Bayesian methods for regional-scale eutrophication models

Water Research

Volumn 38, Issue 11, 2004, Pages 2764-2774

  Lamon III, E Conrad ^a   Stow, Craig A ^b  

^a LOUISIANA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

Bayesian Treed models; Classification and regression trees; Markov chain Monte Carlo methods; National Eutrophication Survey; Regionalization; Water quality

Indexed keywords

DATA REDUCTION; DECISION MAKING; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; REGRESSION ANALYSIS; RISK ASSESSMENT; TREES (MATHEMATICS); WATER POLLUTION;

BAYESIAN CLASSIFICATION; CLASSIFICATION AND REGRESSION TREE (CART);

EUTROPHICATION;

ARTICLE; BAYES THEOREM; CROSS LINKING; DATA ANALYSIS; DECISION MAKING; EUTROPHICATION; EXPERIMENTAL MODEL; INFORMATION PROCESSING; MOLECULAR INTERACTION; PREDICTION; PRIORITY JOURNAL; PROBABILITY; REGRESSION ANALYSIS; RISK ASSESSMENT; STRESS; TECHNIQUE;

EID: 2942672436     PISSN: 00431354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.watres.2004.03.019     Document Type: Article

References (31)

1. National Research Council (NRC). Assessing the TMDL approach to water quality management., Washington, DC: National Academy Press: 2001.
2. Reckhow K.H., Coffey S.C., Henning M.H., Smith K., Banting R. EUTROMOD. technical guidance and spreadsheet models for nutrient loading and lake eutrophication:1992;North American Lake Management Society, Madison, WI
3. Borsuk M.E., Higdon D., Stow C.A., Reckhow K.H. A Bayesian hierarchical model to predict benthic oxygen demand from organic matter loading in estuaries and coastal zones. Ecol Modell. 143:2001;165-181
4. Reckhow K.H. A random coefficient model for chlorophyll-nutrient relationships in lakes. Ecol Model. 70:1993;35-50
5. Chipman H., George E.I., McCulloch R.E. Bayesian CART model search. J Am Stat Assoc. 93(443):1998;935-947
6. Chipman H., George E., McCulloch R. Bayesian treed models. Mach Learn. 48:2002;299-320
7. Brieman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and regression trees. 1984;Wadsworth International Group, Belmont, CA
8. Clark LA, Pregibon D. Tree based models. In: Chambers, JM, Hastie, TJ, editors. Statistical Models in S. Pacific Grove, CA: Wadsworth and Brooks/Cole Advanced Books and Software; 1992.
9. Lamon E.C., Stow C.A. Sources of variability in microcontaminant data for Lake Michigan salmonids. statistical models and implications for trend detection Can J Fish Aqua Sci. 56(Suppl 1):1999;71-85
10. Sonquist J.N., Morgan J.N. The detection of interaction effects. Monograph 35, Survey Research Center Institute for Social Research. 1964;MI University of Michigan, Ann Arbor
11. Magnuson J.J., Tonn W.N., Banjeree A., Toivonnen J., Sanchez O., Rask M. Isolation vs. extinction in the assembly of fishes in small northern lakes. Ecology. 79(8):1998;2941-2956
12. Mankin P.C., Warner R.E. A regional model of the eastern cottontail and land use changes in Illinois. J Wild Manage. 63(3):1999;956-963
13. Clark W.R., Schmitz R.A., Bogenschutz T.R. Site selection and nest success of ring-necked pheasants as a function of location in Iowa landscapes. J Wild Manage. 63(3):1999;976-989
14. Amrhein J.F., Stow C.A., Wible C. Whole-fish versus filet polychlorinated-biphenyl concentration. an analysis using classification and regression tree models Environ Toxicol Chemi. 18(8):1999;1817-1823
15. Qian S.S., Anderson C.W. Exploring factors controlling variability of pesticide concentrations in the Willamette River Basin using tree-based models. Environ Sci Technol. 33:1999;3332-3340
16. Watters G, Deriso R. Catch per unit effort of bigeye tuna: a new analysis with regression trees and simulated annealing, Inter-American Tropical Tuna Commission, vol. 21(8), Bulletin, La Jolla, CA, 2000.
17. Nerini D., Dubrec J.P., Mante C. Analysis of oxygen rate time series in a strongly polluted lagoon using a regression tree method. Ecol Model. 133(1-2):2000;95-105
18. De'ath G., Fabricius K.E. Classification and regression trees. a powerful yet simple technique for ecological data analysis Ecology. 81(11):2000;3178- 3192
19. Robertson D.M., Saad D.A. Environmental water-quality zones for streams. a regional classification scheme Environ Manage. 31(5):2003;581-602
20. Chambers JM, Freeny AE, Heiberger RM. Analysis of Variance: designed experiments. In: Chambers JM, Hastie TJ, editors. Statistical models in S. Pacific Grove, CA: Wadsworth and Brooks/Cole Advanced Books and Software; 2003.
21. Chipman H., George E.I., McCulloch R.E. Hierarchical priors for bayesian cart shrinkage. Working Paper 98-03. 1998;University of Waterloo, Department of Statistics and Actuarial Science
22. Gelfand A.E., Smith A.F.M. Sampling-based approaches to calculating marginal densities. J Am Stat Assoc. 85:1999;398-409
23. Gilks W.R., Richardson S., Spiegelhalter D.J. Markov chain monte carlo in practice. 1995;Chapman & Hall, London
24. USEPA. National eutrophication survey: data acquisition and laboratory analysis systems for lakes samples. EPA-600/4-75-015; Environmental Monitoring and Support Laboratory, Las Vegas, NE, 1975.
25. Weisberg S. Applied linear regression. 1985;Wiley, New York
26. Schindler D.W. Eutrophication and recovery in experimental lakes. implications in lake management Science. 184:1974;897-899
27. Efron B., Morris C. Data analysis using Stein's estimator and it's generalizations. J Am Stat Assoc. 70:1975;311-319
28. Lamon E.C., Clyde M. Accounting for model uncertainty in prediction of chlorophyll a in Lake Okeechobee. J Agri Biol Stat. 5:2000;297-322
29. Raftery A.E., Madigan D.M., Volinski C.T. Accounting for model uncertainty in survival analysis improves predictive performance (with discussion). Bernardo J.M., Berger J.O., Dawid A.P., Smith A.F.M. Bayesian statistics. vol. 5:1995;Oxford Science Publications Clarendon Press, Oxford, UK
30. Clemen R.T., Reilly T. Making hard decisions with decision tools® 2nd ed:2001;Duxbury Press, Belmont, CA
31. Reckhow K.H. Empirical models for trophic state in Southeastern US. Lakes and Reservoirs, Wat Res Bull. 24(4):1988;723-734