Investigation of the effect of climate change on the distribution range of Prunus eburnea (Spach) Aitch. & Hemsl. using the Maxcent

Document Type : Research Paper

Authors

1 Department of Plant Biology, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran

2 Department of Biodiversity and Ecosystem Management, Environmental Sciences Research Institute, Shahid Beheshti University,Tehran, Iran

3 Department of Mining Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

10.29252/aridbiom.2021.16797.1861

Abstract

Climate change affects plant distribution in vulnerable ecosystems, such as gray almond (Prunus eburnea (Spach) Aitch. & Hemsl.). Gray almond is a member of the Rosaceae family and it has conservational, nutritional and endemic importance and widely distributed in arid and semi-arid regions of Iran. The main objectives of this study are modeling the habitat suitability of the gray almond species using the Maxent model and predicting the effects of climate change on its distribution as well as determining the contribution of environmental variables in modeling the habitat suitability. Evaluating the accuracy of Maxent model using the mean area under the curve index (0.94) indicates the excellent performance of this model. Results showed that the soil depth and the solar radiation layers with 35.4% and 27.3%, respectively, had the highest share of participation in modeling of the habitat suitability of gray almond species. Based on the map of habitat suitability in the current climatic conditions, it is predicted that the southern regions (Hormozgan, Bushehr and Fars provinces), south-west (Kohgiluyeh, Boyer-Ahmad and Chaharmahal Bakhtiari provinces) and southeast (provinces) Sistan and Baluchestan, Kerman and South Khorasan) have the potential of suitable environmental conditions to expand the habitats of this species. In order to evaluate the effects of climate change on the habitat suitability of this species, the Representative Concentration Pathway 8.5 (greenhouse gas emission scenario) related to greenhouse gas emission model CCSM4 in the 2080 was used. According to Maxent model, the area of suitable habitat of this species in the current climatic conditions is 862113 Km2. Predicted based on RCP 8.5 in 2080, suitable areas that this species will lose 36.14% and gain new suitable habitats as much as 8.9%. It is predicted that the area of suitable habitat areas of this species will decrease to 627273 km2 in the future. According to the results, it is necessary to have better ecosystem management of conservation and exploitation of gray almond habitats in future.

Keywords

References

[1]. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J Hijmans, R., Huettmann, F., R Leathwick, J., Lehmann, A., Li, J. and G Lohmann, L. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography. 29(2), 129-151.
[2] Assadi, M. (ed.), 1989-2016: Flora of Iran 1-85. – RIFR, Tehran. (In Farsi).
[3]. Azevedo, M. C. C., Arau´jo, F. G., Cruz-Filho, A. G., Pessanha, A. L. M., Arau´jo Silva M., and Guedes, A. P. P. (2007). Demersal fishes in a tropical bay in southeastern Brazil: Partitioning the spatial, temporal and environmental components of ecological variation. Estuarine, Coastal and Shelf Science. 75, 468–480.
[4]. Buehler, E. C. and Ungar, L. H. (2001). Maximum entropy methods for biological sequence modeling. Workshop on Data Mining in Bioinformatics (BIOKDD). University of Pennsylvania. Philadelphia, 345 p.
[5]. Bugmann, H. K. M. and Solomon, A. M. (2000). Explaining forest composition and biomass across multiple biogeographical regions. Ecological Applications. 10, 95–114.
[6]. Chapman, D. S., and Purse, B. V. (2011). Community versus single-species distribution models for British plants. Journal of Biogeography. 38, 1524–1535.
[7]. Davis, M. B. and Shaw, R. G. (2001). Range shifts and adaptive responses to quaternary climate change. Science. 292, 673–679.
[8]. Dormann, C.F., Schymanski, S.J., Cabral, J., Chuine, I., Graham, C., Hartig, F., Kearney, M., Morin, X., Römermann, C., Schröder, B. and Singer, A. (2012). Correlation and process in species distribution models: bridging a dichotomy. Journal of Biogeography, 39, 2119-2131.
[9]. Elith, J., Graham, H. C., Anderson, P. R., Dudik, M., Ferrier, S., Guisan, A., Hijmans, J. R., Huettmann, F., Leathwick, R. and Lehmann, A. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129-151.
[10]. Elith, J., Kearney, M., and Phillips, S. (2010). The art of modelling range-shifting species. Methods in Ecology and Evolution, 1, 330–342.
[11]. ESRI. 2011. ArcGIS desktop: release 10. Environmental Systems Research Institute, Redlands, CA.
[12]. Evans, M., Merow, C., Record, S., McMahon, S. M. and Enquist. B. J. (2016). Towards process-based range modeling of many species. Trends in Ecology and Evolution, 31, 860–871.
[13]. Fick, S. E, and Hijmans, R. J. (2017) Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315.
[14]. Flory, A. R., Kumar, S., Stohlgren, T. J. and Cryan, P. M. (2012). Environmental conditions associated with bat white‐nose syndrome mortality in the north‐eastern United States. Journal of Applied Ecology. 49(3), 680-689.
[15]. Ghahreman, N., Tabatabaei, M., and Babaeian, I. (2015). Investigation of uncertainty in the IPCC AR5 precipitation and temperature projections over Iran under RCP scenarios. Un climate change conference. Paris.
[16]. Glenn, M., Robert, E., Brian, H., David, R. F., Jonathan, H., and Dana, M. (2002). Vegetation variation across Cape Cod, Massachusetts: environmental and historical determinants. Journal of Biogeography, 29, 1439–1454.
[17]. Gogtay, N. J., and Thatte, U. M. (2017). Principles of correlation Analysis. Journal of the Association of Physicians of India. 65, 78-81.
[18]. Gomes, V., IJff, S., Raes, N., Amaral, I. (2017). Species Distribution Modelling: Contrasting presence-only models with plot abundance data. SCIENtIfIC Reports.  8, 1003.
[19]. Hengl, T., De Jesus, J. M., MacMillan, R. A., Batjes, N. H., and Heuvelink, G. B. M. (2014). SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE. 9(8), e105992.
[20]. Hernandez, P. A., Graham, C. H., Master, L. L. and Albert, D. L. (2006). The effect of sample size and species characteristics on performance of different species. Ecography. 29: 773-785.
[21]. Hijmans, R. J. (2017). Raster. Introduction to the 'raster' package. Version 2.6-7 R package. Available: http://CRAN.R-project.org/package = Raster.
[22]. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. and Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology. 25(15), 1965-1978.
[23]. Hirzel, A. H., J. Hausser, D., Chessel, P. and Perrin, N. (2002). Ecological niche factor analysis: how to compute habitat-suitability maps without absence data. Ecology, 73(22): 2027-2036.
[24]. Jump, A. and Penuelas, J. (2005). Running to stand still: adaptation and the response of plants to rapid climate change. Ecology Letters, 8, 1010-1020.
[25]. Kantar, M. B., Sosa, C. C., Khoury, C. K., Castañeda-Álvarez, N. P., Achicanoy, H. A., Bernau, V., Kane, N. C., Marek, L., Seiler, G and Rieseberg, L. H. (2015). Ecogeography and utility to plant breeding of the crop wild relatives of sunflower (Helianthus annuus L.). Frontal Plant Science. 6, 841.
[26]. Khalasi Ahwazi, L., Zare Chahouki, M. A. and Hosseini, S. Z. (2015). Modeling geographic distribution of Artemisia sieberi and Artemisia aucheri using presence-only modelling methods (MAXENT and ENFA). Journal of Renewable Natural Resources Research. 6(1), 57-73. (In Farsi)
[27]. Khatamsaz, M. 1993. Flora of Iran (Family Rosaceae). Vol. 6. Research Institute of Forest and Rangelands press, Tehran, 274315. (In Farsi).
[28]. Kramer, K., Degen, B., Buschbom, J., Hickler, T., Thuiller, W., Sykes, M. T. and Winter, W. (2010). Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change—Range, abundance, genetic diversity and adaptive response. Forest Ecology and Management. 259, 2213–2222.
[29]. Liu, C., White, M. and Newell, G. (2011). Measuring and comparing the accuracy of species distribution models with presence–absence data. Ecography, 34, 232–243.
[30]. Mazangi, A., Ejtehadi, H., Mirshamsi, O., Ghasemzade, F. and Hosseiynian, S.S. (2016). Effects of climate change on the distribution of endemic Ferula xylorhachis Rech.f. (Apiaceae: Scandiceae) in Iran: Predictions from ecological niche models. Russian Journal of Ecology. 47, 349–354.
[31]. Nussey, D. H., Postma, E., Gienapp, P. and Visser, M.E. (2005). Selection on heritable phenotypic plasticity in a wild bird population. Science. 310, 304–306.
[32]. Parmesan, C. (2006). Ecological and evolutionairy responses to recent climate change. Annual Review of Ecology and Systematics. 37, 912–929.
[33]. Phillips, S. J., Anderson, R. P. and Schapired, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231–259.
[34]. Rahmani, R., Neji, M., Belgacem, M., and Debuba, M. (2020). Potential distribution and the habitat suitability of the African mustard (Brassica tournefortii) in Tunisia in the context of climate change. Arabian journal of geoscience. https://dx.doi.org/10.1007/s12517-020-05467-8
[35]. Rechinger, K. H. (1963-1992). Flora Iranica Graz. pp: 1-171.
[36]. Rehfeldt, G. E., Tchebakova, N. M., Parfenova, Y. I., Wykoff, W. R., Kuzmina, N. A. and Milyutin, L. I. (2002). Intraspecific responses to climate in Pinus sylvestris. Global Change Biology. 8, 912–929.
[37]. Rezaei, S. A., and Arzani, H. (2007). The use of soil surface attributes in rangelands capability assessment. Iranian journal of Range and Desert Research. 14(2), 232-248.
[38]. Sexton, J. P., Hangartner, S. B. and Hoffmann, A. A. (2014). Genetic isolation by environment or distance: which pattern of gene flow is most common? Evolution. 68, 1–15.
[39]. Sheth, S. N. and Angert, A. L. (2014). The evolution of environmental tolerance and range size: a comparison of geographically restricted and widespread Mimulus. Evolution, 68, 2917–2931.
[40]. Skelly, D. K., Joseph, L. N., Possingham, H. P., Freidenburg, L. K., Farrugia, T. J., Kinnison, M. T. and Hendry, A. P. (2007). Evolutionary responses to climate change. Conservation Biology. 21, 1353–1355.
[41]. Stuart, N. (2015). ArcGeomorphometry: A toolbox for geomorphometric characterization of DEMs in the ArcGIS environment. Computers and Geosciences. https://doi.org/10.1016/j.cageo.2015.09.020.
[42]. Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240, 1285–1293.
[43]. Titeux, N., Henle, K., Mihoub, J. B., Regos, A., Geijzendorffer, I. R., Cramer, W., Verburg, P.H. and Brotons, L. (2017). Global scenarios for biodiversity need to better integrate climate and land use change. Diversity and distributions, 23(11):1231–1234.
[44]. Wang, C., Liu, C., Wan, J. and Zhang, Z. (2016). Climate change may threaten habitat suitability of threatened plant species within Chinese nature reserves. PeerJ. 14;4:e2091. doi: 10.7717/peerj.2091. PMID: 27326373; PMCID: PMC4911960.
[45]. Wiens, J.A., Stralberg, D., Jongsomjit, D., Howell, C.A., and Howell, M.A. (2009). Niches, models, and climate change: Assessing the assumptions and uncertainties. PNAS. 106-suppl. 2-19729–19736.
[46]. Willi, Y., Van Busrirk, J. and Hoffmann, A. A. (2006). Limits to the adaptive potential of small populations. Annual Review of Ecology, Evolution, and Systematics. 37, 433–458.
Journal of Arid Biome
Volume 11, Issue 1
September 2021
Pages 63-75

Files

Share

How to cite

Statistics