Abstract
BackgroundThe management of the health of urban lake systems is often reactive and is instigated in response to poor aesthetic quality or physicochemical measurements, rather than from an overall assessment of ecosystem health. Interpreting physicochemical monitoring data in isolation is problematic for two main reasons: the suite of parameters that are monitored may be limited; and the contribution that any single parameter has towards water quality or health varies considerably depending on the nature of the system of interest. Extending monitoring programs to include flora and fauna results in a better dataset of ecosystem status, but also increases the complexity in interpreting whether the status is good or poor.ResultsThis paper details a process by which a large set of quantitative biological, physical, chemical and social indicators may be transformed into a simple, but informative, numerical index that represents the overall ecosystem health, while also identifying the likely source and scale of pressure for remedial management action. The flexibility of the proposed approach means that it can be readily adapted to other lake systems and environments, or even to include or exclude different indicators. A case study is presented in which the model is used to assess a comprehensive longitudinal dataset that resulted from monitoring a constructed urban lake in Southeast Queensland, Australia.ConclusionsThe sensitivity analysis and case study indicate that the model identifies how changes in individual monitoring parameters result in changes in overall ecosystem health, and thus illustrates its potential as a lake management tool.
Highlights
The management of the health of urban lake systems is often reactive and is instigated in response to poor aesthetic quality or physicochemical measurements, rather than from an overall assessment of ecosystem health
The model described in this paper takes a similar approach to Cude (2001), but illustrates how a much larger, multi-disciplinary indicator set can be evaluated for overall ecosystem health, while maintaining a high degree of sensitivity to individual parameters
The overall ecosystem health index (OEHI) was most sensitive to change in dissolved oxygen saturation and to Results and discussion In order to assess the applicability and effectiveness of the model with real-world data, a case study analysis was conducted by using the model to summarise an existing multi-disciplinary dataset that resulted from a full year of monitoring several urban lakes
Summary
This paper details a process by which a large set of quantitative biological, physical, chemical and social indicators may be transformed into a simple, but informative, numerical index that represents the overall ecosystem health, while identifying the likely source and scale of pressure for remedial management action. The flexibility of the proposed approach means that it can be readily adapted to other lake systems and environments, or even to include or exclude different indicators. A case study is presented in which the model is used to assess a comprehensive longitudinal dataset that resulted from monitoring a constructed urban lake in Southeast Queensland, Australia
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