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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. - PowerPoint PPT Presentation
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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 – B1Temp-1 + B2Turb-1
B0 = 170.14 B1 = 1948.40 B2 = 2315.22
p-value (Turb-1): 0.02p-value (Temp-1): 0.03
Chironomidae = B0 – B1 poly(pH) + B2Turb-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