The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Bayesian belief networks: applications in ecology and natural resource management
Abstract
In this introduction to the following series of papers on Bayesian belief networks (BBNs) we briefly summarize BBNs, review their application in ecology and natural resource management, and provide an overview of the papers in this section. We suggest that BBNs are useful tools for representing expert knowledge of an ecosystem, evaluating potential effects of alternative management decisions, and communicating with nonexperts about making natural resource management decisions. BBNs can be used effectively to represent uncertainty in understanding and variability in ecosystem response, and the influence of uncertainty and variability on costs and benefits assigned to model outcomes or decisions associated with natural resource management. BBN tools also lend themselves well to an adaptive-management framework by posing testable management hypotheses and incorporating new knowledge to evaluate existing management guidelines.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Learning Bayesian networks: The combination of knowledge and statistical data
Heckerman, D., Geiger, D., & Chickering, D. M. (1995) · Machine Learning · doi:10.1007/bf00994016 · Cited by 2,017
(2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_100245 · Cited by 1,424
Boyce, M. S. (1992) · Annual Review of Ecology and Systematics · doi:10.1146/annurev.es.23.110192.002405 · Cited by 1,242
On the Use of Demographic Models of Population Viability in Endangered Species Management
Beissinger, S. R., & Westphal, M. I. (1998) · The Journal of Wildlife Management · doi:10.2307/3802534 · Cited by 1,118
Bayesian Analysis in Expert Systems
Spiegelhalter, D. J., Dawid, A. P., Lauritzen, S. L., & Cowell, R. G. (1993) · Statistical Science · doi:10.1214/ss/1177010888 · Cited by 684
Marcot, B. G., Steventon, J. D., Sutherland, G. D., & McCann, R. K. (2006) · Canadian Journal of Forest Research · doi:10.1139/x06-135 · Cited by 667
Self-citationAn introduction to Bayesian inference for ecological research and environmental decision-making
Ellison, A. M. (1996) · Ecological Applications · doi:10.2307/2269588 · Cited by 482
Marcot, B. G., Holthausen, R. S., Raphael, M. G., Rowland, M. M., & Wisdom, M. J. (2001) · Forest Ecology and Management · doi:10.1016/s0378-1127(01)00452-2 · Cited by 417
Self-citationUnderstanding Qualitative Data: A Framework of Text Analysis Methods
Lacity, M. C., & Janson, M. A. (1994) · Journal of Management Information Systems · doi:10.1080/07421222.1994.11518043 · Cited by 328
Building models for conservation and wildlife management
(1987) · Biological Conservation · doi:10.1016/0006-3207(87)90066-8 · Cited by 277
Cited by
Bayesian networks in environmental modelling
Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., & Salmerón, A. (2011) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2011.06.004 · Cited by 666
Metrics for evaluating performance and uncertainty of Bayesian network models
Marcot, B. G. (2012) · Ecological Modelling · doi:10.1016/j.ecolmodel.2012.01.013 · Cited by 380
Self-citationGarcía-Herrero, S., Mariscal, M. A., Gutiérrez, J. M., & Ritzel, D. O. (2013) · Accident Analysis & Prevention · doi:10.1016/j.aap.2013.04.009 · Cited by 62
Metadata
- Authors
- McCann, R. K., Marcot, B. G., & Ellis, R.
- Venue
- Canadian Journal of Forest Research
- Publisher
- Canadian Science Publishing
- Published
- 2006-12-01
- Volume
- 36
- Issue
- 12
- Pages
- 3053-3062
- ISSN
- 0045-5067 (ISSN-L)
- 1208-6037
- OpenAlex
- W1976452867
- Cited by
- 381
- References
- 30
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Bayesian belief networks: applications in ecology and natural resource management. https://app.cochat.ai/papers/10.1139/x06-238
- The paper
- McCann, R. K., Marcot, B. G., & Ellis, R. (2006). Bayesian belief networks: applications in ecology and natural resource management. Canadian Journal of Forest Research. https://doi.org/10.1139/x06-238
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
