پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,477 |
| تعداد مقالات | 70,016 |
| تعداد مشاهده مقاله | 122,924,761 |
| تعداد دریافت فایل اصل مقاله | 96,137,534 |
مدل‌سازی پیش‌بینی رویشگاه گونۀ دارویی Rheum ribes L. با استفاده از مدل آنتروپی حداکثر (Maxent) در مراتع چاه ترش استان یزد | ||
| نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
| مقاله 7، دوره 71، شماره 2، شهریور 1397، صفحه 379-391 اصل مقاله (1.35 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jrwm.2018.200398.968 | ||
| نویسندگان | ||
| محمدعلی زارع چاهوکی* 1؛ محبوبه عباسی2 | ||
| 1استاد دانشکدۀ منابع طبیعی، دانشگاه تهران | ||
| 2دانشجوی دکتری مرتعداری، دانشکدۀ منابع طبیعی، دانشگاه تهران | ||
| چکیده | ||
| گونۀ Rheum ribes یکی از گیاهان دارویی مهم در جهان به شمار می‌رود. در این مطالعه به منظور تهیۀ نقشۀ پیش‌بینی رویشگاه این گونه از روش آنتروپی حداکثر (Maxent) و از نرمافزار MAXENT استفاده شد. متغیرهای محیطی اندازه‌گیری شده بهعنوان متغیرهای تأثیرگذار بر حضور گونه شامل متغیرهای خاکی از جمله درصد سنگریزه، اسیدیته، هدایت الکتریکی، درصد آهک، درصد گچ، مادةآلی، املاح محلول (کلسیم، سدیم، پتاسیم، منیزیم، کلر، کربنات، بی‌کربنات و سولفات)، درصد شن، رس و سیلت و متغیرهای توپوگرافی منطقه (شیب، جهت و ارتفاع) و نیز متغیر بارندگی بودند. دقت طبقه‌بندی مدل با استفاده از سطح زیر منحنی (AUC) مقدار 95% بهدست آمد (سطح خوب) و ضریب کاپای بهدست آمده از بررسی میزان تطابق نقشة پیش‌بینی با واقعیت زمینی نیز مقدار 92/0 بهدست آمد که در سطح عالی قرار داشت. بررسی نتایج این تحقیق نشان داد که رویشگاه این گونه در خاک‌هایی با اسیدیتۀ پایین (کمتر از 8)، بافت سبک و مادۀ آلی (بیشتر از 4/0 درصد) قرار دارد و حضور این گونه با متغیرهای اسیدیتۀ هر دو عمق و رس عمق اول رابطۀ معکوس و با مادۀ آلی هر دو عمق رابطۀ مستقیم داشت. | ||
| کلیدواژه‌ها | ||
| نقشۀ پیش‌بینی رویشگاه؛ آنتروپی حداکثر (Maxent)؛ Rheum ribes | ||
| عنوان مقاله [English] | ||
| Habitat prediction model medicinal species of Rheum ribes L. with Maximum Entropy model in Chahtorsh rangeland of the Yazd province | ||
| نویسندگان [English] | ||
| Mohammad Ali Zare Chahouki1؛ mahbobe abbasi2 | ||
| 1t | ||
| 2tehran university | ||
| چکیده [English] | ||
| Rheum ribes species is one of the important medicinal plants in the world. In this study were used maximum entropy method (Maxent) and the MAXENT software to this prediction habitat map. Measure environmental variables was soil variables including gravel percentage, pH, electrical conductivity, percent lime, gypsum, organic matter, soluble salts (Ca+, Na+, K+, Mg2+, CL, HCO3, SM and SO2), sand, clay and silt and variable topography (slope, aspect and elevation) and rainfall variable. Those were effective variables on the presence of species. The model classification accuracy using the area under the curve (AUC) was 95% (good Level), and kappa coefficient was obtained 0.92 that measuring from the agreement of prediction maps with ground truth, which is at a high level. The results of this study showed that the habitat of this species is in the soils with low pH (less than 8), clay Low (less than 40%), coarse texture and organic matter more than 4.0 percent. And the presence of this species has inverse relationship with a pH of both the depth and the clay first depth and with has directly relationship to organic matter of both depths. | ||
| کلیدواژه‌ها [English] | ||
| Habitat Prediction map, Maximum entropy, Rheum ribes, Poshtkouh | ||
| مراجع | ||
|
[1] Abd El-Ghani M.M. and Amer, W.M. (2003). Soil–vegetation relationships in a coastal desert plain of southern Sinai, Egypt. Journal of Arid Environments, 55, 607–628. [2] Abd El-Ghani, M.M. (1998). Environmental correlates of species distribution in arid desert ecosystems of eastern Egypt. Journal of Arid Environments, 38, 297–313. [3] Anderson, R.P., D. Lew, D and Peterson, A.T. (2003). Evaluating predictive models of species distributions: criteria for selecting optimal models. Journal of Ecological Modelling, 162, 211–232. [4] Beck, P.S.A., Kalmbach., E., Joly, D., Stien, A. and Nilsen, L. (2005). Modelling local distribution of an Arctic dwarf shrub indicates an important role for remote sensing of snow cover. Journal of Remote Sensing of Environment, 98, 110 – 121. [5] Bohera J.S. and Dorffing, K. (1993). Nutrition of Rice varieties under NaCl salinity. Journal of Plant and Soil, 152, 299-303. [6] Downie, A.L., Numers, Mv. and Boström, Ch. (2013). Influence of model selection on the predicted distribution of the seagrass Zostera marina. Journal of Estuarine, Coastal and Shelf Science, 8, 121-122. [7] El-Demerdash M.A., Zahran, M.A. and Serag, M.S. (1994). On the ecology of the deltaic Mediterranean coastal land, Egypt III. The habitat of saltmarshes of Damietta-Port Said coastal region. Arab Gulf Journal of Scientific Research, 8, 103–119. [8] Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A.L.J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, T.A., Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Soberon, J., Williams, S., Wisz, M.S. and Zimmermann, N.E. (2006). Novel methods improve prediction of species distributions from occurrence data. Journal of Ecography, 29 (2), 129–151. [9] Emad, M., Raghibi, F., Rasouli, S.M., Khanjanzadeh, R., Jozani, S.M. 2010. Rheum ribes. Pooneh Publication, 69P [10] Fallah Huseini, H., Heshmat, R., Mohseni, F., Jamshidi, A.H., Alavi, S.H.R., Ahvazi, M. and Larijani, B. (2008). Effect of stem of Rheum ribes L. based on blood lipids in type II diabetic patients with high blood lipids. Iranian Journal of Medicinal Plants, 3(27), 92 - 97. (In Persian) [11] Fallah Huseini, H., Larijani, B., Fakhrzadeh, H., Akhondzadeh, S., Radjabipour, B., Toliat, T., Heshmat, R. and Heydari, R. (2004). The efficacy of silymarin on hypercholrsterolemic type II diabetic patients. Iranian Journal of Diabetes and Lipid Disorders, 3, 201 - 212. [12] Fisher, F.M., Zak, J.C., Cunningham, G.L and Whitfor, W.G. (1987). Water and nitrogen effects on growth and allocation pattern of creosote bush in northern Chihuahuan Desert. Journal of Range Management, 41,384-391 [13] Graham, C.H., Ferrier, S., Huettman, F., Moritz, C. and Peterson, A.T. (2004). New developments in museum-based informatics and applications in biodiversity analysis. Journal of Trends in Ecology & Evolution. 19 (9), 497–503. [14] Guisan, A. and Zimmermann, N. (2000). Predictive habitat distribution models in ecology. Journal of Ecological Modelling, 135, 147–186. [15] Hosseini, S.Z., Kappas, M., Zare Chahouki, M.A., Gerold, G., Erasni, D. and Rafiei Emem, A. (2013). Modelling potential habitats for Artemisia sieberi and Artemisia aucheri in Poshtkouh area, central Iran using the maximum entropy model and Geostatistics. Journal of Ecological Informatics, 18, 61-68. [16] Hu, B.Y., Zhang, H., Meng, X.L., Wang, F. and Wang, P. (2014). Aloeemodin from rhubarb (Rheum rhabarbarum) inhibits lipopolysaccharide-induced in flammatory responses in RAW264.7 macrophages. Journal of Ethnopharmacol. http://dx.doi.org/10.1016/ j.jep.2014.03.059i. [17] Jafari, M., Zare Chahouki, M.A., Tavili, A. and Kohandel, A. (2005). Soil-vegetation relationships in rangelands of Qom province. Iranian Journal of Pajouhesh & Sazandegi, 73, 110-116. [18] Khalasi Ahwazi, L., Zare Chahouki, M.A. and Hossein, S.Z.A. 2015. Modeling geographic distribution of Artemisia sieberi and Artemisia aucheri using presence-only modelling methods (MAXENT & ENFA). Iranian Journal of Renewable Natural Resources Researches, 6(1): 56-74. (In Persian) [19] Khalighifar, A. 2014. Determine potential habitats of Rheum ribes L. species using genetic algorithm and Maxent models in Isfahan province. Master's thesis natural resources engineering, Isfahan University of Technology. 115P. [20] Miller J., (2005). Incorporating Spatial Dependence in Predictive Vegetation Models: Residual Interpolation Methods, The Professional Geographer, 57(2), 169 184. [21] Miller J., and Franklin, J. (2002). Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Journal of Ecological Modelling, 157(2-3), 227-247. [22] Mun, S.C. and Mun, G.S. (2015). Development of an efficient callus proliferation system for Rheum coreanum Nakai, a rare medicinal plant growing in Democratic People’s Republic of Korea. Saudi Journal of Biological Sciences, 5, 1-7. [23] Negga, H.E. (2007). Predictive Modelling of Amphibian Distribution Using Ecological Survey Data: a case study of Central Portugal, Master thesis, International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands. [24] Nikbakht, M.R., Esnaashari, S. and Heshmati Afshar, F. (2013). Chemical Composition and General Toxicity of Essential Oil Extracted from the Stalks and Flowers of Rheum Ribes L. Growing in Iran. Journal of Reports in Pharmaceutical Sciences, 2(2), 165-170. [25] Turkmen, O., Crka, M. and Suat E. (2005). Initial Evaluation of a New Edible Wild Rhubarb Species (Rheum ribes L.) with a Modified Weighted Scaling Index Method. Pakistan Journal of Biological Sciences, 8(5), 763 - 675. [26] Ozturk, M., Aydogmus Ozturk, F., Emin Duru, M. and Topcu, G. (2007). Antioxidant activity of stem and root extracts of Rhubarb (Rheum ribes): An edible medicinal plant. Journal of Food Chemistry, 103, 623–630. [27] Phillips, S.J., Anderson, R.P. and Schapire, R.E. (2006). Maximum entropy modeling of speciesgeographic distributions. Journal of Ecological Modelling, 190, 231–259. [28] Qin, Z., Zhang, J.E., Tommaso, A.D., Wang, R.L. and Wu, R.S. (2015). Predicting invasions of Wedelia trilobata(L.) Hitchc. with Maxent and GARP models. Journal of Plant Res, 128, 763–775 [29] Sayyah, M., Boostani, H., Pakseresht, S and malayeri, A. (2009). Efficacy of hydroalcoholic extract of Rheum ribes L. in treatment of major depressive disorder. Journal of Medicinal Plants Research Vol. 3(8), 573-575. [30] Sharaf, E., Din, A. and Shaltout, K.H. (1985). On the phytosociology of Wadi Araba in the Eastern Desert of Egypt. Journal of Proceedings of the Egyptian Botanical Society 4, 1311–1325. [31] Sweet, J.A., 1988. Measuring the accuracy of a diagnostic systems. Journal of Science, 240: 1285-1293. [32] Tarkesh, M and Jetschke, M. (2012). Comparison of six correlative models in predictive vegetation mapping on a local scale. Journal of Environ Ecol Stat, 19, 437–457. [34] Zare Chahouki, M.A. (2006). Species distribution modeling in arid and semi–arid area rangeland. PhD Thesis in Range Management. Department of Natural Resources, Tehran University. 180 p [35] Zare Chahouki, M.A., Piry Sahragard, H. (2016). Maxent modelling for distribution of plant species habitats of rangelands (Iran). Polish Journal of Ecology, 3: 303-317. [36] Zare Chahouki, M.A., Piry Sahragard, H. and Azarnivand, H. (2013). Habitat distribution modeling of some halophyte plant species using Maximum Entropy Method (Maxent) in Hoze Soltan rangelands of Qum Province. Iranian Journal of Rangeland, 7(3), 212-221. [37] Zare Chahouki, M.A., Yousefi, M., Zare Arani, M. and Zare Chahoki, A. (2009). Effective factors on presence on Rheum ribes and preparing the predicted map of it’s (Case study: Chah-torosh Rangelands of Yazd province). Iranian Journal of Watershed Management Research (Pajouhesh & Sazandegi), 85, 72-79. [38] Zargari A, and Rhubarbes, S. (1997). Medicinal plant. Volume 4: Sixth edition. Tehran University publication, 234 – 239pp. | ||
|
آمار تعداد مشاهده مقاله: 591 تعداد دریافت فایل اصل مقاله: 456 |
||