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An Open Source Simulation Model for Soil and Sediment Bioturbation

An Open Source Simulation Model for Soil and Sediment Bioturbation
An Open Source Simulation Model for Soil and Sediment Bioturbation
Bioturbation is one of the most widespread forms of ecological engineering and has significant implications for the structure and functioning of ecosystems, yet our understanding of the processes involved in biotic mixing remains incomplete. One reason is that, despite their value and utility, most mathematical models currently applied to bioturbation data tend to neglect aspects of the natural complexity of bioturbation in favour of mathematical simplicity. At the same time, the abstract nature of these approaches limits the application of such models to a limited range of users. Here, we contend that a movement towards process-based modelling can improve both the representation of the mechanistic basis of bioturbation and the intuitiveness of modelling approaches. In support of this initiative, we present an open source modelling framework that explicitly simulates particle displacement and a worked example to facilitate application and further development. The framework combines the advantages of rule-based lattice models with the application of parameterisable probability density functions to generate mixing on the lattice. Model parameters can be fitted by experimental data and describe particle displacement at the spatial and temporal scales at which bioturbation data is routinely collected. By using the same model structure across species, but generating species-specific parameters, a generic understanding of species-specific bioturbation behaviour can be achieved. An application to a case study and comparison with a commonly used model attest the predictive power of the approach.
1932-6203
e28028
Schiffers, Katja
623030a0-358e-490d-afcc-fd82b3ad22aa
Teal, Lorna Rachel
47f2252c-9ae0-4fb2-bf36-3caf175d04c9
Travis, Justin Mark John
eeb29958-d843-49e0-8583-7515a7b7708c
Solan, Martin
c28b294a-1db6-4677-8eab-bd8d6221fecf
Schiffers, Katja
623030a0-358e-490d-afcc-fd82b3ad22aa
Teal, Lorna Rachel
47f2252c-9ae0-4fb2-bf36-3caf175d04c9
Travis, Justin Mark John
eeb29958-d843-49e0-8583-7515a7b7708c
Solan, Martin
c28b294a-1db6-4677-8eab-bd8d6221fecf

Schiffers, Katja, Teal, Lorna Rachel, Travis, Justin Mark John and Solan, Martin (2011) An Open Source Simulation Model for Soil and Sediment Bioturbation. PLoS ONE, 6 (12), e28028. (doi:10.1371/journal.pone.0028028).

Record type: Article

Abstract

Bioturbation is one of the most widespread forms of ecological engineering and has significant implications for the structure and functioning of ecosystems, yet our understanding of the processes involved in biotic mixing remains incomplete. One reason is that, despite their value and utility, most mathematical models currently applied to bioturbation data tend to neglect aspects of the natural complexity of bioturbation in favour of mathematical simplicity. At the same time, the abstract nature of these approaches limits the application of such models to a limited range of users. Here, we contend that a movement towards process-based modelling can improve both the representation of the mechanistic basis of bioturbation and the intuitiveness of modelling approaches. In support of this initiative, we present an open source modelling framework that explicitly simulates particle displacement and a worked example to facilitate application and further development. The framework combines the advantages of rule-based lattice models with the application of parameterisable probability density functions to generate mixing on the lattice. Model parameters can be fitted by experimental data and describe particle displacement at the spatial and temporal scales at which bioturbation data is routinely collected. By using the same model structure across species, but generating species-specific parameters, a generic understanding of species-specific bioturbation behaviour can be achieved. An application to a case study and comparison with a commonly used model attest the predictive power of the approach.

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Published date: 5 December 2011
Organisations: Ocean Biochemistry & Ecosystems

Identifiers

Local EPrints ID: 339685
URI: http://eprints.soton.ac.uk/id/eprint/339685
ISSN: 1932-6203
PURE UUID: a02dcdba-8648-4eaa-9c59-fd73168a97ac
ORCID for Martin Solan: ORCID iD orcid.org/0000-0001-9924-5574

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Date deposited: 28 May 2012 15:56
Last modified: 15 Mar 2024 03:32

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Contributors

Author: Katja Schiffers
Author: Lorna Rachel Teal
Author: Justin Mark John Travis
Author: Martin Solan ORCID iD

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