Journals / Bilge International Journal of Science and Technology Research / 2026 / Cilt: 10 Sayı: 2

The Potential Distribution of Thaumetopoea pityocampa in the Kaz Mountains and Surrounding Areas Under the Influence of Climate Change

İklim Değişiminin Etkisi Altında Kaz Dağları ve Çevresinde Thaumetopoea pityocampa’nın Potansiyel Dağılımı

Gülmen Karaöz Koç(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ)0009-0001-2058-7246
Akın Kıraç(CANAKKALE ONSEKIZ MART UNIVERSITY)0000-0001-5596-2256
Pages
213–227
Publication Date
2026-10-14
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicate that climate change could significantly alter the geographical distribution of the T. pityocampa species and influence the future dynamics of Mediterranean forest ecosystems.

📌 3 Key Takeaways

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Objective & Hypothesis:Climate change is one of the most significant environmental factors influencing the distribution dynamics of forest insect pests.
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Key Findings & Data:Species presence data and bioclimatic variables obtained from the WorldClim database were incorporated into the model.
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Conclusion & Impact:The results indicate that climate change could significantly alter the geographical distribution of the T. pityocampa species and influence the future dynamics of Mediterranean forest ecosystems.
🔬Design:Modeling & Simulation
📍Scope:2081 Verileri
⚙️Variables:İklim Değişikliği · Ekolojik Niş Modellemesi · Orman Zararlısı

Abstract

Climate change is one of the most significant environmental factors influencing the distribution dynamics of forest insect pests. In this study, the current and future potential distribution of Thaumetopoea pityocampa in the Kaz Mountains and surrounding areas was modeled using the Maximum Entropy (MaxEnt) method. Species presence data and bioclimatic variables obtained from the WorldClim database were incorporated into the model. Projections of future habitat suitability were carried out for the period 2081–2100 under the SSP126, SSP245, SSP370 and SSP585 climate scenarios. Model performance was evaluated using AUC (Area Under the Curve), omission rates, and AICc criteria. The MaxEnt model demonstrated high predictive success with training and validation AUC values exceeding 0.95. Response curve analyses revealed that temperature variables BIO5, BIO6 and BIO7 were the most influential environmental factors on species distribution. Distribution maps indicated that habitat suitability would gradually decrease and become fragmented under future climate scenarios. In particular, under high-emission scenarios, significant reductions in suitable habitat areas were observed, while it was determined that only certain local micro-refugia areas could maintain suitable conditions for the species. The most pronounced habitat loss was observed under the SSP585 scenario. The results indicate that climate change could significantly alter the geographical distribution of the T. pityocampa species and influence the future dynamics of Mediterranean forest ecosystems. Consequently, climate-based species distribution models are considered important tools for sustainable forest management, biodiversity conservation, and pest risk analysis.

Özet

İklim değişikliği, orman zararlısı böceklerin dağılım dinamiklerini etkileyen en önemli çevresel faktörlerden biridir. Bu çalışmada, Thaumetopoea pityocampa’nın Kaz Dağları ve çevresindeki mevcut ve gelecekteki potansiyel dağılımı, Maksimum Entropi (MaxEnt) yöntemi kullanılarak modellenmiştir. Modele, türe ait varlık verileri ile WorldClim veri tabanından elde edilen biyoklimatik değişkenler dâhil edilmiştir. Gelecekteki habitat uygunluğu projeksiyonları, 2081–2100 dönemi için SSP126, SSP245, SSP370 ve SSP585 iklim senaryoları altında gerçekleştirilmiştir. Model performansı AUC (Eğri Altında Kalan Alan), ihmal oranları ve AICc kriterleri kullanılarak değerlendirilmiştir. MaxEnt modeli, eğitim ve doğrulama AUC değerlerinin 0,95’in üzerinde olmasıyla yüksek tahmin başarısı göstermiştir. Tepki eğrisi analizleri, sıcaklık değişkenleri olan BIO5, BIO6 ve BIO7’nin tür dağılımı üzerinde en etkili çevresel faktörler olduğunu ortaya koymuştur. Dağılım haritaları, gelecekteki iklim senaryoları altında habitat uygunluğunun kademeli olarak azalacağını ve parçalı bir yapı kazanacağını göstermiştir. Özellikle yüksek emisyon senaryoları altında uygun habitat alanlarında önemli azalmalar gözlenmiş; tür için uygun koşulların yalnızca belirli yerel mikro-sığınak alanlarında korunabileceği belirlenmiştir. En belirgin habitat kaybı SSP585 senaryosu altında gözlemlenmiştir. Sonuçlar, iklim değişikliğinin T. pityocampa türünün coğrafi dağılımını önemli ölçüde değiştirebileceğini ve Akdeniz orman ekosistemlerinin gelecekteki dinamiklerini etkileyebileceğini göstermektedir. Bu nedenle, iklim temelli tür dağılım modelleri; sürdürülebilir orman yönetimi, biyolojik çeşitliliğin korunması ve zararlı risk analizi açısından önemli araçlar olarak değerlendirilmektedir.

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Cite This Article

Koç, G. K., Kıraç, A. (2026). The Potential Distribution of Thaumetopoea pityocampa in the Kaz Mountains and Surrounding Areas Under the Influence of Climate Change. Bilge International Journal of Science and Technology Research, Cilt: 10 Sayı: 2, 213–227. https://ajindex.com/articles/1463552

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References (24)

  1. Barve, N., Barve, V., Jiménez‐Valverde, A., Lira‐Noriega, A., Maher, S. P., Peterson, A. T., Soberón, J., & Villalobos, F. (2011). The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling, 222(11), 1810–1819. https://doi.org/10.1016/j.ecolmodel.2011.02.011
  2. Battisti, A., Larsson, S., & Roques, A. (2017). Processionary moths and associated urtication risk: Global change–driven effects. Annual Review of Entomology, 62, 323-342.
  3. Battisti, A., Stastny, M., Netherer, S., Robinet, C., Schopf, A., Roques, A., & Larsson, S. (2005). Expansion of geographic range in the pine processionary moth caused by increased winter temperatures. Ecological Applications, 15(6), 2084–2096. https://doi.org/10.1890/04-1903
  4. Bonamonte, D., Foti, C., Vestita, M., Angelini, G. (2013). Skin reactions to pine processionary caterpillar Thaumetopoea pityocampa Schiff. The Scientific World Journal, 2013(1), 867431. https://doi.org/10.1155/2013/867431
  5. Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73–77. https://doi.org/10.1016/j.ecolmodel.2013.12.012
  6. Bourougaaoui, A., Robinet, C., Ben Jamâa, M. L., & Laparie, M. (2024). A retrospective analysis on the effects of climate warming on the pine processionary moth at the southern edge of its range. Oikos, 2024, e10989. https://doi.org/10.1111/oik.10989
  7. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer. https://doi.org/10.1007/b97636
  8. Camarero, J., Tardif, J., Gazol, A., & Conciatori, F. (2022). Pine processionary moth outbreaks cause longer growth legacies than drought and are linked to the North Atlantic Oscillation. Science of the total environment, 819, 153041. https://doi.org/10.1016/j.scitotenv.2022.153041
  9. Cürebal, İ., Efe, R., Sönmez, S., & Soykan, A. (2014). Ecogeography of kaz mountain ecosystem. Procedia-Social and Behavioral Sciences, 120, 167-175.
  10. Efe, R., Sönmez, S., Cürebal, İ., & Soykan, A. (2015). Ecological conditions and vegetation of subalpine zone of Kaz Mountain (Mount Ida, NW Turkey). In Climate Change Impacts on High-Altitude Ecosystems (pp. 591-608). Cham: Springer International Publishing.
  11. 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. https://doi.org/10.1111/j.1472-4642.2010.00725.x
  12. Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086 IPCC. (2021). Climate Change 2021: The Physical Science Basis. Cambridge University Press. https://doi.org/10.1017/9781009157896
  13. İpekdal, K., Burban, C., Kerdelhue, C., & Çağlar, S. S. (2015). Distribution of two pine processionary moth species in Turkey evidences a contact zone. Turkish Journal of Zoology, 39(5), 868-876.
  14. Kass, J. M., Pinilla‐Buitrago, G. E., Paz, A., Johnson, B. A., Grisales‐Betancur, V., Meenan, S. I.,. & Anderson, R. P. (2023). wallace 2: a shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions. Ecography, 2023(3), e06547.
  15. Merow, C., Smith, M. J., & Silander, J. A. (2013). A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography, 36(10), 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
  16. Netherer, S., & Schopf, A. (2010). Potential effects of climate change on insect herbivores in European forests — General aspects and the pine processionary moth as specific example. Forest Ecology and Management, 259(4), 831–838. https://doi.org/10.1016/j.foreco.2009.07.034
  17. Petrucco-Toffolo, E., Basso, A., Kerdelhué, C., İpekdal, K., Mendel, Z., Simonato, M., & Battisti, A. (2018). Evidence of potential hybridization in the Thaumetopoea pityocampa‐wilkinsoni complex. Agricultural and Forest Entomology, 20(1), 9-17.
  18. Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  19. Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography, 31(2), 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
  20. Robinet, C., Laparie, M., & Rousselet, J. (2015). Looking beyond the large scale effects of global change: local phenologies can result in critical heterogeneity in the pine processionary moth. Frontiers in Physiology, 6, 334. https://doi.org/10.3389/fphys.2015.00334
  21. Sangüesa‐Barreda, G., Camarero, J., García-Martín, A., Hernández, R., & De La Riva, J. (2014). Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. Forest Ecology and Management, 320, 171-181. https://doi.org/10.1016/j.foreco.2014.03.008
  22. Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285–1293. https://doi.org/10.1126/science.3287615
  23. Uysal, I. (2010). An overview of plant diversity of Kazdagi (Mt. Ida) Forest National Park, Turkey. Journal of Environmental Biology, 31(1), 141.
  24. Kass, J. M., Pinilla‐Buitrago, G. E., Paz, A., Johnson, B. A., Grisales‐Betancur, V., Meenan, S. I.,. & Anderson, R. P. (2023). wallace 2: a shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions. Ecography, 2023(3), e06547.