Ava Moussavi Jessica Satterlee Garfield Kwan

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.

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

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

8. 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

10. 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)

11. 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

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

13. 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)

14. 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

15. 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.

16. Fin