مدیریت بیابان

مدیریت بیابان

تأثیر تغییر اقلیم بر پراکنش جغرافیایی گونه داروئی آویشن کوهی (Boiss and Hohen)Thymus kotschyanus با بهره‌گیری از مدل‌سازی ترکیبی

نوع مقاله : مقاله پژوهشی

نویسندگان
سمانه نظری 1
زینب جعفریان 2
سید جلیل علوی 3
علی اصغر نقی پور 4
1 دانشجوی دکتری علوم مرتع، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران.
2 استاد، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران.
3 دانشیار، دانشکده جنگلداری، منابع طبیعی و علوم دریایی، دانشگاه تربیت مدرس، نور، ایران.
4 استادیار، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران.
10.22034/jdmal.2021.526831.1338
چکیده
پژوهش حاضر با هدف پیش­‌بینی تأثیر تغییر اقلیم بر پراکنش جغرافیایی گونه داروئی آویشن کوهی (Thymus kotschyanus Boiss and Hohen) در استان مازندران انجام شد. اطلاعات مربوط به داده­‌های نقاط مشاهده گونه توسط سامانۀ موقعیت­‌یاب جهانی ثبت شد. پراکنش گونه آویشن کوهی در شرایط حال حاضر و آینده 2050 و 2070 میلادی (1428- 1448 شمسی) تحت تاثیر تغییر اقلیم بر اساس دو سناریوی RCP 4.5 و RCP 8.5 با سری داده مدل‌­های گردش عمومی BCC-CSM1-1،CCSM4  و MRI-CGCM3 و با استفاده از پنج مدل پراکنش گونه­‌ای شامل مدل خطی تعمیم‌­­یافته، مدل جمعی تعمیم­‌یافته، تحلیل طبقه­‌بندی درختی، مدل رگرسیون تقویت ­­شده و روش جنگل تصادفی بررسی شد. برای این منظور لایه­‌های عوامل محیطی شامل شش متغیر زیست-اقلیمی و دو متغیر فیزیوگرافی به عنوان ورودی مدل­‌های پراکنش گونه‌­ای به ­کار رفت. از بین متغیر­های محیطی به ترتیب تغییر فصلی بارندگی، مجموع بارندگی سردترین فصل سال و شاخص هم‌­دمایی بیشترین تأثیر را در مطلوبیت رویشگاه گونه داشت. ارزیابی مدل­‌های اجرا شده، نشان داد که مدل جمعی تعمیم­‌یافته و رگرسیون تقویت­‌شده نسبت به دیگر مدل‌­ها، پیش­‌بینی قابل اعتماد­تری برای تعیین رویشگاه اقلیمی دارد. نتایج نشان داد که تغییر اقلیم محدوده پراکنش گونه را تغییر داده و به سمت ارتفاعات بالاتر در آینده جابجا خواهد کرد. نتایج پژوهش حاضر می­‌تواند برای برنامه­‌ریزی برای حفاظت از رویشگاه گونه داروئی آویشن کوهی و همچنین احیاء و بازسازی آن در بخش وسیعی از کشور به کار رود.
کلیدواژه‌ها
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مطلوبیت رویشگاه
مدل‌های پراکنش گونه‌ای
  1. Akbarzadeh, M. (2003). Medicinal Plants of Labiatae Family in the summer rangelands of Vaz region in Mazandaran Province. Medicinal and Aromatic Plants, 19, 37-46. (in Farsi)
  2. Allouche, O., Tsoar, A., & Kadmon, R. (2006). Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). Applied Ecology, 43, 1223-1232.
  3. Araujo, M. B., & New, M. (2007). Ensemble forecasting of species distributions. Trends in ecology & evolution, 22 (1), 42- 47.
  4. Araujo, M. B., Pearson, R. G., Thuiller, W., & Erhard, M. (2005). Validation of species–climate impact models under climate change. Global Change Biology, 11(9), 1504-1513.
  5. Booth, T. H. (2018). Species distribution modelling tools and databases to assist managing forests under climate change. Forest Ecology and Management, 430, 196-203.
  6. Brandt, J. S., Haynes, M. A., Kuemmerle, T., Waller, D. M., & Radeloff, V. C. (2013). Regime shift on the roof of the world: alpine meadows converting to shrublands in the southern Himalayas. Biological Conservation, 158, 116-127.
  7. Breiman, L. (1984). Classification and regression trees. Belmont, CA: Wadsworth International Group.
  8. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
  9. Broadmeadow, M. S. J., & Matthews, R. (2003). Forests, Carbon and Climate Change: the UK Contribution. Forestry Commission, 1-12.
  10. Calinger, K.M. (2015). A functional group analysis of change in the abundance and distribution of 207 plant species across 115 years in north-central North America. Biodiversity and Conservation, 24, 2439-2457.
  11. Catry, F. X., Rego, F. C., Bacao, F. L., & Moreira, F. (2009). Modelling and mapping the occurrence of wildfire ignitions in Portugal. International Journal of Wildland Fire, 18(8), 921-931.
  12. Chatterjee, S., & Hadi, A.S. (2006). Regression analysis by example, John Wiley & Sons Inc.
  13. Chen, I. C., Hill, J. K., Ohlemuller, R, Roy, D.B., & Thomas, C.D. (2011). Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science, 333(6045), 1024-1026.
  14. Corlett, R. T., & Westcott, D. A. (2013). Will plant movements keep up with climate change? Trends in Ecology & Evolution, 28(8), 482-488.
  15. D’Odorico, P., Fuentes, J. D., Pockman, W. T., Collins, S. L., He, Y., Medeiros, J. S., Dewekker, S., & Litvak, M.E. (2010). Positive feedback between microclimate and shrub encroachment in the northern Chihuahuan desert. Ecosphere, 1(6), 1-11.
  16. Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Animal Ecology, 77, 802–813.
  17. Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The shuttle radar topography mission: Reviews of Geophysics, 45, 1-33.
  18. Farzadmehr, J., & Sangoony, H. (2020).  The effect of climate change on the geographical distribution of wild borage in Khorasan Razavi. Water and Soil Conservation, 27(3), 145-162. (in Farsi)
  19. Fasina, O. O., & Colley, Z. (2008). Viscosity and specific heat of vegetable oils as a function of temperature: 35°c to 180°c. International Journal of Food Properties, 11, 738-746.
  20. Fielding, A. H., & Bell, J. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24 (1), 38-49.
  21. Franklin, J. (2013). Species distribution models in conservation biogeography: developments and challenges. Diversity and Distributions, 19(10), 1217-1223.
  22. Gatti, R. C., Callaghan, T., Velichevskaya, A., Dudko, A., Fabbio, L., Battipaglia, G & Liang, J. (2019). Accelerating upward treeline shift in the Altai Mountains under last- century climate change. Scientific Reports, 9, 7678.
  23. Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C., Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M., Worley, P. H., Yang, Z. L., & Zhang, M. (2011). The community climate system model version 4. Climate, 24(19), 4973-4991.
  24. Ghehsareh Ardestani, E., & Heidari Ghahfarrokhi, Z. (2021). Ensembpecies distribution modeling of Salvia hydrangea under future climate change scenarios in Central Zagros Mountains, Iran. Global Ecology and Conservation, 26, e01488.
  25. Ghorbani, A., Pour nematy, A., Ghasemi, Z. S., & Shokuhian, A. (2017). Comparison of some effective environmental factors on distribution of Dactylis glomerata L. and Thymus kotschyanus Boiss and Hohen in South of Ardabil province. Range and Watershed Manegement,70 (2), 449-464. (in Farsi)
  26. Graham, M. H. (2003). Confronting multicollinearity in ecological multiple regression. Ecology, 84(11), 2809-2815.
  27. Guisan, A., Edwards, T. C & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157, 89-100.
  28. Guisan, A., & Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993-1009.
  29. Guisan, A., Tingley, R., Baumgartner, J. B., Naujokaitis-Lewis, I., Sutcliffe, P. R., Tulloch, A. I. T., Regan, T. J., Brotons, L., Mcdonald-Madden, E., Mantyka-Pringle, C., Martin, T. G., Rhodes, J. R., Maggini, R., Setterfield, S. A., Elith, J., Schwartz, M. W., Wintle, B. A., Broennimann, O., Austin, M., Ferrier, S., Kearney, M. R., Possingham, H. P., & Buckley, Y. M. (2013). Predicting species distributions for conservation decisions. Ecology Letters, 16(12), 1424-1435.
  30. Hao, T., Elith, J., Guillera-Arroita, G., & Lahoz-Monfort, J. J. (2019). A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Diversity and Distributions, 25(5), 839-852.
  31. Hastie, T., & Tibshirani, R. (2004). Generalized additive models. Encyclopedia of statistical sciences. Chichester, UK: John Wiley & Sons Inc.
  32. He, X., Burgess, K.S., Gao, L.M., & Li, D.Z. (2019). Distributional responses to climate change for alpine species of Cyananthus and Primula endemic to the Himalaya-Hengduan Mountains. Plant Diversity, 1(41), 26-32.
  33. Hijmans, R. J., Etten, J. V., Cheng, J., Mattiuzzi, M., Sumner, M., & Greenberg, J. (2017). Raster: geographic data analysis and modeling. R package version 2.3-33, 2016
  34. https://www.worldclim.org
  35. IPCC. (2013). Climate Change 2013: The physical science basis Woking group I contribution to the fifth assessment report of the Intergovemental Panel on Climte Change. Cambridge University Press.
  36. IPCC. (2014). Summary for Policymakers, Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  37. IPCC. (2018). Global Warming of 1.5_C. An IPCC Special Report on the Impacts of Global Warming of 1.5_C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change.
  38. Jafari, M. (2008). Investigation and analysis of climate change factors in Caspian Zone forests for last fifty years, Forest and Poplar Research, 16(2), 326-314. (in Farsi)
  39. Jafarian, Z & Kargar, M. (2017). Distribution Modeling of Protective and Valuable Plant Species in the Tourist Area of Polour Using Generalized Linear Model (GLM) and Generalized Additive Model (GAM). Geography and Development, 15 (46), 117-132. (in Farsi)
  40. Jafarian, Z., & Kargar, M. (2017). Comparison of Random Forest (RF) and Boosting Regression Tree (BRT) For Prediction of Dominant Plant Species Presence in Polour Rangelands, Mazandaran Province. Applied Ecology, 6(1), 41-55. (in Farsi)
  41. Kaky, E., Nolan, V., Alatawi, A., & Gilbert, F. (2020). A comparison between Ensemble and MaxEnt species distribution modeling approaches for conservation: A case study with Egyptian medicinal plants. Ecological Informatics, 60, 101150.
  42. Kolanowska, M., Kras, M., Lipinska, M., Mystkowska, K., Szlachetko, D. L., & Naczk, A. M. (2017). Global warming not so harmful for all plants - response of holomycotrophic orchid species for the future climate change. Scientific Reports, 7(1), 1-13.
  43. Lenoir, J., Gégout, J. C., Guisan, A., Vittoz, P., Wohlgemuth, T., Zimmermann, N. E., Dullinger, S., Pauli, H., Willner, W., & Svenning, J. C. (2010). Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. Ecography, 33, 295-303.
  44. Manish, K., Telwala, Y., Nautiyal, D. C., & Pandit, M. K. (2016). Modelling the impacts of future climate change on plant communities in the Himalaya: a case study from Eastern Himalaya, India. Modeling Earth Systems and Environment, 2(2), 92.
  45. Matthies, D., Brauer, I., Maibom, W., & Tscharntke, T. (2004). Population size and the risk of local extinction: empirical evidence from rare plants. Oikos, 105(3), 481-488.
  46. McCullagh, P. (1984). Generalized linear models. European Journal of Operational Research, 16, 285-292.
  47. Moghimi, J. (2005). Introduction of some important rangeland species for the development and improvement of Iranian rangelands. Arvan Publication, 670p. (in Farsi).
  48. Myers-Smith, I. H., & Hik, D. S. (2018). Climate warming as a driver of tundra shrubline advance. Ecology, 106(2), 547-560.
  49. Naghipour borj, A. A., Ashrafzadeh, M., & Haidarian, M. (2021). Modeling the current and future potential distribution of Fritillaria imperialis under climate change scenarios and using three general circulation models in Iran. Plant Ecosystem Conservation, 8(17), 219-235.
  50. Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., & Toxopeus, A. G. (2014). Where is positional uncertainty a problem for species distribution modelling? Ecography, 37, 191- 203.
  51. Pacifici, M., Foden, W. B., Visconti, P., Watson, J. E. M., Butchart, S. H. M., Kovacs, K. M., Scheffers, B. R. Hole, D. G., Martin, D. G., Akcakaya, H. R., Corlett, R. T., Huntley, B., Bickford, D., Carr, J. A., Hoffmann, A. A., Midgley, G. F., Pearce-Kelly, P., Pearson, R. G., Williams, S. E., Willis, S. G., Young, B., & Rondinini, C. (2015). Assessing species vulnerability to climate change. Nature Climate Change, 5(3), 215-225.
  52. Palmer, G., Hill, J. K, Brereton, T. M., Brooks, D. R., Chapman, J. W., Fox, R., Oliver, T. H., & Thomas, C. D. (2015). Individualistic sensitivities and exposure to climate change explain variation in species’ distribution and abundance changes. Science Advances, 1(9), e1400220.
  53. Parmesan, C., & Hanley, M. E. (2015). Plants and climate change: complexities and surprises. Annals of Botany, 116(6), 849-864.
  54. Phillips, S. J., & Dudik, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161- 175.
  55. Prugh, L.R., Deguines, N., Grinath, J. B., Suding, K. N., Bean, W.T., Stafford, R., & Brashares, J. S. (2018). Ecological winners and losers of extreme drought in California. Nature Climate Change, 8(9), 819-824.
  56. Richardson, A. D., Andy Black, T., Ciais, P., Delbart, N., Friedl, M. A, Gobron, N., Hollinger, D. Y., Kutsch, W. L., Longdoz, B., Luyssaert, S., Migliavacca, M., Montagnani, L., Monger, J. W., Moors, E., Piao, S., Rebmann, C., Reichstein, M., Saigusa, N., Tomelleri, E., Vargas, R., & Varlagin, A. (2010). Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1555), 3227-3246.
  57. Sun, S., Zhang, Y., Huang, D., Wang, H., Cao, Q., Fan, P., Yang, N., Zheng, P., & Wang, R. (2020). Science of the Total Environment The effect of climate change on the richness distribution pattern of oaks (Quercus L.) in China. Science of the Total Environment, 744, 140786.
  58. Teimoori Asl, S., Naghipour, A.A., Ashrafzadeh, M., & Heydarian, M. (2020). Predicting the impact of climate change on potential habitats of Stipa hohenackeriana Trin & Rupr in Centeral Zagros. Rangeland,14(3), 526-538. (in Farsi)
  59. Thuiller, W., Lafourcade, B., Engler, R., & Araujo, M. B. (2009). BIOMOD - A platform for ensemble forecasting of species distributions. Ecography, 32(3), 369-373.
  60. Thuiller, W., Pollock, L. J., Gueguen, M., & Munkemuller, T. (2015). From species distributions to meta-communities. Ecology Letters, 18(12), 1321-1328.
  61. Urban, M. C. (2015). Accelerating extinction risk from climate change. Science, 348(6234), 571-573.
  62. Van Soesbergen, A., & Mulligan, M. (2018). Uncertainty in data for hydrological ecosystem services modelling: potential implications for estimating services and beneficiaries for the CAZ Madagascar. Ecosystem Services, 33, 175-186.
  63. Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J. F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., & Rose, S. K. (2011). The representative concentration pathways: an overview. Climatic Change, 109(1), 5-31.
  64. Venkataraman, K., Tummuri, S., Medina, A., & Perry, J. (2016). 21st century drought outlook for major climate divisions of Texas based on CMIP5 multimodel ensemble: Implications for water resource management. Hydrology, 534, 300-316.
  65. Vilar, L., Woolford, D. G., Martell, D. L., & Pilar Martin, M. (2010). A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain. International Journal of Wildland Fire, 19(3), 325-337.
  66. Wan, J., Wang, C., Yu, J., Nie, S., Han, S., Liu, J., Zu, Y., & Wang, Q. (2016). Developing conservation strategies for Pinus koraiensis and Eleutherococcus senticosus by using model-based geographic distributions. Forestry Research, 27(2), 389-400.
  67. Wu, J., & Gao, X. J. (2013). A gridded daily observation dataset over China region and comparison with the other datasets. Chinese Journal of Geophysics, 56, 1102–1111.
  68. Yukimoto, S., Adachi, Y., Hosaka, M., Sakami, T., Yoshimura, H., Hirabara, M., Tanaka, T. Y., Shindo, E., Tsujino, H., Deushi, M., Mizuta, R., Yabu, S., Obata, a, Nakano, H., Koshiro, T., Ose, T., & Kitoh, A. (2012). A New Global Climate Model of the Meteorological Research Institute: MRI-CGCM3-Model Description and Basic Performance. Meteorological Society of Japan, 90, 23-64.
  69. Zhang, Z., Capinha, C., Weterings, R., McLay, C.L., Xi, D., Lu, H., & Yu, L. (2019). Ensemble forecasting of the global potential distribution of the invasive Chinese mitten crab, Eriocheir sinensis. Hydrobiologia, 826(1), 367-377.
  70. Zu, K., Wang, Z., Zhu, X., Lenoir, J., Shrestha, N., Lyu, T., Luo, A., Li, Y, Ji, C, Peng, S., Meng, J., & Zhou, J. (2021). Upward shift and elevational range contractions of subtropical mountain plants in response to climate change. Science of the Total Environment, 783, 146896.
مدیریت بیابان
دوره 9، شماره 3 - شماره پیاپی 19
6 مقاله
پاییز 1400
صفحه 1-16

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