Parameterization and Prediction of Community Interaction Models Using Stable-State Assumptions and Computational Techniques: an Example From the High Rocky Intertidal

This source preferred by Rick Stafford

Authors: Stafford, R., Davies, M.S. and Williams, G.A.

Journal: Biological Bulletin

Volume: 215

Pages: 155-163

ISSN: 0006-3185

This data was imported from PubMed:

Authors: Stafford, R., Davies, M.S. and Williams, G.A.

Journal: Biol Bull

Volume: 215

Issue: 2

Pages: 155-163

ISSN: 0006-3185

DOI: 10.2307/25470696

Many ecological communities exist in a stable state where, if undisturbed, no net change will occur in the populations or in the interactions between the component parts of the system. In this paper we present computational methods (evolutionary algorithms and random searches) to parameterize mathematical models that describe communities in stable states. The initial parameterization of the model requires only "best guess" estimates for parameters and can therefore be used in data-poor situations. The technique locates the stable state that occurs with minimum deviation from these parameters. Alternative stable states in which the community may exist after a disturbance event can also be assessed using this technique, even though the number of alternative states may be large. Using available but incomplete data from an intertidal grazer/biofilm community, we created a prediction of the dynamics of both a pre- and post-disturbance community. Using limited data, we then predicted the most likely post-disturbance community, which proved to be a good match to experimental data, indicating the usefulness of this technique as a predictive tool.

This data was imported from Scopus:

Authors: Stafford, R., Davies, M.S. and Williams, G.A.

Journal: Biological Bulletin

Volume: 215

Issue: 2

Pages: 155-163

ISSN: 0006-3185

Many ecological communities exist in a stable state where, if undisturbed, no net change will occur in the populations or in the interactions between the component parts of the system. In this paper we present computational methods (evolutionary algorithms and random searches) to parameterize mathematical models that describe communities in stable states. The initial parameterization of the model requires only "best guess" estimates for parameters and can therefore be used in data-poor situations. The technique locates the stable state that occurs with minimum deviation from these parameters. Alternative stable states in which the community may exist after a disturbance event can also be assessed using this technique, even though the number of alternative states may be large. Using available but incomplete data from an intertidal grazer/biofilm community, we created a prediction of the dynamics of both a pre- and post-disturbance community. Using limited data, we then predicted the most likely post-disturbance community, which proved to be a good match to experimental data, indicating the usefulness of this technique as a predictive tool. © 2008 Marine Biological Laboratory.

This data was imported from Web of Science (Lite):

Authors: Stafford, R., Davies, M.S. and Williams, G.A.

Journal: BIOLOGICAL BULLETIN

Volume: 215

Issue: 2

Pages: 155-163

ISSN: 0006-3185

DOI: 10.2307/25470696

This data was imported from Europe PubMed Central:

Authors: Stafford, R., Davies, M.S. and Williams, G.A.

Journal: The Biological bulletin

Volume: 215

Issue: 2

Pages: 155-163

eISSN: 1939-8697

ISSN: 0006-3185

Many ecological communities exist in a stable state where, if undisturbed, no net change will occur in the populations or in the interactions between the component parts of the system. In this paper we present computational methods (evolutionary algorithms and random searches) to parameterize mathematical models that describe communities in stable states. The initial parameterization of the model requires only "best guess" estimates for parameters and can therefore be used in data-poor situations. The technique locates the stable state that occurs with minimum deviation from these parameters. Alternative stable states in which the community may exist after a disturbance event can also be assessed using this technique, even though the number of alternative states may be large. Using available but incomplete data from an intertidal grazer/biofilm community, we created a prediction of the dynamics of both a pre- and post-disturbance community. Using limited data, we then predicted the most likely post-disturbance community, which proved to be a good match to experimental data, indicating the usefulness of this technique as a predictive tool.

The data on this page was last updated at 04:47 on December 18, 2017.