Predicting the effect of climate change on the geographic distribution of the endemic Fritillaria aurea in Türkiye

Öz

Fritillaria aurea is a rare, high altitude, endemic, and bulbous plant species in Türkiye. Although it is classified as least concern according to IUCN criteria, the species has a narrow distribution. This study utilized ensemble modeling to forecast potential future changes in suitable habitats for F. aurea by two Shared Socio-Economic Pathways (SSPs: SSP 1-2.6 and 5-8.5). These pathways were constructed using two General Circulation Models (GCMs) and covered the years 2035, 2055, and 2085. The results showed that the minimum temperature of the coldest month (bio6), mean temperature of the wettest quarter (bio8), and precipitation of the warmest quarter (bio18) have the largest influence on the potential species distribution. The ensemble model predicted that the highly suitable habitats of F. aurea would contract under all future SSP scenarios and it would lose almost all of its potential highly suitable distribution areas by the end of the century. The remained population of F. aurea could possibly harbour in only minor areas of the North Anatolian Mountains in the north and Taurus Mountains in the south. The results of the study could contribute to establishing conservation strategies and natural resource management policies for F. aurea against the potential impacts of climate change. The highly suitable habitats under pessimistic scenarios at the end of this century projected by the present study can be determined as protected areas for the species.

Anahtar Kelimeler

• CMIP6
• global warming
• habitat prediction
• ensemble model
• plant species distribution

İklim değişikliğinin Türkiye’deki endemik Fritillaria aurea'nın coğrafi dağılımına etkisinin tahmin edilmesi

Öz

Fritillaria aurea, Türkiye'de nadir bulunan, yüksek rakımlı, endemik ve soğanlı bir bitki türüdür. IUCN kriterlerine göre az endişe kategorisinde sınıflandırılmasına rağmen, dar yayılışlı bir türdür. Bu çalışma, F. aurea için uygun habitatlarda gelecekteki potansiyel değişiklikleri iki Paylaşılan Sosyo-Ekonomik Yol (SSP'ler: SSP 1-2.6 ve 5-8.5) aracılığıyla tahmin etmek için topluluk modellemeyi kullanmıştır. Bu yollar 2035, 2055 ve 2085 yıllarını kapsayan iki Genel Dolaşım Modeli (GCM) kullanılarak oluşturulmuştur. Sonuçlar, en soğuk ayın minimum sıcaklığının (bio6), en yağışlı çeyreğin ortalama sıcaklığının (bio8) ve en sıcak çeyreğin yağışının (bio18) potansiyel tür dağılımı üzerinde en büyük etkiye sahip olduğunu gösterdi. Topluluk modeli, F. aurea'nın son derece uygun habitatlarının gelecekteki tüm SSP senaryoları altında daralacağını ve yüzyılın sonuna kadar potansiyel olarak son derece uygun dağıtım alanlarının neredeyse tamamını kaybedeceğini öngördü. F. aurea’nın geriye kalan popülasyonunun kuzeyde Kuzey Anadolu Dağları’nın ve güneyde Toros Dağları'nın yalnızca çok küçük bir kısmında barınması muhtemeldir. Çalışmanın sonuçları, F. aurea'nın iklim değişikliğinin olası etkilerine karşı koruma stratejileri ve doğal kaynak yönetimi politikalarının oluşturulmasına katkı sağlayabilir. Bu çalışmanın öngördüğü, bu yüzyılın sonundaki kötümser senaryolar altında son derece uygun habitatlar, tür için korunan alanlar olarak belirlenebilir.

Anahtar Kelimeler

• CMIP6
• küresel ısınma
• habitat tahmini
• topluluk modeli
• bitki türlerinin dağılımı

Kaynakça

1. Allen MR, Dube OP, Solecki W, Aragón-Durand F, Cramer, W et al. (2018). Framing and Context: Global Warming of 1.5°C. In: Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds.). 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. Cambridge: Cambridge University Press, pp. 49-91.
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. https://doi.org/10.1111/j.1365-2664.2006.01214.x.
3. Beery S, Cole E, Parker J, Perona P, Winner K (2021). Species distribution modeling for machine learning practitioners: A review. In: COMPASS '21. Proceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies Australia, pp. 329-348. https://doi.org/10.1145/3460112.3471966.
4. Billings WD, Mooney HA (1968). The ecology of arctic and alpine plants. Biological Reviews 43: 481-529. https://doi.org/1010.1111/j.1469-185X.1968.tb00968.x.
5. Chen IC, Hill JK, Ohlemüller R, Roy DB, Thomas CD (2011). Rapid range shifts of species associated with high levels of climate warming. Science 333: 1024-1026. https://doi.org/10.1126/science.1206432.
6. Da Silva JMC, Rapini A, Barbosa LCF, Torres RR (2019). Extinction risk of narrowly distributed species of seed plants in Brazil due to habitat loss and climate change. PeerJ 7: e7333. https://doi.org/10.7717/peerj.7333.
7. Deniz A, Toros H, Incecik S (2011). Spatial variations of climate indices in Turkey. International Journal of Climatology 31: 394-403. https://doi.org/10.1002/joc.2081.
8. Diaz HF, Eischeid JK (2007). Disappearing “alpine tundra” Köppen climatic type in the western United States. Geophysical Research Letters 34: L18707. https://doi.org/10.1029/2007GL031253.

9. Dubos N, Montfor F, Grinand C, Nourtier M, Deso G, Probst JM, Razafimanahaka HJ, Andriantsimanarilafy RR, Rakotondrasoa, EF, Razafindraibe P, Jenkins R, Crottinih A (2022). Are narrow-ranging species doomed to extinction? Projected dramatic decline in future climate suitability of two highly threatened species. Perspectives in Ecology and Conservation 20: 18-28. https://doi.org/10.1016/j.pecon.2021.10.002.
10. Elith J, Leathwick JR (2009). Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40: 677-697. https://doi.org/10.1146/annurev.ecolsys.110308.120159.
11. Guisans A, Theurillat JP (2000). Assessing alpine plant vulnerability to climate change: a modeling perspective. Integrated assessment 1: 307-320. https://doi.org/10.1023/A:1018912114948.
12. Gutjahr O, Putrasahan D, Lohmann K, Jungclaus JH, Von Storch JS, Brüggemann N, Haak H, Stössel A (2019). Max Planck Institute earth system model (MPI-ESM1. 2) for the high-resolution model intercomparison project (HighResMIP). Geoscientific Model Development 12: 3241-328. https://doi.org/10.5194/gmd-12-3241-2019.
13. Hamid M, Khuroo AA, Malik AH, Ahmad R, Singh CP, Dolezal J, Haq SM (2020). Early evidence of shifts in alpine summit vegetation: a case study from Kashmir Himalaya. Frontiers in Plant Science 11: 421. https://doi.org/10.3389/fpls.2020.00421.
14. Inouye DW (2020). Effects of climate change on alpine plants and their pollinators. Annals of the New York Academy of Sciences 1469: 26-37. https://doi.org/10.1111/nyas.14104.
15. IPCC (2021). Summary for Policymakers. In: Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis MI, Huang M, Leitzell K, Lonnoy E, Matthews JBR, Maycock TK, Waterfield T, Yelekçi O, Yu R, Zhou B (eds), Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, pp. 3-32. https://doi.org/10.1017/9781009157896.001.
16. Işık K (2011). Rare and endemic species: why are they prone to extinction?. Turkish Journal of Botany 35: 411-417. https://doi.org/10.3906/bot-1012-90.
17. IUCN (2022). The IUCN (International Union for Conservation of Nature) Red List of Threatened Species. Ver. 2020-2. Red List Guidance Documents - IUCN standards and petitions committee. Guidelines for using the IUCN Red List Categories and Criteria. Version 15.1 Website: http://www.iucnredlist.org [accessed 23 January 2024].
18. Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Zimmermann NE, Linder HP, Kessler M (2021). Climatologies at high resolution for the earth’s land surface areas. EnviDat. https://doi.org/10.16904/envidat.228.v2.1.
19. Kenar N, Kikvidze Z (2019). Climatic drivers of woody species distribution in the Central Anatolian forest-steppe. Journal of Arid Environments 169: 34-41. https://doi.org/10.1016/j.jaridenv.2019.104012.
20. Kobiv Y (2017). Response of rare alpine plant species to climate change in the Ukrainian Carpathians. Folia Geobotanica 52: 217-226. https://doi.org/10.1007/s12224-016-9270-z.
21. Lisovski S, Ramenofsky M, Wingfield JC (2017). Defining the degree of seasonality and its significance for future research. Integrative and Comparative Biology 57: 934-942. https://doi.org/10.1093/icb/icx040.
22. Liu X, Chen B (2000). Climatic warming in the Tibetan Plateau during recent decades. International Journal of Climatology: A Journal of the Royal Meteorological Society 20: 1729-1742. https://doi.org/10.1002/1097-0088(20001130)20:14%3C1729::AID-JOC556%3E3.0.CO;2-Y.
23. Mantyka‐Pringle CS, Martin TG, Rhodes JR (2012). Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta‐analysis. Global Change Biology 18: 1239-1252. https://doi.org/10.1111/j.1365-2486.2011.02593.x.
24. Meinshausen M, Nicholls ZR, Lewis J, Gidden MJ, Vogel E, Freund M, Beyerle U, Gessner C, Nauels A, Bauer N, Canadell JG, Daniel JS, Andrew J, Krummel PB, Luderer G, Meinshausen N, Montzka SA, Rayner PJ, Reimann S (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geoscientific Model Development 13: 3571-3605. https://doi.org/10.5194/gmd-13-3571-2020.
25. Naimi B, Hamm NA, Groen TA, Skidmore AK, Toxopeus AG (2014). Where is positional uncertainty a problem for species distribution modelling? Ecography 37: 191-203. https://doi.org/10.1111/j.1600-0587.2013.00205.x.
26. Pearson RG (2007). Species’ distribution modeling for conservation educators and practitioners. Synthesis. American Museum of Natural History Lessons in Conservation 50: 54-89.
27. QGIS Development Team (2021). QGIS Geographic Information System, QGIS Association. Website: http://www.qgis.org [accessed 15 March 2022].
28. R Core Team (2013). R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing. Website: http://www.R-project.org/ [accessed 05 February 2022].
29. Rix EM (1984). Fritillaria L. In: Davis, P. H. (ed.), Flora of Turkey and the east Aegean Islands Vol. 8: 284-302. Edinburgh: Edinburgh University Press.
30. Singh CP (2008). Alpine ecosystems in relation to climate change. ISG Newsletter 14: 54-57.
31. Tekşen M (2018). Fritillaria L. In: Güner A, Kandemir A, Menemen Y, Yıldırım H, Aslan S, Ekşi G, Güner I, Çimen AÖ (eds.), Resimli Türkiye Florası Vol. 2. İstanbul: ANG Vakfı Nezahat Gökyiğit Botanik Bahçesi Yayınları, pp. 800-876.
32. Tekşen M, Aytaç Z (2011). The revision of the genus Fritillaria L. (Liliaceae) in the Mediterranean region (Turkey). Turkish Journal of Botany 35: 447-478. https://doi.org/10.3906/bot-0812-9.
33. Tekşen M, Çimen AÖ, Yıldırım H (2024). Fritillaria nevzatcaglari (Liliaceae), a new species from southern Anatolia, Turkey. Annales Botanici Fennici 61: 41-46. https://doi.org/10.5735/085.061.0107
34. Testolin R, Attorre F, Jiménez‐Alfaro B (2020). Global distribution and bioclimatic characterization of alpine biomes. Ecography 43: 779-788. https://doi.org/10.1111/ecog.05012
35. Thiers B (2024) [continuously updated]: Index Herbariorum: A global directory of public herbaria and associated staff. New York Botanical Garden’s Virtual Herbarium. Website: http://sweetgum.nybg.org/ih/ [accessed 23 January 2024].
36. Thuiller W, Lafourcade B, Engler R, Araújo MB (2009). BIOMOD–a platform for ensemble forecasting of species distributions. Ecography 32: 369-373. https://doi.org/10.1111/j.1600-0587.2008.05742.x.
37. Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC (2005). Climate change threats to plant diversity in Europe. Proceedings of the National Academy of Sciences 102: 8245-8250. https://doi.org/10.1073/pnas.0409902102.
38. Woodward FI, Williams BG (1987). Climate and plant distribution at global and local scales. Vegetatio 69: 189-197.
39. Yukimoto S, Kawai H, Koshiro T, Oshima N, Yoshida K, Urakawa S, Tsujino H, Deushi M, Tanaka T, Hosaka M, Yabu S, Yoshimura H, Shindo E, Mizuta R, Obata A, Adachi Y, Ishii M (2019). The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan 97: 931-965. https://doi.org/10.2151/jmsj.2019-051.