Data collected from lakes at national (regional) scales and state (local) scales can provide different insights regarding relationships between environmental factors and biological responses, and combining these two types of data can potentially yield more precise and accurate understanding of ecological phenomena. National data can include many measures, cover large spatial areas, and span broad environmental gradients. Because of these characteristics, analyses of these data can yield accurate estimates of relationships among different lake characteristics. However, the number of samples in a national data set that is available for estimating a relationship specific to waterbodies within a smaller region, like a single state, is limited. Conversely, state monitoring data provide intensive sampling of lakes within a smaller area, but these data span a narrower range of conditions and may only include a subset of relevant measurements. Here, a Bayesian network model is described that represents the causal linkages between observations of chlorophyll a concentration, cyanobacterial biovolume, and microcystin concentration. This network model was fit to national data and provided a context for modeling observations of chlorophyll a and microcystin collected from lakes in Iowa. Using the knowledge inherent in the national network model improved the accuracy of predictions of microcystin concentrations in Iowa compared to a model based only on Iowa data.
Keywords: Bayesian network; Chlorophyll; Cyanobacteria; Cyanotoxin; Microcystin.
Published by Elsevier B.V.