Jane Caffrey Center for Environmental Diagnostics and Bioremediation University of West Florida

1. Combining Long-term And High Frequency Water Quality Data To Understand Ecological Processes In Estuaries Jane Caffrey Center for Environmental Diagnostics and Bioremediation University of West Florida

2. Acknowledgements • Data • Thomas Chapin, USGS and Hans Jannasch, MBARI • Scott Phipps, Weeks Bay NERR and John Haskins, Elkhorn Slough NERR • Funding - CICEET and NOAA NERR J.M. Caffrey, UWF

3. Outline of talk • Calculation of metabolic rates (primary production, respiration and net ecosystem metabolism) from DO data • Data sondes deployed at NERR • Salinity, temperature, dissolved oxygen, turbidity, pH • Understanding short term variability in estuarine processes • Deployment of in-situ NO3- analyzers (developed by Ken Johnson, MBARI) • Linking physical, chemical and biological processes J.M. Caffrey, UWF

4. National Estuarine Research Reserve System J.M. Caffrey, UWF

5. Background • Dissolved oxygen data collected every half hour between 1995-2001. • Uses diurnal changes in water column oxygen concentrations to estimate primary production, respiration and net ecosystem metabolism • Developed by H.T. Odum in 1950s • Describes the trophic status of the water body • Autotrophic: P > R • Heterotrophic: R > P J.M. Caffrey, UWF

6. Night respiration Net apparent production Dissolved Oxygen Diurnal changes in DO result from photosynthesis and respiration Gross production= NAP + respiration Net Ecosystem Metabolism (NEM) = NAP - respiration J.M. Caffrey, UWF

7. Assumptions • Respiration rate is constant in light and dark • System is well mixed vertically • No advection of water masses with different DO concentrations is occurring – or biology dominates over physics J.M. Caffrey, UWF

8. 30 25 20 Gross production gO2/m2/d 15 10 5 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Primary ProductionWeeks Bay J.M. Caffrey, UWF

9. Temperature effectsNorth Inlet-Winyah Bay, SC - Oyster Landing 16 12 Total respiration gO2/m3/d r = 0.71 8 4 0 0 5 10 15 20 25 30 35 Temperature °C • Temperature versus metabolic rate correlations • Gross production – 23 sites • Total Respiration – 26 sites • Net ecosystem metabolism – 19 sites J.M. Caffrey, UWF

10. 60 50 40 Gross production,g O2/m3/d 30 r = 0.39 20 10 0 0 5 10 15 20 25 30 35 40 Salinity Salinity effectsElkhorn Slough, CA – Azevedo Pond • Salinity versus metabolic rate correlations • Gross Production – 16 sites • Total Respiration –12 sites • Net ecosystem metabolism – 13 sites J.M. Caffrey, UWF

11. Net ecosystem by habitat SAV open water mangrovemarsh creeksupland 1 0 -1 -2 -3 g O2 m-2 d-1 -4 -5 -6 -7 -8 HUD Sawkill NAR T-wharf CBM Jug Bay ACE St Pierre JOB Station 9 APA East Bay PAD Bay View ACE Big Basin JOB Station 10 GRB Great Bay WKB Fish River HUD Tivoli South ELK South Marsh WKB Weeks Bay NAR Potters Cove CBM Patuxent Park WQB Central Basin NIW Oyster Landing NIW Thousand Acre ELK Azevedo Pond CBV Taskinas Creek CBV Goodwin Island RKB Blackwater River PAD Joe Leary Slough RKB Upper Henderson GRB Squamscott River J.M. Caffrey, UWF

12. Conclusions • Water quality monitoring data is useful for estimating metabolic rates • within site variability • temperature • salinity • nutrient concentration • chlorophyll concentration • Among site variability • habitat (organic matter loading) • nutrient loading • residence time J.M. Caffrey, UWF

13. Understanding Temporal Patterns Continuous measurements give greater temporal resolution than discrete measurements J.M. Caffrey, UWF

14. 25 160 20 120 15 Rainfall mm Salinity PSU 80 10 40 5 0 0 J F M A M J J A S O N D Relating Runoff to Estuarine Processes Rainfall in the Weeks Bay watershed leads to reduced salinity at the head of the estuary J.M. Caffrey, UWF

15. In-situ nutrient analysis J.M. Caffrey, UWF

16. Seasonal patterns in rainfall, temperature, salinity and nitrate concentrations in Elkhorn Slough, CA J.M. Caffrey, UWF

17. Winter rains lead to extended periods of high NO3- concentrations in Elkhorn Slough, CA J.M. Caffrey, UWF

18. Relating Runoff to Nutrient Loading 80 30 60 20 Rainfall, mm concentration, µM 40 , 10 Salinity - 3 20 NO 0 0 1/3 1/17 1/31 2/14 2/28 High NO3- concentrations associated with runoff events in Weeks Bay, AL during winter rains J.M. Caffrey, UWF

19. Seasonal differences in NO3- concentrations following runoff events J.M. Caffrey, UWF

20. What factors contribute to variability? • Harmonic regression analysis • choose periods of interest: tidal 12.5h, diurnal 24h, and lunar 29.5d • Fit data to linear regression • Run full models with all periods and reduced models to look at contributions of different components J.M. Caffrey, UWF

21. Elkhorn Slough • Lunar signal most important during winter, capturing runoff events. • Spring-neap forcing of deep Monterey Bay water into Slough (Chapin et al. 2004) • Diurnal signal dominates during summer when biological processes dominate. J.M. Caffrey, UWF

22. Weeks Bay Lunar and diurnal signals also important in Weeks Bay. Not surprising that tidal signal is weak because tides are diurnal rather than semidiurnal. J.M. Caffrey, UWF

23. NO3- inputs enhance gross production in Weeks Bay J.M. Caffrey, UWF

24. And Elkhorn Slough J.M. Caffrey, UWF

25. Conclusions and Challenges • In situ instruments allow you to examine short term temporal variations, e.g. runoff events • Water quality monitoring data (DO) can be used to estimate metabolic rates. • How to link these time series together to examine how events at different time scales affect ecological processes J.M. Caffrey, UWF

26. Nitrogen Loading 1 N 0 2 R = 0.30 -1 i I M e -2 E -3 Net ecosystem metabolism, g O2 m-2 d-1 c B -4 -5 -6 C -7 0 5 10 15 20 25 Nitrogen loading mmol m-2 d-1 J.M. Caffrey, UWF