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Forest patch connectivity diagnostics and prioritization using graph theory

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  • Shanthala Devi, B.S.
  • Murthy, M.S.R.
  • Debnath, Bijan
  • Jha, C.S.

Abstract

Landscape level forest connectivity regulates species level biodiversity, wildlife movement, seed dispersal and ecological factors. Geospatial assessment of forest connectivity at the landscape level is realized as one of the important frameworks to prioritize the biodiversity conservation strategies. The paper presents an approach to identify the optimal threshold distance and set of forest patches (component) using graph theory to propose potential connectivity alternatives over fragmented tropical deciduous forest tracts in parts of Eastern Ghats of India. The study analyzed 598 forest patches (derived from remote sensing data) constituting an area of 3502.87km2 which is distributed over 10,807km2. Optimal threshold distance and components were derived using graph theory based connectivity indices namely, Integral Index of Connectivity importance value (dIIC), Landscape Coincidence Probability importance value (dLCP) and also addressing patch size, number, inter distance and relative importance of each patch in the totality of the landscape. The study identified a total of 191 components at an optimal threshold distance of 250m for potential connectivity. A component consisting of 145 patches of which 5 patches having high relative importance value within the component were identified for prioritizing the connectivity efforts. Further analysis on the three fractions of dIIC (dIICintra, dIICflux and dIICconnector) justified the importance of individual patches for connectivity. Proposed connectivity could enhance the forest habitat network through these potential patches.

Suggested Citation

  • Shanthala Devi, B.S. & Murthy, M.S.R. & Debnath, Bijan & Jha, C.S., 2013. "Forest patch connectivity diagnostics and prioritization using graph theory," Ecological Modelling, Elsevier, vol. 251(C), pages 279-287.
    • Handle: RePEc:eee:ecomod:v:251:y:2013:i:c:p:279-287
    DOI: 10.1016/j.ecolmodel.2012.12.022
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    References listed on IDEAS

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    1. Bodin, Örjan & Saura, Santiago, 2010. "Ranking individual habitat patches as connectivity providers: Integrating network analysis and patch removal experiments," Ecological Modelling, Elsevier, vol. 221(19), pages 2393-2405.
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    2. Bo Mu & Guohang Tian & Gengyu Xin & Miao Hu & Panpan Yang & Yiwen Wang & Hao Xie & Audrey L. Mayer & Yali Zhang, 2021. "Measuring Dynamic Changes in the Spatial Pattern and Connectivity of Surface Waters Based on Landscape and Graph Metrics: A Case Study of Henan Province in Central China," Land, MDPI, vol. 10(5), pages 1-21, May.
    3. Almpanidou, Vasiliki & Mazaris, Antonios D. & Mertzanis, Yorgos & Avraam, Ioannis & Antoniou, Ioannis & Pantis, John D. & Sgardelis, Stefanos P., 2014. "Providing insights on habitat connectivity for male brown bears: A combination of habitat suitability and landscape graph-based models," Ecological Modelling, Elsevier, vol. 286(C), pages 37-44.
    4. Shiyi Guo & Kaoru Saito & Weida Yin & Chang Su, 2018. "Landscape Connectivity as a Tool in Green Space Evaluation and Optimization of the Haidan District, Beijing," Sustainability, MDPI, vol. 10(6), pages 1-14, June.
    5. Jia Liu & Jianjun Chen & Yanping Yang & Haotian You & Xiaowen Han, 2023. "Construction and Optimization of an Ecological Network in the Yellow River Source Region Based on MSPA and MCR Modelling," IJERPH, MDPI, vol. 20(4), pages 1-17, February.
    6. Simone Valeri & Laura Zavattero & Giulia Capotorti, 2021. "Ecological Connectivity in Agricultural Green Infrastructure: Suggested Criteria for Fine Scale Assessment and Planning," Land, MDPI, vol. 10(8), pages 1-16, July.

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