charles p. hawkins aquatic, watershed, and earth resources utah state university
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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. - PowerPoint PPT PresentationTRANSCRIPT
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Use of Predictive Models in Aquatic Biological
Assessment:
theory and application to the Colorado REMAP/NAWQA
datasetCharles P. Hawkins
Aquatic, Watershed, and Earth ResourcesUtah State University
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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.
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Basic Concepts(The Expected Taxa)
SpeciesReplicate Sample Number Freq
(Pc)1 2 3 4 5 6 7 8 9 10
A * * * * * * * * * * 1.0
B * * * * * * * * 0.8
C * * * * * 0.5
D * * * * * 0.5
E * 0.1
Sp Count
3 3 3 2 4 3 2 2 4 3 2.9Species Richness is the Unit of Currency.E = ∑ Pc = number of species / sample = 2.9.
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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 0.5 *
E 0.1
F 0 *
Expected Sp Count 2.9 3 2 2 1
O/E 1.03 0.69 0.69 0.34
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Environmental Gradient
Pro
babili
ty o
f C
aptu
reThis is the Challenge:
Estimating the Probabilities of Capture of Many Different Taxa that Exhibit Individualistic
Distributions
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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.
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RIVPACS-type Models: 9 Steps1. Establish a network of reference sites.
2. Establish standard sampling protocols.
3. Classify sites based on biological similarity.
4. Calculate taxa frequencies of occurrence (Fi,g) within each class.
5. Derive a model for estimating probabilites of a site belonging to each group (Pg).
6. Estimate pc’s by weighting Fi,g by Pg.
For each assessed site:
7. Sum pc’s to estimate E.
8. Calculate O/E.
9. Determine if observed O/E is different from reference?
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Creating RIVPACS Models1. Establish a
network of reference sites that span the range of environmental conditions in the region of interest.
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Creating RIVPACS Models2. Use standard protocols to sample biota
and habitat features.
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Creating RIVPACS Models3. Classify sites in terms of their
compositional similarity.
Dissimilarity0 0.2 0.4 0.6 0.8 1
Gro
up
A
D
B
C
Cluster analysis shows
4 ‘groups’ of sites
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Creating RIVPACS Models4. 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 0.25 1.00 0
B 0.80 0.99 0.21 0.36 0.87 0
C 0.60 0 0.16 0.28 0.98 0.05
D 0.10 0.54 0.09 0.29 1.00 0
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Creating RIVPACS Models4. 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
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Creating RIVPACS Models
New site
When we go to a new site, how do we know which biological group it should belong to?
5. 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
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Creating RIVPACS Models
6. Develop site-specific ferquencies for each taxon to occur at the new site
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
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Creating RIVPACS Models
7. Sum pc’s to estimate the number of taxa (E) that should be observed at the site based on standard sampling.
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
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Creating RIVPACS Models
8. Calculate O/E by comparing the number of predicted taxa that were collected (O) with E.
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 3
O/E = 3 / 4.01 = 0.75
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Creating RIVPACS Models9. Determine if the O/E value is
significantly different from the reference condition by comparing against model predictions and error.
1E
O
O/E
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Intermission
What’s confusing so far?
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Applying RIVPACS to Colorado:
REMAP and NAWQA data 47 Reference Sites
37 REMAP sites10 NAWQA sites112 Taxa used in the model
123 Test Sites/Samples68 REMAP55 NAWQA
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D
C
F
G
P
A
B
Spatial Distribution of Reference SitesColors indicate 7 different biologically defined ‘classes’
Circles = REMAP, Stars = NAWQA
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Predictor Variablesin the Colorado Model
Variable F-valueDay Past 1 Jan 12.35Latitude 6.44Stream Length (log)
3.61
Substrate D50 (log) 2.83
% Ks Basin Lithology
2.61
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0 10 20 30Expected # Taxa
0
10
20
30
Obs
erve
d #
Tax a
r2 = 0.66
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0.0 0.5 1.0 1.50.0 0.5 1.0 1.5O/E
The Distribution of Estimated O/E
Values for Reference SitesMean = 1.00
SD = 0.1610th Percentile = 0.8490th Percentile = 1.16
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D
C
F
G
P
A
B
Spatial Distribution of ‘Test’ SitesCircles = 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.
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0.0 0.5 1.0 1.50.0 0.5 1.0 1.5O/E
The Distribution of Estimated O/E Values for Test
Samples77% of test samples had O/E values outside the threshold values of 0.84 and 1.16.
Mean O/E ValuesREMAP: 0.67NAWQA: 0.69
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D
C
F
G
P
A
B
Blue:>0.84 and <1.16Yell: 0.64–0.84, >1.16Red: <0.64Gray: No Assessment
Colorado Test SiteO/E values
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Relationships between O/Eand
Potential Stressors
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0 1 2 3 40.0
0.5
1.0
1.5
log Dissolved Copper (ug/L)
O/E
O/E Response to Stressors
Response to dissolved copper shows 2 thresholds:~ 2.5 and 30 ug/L.