Back to The Ecology Plexus Page...
Back to The Plexus Home Page...
Bayesian Network Models
in EcologyBruce G. Marcot
updated 16 September 2024Contents:
Introduction to Bayesian Network Modeling
Guidelines & Considerations for Developing, Testing, and Documenting Bayesian Network Models
Papers & Publications on Bayesian Network Modeling
Talks on Bayesian Network and Decision Support Modeling
Bayesian Network Software & Related Resources
Also see:
Structured Decision Making, Expert Systems,
Expert Paneling, & Expert Elicitation in Ecology
Introduction to Bayesian Network ModelingWhat are "Bayesian networks?" ...
a harmless, simple introduction with virtually no math.
[PDF versionbut without the animations]
Guidelines & Consideration for Developing, Testing,
and Documenting Bayesian Network ModelsA set of guidelines you can follow.
Also see publications below on Guidelines and related topics.Bayesian network modeling metrics of performance and uncertainty.
A summary of approaches to conducting model sensitivity analysis,
scenario analysis, model complexity, prediction performance, uncertaintyTemplate for conducting and documenting guided peer reviews of Bayesian network models.
Microsoft Word document format. Follows modeling procedures of Marcot (2006), below.
Template for scheduling and tracking peer reviews of Bayesian network models.
Microsoft Excel spreadsheet format. Follows modeling procedures of Marcot (2006), below.
Papers & Publications on Bayesian Network Modeling
by Bruce Marcot
Bayesian network modeling: (listed chronologically from most recent)Stantial, M. L., A. M. V. Fournier, A. J. Lawson, B. G. Marcot, M. S. Woodrey, and J. E. Lyons. 2024. RE-ARMing salt marshes: a resilience-experimentalist approach to prescribed fire and bird conservation in high marshes of the Gulf of Mexico. Frontiers in Conservation Science 5:1426646. https://doi.org/10.3389/fcosc.2024.1426646.
Rowland, F. E., C. J. Kotalik, B. G. Marcot, J. E. Hinck, and D. M. Walters. 2024. A novel approach to assessing natural resource injury with Bayesian networks. Integrated Environmental Assessment and Management 20(2):562-573. https://doi.org/10.1002/ieam.4836.
Marcot, B. G., K. M. Thorne, J. A. Carr, and G. G. Guntenspergen. 2023. Foundations of modeling resilience to sea-level rise of tidal saline wetlands along the U.S. Pacific Coast. Landscape Ecology 38:3061-3080. https://doi.org/10.1007/s10980-023-01762-3.
Marcot, B. G., P. Scott, and T. I. Burgess. 2023. Multivariate Bayesian analysis to predict invasiveness of Phytophthora pathogens. Ecosphere 14(6):e4573. https://doi.org/10.1002/ecs2.4573.
Penman, T. D., S. McColl-Gausden, B. G. Marcot, and D. Ababei. 2022. Population viability analysis using Bayesian networks. Environmental Modelling & Software. 147:105242. https://doi.org/10.1016/j.envsoft.2021.105242. [PDF]
Also available from: https://www.fs.usda.gov/treesearch/pubs/64144Meurisse, N., B. G. Marcot, O. Woodberry, B. Barratt, and J. Todd. 2021. Risk analysis frameworks used in biological control and introduction of a novel Bayesian network tool. Risk Analysis [Early View]: https://doi.org/10.1111/risa.13812. [PDF with Supplements]
Also available from: https://www.fs.usda.gov/treesearch/pubs/65011Marcot, B. G. 2021. EcoQBNs: First application of ecological modeling with quantum Bayesian networks. Entropy 23(4):441; https://doi.org/10.3390/e23040441. [PDF]
Also available from: https://www.fs.usda.gov/treesearch/pubs/62574Cronin, J. P., B. E. Tirpak, L. L. Dale, V. L. Brink, J. M. Tirpak, J. Gore, and B. G. Marcot. 2021. Strategic habitat conservation for beach mice: estimating habitat objectives and the efficiency of management scenarios. Journal of Wildlife Management 85(2):324-339. [PDF]
Marcot, B. G., and A. Hanea. 2021. What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? Computational Statistics 36(3):2009-2031. https://doi.org/10.1007/s00180-020-00999-9 [PDF]
Penman, T. D., B. Cirulis, and B. G. Marcot. 2020. Bayesian decision network modeling for environmental risk management: a wildfire case study. Journal of Environmental Management 270:110735. [PDF]
Martin, C. D., S. D. Jewell, M. H. Hoff, C. E. Givens, and B. G. Marcot. 2020. Comparing invasive species risk screening tools FISRAM, ERSS, FISK, and AS-ISK, as a response to Hill et al. (2020). Management of Biological Invasions 11(2):342-355. [PDF]
Marcot, B. G., I. Woo, K. Thorne, C. Freeman, and G. R. Guntenspergen. 2020. Habitat of the endangered salt marsh harvest mouse (Reithrodontomys raviventris) in San Francisco Bay. Ecology and Evolution 10(2):662-677. [PDF]
Sundar, K. S. G., R. Koju, B. Maharjan, B. G. Marcot, S. Kittur, and K. R. Gosai. 2019. First assessment of factors affecting breeding success of storks in lowland Nepal using Bayesian Network models. Wildfowl 69:45-69. [PDFarticle] [PDFsupplements]
Also available from: https://www.fs.usda.gov/treesearch/pubs/59230Marcot, B. G., and K. M. Reynolds. 2019. EMDS Has a GeNIe With a SMILE. Research Note PNW-RN-581. USDA Forest Service, Pacific Northwest Research Station and Pacific Northwest Region, Portland, Oregon. 4 pp. [PDF]
Also available from: https://www.fs.usda.gov/treesearch/pubs/58510Marcot, B. G., M. H. Hoff, C. D. Martin, S. D. Jewell, and C. E. Givens. 2019. A decision advisory system for identifying potentially invasive and injurious freshwater fishes. Management of Biological Invasions 10(2):200-226. [PDF]
Also available from: https://www.fs.usda.gov/treesearch/pubs/58084
View and download the FISRAM Bayesian network model: http://www.abnms.org/bnrepo/bn?bnId=198Marcot, B. G., and T. D. Penman. 2019. Advances in Bayesian network modelling: Integration of modelling technologies. Environmental Modelling & Software 111:386-393. [PDF]
Also available from: https://www.fs.usda.gov/treesearch/pubs/58773Wiest, W. A., M. D. Correll, B. G. Marcot, B. J. Olsen, C. S. Elphick, T. P. Hodgman, G. R. Guntenspergen, and W. G. Shriver. 2018. Estimates of tidal-marsh bird densities using Bayesian networks. Journal of Wildlife Management 83(1):109-120. [PDF]
Supplemental Material [PDF]
Main text and abstract also available from: https://www.fs.usda.gov/treesearch/pubs/57729Pawson, S. M., B. G. Marcot, and O. Woodberry. 2017. Predicting forest insect flight activity: a Bayesian network approach. PLoS ONE 12(9):e0183464, https://doi.org/10.1371/journal.pone.0183464. PDF
Havron, A., C. Goldfinger, S. Henkel, B. G. Marcot, C. Romsos, and L. Gilbane. 2017. Mapping marine habitat suitability and uncertainty using Bayesian networks: a case study of northeastern Pacific benthic macrofauna. Ecosphere 8(7):e01859. doi: 10.1002/ecs2.1859. Find it here.
Marcot, B. G. 2017. Common quandaries and their practical solutions in Bayesian network modeling. Ecological Modelling 358:1-9. PDF
Fortin, J. K., K. D. Rode, G. V. Hilderbrand, J. Wilder, S. Farley, C. Jorgensen, and B. G. Marcot. 2016. The impacts of human recreation on brown bears (Ursus arctos): a review and new management tool. PLoS ONE 11(1):e0141983. doi:10.1371/journal.pone.0141983. PDF
The Netica Bayesian network model is available from the Australasian Bayesian Network Modelling Society's model repository, at: http://abnms.org/bnrepo/bn?bnId=145Marcot, B. G., and M. G. Raphael. 2012. Conservation of martens, sables, and fishers in multispecies bioregional assessments. Pp. 451-470 in: K. B. Aubry, W. J. Zielinski, M. G. Raphael, G. Proulx, and S. W. Buskirk, eds. Biology and conservation of martens, sables, and fishers: a new synthesis. Cornell University Press, Ithaca, New York. 580 pp.
[Includes an example of a Bayesian network model of Pacific marten.]Marcot, B. G. 2012. Metrics for evaluating performance and uncertainty of Bayesian network models. Ecological Modelling 230:50-62. PDF
Marcot, B. G., C. Allen, S. Morey, D. Shively, and R. White. 2012. An expert panel approach to assessing potential effects of bull trout reintroduction on federally listed salmonids in the Clackamas River, Oregon. North American Journal of Fisheries Management 32(3):450-465. PDF
[Includes a Bayesian network food web model in Appendix 3.]
The Netica Bayesian network model is available from the Australasian Bayesian Network Modelling Society's model repository, at: http://abnms.org/bnrepo/bn?bnId=149Pauli, J. N., J. P. Whiteman, B. G. Marcot, T. M. McClean, and M. Ben-David. 2011. DNA-based approach to aging martens (Martes americana and M. caurina). Journal of Mammalogy 92(3):500-510. PDF
The Netica Bayesian network model is available from the Australasian Bayesian Network Modelling Society's model repository, at: http://abnms.org/bnrepo/bn?bnId=156van Rooij, T., D. I. Rumiz, A. R. Montellano, U. Remillard, X. Fernandez, R. Arispe, J. C. Herrera, W. Townsend, R. S. Miserendino, D. Caba, T. Muñoz, and B. Marcot. 2008. Using the landscape species concept to model habitat suitability for threatened species in Bolivia’s dry tropical forest (preliminary report). FCBC (Foundation for the Conservation of the Chiquitano Forest), Santa Cruz de la Sierra, Bolivia. 24 pp.
[Includes Bayesian network models on jaguar populations.]Marcot, B. G. 2007. Étude de cas n°5: gestion de ressources naturelles et analyses de risques (Natural resource assessment and risk management). Pp. 293-315 in: P. Naim, P.-H. Wuillemin, P. Leray, O. Pourret, and A. Becker, eds. Réseaux bayésiens (Bayesian networks) [in French]. Eyrolles, Paris, France. PDF preprint in English
Marcot, B. G. 2006. Habitat modeling for biodiversity conservation. Northwestern Naturalist 87(1):56-65. PDF
[Discusses various modeling approaches including use of Bayesian belief networks.]
Abstract published as: Marcot, B. G. 2005. Habitat modeling for biodiversity conservation (abstract). Northwestern Naturalist 86(2):107.McNay, R. S., A. M. Doucette, R. K. McCann, D. C. Heard, B. G. Marcot, R. Sulyma, and R. Ellis. 2003. An assessment of conservation policy for pine-lichen winter ranges used by caribou in north-central British Columbia. CLUPE Project: Implementation of the Mackenzie LRMP Caribou Management Strategy. Wildlife Infometrics Report No. 049. Wildlife Infometrics Inc. Mackenzie, British Columbia, Canada. 27 pp.
Marcot, B. G., R. S. Holthausen, M. G. Raphael, M. M. Rowland, and M. J. Wisdom. 2001. Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management 153(1-3):29-42. PDF
[Also see below for more information on this publication and access to the models used.]Raphael, M. G., M. J. Wisdom, M. M. Rowland, R. S. Holthausen, B. C. Wales, B. G. Marcot, and T. D. Rich. 2001. Status and trends of habitats of terrestrial vertebrates in relation to land management in the interior Columbia River Basin. Forest Ecology and Management 153(1-3):63-87. PDF
Bayesian network modeling of future Pacific walrus habitat & populations:
Jay, C. V., B. G. Marcot, and D. C. Douglas. 2011. Projected status of the Pacific walrus (Odobenus rosmarus divergens) in the 21st century. Polar Biology 34(7):1065-1084. PDF
> With online resource (supplementary material) PDF
The Netica Bayesian network model is available from the Australasian Bayesian Network Modelling Society's model repository, at: http://abnms.org/bnrepo/bn?bnId=147Jay, C. V., B. G. Marcot, and D. C. Douglas. 2010. Projected status of the Pacific walrus (Odobenus rosmarus divergens) in the 21st century. Administrative Report Submitted to the U.S. Fish and Wildlife Service. U.S. Geological Survey, Alaska Science Center. Anchorage, Alaska. 90 pp.
Bayesian network modeling of future polar bear habitat & populations:Atwood, T. C., B. G. Marcot, D. C. Douglas, J. F. Bromaghin, A. M. Pagano, and S. C. Amstrup. In prep. Assessing the cumulative influence of biotic and anthropogenic stressors on polar bears to inform circumpolar conservation planning.
Marcot, B. G., T. C. Atwood, D. C. Douglas, J. F. Bromaghin, A. M. Pagano, and S. C. Amstrup. 2023. Incremental evolution of modeling a prognosis for polar bears in a rapidly changing Arctic. Ecological Indicators 156:111130. https://doi.org/10.1016/j.ecolind.2023.111130.
Atwood, T. C., B. G. Marcot, D. C. Douglas, S. C. Amstrup, K. D. Rode, G. M. Durner, and J. F. Bromaghin. 2016. Forecasting the relative influence of anthropogenic stressors on polar bears. Ecosphere 7(6):DOI: 10.1002/ecs2.1370. PDF
The Netica Bayesian network model is available from the Australasian Bayesian Network Modelling Society's model repository, at: http://abnms.org/bnrepo/bn?bnId=168Atwood, T. C., B. G. Marcot, D. C. Douglas, S. C. Amstrup, K. D. Rode, G. M. Durner, and J. F. Bromaghin. 2015. Evaluating and ranking threats to the long-term persistence of polar bears. U.S. Geological Survey, Open-File Report 2014-1254. http://dx.doi.org/10.3133/ofr20141254. Anchorage, Alaska. 114 pp. PDF
> Also available from http://pubs.usgs.gov/of/2014/1254/Amstrup, S. C., E. T. DeWeaver, D. C. Douglas, B. G. Marcot, G. M. Dumer, C. M. Bitz, and D. A. Bailey. 2010. Greenhouse gas mitigation can reduce sea-ice loss and increase polar bear persistence. Nature 468(7326):955-958. PDF
> With online resource (supplementary material) PDF
The Netica Bayesian network model is available from the Australasian Bayesian Network Modelling Society's model repository, at: http://abnms.org/bnrepo/bn?bnId=146
> Note: A news item on this article by the National Science Foundation can be found here
> Additional news items from Oregon Public BroadcastingAmstrup, S. C., B. G. Marcot, and D. C. Douglas. 2007. Forecasting the range-wide status of polar bears at selected times in the 21st century. Administrative Report. U.S. Geological Survey, Alaska Science Center. Anchorage, Alaska. 126 pp. PDF
Also available from: http://treesearch.fs.fed.us/pubs/33235 PDFAmstrup, S. C., B. G. Marcot, and D. C. Douglas. 2008. Forecasting the range-wide status of polar bears at selected times in the 21st century: addition of model outcomes for the decade 2020-2029. Administrative Report. U.S. Geological Survey, Alaska Science Center. Anchorage, Alaska. 6 pp.
Amstrup, S. C., B. G. Marcot, and D. C. Douglas. 2008. A Bayesian network modeling approach to forecasting the 21st century worldwide status of polar bears. Pp. 213-268 in: E. T. DeWeaver, C. M. Bitz, and L.-B. Tremblay, eds. Arctic sea ice decline: observations, projections, mechanisms, and implications. Geophysical Monograph 180. American Geophysical Union, Washington, D.C. PDF
The Netica Bayesian network model is available from the Australasian Bayesian Network Modelling Society's model repository, at: http://abnms.org/bnrepo/bn?bnId=146Amstrup, S. C., H. Caswell, E. DeWeaver, I. Stirling, D. C. Douglas, B. G. Marcot, and C. M. Hunter. 2009. Rebuttal of "polar bear population forecasts: a public-policy forecasting audit". Interfaces 39(4):353-369. PDF
Special issue section on Bayesian network modeling, in Canadian Journal of Forest Research:
Marcot, B. G., J. D. Steventon, G. D. Sutherland, and R. K. McCann. 2006. Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Research 36:3063-3074. PDF
McCann, R., B. G. Marcot, and R. Ellis. 2006. Bayesian belief networks: applications in natural resource management. Canadian Journal of Forest Research 36:3053-3062. PDF
McNay, R. S., B. G. Marcot, V. Brumovsky, and R. Ellis. 2006. A Bayesian approach to evaluating habitat suitability for woodland caribou in north-central British Columbia. Canadian Journal of Forest Research 36:3117-3133. PDF
Nyberg, J. B., B. G. Marcot, and R. Sulyma. 2006. Using Bayesian belief networks in adaptive management. Canadian Journal of Forest Research 36:3104-3116. PDF
Two-part series on use of Bayesian network modeling for characterizing species at risk, in Ecology and Society:
Marcot, B. G. 2006. Characterizing species at risk I: modeling rare species under the Northwest Forest Plan. Ecology and Society 11(2):10. [online] http://www.ecologyandsociety.org/vol11/iss2/art10/ ... or article PDF
Marcot, B. G., P. A. Hohenlohe, S. Morey, R. Holmes, R. Molina, M. Turley, M. Huff, and J. Laurence. 2006. Characterizing species at risk II: using Bayesian belief networks as decision support tools to determine species conservation categories under the Northwest Forest Plan. Ecology and Society 11(2):12. [online] http://www.ecologyandsociety.org/vol11/iss2/art12/ ... or article PDF
Textbook on Bayesian Network Applications:Pourret, O., P. Naïm, and B. Marcot, editors. 2008. Bayesian networks: a practical guide to applications. Wiley. 428 pp.
Available from Wiley and from Amazon.com. See a synopsis, descriptions from the publisher and authors, and excerpts at Amazon.com.uk.
See lots more about this book -- background, models and applications, new material -- at "Bayesian Networks ~ Applications" ... a website created by the book editors.
Netica Models from Paper on Constructing Bayesian Networks
Marcot, B. G., R. S. Holthausen, M. G. Raphael, M. Rowland, and M. Wisdom. 2001. Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecology and Management 153(1-3):29-42.
Download the paperModels mentioned in the paper:
Note: Download these models (they're ASCII files) by right-clicking on the following links. To run these models you need the Bayesian network program Netica, available from Norsys, Inc., at: http://www.norsys.com
Model 1: (from Fig. 1)
General structure of a Bayesian belief network (BBN) model for evaluating population viability outcomes of wildlife species, showing 6 shells of nodes. See Appendix 1 for description of node names. The state of nature nodes (shells 2-5) can depict parameters as multiple discrete values (as shown here) or as continuous values.Figure 2:
Example BBNs depicting population response of a wildlife species, Townsend’s big-eared bat (Corynorhinus townsendii), in the interior Columbia River Basin, U.S.A., at 3 levels of geographic resolution.
Model 2a: (from Fig. 2a) - Site-specific BBN model: relations of site-specific key environmental correlates (KECs);
Model 2b: (from Fig. 2b) - Subwatershed BBN model: relations of subwatershed-scale KECs and their GIS proxies;
Model 2c: (from Fig. 2c) - Basin BBN model: overall population outcome.
Other Examples of Bayesian Network Models
In addition to the Bayesian network models presented in the various publications listed above, other examples using the Netica modeling shell can be found here:
on the New Models page of our Bayesian Networks Applications web site
on the Bayes Net Library page of Norsys, Inc., which has posted a number of my models under Real World Applications
an example of conversion of the bat Bayesian network model (presented in Figure 2a in Marcot et al. 2001, cited above), as an interactive question-and-answer interface using Netica's "AutoNetica" feature
Guidelines for Developing Bayesian NetworksI developed the following guidelines as part of my team work on the Interior Columbia Basin Ecosystem Management Project of USDA Forest Service and USDI Bureau of Land Management. They have served us well. They served as the basis for some of the publications listed above.
A Process for Creating Bayesian Belief Network Models of Species-Environment Relations (note: much of this was reworked and updated for the journal publication listed above, Marcot, B. G., J. D. Steventon, G. D. Sutherland, and R. K. McCann. 2006. Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Research 36:3063-3074. You may wish to use that publication instead.)
Methods for Peer Review Updating of Bayesian Belief Network Species Models
For other Bayesian network modeling guidelines, also see the lists of publications above.
Talks on Bayesian Network and Decision Support Modeling
by Bruce MarcotClick here for talk abstracts; also, here are some online presentations:
Marcot, B. G. 2021. The many faces of probability. Australasian Bayesian Network Modelling Society, Webinar Series, presented 1 September 2021. View on YouTube: https://www.youtube.com/watch?v=quIWe0bmuuA
Marcot, B. G. 2020. To all the Bayesian Networks I’ve loved before. Presented at: Annual Conference of the Australasian Bayesian Network Modelling Society (ABNMS), 9-10 December 2020, held virtually as video-conference presentations. View presentation.
Marcot, B. G. 2014. Of confidence, control, and cause: using Bayesian networks for management decisions. Presented 26 November 2014 at the Sixth Annual Conference of the Australasian Bayesian Network Modelling Society, Rotorua, New Zealand. [Invited keynote address]. View here ... or PDF version
Bayesian Network Software & Related Resources
AgenaRisk
Australasian Bayesian Network Modelling Society (also see their Resources page)
Bayesian Intelligence
BUGS & WinBUGS
Elvira modeling system
BayesFusion - GeNIe & SMILE (Structural Modeling, Inference, and Learning Engine)
Hugin Expert
International Society for Bayesian Analysis
Microsoft Belief Network (MSBN)
Norsys Software Corp. - Netica
SamIam
For Causal Diagramming, Concept Mapping:
DAGitty
Cmap
FCMapper
