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Natural Selection in a Model Ocean Mick Follows, Scott Grant, Stephanie Dutkiewicz, Penny Chisholm

Natural Selection in a Model Ocean Mick Follows, Scott Grant, Stephanie Dutkiewicz, Penny Chisholm MIT. Ocean productivity regulates distribution and storage of nutrients and carbon: biological pumps. Composition and functional characteristics of pelagic ecosystem vary in space and time.

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Natural Selection in a Model Ocean Mick Follows, Scott Grant, Stephanie Dutkiewicz, Penny Chisholm

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  1. Natural Selection in a Model Ocean Mick Follows, Scott Grant, Stephanie Dutkiewicz, Penny Chisholm MIT

  2. Ocean productivity regulates distribution and storage of nutrients and carbon: biological pumps

  3. Composition and functional characteristics of pelagic ecosystem vary in space and time... coccolithophores – CaCO3 structural material diatoms – Si structural material picoplankton diazotrophs – fix nitrogen

  4. ...affecting efficiency/quality of export: e.g. recycling “microbial loop” vs. exporting diatom blooms

  5. Biogeography: What are the dynamics underlying provinces? (Longhurst)

  6. Johnson et al., (2006) Prochlorococcus ecotypes along AMT section

  7. Models of the Marine Ecosystem Volterra (1928), Cushing (1935) Riley (1946)

  8. Nutrient conservation NPZ models... e.g. Fasham et al. (1990)

  9. recent biogeochemical models begin to represent functional diversity in the ecosystem (e.g. Moore et al., 2002; Gregg et al., 2002; Chai et al.; 2002; Dutkiewicz et al., 2005)

  10. Multiple functional groups of phytoplankton simplified example... Functional group characteristics imposed by parameter values

  11. Prochlorococcus ecotypes (Johnson et al., 2006)

  12. AMT observations Johnson et al. (2006) From modeling point of view, reveals... More complexity: functional diversity within species More simplicity: well defined functional differences between otherwise very closely related organisms

  13. Simplify modeling approach by introducing explicit natural selection: • Many possible functional groups (10's – 100's) • Nutrient conservation (physical principle) • Natural selection (ecological principle) • Generic phytoplankton • assign “functions” randomly • choose sensitivities randomly within prescribed ranges

  14. Multiple functional groups: generalized system... Parameter values assigned with some randomness Successful functional groups determined by competition

  15. “Random” assignment of functional properties (trade-offs?)

  16. sub-tropical 1-dimensional model seasonal cycle initially 100 functional groups phyto (log scale) temp & PAR nutrients

  17. Ensemble averages phyto nutrients

  18. max growth rate Kpo4 Kno3 Kpar Kinhib Npref Topt

  19. Why do only a handful of functional groups persist in each case? • Reflects number of potentially limiting resources (Tilman, 1977) • Also sensitive to physical environment, e.g. scales of turbulent variation (Tozzi et al., 2004) Tilman (1977)

  20. Applying principle of competition simplifies model construction • Level of diversity emerges, not imposed • Self-selects “functional groups” according to physical conditions and nutrient availability • Do plausible biological regimes and “ecotypes” emerge?

  21. Johnson et al., (2006) Prochlorococcus ecotypes along AMT section

  22. global circulation model • 30 functional groups of phytoplankton • 2 grazers • nutrients NO3, NH4, NO2, PO4, Si, Fe • phytoplankton functions and parameter values set by random process • ensemble approach

  23. Single ensemble member (Iseed 5007) annual mean surface phyto (uM P) after 5 yrs

  24. annual mean phyto (P), 0-120m (Iseed 5007)

  25. annual mean nutrients, 0-120m (Iseed 5007)

  26. Prochlorococcus Synechococcus obs (log) model (log) model (linear)

  27. Observed Modeled NO3 NH4 NO2

  28. Johnson et al., (2006) observed modeled

  29. Outlook Natural selection approach appropriate for modeling ocean ecosystems and biogeochemical cycles Enables focus on underlying dynamics of model, not tuning of parameter values Dynamic ecosystem approach can adapt to different climate/nutrient environments Ensemble approach provides statistical viewpoint (c.f. adaptive approach?) Prochlorococcus ecotype observations provide well defined system – can model help interpret the observed structures?

  30. Single ensemble member (Iseed 17656) annual mean surface phyto (uM P) after 5 yrs

  31. annual mean phyto(P), 0-120m (Iseed 17656)

  32. annual mean nutrients, 0-120m (Iseed 17656)

  33. Prochlorococcus Diversity within species...

  34. Productivity of the oceans controlled by • Availability of nutrients (light, phosphorus, nitrogen iron...) • Significant role for wind-driven, upper ocean circulation

  35. ... and quality of sinking particulate material association of organic carbon with CaCO3 and opal, >2000m Klaas and Archer (2002)

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