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Spatial and temporal patterns in food web accumulation of Hg

Spatial and temporal patterns in food web accumulation of Hg. San Francisco Mercury Coordination Meeting February 20, 2008. Questions addressed. Spatial patterns: Where is most bioavailable mercury in the Bay? Using small fish as biosentinels Comparing with sediment MeHg data Regions

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Spatial and temporal patterns in food web accumulation of Hg

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  1. Spatial and temporal patterns in food web accumulation of Hg San Francisco Mercury Coordination Meeting February 20, 2008

  2. Questions addressed • Spatial patterns: Where is most bioavailable mercury in the Bay? • Using small fish as biosentinels • Comparing with sediment MeHg data • Regions • Habitat types and conditions • Temporal patterns: How does mercury vary in small fish from year to year?

  3. Spatial patterns: Bay margins vs. open waters

  4. Spatial patterns: Bay margins vs. open waters

  5. Spatial patterns: Organized by species habits Wetlands/margins Open waters

  6. Spatial patterns: Regional patterns across Bay • 2005 elevated in southern stations (significant) Hg wet weight (g/g) • 2006 elevated in Pt. Isabel (significant)

  7. Spatial patterns including salt pond data Includes data provided by C. Eagles-Smith and J. Ackerman

  8. 2005 elevated in southern stations (not significant) • 2006 elevated in southern stations, Pt. Isabel, and Richardson Bay (significant)

  9. Potential explanations for spatial patterns: • High sediment MeHg in southern stations, Richardson Bay • Suggests linkage: fish vs. sediment MeHg • Suggests spatial gradient Source: RMP

  10. Sediment MeHg may be correlated with topsmelt Hg • Topsmelt 2006 data • RMP and Calfed • sediment data within 1.5 km disk of fish • R2 = 0.61

  11. Sediment MeHg higher in shallow/nearshore sites Sediment MeHg: < 1 ng/g 1 – 2 ng/g > 2 ng/g

  12. MeHg Hotspots in Sediment < 2 ng/g Concentrations highest close to shoreline and surrounded by land > 2 ng/g

  13. Hypothesis: Importance of enclosed areas like Pt. Isabel and Richardson Bay

  14. Temporal Patterns

  15. How Hg varies from year to year?Mississippi silverside • Station effect • Year effect • Interaction term not significant • Disconnect from patterns seen in Delta/Suisun Bay (Slotton et al.)

  16. Topsmelt • Station effect • Year effect • Similar to Slotton et al. 2006 higher than 2005!

  17. Relative importance of station vs. year effect

  18. Summary • Interannual variation differs among species (habitat types) • Elevated in some salt ponds • Apparent association between sediment and biosentinel MeHg New • Hg entering food web at Bay margins, tributaries, wetlands, salt ponds • South Bay and nearshore or enclosed hotspots (Pt. Isabel, Richardson Bay) Q1 Q2 • Sediment net methylation may be locally important process for food web accumulation

  19. Next step: more intensive survey • Spatial survey of about 40 stations/yr (3 years) • Annual monitoring at 8 stations to determine trends • Monthly monitoring at 2 stations to determine seasonal variation • External collaborations • Combining efforts with UC Davis team • Diffusive Gradient in Thinfilm Devices, Bivalves (Hintelmann and Best) • Stable Hg isotopes in fish (Blum)

  20. Collaboration/Data Sharing… Andy Jahn Mark Sandheinrich Kristen Cayce Collin Eagles-Smith Josh Ackerman Wes Heim SF Bay Regional Board Field/Lab Work and Site Access… Katie Harrold Aroon Melwani John Oram Carrie Austin Max Delaney Fred Hetzel Richard Looker April Robinson Meg Sedlak SF Bay National Wildlife Refuge USFWS Stockton USGS BRD California State Parks CA Department Fish & Game Interagency Ecological Program

  21. SUPPORTING INFORMATION:

  22. Spatial survey • Targeting 40 locations • Multiple interrelated factors • Land use, land cover, and Hg sources • Spatial location in Bay • Subtidal hydrology and bathymetry • Sediment physical and chemical parameters

  23. Workplan: Specific questions to address • Where is mercury entering the Bay food web? • What habitats, conditions, or factors help to identify hotspots of food web accumulation in Bay margins? • Are there interannual trends in MeHg bioaccumulation resulting from wetland and margin restoration activities? • What are the best biomonitoring tools for characterizing hotspots of MeHg bioaccumulation?

  24. Spatial survey potential design • Focus on four types of location – test hypothesis of effect • Include spatial gradient from North to South Bay • Also consider subtidal bathymetery/hydrology • Focus on topsmelt and Mississippi silverside

  25. Potential sampling locations – • E.g., POTW outfalls: • Fairfield-Suisun • Palo Alto • Sunnyvale • San Jose

  26. Coordinate with SBMP sites: Improve understanding of wetland – Bay linkages Topsmelt Silversides Marsh fish Brine flies Song sparrows

  27. Trend analysis – a multiple station BACI design

  28. Trend Sampling Locations Hamilton Benicia Park China Camp Control Point Isabel Impact (Restoration) Oakland Middle Harbor Candlestick Point Eden Landing Bird Island/ Steinberger Slough Newark Slough Alviso Slough

  29. Trend Sampling Locations Hamilton Benicia Park China Camp Control Point Isabel Impact (Restoration) Oakland Middle Harbor Candlestick Point Eden Landing Bird Island/ Steinberger Slough Newark Slough Alviso Slough

  30. Monthly sampling locations Additional North Bay Station Sampled by USFWS Martin Luther King Shoreline

  31. MLK Shoreline Location

  32. Collection of additional parameters • Aimed at better understanding mechanisms for spatial variation in bioavailable Hg • GIS spatial parameters • Sediment parameters

  33. GIS spatial parameters

  34. Sediment parameters Sediment parameters: redox, TON, grain size, total and methyl Hg Duplicate sediment samples at subset of 20 stations

  35. Questions for the Workgroup • Is the general approach appropriate? • Indicators selected • Allocation of effort to spatial vs. interannual vs. monthly vs. tool comparison • Spatial survey design • Hypothesis testing approach • Sampling sites (wetlands, POTWs) • Trend sampling • Annual sampling sites • Monthly sampling sites • Additional parameters • Sediment parameters

  36. Annual monitoring of trend stations

  37. Station effect

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