Abstract
The tea plant (Camellia sinensis) that is cultivated in Türkiye is mainly grown in the eastern Black Sea region and is a significant crop for the Turkish economy. Climate change can have significant effects on the tea plant, including changes in its growth, physiology, and distribution. Here, we aimed to predict the current and future distribution patterns of the tea plant under four shared socioeconomic pathway (SSP) emission scenarios using ecological niche modeling in Türkiye. It was used 541 occurrence data for the tea plant in five provinces and nine bioclimatic variables were used in the modeling. The model produced an excellent simulation result with an AUC of 0.989. Our results showed that when compared with the current climate according to both four different scenarios and three different periods, it is predicted that the available suitable habitats will decrease and even excellent habitats will disappear, especially in the 2081–2100 time frame and SSP5-8.5 scenario. In the change analysis, it was determined that the tea plant will experience losses but there will be gains, and these gains show that it is predicted that the distribution range of tea will expand towards Rize and Artvin provinces. The findings obtained constitute a useful scientific foresight for the measures to be taken to increase the effectiveness of the conservation, management, and cultivation of the tea plant.
Similar content being viewed by others
Data availability
The datasets generated during and/or analyzed during the current study are available in this study.
References
Adiloğlu A, Adiloğlu S (2006) An investigation on nutritional status of tea (Camellia sinensis L.) grown in eastern black sea region of Turkey. Pak J Biol Sci 9(3):365–370
Atalay D, Erge HS (2017) Determination of some physical and chemical properties of white, green and black teas (Camellia sinensis). GIDA 42(5):494–504
Beringer T, Kulak M, Müller C, Schaphoff S, Jans Y (2019) First process-based simulations of climate change impacts on global tea production indicate large effects in the World’s major producer countries. Environ Res Lett 15:034023. https://doi.org/10.1088/1748-9326/ab649b
Brown JL, Bennett JR, French CM (2017) SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Peer J 5:e4095. https://doi.org/10.7717/peerj.4095
Cobos ME, Peterson AT, Barve N, Osorio-Olvera L (2019) kuenm: an R package for detailed development of ecological niche models using MaxEnt. PeerJ 7:e6281. https://doi.org/10.7717/peerj.6281
Das P, Chettri V, Ghosh S, Ghosh C (2023) Micromorphological studies of the leaf and stem of Camellia sinensis (L.) Kuntze with reference to their taxonomic significance. Microsc Res Tech 86:465–472
de Costa WAJM, Mohotti AJ, Wijeratne MA (2007) Ecophysiology of tea. Braz J Plant Physiol 19:299–332
Demir N, Bostan SZ (2021) Yaş çay (Camelia sinensis L.) Verimi ve Kalite Özelliklerinin Güneşlenme Durumu ve Sürgün Dönemlerine Göre Değişimi. Bahçe 50(2):103–110 ((in Turkish))
Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697
Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JMCM, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2):129–151
Eminoğlu A, Dizman YA, Güzel Ş, Beldüz AO (2018) Molecular and in silico cloning, identification, and preharvest period expression analysis of a putative cytochrome P450 monooxygenase gene from Camellia sinensis (L.) Kuntze (tea). Turk J Biol 42(1):1–11
Ercisli S, Orhan E, Ozdemir O, Sengul M, Gungor N (2008) Seasonal variation of total phenolic, antioxidant activity, plant nutritional elements, and fatty acids in tea leaves (Camellia sinensis var. sinensis clone Derepazari 7) grown in Turkey. Pharm Biol 46:683–687
Ercisli S, Demir F, Budak G, Karabulut A (2009) Determination of elemental variations in tea leaves (Camellia sinensis L.) in different harvest time by WDXRF spectrometry. Asian J Chem 21:1313–1317
Ercisli S (2012) The tea industry and improvements in Türkiye. In: Chen L, Apostolides Z, Chen ZM (ed) Global Tea Breeding, Advanced Topics in Science and Technology in China. Springer, Berlin, pp 309–321
Ertürk Ö, Çıl E, Ayvaz MÇ, Bagdatlı E (2023) Comparison of biological activities and bioactive components of seed, leaf, and blossom parts of Camellia sinensis (L.) Kuntze and commercial black tea. J Chem Soc Pak 45(2):161–172
Erturk Y, Ercisli S, Sengul M, Eser Z, Haznedar A, Turan M (2010) Seasonal variation of total phenolic, antioxidant activity and minerals in fresh tea shoots (Camellia sinensis var. sinensis). Pak J Pharm Sci 23:69–74
FAO (2016) Report of the working group on climate change of the FAO intergovernmental group on tea. Available: https://www.fao.org/3/a-i5743e.pdf. Accessed 30 Mar 2023
Fick SE, Hijmans RJ (2017) WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315. https://doi.org/10.1002/joc.5086
Ficetola GF, Thuiller W, Miaud C (2007) Prediction and validation of the potential global distribution of a problematic alien invasive species—the American bullfrog. Divers Distrib 13:476–485. https://doi.org/10.1111/j.1472-4642.2007.00377.x
Görür FK, Keser R, Akçay N, As N, Dizman S (2012) Annual effective dose and concentration levels of gross α and β in Turkish market tea. Iran J Radiat Res 10(2):67–72
Gunathilaka RD, Smart JC, Fleming CM (2017) The impact of changing climate on perennial crops: the case of tea production in Sri Lanka. Clim Chang 140:577–592. https://doi.org/10.1007/s10584-016-1882-z
Gunathilaka RPD, Smart JCR, Fleming CM, Hasan S (2018) The impact of climate change on labour demand in the plantation sector: the case of tea production in Sri Lanka. Aust J Agric Resour Econ 62:480–500. https://doi.org/10.1111/1467-8489.12262
IPCC (2022) Climate change 2022: impacts, adaptation, and vulnerability. In: Pörtner H-O, Roberts DC, Tignor M, Poloczanska ES, Mintenbeck K, Alegría A, Craig M, Langsdorf S, Löschke S, Möller V, Okem A, Rama B (eds) Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 3056
Jayasinghe SL, Kumar L (2021) Potential ımpact of the current and future climate on the yield, quality, and climate suitability for tea [Camellia sinensis (L.) O. Kuntze]: a systematic review. Agronomy 11:619. https://doi.org/10.3390/agronomy11040619
Jayasinghe SL, Kumar L (2019) Modeling the climate suitability of tea [Camellia sinensis (L.) O. Kuntze] in Sri Lanka in response to current and future climate change scenarios. Agric for Meteorol 272:102–117. https://doi.org/10.1016/j.agrformet.2019.03.025
Kafkas S, Ercişli S, Doğan Y, Ertürk Y, Haznedar A, Sekban R (2009) Polymorphism and genetic relationships among tea genotypes from Turkey revealed by amplified fragment length polymorphism markers. J Am Soc Hortic Sci 134(4):428–434
Leary N, Conde C, Kulkarni J (2009) Climate change and vulnerability. Earthscan, London
Liao J, Wu Z, Wang H, Xiao S, Mo P, Cui X (2023) Projected effects of climate change on species range of Pantala flavescens, a wandering glider dragonfly. Biology 12:226. https://doi.org/10.3390/biology12020226
Li Yp, Gao X, An Q, Sun Z, W Hb (2022) Ecological niche modeling based on ensemble algorithms to predicting current and future potential distribution of African swine fever virus in China. Sci Rep 12:15614. https://doi.org/10.1038/s41598-022-20008-x
Lou W, Sun S, Wu L, Sun K (2015) Effects of climate change on the economic output of the Longjing-43 tea tree, 1972–2013. Int J Biometeorol 59:593–603. https://doi.org/10.1007/s00484-014-0873-x
Merow C, Smith MJ, Edwards TC Jr, Guisan A, McMahon SM, Normand S, Thuiller W, Wüest RO, Zimmermann NE, Elith J, Elith J (2014) What do we gain from simplicity versus complexity in species distribution models? Ecography 37(12):1267–1281. https://doi.org/10.1111/ecog.00845
Muscarella R, Galante PJ, Soley-Guardia M, Boria RA, Kass JM, Uriarte M, Anderson RP (2014) ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for MaxEnt ecological niche models. Methods Ecol Evol 5:1198–1205. https://doi.org/10.1111/2041-210X.12261
Owens HL, Campbell LP, Dornak L, Saupe EE, Barve N, Soberon J et al (2013) Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecol Model 263:10–18. https://doi.org/10.1016/j.ecolmodel.2013.04.011
Özdemir F, Nadeem HŞ, Akdoğan A, Dinçer C, Topuz A (2018) Effect of altitude, shooting period, and tea grade on the catechins, caffeine, theaflavin, and thearubigin of Turkish black tea. Turk J Agric for 42:334–340. https://doi.org/10.3906/tar-1710-21
Özyazıcı G, Özyazıcı MA, Özdemir O, Sürücü A (2010) Some physical and chemical properties of tea grown soils in Rize And Artvin provinces. Anadolu J Agric Sci 25(2):94–99
Peterson AT (2006) Uses and requirements of ecological niche models and related distributional models. Bioinformatics 3:59–72
Peterson AT, Papes M, Soberon J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model 213(1):63–72. https://doi.org/10.1111/ecog.03049
Phillips SJ, Anderson RP, Dudík M, Schapire RE, Blair M (2017) Opening the black box: an open-source release of MaxEnt. Ecography 40:887–893
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Rivera R, Pinochet J, Brante A (2022) Ecological niche dynamics of three invasive marine species under the conservatism and shift niche hypotheses. Aquat Invasions 17(4):453–475
Saraç DU, Özkan ZC, Akbulut S (2013) Ethnobotanic features of Rize/Turkey province. Biodicon 6(3):57–66
Seyis F, Yurteri E, Ozcan A, Savsatli Y (2018) Organic tea production and tea breeding in Turkey: challenges and possibilities. Ekin J 4(1):60–69
Sillero N (2011) What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecol Model 222:1343–1346
Sillero N, Arenas-Castro S, Enriquez-Urzelai U, Vale CG, SousaGuedes D, Martínez-Freiría F, Real R, Barbosa AM (2021) Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling. Ecol Model 456:109671
Sitienei B, Juma S, Opere E (2017) On the use of regression models to predict tea crop yield responses to climate change: a case of nandi east, sub-county of Nandi County Kenya. Climate 5:54. https://doi.org/10.3390/cli5030054
Şavşatli Y, Özcan A, Civelekoglu O (2021) Variation of antioxidant activity and total phenolic content of tea (Camellia sinensis L. O. Kuntze) genotypes. KSU J Agric Nat 24(1):40–48
Taylor S (2003) Tea types. Production, and trade. In: Cabellero B (ed) Encyclopedia of Food Sciences and Nutrition, 2nd edn. Elsevier Science Ltd, pp 5737–5743
Tercan HS, Ayanoğlu F, Bahadırlı NP (2016) Determination of heavy metal contents and some basic aspects of widely used herbal teas in Turkey. Rev Chim 67:1019–1022
Thuiller W, Lavorel S, Araújo MB (2005) Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob Ecol Biogeogr 14:347–357. https://doi.org/10.1111/j.1466-822X.2005.00162.x
TSMS (2022) Turkish state meteorological service. Assessment date: 30/11/2022. https://www.mgm.gov.tr/FILES/resmi-istatistikler/parametreAnalizi/2021-ortalama-sicaklik.pdf at available
Yilmaz G, Kandemir N, Kinalioglu K (2004) Effects of different pruning intervals on fresh shoot yield and some quality properties of Tea (Camellia sinensis (L.) O. Kuntze) in Turkey. Pak J Biol Sci 7(7):1208–1212
Yogurtcu B, Aygun A (2021) Characterization of tea (Camellia sinensis l.) genotypes grown in Turkey by ISSR markers. Appl Ecol Environ Res 19:4103–4114
Wang Y, Xie B, Wa F, Xiao Q, Dai L (2007) Application of ROC curve analysis in evaluating the performance of alien species’ potential distribution models. Biodivers Sci 15:365–372
Warren DL, Seifert SN (2011) Ecological niche modeling in MaxEnt: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335–342
Wijeratne M, Anandacoomaraswamy A, Amarathunga M, Ratnasiri J, Basnayake B, Kalra N (2007) Assessment of impact of climate change on productivity of tea (Camellia sinensis L.) plantations in Sri Lanka. J Natl Sci Found Sri Lanka 35:119
Wiens JJ, Graham CH, Moen DS, Smith SA, Reeder TW (2006) Evolutionary and ecological causes of the latitudinal diversity gradient in hylid frogs: treefrog trees unearth the roots of high tropical diversity. Am Nat 168:579–596. https://doi.org/10.1086/507882
Worth JRP, Harrison PA, Williamson GJ, Jordan GJ (2015) Whole range and regional-based ecological niche models predict differ- ing exposure to 21st century climate change in the key cool temperate rainforest tree southern beech (Nothofagus cunninghamii). Austral Ecol 40:126–138. https://doi.org/10.1111/aec.12184
Wu T, Lu Y, Fang Y, Xin X, Li L, Li W, Jie W, Zhang J, Liu Y, Zhang L et al (2019) The Beijing climate center climate system model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geosci Model Dev 12:1573–1600. https://doi.org/10.5194/gmd-12-1573-2019
Yan X, Wang S, Duan Y, Han J, Huang D, Zhou J (2021) Current and future distribution of the deciduous shrub Hydrangea macrophylla in China estimated by MaxEnt. Ecol Evol 11:16099–16112. https://doi.org/10.1002/ece3.8288
Yazıcı K (2021) Tea agriculture in Turkey. In: Pakyürek M (ed) Current studies on fruit science, İksad publishing house, Ankara, Türkiye, pp 281–300
Zhao Y, Zhao M, Zhang L, Wang C, Xu Y (2021) Predicting possible distribution of tea (Camellia sinensis L.) under climate change scenarios using MaxEnt model in China. Agriculture 11:1122. https://doi.org/10.3390/agriculture11111122
Acknowledgements
We thank the general directorate of Çaykur for providing tea occurrence data.
Author information
Authors and Affiliations
Contributions
Serkan Gül: conceptualization (lead), formal analysis (lead), methodology (lead), visualization (lead), writing—original draft (lead), and writing—review and editing (lead). Şule Güzel İzmirli: conceptualization (supporting), data curation (lead), writing—original draft (supporting), and writing—review and editing (supporting).
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
İzmirli, Ş.G., Gül, S. Modeling of current and future distributions of Camellia sinensis in Türkiye under climate change. Theor Appl Climatol 154, 1323–1332 (2023). https://doi.org/10.1007/s00704-023-04627-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-023-04627-6