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Aquatic, Watershed, and Earth Resources

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Presentation on theme: "Aquatic, Watershed, and Earth Resources"— Presentation transcript:

1 Aquatic, Watershed, and Earth Resources
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 Replicate Sample Number Freq (Pc) 1 2 3 4 5 6 7 8 9 10 A * 1.0 B 0.8 C 0.5 D E 0.1 Sp Count 2.9 Species Richness is the Unit of Currency. E = ∑ Pc =  number of species / sample = 2.9.

4 O/E as a Measure of Impairment
Expected Biota Observed Biota Species Pc O1 O2 O3 O4 A 1.0 * B 0.8 C 0.5 D E 0.1 F Expected Sp Count 2.9 3 2 1 O/E 1.03 0.69 0.34

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

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

11 Creating RIVPACS Models
Estimate frequencies of occurrence of each taxon in each biotic group. Group Sp 1 Sp 2 Sp 3 Sp 4 Sp 5 Sp 6 A 0.33 0.89 0.25 1.00 B 0.80 0.99 0.21 0.36 0.87 C 0.60 0.16 0.28 0.98 0.05 D 0.10 0.54 0.09 0.29

12 Creating RIVPACS Models
Estimate frequencies of occurrence of each taxon in each biotic group. Color Group Species 1 freq. Red Group A 0.33 Green Group B 0.80 Yellow Group C 0.60 Blue Group D 0.10

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
Species Pc 1 0.70 2 0.92 3 0.86 4 0.63 5 0.51 6 0.32 7 0.07 8 0.00 E 4.01 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
Species Pc O 1 0.70 * 2 0.92 3 0.86 4 0.63 5 0.51 6 0.32 7 0.07 8 0.00 E 4.01 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 Intermission What’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 Variables in the Colorado Model
F-value Day Past 1 Jan 12.35 Latitude 6.44 Stream Length (log) 3.61 Substrate D50 (log) 2.83 % Ks Basin Lithology 2.61

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 The Distribution of Estimated O/E Values for Test Samples
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: –0.84, >1.16 Red: <0.64 Gray: No Assessment Colorado Test Site O/E values

27 Relationships between O/E and Potential 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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