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Overcoming data constraints to create meaningful ecological models

Lester, Rebecca E. and Fairweather, Peter G. 2010, Overcoming data constraints to create meaningful ecological models, in IEMSS 2010 : Modelling for environment's sake : Proceedings of the 5th International Congress on Environmental Modelling and Software, IEMSS, [Ottawa, Ontario], pp. 1-9.

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Title Overcoming data constraints to create meaningful ecological models
Author(s) Lester, Rebecca E.
Fairweather, Peter G.
Conference name Environmental Modelling and Software. Congress (5th : 2010 : Ottawa, Ontario)
Conference location Ottawa, Ontario
Conference dates 5-8 Jul. 2010
Title of proceedings IEMSS 2010 : Modelling for environment's sake : Proceedings of the 5th International Congress on Environmental Modelling and Software
Editor(s) Swayne, D.
Yang, Wanhong
Voinov, A
Rizzoli, A.
Filatova, T.
Publication date 2010
Conference series Environmental Modelling and Software International Congress
Start page 1
End page 9
Total pages 9
Publisher IEMSS
Place of publication [Ottawa, Ontario]
Keyword(s) data limitations
ecological response models
statistical modelling
Summary Many techniques used to model ecosystems cannot be meaningfully applied to large-scale ecological problems due to data constraints. Disparate collection methods, data types and incomplete data sets, or limited theoretical understanding mean that a wide range of modelling techniques used to model physical processes or for problems specific to species or populations cannot be used at an ecosystem scale. In developing an ecological response model for the Coorong, a South Australian hypersaline estuary, we combined several flexible modelling approaches in a statistical framework to develop an approach we call ‘ecosystem states’. This model uses simulated hydrodynamic conditions as input to predict one of a suite of states per space and time, allowing prediction of likely ecological conditions under a variety of scenarios. Each ecosystem state has defined sets of biota and physico-chemical parameters. The existing model is limited in that its predictions have yet to be tested and, as yet, no spatial or temporal connectivity has been incorporated into simulated time series of ecosystem states. This approach can be used in a wide range of ecosystems, where enough data are available to model ecosystem states. We are in the process of applying the technique to a nearby lake system. This has been more difficult than for the Coorong as there is little overlap in the spatial and temporal coverage of biological data sets for that region. The approach is robust to low-quality biological data and missing environmental data, so should suit situations where community or management monitoring programs have occurred through time.
Language eng
Field of Research 059999 Environmental Sciences not elsewhere classified
Socio Economic Objective 970105 Expanding Knowledge in the Environmental Sciences
HERDC Research category E1.1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30054780

Document type: Conference Paper
Collections: School of Life and Environmental Sciences
Centre for Integrative Ecology
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