1. Results From Comparative Ecosystem Studies within GLOBEC: Examples from SPACC, CCC and ESSAS Ken Drinkwater Institute of Marine Research and Bjerknes Center for Climate Research, Bergen, Norway US GLOBEC Meeting Boulder, Colorado Feb. 17, 2009

2. Outline Why comparative studies? Types of comparisons Examples Species –Cod, Lobster Ecosystems – Upwelling, Subarctic Seas Problems Concluding Remarks

3. Why Comparative Studies of Ecosystems? 3. Ecosystems are complex – can help determine what is a fundamental process and what is unique. 2. Provides insights that one cannot obtain by looking at a single ecosystem 4. Increases statistical degrees of freedom 5. Sharing approaches and methodologies 1.Cannot run controlled experiments on ecosystems

4. Types of Comparisons 3. Same species but different geographic (hydrographic) regions, e.g. Atlantic cod, American lobster. 1. Same ecosystem type but different geographic regions (upwelling regions, subarctic seas) There are many types of comparative studies. 4. Same ecosystem (geographic area) but at different times (forcing), e.g. cold period vs. warm period. 2. Different ecosystems, e.g. tropics vs. polar ecosystems.

5. Atlantic Cod (Gadus morhua) CCC (Cod and Climate Change)

6. Cod Distribution

7. Temperature plays a large role in determining the Growth Rate of cod The relative size of a 4- year old as a function of mean bottom temperature. Brander 1994

8. The same information shown graphically, i.e. the relative size of the age 4 cod at different temperatures.

9. Cod Recruitment and Temperature Mean Annual Bottom Temperature 11 10 9 8 7 6 4 3 2 Temperature Anomaly Warm Temperatures decrease Recruitment Warm Temperatures increase Recruitment Log 2 Recruitment Anomaly Faroes Iceland Newfoundland North Sea Irish Sea -2 -1 0 1 2 -2 -1 0 1 -2 -1 0 1 2 Georges Bank 4 W. Greenland 4 2 0 -2 - 4 2 0 -2 -1 0 1 2 4 2 0 -2 Celtic Sea -2 4 2 0 4 2 0 4 2 0 4 2 0 4 2 0 4 2 0 Barents Sea

10. American Lobster (Homarus americanus)

11. Lobster Landings Magdalen Island In the late 1980s landings rose dramatically with suggestions that it was due to good management.

12. Again a dramatic rise in landings in the late 1980s- early 1990s and claims that management was working well.

13. US and Canadian Lobster Landings The US and Canadian landings for each of the lobster management regions showed similar trends (except for 4 out of 34) with different management strategies. Could not have been management strategies. It was not increased effort.

14. SPACC (Small Pelagics and Climate Change)

15. Kawasaki, 1983; Bakun, 1997 Bakun, 1989; 1997 Synchrony in Upwelling Areas Lead to much research on ecological teleconnections.

16. Also found tendency for anchovy and sardines to be out of phase (but not always or everywhere). Lluch-Belda et al., 1989

17. ESSAS (Ecosystem Studies of Sub-Arctic Seas)

18. Coccolithophore Bloom at the eastern entrance to the Barents Sea SSTs in the Labrador Sea NORCAN (Norway-Canada Comparison of Marine Ecosystems)

19. NORCAN Workshop Funded by NRC and DFO Held in Bergen in December 2005 Meeting in St. John’s May 2006 and writing groups met in Bergen 2007 Decided to write papers along discipline lines – physical oceanography, phytoplankton, zooplankton, fish (3) and marine mammals. Drafts nearing completion and hope to submit mid-2009

20. FUGLØYA - BJØRNØYA VARDØ-N KOLA SEAL ISLAND BONAVISTA Ocean Temperature Trends between two regions Out-Of-Phase prior to mid-1990s Similar Trends Recently

21. INDEX 19501951195219531954195519561957195819591960 NAO -0.25-0.09-0.560.60-0.400.981.25-0.480.640.941.29 NUUK 0.800.511.090.430.571.320.180.981.480.721.60 IQUALUIT 0.360.160.780.180.171.92-0.03-0.061.02-0.041.10 CARTWRIGHT 0.241.271.720.810.421.500.22-0.051.480.621.50 ST JOHN'S -0.421.881.441.090.74-0.260.04-0.721.20-1.010.87 NL SEA ICE ICEBERGS 0.441.091.081.020.651.020.99-0.251.110.100.73 S27 SURFACE T -0.151.131.160.37-0.92-1.73-0.14-1.570.52-0.840.40 S27 BOTTOM T -1.111.011.061.050.580.750.77-0.031.10-0.601.19 S27 AGERAGED T 0.311.920.902.830.141.271.28-1.102.470.590.52 HAMILTON BANK SURFACE T 0.501.650.74-0.151.45-1.67-0.86-1.160.12-0.21-0.06 HAMILTON BANK BOTTOM T -0.631.14-0.870.34-1.23-0.09-0.13-0.730.47-0.47-0.10 FLEMISH CAP SURFACE T -1.271.061.420.130.190.08-0.30-0.620.50-0.160.36 FLEMISH CAP BOTTOM T -0.440.600.320.330.921.220.11-0.540.90-0.450.69 SEAL ISLAND AVG T 1.13 -0.170.56-0.860.39-0.070.000.020.831.61 BONAVISTA AVG T -0.57-0.05-0.52-0.070.09 0.710.821.060.400.73 FLEMISH CAP AVG T 1.412.011.240.100.030.320.031.290.451.14 ST. PIERRE BANK BT -0.460.641.84-0.841.30-1.16-1.99-0.541.29-1.51-0.51 SEAL ISLAND CIL 0.230.850.060.21-0.680.95-0.19-0.680.28-0.32-0.20 BONAVISTA CIL -0.020.52-0.390.740.05-0.031.061.370.910.380.64 FLEMISH CAP CIL 0.900.941.86-0.08-0.280.58-0.521.781.470.94 S27 SURFACE S 0.50-0.25-0.690.321.090.480.84-0.33-0.050.770.13 S27 AVERAGED S 0.640.74-0.580.121.03-0.541.63-0.43-1.08-0.45-0.14 SEAL ISLAND AVG S 1.05 -0.060.60-0.120.920.600.66-0.770.140.47 BONAVISTA AVG S 1.390.170.040.531.63-0.081.632.240.65-0.080.53 FLEMISH CAP AVG S -0.830.150.341.020.441.611.71-0.730.830.34 2.2619.6312.9114.667.867.5310.10-1.9817.662.0915.77 Normalized indices (blue-cold, fresh; red- warm, saline) used to estimate overall index for both Labrador and Norwegian regions.

22. These standardized anomalies also show change from out of phase prior to the mid-1990s and in phase since then.

23. NORTH ATLANTIC WINTER SLP FIELDS MEANANOMALY 1991 2000 2003 HISTORICAL PATTERN- COLD IN WEST WARM IN EAST EASTWARD DISPLACEMENT WESTWARD DISPLACEMENT

25. (Marine Ecosystem Comparisons of Norway and the United States) MENU NOAA Fisheries

26. MENU Workshop Funded by NRC Held outside Bergen in March 2007 Brought data to the table Divided into 2 groups: (1) response to recent changes and (2) structure and function of ecosystem. Five papers have been accepted for publication in PiO.

27. Eastern Bering Sea (EBS) Gulf of Alaska (GOA) ( NOR/BAR ) Alaska Russia Canada USA Greenland GB Norwegian Sea Barents Sea GOM Area Latitude Gulf of Maine / Georges Bank (GOM/GB) Menu Regions

28. Highly Advective Systems Strong Tidal Currents and Mixing in subregions Mean Circulation

29. Monthly mean sea-surface temperature anomalies 1900-2006

30. Correlations between annual heat fluxes and SST temperatures Pacific Ecosystems: Significant correlations, 25- 30% of SST variance accounted for. Atlantic Ecosystems: Weak and non-significant correlations. Suggests that warming due to advection in the Atlantic while in Pacific air-sea fluxes play a significant role.

31. Gulf of Alaska Gulf of Maine Barents Sea In GoA surface freshening due to local runoff In GoM freshening due to advection from the North (Arctic?) In Barents Sea increasing salinity due to higher salinity in Atlantic Water. Salinity

32. SeaWiFS climatology – Chl. a (Apr-Jun) Bering Sea / Gulf of Alaska Norwegian Sea / Barents Sea Gulf of Maine / Georges Bank Source: http://oceancolor.gsfc.nasa.gov/cgi/level3.pl Mueter et al., in press

33. Productivity increases with nitrate content of deep source waters Approximate range Nitrate in source waters and total annual primary production GOM/GB Mueter et al., in press

34. Effect of SST on primary production, 1998-2006 246810 200 300 400 500 Annual mean SST (°C) Total annual net primary production (gC m -2 ) Barents Sea (P = 0.093) Norwegian Sea (n.s.) Bering Sea (P = 0.039) Gulf of Maine/ Georges Bank (P < 0.001) Gulf of Alaska (n.s.) Mueter et al., in press

35. MENUII NOAA Fisheries

36. MENUII With the success of MENU, NOAA and IMR administrators encouraged MENU participants to submit full proposals Decided that emphasis would be model comparisons and ecosystem indicators 4 types of models: ECOPATH, production models, biophysical models (3-D hydrodynamic models up to zooplankton) and system models (includes fish and fisheries (ATLANTIS)

37. Ecosystems are created in Atlantis three-dimensionally, using linked polygons that represent major geographical features. Information is added on local oceanography, chemistry and biology such as currents, nutrients, plankton, invertebrates and fish. The model then simulates ecological processes such as: consumption and production, waste production, migration, Predation, habitat dependency, mortality. The Atlantis framework used for management strategy evaluation incorporates a range of sub-models for each major step in the management cycle. They simulate the marine environment, the behaviour of industry, fishery monitoring and assessment processes, and management actions and implementation. ATLANTIS

38. MENUII Same model different regions Different models for same region Determine what we learned from each of the models A good forecaster (modeller) is not smarter than everyone else, he merely has his ignorance better organised. -Anonymous

39. MENUII Norwegian component funded by RCN (2009-2011) US component submitted to CAMEO but not funded in first round. Hoping to obtain funds to carry out work from other sources.

40. Prediction is difficult, especially if it involves the future. Nils Bohr Prediction is easy, getting it right is the difficult part!

41. What does ”right” imply? -Some quantifiable measure of how well the model fits the observations -For future projections where we won’t have observations need some quantifiable measure of the uncertainty.

42. Observationalists and Modellers need to work closer together -Modeller’s to help determine what, where and how often observationalists should measure. -Observationalists should provide more feedback on model results (requires available model results, positive criticisms) -All motherhood statements but not generally done

43. Some Problems For data comparisons, the data or datasets should be similar. Not always possible. Forcing is based on large model and data based datasets (e.g. NCEP) that usually have some problems When using different models there is the difficulty of knowing if one is comparing ecosystems or models

44. Concluding Remarks Comparative studies are a useful way to gain insights into marine ecosystems They often lead to shifts in our thinking about what is important and what is not. Bring comparative datasets to the table For models need to develop new and better measures of uncertainty Need to make sure that observationalists and modelers do not work independently.

45. Thank You!

47. SST Anomalies in the North Atlantic during 1990-1994 HISTORICAL PATTERN- COLD IN WEST WARM IN EAST SST Anomalies in the North Atlantic during 2004 BROAD-SCALE WARMING NOAA Optimum Interpolation SST, NOAA-CIRES Climate Diagnostics Center

48. Maximum SST 1997-2006

49. Sea-Ice Cover Anomalies Bering Sea Barents Sea

50. Zooplankton anomalies: Evidence of top-down and bottom-up control Barents Sea Norwegian Sea Gulf of Maine / Georges Bank Bering Sea Normalized anomaly (Biovolume) Normalized anomaly (Biomass ) Napp & Shiga (unpublished) Based on Valdés et al. (2006) r = 0.60 P = 0.002 Mueter et al., in press

51. Fish: SST & cod recruitment Barents Sea (Atlantic cod) Georges Bank (Atlantic cod) Bering Sea (Pacific cod) Gulf of Alaska (Pacific cod) SST anomaly Log(Recruitment) anomaly 1977-2005 only! Georges Bank and Barents Sea figures from: Planque & Frédou (1999) Mueter et al., in press