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Modeling the Coupling of Ocean Ecology and Biogeochemistry Modeling the coupling of ocean ecology and biogeochemistry The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Dutkiewicz, S., M. J. Follows, and J. G. Bragg. “Modeling the Coupling of Ocean Ecology and Biogeochemistry.” Global Biogeochemical Cycles 23, no. 4 (November 3, 2009): n/a–n/a. © 2009 American Geophysical Union As Published http://dx.doi.org/10.1029/2008gb003405 Publisher American Geophysical Union (AGU) Version Final published version Citable link http://hdl.handle.net/1721.1/97896 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 23, GB4017, doi:10.1029/2008GB003405, 2009 Click Here for Full Article Modeling the coupling of ocean ecology and biogeochemistry S. Dutkiewicz,1 M. J. Follows,1 and J. G. Bragg2,3 Received 15 October 2008; revised 19 April 2009; accepted 14 July 2009; published 3 November 2009. [1] We examine the interplay between ecology and biogeochemical cycles in the context of a global three-dimensional ocean model where self-assembling phytoplankton communities emerge from a wide set of potentially viable cell types. We consider the complex model solutions in the light of resource competition theory. The emergent community structures and ecological regimes vary across different physical environments in the model ocean: Strongly seasonal, high-nutrient regions are dominated by fast growing bloom specialists, while stable, low-seasonality regions are dominated by organisms that can grow at low nutrient concentrations and are suited to oligotrophic conditions. In the latter regions, the framework of resource competition theory provides a useful qualitative and quantitative diagnostic tool with which to interpret the outcome of competition between model organisms, their regulation of the resource environment, and the sensitivity of the system to changes in key physiological characteristics of the cells. Citation: Dutkiewicz, S., M. J. Follows, and J. G. Bragg (2009), Modeling the coupling of ocean ecology and biogeochemistry, Global Biogeochem. Cycles, 23, GB4017, doi:10.1029/2008GB003405. 1. Introduction simulations can become sufficiently complex that we may need to return to more idealized frameworks to interpret [2] Phytoplankton community structure in the world’s them. ocean is understood to regulate important biogeochemical [3] Resource competition theory [Tilman, 1977] (perti- pathways, notably the export of organic carbon to the deep nent aspects of which are briefly outlined in section 1.1) is ocean. The contrast between blooms of aggregating and an established ecological framework for considering the sinking diatoms and a population of picoplankton locked in connections between ecology and resource availability. a tightly coupled microbial loop is clearly important [e.g., Recent empirical and theoretical studies [Falkowski and Pomeroy, 1974; Laws et al., 2000]. The quality of exported Oliver, 2007; Litchman et al., 2007; Irwin et al., 2006; particles, for example their association with particular Wilson et al., 2007, and references therein; Tozzi et al., mineral ballast, is significant in setting the depth at which 2004] emphasize the application of resource competition they are processed and respired [e.g., Armstrong et al., theory as a framework for interpreting the regulation of 2002; Klaas and Archer, 2002]. Community structure, in phytoplankton community structure. turn, is strongly influenced by the physical and chemical [4] Here we examine a model of marine ecosystems environment; the availability of a variety of essential (described further in section 1.2) in which phytoplank- resources and the variability of the environment [Margalef, ton community structure is explicitly ‘‘self-assembling’’ 1968; Tozzi et al., 2004]. The ecology and biogeochemistry [Follows et al., 2007]. A relatively large number of phyto- of the oceans are tightly interconnected. How is this plankton types are initialized each with physiological traits complex, ecobiogeochemical system organized? How does and functionalities stochastically chosen from plausible the physical and chemical environment dictate ecological ranges. A subset of the virtual organisms persist at high regimes? Numerical models of ocean biogeochemical cycles abundances, according to their ability to compete for are a tool with which we can simulate aspects of this resources and susceptibility to predation, among many other coupled system, elucidating and illustrating governing factors. We ask to what extent is resource competition mechanisms and interactions [e.g., Moore et al., 2002; Le theory a qualitative and quantitative tool with which to Que´re´ et al., 2005; Hood et al., 2006]. Such numerical interpret this complex and flexible ecosystem model? 1.1. Resource Competition Theory 1 Department of Earth, Atmospheric and Planetary Sciences, Massachu- [5] Resource competition theory [Tilman, 1977, 1982] setts Institute of Technology, Cambridge, Massachusetts, USA. provides a framework for interpreting the relationship 2Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. between organisms and their resource environment. Here 3Now at CSIRO Plant Industry, Canberra, ACT, Australia. we recap some essential elements using a highly simplified example that implicitly assumes a local balance where the Copyright 2009 by the American Geophysical Union. physical transport of organisms can be neglected. Consider 0886-6236/09/2008GB003405$12.00 GB4017 1of15 GB4017 DUTKIEWICZ ET AL.: COUPLING OF ECOLOGY AND BIOGEOCHEMISTRY GB4017 a single photoautotroph (P), nourished by a single macro- nutrients may not be limiting, N 1, and the grazer NþkN nutrient (N) which is supplied to the system with the rate S: population small, m mm. In these conditions, equation (2) reduces to suggest that the fitness of a particular phyto- dN N plankton type, P, is related to its per capita growth rate, ¼mm P þ S ð1Þ dt N þ kN depends only on mm: 1 dP mm: ð5Þ dP N P dt ¼ mm P À mP ð2Þ dt N þ kN In this limit, organisms most able to take advantage of the abundant nutrients will dominate [Stewart and Levin, 1973]. Here, mm is a maximal growth rate, a function of light and We will refer to these organisms as ‘‘r strategy’’ types temperature. Nutrient limitation is parameterized as a [McArthur and Wilson, 1967; Kilham and Hecky, 1988]. In Monod function where kN is the half-saturation constant, high-nutrient regions, the organism with the fastest max- and m represents a simple parameterization of sinking, imum growth rate, mm, will dominate in bloom periods grazing, viral lysis and other loss terms (While we illustrate [Stewart and Levin, 1973]. the theory assuming simple Monod growth, we note that [8] Thus it seems likely that the utility of R* in predicting analogous expressions can also be obtained when assuming competitive outcomes among marine phytoplankton and a flexible internal stores model [see Tilman, 1977]). In ambient nutrient concentrations will be restricted to certain completely steady conditions, when the system has come to ocean physical environments. Here we ask, using a com- equilibrium, plex, self-assembling model of the marine ecosystem and biogeochemical cycles, where and when is this framework kN m of resource competition theory a useful tool? In what N ¼ ¼ R* ð3Þ mm À m regions does it have qualitative and quantitative diagnostic power and what other factors determine the extent of those regions? To what extent does phytoplankton physiology S regulate the nutrient environment of the oceans? P ¼ ð4Þ m 1.2. A Self-Assembling Model of Phytoplankton Communities The equilibrium resource concentration, N, is often denoted by R*[Tilman, 1977]. Equation (3) suggests that the [9] We briefly describe the three-dimensional ocean mod- ambient concentration of the limiting resource is determined el and some basic features of its biogeography. The model by characteristics of the organism including its maximum has been discussed previously by Follows et al. [2007]. It is based on a coarse resolution (1° Â 1° horizontally, 24 levels) growth rate (mm), nutrient half-saturation constant (kN), and mortality rate (m). The equilibrium assumes a tight coupling configuration of the MITgcm [Marshall et al., 1997] con- of source and sink terms, so R* reflects a combination of strained to be consistent with altimetric and hydrographic observations (the ECCO-GODAE state estimates [Wunsch both bottom-up (kN, mm) and top-down (m) characteristics. If multiple organism types are present the ambient resource and Heimbach, 2007]). We transport inorganic and organic concentration will be drawn down to the lowest R* amongst forms of nitrogen, phosphorus, iron and silica, and resolve the organisms present and other organisms will be excluded many tens of phytoplankton types as well as two simple over time [Stewart and Levin, 1973]. In the presence of grazers. The biogeochemical and biological tracers interact multiple, potentially limiting resources coexistence can through the formation, transformation and remineralization occur [Tilman, 1977] up to the number of resources (or of organic matter. Excretion and mortality transfer living other limiting factors [Armstrong and McGehee, 1980]). organic material into sinking particulate and dissolved The organisms that dominate in this steady
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