Allometric scaling and taxonomic variation in nutrient utilization traits and maximum growth rate of phytoplankton

Nutrient utilization traits can be used to link the ecophysiology of phytoplankton to population dynamic models and the structure of communities across environmental gradients. Here we analyze a comprehensive literature compilation of four traits: maximum nutrient uptake rate; the half-saturation constant for nutrient uptake; the minimum subsistence quota, measured for nitrate and phosphate; and maximum growth rate. We also use these traits to analyze two composite traits, uptake affinity and scaled uptake affinity. All traits tend to increase with cell volume, except for scaled uptake affinity and maximum growth rate, which tend to decline with cell volume. Most scaling relationships are the same for freshwater and marine species, although important differences exist. Most traits differ on average between major taxa, but between-taxon variation is nearly always due to between-taxon variation in volume. There is some evidence for between-trait correlations that could constrain trait evolution, but these correlations are difficult to disentangle from correlation driven by cell volume. These results should enhance the parameterization of models that use size or taxonomic group to structure physiological variation in phytoplankton communities. Nutrient utilization traits have long been used to link the ecophysiology of phytoplankton to competitive interactions and the structure of communities across environmental gradients (Eppley et al. 1969; Tilman 1982). Such traits can be used to parameterize models that predict population dynamics and competitive outcomes in the laboratory (Grover 1991), implying that trait-based models may allow a mechanistic understanding of natural dynamics and distributions (Litchman and Klausmeier 2008). Many traits have been shown to scale allometrically with cell volume, such as maximum nutrient uptake rate, the half-saturation constant for uptake, subsistence quotas, and uptake affinity (Shuter 1978; Grover 1989; Litchman et al. 2007; Tambi et al. 2009). Such scaling relationships allow the parameterization of models that predict how optimal size, or community size structure, should vary with the nutrient supply regime. For example, the relative abundance of large cells is predicted to increase with increasing nitrogen supply (Irwin et al. 2006), and nitrogen supplied in pulses may allow large cells to outcompete small cells (Stolte and Riegman 1996; Litchman et al. 2009). Likewise, major phytoplankton taxa often differ in average trait values (Smayda 1997; Litchman et al. 2007), and this can permit a more mechanistic understanding of patterns in taxonomic structure and the biogeochemical effects of taxonomic variation. For example, annual fluctuation in the relative abundance of marine diatoms, coccolithophores, and prasinophytes can be predicted from a model that incorporates taxonomic differences in nutrient and light utilization traits (Litchman et al. 2006); and predicted seasonal patterns of oceanic CO2 uptake are altered by the inclusion of a separate functional group representing coccolithophores (Signorini et al. unpubl.). In order to better quantify interspecific variation in nutrient utilization traits, we have comprehensively compiled trait data from published studies. Our compilation includes nitrate and phosphate utilization traits of both freshwater and marine species, as nitrogen and phosphorus are the main limiting nutrients in both environments (Hecky and Kilham 1988; Elser et al. 2007). We have also compiled maximum growth rates for a large number of freshwater and marine species. We use this compilation to ask a series of questions: (1) what are the power-law exponents that describe how each trait scales with volume; (2) do the exponents differ between marine and freshwater species; (3) do average trait values differ between taxa; (4) do between-taxon differences exist when controlling for between-taxon variation in cell volume; (5) how do empirical power-law exponents compare to theoretical predictions; and (6) is there evidence for trade-offs among traits that would constrain the evolution of competitive ability. Many of these questions have been addressed previously, in separate analyses using smaller datasets or a subset of traits (Banse 1976; Shuter 1978; Grover 1989; Tang 1995; Smayda 1997; Litchman et al. 2007). In this study we synoptically address these questions using a thorough collection of currently available data.