Assessment of Habitat Suitability Using GIS-Based Multi-Criteria Methods

Yıl 2024, Cilt: 7 Sayı: 13, 26 - 36, 15.07.2024

Cumhur Güngöroğlu , Oğulcan Gürsoy

Öz

Habitat suitability studies in wildlife management provide important bases for the conservation, development and use of species and their habitats. The habitat suitability index is a useful tool for understanding the quality and potential spatial distribution of habitat provided to wild animals. There are basic approaches to the concepts of habitat selection and habitat quality, such as evaluating the environmental conditions of the habitat as adaptability to ensure the continuity of species individuals and populations. Habitat quality is easiest to conceptualize in terms of a single wildlife species rather than multiple species. GIS is used as a central tool to produce geo-referenced ecological data needed in habitat suitability analysis models, running the models, and presenting the analysis results. The advantages of using GIS in habitat suitability studies are linked to the possibilities of considering habitat factors at different scales, combining habitat suitability assessments for various species and weighting different species in different ways, and integrating expert knowledge with empirical models. In this study, it is aimed to introduce the prominent requirements and some methods used in the evaluation of habitat suitability with GIS-based multi-criteria methods. Finally, basic approaches have been made on how multi-criteria decision-making methods integrated with GIS can be used, especially in evaluating the habitat suitability of multiple species.

Anahtar Kelimeler

Habitat Suitability, GIS, Multi-Criteria Analysis

HABİTAT UYGUNLUĞUNUN CBS TABANLI ÇOK KRİTERLİ YÖNTEMLERLE DEĞERLENDİRİLMESİ

Öz

Yaban hayatı yönetiminde habitat uygunluğu çalışmaları türlerin ve onların yaşam alanlarının korunması, geliştirilmesi ve kullanımı için önemli altlıklar sağlamaktadır. Habitat uygunluk indeksi yaban hayvanlarına sağlanan habitatının kalitesini ve potansiyel mekânsal dağılımını anlamak içinde yararlı bir araçtır. Habitat seçimi ve habitat kalitesi kavramlarında habitata ait çevresel koşulların tür bireylerinin ve popülasyonlarının devamlılığını sağlamaya yönelik uyum yeteneği olarak değerlendirilmesi şeklinde temel yaklaşımlar bulunmaktadır. Habitat kalitesinin, tek bir yaban hayvan türüne göre kavramsallaştırılması çoklu türe göre en kolay olanıdır. CBS habitat uygunluk analizi modellerinde ihtiyaç duyulan coğrafi referanslı ekolojik verilerin üretilmesi, modellerin çalıştırılması ve analiz sonuçlarının sunulması için merkezi bir araç olarak kullanılmaktadır. CBS’nin habitat uygunluğu çalışmalarında kullanılmasının avantajları, habitat faktörlerini farklı ölçeklerde dikkate alma, çeşitli türler için habitat uygunluk değerlendirmelerini birleştirme ve farklı türleri farklı şekillerde ağırlıklandırma ve ampirik modeller ile uzman bilgisini entegre etme olanaklarıyla bağlantılıdır. Bu çalışmada habitat uygunluğunun CBS tabanlı çok kriterli yöntemlerle değerlendirilmesinde öne çıkan gereksinimler ve kullanılan bazı yöntemlerin tanıtılması amaçlanmıştır. Son olarak CBS ile entegre edilmiş çok kriterli karar verme yöntemlerin özellikle çoklu türlerin habitat uygunluğunun değerlendirilmesinde nasıl kullanılabileceği üzerine temel yaklaşımlarda bulunulmuştur.

Anahtar Kelimeler

Habitat Uygunluğu, CBS, Çok Kriterli Analizler

Kaynakça

• Bailey, L., Adams, M. 2005. "Occupancy Models to Study Wildlife". U.S. Geological Survey, Fact Sheet 2005-3096.
• Bellamy, C., Scott, C., Altringham, J. (2013). “Multiscale, presence-only habitat suitability models: fine-resolution maps for eight bat species”. Journal of Applied Ecology, 50 (4):892 -901.
• Bilgin, C.C., (proje yürütücüsü). (2014). “Dünyadaki Tek Otokton Alageyik (Dama dama) Populasyonun Yeni Uygun Alanlara Aşılanmasının Yayılış Modellemesi, Alan Değerlendirmesi ve Populasyon Yaşayabilirlik Analizi Yöntemleriyle Tasarımı ve İlk Aşılamanın Telemetri ile İzlenmesi”, Proje Sonuç Raporu, TÜBİTAK Proje No: 110 O 563.
• Bragin, N., Amgalanbaatar, S., Wingard, G., Reading, R.P. (2017). “Creating a model of habitat suitability using vegetation and ruggedness for Ovis ammon and Capra sibirica (Artiodactyla: Bovidae) in Mongolia”. Journal of Asia-Pacific Biodiversity, 10:390-395.
• Broekman, M. J., Hilbers, J. P., Huijbregts, M. A., Mueller, T., Tucker, M. A. (2022). “Evaluating expertbased habitat suitability information of terrestrial mammals with GPS-tracking data”. Global Ecology and Biogeography, 31:1526-1541.
• Boyce, M. S., McDonald, L.L. (1999). “Relating populations to habitats using resource selection functions”. Trends in Ecology & Evolution, 14:268-272.
• CREAX AGENCY. “Deforestation: Destruction of habitat. Perspective of humanity's impact on climate change”. Generative IA By CREAX AGENCY. https://stock.adobe.com (20.06.2024)
• Ertuğrul, E.T., Mert, A., Oğurlu, İ., (2017).” Mapping habitat suitabilities of some wildlife species in Burdur Lake District”. Turkish Journal of Forestry, 18(2):149-154.
• ESRI. “ArcGIS Spatial Analyst-Suitability Modelling”. By Johnston,K.M., Grahan, E. https:// proceedings.esri.com/library/userconf/proc15/tech-workshops/tw_381-28.pdf (05.07.2024)
• 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.
• Ford, A. T., Goheen, J. R., Otieno, T. O., Bidner, L., Isbell, L. A., Palmer, T. M., Ward, D., Woodroffe, R., Pringle, R. M. (2014). “Large carnivores make savanna tree communities less thorny”. Science, 346:346-349.
• Guisan, A., Tingley, R., Baumgartner, J.B.,… Buckley, Y.M. (2013). “Predicting species distributions for conservation decisions”. Ecology Letters, 16:1424-1435.
• Güngöroğlu, C. (2011). “Ekoloji tabanlı envanter, planlama ve yönetim uygulamalarında CBS’nin kullanılması”. TMMOB Coğrafi Bilgi Sistemleri Kongresi, 31.10-4.11.2011.Antalya
• Hale, R., Swearer, S.E. (2016). “Ecological traps: current evidence and future directions”. Proceedings of the Royal Society B: Biological Sciences. 283: 20152647.
• Hall, L.S., Krausman, P. R., Morrison, M. L. (1997). “The Habitat concept and a plea for standard terminology”. Wildlife Society Bulletin, 25:171-182.
• Hobbs, N. T., Hanley. T.A.(1990). “Habitat evaluation: do use/availability data reflect carrying capacity?” Journal of Wildlife Management, 54:515-522.
• Johnson, M. D. (2005). “Habitat Quality: A Brief Review For Wildlife Biologists”. Transactions of the Western Section of The Wildlife Society, 41:31-40.
• Latif, Q.S., Saab, V.A., Haas, J.R., Dudley, J.G. (2018). FIRE-BIRD: A GIS-based toolset for applying habitat suitability models to inform land management planning. Gen. Tech. Rep. RMRS-GTR-391. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 74 S.
• Lissovsky, A.A., Dudov, S.V. (2021). “Species-Distribution Modeling: Advantages and Limitations of Its Application. 2. MaxEnt”. Biology Bulletin Reviews, 11 (3):135-146.
• MacKenzie, D. I., Nichols, J.D., Lachman, G. B., Droege, S., Royle, J.A., Langtimm, C. A. (2002). “Estimating Site Occupancy Rates when Detection Probabilities Are Less than One.” Ecology, 83: 2248–55.
• Mackenzie, D.I., Royle, J.A. (2005). “Designing occupancy studies: general advice and allocating survey effort”. Journal of Applied Ecology, 42:1105–1114.
• MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Bailey, L.L., Hines, J.E. (2006). “Occupancy Estimation and Modeling. Inferring patterns and dynamics of species occurrence”. Academic Press,USA,
• Manley, B. F. J., McDonald, L. L., Thomas, D.L., McDonald, T.L., Erickson, W.P. (2002). “Resource selection by animals”. Second edition. Kluwer Academic Publishers, Dordrecht, Netherlands.
• Matthiopoulos, J., Fieberg, J., Aarts, G., Beyer, H. L., Morales, J. M., Haydon,, D.T. (2015). “Establishing the link between habitat selection and animal population Dynamics”. Ecological Monographs, 85, 413-436.
• Morrison, M.L., Marcot, B. G., Mannan, R. W. (1998). “Wildlife-habitat relationships: concepts and applications”. Second edition. The University of Wisconsin Press, Madison, USA.
• Muhammed, K., Anandhi, A., Chen, G. (2021). “Comparing Methods for Estimating Habitat Suitability”. Land, 11, 1754.
• Northrup, J.M., Wal, E.V., Bonar, M., Fieberg, J., Michel P., Laforge, M.P., Leclerc, M., Prokopenko, C.M., Gerber, B.D. (2022). “Conceptual and methodological advances in habitat-selection modeling: guidelines for ecology and evolution”. Ecological Applications, 32(1), e02470.
• Oruç, M.S., Mert, A., Özdemir, İ. (2017). “Eskişehir Çatacık Yöresinde, Çevresel Değişkenler Kullanılarak Kızılgeyik İçin (Cervus elaphus L.) Habitat Uygunluğunun Modellenmesi”. Bilge International Journal of Science and Technology Research, 1 (2):135-142.
• Questad, E.J., Kellner, J.R., Kinney, K., Cordell, S., … Tucker, B. (2014). “Mapping habitat suitability for at-risk plant species and its implications for restoration and reintroduction”. Ecological Applications, 24(2):385-395.
• Paudel, K.P., Hais, M., Kindlmann, P. (2015). “Habitat suitability models of mountain ungulates: identifying potential areas for conservation”. Zoological Studies, 54, 37.
• Phillips, S.J., Dudík, M. (2008). “Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation”, Ecography, 31 (2):161-175.
• Phillips, S.J., Anderson, R.P., Schapire, R.E. (2006). “Maximum entropy modeling of species geographic distributions”. Ecological Modelling, 190 (3-4):231–259.
• Phillips, S.J., Anderson, R.P., Dudík, M., Schapire, R.E., Blair, M.E. (2017). “Opening the black box: an opensource release of Maxent”, Ecography, 40 (7):887–893.
• Phillips, S.J., Dudík, M., Schapire, R.E., (2019).” Maxent software for modeling species niches and distributions”, Version 3.4.1, 2019. http://biodiversityinformatics.amnh.org/open_source/maxent.
• Pulliam, H. R. (2000). “On the relationship between niche and distribution”. Ecology Letters, 3: 349-361.
• Shafer, A., Northrup, J. M., White, K. S., Boyce, M. S., Cote, S. D., Coltman, D. W. (2012). “Habitat selection predicts genetic relatedness in an alpine ungulate”. Ecology, 93:1317-1329.
• Sohl, T. L. (2014). “The relative impacts of climate and land-use change on conterminous United States bird species from 2001 to 2075”. PLoS One, 9, e112251.
• Store, R., Kangas, J. (2001). “Integrating spatial multi-criteria evaluation and expert knowledge for GIS-based habitat suitability modelling”. Landscape and Urban Planning, 55:79-93.
• Store, R., Jokimaki, J. (2003). “A GIS-based multi-scale approach to habitat suitability modeling”. Ecological Modelling, 169:1-15.
• Su, H., Bist, M., Li, M. (2021). “Mapping habitat suitability for Asiatic black bear and red panda in Makalu Barun National Park of Nepal from Maxent and GARP models”. Scientifc Reports, 11, 14135.
• Tardy, O., Masse, A., Pelletier, F., Mainguy, J., Fortin, D. (2014). “Density-dependent functional responses in habitat selection by two hosts of the raccoon rabies virus variant”. Ecosphere, 5, art132
• Theuerkauf, S.J., Lipcius, R.N. (2016). “Quantitative Validation of a Habitat Suitability Index for Oyster Restoration”. Frontiers in Marine Science, 3:64.
• U.S. EPA (2008) Predicting future introductions of nonindigenous species to the Great Lakes. National Center for Environmental Assessment, Washington, DC; EPA/600/R-08/066F.
• WWF. Wildlife https://explore.panda.org/wildlife (16.06.2024)
• Yackulic, C.B., Chandler, R., Zipkin, E.F., Royle, J.A., Nichols, J.D., Grant, E.H.C., Veran, S. (2013). “Presence-only modelling using MAXENT: when can we trust the inferences?” Methods in Ecology and Evolution, 4:236-243.
• Zabihi, K., Paige, G.B., Hild, A.L., Miller, S.N., Wuenschel, A., Holloran, M.J. 2017. "A fuzzy logic approach to analyse the suitability of nesting habitat for greater sage-grouse in western Wyoming". Journal of Spatial Science, 62 (2): 215–234.