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Fish O/E Modeling. Aquatic Life/Nutrient Workgroup August 11, 2008. Discussion Topics. Reference site data Evaluation of fish O/E indices for “speciose” streams Initial site classification and predictive modeling

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Fish o e modeling

Fish O/E Modeling

Aquatic Life/Nutrient Workgroup

August 11, 2008


Discussion topics
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


Reference site data
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



Evaluation of o e indices
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 = Σpc

  • Compare O (observed) to E to determine impairment


Initial classification of reference sites
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


Cluster analysis dendrogram

Indicator Species

Cluster Analysis Dendrogram

BHS, MTS

CPM not

included

Not-Trout

Western

SPD, RTC, FMS

  • 9 classes (or species groups) based on species composition

  • Indicator spp = BHS, SPD, TRT, WHS, FHC, PKF (no CPM)

Brook Trout

“Cold Water”

Cutthroat Trout

Trout

Rainbow Trout

WHS, CRC, CSH, JOD, ORD,

LGS, IOD, PTM, BMS

Brown Trout

FHC, BBH, RDS, LND, SMM,

CCF, SNF, BBF

PKF, FMW, STR, SAH, BMW,

BST, ARD

“Warm Water”

Eastern



Modeling biotic environmental relationships
Modeling Biotic-Environmental Relationships

Variables extracted from 403 samples

Product from Classifications

Cont.


Model prediction errors w trout
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


Affects from introduced trout
Affects From Introduced Trout

  • SPD and BHS groups are vulnerable to introduced trout; WHS slightly less vulnerable

  • Trout presence has muddled predictions in the West

Trout Thermal Limits

(17.5 o C) *

* Source = Utah State Univ.


Model prediction errors w o trout
Model Prediction Errors w/o Trout

  • Overall, predictions improve w/o trout

  • BHS error drops to 31%


Estimating probability of capture
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”


Initial modeling results
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


Initial modeling outcome
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”)


Benefits of individual species modeling
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


Individual species modeling
Individual Species Modeling

  • Modeled 18 fish species


Model types used
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


Model results
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


Model results1
Model Results

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

AUC = Area Under Operator Receiver Curve


Applicability
Applicability

  • Can use this type of mapping for all 18 spp

  • Probability (of capture) of finding that spp wired into each pixel


Ongoing work
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…


Summary
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


Questions
Questions?

Oncorhynchus clarki stomias

Catostomus discobolus

Cottus bairdii


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