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 Individual species models as an alternative

management tool for species of interest/concern Continuing efforts

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

Reference Site Map

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 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 DendrogramCO-Fish-Classification

Information Remaining (%)100 75 50 25 0

BHS, MTS

Indicator Species

Brook Trout

Cutthroat Trout

Rainbow Trout

Brown Trout

SPD, RTC, FMS

“Cold Water”

“Warm Water”

Trout

Not-TroutWestern

Eastern

CPM notincluded

WHS, CRC, CSH, JOD, ORD, LGS, IOD, PTM, BMS

FHC, BBH, RDS, LND, SMM,CCF, SNF, BBFPKF, 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)

Classes mapped by indicator spp

Modeling Biotic-Environmental Relationships

Product from Classifications

Variables extracted from 403 samples

Cont.

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

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

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

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

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

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 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

Modeled 18 fish species

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

AUC = Area Under Operator Receiver Curve

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

Model Results

AUC = Area Under Operator Receiver Curve

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

Applicability

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

each pixel

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

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?Oncorhynchus clarki stomias

Catostomus discobolus

Cottus bairdii