Research Article
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Comparative assessment of AHP and FRM approaches for susceptibility mapping of pine processionary moth

Year 2025, Volume: 49 Issue: 3, 291 - 306, 30.09.2025

Ali Berat Bulut , Fatih Sivrikaya , Gonca Ece Özcan

https://doi.org/10.16970/entoted.1707815

Abstract

This study aims to develop susceptibility maps for the Pine Processionary Moth (PPM) via multi-criteria decision-making methodologies. This study utilized data on forest stands affected by PPM damage within the Nurdağı Forest Planning Unit in Gaziantep province from 2018 to 2024. Parameters including stand structure, crown closure, development stage, elevation, slope, aspect, annual mean temperature, solar radiation, and annual mean precipitation parameters were used to create the PPM susceptibility maps according to the Analytical Hierarchy Process (AHP) and the Frequency Ratio Method (FRM). Their precision was evaluated by Relative Operating Characteristic (ROC) analysis. The AHP model indicates that 73% of the forest stands with PPM damage fall into the high and extreme susceptibility groups, whereas the FRM model shows that 68% of such forest stands are similarly categorized. The AUC values for the FRM and AHP models were determined to be 0.830 and 0.835, respectively. The results reveal that the PPM susceptibility maps generated using the AHP and FRM models are reliable.

Keywords

Annual mean precipitation Geographic Information Systems ROC Thaumetopoea wilkinsoni

References

  • Aksoy, H., 2023. Determination of landslide susceptibility with Analytic Hierarchy Process (AHP) and the role of forest ecosystem services on landslide susceptibility. Environmental Monitoring and Assessment, 195 (12): 1525 (1-22).
  • Alkan Akıncı, H., Ç. Genç & H. Akıncı, 2022. Susceptibility assessment and mapping of Ips typographus (L.) (Coleoptera: Curculionidae) in oriental spruce forests in Artvin, Turkey. Journal of Applied Entomology, 146 (9): 1185-1199.
  • Atanasova-Pacemska, T., M. Lapevski & R. Timovski, 2014. “Analytical Hierarchical Process (AHP) method application in the process of selection and evaluation, 373-380”. UNITECH'14 International Scientific Conference, (21-22 November 2014, Gabrovo, Bulgaria) 429 pp.
  • Aukema, B. H., A. L. Carroll, J. Zhu, K. F. Raffa, T. A. Sickley & S. W. Taylor, 2006. Landscape level analysis of mountain pine beetle in British Columbia, Canada: spatiotemporal development and spatial synchrony within the present outbreak. Ecography, 29 (3): 427-441.
  • Avcı, M., 2000. Türkiye'nin farklı bölgelerinde Thaumetopoea pityocampa (Den. and Schiff.) (Lep.: Thaumetopoeidae)'nın yumurta koçanlarının yapısı, parazitlenme ve yumurta bırakma davranışları üzerine araştırmalar. Türkiye Entomoloji Dergisi, 24 (3): 167-178 (in Turkish with abstract in English).
  • Azcárate, F. M., J. Seoane & M. Silvestre, 2023. Factors affecting pine processionary moth (Thaumetopoea pityocampa) incidence in Mediterranean pine stands: A multiscale approach. Forest Ecology and Management, 529 (1): 120728 (1-9).
  • Barbaro, L., A.M. Dulaurent, K. Payet, S. Blache, F. Vetillard & A. Battisti, 2013. Winter bird numerical responses to a key defoliator in mountain pine forests. Forest Ecology and Management. 296 (1): 90-97.
  • Battisti, A., M. Stastny, S. Netherer, C. Robinet, A. Schopf, A. Roques & S. Larsson, 2005. Expansion of geographic range in the pine processionary moth caused by increased winter temperatures. Ecological Applications, 15 (6): 2084-2096.
  • Battisti, A., M. Stastny, E. Buffo & S. Larsson, 2006. A rapid altitudinal range expansion in the pine processionary moth produced by the 2003 climatic anomaly. Global Change Biology 12 (4): 662-671.
  • Beck, H. E., N. E. Zimmermann, T. R. McVicar, N. Vergopolan, A. Berg & E. F. Wood, 2018. Present and future Koppen-Geiger climate classification maps at 1-km resolution. Scientific Data 7 (1): 1-2.
  • Bentekhici, N., S. Bellal & A. Zegrar, 2020. Contribution of remote sensing and GIS to mapping the fire risk of Mediterranean forest case of the forest massif of Tlemcen. Natural Hazards 104 (1): 811-831.
  • Bonamonte, D., C. Foti, M. Vestita & G. Angelini, 2013. Skin reactions to pine processionary caterpillar Thaumetopoea pityocampa Schiff. The Scientific World Journal, 867431 (1-6).
  • Bulut, A. B., 2024. Çam Kese Böceğinin (Thaumetopoea wilkinsoni) Duyarlılık Haritalarının Coğrafi Bilgi Sistemleri Tabanlı Çok Kriterli Karar Verme Teknikleri İle Hazırlanması, Kastamonu Üniversitesi, Fen Bilimleri Enstitüsü. (Unpublished) Yüksek Lisans Tezi, Kastamonu, Türkiye, 69 s (in Turkish with abstract in English).
  • Buxton, R. D., 1983. Forest management and the Pine Processionary Moth. Outlook on Agriculture 12 (1): 34-39. Campbell, J. E & M. Shin, 2011. Essentials of Geographic Information Systems. Saylor Foundation, 260 pp.
  • Campoa, J., T. Calvao, P. N. Firmino & C. S. Pimentel, 2021. Disentangling the effects of climate and defoliation on forest growth: The case of an outbreak of a Thaumetopoea pityocampa population with a shifted phenology in a Pinus pinaster monoculture. Forest Ecology and Management, 498 (1): 119548 (1-9).
  • Can, P. & İ. M. Özçankaya, 2003. Ege Bölgesi ağaçlandırma alanlarında çam kese böceği (Thaumetopoea pityocampa (Den. & Schiff.)) yumurta parazitoidlerinin belirlenmesi. Ege Ormancılık Araştırma Müdürlüğü, Teknik Bülten Serisi, 22 (1): 42-53 (in Turkish).
  • Chen, Y., J. Yu & S. Khan, 2010. Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling & Software, 25 (12): 1582-1591.
  • Çanakçıoğlu, H. & T. Mol, 1998. Orman Entomolojisi Zararlı ve Yararlı Böcekler. İstanbul Üniversitesi Orman Fakültesi Yayınları, İstanbul. 302 pp (in Turkish).
  • Dajoz, R., 1998. Insects and The Forest: Role and Diversity of Insects in The Forest Environment. Technique et Documentation Lavoisier, 680 pp.
  • Drobne, S. & A. Lisec, 2009. Multi-attribute decision analysis in GIS: weighted linear combination and ordered weighted averaging. Informatica, 33 (4): 459-474.
  • Durkaya, A., B. Durkaya & İ. Dal, 2009. The effects of the pine processionary moth on the increment of crimean pine trees in Bartin, Turkey. African Journal of Biotechnology, 8 (10): 2356-2361.
  • Erener, A. & H. S. B. Düzgün, 2010. Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides, 7 (1): 55-68.
  • Erkan, N., 2011. Impact of pine processionary moth (Thaumetopoea wilkinsoni Tams) on growth of Turkish red pine (Pinus brutia Ten.). African Journal of Agricultural Research, 6 (21): 4983-4988.
  • FAO, 2009. Thaumetopoea pityocampa. In Global Review of Forest Pests and Diseases: PART II Profiles of Selected Forest Pests Insect Pests, 156 (2): 116-119.
  • Fettig, C. J., J. M. Egan, H. Delb, J. Hilszczanski, M. Kautz, A. S. Munson, J. T. Novak & J. F. Negrón, 2022. “Management Tactics to Reduce Bark Beetle Impacts in North America and Europe Under Altered Forest and Climatic Conditions, 345-394”. In: Bark Beetle Management, Ecology, and Climate Change (Eds. K. J. K. Gandhi & R. W. Hofstetter). Academic Press, 408 pp.
  • FMP, 2022. Forest Management Plans of Nurdağı Forest Planning Unit. General Directorate of Forestry, Ankara, 316 pp.
  • Gheshlaghi, H.A., B. Feizizadeh & B. Blaschke, 2020. GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. Journal of Environmental Planning Management, 63 (3): 481-499.
  • Greene, R., R. Devillers, J. E. Luther & B. G. Eddy, 2011. GIS‐based multiple‐criteria decision analysis. Geography Compass, 5 (6): 412-432.
  • Griscom, B. W., J. Adams, P. W. Ellis, R. A. Houghton, G. Lomax, D. A. Miteva, W. H. Schlesinger, D. Shoch, J. V. Siikamäki, P. Smith, P. Woodbury, C. Zganjar, A. Blackman, J. Campari, R. T. Conant, C. Delgado, P. Elias, T. Gopalakrishna, M. R. Hamsik, M. Herrero, J. Kiesecker, E. Landis, L. Laestadius, S. M. Leavitt, S. Minnemeyer, S. Polasky, P. Potapov, F. E. Putz, J. Sanderman, M. Silvius, E. Wollenberg & J. Fargione, 2017. Natural climate solutions. Proceedings of the National Academy of Sciences, 114 (44): 11645-11650.
  • Hong, H., S. A. Naghibi, M. Moradi Dashtpagerdi, H. R. Pourghasemi & W. Chen, 2017. A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arabian Journal of Geosciences, 10 (7): 1-14.
  • İpekdal, K., 2005. Çam Kese Böceği Thaumetopoea pityocampa (Denis. & Schiffermüller, 1775) (Lepidoptera: Thaumetopoeidae)’nın Biyolojisi ve Mücadelesi Üzerine Araştırmalar. Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, (Unpublished) Yüksek Lisans Tezi, Ankara. 135 s (in Turkish with abstract in English).
  • İpekdal, K., C. Burban, C. Kerdelhue & S. S. Çağlar, 2015. Distribution of two pine processionary moth species in Turkey evidences a contact zone. Turkish Journal of Zoology, 39 (5): 868-876.
  • Jactel, H., L. Barbaro, A. Battisti, A. Bosc, M. Branco, E. Brockerhoff, B. Castagneyrol, A.M. Dulaurent, J.A. Hódar, J.S. Jacquet, E. Mateus, M.R. Paiva, A. Roques, J.C. Jean-Charles Samalens, H. Santos & F. Schlyter, 2014. “Insect-tree interactions in Thaumetopoea pityocampa. Processionary Moths and Climate Change, 265-310”. In: Processionary Moths and Climate Change: An Update (Eds: A. Roques). Springer, France, 427 pp.
  • Jactel, H., X. Moreira & B. Castagneyrol, 2021. Tree diversity and forest resistance to insect pests: patterns, mechanisms, and prospects. Annual Review of Entomology, 66 (1): 277-296.
  • Kanat, M. & F. Sivrikaya, 2005. Çam kese böceğinin kızılçam ağaçlarında çap artımı üzerine etkileri. Kahramanmaraş Sütçü İmam Üniversitesi Fen ve Mühendislik Dergisi, 8 (2): 74-78 (in Turkish with abstract in English).
  • Kanat, M., M. H. Alma & F. Sivrikaya, 2005. Effect of defoliation by Thaumetopoea pityocampa (Den. & Schiff.) (Lepidoptera: Thaumetopoeidae) on annual diameter increment of Pinus brutia Ten. in Turkey. Annals of Forest Science, 62 (1): 91-94.
  • Kara, S., 2023. Yangın Risk Haritalarının Coğrafi Bilgi Sistemleri Tabanlı Çok Kriterli Karar Verme Teknikleri Ile Hazırlanması. Kastamonu Üniversitesi Fen Bilimleri Enstitüsü, (Unpublished) Yüksek Lisans Tezi, Kastamonu, Türkiye. 99 s (in Turkish with abstract in English).
  • Kärvemo, S., T. P. Van Boeckel, M. Gilbert, J. C. Grégoire & M. Schroeder, 2014. Large-scale risk mapping of an eruptive bark beetle-importance of forest susceptibility and beetle pressure. Forest Ecology and Management, 318 (1): 158-166.
  • Kautz, M., A. J. Meddens, R. J. Hall & A. Arneth, 2017. Biotic disturbances in Northern Hemisphere forests-a synthesis of recent data, uncertainties and implications for forest monitoring and modelling. Global Ecology and Biogeography, 26 (5): 533-552.
  • Kerdelhué, C., L. Zane, M. Simonato, P. Salvato, J. Rousselet, A. Roques & A. Battisti, 2009. Quaternary history and contemporary patterns in a currently expanding species. BMC Evolutionary Biology, 9 (1): 1-14.
  • Keten, A., S. Akbulut & Z. Kahraman, 2010. Düzce’de bazı konukçu ağaç özelliklerinin çam keseböceğinin yumurta koyma yeri tercihleri üzerine etkileri. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi, 6 (1): 12-19 (in Turkish with abstract in English)
  • Kumar, D. & C. P. Garg, 2017. Evaluating sustainable supply chain indicators using fuzzy AHP: Case of Indian automotive industry. Benchmarking: An International Journal, 24 (6): 1742-1766.
  • Kumar, R. & A. Indrayan, 2011. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatrics, 48 (4): 277-287.
  • Kunegel-Lion, M. & M. A. Lewis, 2020. Mountain pine beetle outbreak duration and pine mortality depend on direct control effort. Journal of Environmental Management, 260 (1): 110167 (1-11).
  • Logan, J. A., J. Régnière & J. A. Powell, 2003. Assessing the impacts of global warming on forest pest dynamics. Frontiers in Ecology and the Environment, 1 (3): 130-137.
  • Naghibi, S. A., M. Vafakhah, H. Hashemi, B. Pradhan & S. J. Alavi, 2020. Water resources management through flood spreading project suitability mapping using frequency ratio, k-nearest neighbours, and random forest algorithms. Natural Resources Research, 29 (3): 1915-1933.
  • Nandi, A. & A. Shakoor, 2010. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110 (1-2): 11-20.
  • Ollikainen, M., 2014. Forestry in bioeconomy-smart green growth for the humankind. Scandinavian Journal of Forest Research, 29 (4): 360-366.
  • Onaran, M. A. & M. Katı, 2010. Çam kese böceği (Thaumetopoeo pityocampa Shiff) ile biyolojik mücadele. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 12 (2): 21-27 (in Turkish with abstract in English).
  • Özcan, G. E., F. Sivrikaya, O. E. Sakici & K. Enez, 2022. Determination of some factors leading to the infestation of Ips sexdentatus in crimean pine stands. Forest Ecology and Management, 519 (1): 120316 (1-10).
  • Parlak, S., İ. M. Özçankaya, M. Batur, M. E. Akkaş, Z. Boza & Ö. Toprak, 2019. Determining the edge effect of pine processionary moth (Thaumetopoea pityocampa) in its horizontal distribution in the stand. Journal of Forestry Research, 30 (1): 347-352.
  • Pourghasemi, H., B. Pradhan & C. Gokceoglu, 2012. Application of fuzzy logic and Analytical Hierarchy Process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63 (2): 965-996,
  • Pourghasemi, H. R., H. R. Moradi & S. M. Fatemi Aghda, 2013. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural Hazards, 69 (1): 749-779.
  • Pourghasemi, H. R., M. Beheshtirad & B. Pradhan, 2016. A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards and Risk, 7 (2): 861-885.
  • Pradhan, B. & S. Lee, 2009. Delineation of Landslide Hazard Areas Using Frequency Ratio, Logistic Regression and Artificial Neural Network Model at Penang Island. Environmental Earth Science, 60 (1): 1037-1054.
  • Régolini, M., B. Castagneyrol, A. M. Dulaurent-Mercadal, D. Piou, J. C. Samalens & H. Jactel, 2014. Effect of host tree density and apparency on the probability of attack by the pine processionary moth. Forest Ecology and Management, 334 (1): 185-192.
  • Saaty, T. L., 1980. The Analytic Hierarchy Process. Springer, McGraw-Hill, New York. 360 pp.
  • Sari, F., 2021. Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Türkiye: A comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management, 480 (1): 118644 (1-16).
  • Seidl, R., D. Thom, M. Kautz, D. Martin-Benito, M. Peltoniemi, G. Vacchiano, J. Wild, D. Ascoli, M. Petr, J. Honkaniemi, M. J. Lexer, V. Trotsiuk, P. Mairota, M. Svoboda, M. Fabrika, T. A. Nagel & C. P. Reyer, 2017. Forest disturbances under climate change. Nature Climate Change, 7 (6): 395-402.
  • Senf, C., R. Seidl & P. Hostert, 2017. Remote sensing of forest insect disturbances: Current state and future directions. International Journal of Applied Earth Observation and Geoinformation, 60 (1): 49-60.
  • Sevinc, V., O. Kucuk & M. Goltas, 2020. A Bayesian network model for prediction and analysis of possible forest fire causes. Forest Ecology and Management, 457 (1): 117723 (1-11).
  • Sivrikaya, F. & Küçük, Ö. 2022. Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics, 68 (1): 101537 (1-15).
  • Sivrikaya, F., G. E. Özcan, K. Enez & O. E. Sakici, 2022. Comparative study of the analytical hierarchy process, frequency ratio, and logistic regression models for predicting the susceptibility to Ips sexdentatus in crimean pine forests. Ecological Informatics, 71 (1): 101811 (1-11).
  • Tahri, M., J. Kašpar, A. L. Madsen, R. Modlinger, K. Zabihi, R. Marušák & H. Vacik, 2022. Comparative study of fuzzy-AHP and BBN for spatially-explicit prediction of bark beetle predisposition. Environmental Modelling & Software, 147 (1): 105233 (1-17).
  • Ürker, O. & A. Günlü, 2024. Identification of plantation areas for the endangered oriental sweetgum tree (Liquidambar orientalis Miller, 1768) in Türkiye. International Journal of Environmental Science and Technology, 21 (1): 153-168.
  • Vacik, H & M. J. Lexer, 2014. Past, current and future drivers for the development of decision support systems in forest management. Scandinavian Journal of Forest Research, 29 (Suplement 1): 2-19.
  • van Lierop, P., E. Lindquist, S. Sathyapala & G. Franceschini, 2015. Global forest area disturbance from fire, insect pests, diseases and severe weather events. Forest Ecology and Management, 352 (1): 78-88.
  • Vasquez, M. C. V., C. F. Chen, Y. J. Lin, Y. C. Kuo, Y. Y. Chen, D. Medina & K. Diaz, 2020. Characterizing spatial patterns of pine bark beetle outbreaks during the dry and rainy season’s in Honduras with the aid of geographic information systems and remote sensing data. Forest Ecology and Management, 467 (1): 118162 (1-10).
  • Yesilnacar, E. K., 2005. The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey. The University of Melbourne, Department of Geomatics, (Unpublished9 Dissertation, Melbourne, Australia, 423 pp.
  • Ziouche, S., F. Baali, D., Moutassem & Z. E. Djazouli, 2017. Strategies for choosing location of winter nests of Thaumetopoea pityocampa (Denis & Schiffermüller, 1775) in three pine forests of Bordj Bou Arreridj region (Algeria). AgroBiologia, 7 (2): 412-426.

Çam kese böceğinin duyarlılık haritalamasında AHP ve FRM yaklaşımlarının karşılaştırmalı değerlendirmesi

Year 2025, Volume: 49 Issue: 3, 291 - 306, 30.09.2025

Ali Berat Bulut , Fatih Sivrikaya , Gonca Ece Özcan

https://doi.org/10.16970/entoted.1707815

Abstract

Bu çalışmada, çok kriterli karar verme metodolojileri kullanılarak Çam Kese Böceği (ÇKB) için duyarlılık haritalarının geliştirilmesi amaçlanmıştır. Çalışmada, Gaziantep ili Nurdağı Orman İşletme Şefliğinde 2018-2024 yıllarındaki ÇKB zararı olan meşcere verileri kullanılmıştır. Meşcere yapısı, kapalılık, gelişim çağı, yükselti, eğim, bakı, yıllık ortalama sıcaklık, güneş radyasyonu ve yıllık ortalama yağış parametreleri ÇKB duyarlılık haritalarının oluşturulmasında kullanılmıştır. ÇKB duyarlılık haritaları Analitik Hiyerarşi Süreci (AHP) ve Frekans Oranı Yöntemi (FRM) kullanılarak geliştirilmiş ve doğrulukları Göreceli İşletme Karakteristiği (ROC) analizi ile değerlendirilmiştir. AHP modeli, ÇKB zararı olan ormanlık alanların %73'ünün yüksek ve aşırı duyarlılık gruplarına girdiğini gösterirken, FRM modeli bu ormanlık alanların %68'inin benzer şekilde kategorize edildiğini göstermektedir. FRM ve AHP modelleri için AUC değerleri sırasıyla 0,830 ve 0,835 olarak belirlenmiştir. Sonuçlar, AHP ve FRM modelleri kullanılarak oluşturulan ÇKB duyarlılık haritalarının güvenilir sonuçlar verdiğini göstermiştir.

Keywords

Yıllık ortalama yağış Coğrafi Bilgi Sistemi ROC Thaumetopoea wilkinsoni

References

  • Aksoy, H., 2023. Determination of landslide susceptibility with Analytic Hierarchy Process (AHP) and the role of forest ecosystem services on landslide susceptibility. Environmental Monitoring and Assessment, 195 (12): 1525 (1-22).
  • Alkan Akıncı, H., Ç. Genç & H. Akıncı, 2022. Susceptibility assessment and mapping of Ips typographus (L.) (Coleoptera: Curculionidae) in oriental spruce forests in Artvin, Turkey. Journal of Applied Entomology, 146 (9): 1185-1199.
  • Atanasova-Pacemska, T., M. Lapevski & R. Timovski, 2014. “Analytical Hierarchical Process (AHP) method application in the process of selection and evaluation, 373-380”. UNITECH'14 International Scientific Conference, (21-22 November 2014, Gabrovo, Bulgaria) 429 pp.
  • Aukema, B. H., A. L. Carroll, J. Zhu, K. F. Raffa, T. A. Sickley & S. W. Taylor, 2006. Landscape level analysis of mountain pine beetle in British Columbia, Canada: spatiotemporal development and spatial synchrony within the present outbreak. Ecography, 29 (3): 427-441.
  • Avcı, M., 2000. Türkiye'nin farklı bölgelerinde Thaumetopoea pityocampa (Den. and Schiff.) (Lep.: Thaumetopoeidae)'nın yumurta koçanlarının yapısı, parazitlenme ve yumurta bırakma davranışları üzerine araştırmalar. Türkiye Entomoloji Dergisi, 24 (3): 167-178 (in Turkish with abstract in English).
  • Azcárate, F. M., J. Seoane & M. Silvestre, 2023. Factors affecting pine processionary moth (Thaumetopoea pityocampa) incidence in Mediterranean pine stands: A multiscale approach. Forest Ecology and Management, 529 (1): 120728 (1-9).
  • Barbaro, L., A.M. Dulaurent, K. Payet, S. Blache, F. Vetillard & A. Battisti, 2013. Winter bird numerical responses to a key defoliator in mountain pine forests. Forest Ecology and Management. 296 (1): 90-97.
  • Battisti, A., M. Stastny, S. Netherer, C. Robinet, A. Schopf, A. Roques & S. Larsson, 2005. Expansion of geographic range in the pine processionary moth caused by increased winter temperatures. Ecological Applications, 15 (6): 2084-2096.
  • Battisti, A., M. Stastny, E. Buffo & S. Larsson, 2006. A rapid altitudinal range expansion in the pine processionary moth produced by the 2003 climatic anomaly. Global Change Biology 12 (4): 662-671.
  • Beck, H. E., N. E. Zimmermann, T. R. McVicar, N. Vergopolan, A. Berg & E. F. Wood, 2018. Present and future Koppen-Geiger climate classification maps at 1-km resolution. Scientific Data 7 (1): 1-2.
  • Bentekhici, N., S. Bellal & A. Zegrar, 2020. Contribution of remote sensing and GIS to mapping the fire risk of Mediterranean forest case of the forest massif of Tlemcen. Natural Hazards 104 (1): 811-831.
  • Bonamonte, D., C. Foti, M. Vestita & G. Angelini, 2013. Skin reactions to pine processionary caterpillar Thaumetopoea pityocampa Schiff. The Scientific World Journal, 867431 (1-6).
  • Bulut, A. B., 2024. Çam Kese Böceğinin (Thaumetopoea wilkinsoni) Duyarlılık Haritalarının Coğrafi Bilgi Sistemleri Tabanlı Çok Kriterli Karar Verme Teknikleri İle Hazırlanması, Kastamonu Üniversitesi, Fen Bilimleri Enstitüsü. (Unpublished) Yüksek Lisans Tezi, Kastamonu, Türkiye, 69 s (in Turkish with abstract in English).
  • Buxton, R. D., 1983. Forest management and the Pine Processionary Moth. Outlook on Agriculture 12 (1): 34-39. Campbell, J. E & M. Shin, 2011. Essentials of Geographic Information Systems. Saylor Foundation, 260 pp.
  • Campoa, J., T. Calvao, P. N. Firmino & C. S. Pimentel, 2021. Disentangling the effects of climate and defoliation on forest growth: The case of an outbreak of a Thaumetopoea pityocampa population with a shifted phenology in a Pinus pinaster monoculture. Forest Ecology and Management, 498 (1): 119548 (1-9).
  • Can, P. & İ. M. Özçankaya, 2003. Ege Bölgesi ağaçlandırma alanlarında çam kese böceği (Thaumetopoea pityocampa (Den. & Schiff.)) yumurta parazitoidlerinin belirlenmesi. Ege Ormancılık Araştırma Müdürlüğü, Teknik Bülten Serisi, 22 (1): 42-53 (in Turkish).
  • Chen, Y., J. Yu & S. Khan, 2010. Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environmental Modelling & Software, 25 (12): 1582-1591.
  • Çanakçıoğlu, H. & T. Mol, 1998. Orman Entomolojisi Zararlı ve Yararlı Böcekler. İstanbul Üniversitesi Orman Fakültesi Yayınları, İstanbul. 302 pp (in Turkish).
  • Dajoz, R., 1998. Insects and The Forest: Role and Diversity of Insects in The Forest Environment. Technique et Documentation Lavoisier, 680 pp.
  • Drobne, S. & A. Lisec, 2009. Multi-attribute decision analysis in GIS: weighted linear combination and ordered weighted averaging. Informatica, 33 (4): 459-474.
  • Durkaya, A., B. Durkaya & İ. Dal, 2009. The effects of the pine processionary moth on the increment of crimean pine trees in Bartin, Turkey. African Journal of Biotechnology, 8 (10): 2356-2361.
  • Erener, A. & H. S. B. Düzgün, 2010. Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides, 7 (1): 55-68.
  • Erkan, N., 2011. Impact of pine processionary moth (Thaumetopoea wilkinsoni Tams) on growth of Turkish red pine (Pinus brutia Ten.). African Journal of Agricultural Research, 6 (21): 4983-4988.
  • FAO, 2009. Thaumetopoea pityocampa. In Global Review of Forest Pests and Diseases: PART II Profiles of Selected Forest Pests Insect Pests, 156 (2): 116-119.
  • Fettig, C. J., J. M. Egan, H. Delb, J. Hilszczanski, M. Kautz, A. S. Munson, J. T. Novak & J. F. Negrón, 2022. “Management Tactics to Reduce Bark Beetle Impacts in North America and Europe Under Altered Forest and Climatic Conditions, 345-394”. In: Bark Beetle Management, Ecology, and Climate Change (Eds. K. J. K. Gandhi & R. W. Hofstetter). Academic Press, 408 pp.
  • FMP, 2022. Forest Management Plans of Nurdağı Forest Planning Unit. General Directorate of Forestry, Ankara, 316 pp.
  • Gheshlaghi, H.A., B. Feizizadeh & B. Blaschke, 2020. GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. Journal of Environmental Planning Management, 63 (3): 481-499.
  • Greene, R., R. Devillers, J. E. Luther & B. G. Eddy, 2011. GIS‐based multiple‐criteria decision analysis. Geography Compass, 5 (6): 412-432.
  • Griscom, B. W., J. Adams, P. W. Ellis, R. A. Houghton, G. Lomax, D. A. Miteva, W. H. Schlesinger, D. Shoch, J. V. Siikamäki, P. Smith, P. Woodbury, C. Zganjar, A. Blackman, J. Campari, R. T. Conant, C. Delgado, P. Elias, T. Gopalakrishna, M. R. Hamsik, M. Herrero, J. Kiesecker, E. Landis, L. Laestadius, S. M. Leavitt, S. Minnemeyer, S. Polasky, P. Potapov, F. E. Putz, J. Sanderman, M. Silvius, E. Wollenberg & J. Fargione, 2017. Natural climate solutions. Proceedings of the National Academy of Sciences, 114 (44): 11645-11650.
  • Hong, H., S. A. Naghibi, M. Moradi Dashtpagerdi, H. R. Pourghasemi & W. Chen, 2017. A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arabian Journal of Geosciences, 10 (7): 1-14.
  • İpekdal, K., 2005. Çam Kese Böceği Thaumetopoea pityocampa (Denis. & Schiffermüller, 1775) (Lepidoptera: Thaumetopoeidae)’nın Biyolojisi ve Mücadelesi Üzerine Araştırmalar. Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, (Unpublished) Yüksek Lisans Tezi, Ankara. 135 s (in Turkish with abstract in English).
  • İpekdal, K., C. Burban, C. Kerdelhue & S. S. Çağlar, 2015. Distribution of two pine processionary moth species in Turkey evidences a contact zone. Turkish Journal of Zoology, 39 (5): 868-876.
  • Jactel, H., L. Barbaro, A. Battisti, A. Bosc, M. Branco, E. Brockerhoff, B. Castagneyrol, A.M. Dulaurent, J.A. Hódar, J.S. Jacquet, E. Mateus, M.R. Paiva, A. Roques, J.C. Jean-Charles Samalens, H. Santos & F. Schlyter, 2014. “Insect-tree interactions in Thaumetopoea pityocampa. Processionary Moths and Climate Change, 265-310”. In: Processionary Moths and Climate Change: An Update (Eds: A. Roques). Springer, France, 427 pp.
  • Jactel, H., X. Moreira & B. Castagneyrol, 2021. Tree diversity and forest resistance to insect pests: patterns, mechanisms, and prospects. Annual Review of Entomology, 66 (1): 277-296.
  • Kanat, M. & F. Sivrikaya, 2005. Çam kese böceğinin kızılçam ağaçlarında çap artımı üzerine etkileri. Kahramanmaraş Sütçü İmam Üniversitesi Fen ve Mühendislik Dergisi, 8 (2): 74-78 (in Turkish with abstract in English).
  • Kanat, M., M. H. Alma & F. Sivrikaya, 2005. Effect of defoliation by Thaumetopoea pityocampa (Den. & Schiff.) (Lepidoptera: Thaumetopoeidae) on annual diameter increment of Pinus brutia Ten. in Turkey. Annals of Forest Science, 62 (1): 91-94.
  • Kara, S., 2023. Yangın Risk Haritalarının Coğrafi Bilgi Sistemleri Tabanlı Çok Kriterli Karar Verme Teknikleri Ile Hazırlanması. Kastamonu Üniversitesi Fen Bilimleri Enstitüsü, (Unpublished) Yüksek Lisans Tezi, Kastamonu, Türkiye. 99 s (in Turkish with abstract in English).
  • Kärvemo, S., T. P. Van Boeckel, M. Gilbert, J. C. Grégoire & M. Schroeder, 2014. Large-scale risk mapping of an eruptive bark beetle-importance of forest susceptibility and beetle pressure. Forest Ecology and Management, 318 (1): 158-166.
  • Kautz, M., A. J. Meddens, R. J. Hall & A. Arneth, 2017. Biotic disturbances in Northern Hemisphere forests-a synthesis of recent data, uncertainties and implications for forest monitoring and modelling. Global Ecology and Biogeography, 26 (5): 533-552.
  • Kerdelhué, C., L. Zane, M. Simonato, P. Salvato, J. Rousselet, A. Roques & A. Battisti, 2009. Quaternary history and contemporary patterns in a currently expanding species. BMC Evolutionary Biology, 9 (1): 1-14.
  • Keten, A., S. Akbulut & Z. Kahraman, 2010. Düzce’de bazı konukçu ağaç özelliklerinin çam keseböceğinin yumurta koyma yeri tercihleri üzerine etkileri. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi, 6 (1): 12-19 (in Turkish with abstract in English)
  • Kumar, D. & C. P. Garg, 2017. Evaluating sustainable supply chain indicators using fuzzy AHP: Case of Indian automotive industry. Benchmarking: An International Journal, 24 (6): 1742-1766.
  • Kumar, R. & A. Indrayan, 2011. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatrics, 48 (4): 277-287.
  • Kunegel-Lion, M. & M. A. Lewis, 2020. Mountain pine beetle outbreak duration and pine mortality depend on direct control effort. Journal of Environmental Management, 260 (1): 110167 (1-11).
  • Logan, J. A., J. Régnière & J. A. Powell, 2003. Assessing the impacts of global warming on forest pest dynamics. Frontiers in Ecology and the Environment, 1 (3): 130-137.
  • Naghibi, S. A., M. Vafakhah, H. Hashemi, B. Pradhan & S. J. Alavi, 2020. Water resources management through flood spreading project suitability mapping using frequency ratio, k-nearest neighbours, and random forest algorithms. Natural Resources Research, 29 (3): 1915-1933.
  • Nandi, A. & A. Shakoor, 2010. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110 (1-2): 11-20.
  • Ollikainen, M., 2014. Forestry in bioeconomy-smart green growth for the humankind. Scandinavian Journal of Forest Research, 29 (4): 360-366.
  • Onaran, M. A. & M. Katı, 2010. Çam kese böceği (Thaumetopoeo pityocampa Shiff) ile biyolojik mücadele. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 12 (2): 21-27 (in Turkish with abstract in English).
  • Özcan, G. E., F. Sivrikaya, O. E. Sakici & K. Enez, 2022. Determination of some factors leading to the infestation of Ips sexdentatus in crimean pine stands. Forest Ecology and Management, 519 (1): 120316 (1-10).
  • Parlak, S., İ. M. Özçankaya, M. Batur, M. E. Akkaş, Z. Boza & Ö. Toprak, 2019. Determining the edge effect of pine processionary moth (Thaumetopoea pityocampa) in its horizontal distribution in the stand. Journal of Forestry Research, 30 (1): 347-352.
  • Pourghasemi, H., B. Pradhan & C. Gokceoglu, 2012. Application of fuzzy logic and Analytical Hierarchy Process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63 (2): 965-996,
  • Pourghasemi, H. R., H. R. Moradi & S. M. Fatemi Aghda, 2013. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural Hazards, 69 (1): 749-779.
  • Pourghasemi, H. R., M. Beheshtirad & B. Pradhan, 2016. A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomatics, Natural Hazards and Risk, 7 (2): 861-885.
  • Pradhan, B. & S. Lee, 2009. Delineation of Landslide Hazard Areas Using Frequency Ratio, Logistic Regression and Artificial Neural Network Model at Penang Island. Environmental Earth Science, 60 (1): 1037-1054.
  • Régolini, M., B. Castagneyrol, A. M. Dulaurent-Mercadal, D. Piou, J. C. Samalens & H. Jactel, 2014. Effect of host tree density and apparency on the probability of attack by the pine processionary moth. Forest Ecology and Management, 334 (1): 185-192.
  • Saaty, T. L., 1980. The Analytic Hierarchy Process. Springer, McGraw-Hill, New York. 360 pp.
  • Sari, F., 2021. Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Türkiye: A comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management, 480 (1): 118644 (1-16).
  • Seidl, R., D. Thom, M. Kautz, D. Martin-Benito, M. Peltoniemi, G. Vacchiano, J. Wild, D. Ascoli, M. Petr, J. Honkaniemi, M. J. Lexer, V. Trotsiuk, P. Mairota, M. Svoboda, M. Fabrika, T. A. Nagel & C. P. Reyer, 2017. Forest disturbances under climate change. Nature Climate Change, 7 (6): 395-402.
  • Senf, C., R. Seidl & P. Hostert, 2017. Remote sensing of forest insect disturbances: Current state and future directions. International Journal of Applied Earth Observation and Geoinformation, 60 (1): 49-60.
  • Sevinc, V., O. Kucuk & M. Goltas, 2020. A Bayesian network model for prediction and analysis of possible forest fire causes. Forest Ecology and Management, 457 (1): 117723 (1-11).
  • Sivrikaya, F. & Küçük, Ö. 2022. Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics, 68 (1): 101537 (1-15).
  • Sivrikaya, F., G. E. Özcan, K. Enez & O. E. Sakici, 2022. Comparative study of the analytical hierarchy process, frequency ratio, and logistic regression models for predicting the susceptibility to Ips sexdentatus in crimean pine forests. Ecological Informatics, 71 (1): 101811 (1-11).
  • Tahri, M., J. Kašpar, A. L. Madsen, R. Modlinger, K. Zabihi, R. Marušák & H. Vacik, 2022. Comparative study of fuzzy-AHP and BBN for spatially-explicit prediction of bark beetle predisposition. Environmental Modelling & Software, 147 (1): 105233 (1-17).
  • Ürker, O. & A. Günlü, 2024. Identification of plantation areas for the endangered oriental sweetgum tree (Liquidambar orientalis Miller, 1768) in Türkiye. International Journal of Environmental Science and Technology, 21 (1): 153-168.
  • Vacik, H & M. J. Lexer, 2014. Past, current and future drivers for the development of decision support systems in forest management. Scandinavian Journal of Forest Research, 29 (Suplement 1): 2-19.
  • van Lierop, P., E. Lindquist, S. Sathyapala & G. Franceschini, 2015. Global forest area disturbance from fire, insect pests, diseases and severe weather events. Forest Ecology and Management, 352 (1): 78-88.
  • Vasquez, M. C. V., C. F. Chen, Y. J. Lin, Y. C. Kuo, Y. Y. Chen, D. Medina & K. Diaz, 2020. Characterizing spatial patterns of pine bark beetle outbreaks during the dry and rainy season’s in Honduras with the aid of geographic information systems and remote sensing data. Forest Ecology and Management, 467 (1): 118162 (1-10).
  • Yesilnacar, E. K., 2005. The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey. The University of Melbourne, Department of Geomatics, (Unpublished9 Dissertation, Melbourne, Australia, 423 pp.
  • Ziouche, S., F. Baali, D., Moutassem & Z. E. Djazouli, 2017. Strategies for choosing location of winter nests of Thaumetopoea pityocampa (Denis & Schiffermüller, 1775) in three pine forests of Bordj Bou Arreridj region (Algeria). AgroBiologia, 7 (2): 412-426.
There are 70 citations in total.

Details

Primary Language English
Subjects Forest Entomology and Forest Protection
Journal Section Articles
Authors

Ali Berat Bulut 0009-0006-0772-6092

Fatih Sivrikaya 0000-0003-0860-6747

Gonca Ece Özcan 0000-0003-0141-1031

Publication Date September 30, 2025
Submission Date May 29, 2025
Acceptance Date August 24, 2025
Published in Issue Year 2025 Volume: 49 Issue: 3

Cite

APA Bulut, A. B., Sivrikaya, F., & Özcan, G. E. (2025). Comparative assessment of AHP and FRM approaches for susceptibility mapping of pine processionary moth. Turkish Journal of Entomology, 49(3), 291-306. https://doi.org/10.16970/entoted.1707815
AMA Bulut AB, Sivrikaya F, Özcan GE. Comparative assessment of AHP and FRM approaches for susceptibility mapping of pine processionary moth. TED. September 2025;49(3):291-306. doi:10.16970/entoted.1707815
Chicago Bulut, Ali Berat, Fatih Sivrikaya, and Gonca Ece Özcan. “Comparative Assessment of AHP and FRM Approaches for Susceptibility Mapping of Pine Processionary Moth”. Turkish Journal of Entomology 49, no. 3 (September 2025): 291-306. https://doi.org/10.16970/entoted.1707815.
EndNote Bulut AB, Sivrikaya F, Özcan GE (September 1, 2025) Comparative assessment of AHP and FRM approaches for susceptibility mapping of pine processionary moth. Turkish Journal of Entomology 49 3 291–306.
IEEE A. B. Bulut, F. Sivrikaya, and G. E. Özcan, “Comparative assessment of AHP and FRM approaches for susceptibility mapping of pine processionary moth”, TED, vol. 49, no. 3, pp. 291–306, 2025, doi: 10.16970/entoted.1707815.
ISNAD Bulut, Ali Berat et al. “Comparative Assessment of AHP and FRM Approaches for Susceptibility Mapping of Pine Processionary Moth”. Turkish Journal of Entomology 49/3 (September2025), 291-306. https://doi.org/10.16970/entoted.1707815.
JAMA Bulut AB, Sivrikaya F, Özcan GE. Comparative assessment of AHP and FRM approaches for susceptibility mapping of pine processionary moth. TED. 2025;49:291–306.
MLA Bulut, Ali Berat et al. “Comparative Assessment of AHP and FRM Approaches for Susceptibility Mapping of Pine Processionary Moth”. Turkish Journal of Entomology, vol. 49, no. 3, 2025, pp. 291-06, doi:10.16970/entoted.1707815.
Vancouver Bulut AB, Sivrikaya F, Özcan GE. Comparative assessment of AHP and FRM approaches for susceptibility mapping of pine processionary moth. TED. 2025;49(3):291-306.
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