A trait-based approach for downscaling complexity in plankton ecosystem models

Merico, Agostino; Bruggeman, Jorn; Wirtz, Kai W.

doi:10.1016/j.ecolmodel.2009.05.005

Cited by 84 publications

(141 citation statements)

References 45 publications

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“…Adaptive dynamics have been employed to study predator-prey coevolution (e.g., Abrams and Matsuda 1997) and increasingly also community dynamics and their potential to adapt to environmental changes (Norberg 2004;Savage et al 2007). The multi-species and dynamic trait approach gives similar results when based on comparable assumptions (Merico et al 2009). …”

Section: Physiologically Structured Modelsmentioning

confidence: 83%

“…A continuous trait value distribution describes the relative importance of the functionally different units, where the mean trait value reflects the strategy of the most abundant units and the variance the functional diversity. The trait value distribution may continuously change when growth conditions are altered, which reflects an increase in the share of species better suited for the current environment (Wirtz and Eckhardt 1996;Merico et al 2009). Adaptive dynamics have been employed to study predator-prey coevolution (e.g., Abrams and Matsuda 1997) and increasingly also community dynamics and their potential to adapt to environmental changes (Norberg 2004;Savage et al 2007).…”

Section: Physiologically Structured Modelsmentioning

confidence: 99%

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Challenges and opportunities for integrating lake ecosystem modelling approaches

et al. 2010

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A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: superindividual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.

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Section: Physiologically Structured Modelsmentioning

confidence: 83%

Section: Physiologically Structured Modelsmentioning

confidence: 99%

Challenges and opportunities for integrating lake ecosystem modelling approaches

et al. 2010

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“…But rather than solving the underlying trait discrete model directly, we shall adopt a different and more efficient approach. In the following, however, we reduce the model complexity further by deriving simplified aggregate equations for the evolution of the first three moments of c. In much of the prior literature, the moment equations are derived directly from the discrete model (Clement and Wood, 1980;Norberg et al, 2001;Merico et al, 2009). Here, we find it more convenient to match the time-dependent Gaussian ansatz function…”

Section: Trait Diffusionmentioning

confidence: 99%

“…We will now study the trait diffusion approach in the context of a previously published chemostat model (Merico et al, 2009), which describes the adaptive dynamics of a phytoplankton community P, via temporal changes in the mean trait edibility, subject to a constant nutrient input N and to variable grazing pressure by zooplankton Z. P, N, and Z are expressed as concentrations. The phytoplankton abundance P evolves according to the trait diffusion Equation (7), in which we assume that the loss rate per unit of phytoplankton is given by…”

Section: Npz Modelmentioning

confidence: 99%

“…The half-saturation is a function of the trait x and reflects a trade-off relationship between nutrient harvesting abilities and edibility of phytoplankton. This relationship was mechanistically derived by Merico et al (2009) by assuming that phytoplankton partition assimilated energy over three pools: (1) generic biomass, (2) nutrient harvesting biomass, with allocation coefficient α, and (3) defense biomass, with allocation coefficient β. The fraction of energy allocated to generic biomass is indicated with κ, while the remaining fraction (1 − κ) is partitioned between nutrient harvesting and defense pools.…”

Section: Npz Modelmentioning

confidence: 99%

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Sustaining diversity in trait-based models of phytoplankton communities

et al. 2014

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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.

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