Fish OE Modeling

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Fish O/E Modeling

• Aquatic Life/Nutrient Workgroup
• August 11, 2008

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Discussion Topics

• 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

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Reference Site Data

• Data from 182 reference sites
• 151 sites from CO Division of Wildlife
• Sites from EMAP-West
• 4 samples contained 0 fish
• 36 native species used
• All trout considered native or desirable
• All cutthroats lumped in cutthroat group

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Reference Site Map
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Evaluation of O/E Indices

• Classify streams based on taxa composition
• What streams are similar biologically?
• Model biotic-environment relationships
• 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 Spc
• Compare O (observed) to E to determine impairment

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Initial Classification of Reference Sites

• 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
• 10 classes selected

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Cluster Analysis Dendrogram
WHS, CRC, CSH, JOD, ORD, LGS, IOD, PTM, BMS
FHC, BBH, RDS, LND, SMM, CCF, SNF, BBF
PKF, FMW, STR, SAH, BMW, BST, ARD

• 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

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Modeling Biotic-Environmental Relationships
Variables extracted from 403 samples
Product from Classifications
Cont.
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Model Prediction Errors w/ Trout

• 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

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Affects From Introduced Trout
Trout Thermal Limits (17.5 o C)
Source Utah State Univ.

• SPD and BHS groups are vulnerable to introduced
  trout WHS slightly less vulnerable
• Trout presence has muddled predictions in the West

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Model Prediction Errors w/o Trout

• Overall, predictions improve w/o trout
• BHS error drops to 31

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Estimating Probability of Capture

• 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

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Initial Modeling Results

• 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
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Initial Modeling Outcome

• 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)

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Benefits of Individual Species Modeling

• 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
• Alleviates mountains issue

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Individual Species Modeling

• Modeled 18 fish species

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Model Types Used

• 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

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Model Results

• Values range from 0 to 1
• 1 perfect model
• Many models above 0.8 ? should see good
  predictions

AUC Area Under Operator Receiver Curve
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Model Results

• Those potentially affected by trout
  introductions BHS, SPD WHS (indicator spp)
  MTS (which groups w/ BHS)

AUC Area Under Operator Receiver Curve
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Applicability

• Can use this type of mapping for all 18 spp
• Probability (of capture) of finding that spp
  wired into each pixel

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Ongoing Work

• 13 additional reference sites added to modeling
  in July 08 (emphasis on plains and San Luis V.)
• Will attempt using Similarity Coefficients
• 2 samples are x similar to ea. other
• Will attempt a John Van Sickle (EPA) Similarity
  Index approach
• How similar is O to E?
• Niche modeling i.e. where spp should be

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Summary

• Traditional RIVPACS modeling approach did NOT
  work model not bad, just too course
• Alternative approaches explored
• Individual spp modeling best performing approach
• Demonstrates strong utility in regulatory
  framework
• Modeling moving forward towards completion

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Questions?
Oncorhynchus clarki stomias
Catostomus discobolus
Cottus bairdii