The effect of climate change on the geographical distribution of wild borage in Khorasan Razavi

Document Type : Complete scientific research article


1 Department of Nature engineering and medicinal plants, University of Torbat heydarieh

2 university of Torbat heydarieh


Background and objectives: Climate change which has begun due to human activities in the past centuries, intensified through positive feedbacks as time goes by. Changes in the characteristics of the environment of organisms definitely affect their lives. In the last decade, many studies have focused on the type, size, and extent of this effect on different organisms (including plants) in various ecosystems. In this study, the effect of climate change on the geographical distribution of Anchusa italica in Khorasan Razavi province was investigated at 2080 with RCP 8.5 and under the HadGem2 general circulation model. Despite its medicinal, conservational, economic and even forage importance, this species has never been studied. The impact of climate change on important species can only be examined through modeling, because modeling studies in the natural sciences are the key of many issues that cannot be tracked in normal studies.
Materials and methods: At first, field studies were carried out including the record of geographical coordinates of the species presence in the rangelands of Khorasan Razavi province in late spring of 2018 (when the plant was flowering and easily visible and identified) and 113 presence points was recorded for Anchusa italica. Then the 19 bioclimatic variables alongside three topographic variables were used as inputs to GBM model. This model, which is a decision trees based methods, uses numerous incomplete trees to form the final model, and the algorithm required in this study was programmed in the R environment. The model simulates the relationship between the presence of species and climatic and topographical conditions, and then uses this simulation to study the effect of changes in climatic factors on the distribution of species. Two validation indices (AUC and TSS) were used to determine the model's potency.
Results: The maps of suitable areas for wild borage in current climatic conditions and under climate change in 2080 were prepared and the model assessment indices showed that the model has a high ability to predict suitable sites for species presence (AUC = 0.974 and TSS = 0.87). Habitat suitability of this species was highly affected by (BIO1), (BIO2), (BIO15) and (BIO12). Results showed that in comparison with current conditions, about 40% of the climatic suitable area for this species will be diminished with the RCP 8.5 (from the IPCC scenario series). The areas where changes in the habitat of this species occur are displayed in a map.
Conclusion: The effect of climate change on plants is divided into three groups: Adaptation, Shifting and Extinction. According to the results of this study, in the case of wild borage, physiological adaptation is not likely to be possible in the short term (less than few centuries to few millennia). So if this plant fails to alter its distribution range (Shifting or displacement), will have to choose the third way (local extinction or get limited to refuges). Therefore, it is necessary to plan local, national and regional programs to maintain and extend the current habitats of this biological, ecological, medicinal and economic important species and providing access to new habitats for it; alongside the preventive activities of climate change.


 1.Al-Snafy, A. 2014. The pharmacology of Anchusa italica and Anchusa strigosa, a review. Inter. J. Pharm. Pharmaceut. Sci. 6: 4. 7-10.
2.Allouche, O., Tsoar, A., and Kadmon, R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43: 1223-32.
3.Anderson, R.P. 2013. A framework for using niche models to estimate impacts of climate change on species distributions. Annals of the New York Academy of Sciences. 1297: 8-28.
4.Araujo, M.B., and Guisan, A. 2006. Five (or so) challenges for species distribution modelling. J. Biogeograph. 33: 1677-88.
5.Archer, S.R., and Predick, K.I. 2008. Climate change and ecosystems of the southwestern United States. Rangelands. 30: 23-8.
6.Attorre, F., Francesconi, F., Taleb, N., Scholte, P., Saed, A., Alfo, M., and Bruno, F. 2007. Will dragonblood survive the next period of climate change? Current and future potential distribution of Dracaena cinnabari (Socotra, Yemen). Biological Conservation. 138: 430-9.
7.Aurambout, J., Finlay, K., Luck, J., and Beattie, G. 2009. A concept model to estimate the potential distribution of the Asiatic citrus psyllid in Australia under climate change-A means for assessing biosecurity risk. Ecological Modelling. 220: 2512-24.
8.Bakkenes, M., Alkemade, J., Ihle, F., Leemans, R., and Latour. J. 2002. Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050. Global change biology. 8: 390-407.
9.Braunisch, V., Coppes, J., Arlettaz, R., Suchant, R., Schmid, H., and Bollmann, K. 2013. Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change. Ecography. 36: 971-83.
10.Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.I. 1984. Classification and regression trees. Taylor & Francis, California, 368p.
11.Collevatti, R.G., Nabout, J.C., and Diniz-Filho, J.A.F. 2011. Range shift and loss of genetic diversity under climate change in Caryocar brasiliense, a Neotropical tree species. Tree Genetics & Genomes. 7: 1237-47.
12.DE’ATH, G. 2007. Boosted trees for ecological modeling and prediction. Ecology. 88: 243-51.
13.Elith, J., Leathwick, J.R., and Hastie, T. 2008. A working guide to boosted regression trees. J. Anim. Ecol. 77: 802-13.
14.Flom, P.L. 1999. Multicollinearity diagnostics for multiple regression: A Monte Carlo study. ETD Collection for Fordham University, 155p.
15.Ghahreman, A. 2006. Basic Botany. Tehran: University of Tehran Press, 492p.
16.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. Inter. J. Climatol. 25: 1965-78.
17.Hussain, H.Z., Al-Baldawy, M., andAl-Ani, R. 2014. Efficiency of borage (Anchusa italica) and french jasmin powders (Calotropis procera) in detoxification of ochiratoxin A and deoxynivalenol in poultry diet. J. Exp. Biol. Agric. Sci. 2: 5. 484-488.
18.IPCC. 2001. Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York, 54p.
19.IPCC. 2007. Climate change 2007: The physical science basis. Agenda. 6: 333.
20.Iverson, L.R., and McKenzie, D. 2013. Tree-species range shifts in a changing climate: detecting, modeling, assisting. Landscape ecology. 28: 879-89.
21.Keith, D.A., Akçakaya, H.R., Thuiller, W., Midgley, G.F., Pearson, R.G., Phillips, S.J., Regan, H.M., Araújo, M.B., and Rebelo, T.G. 2008. Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models. Biology Letters. 4: 560-3.
22.Khatamsaz, M. 2002. Flora of Iran (Boraginaceae), No. 39. Tehran: Research Institute of Forests and Rangelands Press, 508p.
23.King, D.A., Bachelet, D.M., and Symstad, A.J. 2013. Climate change and fire effects on a prairie–woodland ecotone: projecting species range shifts with a dynamic global vegetation model. Ecology and evolution. 3: 5076-97.
24.Lawler, J.J., White, D., Neilson, R.P., and Blaustein, A.R. 2006. Predicting climateÔÇÉinduced range shifts: model differences and model reliability. Global change biology. 12: 1568-84.
25.Malmir, M., Mohamadrezapour, O., Sharifazari, S., and Ghandehari, Gh. 2016. J. Water Soil Cons. 23: 317-326.
26.Merlani, M., Barbakadze, V., Gogilashvili, L., and Amiranashvili, L. 2017. Antioxidant Activity of caffeic Acid-Derived Polymer from Anchusa italica. Bulletin of the Georgian national academy of sciences, 11: 2. 123-127.
27.Mohammadi, S., Ebrahimi, E., Shahriari Moghadam, M., and Bosso, L. 2019. Modelling current and future potential distributions of two desert jerboas under climate change in Iran, Ecological Informatics, 52: 7-13.
28.Morin, X., and Thuiller, W. 2009. Comparing niche-and process-based models to reduce prediction uncertainty in species range shifts under climate change. Ecology. 90: 1301-13.
29.Peterson, A.T., Sánchez-Cordero, V., Soberón, J., Bartley, J., Buddemeier, R.W., and Navarro-Sigüenza, A.G. 2001. Effects of global climate change on geographic distributions of Mexican Cracidae. Ecological Modelling. 144: 21-30.
30.Ridgeway, G. 1999. The state of boosting. Computing Science and Statistics. 31: 172-81.
31.Sangoony, H., Vahabi, M., Tarkesh, M., Soltani, S. 2016. Range shift of Bromus tomentellus as a reaction to climate change in central zagros, Iran. Applied ecology and environmental research.
14: 85-100.
32.Schapire, R.E. 2003. The boosting approach to machine learning - an overview. MSRI Workshop on Nonlinear Estimation and Classification. Newyork: Springer. Pp: 1-23.
33.Sohrabian, E., Meftah Halaghi, M., Ghorbani, KH., Golian, S., and Zakerinia, M. 2015. Effects of climate change on runoff from rainfall (Case study: Galikesh Watershed in Golestan). J. Water Soil Cons. 22: 2. 111-125.
34.Swets, K. 1988. Measuring the accuracy of diagnostic systems. Science. 240: 1285-93.
35.Thuiller, W. 2003. BIOMOD-optimizing predictions of species distributions and projecting potential future shifts under global change. Global change biology.9: 1353-62.
36.Thuiller, W. 2007. Biodiversity: climate change and the ecologist. Nature.448: 550-2.
37.Thuiller, W., Lavorel, S., Araújo, M.B., Sykes, M.T., and Prentice, I.C. 2005. Climate change threats to plant diversity in Europe. Proceedings of the National Academy of Sciences of the united States of America. 102: 8245-50.
38.Williams, J.E., and Blois, J.L. 2018. Range shifts in response to past and future climate change: Can climate velocities and species’ dispersal capabilities explain variation in mammalian range shifts? J. Biogeograph. 45: 9. 2175-2189.
39.Wiens, J.A., Stralberg, D., Jongsomjit, D., Howell, C.A., and Snyder, M.A. 2009. Niches, models and climate change: assessing the assumptions and uncertainties. Proceedings of the National Academy of Sciences, 106: 729-736.
40.Xu, Z. 2014. Potential distribution of invasive alien species in the upper Ili river basin: determination and mechanism of bioclimatic variables under climate change. Environmental Earth Sciences. Pp: 1-8.
41.Zimbres, B., de Aquino, P.D., Machado, R., Silveira, L., Jácomo, A., Sollmann, R., Tôrres, N., Furtado, M., and Marinho-Filho, J. 2012. Range shifts under climate change and the role of protected areas for armadillos and anteaters. Biological Conservation. 152: 53-61.