Bio-optics in Integrated Ocean Observing Networks: Potential for Studying Harmful Algal Blooms

2008 
Ships have a difficult time sampling the time and space scales relevant for understanding how physics and chemistry regulate marine ecosystems (National Research Council 2000, 2003, Figure 3.1); therefore the biology in the oceans is chronically undersampled. This is a major problem for policy makers and local governments charged with managing coastal resources who are increasingly being asked to develop ‘ecosys­ tem-based management’ plans (US Ocean Commission on Ocean Policy, 2004). This is especially true in regions where Harmful Algal Blooms (HABs) are present (Hal­ legraeff, 1993). Ecosystem-based management requires a quantitative understanding of the processes controlling marine food webs. These processes include physical (mixing, stratification, advection) and chemical (micro, macro, and organic nutrients) factors as well as trophic interactions within the marine food webs. These food-web interactions are especially difficult to study as they are modulated by complex climate (global warm­ ing, storms, winter cooling/summer heating), pelagic (mixing and stratification), litho­ spheric (nutrient weathering), and anthropogenic (terrestrially derived macro and micro nutrients, buoyant plumes, human grazing pressures) forcing functions. Understanding these food webs require an integrated view of physics, chemistry, and phytoplankton community dynamics. Phytoplankton communities often appear to be relatively stable and diverse (the paradox of the plankton, described by Hutchinson, 1961); however the communi­ ties occasionally undergo sudden and dramatic fluctuations where the diverse phy­ toplankton communities shift to rapidly growing monospecific populations. Harm­ ful algal blooms (HABs) are one such manifestation of this phenomena. Theoretical constructs for community shifts have been developed as disturbance hypotheses in ecology (e.g. Paine and Vadas, 1969; Connell, 1978) and catastrophe hypotheses in geology (Berggren and van Couvering, 1984). These theories underlie many of the
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