A model for <i>Acartia tonsa</i>: effect of turbulence and consequences for the related physiological processes

Caparroy, Philippe; Carlotti, François

doi:10.1093/plankt/18.11.2139

Cited by 23 publications

(16 citation statements)

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“…In a majority of articles (see [7,8,33] for instance), the copepod is represented by an analytical model. These models try to describe each ''process'' of the organism in terms of input flow, output flow and transfer function.…”

Section: Internal Processesmentioning

confidence: 99%

“…Caparroy [7] proposes a model synthesizing the various models developed before. This model takes into account the activities of capture and ingestion using five coupled differential equations.…”

Section: Internal Processesmentioning

confidence: 99%

“…One usually models these processes by introducing different coefficients. For instance, the effect of turbulence is modeled by a more or less important encounter rate coefficient [7,8]. In this work, we use equations developed by Caparroy [7] to model internal processes that govern satiety.…”

Section: Internal Processesmentioning

confidence: 99%

“…By measuring the quantity of nitrogen ingestion, observations show that outputs relating to the behavior of the copepod (the excretion rate for example) vary according to the type of distribution of food. For example, a turbulent environment, favorable to an overall mixing, can increase the encounter rate of the copepod with the cells of phytoplankton and increases the ingestion rate [7]. So, the only point that is modeled with regard to phytoplankton is its spatial distribution.…”

Section: The Phytoplanktonmentioning

confidence: 99%

“…The unit of time of simulation is fixed to the duration corresponding to the time necessary to perform the shortest action, i.e. the handling time of a particle of small phytoplankton by the copepod, 1/20 s [7].…”

Section: Numerical Aspects Of the Modelmentioning

confidence: 99%

See 4 more Smart Citations

Scale Transfer Modeling: Using Emergent Computation for Coupling an Ordinary Differential Equation System with a Reactive Agent Model

Duboz

Ramat

Preux

2003

Systems Analysis Modelling Simulation

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This article deals with the coupling of analytical models with individual based models design with the reactive agents paradigm. Such a coupling of models of different natures is motivated by the need to find a way to model scale transfer in large complex systems, i.e. to model how low level of organization can be made to influence upper level and vice versa. This is a fundamental issue, and more particularly in ecological modeling where models are a real scientific tool of investigation. Individuals and populations are not described at the same scale of time and space but it is known that they act on each others. Based on this example, we model individuals in their environment and the population dynamics. While behavior is best modeled using an algorithmic framework (the reactive agent paradigm), population dynamics (because of the number of interacting entities) is best modeled using numerical models. We propose the use of the concept of emergent computation as a framework for coupling heterogeneous formalisms. In the same time, it is crucial to be aware of the consequences of the simplifications and of the choices that are made in the reactive agent model, such as the topology of space and various parameters. In this article, we discuss these issues and our approach on a case study drawn from marine ecology and we show that it is possible to find classical mathematical functional responses with a reactive agent system. Then, we propose a methodology to deal with the coupling of heterogeneous formalism useful in any kind of system modeling.

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Section: Internal Processesmentioning

confidence: 99%

Section: Internal Processesmentioning

confidence: 99%