Aquatic GAP in the Tallapoosa River Basin, Alabama and Georgia

Elise Irwin, Jim Peterson, Mary FreemanU.S. Geological Survey

Bud Freeman and Liz KramerUniversity of Georgia

AtlantaAtlanta

R&D Aquatic GAP Project

• Developing and testing standards for aquatic GAP in three basins

• Building predictive models

• Combine with terrestrial GAP projects in southeast

Models• Logit models

– Goodness-of-fit using Hosmer-Lemshow test

– ANOVA on residuals to examine spatial dependence• Hierarchical models used when dependence occurred

– AIC model selection and X-validation

• Non-parametric models– k-nearest neighbor analysis (CATDAT)

– Monte Carlo tests to examine individual predictors

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Percina sp. E. tallapoosae Cottus sp. E.chuckwachatte

C. gibbsi C. halli C. englishi L. altilis E. flava

Species

Ove

ral C

V e

rro

r ra

te

Logit 92

Logit 98

KNN 92

KNN 98

Results• Non-parametric tests were better• The 98 LU/LC data were better model predictors• Spatial interdependence for three species

KNN 98

Variable Model # Neighbors Overall Present Absent Present AbsentPercina sp. ELEV_DEM PROPFORW ISOLATED FOREST 7 20.4 18.2 22.0 26.5 14.8Etheostoma tallapoosae ISOLATED URBAN LINK_100 PROPCLEA 4 26.2 28.6 22.5 16.7 36.7Cottus sp. isolated SLOPE ORD_100k URBAN IMPDEN FOREST 10 22.3 13.9 41.9 17.3 35.7E. chuckwachatte LINK_100 litt_tal PROPFORW ISOLATED 5 10.7 8.0 11.5 28.1 2.8Cyprinella gibbsi SLOPE ISOLATED ORD_100K FOREST 9 21.4 12.3 32.6 23.1 18.4Cambarus halli SLOPE ord_100k 9 29.9 24.5 38.2 24.5 38.2C. englishi ord_100k isolated 6 17.2 42.1 10.3 38.9 11.6Lampsilis altilis* LINK_100 LITT_TAL IMPDEN 10 28.6 6.3 47.4 37.5 9.1Elimia flava* slope link_100 PROPCLEA 11 10.0 20.0 0.0 0.0 16.7

* sample size low

Cross-validated errorsClassification errors Prediction errors

1993 impoundments in the Upper Tallapoosa

Conserve?Restore?

Setting Management Priorities

*DSS is our ultimate goal

Reducing Uncertainty: Bayesian Learning

Prior Estimate Posterior Estimate

New Information

Flint River Basin

• 341 sites = faunal data

• Hierarchical models– Detection probabilities

• Decision support models include– Faunal response

– Flow –habitat relations

– Basin hydrology, geology, channel type

• Evaluate effects on biotic under different management scenarios

Spatially Explicit OutputsStreamflow policy A Streamflow policy B

BioticIntegrity

HighMediumLow