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A survival model of the effects of bottom-water hypoxia on the population density of an estuarine clam (Macoma balthica)
Borsuk, M.E.; Powers, S.P.; Peterson, C.H. (2002). A survival model of the effects of bottom-water hypoxia on the population density of an estuarine clam (Macoma balthica). Can. J. Fish. Aquat. Sci. 59(8): 1266-1274.
In: Canadian Journal of Fisheries and Aquatic Sciences = Journal canadien des sciences halieutiques et aquatiques. National Research Council Canada: Ottawa. ISSN 0706-652X, more
Peer reviewed article  

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    Brackishwater molluscs; Hypoxia; Population density; Macoma balthica (Linnaeus, 1758) [WoRMS]; Marine

Authors  Top 
  • Borsuk, M.E., correspondent
  • Powers, S.P.
  • Peterson, C.H.

    The effect of bottom-water hypoxia on the population density of the clam Macoma balthica is estimated using a survival-based approach. We used Bayesian parameter estimation to fit a survival model to times-to-death corresponding to multiple dissolved oxygen (DO) concentrations assessed from scientific experts. We describe guidelines for ensuring the accuracy of such assessments and claim that elicitation of quantities that pertain to measurable variables of interest, rather than unobservable parameters, should improve the use of judgment-based information in Bayesian analyses. When directly relevant data are lacking, predictions based on subjective assessments can serve as the basis for preliminary management decisions and additional data collection efforts. To inform pending water quality controls for the Neuse River estuary, North Carolina, we combined the survival model with a model describing the time dependence of DO. For current conditions, the mean summer survival rate is predicted to be only 11%. However, if sediment oxygen demand (SOD) is reduced as a result of nutrient management, summer survival rates will increase, reaching 23% with a 25% reduction in SOD and 46% with a 50% SOD reduction. Full model predictions are expressed as probabilities to provide a quantitative basis for risk-based decision-making and experimental design.

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