Embedding individual-based plankton ecosystem models in a finite element ocean model
File(s)
Author(s)
Lange, Michael
Type
Thesis
Abstract
Computational models of the ocean plankton ecosystem are traditionally based on simulating entire populations of microbes using sets of coupled differential equations. However, due to recent advances in high-performance computing, a new class of individual-based models (IBM) has come to the fore, which uses computational agents to model individual sub-populations of marine plankton. Although computationally more expensive, these agent-based models offer features that cannot be re-created using population-level dynamics, such as individual life cycles, intra-population variability and an increased stability over parameter ranges.
The main focus of this thesis is the implementation and verification of an embedded modelling framework for creating agent-based plankton ecology models in Fluidity-ICOM, a state-of-the-art ocean model that solves the Navier-Stokes equations on adaptive unstructured finite element meshes. Since Fluidity-ICOM provides an interface for creating population-based ecology models, a generic agent-based framework not only enables the integration of existing plankton IBMs with adaptive remeshing technology, but also allows individual and population-based components to be used within a single hybrid ecosystem.
This thesis gives a full account of the implementation of such a framework, focusing in particular on the movement and tracking of agents in an unstructured finite element mesh and the coupling mechanism used to facilitate agent-mesh and agent-agent interactions. The correctness of the framework is verified using an existing agent-based ecosystem model with four trophic levels, which is shown to settle on a stationary annual attractor given a stable cycle of annual forcing. A regular cycle of phytoplankton primary production and zooplankton reproduction is achieved using a purely agent-based implementation and a hybrid food chain version of the model, where the two top-level components of the ecosystem are modelled using Eulerian field equations. Finally, a standalone phytoplankton model is used to investigate the effects of vertical mesh adaptivity on the ecosystem in a three-dimensional mesh.
The main focus of this thesis is the implementation and verification of an embedded modelling framework for creating agent-based plankton ecology models in Fluidity-ICOM, a state-of-the-art ocean model that solves the Navier-Stokes equations on adaptive unstructured finite element meshes. Since Fluidity-ICOM provides an interface for creating population-based ecology models, a generic agent-based framework not only enables the integration of existing plankton IBMs with adaptive remeshing technology, but also allows individual and population-based components to be used within a single hybrid ecosystem.
This thesis gives a full account of the implementation of such a framework, focusing in particular on the movement and tracking of agents in an unstructured finite element mesh and the coupling mechanism used to facilitate agent-mesh and agent-agent interactions. The correctness of the framework is verified using an existing agent-based ecosystem model with four trophic levels, which is shown to settle on a stationary annual attractor given a stable cycle of annual forcing. A regular cycle of phytoplankton primary production and zooplankton reproduction is achieved using a purely agent-based implementation and a hybrid food chain version of the model, where the two top-level components of the ecosystem are modelled using Eulerian field equations. Finally, a standalone phytoplankton model is used to investigate the effects of vertical mesh adaptivity on the ecosystem in a three-dimensional mesh.
Version
Open Access
Date Issued
2013-09
Date Awarded
2014-03
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Field, Anthony
Gorman, Gerard
Sponsor
Engineering and Physical Sciences Research Council
Publisher Department
Computing
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)