Use of Centrality Metrics to Protect Wildlife Ecology and Habitat Connectivity Analysis

Year 2021, Volume: 21 Issue: 3, 268 - 276, 31.12.2021

Huriye Simten Sütün

https://doi.org/10.17475/kastorman.1049353

Abstract

Aim of study: Aim of this study was to conduct a habitat connectivity analysis using centrality metrics to protect the regional wildlife-habitat connections of the Siirt landscape, and to ensure the continuity of ecological flows in the landscape.
Area of study: Increasing urbanisation trend in recent years and human impact on natural resources cause the diversity in Siirt landscape to be negatively affected. Centrality metrics were sampled in Siirt landscape in terms of re-establishing and maintaining the connectivity in the landscape.
Material and methods: Environmental Plan was used to define the core areas. Land cover/land uses were digitised using Geographical Information Systems. Firstly, landscape connectivity corridors defined with least-cost-path and secondly, current flow centrality was used with circuit theory.
Main results: The core area with the strongest flow centrality was forest, with a value of 14.6, and the core area with the weakest flow centrality was marsh at 8.23. The core areas that establish the easiest and strongest connection with each other are wetland-pasture, pasture-geologically reserved area, and pasture-ecological area.
Highlights: The greater the distance between core areas, the weaker the degree of connectivity between species and habitats. Increasing the distance between core areas negatively affects the ecological flow.

Keywords

Centrality Metrics, Circuit Theory, Habitat Connectivity, Least-Cost-Path, Ecological Flow.

Yaban Hayatı Ekolojisini Korumak Amacıyla Merkezlik Metriklerinin Kullanılması ve Habitat Bağlantılılık Analizi

Abstract

Çalışmanın amacı: Bu çalışmanın amacı. Siirt peyzajının bölgesel yaban hayatı ve habitat bağlantılarını korumak ve peyzajdaki ekolojik akışların sürekliliğini sağlamak için merkezlik metriklerini kullanarak bir habitat bağlantı analizi yapmaktır.
Çalışma alanı: Son yıllarda artan kentleşme eğilimi ve doğal kaynaklar üzerindeki insan etkisi Siirt coğrafyasındaki çeşitliliğin olumsuz etkilenmesine ve parçalanmanın artmasına neden olmaktadır. Peyzajdaki bağlantının yeniden kurulması ve sürdürülmesi açısından merkezlik metrikleri Siirt peyzajında örneklenmiştir.
Materyal ve yöntem: Siirt peyzajında çekirdek alanları tanımlamak için 1/100.000 ölçekli Çevre Düzeni Planı kullanılmıştır. Bu plandaki tüm arazi örtüsü/arazi kullanımları Coğrafi Bilgi Sistemleri kullanılarak sayısallaştırılmıştır. En düşük maliyetli yol yöntemi ile peyzaj bağlantı koridorları tanımlanmış, merkezlik metrikleri için de devre teorisi kullanılmıştır.
Temel sonuçlar: Siirt peyzajında en güçlü akış merkeziliğine sahip çekirdek alan 14.6 değeriyle orman, en zayıf akış merkeziliğine sahip çekirdek alan 8.23 ile bataklıktır. Birbiriyle en kolay ve en güçlü bağlantıyı kuran çekirdek alanların sulak alan-mera, mera-jeolojik açıdan sakıncalı alan ve mera-ekolojik alan olduğu görülmüştür.
Araştırma vurguları: Çekirdek alanlar arasındaki mesafe ne kadar büyükse, türler ve habitatlar arasındaki bağlantı derecesi o kadar zayıftır. Çekirdek alanlar arasındaki mesafenin artması ekolojik akışı olumsuz etkilemektedir.

Keywords

Merkezlik metrikleri, Devre teorisi, Habitat Bağlantılılığı, En Düşük Maliyetli Yol, Ekolojik Akış.

References

• Allen, A. M. & Singh, N. J. (2016). Linking movement ecology with wildlife management and conservation. Frontiers in Ecology and Evolution, 3(155). doi:10.3389/fevo.2015.00155.
• Borgatti, S. (2005). Centrality and Network Flow. Social Networks, 27, 55-71. doi:10.1016/j.socnet.2004.11.008.
• Bunn, A. G., Urban, D. L. & Keitt, T. H. (2000). Landscape connectivity: A conservation application of graph theory. Journal of Environmental Management, 59(4), 265-278. doi:https://doi.org/10.1006/jema.2000.0373.
• Carroll, C., McRae, B. H. & Brookes, A. (2012). Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of Gray wolf populations in Western North America. Conservation Biology, 26(1), 78-87.
• Cushman, S. A. & Landguth, E. (2010). Scale dependent inference in landscape genetics. Landscape Ecology, 25, 967-979. doi:10.1007/s10980-010-9467-0.
• Cushman, S. A., McKelvey, K. S., Hayden, J. & Schwartz, M. K. (2006). Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. The American Naturalist, 168(4), 486-499. doi:10.1086/506976.
• D’Elia, J., Brandt, J., Burnett, L. J., Haig, S. M., Hollenbeck, J., Kirkland, S. & Young, R. (2020). Applying circuit theory and landscape linkage maps to reintroduction planning for California Condors. Plos One, 14(12), e0226491. doi:10.1371/journal.pone.0226491.
• Dutta, T., Sharma, S., McRae, B. H., Roy, P. S. & DeFries, R. (2016). Connecting the dots: mapping habitat connectivity for tigers in central India. Regional Environmental Change, 16(1), 53-67. doi:10.1007/s10113-015-0877-z.
• Dyer, R. J., Nason, J. D. & Garrick, R. C. (2010). Landscape modelling of gene flow: improved power using conditional genetic distance derived from the topology of population networks. Mol Ecol, 19(17), 3746-3759. doi:10.1111/j.1365-294X.2010.04748.x.
• Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. Annual Review of Ecology, Evolution, and Systematics, 34(1), 487-515. doi:10.1146/annurev.ecolsys.34.011802.132419.
• Forman, R. T. T. (1983). An ecology of the landscape. BioScience, 33(9), 535-535. doi:10.1093/bioscience/33.9.535.
• Forman, R. T. T. (1995). Land mosaics: The ecology of landscapes and regions Cambridge. New York: Cambridge University Press.
• Forman, R. T. T. & Godron, M. (1986). Landscape ecology. New York: Wiley.
• Fraser, K. C., Davies, K. T. A., Davy, C. M., Ford, A. T., Flockhart, D. T. T. & Martins, E. G. (2018). Tracking the conservation promise of movement ecology. Frontiers in Ecology and Evolution, 6(150). doi:10.3389/fevo.2018.00150.
• Hanks, E. M. & Hooten, M. (2013). Circuit theory and model-based inference for landscape connectivity. Journal of the American Statistical Association, 108(501), 22-33. doi:10.1080/01621459.2012.724647.
• Hanski, I. & Ovaskainen, O. (2003). Metapopulation theory for fragmented landscapes. Theoretical Population Biology 64(1), 119-127. doi:10.1016/s0040-5809(03)00022-4.
• Kindlmann, P. & Burel, F. (2008). Connectivity measures: A review. Landscape Ecology, 23(8), 879-890. doi:10.1007/s10980-008-9245-4.
• Locke, H., Ellis, E., Venter, O., Schuster, R., Ma, K., Shen, X. & Watson, J. (2019). Three global conditions for biodiversity conservation and sustainable use: An implementation framework. Proceedings of the National Science Council, 6. doi:10.1093/nsr/nwz136.
• Lookingbill, T. R., Gardner, R. H., Ferrari, J. R. & Keller, C. E. (2010). Combining a dispersal model with network theory to assess habitat connectivity. Ecological Applications, 20(2), 427-441. doi:https://doi.org/10.1890/09-0073.1.
• Lundberg, J. & Moberg, F. (2003). Mobile link organisms and ecosystem functioning: Implications for ecosystem resilience and management. Ecosystems, 6(1), 0087-0098. doi:10.1007/s10021-002-0150-4. McRae, B. H. (2006). Isolation by resistance. Evolution, 60(8), 1551-1561. doi:https://doi.org/10.1111/j.0014-3820.2006.tb00500.x.
• McRae, B. H. (2012). Centrality Mapper Connectivity Analysis Software. Retrieved from http://www.circuitscape.org/linkagemapper.
• McRae, B. H. & Beier, P. (2007). Circuit theory predicts gene flow in plant and animal populations. Proceedings of the National Academy of Sciences, 104(50), 19885-19890. doi:10.1073/pnas.0706568104.
• McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. (2008). Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology, 89(10), 2712-2724. doi:10.1890/07-1861.1.
• McRae, B. H. & Kavanagh, D. M. (2011). Linkage Mapper Connectivity Analysis Software. Retrieved from http://www.circuitscape.org/linkagemapper.
• Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D. & Smouse, P. E. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105(49), 19052-19059. doi:10.1073/pnas.0800375105.
• Newman, M. E. J. (2010). Networks: An introduction. New York: Oxford University Press.
• Owen-Smith, N., Fryxell, J. M. & Merrill, E. H. (2010). Foraging theory upscaled: the behavioural ecology of herbivore movement. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1550), 2267-2278. doi:10.1098/rstb.2010.0095.
• Rachlow, J. L. (2008). Wildlife Ecology. In S. E. Jørgensen & B. D. Fath (Eds.), Encyclopedia of ecology, 3790-3794. Oxford: Academic Press.
• Rayfield, B., Fortin, M. J. & Fall, A. (2011). Connectivity for conservation: a framework to classify network measures. Ecology, 92(4), 847-858. doi:https://doi.org/10.1890/09-2190.1.
• Rempel, R. (2015). Spatial ecology program-analysis tools/patch analyst. Ontario, Ontario, United States of America: Queens Press, Ontario Ministry of Natural Resources and Forestry. Retrieved from http://www.cnfer.on.ca/SEP/.
• Saura, S. & Pascual-Hortal, L. (2007). A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study. Landscape and Urban Planning, 83(2), 91-103. doi:https://doi.org/10.1016/j.landurbplan.2007.03.005.
• Saura, S. & Rubio, L. (2010). A common currency for the different ways in which patches and links can contribute to habitat availability and connectivity in the landscape. Ecography, 33(3), 523-537. doi:https://doi.org/10.1111/j.1600-0587.2009.05760.x.
• Shi, F., Liu, S., An, Y., Sun, Y., Zhao, S., Liu, Y. & Li, M. (2020). Spatio-temporal dynamics of landscape connectivity and ecological network construction in Long Yangxia Basin at the Upper Yellow River. Land, 9(8), 265. Retrieved from https://www.mdpi.com/2073-445X/9/8/265.
• Taylor, P. D., Fahrig, L., Henein, K. & Merriam, G. (1993). Connectivity is a vital element of landscape structure. Oikos, 68, 571-573.
• Tischendorf, L. & Fahrig, L. (2000). On the usage and measurement of landscape connectivity. Oikos, 90(1), 7-19. doi:10.1034/j.1600-0706.2000.900102.x.
• Urban, D. L., Minor, E. S., Treml, E. A. & Schick, R. S. (2009). Graph models of habitat mosaics. Ecology Letters, 12(3), 260-273. doi:https://doi.org/10.1111/j.1461-0248.2008.01271.x
• Xun, B., Yu, D. & Liu, Y. (2014). Habitat connectivity analysis for conservation implications in an urban area. Acta Ecologica Sinica, 34(1), 44-52. doi:https://doi.org/10.1016/j.chnaes.2013.11.006.