Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation

By Bruce G. Marcot, J. D. Steventon, G. Sutherland, Robert K. Mccann. 2006

681 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation, Probabilistic & Bayesian Methods

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Citations in the corpus

Bayesian Structure Learning with Generative Flow Networks (2022, background): “…representing uncertainty about the world in multiple scientiﬁc domains, including medical diagnosis (Lau-ritzen and Spiegelhalter, 1988; Heckerman and Nathwani, 1992), molecular biology (Friedman, 2004; Sebastiani et al., 2005), and ecological modeling (Varis and Kuikka, 1999; Marcot et al., 2006).”
A review of Bayesian belief networks in ecosystem service modelling (2013, methodology): “To prevent this dilution of interactions, it is recommended to limit the number of node layers or sequential relationships to less than five (Marcot et al., 2006).”
Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks (2018, background): “The variance reduction values range from 0 to 100%, where a higher value indicates a higher influence [18,49].”
Bayesian belief networks: applications in ecology and natural resource management (2006, positive): “Additionally, sensitivity analyses can be used to help build the model correctly (Marcot et al. 2006), aid in identifying restoration and research priorities (Nyberg et al.”
Projected status of the Pacific walrus (Odobenus rosmarus divergens) in the twenty-first century (2011, methodology): “Initial model development We developed a BN model, using the software Netica® (Norsys, Inc.) and following the guidelines in Marcot et al. (2006), to depict potential effects of environmental condi tions and anthropogenic stressors on the future status of the Pacific walrus population.”
Metrics for evaluating performance and uncertainty of Bayesian network models (2012, neutral): “…information of Q from a finding for variable F, calculated as = H(Q ) − H(Q |F) = ∑ q ∑ f P(q, f )log2[P(q, f )] P(q)P(f ) here H(Q) is the entropy of Q before any new findings, H(Q|F) s the entropy of Q after new findings from variable F, and Q is easured in information bits (Marcot et al., 2006).”
A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India) (2016, methodology): “It is based on Bayes' theorem to represent graphically and probabilistically correlative and causal relationships among variables (Marcot et al., 2006).”
Forecasting the relative influence of environmental and anthropogenic stressors on polar bears (2016, methodology): “We conducted sensitivity and influence analyses with the modeling shell Netica using methods and interpretations described in Marcot et al. (2006), Amstrup et al. (2008), Jay et al. (2011), and Marcot (2012).”
The role of expert opinion in environmental modelling (2012, background): “in grazed landscapes 20 yes Martin et al. (2005); Kuhnert et al.”