Chironomid Abundance as An Indicator of Water

Conditions in Treatment Wetlands and Biofilters of

Victoria, Australia

Ava MoussaviJessica Satterlee

Garfield Kwan

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

The Millennium Drought

Melbourne

Bureau of Meteorology, 2011

Sparked widespread use of alternate water sources◦ Recycled water◦ Rainwater harvesting

Alternate Water Sources

Grant et al. 2012

Western Treatment Plant

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

Potential Risk

Larval stage of midges

Thrive in anoxic conditions

Feed on organic matter

Associated with degraded wetland conditions

Chironomids as Indicators?

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

Objective

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 Collection

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)

Data Analysis

Results

Chironomidae = B0 – B

1Temp-1 + B

2Turb-1

B0 = 170.14

B1 = 1948.40

B2 = 2315.22

p-value (Turb-1): 0.02p-value (Temp-1): 0.03

Chironomidae = B0 – B

1 poly(pH) + B

2Turb-1

B0 = -34.56

B1 = 1.30

B2 = 1505.51

Results

• Chironomid abundance can be predicted from temperature and turbidity (top ranked model) or pH and turbidity (second model)

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

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.

Conclusion

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

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.

Acknowledgements

Fin