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Ava Moussavi Jessica Satterlee Garfield Kwan

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Ava Moussavi Jessica Satterlee Garfield Kwan

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  1. Chironomid Abundance as An Indicator of Water Conditions in Treatment Wetlands and Biofilters of Victoria, Australia Ava Moussavi Jessica Satterlee Garfield Kwan

  2. The Millennium Drought • Started in the late 1990s and lasted more than a decade Melbourne Bureau of Meteorology, 2011

  3. Alternate Water Sources • Sparked widespread use of alternate water sources • Recycled water • Rainwater harvesting Grant et al. 2012 Western Treatment Plant

  4. Potential Risk • Wastewater and stormwater recycling can be a potential risk to human and ecosystem health if methods for water treatment do not perform optimally.

  5. Chironomids as Indicators? • Larval stage of midges • Thrive in anoxic conditions • Feed on organic matter • Associated with degraded wetland conditions

  6. Objective • The objective of this project was to assess the relationship between chironomidabundance and overall water quality.

  7. Data Collection • Water quality parameters were measured at 2 biofilters and 3 constructed wetlands in Melbourne, Australia • Chironomids • Chlorophyll concentrations • Dissolved oxygen and temperature • Conductivity, Turbidity, ORP, and pH

  8. Data Analysis • Virtual Beach 2.3 was used to perform multiple linear regression • Identified correlations between chironomid abundance and water quality parameters: • Chlorophyll Content • Dissolved Oxygen (DO) • Temperature • pH • Conductivity • Turbidity • Oxidation Reduction Potential (ORP)

  9. Results Chironomidae = B0 – B1Temp-1 + B2Turb-1 B0 = 170.14 B1 = 1948.40 B2 = 2315.22 p-value (Turb-1): 0.02 p-value (Temp-1): 0.03

  10. Results Chironomidae = B0 – B1 poly(pH) + B2Turb-1 B0 = -34.56 B1 = 1.30 B2 = 1505.51

  11. Discussion • Chironomid abundance can be predicted from temperature and turbidity (top ranked model) or pH and turbidity (second model) • Turbidity is the most credible explanatory variable because it appears in both top-ranked models, and was identified as an important correlate in a preliminary Classification Tree analysis (data not shown) • Chironomid abundance can be predicted from temperature and turbidity (top ranked model) or pH and turbidity (second model) • Turbidity is the most credible explanatory variable because it appears in both top-ranked models, and was identified as an important correlate in a preliminary Classification Tree analysis (data not shown) • Data set is small and more advanced analytical techniques for categorical data would need to be explored • Chironomid abundance can be predicted from temperature and turbidity (top ranked model) or pH and turbidity (second model)

  12. Conclusion • Our study has identified temperature, pH and turbidity as possible indicators of chironomid abundance, but our data/methods are insufficient for us to conclude that these water quality parameters can be used to predict chironomid abundance. Future Direction • Increase sampling size and sampling intensity • Survey alternative variables i.e. wetland birds • Use advanced statistical tools (Generalized Linear Models, Classification Tree analysis) that permit evaluation of categorical variables • Functional role of chironomidae

  13. Acknowledgements • We want to thank Stanley Grant, Sunny Jiang, Megan Rippy, Andrew Mehring, Alex McCluskey, Laura Weiden, Nicole Patterson, and Leyla Riley, the faculty of University of California - Irvine, and the staff of University of Melbourne for contributing and facilitating our research. We also want to thank NSF for funding this research.

  14. Fin