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Charles P. Hawkins Aquatic, Watershed, and Earth Resources Utah State University

Use of Predictive Models in Aquatic Biological Assessment: theory and application to the Colorado REMAP/NAWQA dataset. Charles P. Hawkins Aquatic, Watershed, and Earth Resources Utah State University. Basic Concepts.

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Charles P. Hawkins Aquatic, Watershed, and Earth Resources Utah State University

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  1. Use of Predictive Models in Aquatic Biological Assessment:theory and application to the Colorado REMAP/NAWQA dataset Charles P. Hawkins Aquatic, Watershed, and Earth Resources Utah State University

  2. Basic Concepts • Predictive models base assessments on the compositional similarity between observed and expected biota. • Major issues: • Understanding the units of measure. • Predicting the expected taxa.

  3. Basic Concepts(The Expected Taxa) Species Richness is the Unit of Currency. E = ∑ Pc=  number of species / sample = 2.9.

  4. O/E as a Measure of Impairment

  5. Probability of Capture Environmental Gradient This is the Challenge: Estimating the Probabilities of Capture of Many Different Taxa that Exhibit Individualistic Distributions

  6. The basic approach to modeling pc’s and estimating E was worked out by Moss et al.*River InVertebrate Prediction and Classification System(RIVPACS) *Moss, D., M. T. Furse, J. F. Wright, and P. D. Armitage. 1987. The prediction of the macro-invertebrate fauna of unpolluted running-water sites in Great Britain using environmental data. Freshwater Biology 17:41-52.

  7. RIVPACS-type Models: 9 Steps • Establish a network of reference sites. • Establish standard sampling protocols. • Classify sites based on biological similarity. • Calculate taxa frequencies of occurrence (Fi,g) within each class. • Derive a model for estimating probabilites of a site belonging to each group (Pg). • Estimate pc’s by weighting Fi,g by Pg. For each assessed site: • Sum pc’s to estimate E. • Calculate O/E. • Determine if observed O/E is different from reference?

  8. Creating RIVPACS Models • Establish a network of reference sites that span the range of environmental conditions in the region of interest.

  9. Creating RIVPACS Models • Use standard protocols to sample biota and habitat features.

  10. Group A B C D Creating RIVPACS Models • Classify sites in terms of their compositional similarity. Dissimilarity 0.2 0.4 0.8 1 0 0.6 Cluster analysis shows 4 ‘groups’ of sites

  11. Creating RIVPACS Models • Estimate frequencies of occurrence of each taxon in each biotic group.

  12. Creating RIVPACS Models • Estimate frequencies of occurrence of each taxon in each biotic group.

  13. Creating RIVPACS Models When we go to a new site, how do we know which biological group it should belong to? • Derive a model from environmental features (not biology) to predict the probabilities that a new site belongs to each of the biological groups. • Discriminant Function Model (for example): • Group is predicted by elevation, watershed area, geology New site

  14. Creating RIVPACS Models • Develop site-specific ferquencies for each taxon to occur at the new site • Model predicts the probability that the new site is a member of each of the groups • These probabilities are used to adjust site-specific frequencies of occurrence New site

  15. Creating RIVPACS Models • Sum pc’s to estimate the number of taxa (E) that should be observed at the site based on standard sampling.

  16. Creating RIVPACS Models • Calculate O/E by comparing the number of predicted taxa that were collected (O) with E. O/E = 3 / 4.01 = 0.75

  17. Creating RIVPACS Models • Determine if the O/E value is significantly different from the reference condition by comparing against model predictions and error. O 1 E O/E

  18. IntermissionWhat’s confusing so far?

  19. Applying RIVPACS to Colorado:REMAP and NAWQA data • 47 Reference Sites 37 REMAP sites 10 NAWQA sites 112 Taxa used in the model • 123 Test Sites/Samples 68 REMAP 55 NAWQA

  20. Spatial Distribution of Reference Sites Colors indicate 7 different biologically defined ‘classes’ Circles = REMAP, Stars = NAWQA

  21. Predictor Variablesin the Colorado Model

  22. r2 = 0.66

  23. The Distribution of Estimated O/E Values for Reference Sites Mean = 1.00 SD = 0.16 10th Percentile = 0.84 90th Percentile = 1.16 O/E

  24. Spatial Distribution of ‘Test’ Sites Circles = REMAP Sites, Stars = NAWQA Sites Assessments could not be made on 16 of 118 samples (14% of total) because values of predictor variables were outside of the experience of the model.

  25. Mean O/E Values REMAP: 0.67 NAWQA: 0.69 The Distribution of Estimated O/E Values for Test Samples 77% of test samples had O/E values outside the threshold values of 0.84 and 1.16. O/E

  26. Blue: >0.84 and <1.16 Yell: 0.64–0.84, >1.16 Red: <0.64 Gray: No Assessment Colorado Test Site O/E values

  27. Relationships between O/EandPotential Stressors

  28. O/E Response to Stressors Response to dissolved copper shows 2 thresholds: ~ 2.5 and 30 ug/L. O/E log Dissolved Copper (ug/L)

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