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Oceanic Phytoplankton Communities Biogeosciences Discuss., 3, 607–663, 2006 Biogeosciences www.biogeosciences-discuss.net/3/607/2006/ Discussions BGD © Author(s) 2006. This work is licensed 3, 607–663, 2006 under a Creative Commons License. Biogeosciences Discussions is the access reviewed discussion forum of Biogeosciences Oceanic phytoplankton communities E. Litchman et al. Multi-nutrient, multi-group model of Title Page present and future oceanic phytoplankton Abstract Introduction communities Conclusions References E. Litchman1,2, C. A. Klausmeier2,3, J. R. Miller1, O. M. Schofield1, and Tables Figures P. G. Falkowski1 J I 1Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ 08901, USA 2 Michigan State University, Kellogg Biological Station, MI 49060, USA J I 3Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, Back Close USA Received: 16 January 2006 – Accepted: 20 January 2006 – Published: 19 June 2006 Full Screen / Esc Correspondence to: E. Litchman ([email protected]) Printer-friendly Version Interactive Discussion EGU 607 Abstract BGD Phytoplankton community composition profoundly influences patterns of nutrient cy- cling and the structure of marine food webs; therefore predicting present and future 3, 607–663, 2006 phytoplankton community structure is of fundamental importance to understanding how 5 ocean ecosystems are influenced by physical forcing and nutrient limitations. In this pa- Oceanic per, we develop a mechanistic model of phytoplankton communities that includes multi- phytoplankton ple taxonomic groups, test the model at two contrasting sites in the modern ocean, and communities then use the model to predict community reorganization under different global change scenarios. The model includes three phytoplankton functional groups (diatoms, coccol- E. Litchman et al. 10 ithophores, and prasinophytes), five nutrients (nitrate, ammonium, phosphate, silicate and iron), light, and a generalist zooplankton grazer. Each taxonomic group was pa- rameterized based on an extensive literature survey. The model successfully predicts Title Page the general patterns of community structure and succession in contrasting parts of the Abstract Introduction world ocean, the North Atlantic (North Atlantic Bloom Experiment, NABE) and subarc- 15 tic North Pacific (ocean station Papa, OSP). In the North Atlantic, the model predicts Conclusions References a spring diatom bloom, followed by coccolithophore and prasinophyte blooms later in Tables Figures the season. The diatom bloom becomes silica-limited and the coccolithophore and prasinophyte blooms are controlled by nitrogen, grazers and by deep mixing and de- creasing light availability later in the season. In the North Pacific, the model reproduces J I 20 the low chlorophyll community dominated by prasinophytes and coccolithophores, with J I low total biomass variability and high nutrient concentrations throughout the year. Sen- sitivity analysis revealed that the identity of the most sensitive parameters and the Back Close range of acceptable parameters differed between the two sites. Full Screen / Esc Five global change scenarios are used to drive the model and examine how commu- 25 nity dynamics might change in the future. To estimate uncertainty in our predictions, we Printer-friendly Version used a Monte Carlo sampling of the parameter space where future scenarios were run using parameter combinations that produced adequate modern day outcomes. The Interactive Discussion first scenario is based on a global climate model that indicates that increased green- EGU 608 house gas concentrations will cause a later onset and extended duration of stratification and shallower mixed layer depths. Under this scenario, the North Atlantic spring diatom BGD bloom occurs later and is of a smaller magnitude, but the average biomass of diatoms, 3, 607–663, 2006 coccolithophores and prasinophytes will likely increase. In the subarctic North Pacific, 5 diatoms and prasinophytes will likely increase along with total chlorophyll concentra- tion and zooplankton. In contrast, coccolithophore densities do not change at this site. Oceanic Under the second scenario of decreased deep-water phosphorus concentration, coc- phytoplankton colithophores, total chlorophyll and zooplankton decline, as well as the magnitude of communities the spring diatom bloom, while the average diatom and prasinophyte abundance does E. Litchman et al. 10 not change in the North Atlantic. In contrast, a decrease in phosphorus in the North Pacific is not likely to change community composition. Similarly, doubling of nitrate in deep water does not significantly affect ecosystems at either site. Under decreased Title Page iron deposition, coccolithophores are likely to increase and other phytoplankton groups and zooplankton to decrease at both sites. An increase in iron deposition is likely to in- Abstract Introduction 15 crease prasinophyte and diatom abundance and decrease coccolithophore abundance at both sites, although more dramatically at the North Pacific site. Total chlorophyll and Conclusions References zooplankton are also likely to increase under this scenario at both sites. Based on Tables Figures these scenarios, our model suggests that global environmental change will inevitably alter phytoplankton community structure and potentially impact global biogeochemical J I 20 cycles. J I 1 Introduction Back Close Although they account for less than 1% of the photosynthetic biomass on Earth, Full Screen / Esc oceanic phytoplankton are responsible for upwards of 45% of global net primary pro- duction (Field et al., 1998). The fate of net primary production in the oceans is, how- Printer-friendly Version 25 ever, critically dependent on community composition (Doney et al., 2002; Falkowski et Interactive Discussion al., 2003). For example, phytoplankton communities dominated by diatoms are associ- ated with significantly higher carbon export production than flagellate-dominated com- EGU 609 munities (Dugdale and Wilkerson, 1998; Smetacek, 1999). Coccolithophorids influ- ence alkalinity, the production of calcite and dimethyl sulfide (DMS) and ocean albedo BGD (Balch et al., 1992; Iglesias-Rodriguez et al., 2002; Holligan and Robertson, 1996; 3, 607–663, 2006 Tyrrell et al., 1999). Understanding the factors that determine the distribution of key 5 phytoplankton groups and their patterns of succession is a fundamentally important but elusive goal in marine ecology. Oceanic In this paper we develop a mechanistic model of phytoplankton community struc- phytoplankton ture and first apply it to two characteristic and biogeochemically important regions of communities the open ocean (the North Atlantic and North Pacific) to describe patterns of seasonal E. Litchman et al. 10 succession in the modern ocean. Our model aims to capture general patterns of sea- sonal cycles in phytoplankton and apply it to contrasting regions of the global ocean, similarly to the models of Evans and Parslow (1985) and Fasham et al. (1990). We Title Page are interested in the long-term behavior of the model when the effects of initial condi- tions fade away. By examining the steady state predictions, we can explore the long- Abstract Introduction 15 term shifts in the community resulting from human impacts. While Evans and Parslow (1985) considered seasonal cycles of phytoplankton as a whole, we develop a model Conclusions References to resolve seasonal phytoplankton dynamics at the level of functional groups. Func- Tables Figures tional groups in phytoplankton are defined as groups of “organisms related through common biogeochemical processes” and are not necessarily phylogenetically related J I 20 (Iglesias-Rodr´ıguez et al., 2002). However, some of the major taxonomic groups of marine phytoplankton represent distinctly different functional groups (e.g., diatoms as J I a major silicifying group and coccolithophores as a major calcifying group). Here we Back Close explicitly consider the following major taxonomic groups of eukaryotic phytoplankton: diatoms, coccolithophores and prasinophytes. Each group is parameterized based on Full Screen / Esc 25 an extensive compilation of the experimental data on nutrient uptake and growth kinet- ics, allowing us to meaningfully constrain key model parameters. To obtain a greater Printer-friendly Version taxonomic resolution one must consider multiple nutrients (two forms of inorganic ni- trogen, inorganic phosphorus, silica and iron). This allows us to adequately separate Interactive Discussion controls for different functional groups as the groups appear to be differentiated in their EGU 610 competitive abilities for these nutrients. After testing the model against modern data, we use it to explore how phytoplank- BGD ton community structure and patterns of seasonal succession may shift in response to 3, 607–663, 2006 global change. We have two main goals: (1) to identify and model potential mecha- 5 nisms of the community changes in the contemporary ocean, and (2) to examine how potential changes in ocean mixing and nutrient availability potentially influence phyto- Oceanic plankton community composition over the next century. In addressing these goals, we phytoplankton ask whether a relatively simple model can recreate general patterns in phytoplankton communities distribution, seasonal succession in the modern ocean. If so, what are the inferred E. Litchman et al. 10 mechanisms responsible for shifts in community composition? Because our primary goal is to describe phytoplankton community dynamics,
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