1. John Cullen: Ocean Sciences 2006 Describing and Predicting Environmental Influences on Phytoplankton Communities and Food Web Structure John J. Cullen Department of Oceanography, Dalhousie University Halifax, Nova Scotia, Canada B3H 4J1 Microbial Oceanography Summer Course: Genomes to Biomes University of Hawai‘i June 27, 2007 Supported by NSERC, NOPP & ONR International Coalition of Ocean Observation Laboratories

2. John Cullen: Agouron Institute 2007 A principal goal of microbial oceanography Describing and explaining the distributions and activities of marine microbes marine.rutgers.edu/opp/

3. John Cullen: Agouron Institute 2007 marine.rutgers.edu/opp/ …and using this information to describe the causes and consequences of variations in key biogeochemical processes

4. John Cullen: Agouron Institute 2007 Key biogeochemical processes Primary production Nitrogen fixation Denitrification Trace gas production …and the many other processes that make those processes possible

5. John Cullen: Agouron Institute 2007 This can be achieved only through an integrated approach Rothstein et al., Oceanography Mag. The role of the oceans in Earth systems ecology, and the effects of climate variability on the ocean and its ecosystems, can be understood only by observing, describing, and ultimately predicting the state of the ocean as a physically forced ecological and biogeochemical system.

6. John Cullen: Agouron Institute 2007 Arguably, this represents the state of the art PARADIGM Global Biogeochemistry - Ecology - Circulation model (Doney and colleagues) Rothstein et al. 2006 – Oceanography Magazine

7. John Cullen: Agouron Institute 2007 Physiological Ecology Including Gene Expression Genomics & the other ‘omics Theory / Models Process Studies Hydrological & Atmospheric Optics [+Acoustics] Physical Chemical & Biological Oceanography Assimilative biogeochemical models Integrated interdisciplinary sensor systems Ultimate Target

8. John Cullen: Agouron Institute 2007 Biological oceanography and phytoplankton ecology marine.rutgers.edu/opp/ Describe the causes and consequences of variations in primary productivity (and food web structure)

9. John Cullen: Agouron Institute 2007 An overview of established approaches to marine prediction Gordon Riley

10. John Cullen: Agouron Institute 2007 Approach #1: Observation, analysis, inference (empirical, diagnostic models) Behrenfeld and Falkowski 1997b L&O Modeling the pattern in the measurements — not necessarily primary productivity!

11. John Cullen: Agouron Institute 2007 Result: Quantitative predictions that are as good as the data & assumptions that go into them Inputs/Assumptions of the Productivity Model(s) Validity of the Statistical Analysis

12. John Cullen: Agouron Institute 2007 Approach #2: Observation, analysis, inference (qualitative, mechanistic, predictive models) Arrigo, Nature (2005)

13. John Cullen: Agouron Institute 2007 The testing of qualitative, mechanistic, predictive models may be messy Arrigo, Nature (2005)

14. John Cullen: Agouron Institute 2007 Approach #3: Prognostic models (quantitative, mechanistic, predictive models) Doney, S. C., M. R. Abbott, J. J. Cullen, D. M. Karl, and L. Rothstein. 2004. From genes to ecosystems: the ocean’s new frontier. Frontiers in Ecology and the Environment 2: 457-466.

15. John Cullen: Agouron Institute 2007 Complexity is added to increase realism and to test hypotheses Doney, S. C., M. R. Abbott, J. J. Cullen, D. M. Karl, and L. Rothstein. 2004. From genes to ecosystems: the ocean’s new frontier. Frontiers in Ecology and the Environment 2: 457-466.

16. John Cullen: Agouron Institute 2007 Conventionally tested by the “LPG” criterion — but that is changing Follows, M. J., S. Dutkiewicz, S. Grant, and S. W. Chisholm. 2007. Emergent biogeography of microbial communities in a model ocean. Science 315: 1843-1846. L ooks P retty G ood!

17. John Cullen: Ocean Sciences 2006 Approaches to Ecosystem Modeling Maud Guarrracino - Lunenburg Bay

18. John Cullen: Agouron Institute 2007 Fundamentally, ecosystem models should predict population dynamics Daughter Cell Daughter Cell Accumulate (Bloom) Be eaten Blow up (viral lysis) Sink Die (e.g., apoptosis) Single Cell GrowthLoss A bit weird, because “population” refers to species, and many species are often lumped

19. John Cullen: Agouron Institute 2007 Biogeochemical models must include functional groups

20. John Cullen: Agouron Institute 2007 Environmental Influences on the Growth and Chemical Composition of Phytoplankton Essential Knowledge: Daughter Cell Daughter Cell Cell Division Doubled Biomass Photosynthesis Nutrient Uptake Single Cell LIGHT NUTRIENTS Growth rate Chemical composition Biogeochemical transformations

21. John Cullen: Agouron Institute 2007 Critical to know the Environmental Influences on the Growth of Phytoplankton: Temperature Light Daylength Nutrients Growth vs Temperature

22. John Cullen: Agouron Institute 2007 Figuring out what to do with exploding knowledge of biological complexity in the ocean Challenge: Venter et al. Science 2004

23. John Cullen: Agouron Institute 2007 Growth vs Temperature

24. John Cullen: Agouron Institute 2007 Many other things define the growth- niche of marine phytoplankton From Margalef et al. 1979 in Cullen et al. Oceanography Mag. 2007

25. John Cullen: Agouron Institute 2007 http://jaffeweb.ucsd.edu/pages/celeste/copepods.html And cell division is only half of the battle!

26. John Cullen: Agouron Institute 2007 Reduction of loss can be as good as an increase of growth rate

27. John Cullen: Agouron Institute 2007 So rapid growth is not the only strategy for survival / selection www.andyslocum.com/images/tortoise&hare.jpg It’s the gene, stupid!

28. John Cullen: Agouron Institute 2007 Top-down or bottom-up control? Sverdrup’s (1955) map of productivity based on vertical convection, upwelling and turbulent diffusion Global productivity estimated from remote sensing (Falkowski et al. 1998) As presented by John McGowan (Oceanography Mag., 2004)

29. John Cullen: Agouron Institute 2007 Top-down control

30. John Cullen: Agouron Institute 2007 Global test of the top-down hypothesis? Myers and Worm Nature 2003 Decline of fish stocks since 1960

31. John Cullen: Agouron Institute 2007 Bottom-up processes

32. John Cullen: Agouron Institute 2007 A Tool for Making Sense of Physically Forced Ecosystem Dynamics: Margalef’s Mandala

33. John Cullen: Agouron Institute 2007 Not simply temperature, irradiance, nutrients Cullen et al. 2002, The Sea LARGER CELLS HIGHER BIOMASS TRANSIENT & SELF-LIMITING SELECTION FOR RAPID GROWTH (DIATOMS) SMALLER CELLS HIGH TURNOVER COMPETITION FOR NUTRIENTS RETENTION BY RECYCLING (MICROBIAL LOOP) LARGER CELLS HIGHER BIOMASS SLOWER TURNOVER SELECTIVE PRESSURE TO SEQUESTER NUTRIENTS AND MINIMIZE LOSSES (e.g., NOXIOUS/TOXIC BLOOMS) Nutrients —> LOW BIOMASS SLOW TURNOVER ADAPTATIONS FOR EFFICIENT USE OF LIGHT&NUTRIENTS (HIGH-LATITUDE “HNLC”) Turbulence —> Potential for Production and Export —> Event-Scale Forcing —> <— Succession High Turbulence and Low Nutrients High Turbulence and High Nutrients Low Turbulence and Low Nutrients Low Turbulence and High Nutrients

34. John Cullen: Agouron Institute 2007 The challenge: quantitative description of the niches of functional groups

35. John Cullen: Agouron Institute 2007 Critical to know the Environmental Influences on the Growth of Phytoplankton: Temperature Light Daylength Nutrients Growth vs Temperature

36. John Cullen: Agouron Institute 2007 Surely, all this means something! Keep in the back of your mind: Delong et al. Nature 2006

37. John Cullen: Agouron Institute 2007 Acclimated growth rate: genotypic Species evolve different functions through adaptation and selection Cullen et al. in prep Ecosystem modeling ground zero:

38. John Cullen: Agouron Institute 2007 µ as a function of T and E may not be relevant for describing growth rate in dynamic environments Alexandrium fundyense Growth determined using the method of Brand and Guillard Brand, L. E. and Guillard, R. R. L. (1981). A method for the rapid and precise determination of acclimated phytoplankton reproduction rates. J. Plankton Res. 3: 191-201.

39. John Cullen: Agouron Institute 2007 But it is an excellent start for identifying niches

40. John Cullen: Agouron Institute 2007 Photosynthesis vs Irradiance: phenotypic Species develop different functions through acclimation

41. John Cullen: Agouron Institute 2007 Photosynthesis vs Irradiance: phenotypic Strategies to respond to environmental variability are adaptations Layer former Mixer

42. John Cullen: Agouron Institute 2007 Adaptations to oceanic vs coastal conditions

43. John Cullen: Agouron Institute 2007 Oceanic species has much reduced capability for regulation Rough and ready coastal mixer Lean but valiant oceanic survivor

44. John Cullen: Agouron Institute 2007 Niches abound!

45. John Cullen: Agouron Institute 2007 But what about quantitative prediction? Rothstein et al. Oceanography Mag. 2006

46. John Cullen: Agouron Institute 2007 Growth must be described as a function of environmental conditions

47. John Cullen: Agouron Institute 2007 Functions can be Developed (Daylength, Irradiance, Temperature, Nutrients) McGillicuddy, Stock, Anderson, and Signell WHOI

48. John Cullen: Agouron Institute 2007 Requires a huge amount of work with cultures Algae should be acclimated to each set of conditions –This can require several weeks Conditions in nature are almost never so stable –Phytoplankton are subject to vertical mixing –Vertical migration All combinations of Daylength, Irradiance, Nutrients and Temperature are essentially impossible to test …but

49. John Cullen: Agouron Institute 2007 Summary: µ as a function of environmental conditions Good for identifying environmental ranges and optima

50. John Cullen: Agouron Institute 2007 Summary: µ as a function of environmental conditions Excellent for describing differences between species (and variations among strains of the same species) Ancient example: Gallagher, J. C. (1982). Physiological variation and electrophoretic banding patterns of genetically different seasonal populations of Skeletonema costatum (Bacillariophyceae). J. Phycol. 18: 148-162. Eppley, R. W. 1972. Temperature and phytoplankton growth in the sea. Fish. Bull. 70: 1063-1085. Even more ancient:

51. John Cullen: Agouron Institute 2007 Can we assume that adaptation provides all the raw genetic material for aggregate super-bugs? see Eppley, R. W. 1972. Temperature and phytoplankton growth in the sea. Fish. Bull. 70: 1063-1085. Figure from C.B. Miller, “Biological Oceanography” after Smayda, 1976

52. John Cullen: Agouron Institute 2007 A novel approach

53. John Cullen: Agouron Institute 2007 Everything (well, a lot of things) is everywhere and the environment selects Natural selection in silico

54. John Cullen: Agouron Institute 2007 The microbial assemblage structures the chemical environment Only some of the ecotypes survive – the number corresponds to environmental complexity Rothstein et al. 2006

55. John Cullen: Agouron Institute 2007 A test of our understanding of what structures marine ecosystems (LPG)

56. John Cullen: Agouron Institute 2007 Future steps: Assembly and natural selection of microbial communities guided by metagenomics

57. John Cullen: Agouron Institute 2007 Future tests: How much biological complexity is needed to describe and predict ecological and biogeochemical variability in the sea? Dave Karl, Nature 2002

58. John Cullen: Agouron Institute 2007Conclusions Relationships between environmental variability and microbial diversity must be described and ultimately predicted to understand the ecology and biogeochemistry of the sea Niches abound: wide range of environmental tolerances specialized life-styles (nutrient requirements, alternate modes of photosynthesis physiological plasticity vs specialization for stable environments Complexity will never be fully described with numerical models The degree of model complexity can and should be related to the ecological/biogeochemical question and its scale. This can be done — but models must be verified by measurements! (tomorrow’s lecture) Mahalo!