1. Fish O/E Modeling Aquatic Life/Nutrient Workgroup August 11, 2008

2. 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

3. 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

4. Reference Site Map

5. 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

6. 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

7. Cluster Analysis Dendrogram BHS, MTS Indicator Species Brook Trout Cutthroat Trout Rainbow Trout Brown Trout SPD, RTC, FMS “Cold Water” “Warm Water” Trout Not-Trout Western Eastern CPM not included 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)

8. Classes mapped by indicator spp

9. Modeling Biotic-Environmental Relationships Product from Classifications Variables extracted from 403 samples Cont.

10. 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

11. 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.

12. Model Prediction Errors w/o Trout Overall, predictions improve w/o trout BHS error drops to 31%

13. 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”

14. 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

15. 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”)

16. 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

17. Individual Species Modeling Modeled 18 fish species

18. 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

19. 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

20. Model Results AUC = Area Under Operator Receiver Curve Those potentially affected by trout introductions: BHS, SPD & WHS (indicator spp) + MTS (which groups w/ BHS)

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

22. 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…

23. 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

24. Questions? Oncorhynchus clarki stomias Catostomus discobolus Cottus bairdii