Ilgaz Dağı Yaban Hayatı Geliştirme Sahası’nda memeli türlerinin habitat uygunluk modellemesi ve haritalanması

Abstract

Yaban hayvanlarının beslenme, barınma ve üreme gibi yaşamsal ihtiyaçlarını karşıladıkları habitatlar günümüzde iklim değişikliği ve antropojenik tehditlerin etkisi altındadır. Gün geçtikçe nitelik ve nicelik bakımından bozulan habitatların yönetimi ve korunması, yaban hayvanlarının geleceği açısından giderek daha önemli hale gelmektedir. Bu çalışmada Batı Karadeniz Bölgesi’nin önemli yaban hayatı alanlarından biri olan Ilgaz Dağı Yaban Hayatı Geliştirme Sahası’nda (YHGS) bulunan memeli türlerinin habitat uygunluk modellemelerinin yapılması ve potansiyel habitat uygunluk haritalarının ortaya konulması amaçlanmıştır. Türlerin tespiti amacıyla 2021–2023 yılları arasında elde edilen 38 bağımsız fotokapan kaydı ile arazi çalışmaları sırasında belirlenen 4 iz/dışkı kaydından elde edilen varlık verileri maksimum entropi yaklaşımı (MaxEnt) ile analiz edilmiştir. Çalışma sonucunda alanda en yaygın türler olarak belirlenen bozayı (Ursus arctos), karaca (Capreolus capreolus), kurt (Canis lupus) ve yaban tavşanının (Lepus europaeus) habitat uygunluk modelleri ve potansiyel yayılış haritaları oluşturulmuştur. Modellerin performansını gösteren AUC değerleri 0,85–0,93 arasında bulunmuştur. Araştırma sonucunda söz konusu dört türün habitat uygunluğunu etkileyen en önemli faktörün yola olan uzaklık olduğu, diğer önemli faktörlerin ise türe bağlı olarak suya olan uzaklık, yükseklik, bitki örtüsü sınıfları, eğim, bakı ve solar radyasyon olduğu belirlenmiştir.

Keywords

• Fotokapan
• MaxEnt
• Tür dağılım modeli
• Yaban hayatı yönetimi

Supporting Institution

Kastamonu Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

Kü-BAP01/2020-69

Habitat Suitability Modeling and Mapping of Mammal Species in the Ilgaz Mountain Wildlife Development Area

Abstract

Habitats where wildlife meet their vital needs such as feeding, sheltering, and reproduction are currently under the influence of climate change and anthropogenic threats. The management and conservation of habitats, which are deteriorating day by day in terms of quality and quantity, are becoming increasingly important for the future of wildlife. This study aimed to perform habitat suitability modeling of mammal species inhabiting the Ilgaz Mountain Wildlife Development Area (WDA), one of the important wildlife areas of the Western Black Sea Region, and to generate potential habitat suitability maps. Presence data obtained from 38 independent camera trap records collected between 2021 and 2023 and 4 track/scat records identified during field studies were analyzed using the Maximum Entropy (MaxEnt) approach. As a result of the study, habitat suitability models and potential distribution maps were generated for brown bear (Ursus arctos), roe deer (Capreolus capreolus), wolf (Canis lupus), and European hare (Lepus europaeus), which were identified as the most common species in the area. The AUC values indicating model performance were found to range between 0.85 and 0.93. The results of the study revealed that the most important factor affecting the distribution of these four species was distance to roads, while the other important factors varied depending on the species and included distance to water, elevation, vegetation classes, slope, aspect, and solar radiation.

Keywords

• Camera trap
• MaxEnt
• Species distribution model
• Wildlife management

Project Number

Kü-BAP01/2020-69

Thanks

Bu araştırmanın gerçekleştirilmesine katkı sağlayan Kastamonu Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi’ne (Proje No: KÜ-BAP01/2020-69) ve arazi çalışmaları sürecinde verdikleri desteklerden dolayı Kastamonu Doğa Koruma ve Milli Park Müdürlüğü’ne teşekkür ederiz.

References

1. Acarer, A. (2024). Saz kedisi (Felis chaus S.) habitat uygunluk modellemesi ve haritalaması. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi, 25(2), 154-166.
2. Allouche, O., Steinitz, O., Rotem, D., Rosenfeld, A., & Kadmon, R. (2008). Incorporating distance constraints into species distribution models. Journal of Applied Ecology, 45, 599–609.
3. Araújo, M.B., & Peterson, A.T. (2012). Uses and misuses of bioclimatic envelope modeling. Ecology, 93: 1527–1539.
4. Avcı, M., Oğurlu, İ., & Sarıkaya, O. (2005). Kasnak meşesi tabiatı koruma alanı faunası üzerine araştırmalar. In Korunan Doğal Alanlar Sempozyumu, (8-10 Eylül, Isparta, s. 599-606).
5. Baldwin, R.A. (2009). Use of Maximum entropy modeling in wildlife research. Entropy, 11(4), 854-866. https://doi.org/10.3390/e11040854.
6. Bao, X., Zhou, P., Zhang, M., Fang, Y., & Zhang, Q. (2024). MaxEnt-based habitat suitability assessment for Vaccinium mandarinorum: Exploring industrial cultivation opportunities. Forests, 15(12),2254.
7. Bascompte, J. (2009). Mutualistic networks. Frontiers in Ecology and the Environment, 7, 429–436.
8. Benítez-López, A., Alkemade, R., & Verweij, P.A. (2010). The impacts of roads and other infrastructure on mammal and bird populations: A meta‐analysis. Biological Conservation, 143(6), 1307–1316.

9. Broekhuis, F., & Gopalaswamy, A.M. (2016). Counting cats: Spatially explicit population estimates of cheetah (Acinonyx jubatus) using unstructured sampling data. Plos One, 11(5), e0153875.
10. Chain-Guadarrama, A., Finegan, B., Vilchez, S., & Casanoves, F. (2012). Determinants of rain-forest floristic variation on an altitudinal gradient in southern Costa Rica. Journal of Tropical Ecology, 28, 463–481.
11. Copernicus Land Monitoring Service (2018). High Resolution Layer Imperviousness (2012). European Environment Agency. Denmark. https://doi.org/10.2909/dd62f02d-a9e0-4e4f-906c-d5a913062deb.
12. Çalışkan, G., Tanrısever, C., & Mısırlı, İ. (2021). Ilgaz Dağı Milli Parkı’nda yer alan köylerin kırsal turizm algısı. Türk Turizm Araştırmaları Dergisi, 5(1), 313-332.
13. De Angelis, A., Ricotta, C., Conedera, M., & Pezzatti, G.B. (2015). Modelling the meteorological forest fire niche in heterogeneous pyrologic conditions. Plos One, 10(2), e0116875. https:// doi. org/ 10. 1371/journ al. pone. 01168 75
14. Doumenge, C. (1998). Forest diversity, distribution, and dynamique in the Itombwe Mountains, south-Kivu, Congo Democratic Republic. Mountain Research and Development, 18(3):249-264.
15. Edosa, B.T., & Erena, M.G. (2024). Wildlife habitat suitability analysis and mapping the former dhidhessa wildlife sanctuary using GIS-based analytical hierarchal process and weighted linear combination methods. Heliyon, 10(13),e33921.
16. Eilu, G., Hafashimana, D.L.N., & Kasenene, J.M. (2004). Density and species diversity of trees in four tropical forests of the Albertine rift, western Uganda. Diversity and Distributions, 10, 303–312.
17. 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.
18. Esri, (2021). ArcMap (Version 10.8) [Computer software]. Environmental Systems Research Institute. Redlands, CA, USA. https://www.esri.com/
19. Evcin, Ö. (2018). Kastamonu ve Sinop'ta karacanın (Capreolus capreolus) popülasyon ekolojisi (Doktora Tezi, Kastamonu Üniversitesi, Fen Bilimleri Enstitüsü).
20. Evcin, Ö. (2022). Monitoring the annual change of fauna in Ilgaz Mountain Wildlife Reserve by using biodiversity indices. Journal of Biometry Studies, 2(1), 7-14.
21. Evcin, Ö., Küçük, Ö., & Aktürk, E. (2019). Habitat suitability model with maximum entropy approach for European roe deer (Capreolus capreolus) in the Black Sea Region. Environmental Monitoring and Assessment, 191(11), 669.
22. Fernández-Juricic, E., Venier, M.P., Renison, D., & Blumstein, D.T. (2005). Sensitivity of wildlife to spatial patterns of recreationist behavior. Biological Conservation, 125(2), 225-235.
23. Forman, R.T.T., & Alexander, L.E. (1998). Roads and their major ecological effects. Annual Review of Ecology and Systematics, 29,207-231. https://www.annualreviews.org/doi/10.1146/annurev.ecolsys.29.1.207
24. Gittleman, J.L., & Harvey, P.H. (1982). Carnivore home-range size, metabolic needs and ecology. Behavioral Ecology and Sociobiology, 10(1), 57–63.
25. Google Earth, (2023). Google Earth (Version 9.4) [Computer software]. California, USA. https://www.google.com/earth/
26. Gómez-Mendoza, L., & Arriaga, L. (2007). Modeling the effect of climate change on the distribution of oak and pine species of Mexico. Conservation Biology, 21: 1545–1555.
27. Guisan, A., Tingley, R., Baumgartner, J.B., Naujokaitis-Lewis, I., Sutcliffe, P.R., Tulloch, A.I.T., Regan, T.J., Brotons, L., Mcdonald-Madden, E., Mantyka-Pringle, C., Martin, T.G., Rhodes, J.R., Maggini, R., Setterfield, S.A., Elith, J., Schwartz, M.W., Wintle, B.A., Broennimann, O., Austin, M., Ferrier, S., Kearney, M.R., Possingham, H.P., & Buckley, Y.M. (2013). Predicting species distributions for conservation decisions. Ecology Letters, 16, 1424–1435.
28. Gülçin, D., Evcin, Ö., Çiçek, K., Velázquez, J., & Özcan, A.U. (2026). Climate change-induced shifts in habitat suitability for the European roe deer (Capreolus capreolus L., 1758) across Europe and Türkiye. Mammal Research, 71(2), 21.
29. Gürkan, N. (2019). Sinop Boyabat Yöresinde kurt (Canis lupus)'un habitat uygunluk modeli ile potansiyel yayılışının tahmini (Doktora Tezi, Kastamonu Üniversitesi, Fen Bilimleri Enstitüsü).
30. Harte, J., Zillio, T., Conlisk, E., & Smith, A. B. (2008). Maximum entropy and the state‐variable approach to macroecology. Ecology, 89(10), 2700-2711.
31. Hemp, A. (2009). Climate change and its impact on the forests of Kilimanjaro. African Journal of Ecology, 47, 3–10.
32. Hernandez, P.A., Franke, I., Herzog, S.K., Pacheco, V., Paniagua, L., Quintana, H.L., Soto, A., Swenson, J., Tovar, C., Valqui, T., & Vargas, J. (2008). Predicting species distributions in poorly-studied landscapes. Biodiversity and Conservation, 17(6), 1353-1366.
33. Hiddink, J.G., Jennings, S., & Kaiser, M.J. (2007). Assessing and predicting the relative ecological impacts of disturbance on habitats with different sensitivities. Journal of Applied Ecology, 44(2), 405-413.
34. Hirzel, A., Hausser, J., Chessel, D., & Perrin, N. (2002). Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology, 83, 2027–2036.
35. Hota, D., Rajasekar, K., Parashar, T., Sobti, S., Roy, A., & Ajitha, P. (2025). Using the MaxEnt algorithm to predict habitat suitability under climate change scenarios. Natural and Engineering Sciences, 10(2), 270-283.
36. Ibrahim Mahmoud, M., Duker, A., Conrad, C., Thiel, M., Shaba Ahmad, H. (2016). Analysis of settlement expansion and urban growth modelling using geoinformation. Remote Sensing, 8(3), 220. Kalleci, B. (2023). Kastamonu Ilgaz Dağı Yaban Hayatı Geliştirme Sahası ve Gavurdağı Yaban Hayatı Geliştirme Sahası arasındaki yaban hayatı ekolojik koridorlarının belirlenmesi (Yüksek Lisans Tezi, Kastamonu Üniversitesi, Fen Bilimleri Enstitüsü).
37. Kalleci, B., & Evcin, Ö. (2025). Using circuit theory to identify important ecological corridors for large mammals between wildlife refuges. Diversity, 17(8), 542.
38. Kellner, C.J., Brawn, J.D. & Karr, J.R. (1992). What is habitat suitability and how should it be measured? In D.R. McCullough & R.H. Barrett (Eds.), Wildlife 2001: Populations (p.476-488). Springer, Dordrecht, The Netherlands.
39. Kija, H.K., Ogutu, J.O., Mangewa, L.J., Bukombe, J., Verones, F., Graae, B.J., Kideghesho, J.R., Said, M.Y., & Nzunda, E.F. (2020). Spatio-temporal changes in wildlife habitat quality in the greater Serengeti ecosystem. Sustainability, 12(6), 2440.
40. Kuloba, M.B., Gils, V.H., Duren, V.I., Muya, M.S., & Ngene, M.S. (2015). Modeling cheetah Acinonyx jubatus fundamental niche in Kenya. International Journal of Environmental Monitoring and Analysis, 3(5), 317-330.
41. Liu, T., Jiang, Z., Wang, W., Wang, G., Song, X., Xu, A., & Li, C. (2023). Changes in habitat suitability and population size of the endangered Przewalski's gazelle. Global Ecology and Conservation, 43, e02465.
42. Lobo, J.M. (2008). More complex distribution models or more representative data? Biodiversity Informatics, 5, 15–19.
43. Mattson, D.J., Blanchard, B.M., Knight, R.R. (1992). Yellowstone grizzly bear mortality, human habituation, and whitebark pine seed crops. The Journal of Wildlife Management, 56(3), 432–442.
44. Mysterud, A., & Østbye, E. (1999). Cover as a habitat element for temperate ungulates: effects on habitat selection and demography. Wildlife Society Bulletin, 27(2), 385–394.
45. Naqibzadeh, A., Sarhangzadeh, J., & Sayedi, N. (2021). Habitat suitability modeling of goitered gazelle (Gazella subgutturosa) by ecological niche factor analysis in the Bidouyeh protected area, Iran. Journal of Wildlife and Biodiversity, 5(4), 15-27.
46. Narouei-Khandan, H., Harmon, C., Harmon, P., Olmstead, J., Zelenev, V.V., van der Werf, W., Worner, S.P., Senay, S.D., & van Bruggen A.H.C. (2017). Potential global and regional geographic distribution of Phomopsis vaccinii on Vaccinium species projected by two species distribution models. European Journal of Plant Pathology, 148, 919–30.
47. NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. Sioux Falls, South Dakota, USA. https://doi.org/10.5067/ASTER/ASTGTM.003
48. Northrup, J.M., Pitt, J., Muhly, T.B., Stenhouse, G.B., Musiani, M., & Boyce, M.S. (2012). Vehicle traffic shapes grizzly bear behaviour on a multiple‐use landscape. Journal of Applied Ecology, 49(5), 1159–1167.
49. Oğul, A., Tuttu, U., Öner, M.N., Yorulmaz, T., & Özcan, A.U., (2022). Ilgaz Dağı’nda farklı orman kuruluşlarındaki bazı yaban hayvanlarının (Classis: Mammalia) aktivitelerinin belirlenmesi. Anadolu Orman Araştırmaları Dergisi, 8(1), 29-36.
50. Peters, D. S., & Cross, F.A. 1992. What is coastal fish habitat? In R. H. Stroud (Ed.), Stemming the tide of coastal fish habitat loss: proceedings of a symposium on conservation of coastal fish habitat (pp.17-22). Baltimore, MD, 7–9 March 1991.
51. Petitpierre, B., Kueffer, C., Broennimann, O., Randin, C., Daehler, C., & Guisan, A. (2012). Climatic niche shifts are rare among terrestrial plant invaders. Science, 335, 1344–1348.
52. Phillips, S.J., Anderson, R.P., & Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.
53. Pickering, C.M., & Green, K. (2009). Vascular plant distribution in relation to topography, soils and micro-climate at five GLORIA sites in the Snowy Mountains, Australia. Australian Journal of Botany, 57, 189–199.
54. Pimentel, D., Berger, B., Filiberto, D., Newton, M., Wolfe, B., Karabinakis, E., Clark, S., Poon, E., Abbett, E., & Nandagopal, S. (2004). Water resources: agricultural and environmental issues. BioScience, 54(10), 909-918.
55. Price, H.I.M., Gratzer, G., Duguma, L.A., Kohler, T., Maselli, D., & Romeo, R. (2011). Mountain forests in a changing world: realizing values, addressing challenges. Rome: FAO/Mountain Partnership Secretariat.
56. Radosavljevic, A., & Anderson, R.P. (2014). Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41(4), 629-643.
57. Raxworthy, C.J., Martinez-Meyer, E., Horning, N., Nussbaum, R.A., Schneider, G.E., Ortega-Huerta, M.A., & Townsend Peterson, A. (2003). Predicting distributions of known and unknown reptile species in Madagascar. Nature, 426, 837–841.
58. Said, S., & Servanty, S. (2005). The influence of landscape structure on female roe deer home-range size. Landscape Ecology, 20(8), 1003–1012.
59. Slater, H., & Michael, E. (2012). Predicting the current and future potential distributions of lymphatic filariasis in Africa using maximum entropy ecological niche modelling. PloS one, 7(2), e32202.
60. Süel, H., Şentürk, Ö., Mert, A., Özdemir, S., & Yalçınkaya, B. (2018). Habitat suitability modeling and mapping. In Proceedings of VI. International Multidisciplinary Congress of Eurasia, (July 24-26, Barcelona, Spain, pp. 2018).
61. T.C. Orman ve Su İşleri Bakanlığı, (2012). Kastamonu Ilgaz Dağı Yaban Hayatı Geliştirme Sahası yönetim ve gelişme planı. Doğa Koruma ve Milli Parklar Genel Müdürlüğü, 10. Bölge Müdürlüğü, Sinop, Türkiye.
62. Taylor, D.A.R., & Tuttle, M.D. (2012). Water for Wildlife: A handbook for ranchers and range managers. Austin: Bat Conservation International.
63. Trombulak, S.C., & Frissell, C.A. (2000). Review of ecological effects of roads on terrestrial and aquatic communities. Conservation Biology, 14(1), 18-30.
64. Tse-ring, K., Sharma, E., Chettri, N., & Shrestha, A., (2010). Climate change vulnerability of mountain ecosystems in the eastern himalayas; synthesis report. Kathmandu, Nepal: International Centre for Integrated Mountain Development.
65. Uzun, G., & Evcin, Ö. (2025). Determination of dispersal corridors used by large mammals between close habitats. Diversity, 17(3), 165.
66. van Dam, N. M. (2009). How plants cope with biotic interactions. Plant Biology, 11,1–5.
67. Wang, P., Feng, B., Zhang, L., Fan, X., Tang, Z., Dong, X., Zhang, J., Zhou, C., & Bai, W. (2023). Assessment of habitat suitability and connectivity across the potential distribution landscape of the sambar (Rusa unicolor) in Southwest China. Frontiers in Conservation Science, 3, 909072.
68. Weaver, J.L., Paquet, P.C., & Ruggiero, L.F. (1996). Re-silience and conservation of large carnivores in the RockyMountains. Conservation Biology, 10,964–976.
69. Zhu, L., Sun, O.J., Sang, W., Li, Z., & Ma, K. (2007). Predicting the spatial distribution of an invasive plant species (Eupatorium adenophorum) in China. Landscape Ecology, 22, 1143–1154.