Application of Bayesian Belief Networks for the prediction of macroinvertebrate taxa in rivers

Annales de Limnologie

Volumn 40, Issue 3, 2004, Pages 181-191

  Adriaenssens, V ^a   Goethals, P L M ^a   Charles, J ^a   De Pauw, N ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

Decision support; Ecological modelling; River management; Uncertainty

Indexed keywords

DECISION SUPPORT SYSTEM; ECOLOGICAL MODELING; MACROINVERTEBRATE; PREDICTION; RIVER MANAGEMENT;

ASELLIDAE; GAMMARIDAE;

EID: 4944223815     PISSN: 00034088     EISSN: None     Source Type: Journal    
DOI: 10.1051/limn/2004016     Document Type: Article

References (52)

1. Adriaenssens V., Goethals P.L.M. & De Pauw N. 2003. - Fuzzy knowledge-based models for Gammarus and Asellus in watercourses in Flanders (Belgium). Ecol. Model., in press.
2. Anderson J. L. 1998. - Embracing uncertainty: the interface of Bayesian statistics and cognitive psychology. Conserv. Ecol. Online. http://www. ecologyandsociety.org/vol2/issl/art2/
3. Bernardo J. & Smith A. 1994. - Bayesian Theory. John Wiley and Sons, Chichester.
4. Borsuk M.E., Reichert P. & Burkhardt-Holm P. 2002. - A Bayesian network for investigating the decline in fish catch in Switzerland. Pages 108-113 in Rizzoli A.E., Jakeman A.J. (Eds.) Integrated Assessment and Decision Support. Proceedings of the 1st biennial meeting of the International Environmental Modelling and Software Society, Lugano, Switzerland.
5. Borsuk M., Stow C.A. & Reckhow K.H. 2004. - A Bayesian network of eutrophication models for synthesis, prediction and uncertainty analysis. Ecol. Model., 173, 219-239.
6. Buckland S.T. & Elston D.A. 1993. - Empirical models for the spatial distribution of wildlife. J. Appl. Ecol., 30, 478-95.
7. Cohen J. 1960. - A coefficient of agreement for nominal scales. Educ. Psychol. Meas., 20(1), 37-46.
8. Cohen Y. 1988. - Bayesian estimation of clutch size for scientific and management purposes. J. Wildl. Manage., 52 (4), 787-793.
9. Crome F.H.J., Thomas M.R. & Moore L.A. 1996. - A novel Bayesian approach to assessing impacts of rain forest logging. Ecol. Appl., 6, 1095-1103.
10. Dedecker A., Goethals P.L.M., Gabriels W. & De Pauw N. 2004a. - Optimization of Artificial Neural Network (ANN) design for prediction of macroinvertebrates in the Zwalm river basin. Ecol. Model., 174, 161-173.
11. Dedecker A., Goethals P.L.M., D'heygere T, Gevrey M., Lek S. & De Pauw N. 2004b - Habitat preference study of Asellus (Crustacea, Isopoda) by applying input variable contribution methods to Artificial Neural Network models. Water Res., submitted.
12. Delcher A.L., Grove A.J., Kasif S. & Pearl J. 1996. - Logarithmic-time updates and querys in probabilistic networks. J. Artif. Intell. Res., 4, 37-59.
13. Dennis B. 1996. - Discussion: should ecologists become Bayesians? Ecol. Appl., 6, 1104-1123.
14. De Pauw N. & Vanhooren G. 1983. - Method for biological quality assessment of watercourses in Belgium. Hydrobiologia, 100, 153-183.
15. De Pauw N., Lambert V., Van Kenhove V. & bij de Vaate A. 1994. - Performance of two artificial substrate samplers for macroinvertebrates in biological monitoring of large and deep rivers and canals in Belgium and the Netherlands. Environ. Monit. Assess., 30, 25-47.
16. D'heygere T., Goethals P.L.M. & De Pauw N. 2003. - Use of genetic algorithms to select input variables in artificial neural network models for the prediction of benthic macroinvertebrates. Ecol. Model., 160, 291-300.
17. D'heygere T., Goethals P.L.M., Dedecker A., Adriaenssens V & De Pauw N. 2004. -Development of a monitoring network to model the habitat suitability of macroinvertebrates in the Zwalm river basin (Flanders, Belgium). Aquat. Ecol., submitted.
18. Dixon P. & Ellison A.M. 1996. - Introduction: ecological applications of Bayesian inference. Ecol. Appl., 6, 1034-1035.
19. Ellison A.M. 1996. - An introduction to Bayesian inference for ecological research and environmental decision-making. Ecol. Appl., 6, 1036-1046.
20. EU 2000. - Directive of the European Parliament and of the Council 2000/60/EC establishing a framework for community action in the field of water policy. European Union, The European Parliament, The Council, PE-CONS 3639/1/00 REV 1 EN, 62 p. + annexes.
21. Fielding A.H. & Bell J.F. 1997. - A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv., 24, 38-49.
22. Fleishman E., Mac Nally R., Fay J.P. & Murphy D.D. 2001. - Modeling and predicting species occurrence using broad-scale environmental variables: an example with butterflies of the Great Basin. Conserv. Biol., 15, 1674-1685.
23. Fleishman E., Mac Nally R. & Fay J.P. 2002. - Validation tests of predictive models of butterfly occurrence based on environmental variables. Conserv. Biol., 17, 806-817.
24. Gelman A., Carlin J., Stern H. & Rubin D. 1995. - Bayesian data analysis. Chapman and Hall, London.
25. Goethals P.L.M. & De Pauw N. 2001. - Development of a concept for integrated ecological river assessment in Flanders (Belgium). J. Limnol., 60 (1), 7-16.
26. Haas T.C., Mowrer H.T. & Shepperd W.D. 1994. - Modelling aspen stand growth with a temporal Bayes Network. Artif. Insemen. Appl., 8, 15-28.
27. IBN 1984. - Norme Belge T 92-402. Biological water quality. Determination of a biotic index based on aquatic macroinvertebrates. Institut Belge de Normalisation, Brussels, Belgium, 11 p (in French).
28. Landis J.R. & Koch G.G. 1977. - The measurements of observer agreement for categorical data. Biometrics, 33, 159-174.
29. Lee D.C. & Riemann B.E. 1997. Population viability assessment of salmonids by using probabilistic networks. N. Am. J. Fish. Manage., 17, 1144-1157.
30. MacNeil C., Dick J.T.A., Bigsby E., Elwood R.W., Montgomery W.I., Gibbins C.N. & Kelly, D.W. 2002. - The validity of the Gammarus:Asellus ratio as an index of organic pollution: abiotic and biotic influences. Wat. Res., 36, 75-84.
31. Maeckelberghe H. 2003. - The quality of the Flemish surface water and wastewater discharges in 2002. A commentary explanation of the results of monitoring networks of the Flemish Environment Agency in 2002. Water, 10, 1-6. (in Dutch).
32. Manel S., Williams H.C. and Ormerod S.J. 2001. - Evaluating presence-absence models in ecology: the need to account for prevalence. J. Appl. Ecol., 38, 921-931.
33. Marcot B.C., Holthausen R.S., Raphael M.G., Rowland M. & Wisdom M. 2001. - Using Bayesian Belief Networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. Forest Ecol. Manag., 153, 29-42.
34. Murphy K.P. 2001. - The Bayes Nets Toolbox for MATLAB. Computing science and Statistics, volume 33.
35. O'Connor M.A. & Walley W.J. 2002. - A River Pollution Diagnostic System (RPDS) for England and Wales. Water, Sci. Technol., 46, 17-23.
36. Olson R.L., Willers J.L. & Wagner T.L. 1990. - A framework for modelling uncertain reasoning in ecosystem management II. Bayesian Belief Networks. AI Appl. Nat. Res. Man., 4, 11-24.
37. Pearl J. 1988. - Probabilistic reasoning in intelligent systems. Morgan Kauffman Publishers, San Francisco, CA, USA.
38. Pearl J. 2000. - Causality. Models, Reasoning and Inference. Cambridge University Press, 500 p.
39. Peot M.A. & Shachter R.D. 1991. - Fusion and propagation with multiple observations in belief networks. Artif. Intell., 48, 299-318.
40. Prati L., Pavanello R. & Pesarin F. 1971. - Assessment of surface water quality by a single index of pollution. Wat. Res., 5, 741-751.
41. Raphael M.G., Wisdom M.J., Rowland M.M., Holthausen R.S., Wales B.C., Marcot G. & Rich T.D. 2001. Status and trends of habitats of terrestrial vertebrates in relation to land management in the interior Columbia River Basin. Forest Ecol. Manag., 153, 63-78.
42. Reckhow K.H. 2002. - Bayesian Approaches in Ecological Analysis and Modeling. in Canham, C. D., Cole, J. J. & Lauenroth, W. K. (Eds.) The Role of Models in Ecosystem Science. Princeton University Press, in press.
43. Regan H.M. 2002. - A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol. Appl., 12, 618-628.
44. Reichert P. & Omlin M. 1999. - A comparison of techniques for the estimation of model prediction uncertainty. Ecol. Model., 115, 45-59.
45. Shannon C.E. & Weaver W. 1963 - Mathematical theory of communication. Urbana, IL: University of Illinois Press.
46. Tattari S., Schultz T. & Kuussaari M. 2003. - Use of belief network modelling to assess the impact of buffer zones on water protection and biodiversity. Agr., Ecosyst., Environ., 96, 119-132.
47. Trigg D.J., Walley W.J. & Ormerod S.J. 2000. - A prototype Bayesian belief network for the diagnosis of acidification in Welsh rivers. ENVIROSOFT 2000, Bilbao, Spain. In: Brebbia C.A., Ibarra-Berastegui G., Zannetti P. (Eds.), Development and Application of Computer Techniques to Environmental Studies.
48. Vehtari A. & Lampinen J. 2002. - Bayesian input variable selection using posterior probabilities and expected utilities. Helsinki University of Technology, Laboratory of Computational Engineering publications.
49. Walley W.J. & Dzeroski S. 1995. - Biological monitoring: a comparison between bayesian, neural and machine learning methods of water quality classification. Pages 229-240 in R. Denzer, G. Schimak & D. Russell, Eds. Environmental Software Systems. Chapman & Hall, London.
50. Walley W.J., O'Connor M.A., Trigg D.J. & Martin R.W. 2002. - Diagnosing and predicting river health from biological survey data using pattern recognition and plausible reasoning. R&D Technical Report E1-056/TR. Environment Agency, Swindon, UK.
51. Witten L.H. & Frank E. 2000. - Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann Publishers, San Francisco.
52. Wolfson L.J., Kadane J.B. & Small M.J. 1996. - Bayesian environmental policy decisions: two case studies. Ecol. Appl., 6, 1056-1066.