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Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution

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  • Meineri, Eric
  • Dahlberg, C. Johan
  • Hylander, Kristoffer

Abstract

Landscape physiography affects temperature, soil moisture and solar radiation. In turn, these variables are thought to determine how species are distributed across landscapes. Systems involving direct and indirect associations between variables can be described using path models. However, studies applying these to species distribution modelling are rare.

Suggested Citation

  • Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
    • Handle: RePEc:eee:ecomod:v:313:y:2015:i:c:p:127-136
    DOI: 10.1016/j.ecolmodel.2015.06.028
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    References listed on IDEAS

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    2. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).

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