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Integrating Biological Data in Predictive Water Quality Models Using Multivariate Analysis

This study discusses the application of user-friendly multivariate analysis techniques to enhance predictive water quality models by linking them to biological data. By employing PRIMER software and methodologies such as cluster and ordination techniques, this research aims to create a coherent framework for interpreting community structures and calculating environmental impacts. It highlights the importance of integrating biological and environmental variables for effective aquatic biological modeling while addressing the challenges of multivariate data presentation and analysis.

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Integrating Biological Data in Predictive Water Quality Models Using Multivariate Analysis

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  1. User-Friendly Multivariate Analysis for Linking Predictive Water Quality Models to Biological Data Janna Owens

  2. Water Quality Monitoring • Physical, chemical and biological assessments • Calculate environmental impacts • Create models of water processes as predictive tools for physical/chemical data • Ideally, a compatible framework would integrate biological data

  3. PRIMER software • Plymouth Research Routines in Multivariate Ecological Research • Coherent strategy for interpretation of community structure • Wide range univariate/multivariate routines • Ease of use and comprehension

  4. Predictive Model

  5. Aquatic Biological Modeling • Deterministic models do not directly evaluate larger biological organisms • Won’t simulate many aspects of complex community • Statistical data modeling integrates biological and environmental variables • Basic methodologies: Cluster and Ordination

  6. techniques to classify objects Biological classification verified by environmental variables Difficult to use with environmental gradients Requires extensive database Mutivariate data presented in 2 dimensions Sample (dis)similarity represented by proximity in space Determines variables that affect biological data Spatial distortion possible without caution Cluster vs. Ordination

  7. Cluster Analysis ?

  8. Hierarchical Cluster Unstable Stable

  9. Ordination Analysis Multi-Variable

  10. PCA PC Eigenvalue %Variation Cum.%Variation 1 199 39.0 39.0 2 130 25.5 64.5 3 68.6 13.4 77.9 4 35.7 7.0 84.9 5 20.3 4.0 88.9

  11. PCA cont.

  12. MDS Stable sites: Less Urbanization Unstable sites: More Urbanization

  13. MDS Trajectory

  14. ANOSIM

  15. Dominance Curves Sites ~ 67% Cumulative Dominance (%) ~ 27% 3 Species rank by abundance

  16. Aggregation   ;-)   ;-)

  17. Phylogenetic Diversity

  18. TAXTEST 95%

  19. Sample Spreadsheet

  20. Applications • More productive data mining • Allow merging of historical and diverse sample efforts • Comparison to a variety of predictive models to assess trends • Universal comprehension

  21. Acknowledgments • US EPA Region IV • Dr. Andrew Simon, USDA, National Sedimentation Lab • Drs. Angus and Marion, UAB • Clarke, K.R. and Warwick, R.M. 1993. Change in Marine Communities: An approach to Statistical Analysis and Interpretation, Bourne Press Ltd., Bournemouth, U.K.

  22. Questions?

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