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Chironomid Abundance as An Indicator of Water Conditions in Treatment Wetlands and Biofilter s of Victoria, Australia. Ava Moussavi Jessica Satterlee Garfield Kwan. The Millennium Drought. Started in the late 1990s and lasted more than a decade. Melbourne. Bureau of Meteorology, 2011.

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Chironomid Abundance as An Indicator of Water Conditions in Treatment Wetlands and Biofilters of Victoria, Australia

Ava Moussavi

Jessica Satterlee

Garfield Kwan


The Millennium Drought

  • Started in the late 1990s and lasted more than a decade

Melbourne

Bureau of Meteorology, 2011


Alternate Water Sources

  • Sparked widespread use of alternate water sources

    • Recycled water

    • Rainwater harvesting

Grant et al. 2012

Western Treatment

Plant


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.


Chironomids as Indicators?

  • Larval stage of midges

  • Thrive in anoxic conditions

  • Feed on organic matter

  • Associated with degraded wetland conditions


Objective

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


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


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)


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


Results

Chironomidae = B0 – B1 poly(pH) + B2Turb-1

B0 = -34.56

B1 = 1.30

B2 = 1505.51


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)


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


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.


Fin


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