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:: Volume 9, Issue 19 (3-2022) ::
PEC 2022, 9(19): 137-155 Back to browse issues page
Predicting the geographical distribution of Alopecurus textilis Boiss rangeland species on basis Consensus approach of climate change in Mazandaran province
Samaneh Nazari1 , Zeinab Jafarian * , Jalil Alavi3 , Ali asghar Naghipoor4
1- -
Sari Agricultural Sciences and Natural Resources University, Sari Agricultural Sciences and Natural Resources University , z.jafarian@sanru.ac.ir
3- Tarbiat Modares University
4- Shahrekord University
Abstract:   (2176 Views)
The climate changes have an important role in distribution of plant species. Statistical species distribution models (SDMs) are widely used to predict the changes in species distribution under climate change scenarios. In the peresent study, the distribution of Alopecurus textilis in the current and future climate condition (2050) under the influence of climate change and two scenarios of RCP 4.5 and RCP 8.5 with the GCM data series of general circulation models BCC-CSM1-1، CCSM4 and MRI-CGCM3 by using of Five species distribution models such as Generalized Linear Model, Generalized Additive Model, Classification Tree Analyses, Generalized Boosting Model and Random Forest method in Mazandaran province were investigated. For this purpose, the number of 92 species presence data was recorded using Global Positioning System and along with layers of environmental factors including six bioclimatic variables and two physiographic variables were used in modeling. Among the environmental variables, mean temperature of driest quarter, precipitation seasonality, precipitation of warmest quarter and precipitation of coldest quarter had the most impact on the habitat suitability. Modeling evaluation indicated that the random forest and generalized boosting model had better predictions of climatic habitat than other models. Results showed that in comparison with current conditions under the emission scenarios of RCP 4.5 and RCP 8.5 respectively 22.25 and 36.22 percent of the climatic suitable area for this species will be reduced by 2050. Finally, the results of the present study can be an efficient tool for biodiversity protection, ecosystem management, and species re-habitation planning under future climate change scenarios.
Keywords: Climate change scenarios, Habitat suitability, Species distribution models, Model evaluation.
Full-Text [PDF 1039 kb]   (469 Downloads)    
Type of Study: Research | Subject: Special
Received: 2021/02/24 | Accepted: 2021/08/9 | Published: 2022/03/16
References
1. Albuquerque, F.S., Macias-Rodriguez, M.A., Burquez, A., Astudillo-Scalia, Y. 2019. Climate change and the potential expansion of buffelgrass (Cenchrus ciliaris L., Poaceae) in biotic communities of Southwest United States and northern Mexico. Biological Invasions, 21(11): 3335-3347.
2. Allouche, O., Tsoar, A., Kadmon, R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43: 1223-1232.
3. Amissah, L., Mohren, G.M.J., Bongers, F., Hawthorne, W.D., Poorter, L. 2014. Rainfall and temperature affect tree species distribution in Ghana. Journal of Tropical Ecology, 5 (30): 435-446.
4. Araujo, M.B., New, M. 2007. Ensemble forecasting of species distributions. Trends in ecology & evolution, 22 (1): 42–47.
5. 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.
6. 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.
7. 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.
8. Chen, I.C., Hill, J.K., Ohlemuller, R., Roy, D.B., Thomas, C.D. 2011.Rapid range shifts of species associated with levels of climate warming. Science, 333: 1024-1026.
9. Corlett, R.T., Westcott, D.A. 2013. Will plant movements keep up with climate change? Trends in Ecology & Evolution, 28 (8): 482-488.
10. Dagnino, D., Guerrina, M., Minuto, L., Mariotti, M.G., Medail, F., Casazza, G. 2020. Climate change and the future of endemic flora in the South Western Alps: relationships between niche properties and extinction risk. Regional Environmental Change, 20: 121.
11. Dubuis, A., Pottier, J., Rion, V., Pellissier, L., Theurillat, J.P., Guisan, A. 2011. Predicting spatial patterns of plant species richness: A comparison of direct macroecological and species stacking modelling approaches. Diversity and Distributions, 17 (6): 1122-1131.
12. Elith, J., Graham, C.H. 2009. Do they? how do they? why do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32: 66–77.
13. Etterson, J.R., Mazer, S.J. 2016. How climate change affects plants'sex lives. Science, 353: 32- 33.
14. Ferrarini, A., Dai, J., Bai, Y., Alatalo, J. M. 2019. Redefining the climate niche of plant species: A novel approach for realistic predictions of species distribution under climate change. Science of the Total Environment, 671: 1086-1093.
15. Galloway, L.F., Burgess, K.S. 2012. Artificial selection on flowering time: Influence on reproductive phenology across natural light environments. Journal of Ecology, 100: 852- 861.
16. Ghahramani, A., Moore, A.D. 2015. Systemic adaptations to climate change in southern Australian grasslands and livestock: Production, profitability, methane emission and ecosystem function. Agricultural Systems, 133: 158-166.
17. 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.
18. Gillison, A.N. 2019. Plant functional indicators of vegetation response to climate change, past present and future: I. Trends, emerging hypotheses and plant functional modality. Flora, 254: 12-30.
19. Graham, M.H. 2003. Confronting multicollinearity in ecological multiple regression. Ecology, 84 (11): 2809-2815.
20. Guan, B., Guo, H., Chen, S., Li, D., Liu, X., Gong, X., Ge, G. 2020. Ecological Informatics Shifting ranges of eleven invasive alien plants in China in the face of climate change. Ecological Informatics, 55: 101024.
21. Guisan, A., Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling, 135: 147-186.
22. 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.
23. IPCC, 2014 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. IPCC, Geneva, Switzerland, 151.
24. IPCC, Global Warming of 1.5 °C, 2018. 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, Sustainable Development, and Efforts to Eradicate Poverty.
25. 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.
26. Kane, K., Debinski, D.M., Anderson, C., Scasta, J.D., Engle, D.M., Miller, J.R. 2017. Using regional climate projections to guide grassland community restoration in the face of climate change. Frontiers in Plant Science, 8: 1-11.
27. Kurpis, J., Serrato-Cruz, M.A., Feria Arroyo, T.P. 2019. Modeling the effects of climate change on the distribution of Tagetes lucida Cav. (Asteraceae). Global Ecology and Conservation, 20: e00747.
28. Leao, T.C.C., Fonseca, C.R., Peres, C.A., Tabarelli, M. 2014. Predicting extinction risk of Brazilian Atlantic forest angiosperms. Conservation. Biology, 28: 1349 -1359.
29. Lioret, F., Siscart, D., Dalmases, C. 2005. Canopy recovery after drought dieback in holm-oak Mediterranean forests of Catalonia (NE Spain). Global Change Biology, 10: 2092-2099.
30. Ma, Z., Liu, H., Mi, Z., Zhang, Z., Wang, Y., Xu, w., Jiang, L., He, j. 2017. Climate warming reduces the temporal stability of plant community biomass production. Nature Communications, 8: 1-7.
31. Mahmoudi Shamsabad, M., Assadi, M., Parducci, L. 2019. Phylogeography and population genetics of Acanthophyllum squarrosum complex (Caryophyllaceae) in the Irano-Turanian region. Systematics and Biodiversity, 17 (4): 412-421.
32. Menegat, A., Milberg, P., Nilsson, A.T.S., Andersson, L., Vico, G. 2018. Soil water potential and temperature sum during reproductive growth control seed dormancy in Alopecurus myosuroides Huds. Ecology and Evolution, 8 (14): 7186-7194.
33. Mi, C., Huettmann, F., Guo, Y., Han, X., Wen, L. 2017. Why to choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. PeerJ, 5: e2849.
34. Miller, J. 2010. Species distribution modeling. Geography Compass, 4: 490-509.
35. Moraitis, M.L., Valavanis, V.D., Karakassis, I. 2019. Modelling the effects of climate change on the distribution of benthic indicator species in the Eastern Mediterranean Sea. Science of the Total Environment, 667: 16–24.
36. Moritz, C., Agudo, R. 2013. The future of species under climate change: Resilience or decline? Science, 341: 504-508.
37. 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.
38. Niskanen, A.K.J., Niittynen, P., Aalto, J., Vare, H., Luoto, M. 2019. Lost at high latitudes: Arctic and endemic plants under threat as climate warms. Diversity and Distributions, 25: 809-821.
39. Opdam, P., Wascher, D .2004. Climate change meets habitat fragmenta¬tion: linking landscape and biogeographical scale levels in research and conservation. Biological Conservation, 117: 285-297.
40. Oyebanji, O.O., Salako, G., Nneji, L.M., Oladipo, S.O., Bolarinwa, K.A., Chukwuma, E.C., Ayoola, A.O., Olagunju, T.E., Ighodalo, D.J., Nneji, I.C. 2021. Impact of climate change on the spatial distribution of endemic legume species of the Guineo-Congolian forest, Africa. Ecological Indicators, 122: 107282.
41. Pauls, S.U., Nowak, C., Balint, M., Pfenninger, M. 2013. The impact of global climate change on genetic diversity within populations and species. Molecular Ecology, 22: 925-946.
42. Pearson, R.G., Dawson, T.P. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global ecology and biogeography, 12 (5): 361-371.
43. Rana, H.K., Luo, D., Rana, S.K., Sun, H. 2020. Geological and climatic factors affect the population genetic connectivity in Mirabilis himalaica (Nyctaginaceae): Insight from phylogeography and dispersal corridors in the Himalaya-Hengduan Biodiversity Hotspot. Front. Plant Sci, 10:1721.
44. Schippers, P., Verboom, J., Vos, C.C., Jochem, R. 2011. Metapopulation shift and survival of woodland birds under climate change: will species be able to track? Ecography, 34: 909-919.
45. Seddon, A.W.R., Macias-fauria, M., Long, P.R., Benz, D., Willis, K.J. 2016. Sensitivity of global terrestrial ecosystems to climate variability. Nature, 531: 229–232.
46. Sintayehu, D.W. 2018. Impact of climate change on biodiversity and associated key ecosystem services in Africa: a systematic review. Ecosystem Health and Sustainability, 4 (9): 225-239.
47. 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.
48. Syphard, A.D., Franklin, J. 2009. Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors. Ecography, 32(6): 907-918.
49. Thomas, C.D. 2010. Climate, climate change and range boundaries. Diversity and Distributions, 16: 488-495.
50. Thuiller, W. 2004. Patterns and uncertainties of species range shifts under climate change. Global Change Biology, 10: 2020–2027.
51. Thuiller, W., Lafourcade, B., Engler, R., Araujo, M.B. 2009. BIOMOD - A platform for ensemble forecasting of species distributions. Ecography, 32 (3): 369-373.
52. 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.
53. 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. Journal of hydrology, 534: 300-316.
54. 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.
55. Wagner, E. J., Oplinger, R.W. 2017. Effect of overwinter hydration, seed storage time, temperature, and photoperiod on germination of some Carex, Juncus, and Alopecurus species. Aquatic Botany, 137: 39-49.
56. Walck, J.L., Hidayati, S.N., Dixon, K.W., Thompson, K.E.N., Poschlod, P. 2011. Climate change and plant regeneration from seed. Global Change Biology, 17: 2145-2161.
57. Wang, B., Deveson, E.D., Waters, C., Spessa, A., Lawton, D., Feng, P.Y., Liu, D.L. 2019. Future climate change likely to reduce the Australian plague locust (Chortoicetes terminifera) seasonal outbreaks. Science of the Total Environment, 668: 947-957.
58. Weiland, F.S., Van Beek, L.P.H., Weerts, A.H., Bierkens, M.F.P. 2012. Extracting information from an ensemble of GCMs to reliably assess future global runoff change. Journal of Hydrology, 412: 66-75.
59. Wilson, K.L., Skinner, M.A., Lotze, H.K. 2019. Projected 21st-century distribution of canopy-forming seaweeds in the Northwest Atlantic with climate change. Diversity and Distributions, 25: 582–602.
60. Woldearegay, M. 2020. Climate change impacts on the distribution and phenology of plants: A review. Tropical Plant Research, 7 (1): 196-204.
61. Yi, Y.J., Zhou, Y., Cai, Y.P., Yang, W., Li, Z.W., Zhao, X. 2018. The influence of climate change on an endangered riparian plant species: the root of riparian Homonoia. Ecological indicators, 92: 40–50.
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nazari S, jafarian Z, alavi J, naghipoor A A. Predicting the geographical distribution of Alopecurus textilis Boiss rangeland species on basis Consensus approach of climate change in Mazandaran province. PEC 2022; 9 (19) :137-155
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Volume 9, Issue 19 (3-2022) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
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