1. Environmental Change and Altered Marine Food Webs Altered Marine Food Webs 1)Warming I.coastal Antactica (importance of communities) 2)Ozone Depletion II. Antarctica (importance of wind) 3)Eutrohication III. HABs (importance of people) 4)Fishing Pressures IV. Globe (importance of people) Oscar Schofield (oscar@ahab.rutgers.edu)

2. 0 2 4 6 8 10 0.11101001000 Irradiance (  mol photons m -2 s -1 ) Productivity (mg C mg Chl a -2 h -1 )  P max I k = P max /  Respiration  Light Nutrient concentration (can be nitrogen, phosphorus) Nutrient Uptake Ks Nutrients Vmax

3. Nutrient Uptake Varies with Phytoplankton Species

4. Things effecting a food web include: # trophic levels, trophic transfer efficiency What else?

5. Cell size effects the trophic transfer of matter and energy in the food web Cullen et al. (Cullen et al., 2002)

6. Why?

8. 01020304050607080 0 50 100 150 200 250 300 350 Integrated UML Chl a (mg m -3 ) Upper Mixed Layer Depth (m) Station B 1991-1992 Mitchell & Holm Hansen (1991) Integrated Chlorophyll a vs. Upper Mixed Layer Depth

10. 0 0.2 0.4 0.6 0.8 1 00.20.40.60.81 Cryptophytes in the Coastal Ocean (Antarctica) Proportion of total chlorophyll a associated with cryptophytes Proportion of total chlorophyll a associated with diatoms Palmer Station (n=162)

11. Cryptomonas cryophila Thalassiosira antarctica Corethron criophilum Palmer Cryptophytes --> 8 ± 2  m 100  m SEM Micrographs fromMcMinn and Hodgson 1993 10  m

12. The Ice-melt Wall

15. Salinity 33.333.633.8 64°W Palmer Station Antarctic Peninsula % Crypts 02550 65°S

17. From Smith (1994) -0.8 -0.4 0 0.4 0.8 1.2 19451955196519751985 Year Mean Summer Air Temperatures (°C) Faraday Station Signy Station R 2 = 0.64 R 2 = 0.73 Changes over the last 50 years

18. Salps Euphausid superba

19. Other Grazers (copepods) Diatoms & Other Phytoplankton Cryptophytes Autotrophic Carbon Production Krill Salps Sedimentation (Microbial Loop) Respiration (Other Losses) Higher Trophic Levels (fish, penguins, whales) Autotrophic Losses (Not Grazing)

20. 0.001 0.01 0.1 1 10 100 808284868890929496 Krill:Salp YEAR Ice Index 6 4 2 0.001 0.01 0.1 1 10 100 Krill:Salp Ice Index 0.001 0.01 0.1 1 10 100 Krill:Salp Mean Air Temperature (°C) 0 -2-4 From Loeb et al., 1997 6 42 Where have all the good krill gone?

21. 0 10 20 30 40 50 Quetin and Ross 1985 5-10>15 0 20 40 60 80 100 McClatchie and Boyd 1983 5-10>15 0 20 40 60 80 100 Boyd et al. 1984 5-10>15 % Retention by Krill Phytoplankton Size (  m)

22. Other Grazers (copepods) Diatoms & Other Phytoplankton Cryptophytes Autotrophic Carbon Production Krill Salps Sedimentation (Microbial Loop) Respiration (Other Losses) Higher Trophic Levels (fish, penguins, whales) Autotrophic Losses (Not Grazing) Consequences of cryptophytes -Shift grazers to salp community -Decrease carbon to higher trophic levels by ~ 50-60% -Increase carbon flux to benthos by a factor of 3-4 (given one year salp life) -Mobile higher tropic levels move to preferred food source in the south

23. Changes in physical environment impact Antarctic phytoplankton community composition. This will impact elemental cycling and higher trophic levels.

24. HUMANS?

25. Myers and Worm, Nature 423: 280-284

28. Chavez et al. Science 2003

31. Diversity of HAB Toxins Saxitoxins Domoic Acid Ciguatoxins Brevetoxins N N NH OH NH 2 R1R1 R 3 R 2 R4 N H CH3 H COOH CH3 COOH R2R2 O O O O O O O O O O O O OR 1 CH 3 3 H 3 C 3 3 3 3 OH CH3 OH o o o o o o HO CH3 o o o o o o o R2 R1 CH3 Okadaic Acid Unknown Toxins? Azaspiracis Yessotoxin Gymnodimie Spirolides

33. Environmental Controls: 1973 1920 0 100 200 300 Total Number Red Tides 10 Secchi Depth (m) Seto Inland Sea: Manabe and Ishio, 1991 Mar. Poll. Bull. and Honjo, 1993 in Smayda and Shimizu Year 1940 19601980 7 8 9

34. March 28, 2002 – SeaWIFS off the West Florida Shelf Chlorophyll Backscattering (555) Absorption (443) Feature Tracking

35. Red Tide

38. -80-81-82-83-84-85-86-87-88 31 30 29 28 27 26 25 24

39. Figure 2: T-S Diagram Temperature (°C) Salinity (P.S.U.) 34 35 36 24252627

41. Figure 3B: a 670 (m -1 ) 2 4 6 8 10 Time of Day Depth (m) 9am1pm5pm9pm1am5am 1.0 0.0

42. Figure 3C: b 670 (m -1 ) 2 4 6 8 10 Time of Day Depth (m) 9am1pm5pm9pm1am5am 1.0 0.0

43. Figure 3D: c 676 (m -1 ) Time of Day Depth (m) 2 4 6 8 10 9am1pm5pm9pm1am5am 2.0 0.0

44. Figure 5B: bb 440 /b 442 Time of Day Depth (m) 2 4 6 8 10 9am1pm5pm9pm1am5am.05.01

48. Historical, Synoptic, Future in Situ/Remote Field/Error Observations d 0 R 0 Field Initialization Central Forecast Sample Probability Density Mean Select Best Forecast Shooting ESSE Smoothing via Statistical Approximation Minimum Error Variance Within Error Subspace (Sequential processing of Observations) Measurement Model A Posteriori Residules dr (+) Performance/ Analysis Modules OA via ESSE Gridded Residules Synoptic Obs Measurement Model Measurement Error Covariance   ^  cf (-) ^     00 Options/ Assumptions Most Probable Forecast  mp (-) ^ Ensemble Mean  q {  j  ^ Adaptive Error Subspace Learning Convergence Criterion Continue/Stop Iteration Breeding Peripherals Analysis Modules Normalization SVD p Continuous Time Model Errors Q(t) Scalable Parallel Ensemble Forecast + PerturbationsError Subspace Initialization 1jq1jq    1jq1jq ^ ^ ^ u j (o,Ip) with physical constraints +  (+) ^ E(+)  (+) E0E0  (+) ^ Ea(+)a(+)Ea(+)a(+) Field Operation Assumption Key  (-) ^ E(-)  (-) - + + + - + - - - - - + + + d  C  (-) Data Residuals ^ + + + - - +/- + j=1 j=q + ESSE Flow Diagram

49. 26 24 22 20 18 16 14 12 10 8 July, 2001 18192021 2 4 6 8 10 12 Depth (m) Thermistor 2 4 6 8 10 12 Depth (m) July, 2001 18192021 HR COAMPS / ROMS KPP 2 4 6 8 10 12 Depth (m) July, 2001 18192021 MY2.5 Real-Time Ensemble Validation -In an observationally rich environment, ensemble forecasts can be compared to real-time data to assess which model is closer to reality and try to understand why.

52. Phytoplankton off the coast of Florida