10/152022/10/232022/10/26 1401/8/4 ابوالفضل همت‌زاده A. Hemmatzadeh دانشگاه محقق اردبیلی ایران abolfazlvet@gmail.com اباذر اسمعلی‌عوری A. Esmali Ouri دانشگاه محقق اردبیلی ایران esmaliouri@uma.ac.ir رئوف مصطفی‌زاده R. Mostafazadeh دانشگاه محقق اردبیلی ایران raoofmostafazadeh@uma.ac.ir محمد گلشن M. Golshan اداره منابع طبیعی و آبخیزداری، آستارا، گیلان ایران golshan.mohammad@yahoo.com زینب حزباوی Z. Hazbavi دانشگاه محقق اردبیلی ایران z.hazbavi@uma.ac.ir نازیلا علائی N. Alaei دانشگاه ارومیه ایران nazila.alaie96@gmail.com Ecological degradation Index-based approach Land management Landscape integrity پیوستگی سیمای سرزمین تخریب بوم‌شناختی رویکرد شاخص‌محور مدیریت سرزمین 1. Abolhasani, M., A. Sotoudeh and P. Parivar. 2020. Remote sensing application for urban landscape assessment in arid regions (case study: Yazd city, Iran). RS & GIS for Natural Resources 11(3): 22-39. (In Farsi)##2. Aghaei, M., H. Khavarian and R. Mostafazadeh. 2019. Prediction of land use changes using the CA-Markov and LCM models in the Kozehtopraghi watershed in the province of Ardabil. Watershed Management Research 33(3): 91-107. (In Farsi)##3. Ahern, J. and L. Andre, 2003. Applying landscape ecological concepts and metrics in sustainable landscape planning. Landscape and Urban Planning 59: 65-93.##4. Alaei, N., R. Mostafazadeh, A. Esmali-Ouri, M. Sharari and Z. Hazbavi. 2019. Assessment and comparison of landscape connectivity in KoozehTopraghi watershed, Ardabil province. Iranian Journal of Applied Ecology 8(4): 19-34. (In Farsi)##5. Annissa, M. and E. Eyasu. 2021. Class and landscape level habitat fragmentation analysis in the Balemountains national park, southeastern Ethiopia. Heliyon 7: 1-12.##6. Cardille, J.A. and M.G. Turner. 2002. Understanding landscape metrics. In: Gergel, S.E., Turner, M.G. (eds) Learning Landscape Ecology. Springer, New York, NY. ##7. Debarros, F., Vettorazzi, C., Theobald, D. and M. Ballester. 2005. Landscape dynamics of amazonian deforestation between 1984 and 2002 in central Rondonia Brazil, assessment and future scenarios. Forest Ecology and Management 204(1): 69-85.##8. De Montis, A., B. Martin, E. Ortega and A. Ledda. 2017. Landscape fragmentation in Mediterranean Europe: A comparative approach. Land Use Policy 64: 83–94.##9. Darvishi, A., S. Fakheran, A. Soffianian and M. Ghorbani. 2012. Quantifying landscape spatial pattern changes in the Caucasian Black Grouse (Tetrao mlokosiewiczi) habitat in Arasbaran biosphere reserve. Iranian journal of applied Ecology 2(5): 27-38. (In Farsi)##10. Dezhkam, S.S., B. Jabbarian Amiri and A.A. Darvish sefat. 2015. Monitoring the landscape changes using synoptic analysis and satellite images (Case study: Rasht township). Natural Environment, Natural Resources of Iran 68(2): 225-238. (In Farsi)##11. Esfandiyari Darabad, F., M. Hamzeei, R. Mostafazadeh, and N. Alaei. 2021. ‬Spatial variations of landscape metrics in riparian area vegetation of Gharesou river reaches under the effect of different land uses, Ardabil province. Geographical Planning of Space 10(38): 219-234. (In Farsi)##12. Ghanbari, F. and Sh. Shataee. 2011. Investigation on forest extend changes using aerial photos and ASTER imagery (case study: border forests in south and southwest of Gorgan city). Wood & Forest Science and Technology 17(4): 1-18. (In Farsi)##13. Ghosh, A., Madhushree Munshi, G. Areendran and P.K. Joshi. 2012. Pattern space analysis of landscape metrics for detecting changes in forests of Himalayan foothills. Asian Journal of Geoinformatics 12(1): 38-50.##14. Hazbavia, Z., N. Parchami, N. Alaei and L. Babaei. 2020. Assessment and analysis of the Koozeh Topraghi watershed health status, Ardabil province, Iran. Water and Soil Resources Conservation 9(3): 121-141. (In Farsi)##15. Jafari, Sh., A. Alizadeh Shabani and A. Danekar. 2012. Investigation of structural changes in lake Urmia using landscape metrics. Wetland Ecobiology 4: 45-54. (In Farsi)##16. Jafari, F., R. Jafari., H. Bashari. 2017. Assessing the performance of remotely sensed landscape function indices in semi-arid rangelands of Iran. Rangeland Journal 39(3): 253-262.##17. Jose, S. K., Alex C. J., Santhosh Kumar, Abin Varghese and G. Madhu. 2011. Landscape metric modeling a technique for forest disturbance assessment in Shendurney wildlife sanctuary. Environmental Research, Engineering and Management 4(58): 34-41.##18. Kang, N., T. Sakamoto, J. Imanishi, K. Fukamachi, S. Shibata and Y. Morimoto. 2013. Characterizing the historical changes in land use and landscape spatial pattern on the Oguraike floodplain after the Meiji period. Intercultural Understanding 1: 11-16.##19. Kiyani, V. and J. Feghhi. 2015. Investigation of cover/ land use structure of Sefidrod watershed by landscape ecology metrics. Environmental Science and Technology 17: 131-141. (In Farsi)##20. Lam, N.S., W. Cheng, L. Zou and H. Cai. 2018. Effects of landscape fragmentation on land loss. Remote Sensing of Environment 209: 253–262.##21. Linh, N.H.K., S., Erasmi and M. Kappas, 2012. Quantifying land use/cover change and landscape fragmentation in Danang, Vietnam: 1979-2009. Remote Sensing and Spatial Information Sciences 8: 501-506.##22. Liu, S., Y. Dong, L. Deng, Q. Liu, H. Zhaoa and S. Dong. 2014. Forest fragmentation and landscape connectivity change associated with road network extension and city expansion: a case study in the Lancang rive valley, Ecological Indicators 36(1): 160-168.##23. Matsushita, B., W. Yang, W. Chen, Y. Onda and G. Qiu. 2007. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case Study in high-density Cypress forest. Sensors 7: 2636-2651.##24. McGarigal, K. and E. Ene. 2013. FRAGSTATS: Spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site http://www.umass.edu/landeco/research/fragstats/fragstats.html##25. Mir, M., S. Maleki and V. Rahdari. 2021. Application of landscape ecology in spatio-temporal change detection of arid regions, case study: Sistan plain. Iranian Journal of Applied Ecology 10(2): 67-81.##26. Mirzaei, M., A.R., Riahi Bakhtiari, A.R., Salman Mahini and M., GholamaliFard. 2013. Investigation of land cover changes in Mazandaran province using Landscape metrics between 1984- 2010. Iranian Journal of Applied Ecology 2: 37-54. (In Farsi)##27. Mostafazadeh1, R., A. Jafari and F. Keivan-behjou. 2018. Comparing the rangelands structure and degradation of landscape connectivity in Iril sub-watersheds, Ardabil province. Iranian Journal of Applied Ecology 7(1): 41-53. (In Farsi)##28. Nazarnejad, H., M. Hosseini and R. Mostafazadeh. 2019. Analysis of land use change in Balangchai watershed using land features. Geography and Development 54: 75-90. (In Farsi)##29. Nematollahi, Sh., S. Fakheran, A. Soffianian and S. Pourmanafi. 2016. Incorporating the novel landscape index (spatial road disturbance index) for ecological impacts assessment of roads network (case study: eastern part of Isfahan province). The 2nd International Confrence of IALE-IRAN, Isfahan, Iran.##30. Panahandeh, M. and M. Azizi. 2020. Investigation of structural changes in Anzali watershed based on landscape ecology approach. Natural Environment, Natural Resources of Iran 73(2): 227-241. (In Farsi)##31. Pirikiya, M., A. Fallah, H. Amirnejad and J. Mohamadi. 2018. The identification and prioritization of criteria and indicators for assessment of multiple ecosystem services using of multi-criteria decision making techniques Entropy and TOPSIS in Darabkola watershed. Natural Ecosystems of Iran 9(3): 79-100. (In Farsi)##32. Rostamikia, Y., A. Biguzadeh, K. Mirakhorlu and J. Sharifi. 2013. Estimation of forest degradation in the upper forests of East Ardabil using satellite data. First National Conference on Natural Resources Management, Gonbad Kavous University, 1-9. (In Farsi)##33. Safaei, M., R. Jafari, P. Datta, H. Bashari, D. Pothier and B. Koch. 2021. Spatial scale effect of Sentinel-2, Landsat OLI, and MODIS imagery in the assessment of landscape condition of Zagros mountains. Geocarto International 37(18): 5345-5362.##34. Shi, Y., and J. Xiao. 2008. Evaluating landscape changing due to urbanization using remote sensing data: A case study of Shijiazhung, China. International Workshop on Geoscience and Remote Sensing, IEEE Computer Society Washington, DC, USA 21-22 December, pp.508-511.##35. Wang, Y., G. Bonynge, J. Nugranad, M. Traber, A. Ngusaru, J. Tobey, L. Hale, R. Bowen and V. Makota. 2003. Remote Sensing of Mangrove Change along the Tanzania Coast. Marin Geodesy 26 (14): 35-48.## ##

ارزیابی سلامت اکولوژیک رسوبات سطحی سواحل مرجانی خارک و خارکو (خلیج فارس، ایران) Ecological Health Assessment of the Surface Sediments of the Coral Reefs of Khark and Kharko Islands (Persian Gulf, Iran) 1 1 به منظور ارزیابی ریسک اکولوژیک زیستگاه مرجانی جزایر خارک و خارکو در خلیج فارس (استان بوشهر)، در شهریور 1399 نمونه-های رسوب سطحی از هفت ایستگاه جمع آوری شد. این جزایر، به دلیل داشتن آبسنگ های مرجانی دارای ارزش اکولوژیک فوق العاده ای است. میزان عناصر بالقوه سمی، دانه بندی، مواد آلی کل، فسفرکل و نیتروژن کل در رسوبات، به ترتیب با استفاده از طیف‌سنجی جرمی پلاسمای جفت‌شده القایی، الک، روش سوزاندن در کوره، اسپکتروفتومتر و کجلدال اندازه¬گیری شد. میانگین غلظت آلومینیوم و آهن (درصد) و میانگین غلظت نیکل، سرب، روی، وانادیوم، فسفر کل و نیتروژن کل (میلی گرم در کیلوگرم) در رسوبات به ترتیب 0/53±0/76، 55/35±0/0، 19±35، 1/5±2/1، 10±22، 25±40، 7/3±0/0 و 6/2±14/7 ثبت گردید. بر اساس میزان فاکتور غنی شدگی نیکل و روی (به ترتیب در حد متوسط تا خیلی شدید)، منشاء آنها در اطراف خارک می تواند فعالیت انسانی از جمله صنایع نفتی باشد. شاخص بار آلودگی (0/25- 0/06) کلیه ایستگاه ها را بدون آلودگی نشان داد. میزان نیکل در ایستگاه 1 و 7 به ترتیب از استانداردهای"محدوده اثرات متوسط" و "سطح اثرات احتمالی" بیشتر بود که نشان دهنده اثرات زیستی متوسط و احتمالی این فلز بر روی موجودات بستر است. به طور کلی کیفیت اکولوژیک رسوبات سطحی اطراف خارکو نسبت به خارک، بهتر ارزیابی گردید. 2 To assess the ecological risk of the coral reef habitats of Khark and Kharko islands in the Persian Gulf, (Bushehr province), the surface sediment samples were collected from seven stations, in September 2019. The islands have a great ecological value due to the presence of coral reefs. The amounts of potentially toxic elements, sediment texture, total organic matter, total phosphorus and total nitrogen in the sediments were measured by inductively-coupled plasma mass spectrometry, sieve analysis, furnace burning method, spectrophotometer, and Kjeldahl, respectively. The mean concentrations of Al, Fe (%) and Ni, Pb, Zn, V, TP and TN (mg/kg) in the sediments were recorded 0.76±0.53, 0.55±0.35, 35±19, 2.1±1.5, 22±10, 40±25, 0.7±0.3 and 14.7±6.2, respectively. Based on the amount of Ni and Zn enrichment factors (moderate to very sever, respectively), their source around Khark Island could be related to human activities, such as oil industries. Pollution load index (0.06-0.25) showed all stations without pollution. The amount of Ni in stations 1 and 7 were higher than the "range of moderate effect" and "level of possible effects", which indicates the possible biological effects of this element on the benthic organisms. In general, the ecological quality of surface sediments around Kharko was better than Khark Island.   83 101 2022/06/72022/06/142022/07/202022/09/62022/03/242022/09/25 1401/7/3 2022/09/242022/09/282022/10/152022/10/232022/10/262022/11/28 1401/9/7 قاسم قربان زاده زعفرانی Gh. Ghorbanzadeh Zafarani پژوهشکده محیط زیست و توسعه پایدار، حفاظت محیط زیست، تهران ایران ghorbanzadeh110@yahoo.com فرهاد حسینی طایفه F. Hosseini Tayefeh پژوهشکده محیط زیست و توسعه پایدار، حفاظت محیط زیست، تهران ایران farhadtayefeh@gmail.com امین احمدی گیوی A. Ahmadi Givi دانشگاه تهران ایران amin.ahmadi.givi@ut.ac.ir محمد امین طلاب M. A. Tollab اداره کل محیط زیست، بوشهر ایران gh2012eco@gmail.com علی صابر A. Saber حفاظت محیط زیست، تهران ایران gh.ghorbanzadeh@rcesd.ac.ir Sediment Khark and Kharko Isalnds Potentially toxic elements Total phosphorus Total nitrogen رسوب جزایر خارک و خارکو عناصر بالقوه سمی فسفر کل نیتروژن کل 1. Abrahim, G. M. S. and R. J. Parker. 2008. Assessment of heavy metal enrichment factors and the degree of contamination in marine sediments from Tamaki Estuary, Auckland, New Zealand. Coastal and Shelf Science, 136: 227-238.##2. Acevedo-Figueroa, D., B. D. Jimenez and C. J. Rodriguez-Sierra. 2006. Trace metals in sediments of two estuarine lagoons form Puerto Rico. Journal of Environmental Pollution, 141: 336-342.##3. Adamo, P., M. Arienzo, M. Imperato, D. Naimo, G. Nardi and D. Stanzione. 2005. Distribution and partition of heavy metals in surface and sub-surface sediments of Naples city port. Chemosphere, 61: 800–809. ##4. Akhbarizadeh, R. 2017. Ecological risk assessment of potentially toxic elements and organic compounds in the shore and wastewater collecting basins of Khark Island and investigating possible alleviation methods. PhD Thesis, Shiraz University, Shiraz, Iran. (In Farsi).##5. Ali, A. M. A., M. A. Hamed and H. Abd El-Azim. 2011. Heavy metals distribution in the coral reef ecosystems of the Northern Red Sea. Helgoland Marine Research, 65:67–80. ##6. Alloway, B. J. 1995. Heavy Metals in Soils. Blackie Academic and Professional, Chapman and Hall, London, 368 p. ##7. Antony, S., K. Unnikrishnan, S. Aswin, V. D. Vinu, V. Arun and K. AnoopKrishnan. 2022. Heavy metals in coral reef sediments of Kavaratti Island, India: An integrated quality assessment using GIS and pollution indicators. Marine Pollution Bulletin, 180: 113721.##8. ASTM, 2000. Standard test method for moisture, ash and organic matter of peat and other organic soils (test method c). Available online at: https://www.madcad.com/store/subscription/ASTM-D2974-00/. Accessed 25 October 2022.##9. ASTM, 2013. Standard practice for total digestion of sediment samples for chemical analysis of various metals. Available online at: https://www.astm.org/d4698-92r07.html. Accessed 25 October 2022.##10. Bagheri, H., K. Darvish Bastami, T. Sharmad, Z. Bagheri. 2012. Assessment of heavy metal distribution in the Gorgan Bay. Journal of Oceanography, 3 (11):65-72. (In Farsi).##11. Berkowitz, B., I. Dror and B. Yaron. 2008. Contaminant geochemistry: interactions and transport in the subsurface environment. Springer, Heidelberg, p 412.##12. Besson, M., M. Metian, P. Bustamante and L. Hédouin. 2020. Metal(loid)s in superficial sediments from coral reefs of French Polynesia. Marine Pollution Bulletin, 155: 111175.##13. Briand, M., P. Bustamante, X. Bonnet, C. Churlaud and Y. Letourneur. 2018. Tracking trace elements into complex coral reef trophic networks. Science of the Total Environment, 612:1091-1104 ##14. Buchman, M. F. 2008. Screening quick reference tables (SQuiRTs), NOAA OR and R Report; 08-1, Seattle WA, Office of Response and Restoriation Division, National Oceanic and Atmospheric Administration, 34p. ##15. De Mora, S. D. and M. R. Sheikholeslami. 2002. ASTP: Contaminant screening program: Final report: interpretation of Caspian Sea sediment data. Caspian Environment Program (CEP), 27 p.##16. Ghorbanzadeh Zaferani, S. G., A. Machinchian Moradi, R. Mousavi Nadushan, A. R. Sari and S. M. R. Fatemi. 2016. Distribution pattern of heavy metals in the surficial sediment of Gorgan Bay (South Caspian Sea, Iran). Iranian Journal of Fisheries Sciences, 15(3): 1144-1166.##17. Grant, A. and R. Middleton. 1990. An assessment of metal contamination of sediments in the Humber Estuary. Estuarine, Coastal and Shelf Science, 31: 71–85. ##18. Habibpour Gatabi, K. and R. Safari Shali. 2012. Comprehensive manual for using SPSS in survey resrarches, Loya Pub., Tehran. (In Farsi).##19. Hosseini, S. J. 2016. Investigating, studying and determining the causes and effects of the increase in sea urchin population in the coral reefs of Kharg and Kharko islands and the solutions to deal with it. Bushehr. Persian Gulf Research Institute (PGRI) Research Report, Persian Gulf University, Bushehr, Iran. pp.279. (In Farsi).##20. Huang, K. M. and S. Lin. 2003. Consequences and implication of heavy metal spatial variations in sediments of the Keelung River drainage basin, Taiwan. Chemosphere, 53: 1113–1121. ##21. Karbassi A. R., Gh. R. Nabi-Bidhendi and I. Bayati. 2005. Environmental geochemistry of heavy metals in a sediment core of Bushehr, Persian Gulf. Journal of Environmental Health Science & Engineering, 2 (4), 255-260.##22. Long, E. R., D. D. MacDonald, S. L. Smith and F. D. Calder. 1995. Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments. Environmental Management, 19: 18–97.##23. Miller, J. R. and Orbock S. M. Miller. 2007. Contaminated rivers : A Geomorphological-Geochemical Approach to Site Assessment and Remediation, Springer, Dordreeht, The Netherlands, 432 p.##24. Mirza, R., A. Fakhri, I. Faghiri, A. Azimi. 2013. An investigation of nickel and vanadium ratio from oil pollution in sediments and rocky shore oysters (Saccostrea cucullata) in Bushehr coasts (Persian Gulf). Journal of Oceanography, 4 (14) :35-43 (In Farsi).##25. MOOPAM (Manual of Oceanographic Observation and Pollutant Analyses Methods). 2010. Regional Oranization for the Protection of the Marine Environment (ROPME), Fourth Edition.##26. Morillo, J., J. Usero and I. Gracia. 2004. Heavy metal distribution in marine sediments from the southwest coast of Spain. Chemosphere, 55, 431–442.##27. Persaud, D., R. Jaagumagi and A. Hayton. 1993. Guidelines for the protection and management of aquatic sediment quality in Ontario. Ministry of Environment and Energy, Queen's Printer for Ontario, Canada, 39‌p.##28. Plumb, R. H. 1981. Procedure for handling and chemical analysis of sediment and water samples. Technical Report EPA/CE-81-1. Prepared by State University College at Buffalo, Great Lakes Laboratory, Buffalo, NY. US.Environmental Protection Agancy and U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, Ms. pp 3-39.##29. Pouyandegan Mohit‌Zist. 2010. The environmental consequences assessment of the development operation project and increasing the production of Forozan oil field, Bushehr, Iranian Offshore Engineering and Construction Company (IOEC), Iran. (In Farsi).##30. Ranjbar Jafarabadi, A., A. Riyahi Bakhtiari, L. Hedouin, , A. Shadmehri Toosi and T. Cappello. 2018. Spatio-temporal variability, distribution and sources of n-alkanes and polycyclic aromatic hydrocarbons in reef surface sediments of Kharg and Lark coral reefs, Persian Gulf, Iran, Ecotoxicology and Environmental Safety, 163: 307-322.##31. Ranjbar Jafarabadi, A., A. Riyahi Bakhtiari, M. Maisano, P. Pereira and T. Cappello. 2018. First record of bioaccumulation and bioconcentration of metals in Scleractinian corals and their algal symbionts from Kharg and Lark coral reefs (Persian Gulf, Iran), Science of The Total Environment, 640–641: 1500-1511. ##32. Ranjbar, A. J., A. R. Bakhtiyari, A. S. Toosi, C. Jadot. 2017. Spatial distribution, ecological and health risk assessment of heavy metals in marine superficial sediments and coastal seawaters of fringing coral reefs of the Persian Gulf, Iran, 185: 1090-1111 ##33. Ranjbar, P., M. Shafieefar and J. Rezvandoust. 2014. Modeling of oil spill and response in support of decreasing environmental oil effects case study: blowout from Khark subsea pipelines (Persian Gulf), International Journal of Environmental Research., 8(2):289-296.##34. Sabdono, A. 2009. Heavy metal levels and their potential toxic effect on coral Galaxea fascicularis from Java Sea, Indonesia. Research Journal of Environmental Sciences, 3: 96-102.##35. Sakan, S., G. Dević, D. Relić, I. Andelkovic, N. Sakan and D. Dordevic. 2015. Evaluation of sediment contamination with heavy metals: the importance of determining appropriate background content and suitable element for normalization. Environmental Geochemistry and Health, 37: 97–113. ##36. Selvaraj, K., V. Ram Mohan and P. Szefer. 2004. Evaluation of metal contamination in coastal sediments of the Bay of Bengal, India: geochemical and statistical approaches. Marine pollution Bulletin, 49, 174–185.##37. Seshan, B. R. R., U. Natesan and K. Deepthi. 2010. Geochemical and statistical approach for evaluation of heavy metal pollution in core sediments in southeast coast of India. International Journal of Environmental Science and Technology, 7 (2): 291–306.##38. Shakri Motlaq, M., M. Ezam and A. Aliakbari Bidokht. 2015. Study of coastal currents caused by wave breaking on the coast of Khark Island using Mike 21, In: Proceedings of 16th Fluid Dynamics Conference, Kermanshah, Iran, 17-19 November, pp.1-10. (In Farsi). ##39. Sharbaty, S. 2011. Two dimensional simulation of flow pattern in Gorgan Bay by using Mike21 software. Journal of Water and Soil Conservation, 18(4): 241-246.##40. Suresh, G., V. Ramasamy, V. Meenakshisundaram, R. Venkatachalapathy and V. Ponnusamy. 2011. Influence of mineralogical and heavy metal composition on natural radionuclide concentrations in the river sediments, Applied Radiation and Isotopes, 69(10):1466-1474.##41. Vald’es, J., G. Vargas, A. Sifeddine, L. Ortlieb and M. Guinez. 2005. Distribution and enrichment evaluation of heavy metals in Mejillones Bay (23S), Northern Chile: geochemical and statistical approach. Marine pollution Bulletin, 50: 1558–1568. ##42. Woitke, P., J. Wellmitz, D. Helm, P. Kube, P. Lepom and P. Litheraty. 2003. Analysis and assessment of heavy metal pollution in suspended solids and sediments of the river Danube. Chemosphere, 51: 633–642. ##43. Zaker, H. N., 2022. Aliphatic and aromatic hydrocarbons in the coastal sediments of the Kharg Island in the Persian Gulf, Pollution, 8(2): 705-716. ##44. Zare Khosheghbal, M. and A. H. Charkhaabi. 2013. Comparison of enrichment methods of heavy metal pollution studies in south of the Caspian sea sedimentary environment: Case study of Anzali wetland, Watershed Management Research (Pajouhesh & Sazandegi), 98(1): 51-64. (In Farsi).## ##