Predictive assessment of fish health and fish kills in the Neuse River Estuary using elicited expert judgment

Human and Ecological Risk Assessment

Volumn 10, Issue 2, 2004, Pages 415-434

  Borsuk, Mark E ^a,b  

^a DUKE UNIVERSITY   (United States)

^b SWISS FEDERAL INSTITUTE OF AQUATIC SCIENCE AND TECHNOLOGY   (Switzerland)

Author keywords

Coastal eutrophication; Ecological risk assessment; Expert elicitation; Logistic regression; Probability network

Indexed keywords

EID: 4544278784     PISSN: 10807039     EISSN: None     Source Type: Journal    
DOI: 10.1080/10807030490438454     Document Type: Article

References (57)

1. Abramson B, Brown J, Edwards W, et al. 1996. Hailfinder: A Bayesian system for forecasting severe weather. Int J Forecasting 12:57-71
2. Ananth CV and Kleinbaum DG. 1997. Regression models for ordinal responses: A review of methods and applications. Int J Epidemiol 26:1323-33
3. Bender R and Benner A. 2000. Calculating ordinal regression models in SAS and S-Plus. Biom J 42:677-99
4. Borsuk ME, Clemen RT, Maguire LA, et al. 2001. Stakeholder values and scientific modeling in the Neuse River watershed. Group Decis Negotiation 10:355-73
5. Borsuk ME, Higdon D, Stow, CA, et al. 2001. A Bayesian hierarchical model to predict benthic oxygen demand from organic matter loading in estuaries and coastal zones. Ecol Modell 143:165-81
6. Borsuk ME, Stow CA, Luettich, RA, et al. 2001. Modelling oxygen dynamics in an intermittently stratified estuary: Estimation of process rates using field data. Estuar Coast Shelf Sci 52:33-49
7. Borsuk ME, Stow CA, and Reckhow KH. 2003. Integrated approach to total maximum daily load development for Neuse River Estuary using Bayesian probability network model (Neu-BERN). J Water Resour Plan Manage 129:271-82
8. Breitburg DL. 1992. Episodic hypoxia in Chesapeake Bay: Interacting effects of recruitment, behavior, and physical disturbance. Ecol Monogr 62:525-46
9. Breitburg DL, Rose KA, and Cowen, JH. 1999. Linking water quality to larval survival: Predation mortality of fish larvae in an oxygen-stratified water column. Mar Ecol Prog Ser 178:39-54
10. Breitburg DL, Steinberg N, and DuBeau S. 1994. Effects of low dissolved oxygen on predation on estuarine fish larvae. Mar Ecol Prog Ser 104:235-46
11. Burkholder JM and Glasgow HB. 1997. Pfiesteria piscicida and other Pfiesteria-like dinoflagellates: Behavior, impacts, and environmental controls. Limnol Oceanogr 42:1052-75
12. Clemen RT and Winkler RL. 1999. Combining probability distributions from experts in risk analysis. Risk Anal 19:187-203
13. Devore JL. 1991. Probability and Statistics for Engineering and the Sciences. Brooks/Cole Pub. Co., Pacific Grove, CA, USA
14. Diaz RJ and Rosenberg R. 1995. Marine benthic hypoxia: A review of its ecological effects and the behavioural responses of benthic macrofauna. Oceanogr Mar Biol 33:245-303
15. Dobson AJ. 1990. An Introduction to Generalized Linear Models. Chapman & Hall/CRC, New Yory, NY, USA
16. Dykstra MJ and Kane AS. 2000. Pfiesteria piscicida and ulcerative mycosis of Atlantic menhaden - Current status of understanding. J Aquat. Anim Health 12:18-25
17. Eby LA. 2001. Response of a Fish Community to Frequent and Infrequent Disturbances in an Estuarine Ecosystem. Ph.D. Thesis. Duke University, Durham, NC, USA
18. Eby LA and Crowder LB. 2002. Hypoxia-based habitat compression in the Neuse River Estuary: Context-dependent shifts in behavioral avoidance thresholds. Can J Fish Aquat Sci 59:952-65
19. Eby LA, Crowder LB, and McClellan, C. 2000. Neuse River Estuary Modeling and Monitoring Project Stage 1: Effects of Water Quality on Distribution and Composition of the Fish Community. Water Resource Research Institute of the University of North Carolina, Raleigh, NC, USA
20. Eby LA, Crowder LB, and McClellan, C. Effects of habitat degradation from intermittent hypoxic disturbances on juvenile fishes (in review)
21. Ellison AM. 1996. An introduction to Bayesian inference for ecological research and environmental decision-making. Ecol Appl 6:1036-46
22. Evans M, Hastings NAJ, and Peacock JB. 2000. Statistical Distributions. Wiley, New York, NY, USA
23. Goldman RL, Eby LA, Crowder LB, et al. The effects of alterations in coastal ecosystems on incidence of disease in Atlantic menhaden (Brevoortia tyrannus L.) (in review)
24. Golub GH and Van Loan CF. 1983. Matrix Computations. Johns Hopkins University Press, Baltimore, MD, USA
25. Grimm V. 1999. Ten years of individual-based modelling in ecology: What have we learned and what could we learn in the future? Ecol Modell 115:129-48
26. Hall LW, Fisher SA, and Sullivan JA. 1991. A synthesis of water quality and contaminants data for the Atlantic menhaden, Brevoortia tyrannus: Implications for Chesapeake Bay. J Environ Sci Health, Part A 26:1513-44
27. Harrell FEJ. 1998. Design: S functions for biostatistical/epidemiologic modeling, testing, estimation, validation, graphics, and prediction. Available at http://lib.stat.cmu.edn/S/Harrell/
28. Hattis D and Anderson EL. 1999. What should be the implications of uncertainty, variability, and inherent "biases"/ "conservatism" for risk management decision-making? Risk Anal 19:95-107
29. Huston M, DeAngelis D, and Post W. 1988. New computer models unify ecological theory. Bioscience 38:682-91
30. Lichtenstein S, Slovic P, Fischhoff B, et al. 1978. Judged frequency of lethal events. J Exp Psychol: Hum Learn Memory 4:551-78
31. Litaker RW, Vandersea MW, Kibler SR, et al. 2002. Life cycle of the heterotrophic dinoflagellate Pfiesteria Piscicida (Dinophyceae). J Phycol 38:442-63
32. Lumina. 1997. Analytica for Windows, User's Guide, Version 1.1. Lumina Decision Systems, Denver, CO, USA
33. Lunn DJ, Wakefield J, and Racine-Poon A. 2001. Cumulative logit models for ordinal data: A case study involving allergic rhinitis severity scores. Stat Med 20:2261-85
34. McCullagh P. 1980. Regression models for ordinal data (with discussion). J R Stat Soc [Ser B] 42:109-42
35. McNatt RA, Rice JA, and Noga EG. 2000. Impacts of Sublethal Hypoxia on Feeding, Growth, and Immunity of Juvenile Estuarine-Dependent Fish. N.C. Water Resources Research Conference, Water Resources Research Institute of the University of North Carolina, Raleigh, NC, USA
36. McNinch JE and Luettich RA. 2000. Hydrography and Circulation. Neuse River Estuary Modeling and Monitoring Project Final Report: Monitoring Phase, Report No. 325-B, pp 7-42. Water Resources Research Institute of the University of North Carolina, Raleigh, NC, USA
37. Meyer M and Booker J. 1991. Eliciting and Analyzing Expert Judgment: A Practical Guide. Academic Press, London, UK
38. Morgan MG and Henrion M. 1990. Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press, Cambridge, UK
39. NCDMF (NC Division of Marine Fisheries). 2001. Stock Status of Important Coastal Fisheries in North Carolina. Available at http://www.ncfisheries.net/ stocks/atlmenhaden.htm
40. NCDWQ (NC Division of Water Quality). 2000. Estuarine Fish Kills in North Carolina. Available at http://www.esb.enr.state.nc.us/Fishkill/killfact830.pdf
41. NCDWQ (NC Division of Water Quality). 2001. North Carolina Division of Water Quality Fish Kill Report. Available at http://www.esb.enr.state.nc.us/ Fishkill/fishkill00.htm
42. NCDWQ (NC Division of Water Quality). 2001. Phase II of the Total Maximum Daily Load for Total Nitrogen to the Neuse River Estuary, North Carolina. North Carolina Department of Environment and Natural Resources, Raleigh, NC, USA
43. Newman MC and Evans DA. 2002. Enhancing belief during causality assessments: Cognitive idols or Bayes's theorem. In: Newman MC, Roberts MHJ, et al. (eds), Coastal and Estuarine Risk Assessment, Lewis, Boca Raton, FL, USA
44. Pace ML. 2001. Prediction and the aquatic sciences. Can J Fish Aquat Sci 58:1-10
45. Paerl HW, Pinckney JL, Fear JM, et al. 1999. Fish kills and bottom-water hypoxia in the Neuse River and Estuary. Mar Ecol Prog Ser 186:307-9
46. Pearl J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA, USA
47. Pearl J. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK
48. Pihl L, Baden SP, and Diaz RJ. 1991. Effects of periodic hypoxia on distribution of demersal fish and crustaceans. Mar Biol 108:349-60
49. Reckhow KH. 1994. Water quality simulation modeling and uncertainty analysis for risk assessment and decision-making. Ecol Modell 72:1-20
50. Reckhow KH and Gray J. 2000. Neuse River Estuary Modeling and Monitoring Project Stage 1: Executive Summary and Long-Term Modeling Recommendations. Report No. 325-A. Water Resources Research Institute of the University of North Carolina, Raleigh, NC, USA
51. Reckhow KH, Black RW, Stockton TB, et al. 1987. Empirical models of fish response to lake acidification. Can J Fish Aquat Sci 44:1432-42
52. Rose KA. 2000. Why are quantitative relationships between environmental quality and fish populations so elusive? Ecol Appl 10:367-85
53. Rubin ES, Small MJ, Bloyd CN, et al. 1992. Integrated assessment of acid-deposition effects on lake acidification. J Environ Eng-ASCE 118:120-34
54. Rubinstein RY. 1982. Simulation and the Monte Carlo Method. Wiley, New York, NY, USA
55. Small MJ and Fischbeck PS. 1999. False precision in Bayesian updating with incomplete models. Hum Ecol Risk Assess 5:291-304
56. Spetzler CS and Stael von Holstein C-AS. 1975. Probability encoding in decision analysis. Manage Sci 22:340-58
57. Stow CA and Borsuk ME. 2003. Enhancing causal assessment of estuarine fishkills using graphical models. Ecosystems 6:11-9