Exploiting the full potential of Bayesian networks in predictive ecology

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dc.contributor.authorRamazi, Pouria
dc.contributor.authorKunegel-Lion, Mélodie
dc.contributor.authorGreiner, Russell
dc.contributor.authorLewis, Mark A.
dc.date.accessioned2025-04-15T19:22:07Z
dc.date.available2025-04-15T19:22:07Z
dc.date.issued2021
dc.description.abstract1. Although ecological models used to make predictions from underlying covariates have a record of success, they also suffer from limitations. They are typically unable to make predictions when the value of one or more covariates is missing during the testing. Missing values can be estimated but methods are often unreliable and can result in poor accuracy. Similarly, missing values during the training can hinder parameter estimation of many ecological models. Bayesian networks can handle these and other limiting issues, such as having highly correlated covariates. However, they are rarely used to their full potential. 2. Indeed, Bayesian networks are commonly used to evaluate the knowledge of experts by constructing the network manually and often (incorrectly) interpreting the resulting network causally. We provide an approach to learn a Bayesian network fully from observed data, without relying on experts and show how to appropriately interpret the resulting network, both to identify how the variables (covariates and target) are interrelated and to answer probabilistic queries. 3. We apply this method to the case study of a mountain pine beetle infestation and find that the trained Bayesian network has a predictive accuracy of 0.88 AUC. We classify the covariates as primary and secondary in terms of contributing to the prediction and show that the predictive accuracy does not deteriorate when the secondary covariates are missing and degrades to only 0.76 when one of the primary covariates is missing. 4. As a complement to the previous work on constructing Bayesian networks by hand, we show that if instead, both the structure and parameters are learned only from data, we can achieve more accurate predictions as well as generate new insights about the underlying processes.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.description.sponsorshipThe research was partly funded by Alberta Environment & Parks (AEP). This research was also supported by a grant to M.A.L. from the Natural Science and Engineering Research Council of Canada (grant no. NET GP 434810-12) to the TRIA Network, with contributions from Alberta Agriculture and Forestry, Foothills Research Institute, Manitoba Conservation and Water Stewardship, Natural Resources Canada-Canadian Forest Service, Northwest Territories Environment and Natural Resources, Ontario Ministry of Natural Resources and Forestry, Saskatchewan Ministry of Environment, West Fraser and Weyerhaeuser. M.A.L. is also grateful for the support through the NSERC Discovery and the Canada Research Chair Programs. R.G. is grateful for funding from NSERC Discovery and Alberta Machine Intelligence Institute.
dc.identifier.citationRamazi, P., Kunegel-Lion, M., Greiner, R., & Lewis, M. A. (2020). Exploiting the full potential of Bayesian networks in predictive ecology. Methods in Ecology and Evolution, 12(1), 135-149. https://doi.org/10.1111/2041-210x.13509
dc.identifier.urihttps://doi.org/10.1111/2041-210x.13509
dc.identifier.urihttps://hdl.handle.net/1828/21932
dc.language.isoen
dc.publisherMethods in Ecology and Evolution
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectautomatic learning
dc.subjectBayesian network
dc.subjectinvasive species
dc.subjectmachine-learning
dc.subjectmountain pine beetle
dc.subjectpest
dc.subjectrisk modelling
dc.subjectstructure learning
dc.titleExploiting the full potential of Bayesian networks in predictive ecology
dc.typeArticle

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