Modeling Marine Harmful Algal Blooms: Current Status and Future Prospects

A model is a simplification of reality, and the purpose of this chapter is to explore the limitations and potentials for such simplifications to serve useful roles in the management and mitigation of harmful algal blooms (HAB). Others, such as Glibert et al. (2010), have provided overarching reviews on factors that may actually be associated with predicting events; here, the emphasis is upon assessing the state of the art, and how to advance it. Some of the challenges identified stem from issues specific to HAB science, while others apply to plankton research in general; challenges in both have arguably hindered progress in the develop­ ment of HAB forecasting capability and manage­ ment tools. These challenges can best be addressed by closer collaboration among researchers con­ ducting laboratory, field, and modeling work. Improved interactions among these communities can be facilitated by clarification of terminology used in the various subfields (for discussion and an attempt to provide some clarity, see Flynn et al., 2015b). Indeed, models can provide useful dynamic test beds for exploring and testing hypotheses, guiding future iterations of field and laboratory investigations, and providing an improved overall level of understanding. Simplification in modeling can be extreme, as represented by a statistical fit of a regression line through data; and, in some cases, such models can be entirely adequate. At the other end of the spectrum, models may purport to describe tempo­ ral dynamics of dozens of organism types within 3D spatial scenarios. While it may be argued that all models are imperfect and that models are designed specifically to tackle individual questions, such views malign the real value and potential of adequately constructed models in informing us about the real world, how we think it works, and how our understanding may be in error. Errors may reside at conceptual levels as well as in the conversion of understanding into equations and parameter values. Nevertheless, both statistical/ empirical and mechanistic models can provide tools for scientific investigation as well as predic­ tion. Choice of approach depends on the specifics of the application and purpose of the model in that context. The more complex models typically are built upon (and thence should enhance) mechanistic understanding. Complexity does not refer here to factors such as spatial resolution or pure com­ putation load, but rather to the degree of concep­ tual complexity that underpins the description. For biological components, complexity refers more to the level of physiological detail applied to each organism grouping (ecological functional type; Flynn et al., 2015b); complexity does not relate simply to the number of groups, each of which could contain the same very simple conceptual structure differing only in the value ascribed to traits such as organism size or maximum growth rate. Typically, model components describing physi­ ological features of organisms are empirical; that is, they describe behavior that accords with empirical data (i.e., that which is observed). At the extreme, empirical descriptions may relate factors that in reality are only distantly related to each other. Care