This document summarizes research on reservoir water quality and productivity in Ohio. A state-wide database was compiled containing water quality data from 212 reservoir-years sampled between 2006 and 2007. Various water quality metrics were analyzed including secchi transparency, total phosphorus, chlorophyll a, and suspended sediments. A principal components analysis reduced the dimensions to a single composite productivity variable. Reservoirs were then ranked on this scale from hypereutrophic to oligotrophic. The results were then compared to understand trends in productivity across reservoirs and how these may impact sportfish populations over time with changes to watershed land use and reservoir aging. Understanding these relationships can help conceptualize variability in sportfish recruitment and survival.

1. Looking Inland: Ohio Reservoir Water
Quality
Joe Conroy
Fisheries Biologist
Inland Fisheries Research Unit

2. Variable sportfish recruitment & survival
Variable sportfish recruitment
& survival environment
Land use + Shape + Productivity + Zooplankton + Forage fish
+
Understand sportfish variability by
understanding system variability?

3. Understanding productivity variation
 Known governing factors
» Land cover/use
– External effects
» Reservoir type
– Internal effects
 Seek state-wide baseline
» Ongoing perturbations
– Reservoir aging
– Watershed modification
Goal: Assess productivity trends among
reservoirs

4. Roadmap
 Conceptualizing sportfish variability
 Compiling productivity data
 Comparing reservoir productivity

5. Roadmap
 Conceptualizing sportfish variability
 Compiling productivity data: 212 “snapshots”
 Comparing reservoir productivity

6. Ohio Reservoir Productivity Database
 ORPAD built in 2006
» Manage project-specific data
» Archive all inland data
 Stores:
» 2,151 trips (1993–2011)
» 153 reservoirs
» 3,606 samples (1,416 “complete”)
– SD, TSS, TN, TP, Chl

7. Assessing state-wide trends
 Extensive reservoir set
» n = 134
 Summer sampling
» July and/or August
» 2006 and/or 2007
– n = 90 res in 2006
– n = 80 res in 2007
» n = 212 res-yrs
Compare reservoir productivity
statewide

8. Assessing trends: Productivity metrics
 Synthetic examination
» Secchi transparency; SD
» [Total suspended sediment]; TSS
» [Non-volatile suspended sediment]; NVSS
» [Total phosphorus]; TP
» [Total nitrogen]; TN
» [Chlorophyll a]; Chl

9. Assessing trends: Detecting a signal
 Decrease data dimension (6D  1D), examine pattern
 Predictors: Ensure multivariate normality; ordinate
» Variables summarized by reservoir; log-transformed
» NVSS not retained; non-normal data
» Principal components analysis conducted
 Results: Examine ordination
» Generate composite productivity variable

10. Roadmap
 Conceptualizing sportfish variability
 Compiling productivity data
 Comparing reservoir productivity

11. Roadmap
 Conceptualizing sportfish variability
 Compiling productivity data
 Comparing reservoir productivity: One axis

12. One composite productivity variable
 Principal components analysis
» 1D solution (λ = 3.81, R2 = 76.2%)
23650 0.380.9 1306139 11.3715.3 3.4351.9
» PC score re-centered and relativized: 01 scale
» Ranked reservoirs (1134, hypereutrophicoligotrophic)

13. Statewide comparison
 Secchi transparency (cm)

14. Statewide comparison
 Secchi transparency (cm)
 Total P (mg/m3)

15. Statewide comparison
 Secchi transparency (cm)
 Total P (mg/m3)
 Chlorophyll a (mg/m3)

16. Statewide comparison
 Secchi transparency (cm)
 Total P (mg/m3)
 Chlorophyll a (mg/m3)
 Rank

17. Leveraging reservoir productivity data
 Assessing change 1992
» Land use/cover change
–  Urban,  Row crops
» Reservoir aging
–  Volume,  Productivity
Modify fish habitat &
ecosystem function 2006
>5
yd3/ac/y < 0.1
yd3/ac/y

18. Limno/Lower Trophic Team
 Marty Lundquist
 Joel Plott
 Matt Wolfe
 Don Swatzel
 Glenn Trueb
Research Partners
Miami University
 Mike Vanni
 Maria González
The Ohio State University
 Dave Culver
 Stu Ludsin
 Ruth Briland, Sarah Wallace,
Cathy Doyle, Mike Kulasa