1. Spatio-temporal Stochastic Simulation of Connectivity Matrices from Lagrangian Ocean Models

2. The Raw Material: Time series of simulated daily Kij Matrices i

3. Approach

7. Logit Transformation

8. Remove Temporal Trend

9. Remove Spatial Trend

10. Residuals

11. h (lag distance, km) h (lag distance, days) VARIOGRAMS ON PRESENCE/ABSENCE OF SETTLEMENT (INDICATOR VARIABLE, 0/1) Along-Rows (t, i, i)  (t, i, i+h) correlation of settlement at adjacent destinations from same source Time (t,i,j)  (t+h,i,j) correlation of settlement at time t in patch (i,j) with settlement at time t+h in same patch Down-Columns (t, j, j)  (t, j+h, j) correlation of settlement from adjacent sources to the same destination

12. γ(h) h (lag distance, km) h (lag distance, days) VARIOGRAMS ON MAGNITUDE OF SETTLEMENT AT NON-ZERO LOCATIONS Along-Rows (t, i, i)  (t, i, i+h) correlation of settlement at adjacent destinations from same source Time (t,i,j)  (t+h,i,j) correlation of settlement at time t in patch (i,j) with settlement at time t+h in same patch Down-Columns (t, j, j)  (t, j+h, j) correlation of settlement from adjacent sources to the same destination

13. SGEMS ….4D simulation…yay

14. Predicting Alongshore Patterns from Coastal Topgraphy

16. ‘Coastal Anomaly’ Broitman and Kinlan 2006 MEPS, In press

17. Smoothing Scale=1000 km COASTAL STRUCTURE

18. Smoothing Scale=50 km COASTAL STRUCTURE

20. S.Africa WNA Chile

21. What scale of coastal features matter to the process you’re interested in? Correlation between variable of interest and topographic index at each smoothing scale

22. myt bal cht Smoothing scale (km) for topo index Correlation coefficient

23. myt bal cht alongshore lag (km) (negative lags are poleward) Correlation coefficient

24. myt bal cht The “Topographic Response Function” Correlation coefficient Alongshore Lag (km) – positive lags poleward – sorry! Smoothing scale (km) for topo index

25. -50510 -250 -200 -150 -100 -50 0 50 100 150 200 250 alongshore lag (km) Amplitude (  ) Mytilus spp. 05001000 -0.05 0 0.05 0.1 Filter length (km) PC amplitude PC1 PC2 PC3

26. -50510 -250 -200 -150 -100 -50 0 50 100 150 200 250 alongshore lag (km) Amplitude (  ) Balanus glandula 05001000 -0.05 0 0.05 0.1 Filter length (km) PC amplitude PC1 PC2 PC3

27. -50510 -250 -200 -150 -100 -50 0 50 100 150 200 250 alongshore lag (km) Amplitude (  ) Chthamalus spp. 05001000 -0.05 0 0.05 0.1 Filter length (km) PC amplitude PC1 PC2 PC3

28. Myt (74%)Bal (85%)Cht (69%)

29. 45%; ns 87%; *** Balanus predicted from Mytilus Chthamalus predicted from Mytilus

30. Mytilus spp. 10 3 32 34 36 38 40 42 44 46

31. Balanus -126-124-122-120-118-116 32 34 36 38 40 42 44 46 10 -2 10 10 0 1 2 3 32 34 36 38 40 42 44 46

32. Chthamalus -126-124-122-120-118-116 32 34 36 38 40 42 44 46 10 -2 10 10 0 1 2 3 32 34 36 38 40 42 44 46

33. -126-124-122-120-118-116 32 34 36 38 40 42 44 46 10 -2 10 10 0 1 2 3 32 34 36 38 40 42 44 46

34. regressing PeruRecTx and topography Var Explained = 0.687003 Model significance = 0.436829 2.9442 + -0.0014604 * COAST + -0.0024253 * Topo(521,-238) + 0.0067157 * Topo(753,15) + 0.023021 * Topo(58,156) + 0.011621 * Topo(521,-187) + 0.0054632 * Topo(753,-186) + -0.0095863 * Topo(58,103)

35. regressing SemiRecTx and topography Var Explained = 0.795611 Model significance = 0.229436 -0.039258 + 0.00030025 * COAST + 0.024883 * Topo(522,-224) + -0.024115 * Topo(837,-217) + -0.020776 * Topo(62,-233) + -0.015662 * Topo(522,-65) + 0.013502 * Topo(837,-114) + -0.039298 * Topo(62,235)

36. regressing JhelRecTx and topography Var Explained = 0.914106 Model significance = 0.0501796 -0.055945 + 0.00044229 * COAST + 0.0040024 * Topo(519,-237) + -0.0026306 * Topo(253,22) + -0.079962 * Topo(44,102) + -0.0096198 * Topo(519,196) + -0.043207 * Topo(253,-187) + 0.079086 * Topo(44,-175)

37. A Global, Daily, Sub-Kilometer-Scale Index of Wind-Driven Dynamics in Nearshore Ecosystems

38. JPL Model Nowcast – 1km wind field

40. Kelp Dynamics at the California Channel Islands Responses to Ocean Climate, Trophic Structure, and Management

43. Overall Protection of Kelp Habitats Based on 1989-2003 Kelp MapBased on 2004-2005 Kelp Map Area of Kelp in MPA’s in 2004-2005 versus 1989-2003 Baseline 0 0.05 0.1 Fraction of Kelp Habitat in MPAs 11.0% 5.56 km 2 of 50.50 km 2 13.6% 5.56 km 2 of 40.92 km 2 11.8% 4.82 km 2 of 40.92 km 2

44. Kelp Canopy at San Miguel Island

45. Kelp Canopy at Santa Rosa Island

46. Kelp Canopy at Santa Cruz Island

47. Kelp Canopy at Anacapa Island

48. Kelp Canopy at Santa Barbara Island

49. For Comparison: San Nicolas Island

50. For Comparison: Campus Point (Mainland)

51. Before (1989-2002)Before (1999-2002)After (2003-2006) 0 200 400 600 800 1000 1200 Average Kelp Canopy Biomass (US tons) Change in Canopy Area Over Time: All So Cal Islands

52. Change in Canopy Area Over Time: CINMS vs. Other Islands Before (1989-2002)Before (1999-2002)After (2003-2006) 0 200 400 600 800 1000 1200 Average Kelp Canopy Biomass (US tons) Before (1989-2002)Before (1999-2002)After (2003-2006) 0 200 400 600 800 1000 1200 CINMSSan Nicolas, Clemente, Catalina

53. Kelp Biomass at Islands

55. 1989199019911992199319941995199619971998199920002001200220032004200520062007 0 200 400 600 800 1000 1200 1400 1600 1800 Date of Survey Kelp Canopy Biomass (US Tons) Kelp Biomass – CINMS Region

56. CINMS Region Other Islands

57. Patterns Different from Mainland ENSO Index -(SOI) Islands Mainland

58. CINMS Region MBNMS Region (1985-2001) Figure 4

59. Kelp forest state De-forested state From Behrens and Lafferty 2004; based on 1985-2001 data from Kelp Forest Monitoring Project Indirect Effects of Fishing on Kelp Forests?

60. Interesting Pattern at Anacapa Island MCA established

61. F^3 Needs?