Isparta ili’nde doğal yayılış gösteren Orchis anatolica (Boiss.,)’nın potansiyel dağılım haritalaması

Abstract

Giriş ve Hedefler Orchidaceae familyasına ait Orchis anatolica’nın orkide türünün potansiyel dağılım haritalaması incelenmiştir. Yöntemler 2019-2022 yılları Mart-Haziran ayları arasında gerçekleştirilen arazi çalışması sonucunda sahada bu türe ait 30 örnek alanda var verisi elde edilmiştir. Elde edilen veriler doğrultusunda Isparta İli içinde doğal yayılış yapan Orchis anatolica’nın Maksimum Entropi Yöntemi ile potansiyel dağılım haritalamasını yapılmıştır. Potansiyel dağılım haritalaması belirlemek için ise Bio iklim değişkenleri, coğrafi bilgi sistemleri kullanılarak üretilen veya sayısallaştırılan altlık haritalar olmak üzere toplamda birbiriyle korelasyon göstermeyen 22 çevresel değişken ile hedef türe ait var verileri arasındaki ilişkiler MaxEnt yazılımı ile modellenmiş ve haritalanmıştır. Bulgular Orchis anatolica için elde edilen potansiyel dağılım haritalamasına göre (eğitim veri seti AUC: 0,974, test veri seti AUC: 0,953) türünün dağılımını etkileyen çevresel değişkenlerin bonitet, bio10, yaş sınıf, bio12, engebelilik sınıfları olduğu belirlenmiştir. Sonuçlar Orchis anatolica’nın ekolojik özellikleri, coğrafi dağılımları ve habitat özellikleri arasındaki ilişkileri araştırmış ve Isparta bölgesi için türlerin potansiyel dağılım haritasını oluşturmuştur. Isparta ilindeki Orchis çeşitliliğinin ve genetik kaynaklarının korunması ve sürdürülebilir kullanımı için değerli bir temel sağlamaktadır.

Keywords

• Orchis
• salep
• MaxEnt
• Isparta ili

Supporting Institution

Isparta Uygulamalı Bilimler Üniversitesi, Bilimsel Araştırma Projeleri Yönetim Birimi Başkanlığı

Thanks

2020-D1-0059 No`lu Proje ile tezi maddi olarak destekleyen Isparta Uygulamalı Bilimler Üniversitesi Bilimsel Araştırma Projeleri Yönetim Birimi Başkanlığı’na teşekkür ederiz.

Potential distribution mapping of Orchis anatolica (Boiss.) naturally distributed in Isparta province

Abstract

Background and aims This study focused on modeling the potential distribution of Orchis anatolica, a species belonging to the Orchidaceae family. Methods As a result of field studies conducted between March and June from 2019 to 2022, presence data for this species were obtained from 30 sampling sites. Based on the collected data, the potential distribution of Orchis anatolica, which naturally occurs in the Isparta province, was modeled using the Maximum Entropy (MaxEnt) method. To determine the potential distribution, relationships between the presence data of the target species and 22 uncorrelated environmental variables including bioclimatic variables and base maps produced or digitized via geographic information systems were modeled and mapped using MaxEnt software. Results According to the potential distribution model generated for Orchis anatolica (training dataset AUC: 0.974, test dataset AUC: 0.953), the environmental variables most influencing the species' distribution were identified as site index (bonitet), bio10, age class, bio12, and terrain ruggedness classes. Conclusions This study investigated the relationships among the ecological characteristics, geographic distribution, and habitat features of Orchis anatolica, and produced a potential distribution map for the species in the Isparta region. It provides a valuable foundation for the conservation and sustainable use of Orchis diversity and genetic resources in Isparta province.

Keywords

• Orchis
• salep
• MaxEnt
• Isparta province

Thanks

We thank the Isparta University of Applied Sciences Scientific Research Projects Management Unit for providing financial support for this thesis under project number 2020-D1-0059.

References

1. Aertsen, W., Kint, V., Van Orshoven, J., Özkan, K., and Muys, B. 2010. Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling, 221(8), 1119-1130.
2. Akten, M. 2008. Isparta Ovasının Optimal Alan Kullanım Planlaması Üzerine Bir Araştırma (Yayımlanmamış Yüksek Lisans Tezi). Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Orman Mühendisliği Anabilim Dalı, Isparta.
3. Altundag, E., Sevgi, E., Kara, O., Sevgi, O., Tecimen, H. B., Bolat, I. 2012. Studies on the chorology, ecology, morphology and conservation strategies of Orchis anatolica Boiss (Orchidaceae). Journal of Environmental Biology, 33(2), 325.
4. Austrheim, G., Gunilla, E., Olsson, A., Grontvedt, E. 1999. Land–use impact on plant communities in semi-natural sub-alpine grasslands of Budalen, central Norway. Biological Conservation, 87, 369-379.
5. Baldwin, R. A. 2009. Use of maximum entropy modeling in wildlife research. Entropy, 11(4), 854-866.
6. Boubli, J. P., De Lima, M. G. 2009. Modeling the geographical distribution and fundamental niches of Cacajao spp. and Chiropotes israelita in Northwestern Amazonia via a maximum entropy algorithm. International Journal of Primatology, 30, 217-228.
7. Brown, S. R., Ahl, R. S. 2011. The Region 1 Vegetation Mapping Program (VMap) Methodology. USDA Forest Service, Northern Region, CMIA Numbered Report 11-02.
8. Çolak, A. H., Yılmaz, G. 2019. Modeling the distribution of geophyte plants in Turkey with MaxEnt and evaluating the impact of climate change.

9. Çolak, A. H., Yılmaz, G., Akyıldırım Beğen, M. 2020. Distribution and conservation status of some Turkish orchids. Turkish Journal of Botany, 44(3), 245–260.
10. Çolak, A. H., Yılmaz, G., Akyıldırım Beğen, M. 2020. Distribution modeling of some orchid species in Turkey using MaxEnt. Anatolian Journal of Forest Research, 6(2), 123-135.
11. Dayan, E., Bilgin, A., Hancer, M. 1999. Die Karsterscheinungen an den östlichen Hangen des Davras Dağı (Westlicher Taurus): Karren Dolines, Uvalas. Z. Geomorph., 43(3), 321-340.
12. Delforge, P. 2006. Orchids of Europe, North Africa and the Middle East (3rd ed.). A&C Black.
13. DeMatteo, K. E., Loiselle, B. A. 2008. New data on the status and distribution of the bush dog (Speothos venaticus): Evaluating its quality of protection and directing research efforts. Biological Conservation, 141(10), 2494-2505.
14. Ece Tamer, C., Karaman, B., Utku Copur, O. 2006. A traditional Turkish beverage: salep. Food Reviews International, 22(1), 43-50.
15. Elith, J., Graham, C. H., Anderson, R. P., Dudik, M., Ferrier, S., Guisan, A., Huettmann, F., Leathwick, J. R., Peterson, A. T. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129–151.
16. Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., Yates, C. J. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57.
17. Erdem, H. E. 2004. The methodology used in this study is based on the research conducted in the Master's Thesis, “Determining the Economic Value of Biodiversity: The Case of Wild Orchids” Ege University Institute of Science, İzmir.
18. Evans, A., Janssens, S., Jacquemyn, H. 2020. Impact of climate change on the distribution of four closely related Orchis (Orchidaceae) species. Diversity, 12(8), 312.
19. Evans, J. S., Oakleaf, J., Cushman, S. A., Theobald, D. 2014. An ArcGIS toolbox for surface gradient and geomorphometric modeling, version 2.0–0. Laramie, WY. –http://evansmurphywixcom/evansspatial. (accessed: 11 January 2022).
20. Gallant, J. C. 2000. Primary topographic attributes. In Terrain Analysis: Principles and Applications, pp. 51–86.
21. Georgiadis, N., Ritzoulis, C., Charchari, E., Koukiotis, C., Tsioptsias, C., Vasiliadou, C. 2012. Isolation, characterization and emulsion stabilizing properties of polysaccharides from orchid roots (salep). Food Hydrocolloids, 28(1), 68-74.
22. Guisan, A., Weiss, S. B., Weiss, A. D. 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecology, 143(1), 107-122.
23. Güner, A., Özhatay, N., Ekim, T., Baser, K. H. C. 2000. Flora of Turkey and the East Aegean Islands (Vol. 11). Edinburgh: University Press.
24. Hernandez, P. A., Graham, C. H., Master, L. L., Albert, D. L. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29(5), 773-785.
25. Hoenes, B. D., Bender, L. C. 2010. Relative habitat-and browse-use of native desert mule deer and exotic oryx in the greater San Andres Mountains, New Mexico. Human-Wildlife Interactions, 4(1), 12-24.
26. Hossain, M. M. 2011. Therapeutic orchids: traditional uses and recent advances—an overview. Fitoterapia, 82(2), 102-140.
27. Hussein, A., Workeneh, S. 2021. Modeling the Impacts of Climate Changes on the Distribution of Aloe vera Species in Ethiopia. Research Square, 19.
28. Jenness, J. 2006. Topographic Position Index (tpi_jen. avx) extension for ArcView 3.x, v. 1.3 a. Jenness Enterprises.
29. Karakaya, T., Yücel, E. 2021. Potential distribution modelling and mapping of dog rose (Rosa canina L.) in the Nur Mountains of Gaziantep district, Turkey. Applied Ecology and Environmental Research, 19(4), 2741–2760.
30. Kasparek, M., Grimm, U. 1999. European trade in Turkish salep with special reference to Germany. Economic Botany, 396-406.
31. Kayıkçı, S., Oğur, E. 2012. An Investigation on some Orchid Species Distributed in the Province of Hatay. Journal of Anatolian Aegean Agricultural Research Institute, 22(2), 1-12.
32. Kreutz, C. A. J. 1998. Orchids of Turkey: Including the Greek Islands. Kreutz Publishers.
33. Mert, A., Şentürk, Ö., Güney, C.O., Akdemir, D., Özkan, K. 2013. Mapping of Some Distal Variables Available for Mapping Habitat Suitabilities of The Species: A Case Study of Buldan District. The 3rd International Geography Symposium, June 0-13, Antalya, 210.
34. Meteoroloji Genel Müdürlüğü. 2021. Türkiye 2020 Yılı İklim Değerlendirmesi. https://www.mgm.gov.tr/FILES/iklim/yillikiklim/2020-iklim-raporu.pdf.
35. Moisen, G. G., Frescino, T. S. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157(2-3), 209-225.
36. Nigâr, M. 2021. Maxent modeling for predicting the potential habitat and future distribution of a rare species Ophrys apifera Huds. in the Greater Caucasus (Azerbaijan). Труды Мордовского государственного природного заповедника им. ПГ Смидовича, 27, 3-14.
37. Obiakara, M., Etaware, P., Chukwuka, K. 2020. Maximum Entropy Niche Modelling to Estimate the Potential Distribution of Phytophthora megakarya (Brasier & MJ Griffin) in Tropical Regions. European Journal of Ecology, 6(2), 23–40.
38. Özdemir, S., Gülsoy, S., Mert, A. 2020. Predicting the Effect of Climate Change on the Potential Distribution of Crimean Juniper. Kastamonu University Journal of Forestry Faculty, 20(2), 133–142.
39. Özdemir, S., Oğuzoğlu, Ş., Ulusan, M. D. 2014. Creating Base Maps of Environmental Variables That Can Be Used in Habitat Suitability Modeling of Non-Wood Forest Products: Ovacık Mountain Example. National Mediterranean Forest and Environment Symposium, October 22-24.
40. Pal Axel, O., Linda-Maria, M., Hans Henrik, B. 2009. Acidification of sandy grasslands-consequences for plant diversity. Applied Vegetation Science, 12, 350-361.
41. Parker, B. D. 1988. PRAM simulations on bounded-degree networks. The Pennsylvania State University.
42. Pearson, R. G., Raxworthy, C. J., Nakamura, M., Townsend Peterson, A. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34(1), 102-117.
43. Pedersen, H. Æ., Faurholdt, N. 2007. Ophrys: The bee orchids of Europe. Kew Publishing.
44. Philips, Z., Ginnelly, L., Sculpher, M., Claxton, K., Golder, S., Riemsma, R., Glanville, J. 2004. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technology Assessment (Winchester, England), 8(36), 1-158.
45. Riley, S. J., DeGloria, S. D., Elliot, R. 1999. Index that quantifies topographic heterogeneity. Intermountain Journal of Sciences, 5(1–4), 23–27.
46. Rödder, D., Weinsheimer, F. 2009. Will future anthropogenic climate change increase the potential distribution of the alien invasive Cuban treefrog (Anura: Hylidae). Journal of Natural History, 43(19-20), 1207-1217.
47. Sargın, S. 2004. “Isparta Yöresinde Fiziki Çevre Faktörlerinin Yerleşme Birimleri Üzerindeki Etkisi”. Doğu Coğrafya Dergisi, 9, 371-388.
48. Sezik, E. 2002. Destruction and conservation of Turkish orchids. In Biomolecular Aspects of Biodiversity and Utilization, pp. 391-400.
49. Sezik, E. 1967. Research on Turkey's salep family, commercial salep varieties and especially Muğla salep. Doctoral Thesis, Istanbul University, Institute of Science and Technology, Istanbul.
50. Sezik, E., Baykal, T. 1991. The origin of Maraş saleb. Tübitak Nature-Tr. J. of Pharmacy, 1, 10-16.
51. Sezik, E., Özer, B. 1983. The Origin of Kastamonu Saleb and Orchids of Kastamonu Surroundings. TÜBİTAK, Ankara, pp. 35–245.
52. Suárez-Seoane, S., de la Morena, E. L. G., Prieto, M. B. M., Osborne, P. E., de Juana, E. 2008. Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution. Ecological Modelling, 219(1-2), 17-29.
53. Tekinşen, K. K., Güner, A. 2010. Chemical composition and physicochemical properties of tubera salep produced from some Orchidaceae species. Food Chemistry, 121(2), 468-471.
54. Thorn, J. S., Nijman, V., Smith, D., Nekaris, K. A. I. 2009. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus). Diversity and Distributions, 15(2), 289-298.
55. Tığlı, E. H., Fakir, H. 2017. Distribution areas, morphological and phenological characteristics of some natural orchid species in Bucak (Burdur) region. Turkish Journal of Forestry, 18(4), 289-294.
56. Wei, B., Wang, R., Hou, K., Wang, X., Wu, W. 2018. Predicting the current and future cultivation regions of Carthamus tinctorius L. using MaxEnt model under climate change in China. Global Ecology and Conservation, 16, e00477.
57. Wisz, M. S., Hijmans, R. J., Li, J., Peterson, A. T., Guisan, A. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5), 763–773.
58. Yost, A. C., Petersen, S. L., Gregg, M., Miller, R. 2008. Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy and a long-term dataset from Southern Oregon. Ecological Informatics, 3(6), 375-386.
59. Zelený, D., Chytrý, M. 2007. Environmental control of the vegetation pattern in deep river valleys of the Bohemian Massif. Preslia, 79, 205-222.