Pelagic functional group modeling: Progress, challenges and prospects

Abstract

In this paper, we review the state of the art and major challenges in current efforts to incorporate biogeochemical functional groups into models that can be applied on basin-wide and global scales, with an emphasis on models that might ultimately be used to predict how biogeochemical cycles in the ocean will respond to global warming. We define the term ''biogeochemical functional group'' to refer to groups of organisms that mediate specific chemical reactions in the ocean. Thus, according to this definition, ''functional groups'' have no phylogenetic meaning-these are composed of many different species with common biogeochemical functions. Substantial progress has been made in the last decade toward quantifying the rates of these various functions and understanding the factors that control them. For some of these groups, we have developed fairly sophisticated models that incorporate this understanding, e.g. for diazotrophs (e.g. Trichodesmium), silica producers (diatoms) and calcifiers (e.g. coccolithophorids and specifically Emiliania huxleyi). However, current representations of nitrogen fixation and calcification are incomplete, i.e., based primarily upon models of Trichodesmium and E. huxleyi, respectively, and many important functional groups have not yet been considered in open-ocean biogeochemical models. Progress has been made over the last decade in efforts to simulate dimethylsulfide (DMS) production and cycling (i.e., by dinoflagellates and prymnesiophytes) and denitrification, but these efforts are still in their infancy, and many significant problems remain. ARTICLE IN PRESSOne obvious gap is that virtually all functional group modeling efforts have focused on autotrophic microbes, while higher trophic levels have been completely ignored. It appears that in some cases (e.g., calcification), incorporating higher trophic levels may be essential not only for representing a particular biogeochemical reaction, but also for modeling export. Another serious problem is our tendency to model the organisms for which we have the most validation data (e.g., E. huxleyi and Trichodesmium) even when they may represent only a fraction of the biogeochemical functional group we are trying to represent.When we step back and look at the paleo-oceanographic record, it suggests that oxygen concentrations have played a central role in the evolution and emergence of many of the key functional groups that influence biogeochemical cycles in the present-day ocean. However, more subtle effects are likely to be important over the next century like changes in silicate supply or turbulence that can influence the relative success of diatoms versus dinoflagellates, coccolithophorids and diazotrophs. In general, inferences drawn from the paleo-oceanographic record and theoretical work suggest that global warming will tend to favor the latter because it will give rise to increased stratification. However, decreases in pH and Fe supply could adversely impact coccolithophorids and diazotrophs in the future.It may be necessary...

Modeling the seasonal autochthonous sources of dissolved organic carbon and nitrogen in the upper Chesapeake Bay

“…This complexity creates a problem when trying to understand and model DOM cycling because complex models, with many state variables and parameters, often do not perform better than simpler ones (Anderson, 2005;Arhonditsis et al, 2006;Freidrichs et al, 2007;Hood et al, 2006). However, since the cycling of DOM is so complex, it cannot be adequately described using the simple modeling approach of aggregating species into all-encompassing functional groups (as in NPZ models) and adding a DOM pool.…”

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“…How does the physical and chemical environment dictate ecological regimes? Numerical models of ocean biogeochemical cycles are a tool with which we can simulate aspects of this coupled system, elucidating and illustrating governing mechanisms and interactions [e.g., Moore et al, 2002;Le Quéré et al, 2005;Hood et al, 2006]. Such numerical simulations can become sufficiently complex that we may need to return to more idealized frameworks to interpret them.…”

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