Implementing Bayesian network models using expert elicitation for instream barrier assessment.
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| Title: | Implementing Bayesian network models using expert elicitation for instream barrier assessment. |
|---|---|
| Authors: | Franklin, Paul1 (AUTHOR) paul.franklin@niwa.co.nz, Baker, Cindy1 (AUTHOR), Gee, Eleanor1 (AUTHOR), Wilkes, Martin2 (AUTHOR) |
| Source: | Journal of Applied Ecology. Feb2026, Vol. 63 Issue 2, p1-13. 13p. |
| Subjects: | Bayesian analysis, Knowledge acquisition (Expert systems), Freshwater biodiversity, Fishways, Fragmented landscapes, Risk assessment, Fish migration |
| Geographic Terms: | New Zealand |
| Abstract: | Reducing river fragmentation is crucial for restoring freshwater biodiversity. Cost‐effective methods of assessing the likelihood of fish passage at river infrastructure are required for spatial planning of barrier mitigation strategies. A paucity of empirical data on the swimming capabilities and movement behaviour of many fish species presents a challenge for evaluating barrier permeability.We used a combination of expert knowledge and empirical data to define prior probabilities in Bayesian network (BN) models to estimate culvert, ford and weir permeability for multi‐species fish assemblages in New Zealand. The models have been implemented as part of a national fish passage assessment tool. Model outputs are illustrated for a range of structures.Uncertainty associated with incomplete knowledge was explicitly incorporated in the BN models. Experts were most confident in predicting barrier permeability under conditions where fish passage was expected to be poor. Estimates of fish passage success were more varied for conditions considered less likely to impede fish movements. This reflects experts' understanding of the varying swimming and climbing capabilities of different fish species and life stages and how this impacts barrier permeability.The BN models form the basis of a new fish passage assessment tool that has been implemented in New Zealand. Users collect data using a mobile app, recording the key features of instream structures that have been determined to influence the likelihood of successful passage. The BN models are then used to objectively classify the risk to fish passage for each structure.Synthesis and applications. We have demonstrated that BN modelling and expert knowledge can be used for assessing the likelihood of fish passage where empirical data are lacking or sparse. The probabilistic framework is consistent with the need to reflect that many structures are partial barriers to fish movement and cannot be represented accurately in a binary pass/fail classification. This facilitates risk‐based decision‐making, but also suggests that widely used connectivity indices should not ignore the uncertainty in barrier permeability by parameterising connectivity as a single value. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Applied Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194204537 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Implementing Bayesian network models using expert elicitation for instream barrier assessment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Franklin%2C+Paul%22">Franklin, Paul</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> paul.franklin@niwa.co.nz</i><br /><searchLink fieldCode="AR" term="%22Baker%2C+Cindy%22">Baker, Cindy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gee%2C+Eleanor%22">Gee, Eleanor</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wilkes%2C+Martin%22">Wilkes, Martin</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Ecology%22">Journal of Applied Ecology</searchLink>. Feb2026, Vol. 63 Issue 2, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+acquisition+%28Expert+systems%29%22">Knowledge acquisition (Expert systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Freshwater+biodiversity%22">Freshwater biodiversity</searchLink><br /><searchLink fieldCode="DE" term="%22Fishways%22">Fishways</searchLink><br /><searchLink fieldCode="DE" term="%22Fragmented+landscapes%22">Fragmented landscapes</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Fish+migration%22">Fish migration</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22New+Zealand%22">New Zealand</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Reducing river fragmentation is crucial for restoring freshwater biodiversity. Cost‐effective methods of assessing the likelihood of fish passage at river infrastructure are required for spatial planning of barrier mitigation strategies. A paucity of empirical data on the swimming capabilities and movement behaviour of many fish species presents a challenge for evaluating barrier permeability.We used a combination of expert knowledge and empirical data to define prior probabilities in Bayesian network (BN) models to estimate culvert, ford and weir permeability for multi‐species fish assemblages in New Zealand. The models have been implemented as part of a national fish passage assessment tool. Model outputs are illustrated for a range of structures.Uncertainty associated with incomplete knowledge was explicitly incorporated in the BN models. Experts were most confident in predicting barrier permeability under conditions where fish passage was expected to be poor. Estimates of fish passage success were more varied for conditions considered less likely to impede fish movements. This reflects experts' understanding of the varying swimming and climbing capabilities of different fish species and life stages and how this impacts barrier permeability.The BN models form the basis of a new fish passage assessment tool that has been implemented in New Zealand. Users collect data using a mobile app, recording the key features of instream structures that have been determined to influence the likelihood of successful passage. The BN models are then used to objectively classify the risk to fish passage for each structure.Synthesis and applications. We have demonstrated that BN modelling and expert knowledge can be used for assessing the likelihood of fish passage where empirical data are lacking or sparse. The probabilistic framework is consistent with the need to reflect that many structures are partial barriers to fish movement and cannot be represented accurately in a binary pass/fail classification. This facilitates risk‐based decision‐making, but also suggests that widely used connectivity indices should not ignore the uncertainty in barrier permeability by parameterising connectivity as a single value. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Applied Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/1365-2664.70307 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Knowledge acquisition (Expert systems) Type: general – SubjectFull: Freshwater biodiversity Type: general – SubjectFull: Fishways Type: general – SubjectFull: Fragmented landscapes Type: general – SubjectFull: Risk assessment Type: general – SubjectFull: Fish migration Type: general – SubjectFull: New Zealand Type: general Titles: – TitleFull: Implementing Bayesian network models using expert elicitation for instream barrier assessment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Franklin, Paul – PersonEntity: Name: NameFull: Baker, Cindy – PersonEntity: Name: NameFull: Gee, Eleanor – PersonEntity: Name: NameFull: Wilkes, Martin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00218901 Numbering: – Type: volume Value: 63 – Type: issue Value: 2 Titles: – TitleFull: Journal of Applied Ecology Type: main |
| ResultId | 1 |
