fish o/e modeling
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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 - PowerPoint PPT PresentationTRANSCRIPT
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