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Circuit Theory and Model-Based Inference for Landscape Connectivity

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  • Ephraim M. Hanks
  • Mevin B. Hooten

Abstract

Circuit theory has seen extensive recent use in the field of ecology, where it is often applied to study functional connectivity. The landscape is typically represented by a network of nodes and resistors, with the resistance between nodes a function of landscape characteristics. The effective distance between two locations on a landscape is represented by the resistance distance between the nodes in the network. Circuit theory has been applied to many other scientific fields for exploratory analyses, but parametric models for circuits are not common in the scientific literature. To model circuits explicitly, we demonstrate a link between Gaussian Markov random fields and contemporary circuit theory using a covariance structure that induces the necessary resistance distance. This provides a parametric model for second-order observations from such a system. In the landscape ecology setting, the proposed model provides a simple framework where inference can be obtained for effects that landscape features have on functional connectivity. We illustrate the approach through a landscape genetics study linking gene flow in alpine chamois ( Rupicapra rupicapra ) to the underlying landscape.

Suggested Citation

  • Ephraim M. Hanks & Mevin B. Hooten, 2013. "Circuit Theory and Model-Based Inference for Landscape Connectivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 22-33, March.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:501:p:22-33
    DOI: 10.1080/01621459.2012.724647
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    Citations

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    Cited by:

    1. Yanping Yang & Jianjun Chen & Renjie Huang & Zihao Feng & Guoqing Zhou & Haotian You & Xiaowen Han, 2022. "Construction of Ecological Security Pattern Based on the Importance of Ecological Protection—A Case Study of Guangxi, a Karst Region in China," IJERPH, MDPI, vol. 19(9), pages 1-22, May.
    2. Sahar Zarmehri & Ephraim M. Hanks & Lin Lin, 2021. "A Sample Covariance-Based Approach For Spatial Binary Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 220-249, June.
    3. Ephraim M. Hanks & Devin S. Johnson & Mevin B. Hooten, 2017. "Reflected Stochastic Differential Equation Models for Constrained Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 353-372, September.
    4. Jianying Xu & Feifei Fan & Yanxu Liu & Jianquan Dong & Jixing Chen, 2019. "Construction of Ecological Security Patterns in Nature Reserves Based on Ecosystem Services and Circuit Theory: A Case Study in Wenchuan, China," IJERPH, MDPI, vol. 16(17), pages 1-15, September.
    5. Ephraim M. Hanks, 2017. "Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 497-507, April.
    6. Xueping Su & Yong Zhou & Qing Li, 2021. "Designing Ecological Security Patterns Based on the Framework of Ecological Quality and Ecological Sensitivity: A Case Study of Jianghan Plain, China," IJERPH, MDPI, vol. 18(16), pages 1-32, August.
    7. Manuel Wolff & Dagmar Haase & Jörg Priess & Tobias Leander Hoffmann, 2023. "The Role of Brownfields and Their Revitalisation for the Functional Connectivity of the Urban Tree System in a Regrowing City," Land, MDPI, vol. 12(2), pages 1-22, January.

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