The Impact of Climate Change on The Geographic Distribution of Thymus Kotschyanus (Boiss and Hohen) Using Ensemble Modelling

Document Type : Original Article

Authors

1 Ph.D. Student of Rangeland Science, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.

2 Professor, Department of Range Management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.

3 Associated Professor, Department of Forestry, Natural Resources and Marin Science, Tarbiat Modares University, Noor, Iran.

4 Assistant Professor, Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, Iran.

Abstract

This study aimed at predicting the effect of climate change on the geographic distribution of Thymus kotschyanus Boiss and Hohen in Mazandaran province. Species presence data were recorded via the Global Positioning System (GPS). The distribution of T. kotschyanus Boiss and Hohen under current and future climatic conditions 2050 and 2070 (1428- 1448 Solar) applying  two scenarios of RCP 4.5 and RCP 8.5 climate change by using the GCM data series of general circulation models BCC-CSM1-1، CCSM4 and MRI-CGCM3  and the five species distribution models such as Generalized Linear Model, Generalized Additive Model, Classification Tree Analyses, Generalized Boosting Model and Random Forest methods was investigated. For this purpose, layers of environmental factors, including six bioclimatic and two physiographic variables, were used as inputs to species distribution models. Among the environmental variables, precipitation seasonality, precipitation in the coldest quarter, and isotherm had the greatest impact on habitat suitability. The assessment of the modelling indicated that the Generalized Additive Model and Generalized Boosting Model had better predictions of climate habitat than the other models. The results also indicate that climate change will change the range size of the T. kotschyanus Boiss and Hohen, and will move toward higher elevations in the future. The results of the present study may be used to plan habitat protection for the medicinal species of T. kotschyanus Boiss and Hohen, as well as its restoration and rehabilitation in the vast regions of the country.

Keywords


  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 Ecology43, 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 Conservation24 (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 Geophysics56, 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.