Agent-based modeling of the complex life cycle of a cyanobacterium (Anabaena) in a shallow reservoir

The cyanobacterium Anabaena flos-aquae and many other phytoplankton species have a complex life cycle that includes a resting stage (akinete). We present a new agent-based (also known as individual-based) model of Anabaena that includes the formation and behavior of akinetes. The model is part of a coupled Eulerian– Lagrangian model and can reproduce the main features of the observed seasonal and interannual population dynamics in Bugach Reservoir (Siberia), including an unexpectedly large bloom in a year with low nutrient concentrations. Model analysis shows that the internal loading of phosphorus (P) due to germination from the sediment bed is ,10% of the total input. However, most of the long-term nutrient uptake for Anabaena occurs in the sediment bed, which suggests that the sediment bed is not just a convenient overwintering location but may also be the primary source of nutrients. An in silico tracing experiment showed that most water column cells (,90%) originated from cells located in the sediment bed during the preceding winter. An in silico gene knockout experiment (akinete formation is prohibited) showed that the formation of resting stages is of critical importance to the survival of the population on an annual basis. A nutrient-reduction management scenario indicates that Anabaena densities increase because they are less sensitive to water column nutrient levels (because of the sediment bed source) than other species. In lakes and reservoirs, cyanobacteria constitute a problem because they can produce surface scum blooms; low dissolved oxygen (hypoxia, anoxia); toxins; and taste, odor, and aesthetic problems, which interfere with recreation, water supply, and aquatic life (Paerl 1988; Paerl et al. 2001). To exacerbate the problem, cyanobacteria often appear unexpectedly, especially in light of decreasing phosphorus loadings, because present models predict that lower nutrient inputs would favor other species (Downing et al. 2001). In the 1980s, elimination of point sources (,$40M) and partial control of non–point source loads to Lake Delavan (Wisconsin) was followed by a massive cyanobacteria bloom (WDNR 1989). After decades of costly (,$20B) reductions in nutrient inputs to the Great Lakes, cyanobacteria are again a problem in Saginaw Bay (Lake Huron) and other locations (Vanderploeg et al. 2001). Following a large (,$650M) combined sewer overflow (CSO) reduction program, Boston was surprised in 2006 by a massive cyanobacteria bloom in its Charles River (Daley 2006). These examples highlight the complexity of cyanobacteria population dynamics and the inability of present models to predict it. Numerous physical, chemical, and biological factors and their interactions likely contribute to the observed complexity. Here we focus on one specific feature of the cyanobacteria life cycle, the formation of resting stages, which can significantly influence population dynamics (Reynolds 1972; Baker