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. J.M. Caffrey, UWF 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

3. J.M. Caffrey, UWF 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 NO 3 - analyzers (developed by Ken Johnson, MBARI) Linking physical, chemical and biological processes

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

5. J.M. Caffrey, UWF 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

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

7. J.M. Caffrey, UWF 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

8. J.M. Caffrey, UWF Primary Production Weeks Bay 0 5 10 15 20 25 30 1996199719981999200020012002200320042005 Gross production gO2/m2/d

9. J.M. Caffrey, UWF Temperature effects North Inlet-Winyah Bay, SC - Oyster Landing r = 0.71 0 4 8 12 16 05101520253035 Temperature °C Total respiration gO2/m3/d Temperature versus metabolic rate correlations Gross production – 23 sites Total Respiration – 26 sites Net ecosystem metabolism – 19 sites

10. J.M. Caffrey, UWF Salinity effects Elkhorn Slough, CA – Azevedo Pond Salinity versus metabolic rate correlations Gross Production – 16 sites Total Respiration –12 sites Net ecosystem metabolism – 13 sites

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

12. J.M. Caffrey, UWF 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

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

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

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

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

17. J.M. Caffrey, UWF Winter rains lead to extended periods of high NO 3 - concentrations in Elkhorn Slough, CA

18. J.M. Caffrey, UWF Relating Runoff to Nutrient Loading High NO 3 - concentrations associated with runoff events in Weeks Bay, AL during winter rains 0 20 40 60 80 1/31/171/312/142/28 NO 3 - concentration, µM 0 10 20 30 Salinity, Rainfall, mm

19. J.M. Caffrey, UWF Seasonal differences in NO 3 - concentrations following runoff events

20. J.M. Caffrey, UWF 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

21. J.M. Caffrey, UWF 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.

22. J.M. Caffrey, UWF 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.

23. J.M. Caffrey, UWF NO 3 - inputs enhance gross production in Weeks Bay

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

25. J.M. Caffrey, UWF 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

26. J.M. Caffrey, UWF Nitrogen Loading N M i I e E c C B R 2 = 0.30 -7 -6 -5 -4 -3 -2 0 1 0510152025 Nitrogen loading mmol m-2 d-1 Net ecosystem metabolism, g O2 m-2 d-1