Prediction of potential geographic distribution of Capparis spinosa

Year 2024, Volume: 17 Issue: 3, 206 - 215

Alican Çıvğa , Serkan Özdemir , Serkan Gülsoy

https://doi.org/10.46309/biodicon.2024.1384960

Abstract

Capparis spinosa, Türkiye'nin batı ve güney kıyı bölgelerinde yayılış gösteren, ekonomik (örneğin gıda, hayvancılık, ilaç) ve ekolojik (örneğin erozyon kontrolü, orman yangınlarıyla mücadele) öneme sahip tıbbi bir bitkidir. Çevre koşullarının etkisiyle C. spinosa’nın potansiyel dağılım alanlarını simüle etmek için MaxEnt modeli kullanılmıştır. Sonuçlar, C. spinosa için potansiyel uygun alanın 6109 hektar olduğunu ve Babadağ Bölgesi'nde çoğunlukla 1000 metrenin altında dağıldığını göstermiştir. Modele katkı sağlayan değişkenlerin sırasıyla ana kaya, yükselti, topoğrafik pozisyon indeksi ve gölgelenme indeksi olduğu belirlendi. Elde edilen model, AUC değerlerine (Eğitim AUC: 0,909 ve test AUC: 0,906) göre mükemmel performans sergiledi. Çalışmada ortaya çıkan sonuçların türe yönelik ileride yapılacak araştırmalara ışık tutacağı düşünülmektedir.

Keywords

Kapari, habitat uygunluğu, MaxEnt, tür Dağılım modellemesi

Prediction of potential geographic distribution of Capparis spinosa

Abstract

Capparis spinosa is a medicinal plant with economic (e.g., food, animal breeding, medicine) and ecological (e.g., erosion control, fighting wildfires) importance that is distributed in the western and southern coastal regions of Turkey. The MaxEnt model was used to simulate potential distribution areas of C. spinosa with the effect of environmental conditions. The results showed that the potential suitable area of C. spinosa is 6109 hectares, mainly distributed below 1000 meters in Babadağ Region. It was determined that the variables contributing to the model were bedrock, elevation, topographic position index and hillshade index, respectively. The acquired model presented excellent performance according to its AUC values (Training AUC: 0.909 and test AUC: 0.906). It is thought that the results revealed in the study will provide an insight for future investigations to be carried out for the species.

Keywords

Caper, habitat suitability, MaxEnt, species distribution modeling

References

• [1] CFE. (2021). Non-Wood Forest Products Assesment Report of Turkey. The Chamber of Forest Engineers of Turkey, Ankara.
• [2] Giritlioğlu, N., Yildiz, E., & Gürbüz, O. (2020). Effect of Caper Buds (Capparis spp.) on Phenolics, Antioxidant Capacity, and Bioaccessibility on Kombucha Tea Production. Academic Food Journal, 18(4): 390-401. https://doi.org/10.24323/akademik-gida.850909
• [3] Yeğenoğlu, S., & Ayşe, U.Z. (2011). Caper: Its importance from historical and medical perspective. Lokman Hekim Journal, 35-36.
• [4] Kolanci, B.Y. (2020). Antik Dönemde Kapari Bitkisi (Capparis spp.). A life dedicated to Anatolian prehistory: Festschrift for jak yakar, Editors: Barış Gür, Semra Dalkılıç, 263-272, Ankara.
• [5] Şat, İ.G., & Çil, Y.M. (2006). Yeni bir Tarımsal Ürün: Kapari (Capparis spp.). Türkiye, 9, 24-26. [6] Kart, A. (2019). Bioactive, pharmacological effects of caper plant and its use in neurodegenerative diseases. Veterinary Journal of Mehmet Akif Ersoy University, 4(2), 101-107. https://doi.org/10.24880/maeuvfd.615592
• [7] Naudiyal, N., & Schmerbeck, J. (2021). Potential distribution of oak forests in the central Himalayas and implications for future ecosystem services supply to rural communities. Ecosystem Services, 50, 101310. https://doi.org/10.1016/j.ecoser.2021.101310
• [8] Lissovsky, A.A., & Dudov, S.V. (2021). Species-Distribution Modeling: Advantages and limitations of its application. 2. MaxEnt. Biology Bulletin Reviews, 11(3), 265-275. https://doi.org/10.1134/S2079086421030087
• [9] Zhang, P., Grenouillet, G., Dong, X., Zheng, Y., Lek, S., & Chang, J. (2021). Capturing response differences of species distribution to climate and human pressures by incorporating local adaptation: Implications for the conservation of a critically endangered species. Journal of Environmental Management, 284, 111998. https://doi.org/10.1016/j.jenvman.2021.111998
• [10] Oğuzoğlu, Ş., & Özkan, K. (2008). Productivity distribution modelling of Anatolian Black Pine Pinus nigra subsp. pallasiana var. pallasiana in the Türkmen Mountain, Eskişehir. Biyolojik Çeşitlilik ve Koruma, 8(2), 134-140.
• [11] Hosseini, N., Mehrabian, A., & Mostafavi, H. (2022). Modeling climate change effects on spatial distribution of wild Aegilops L. (Poaceae) toward food security management and biodiversity conservation in Iran. Integrated Environmental Assessment and Management, 18(3), 697-708. https://doi.org/10.1002/ieam.4531
• [12] Özdemir, S., Gülsoy, S., & Mert, O.P. (2020). Redicting the effect of climate change on the potential distribution of Crimean Juniper. Kastamonu University Journal of Forestry Faculty, 20(2), 133-142.
• [13] Zhang H, Song J, Zhao H, Li M, & Han W. (2021). Predicting the distribution of the invasive species Leptocybe invasa: Combining MaxEnt and Geodetector Models. Insects, 12(2), 92. https://doi.org/10.3390/insects12020092
• [14] Güngör, H., Solak, M.H., Alli, H., Işiloğlu, M., & Kalmiş, E. (2016). Contributions to the macrofungal diversity of Muğla province (Turkey). Mycotaxon, Link, 131(1): 255-256.
• [15] Mert, A., & Kıraç, A. (2017). Habitat Suitability Mapping of Anatololacerta danfordi (Günter, 1876) in Isparta-Sütçüler District. Bilge International Journal of Science and Technology Research, 1(1), 16-22.
• [16] Mert, A., Özkan, K., Şentürk, Ö., & Negiz, M.G. (2016). Changing the potential distribution of Turkey Oak (Quercus cerris L.) under climate change in Turkey. Polish Journal of Environmental Studies, 25(4), 1633-1638. https://doi.org/10.15244/pjoes/62230
• [17] Evcin, Ö., & Kalleci, B. (2020). Glis glis (Nehring, 1903) (Rodentia: Gliridae) Türünün Kastamonu İlindeki Yayılış Gösterdiği Alanların ve Potansiyel Dağılımının Tespit Edilmesi. Bilge International Journal of Science and Technology Research, 4(2), 73-77. https://doi.org/10.30516/bilgesci.779010
• [18] Chavent, M., Kuentz-Simonet, V., Labenne, A., & Saracco, J. (2014). Multivariate analysis of mixed data: The R package PCAmixdata. arXiv preprint arXiv, 1411.4911.
• [19] Özdemir, S., & Çınar, T. (2023). Determining indicator plant species of Pinus brutia Ten. Site index classes using interspecific correlation analysis in Antalya (Turkey). CERNE, 29, e-103188.
• [20] Ashraf, U., Chaudhry, M.N., Ahmad, S.R., Ashraf, I., Arslan, M., Noor, H., & Jabbar, M. (2018). Impacts of climate change on Capparis spinosa L. based on ecological niche modeling. PeerJ, 6, e5792. https://doi.org/10.7717/peerj.5792
• [21] Fici, S. (2014). A taxonomic revision of the Capparis spinosa group (Capparaceae) from the Mediterranean to Central Asia. Phytotaxa, 174(1), 1-24. http://dx.doi.org/10.11646/phytotaxa.174.1.1
• [22] Rakhimova, T., Vaisova, G. B., Rakhimova, N. K., & Matkarimova, A. (2021). Phytocoenotic distribution of Capparis spinosa L. (Capparaceae) under different ecological conditions in Uzbekistan. Annals of the Romanian Society for Cell Biology, 25(6), 7882-7895.
• [23] Gassó, N., Thuiller, W., Pino, J., & Vilà, M. (2012). Potential distribution range of invasive plant species in Spain. NeoBiota, 12, 25. https://doi.org/10.3897/neobiota.12.2341
• [24] Swets, K.A. (1988). Measuring the accuracy of diagnostic systems. Science, 240, 1285–1293.