Transcript Fish O/E Model Update

Fish O/E Modeling
Aquatic Life/Nutrient Workgroup
August 11, 2008
Discussion Topics
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Reference site data
Evaluation of fish O/E indices for “speciose”
streams
Initial site classification and predictive modeling
Individual species models as an alternative
management tool for species of interest/concern
Continuing efforts
Reference Site Data
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Data from 182 reference sites
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151 sites from CO Division of Wildlife
Sites from EMAP-West
4 samples contained 0 fish
36 “native” species used
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All trout considered native or desirable
All cutthroats lumped in “cutthroat” group
Reference Site Map
Evaluation of O/E Indices
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Classify streams based on taxa composition
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Model biotic-environment relationships
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What streams are similar biologically?
Usage of predictor variables
Use model to estimate site-specific, individual
species probabilities of capture (pc)
E (expected), the number of species predicted at
a site = Σpc
Compare O (observed) to E to determine
impairment
Initial Classification of Reference Sites
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Composition of native or desirable fish species
at reference sites only
Biologically similar sites being grouped together
Cluster analysis/ordination revealed several
relatively distinct groupings of sites based on
species composition
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10 “classes” selected
Cluster Analysis Dendrogram
Indicator Species
BHS, MTS
SPD, RTC, FMS
Brook Trout
CO-Fish-Classification
100
75
Information Remaining (%)
50
25
0
CPM not
included
Not-Trout
Western
“Cold Water”
Cutthroat Trout
Rainbow Trout
Trout
Brown Trout
WHS, CRC, CSH, JOD, ORD,
LGS, IOD, PTM, BMS
FHC, BBH, RDS, LND, SMM,
CCF, SNF, BBF
PKF, FMW, STR, SAH, BMW,
BST, ARD
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“Warm Water”
Eastern
9 classes (or species groups) based on species composition
Indicator spp = BHS, SPD, TRT, WHS, FHC, PKF (no CPM)
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Classes mapped by indicator spp
Modeling Biotic-Environmental
Relationships
Variables
extracted from
403 samples
Product from
Classifications
Cont.
Model Prediction Errors w/ Trout
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No model is completely precise nor accurate; errors must be
quantified
Trout (TRT) predicted correctly 93% of the time
Bluehead sucker (BHS) wants to predict as “TRT” or “SPD” → 100%
error
Affects From Introduced Trout
Trout Thermal Limits
(17.5 o C) *
* Source = Utah State Univ.
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SPD and BHS groups are vulnerable to introduced trout;
WHS slightly less vulnerable
Trout presence has muddled predictions in the West
Model Prediction Errors w/o Trout
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Overall, predictions improve w/o trout
BHS error drops to 31%
Estimating Probability of Capture
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Discriminant model
output use to estimate
“E”
Sum PC (probability of
capture)
Probability of capture
still a quantitative way of
predicting spp in
“individual spp
modeling”
Initial Modeling Results
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A single, statewide
model attempted
Most “speciose” group
has about 6 taxa per
sample on average,
too few for precise O/E
indices
Results indicate that
model too course
Max 13
Initial Modeling Outcome
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Failure to detect 1 spp could result in extensive
deviation in O & E assemblages, which results in
low sensitivity
Not useful in a regulatory-sense
WQCD took a shot at developing a practical
bioassessment tool for fish to complement
macroinvertebrate tools
Next step – decompose model into individual
taxa models (“species modeling”)
Benefits of Individual Species Modeling
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Predicted list of fish species
Best estimate of historical distribution
Antidegradation for high quality sites
Visual tool (when predictions wired into stream
layer)
Statewide application
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Alleviates “mountains” issue
Individual Species Modeling
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Modeled 18 fish species
Model Types Used
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“MaxEnt” (Maximum Entropy) – only uses
presence data
“RF” (Random Forest) – uses observations
from both presence and absence data
Approach not based on normal classification
and regression tree (CART) work – more like
bootstrapping
Model Results
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Values range from 0 to 1
1 = perfect model
Many models above 0.8 → should see good predictions
AUC = Area Under Operator Receiver Curve
Model Results
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Those potentially affected by trout introductions: BHS,
SPD & WHS (indicator spp) + MTS (which groups w/
BHS)
AUC = Area Under Operator Receiver Curve
Applicability
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Can use this type of mapping for all 18 spp
Probability (of capture) of finding that spp wired into
each pixel
Ongoing Work
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13 additional reference sites added to modeling
in July 08 (emphasis on plains and San Luis V.)
Will attempt using “Similarity Coefficients”
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Will attempt a John Van Sickle (EPA) “Similarity
Index” approach
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2 samples are “x” % similar to ea. other
How similar is O to E?
“Niche” modeling – i.e. where spp should be…
Summary
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Traditional RIVPACS modeling approach did
NOT work – model not bad, just too course
Alternative approaches explored
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Individual spp modeling best performing approach
Demonstrates strong utility in regulatory framework
Modeling moving forward towards completion
Questions?
Oncorhynchus clarki stomias
Catostomus discobolus
Cottus bairdii