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PREDICTING ESTUARINE SEDIMENT METAL CONCENTRATIONS AND INFERRED ECOLOGICAL CONDITIONS: AN INFORMATION THEORETIC APPROACH
Citation:
HOLLISTER, J. W., P. AUGUST, J. F. PAUL, AND H. A. WALKER. PREDICTING ESTUARINE SEDIMENT METAL CONCENTRATIONS AND INFERRED ECOLOGICAL CONDITIONS: AN INFORMATION THEORETIC APPROACH. JOURNAL OF ENVIRONMENTAL QUALITY. American Society of Agronomy, MADISON, WI, 37(1):234-244, (2008).
Impact/Purpose:
This manuscript describes a predictive model that uses widely available, broad scale monitoring data (i.e. EMAP and NLCD) to predict sediment metal concentrations in Northeast and Mid-Atlantic estuaries. The impacts of this manuscript are twofold. First, this work builds on prior AED publications, namely Comeleo et al. (1996) and Paul et al. (2002), by expanding the extent of the study area and updating the data. Our findings corroborate those of both prior studies. Second and most importantly, we demonstrate a modeling method, information theoretic model averaging, that is somewhat recent to environmental science. By using these methods as described by Burnham and Anderson (2002), we have updated the general broad scale modeling framework described by Comeleo et al. (1996) and Paul et al. (2002) to a more contemporary mode that is more appropriate to models built from broad scale environmental monitoring data (i.e. EMAP and NLCD). These methods and our demonstration of them are important because they represent a step forward in how we make broad scale predictions of estuarine condition from less than perfect (from a frequentist statistics standpoint) data.
Description:
Empirically derived values associating sediment metal concentrations with degraded ecological conditions provide important information to assess estuarine condition. However, resources limit the number, magnitude, and frequency of monitoring programs to gather these data. As such, models that use available information and simple statistical relationships to predict sediment metal concentrations could provide an important tool for environmental assessment. To address this need, we developed 45 predictive models for the total concentrations of copper, lead, mercury, and cadmium in estuarine sediments along the Southern New England and Mid-Atlantic regions of the United States. Using information theoretic model-averaging approaches, we found total developed land and percent silt/clay of estuarine sediment were the most important variables for predicting the presence of all 4 metals. Estuary area, river flow, tidal range, and total agricultural land varied in their importance. The model-averaged predictions explained 78.4%, 70.5%, 56.4%, and 50.3% of the variation for copper, lead, mercury, and cadmium respectively. Overall prediction accuracies of selected sediment benchmark values (e.g., Long’s effects ranges) were 83.9%, 84.8%, 78.6%, 92.0% for copper, lead, mercury, and cadmium, respectively. Our results further support the generally accepted conclusion that sediment metal concentrations are best described by the physical characteristics of the estuarine sediment and the total amount of urban land in the contributing watershed. More importantly, we demonstrated that broad scale predictive models built from existing monitoring data with information theoretic model-averaging approaches provide reliable predictions of estuarine sediment metal concentrations and show promise for future environmental modeling efforts in other regions.