A Bayesian approach to analyzing ecotoxicoloqical data

Environmental Science and Technology

Volumn 42, Issue 23, 2008, Pages 8978-8984

  Billoir, Elise ^a,b   Delignette Muller, Marie Laure ^a,c   Péry, Alexandre R R ^d   Charles, Sandrine ^a,b  

^a UNIVERSITÉ DE LYON   (France)

^b UNIVERSITÉ LYON 1   (France)

^c VETAGRO SUP   (France)

^d INSTITUT NATIONAL DE L ENVIRONNEMENT INDUSTRIEL ET DES RISQUES INERIS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; INFERENCE ENGINES; PARAMETER ESTIMATION; RISK ASSESSMENT;

BAYESIAN APPROACHES; BAYESIAN INFERENCE; EFFECT CONCENTRATION; ESTIMATION METHODS; NO-OBSERVED-EFFECT-CONCENTRATIONS; PHYSIOLOGICAL PROCESS; POSTERIOR DISTRIBUTIONS; REPRODUCTION TESTS;

PROBABILITY DISTRIBUTIONS;

COPPER; ZINC;

BAYESIAN ANALYSIS; CRUSTACEAN; ECOTOXICOLOGY; ENERGY BALANCE; REPRODUCTION; RISK ASSESSMENT; TOXICITY TEST;

ARTICLE; BAYES THEOREM; CHRONIC TOXICITY; DAPHNIA; DATA ANALYSIS; ECOTOXICITY; GROWTH RATE; NONHUMAN; REPRODUCTION; RISK ASSESSMENT; STATISTICAL ANALYSIS; TOXICITY TESTING; WATER QUALITY;

DAPHNIA;

EID: 57449096777     PISSN: 0013936X     EISSN: None     Source Type: Journal    
DOI: 10.1021/es801418x     Document Type: Article

References (29)

1. International Organization for Standardization: 10706:2000 Water quality = Determination of long term toxicity of substances to Daphnia magna Straus (Cladocera, Crustacea), 2000.
2. Kooijman, S. A. L. M.; Bedaux, J. J. M, Péry, A. R. R.; Jager, T. Biology-based methods. In Water Quality - Guidance document on the Statistical Analysis of Ecotoxicity Data; ISO and OECD: Paris, 2004; Vol. TC 147/ SC 5/ WG 10/ N 0390.
3. Kooijman, S. A. L. M.; Bedaux, J. J. M. The Analysis of Aquatic Toxicity Data; VU University Press: Amsterdam, 1996.
4. Kooijman, S. A. L. M.; Bedaux, J. J. M. Analysis of toxicity tests on Daphnia survival and reproduction. Water Res. 1996, 30, 1711-1723.
5. Jager, T.; Crommentuijn, T.; Van Gestel, C. A. M.; Kooijman, S. A. L. M. Simultaneous modeling of multiple endpoints in life-cycle toxicity tests. Environ. Sci. Technol. 2004, 38, 2894-2900.
6. Jager, T.; Heugens, E. H. W.; Kooijman, S. A. L. M. Making sense of ecotoxicological test results: towards application of processbased models. Ecotoxicology 2006, 15, 305-314.
7. Péry, A. R. R.; Ducrot, V.; Mons, R.; Garric, J. Modelling toxicity and mode of action of chemicals to analyse growth and emergence tests with the midge Chironomus riparius. Aquat. Toxicol. 2003, 65 (3), 281-292.
8. Ducrot, V.; Péry, A. R. R.; Quéau, H.; Mons, R.; Lafont, M.; Garric, J. Rearing and estimation of life-cycle parameters of the tubicifid worm Branchiura sowerbyi: application to ecotoxicity testing. Sci. Total Environ. 2007, 384, 252-263.
9. Billoir, E.; Delignette-Muller, M. L.; Péry, A. R. R.; Geffard, O.; Charles, S. Statistical cautions when estimating DEBtox parameters J. Theor. Biol. 2008, 254 (1), 55-64.
10. Billoir, E.; Péry, A. R. R.; Charles, S. Integrating the lethal and sublethal effects of toxic compounds into the population dynamics of Daphnia magna: a combination of the DEBtox and matrix population models. Ecol. Modell. 2007, 203, 204-214.
11. Gilks, W. R.; Richardson, S.; Spiegelhalter, D. J, Markov Chain Monte Carlo in Practice, Chapman and Hall/CRC: Boca Raton, FL, 1996.
12. Englehardt, J. D.; Swartout, J. Predictive bayesian microbial dose-response assessment based on suggested self-organization in primary illness response: Cryptosporidium parvum. Risk Anal. 2006, 26, 543-554.
13. Link, W. A.; Albers, P. H. Bayesian multimodel inference for dose-response studies. Environ. Toxicol. Chem. 2007, 26, 1867-1872.
14. Klok, C.; Holmstrup, M.; Damgaard, C. Extending a combined dynamic energy budget matrix population model with a bayesian approach to assess variation in the intrinsic rate of population increase. An example in the earthworm Dendrobaena octaedra. Environ. Toxicol. Chem. 2007, 26, 2383-2388.
15. Essington, T. E.; Kitchell, J. F.; Walters, C. J. The von Bertalanffy growth function, bioenergetics, and the consumption rates of fish. Can. J. Fish. Sci. 2001, 58, 2129-2138.
16. Gertner, G. Z.; Fang, S.; Skovsgaard, J. P. A bayesian approach for estimating the parameters of a forest process model based on long-term growth data. Ecol. Modell. 1999, 119, 249-265.
17. Radtke, P. J.; Robinson, A. P. A bayesian strategy for combining predictions from empirical and process-based models. Ecol. Modell. 2006, 190, 287-298.
18. Arhonditsis, G. B.; Qian, S. S.; Stow, C. A.; Lamon, E. C.; Reckhow, K. H. Eutrophication risk assessment using bayesian calibration of process-based models: Application to a mesotrophic lake. Ecol. Modell. 2007, 208, 215-229.
19. OECD Guidelines for Testing of Chemicals. Daphnia magna Reproduction Test, Organization for Economic Co-operation and Development, 1998.
20. Kooijman, S. A. L. M., Dynamic Energy and Mass Budgets in Biological Systems, 2nd ed.; Cambridge University Press: Cambridge, 2000.
21. Lunn, D. J.; Thomas, A.; Best, N.; Spiegelhalter, D. Winbugs - a bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 2000, 10, 325-337.
22. Spiegelhalter, D.; Thomas, A.; Best, N.; Lunn, D. WinBUGS Version 1.4 User Manual; MRC Biostatistics Unit: Cambridge, UK, 2003.
23. Gelman, A. Prior distributions for variance parameters in hierarchical models. Bayesian Anal. 2006, 1, 515-533.
24. Lunn, D. J. WinBugs Differential Interface - Worked Examples, Imperial School of Medicine: London, UK, 2004.
25. Brooks, S. P.; Gelman, A. Alternative methods for monitoring convergence of iterative simulations. J. Comput. Graphical Stat. 1998, 7, 434-455.
26. R Development Core Team R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2007.
27. Woodrow Setzer R. Odesolve: Solvers for Ordinary Differential Equations, R package version 0.5-16, 2006.
28. Plummer, M.; Best, N.; Cowles, K.; Vines, K. Coda: Output analysis and diagnostics for MCMC: R package version 0.11-2, 2007.
29. Spiegalhalter, D. J.; Best, N. G.; Carlin, B. P.; van der Linde, A. Bayesian measures of model complexity and fit (with discussion). J. R. Stat. Soc. B. 2002, 64, 583-640.