Natural Selection in a Model Ocean
Mick Follows, Scott Grant, Stephanie Dutkiewicz, Penny Chisholm MIT Ocean productivity regulates distribution and storageof nutrients and carbon: biological pumps Composition and functional characteristics of pelagic
ecosystem vary in space and time... coccolithophores CaCO3 structural material diatoms Si structural material diazotrophs fix nitrogen picoplankton ...affecting efficiency/quality of export:
e.g. recycling microbial loop vs. exporting diatom blooms Biogeography: What are the dynamics underlying provinces? (Longhurst) Johnson et al., (2006) Prochlorococcus ecotypes along AMT section Models of the Marine Ecosystem
Volterra (1928), Cushing (1935) Riley(1946) Nutrient conservation
NPZ models... e.g. Fasham et al. (1990) 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) Multiple functional groups of phytoplankton
simplified example... Functional group characteristics imposed by parameter values Prochlorococcus ecotypes (Johnson et al., 2006) 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 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 Multiple functional groups:
generalized system... Parameter values assigned with some randomness Successful functional groups determined by competition Random assignment of functional properties(trade-offs?) sub-tropical 1-dimensional model seasonal cycle initially 100 functional groups phyto (log scale) temp & PAR nutrients Ensemble averages phyto nutrients max growth rate Kpo4 Kno3 Kpar
Kinhib Npref Topt Why do only a handful of functional groups persist in each case?
Reflects number ofpotentially limitingresources (Tilman,1977) Also sensitive tophysical environment,e.g. scales of turbulentvariation (Tozzi et al.,2004) Tilman (1977) 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? Johnson et al., (2006) Prochlorococcus ecotypes along AMT section 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 Single ensemble member (Iseed 5007)
annual mean surface phyto (uM P) after 5 yrs annual mean phyto (P), 0-120m(Iseed 5007) annual mean nutrients, 0-120m(Iseed 5007) Prochlorococcus Synechococcus
obs (log) model (log) (linear) Observed Modeled NO3 NH4 NO2 Johnson et al., (2006) observed modeled 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? Single ensemble member (Iseed 17656)
annual mean surface phyto (uM P) after 5 yrs annual mean phyto(P), 0-120m(Iseed 17656) annual mean nutrients, 0-120m(Iseed 17656) Prochlorococcus Diversity within species... Productivity of the oceans controlled by
Availability of nutrients (light, phosphorus, nitrogen iron...) Significant role for wind-driven, upper ocean circulation ... and quality of sinking particulate material
association of organic carbon with CaCO3 and opal,>2000m Klaas and Archer (2002)