Genetic Programming for Ocean Microbial Ecology and Biodiversity John R. Moisan NASA/GSFC Earth...

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  • Slide 1
  • Genetic Programming for Ocean Microbial Ecology and Biodiversity John R. Moisan NASA/GSFC Earth Science Division Code 610.W [email protected] 23 April, 2015
  • Slide 2
  • Outline Overview of Genetic Programming Achitecture Evolving Satellite Algorithms Modeling Phytoplankton Traits GP Evolution of Ecosystem Models Twin Experiment Solutions Global Ocean CDOM/Mixed Layer Dynamics Future Directions
  • Slide 3
  • the simple sad fact is that not for a single clone of a plankton species can we construct a model describing its activity under a realistic range of environmental conditions. This is before we consider the impact of genetic diversity. 1 1 Flynn, K. J., Reply to Horizons Article Plankton functional type modelling: running before we can walk Anderson (2005): II Putting trophic functionality into plankton functional types, J. Plankton Res., 9, 873-875, 2006
  • Slide 4
  • Phytoplankton Ecological Function Adapted from: Litchman et al., J. Plankton Res. (2013) 35(3): 473484 Phytoplankton Trait Type Life History Behavioral Physiological Morphological Feeding Growth, Reproduction, Pop. Expansion Survival Body Size Shape (Volume to Biomass Ratio) Reproductive Fecundity Nutrient/Energy Source Mixotrophy? Defenses Color BioluminescenceMaximum Growth Rate Stoichiometric Requirement/Content Senescence Motility (Pattern/Speed) Basal Metabolic Rate Vertical Migration Photoacclimation Pigment Suite Sexual/asexual Repoduction Reproduction Frequency Microbial Relationships (Phycosphere)
  • Slide 5
  • Phytoplankton Maximum Growth Rates Eppley Curve Eppley, R.W., 1972. Temperature and phytoplankton growth in the sea. Fishery Bull. 70, 10631085.
  • Slide 6
  • Everything is everywhere, but the environment selects*. *Bass Becking, 1934 Follows et al., 2007 Moisan et al., 2002
  • Slide 7
  • Linking parameters to traits for diversity and acclimation Model with 100 phytoplankton groups Gaussian Trait-based model (2-3 equations)
  • Slide 8
  • Genetic Programming Overview 1)Generate initial random population of models/equations 2)Calculate fitness of all individual models/equations 3)Randomly select based on fitness for: Asexual reproduction Sexual reproduction (i.e. Tournament Selection) 4)Carry out genetic mutation (these are rare events ~

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