1 / 24

Fish O/E Modeling

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

chaman
Download Presentation

Fish O/E Modeling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. 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

  8. Classes mapped by indicator spp

  9. Modeling Biotic-Environmental Relationships Variables extracted from 403 samples Product from Classifications 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 • Values range from 0 to 1 • 1 = perfect model • Many models above 0.8 → should see good predictions AUC = Area Under Operator Receiver Curve

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

  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

More Related