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

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Presentation on theme: "Looking Inland: Ohio Reservoir Water Quality Joe Conroy Fisheries Biologist Inland Fisheries Research Unit."— Presentation transcript:

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

2 Variable sportfish recruitment & survival environment Variable sportfish recruitment & survival Land use Shape Productivity Forage fish Zooplankton + 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, R 2 = 76.2%) » PC score re-centered and relativized: 0  1 scale » Ranked reservoirs (1  134, hypereutrophic  oligotrophic) 23      351.9

13 Statewide comparison  Secchi transparency (cm)

14 Statewide comparison  Secchi transparency (cm)  Total P (mg/m 3 )

15 Statewide comparison  Secchi transparency (cm)  Total P (mg/m 3 )  Chlorophyll a (mg/m 3 )

16 Statewide comparison  Secchi transparency (cm)  Total P (mg/m 3 )  Chlorophyll a (mg/m 3 )  Rank

17 2006 Leveraging reservoir productivity data > 5 yd 3 /ac/y < 0.1 yd 3 /ac/y  Assessing change » Land use/cover change –  Urban,  Row crops » Reservoir aging –  Volume,  Productivity 1992 Modify fish habitat & ecosystem function

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


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