The PICES CCCC (North Pacific Marine Science Organization, ClimateChange and Carrying Capacity program) MODEL Task Team achieved a consensus on the structure of a prototype lower trophic level ecosystem model for the North Pacific Ocean, and named it the North Pacific Ecosystem Model for Understanding Regional Oceanography, "NEMURO". Through an extensive dialog between modelers, plankton biologists and oceanographers, an extensive review was conducted to define NEMURO's process equations and their parameter values for distinct geographic regions. We present in this paper the formulation, structure and governing equations of NEMURO as well as examples to illustrate its behavior. NEMURO has eleven state variables: nitrate, ammonium, small and large phytoplankton biomass, small, large and predatory zooplankton biomass, particulate and dissolved organic nitrogen, particulate silica, and silicic acid concentration. Several applications reported in this issue of Ecological Modelling have successfully used NEMURO, and an extension that includes fish as an additional state variable. Applications include studies of the biogeochemistry of the North Pacific, and variations of its ecosystem's lower trophic levels and two target fish species at regional and basin-scale levels, and on time scales from seasonal to interdecadal.5

No abstract

There is growing interest in models of marine ecosystems that deal with the effects of climate change through the higher trophic levels. Such end-to-end models combine physicochemical oceanographic descriptors and organisms ranging from microbes to higher-trophic-level (HTL) organisms, including humans, in a single modeling framework. The demand for such approaches arises from the need for quantitative tools for ecosystem-based management, particularly models that can deal with bottom-up and top-down controls that operate simultaneously and vary in time and space and that are capable of handling the multiple impacts expected under climate change. End-to-end models are now feasible because of improvements in the component submodels and the availability of sufficient computing power. We discuss nine issues related to the development of end-to-end models. These issues relate to formulation of the zooplankton submodel, melding of multiple temporal and spatial scales, acclimation and adaptation, behavioral movement, software and technology, model coupling, skill assessment, and interdisciplinary challenges. We urge restraint in using end-to-end models in a true forecasting mode until we know more about their performance. End-to-end models will challenge the available data and our ability to analyze and interpret complicated models that generate complex behavior. End-to-end modeling is in its early developmental stages and thus presents an opportunity to establish an open-access, community-based approach supported by a suite of true interdisciplinary efforts

No abstract

No abstract

On the basis of the assumption that natural selection should tend to produce organisms optimally adapted to their environments, we consider optimality as a guiding concept for abstracting the behavior of aquatic microorganisms (plankton) to develop models to study and predict the behavior of planktonic organisms and communities. This is closely related to trait-based ecology, which considers that traits and functionality can be understood as the result of the optimization inherent in natural selection, subject to constraints imposed by fundamental processes necessary for life. This approach is particularly well suited to plankton because of their long evolutionary history and the ease with which they can be manipulated in experiments. We review recent quantitative modeling studies of planktonic organisms that have been based on the assumption that adaptation of species and acclimation of organisms maximize growth rate. Compared with mechanistic models not formulated in terms of optimality, this approach has in some cases yielded simpler models, and in others models of greater generality. The evolutionary success of any given species must depend on its interactions with both the physical environment and other organisms, which depend on the evolving traits of all organisms concerned. The concept of an evolutionarily stable strategy (ESS) can, at least in principle, constrain the choice of goal functions to be optimized in models. However, the major challenge remains of how to construct models at the level of organisms that can resolve short-term dynamics, e.g., of phytoplankton blooms, in a way consistent with ESS theory, which is formulated in terms of a steady state.Phytoplankton are an excellent model system for ecological studies because of their small size, short generation times, large population numbers, and ease of manipulation (Litchman and Klausmeier 2008), and the same is true at least to some extent of plankton in general (including bacteria). Furthermore, the long evolutionary histories of phytoplankton (3 billion yr; Hedges et al. 2001), bacteria, and archaea (3-4 billion yr; Battistuzzi et al. 2004) make them particularly suited for examining the concept of optimality. Ecological stability and protection from extinction afforded by high dispersal have permitted planktonic organisms to evolve gradually through millions of years in spite of strong climate variability (Cermeñ o et al. 2010). Beyond basic ecology, there is much interest in understanding the major roles of plankton in the biogeochemical cycles of carbon and nutrients on Earth and as the foundation of aquatic food webs.Deterministic modeling is the primary means of expressing and examining quantitatively our understanding of ecological and biogeochemical systems. In an approach that is complementary to trait-based ecology (McGill et al. 2006;Bruggeman and Kooijman 2007;Litchman and Klausmeier 2008), several recent studies have developed improved models of phytoplankton, bacteria, and zooplankton on the basis of some form of the as...

We present a new, optimization-based model for uptake kinetics of multiple nutrients, which has the same number of parameters (two for each nutrient) as the Michaelis-Menten model. We fit this model and an existing inhibition-based model to data from chemostat experiments at various flow rates (under extreme limitation by both nitrogen [N] and phosphorus [P]) and compared these models and the Michaelis-Menten model to an independent data set for the same species in a chemostat at various N : P input ratios (at constant flow rate). Our model fit the data well, with a slightly higher square error than the much more complex inhibition model. We also successfully applied our model to a data set for a different species under various degrees of vitamin B12-and Plimitation. Our model agrees with measured cell quotas of nonlimiting nutrients when supply ratios differ greatly from the optimal ratio for phytoplankton, whereas the Michaelis-Menten model greatly overestimates the uptake of nonlimiting nutrients at these extreme nutrient supply ratios. The key to our model's success is the optimization of uptake for the limiting nutrient, which results in distinct behavior for limiting versus nonlimiting nutrients, without additional parameters; phytoplankton allocate their internal resources (nitrogen) to optimize uptake of the limiting nutrient, but not in response to changes in ambient nutrient ratios.For ambient nutrient ratios that are very different from the optimal ratio of phytoplankton, straightforward application of separate Michaelis-Menten equations for multiple nutrients greatly overestimates the uptake rates of nonlimiting nutrients compared with data from chemostat experiments (Droop 1974;Rhee 1974). Uptake of the same nutrient is faster when it is limiting than when it is nonlimiting (Rhee 1974; Gotham and Rhee 1981a,b). Droop (1974) developed a parameterization for uptake of nonlimiting nutrients to match his observations, and Gotham and Rhee (1981a,b) developed an inhibition-based model in which the maximum uptake rate of a nutrient is a decreasing function of its cell quota (internal concentration). Both of these approaches yield more accurate uptake rates for nonlimiting nutrients, but both add parameters, which must be determined separately for various nutrients and even for the same nutrient with different ratios of ambient nutrient concentrations.We present a new optimization-based model for uptake kinetics of multiple nutrients. Our uptake model is an extension of the single-nutrient optimal-uptake equation of Pahlow (2005), which is itself an extension of the affinitybased uptake model of Aksnes and Egge (1991).For this study we embedded both our uptake model and the inhibition model of Gotham and Rhee (1981a,b) into a model of phytoplankton growth on multiple nutrients (Legovic and Cruzado 1997;Klausmeier et al. 2004) and applied the resulting models to simulate chemostat experiments. We fit both models to data from chemostat experiments at extreme nitrogen : phosphorus (N : P) input rati...

It is well-established that when equilibrium is attained for two species competing for the same limiting resource in a stable, uniform environment, one species will eliminate the other due to competitive exclusion. While competitive exclusion is observed in laboratory experiments and ecological models, the phenomenon seems less common in nature, where static equilibrium is prevented by the fluctuating physical environment and by other factors that constantly change species abundances and the nature of competitive interactions. Trait-based models of phytoplankton communities appear to be useful tools for describing the evolution of large assemblages of species with aggregate group properties such as total biomass, mean trait, and trait variance, the latter representing the functional diversity of the community. Such an approach, however, is limited by the tendency of the trait variance to unrealistically decline to zero over time. This tendency to lose diversity, and therefore adaptive capacity, is typically "solved" by fixing the variance or by considering exogenous processes such as immigration. Exogenous processes, however, cannot explain the maintenance of adaptive capacity often observed in the closed environment of chemostat experiments. Here we present a new method to sustain diversity in adaptive trait-based models of phytoplankton communities based on a mechanism of trait diffusion through subsequent generations. Our modeling approach can therefore account for endogenous processes such as rapid evolution or transgenerational trait plasticity.