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Kate Malloy David Wade Tony Janicki

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  1. Benthic Macroinvertebrate and Periphyton Monitoring in the Suwannee River Basin in Florida 2: Relationships between Water Quality and Biology Kate MalloyDavid WadeTony Janicki September 23, 2004 Rob Mattson

  2. Objectives: • Determine which water quality variables are most strongly correlated with biology • Determine probability of occurrence of species as a response to water quality

  3. SRWMD Data: • Most frequently occurring benthic invertebrate and periphyton species • 16 water quality parameters: alkalinity, chl a, color, conductivity, DO, NO2+NO3, NH3,TKN, total N, total P, OPO4 ,PH, temperature, TOC, TSS, turbidity

  4. Spearman Correlation • non-parametric • tests statistical significance of bivariate relationship • provides measure of association (positive, negative, strong, weak)

  5. Logistic Regression • Predict the probability of occurrence, p(y), of a species as a function of environmental variables • Concept: an organism has tolerance limits for a given environmental variable (bounded by a minimum and maximum value) • Single logistic regression describes optimum habitat requirements • Multiple logistic regressions provide information on relative importance of each environmental variable

  6. Nitrate + Nitrite • Statistically significant correlations for 16 of the 20 frequently occurring periphyton species • 11 were highly significant (<0.001) • Correlations varied in strength, ranging from 0.62-0.20 • most positively correlated • Example species: • Cocconeis placentula • R=0.62, p<0.001

  7. 1.00 0.75 0. 50 0. 25 0. 01 2.34 Nitrogen Logistic Output p(y) Optimum Tolerance Range Model Domain

  8. Logistic Regression Approach Diatom, Cocconeis placentula

  9. Periphyton Species Summary

  10. Nitrate + Nitrite • Statistically significant correlations for 15 of the 20 frequently occurring invertebrate species • 11 were highly significant (<0.001) • Correlations varied in strength, ranging from 0.59-0.17 • most positively correlated, a few negatively • Example species: • Tricorythodes albilineatus • R=0.59, <0.001

  11. Logistic Regression Approach Mayfly, Tricorythodes albilineatus

  12. Invertebrate Species Summary

  13. Alkalinity • Statistically significant correlations for 13 of the 20 frequently occurring periphyton species • 10 were highly significant (<0.001) • Correlations varied in strength, ranging from 0.61-0.18 • most positively correlated • Example species: • Cocconeis placentula • R=0.61, <0.001

  14. Alkalinity • Statistically significant correlations for 17 of the 20 frequently occurring invertebrate species • 11 were highly significant (<0.001) • Correlations varied in strength, ranging from 0.62-0.15 • most positively correlated, a few negatively • Example species: • Tricorythodes albilineatus • R=0.62, <0.001

  15. Answer Questions: • What are the most sensitive biological indicators for selected environmental variables? • Is there a set of physical/chemical conditions that will result in an expected biological condition?