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Marine Genomic Revolution and Ecosystem Modeling

Explore biological dynamics, environmental genomics, and physiology in marine ecosystems. Learn about population ecology, modeling, and the impact of genomic data on understanding climate change processes. Dive into the complexity of ecosystem state, biomass, and community structure in the ocean environment.

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Marine Genomic Revolution and Ecosystem Modeling

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  1. Biological Dynamics Environmental Genomics Physiology Population Ecology Marine Ecosystem Modeling & Genomics Scott Doney, WHOI Ecosystem State Biomass Primary Productivity Net Production/Export Community Structure Ocean & Climate Atmospheric CO2, DMS, … Ocean/Atmosphere Circulation Dust-Iron Influx, pH Ocean Nutrient Fields

  2. D. Karl (2004) -Regulation of atm. CO2 & other radiative gases -Responses & feedbacks to warming, dust, … -Predictions require mechanistic understanding

  3. “State of the Art” Model -Aggregate into trophic levels/functional groups -Rates/processes from limited culture/field studies -Many aspects empirically based -Data poor for validation (rates, grazing, loss terms)

  4. Marine Genomic Revolution -Leverage advances in Biotech & Human Genome -rapid gene sequencing -O(10) => O(100) marine microbial genomes -“microarrays” => 104-106 simultaneous samples -Environmental genomics: -sample whole communities; -“cultivation independent” techniques Prochlorococcus Genome map Rocap et al. (2003)

  5. Brief Genomic Tutorial Proteins/ Enzymes mRNA DNA -Proteomics -Physiological rates -Phylogeny -Diversity (DNA, rRNA) -Metabolic pathways -Phylogeny -Diversity (DNA, rRNA) -Metabolic pathways -Gene expression -Response to environmental forcing -Gene expression -Response to environmental forcing

  6. Photosynthesis Light-driven proton pump Recent Findings • -dominance of uncultured microbes • large number of species/taxa • -new metabolic pathways • -deeper genetic “potential” (genotype) often unexpressed

  7. Sargasso Sea “Shotgun” Analysis (C. Venter) -filter 2000 liters -collect genome fragments -assemble in silico ~1.2M genes ~1800 new “species” Venter et al. 2004

  8. Bridging Genomics & Ecosystem Modeling -New processes/dynamics -Data for model evaluation species composition metabolic rates -Adapt current models expand predicted variables increase species diversity (more boxes) model cellular metabolism (more complex boxes)

  9. Assay development: Cultures grown with (+P) or without (-P) phosphorus. -P 7.5 nM PO43- 7.5 nM PO43- +P St. 5 St. 2 Molecular Detection of Trichodesmium Phosphorus Stress Dyhrman et al. 2002 12.1 nM PO43-

  10. Genomics & Modeling New model paradigms -multi-scale models (e.g., individual based models; continuous distributions) (more flexible “boxes”) nesting; SGS parameterization -simulate ecological functions, not species “genotype” => “phenotype” (abandon boxes) -ecological/evolutionary rules for ecosystem assembly (e.g., resiliance; optimal energy/mass flow)

  11. Biological Dynamics Environmental Genomics Physiology Population Ecology How do we build a credible model? How much complexity is enough? Do we know the key processes for climate change? Genomic data will help to quantify processes & environmental sensitivity Ecosystem State Biomass Primary Productivity Net Production/Export Community Structure Ocean & Climate Atmospheric CO2, DMS, … Ocean/Atmosphere Circulation Dust-Iron Influx, pH Ocean Nutrient Fields

  12. Phylogentic Tree for nifH (N2 fixation) gene Chesapeake Bay Jenkins et al. (2004)

  13. Assay development: Cultures grown with (+P) or without (-P) phosphorus. -P 7.5 nM PO43- 7.5 nM PO43- +P St. 5 St. 2 Molecular Detection of Trichodesmium Phosphorus Stress 12.1 nM PO43- Species Heterogeneity Dyhrman et al. 2002

  14. Prochlorococcus genome Rocap et al. (2003) Genome Sequences -novel metabolic pathways -

  15. Environmental Controls on Plankton Species Distributions: An Example for Coccolithophores Universal distribution => local selection Step 1: Satellite mapping of coccolithophorid blooms Step 2: Compare with modern physical variables (SST, nutrients, light, mixing depth) Step 3: Develop “conditional probability function” Step 4: Project future using results of climate models SeaWiFS Model estimate (probability distribution) Iglesias-Rodreguiz (2002) Future (2060-2070)

  16. 0.01 Amplified Fragment Length Polymorphism (AFLP) A B 28 27 78 12 43 131 SA 11 NZ Distinct genetic populations of Emiliania huxleyia in Northern and Southern Hemisphere blooms => different physiological responses to environmental forcing

  17. Genes to Physiology Proteins/ Enzymes Proteins/ Enzymes mRNA DNA mRNA • -Phylogeny (evolutionary relationships): • dominance of uncultured microbes • Archea (new Domain of life) • -Diversity • large number species/taxa, many unidentified • relative number of organisms • -Metabolic pathways • new functions or unexpected capabilities • deeper genetic “potential” (genotype)

  18. Genes to Physiology Proteins/ Enzymes Proteins/ Enzymes DNA mRNA -Gene expression -Responses to environmental forcing (physcial, chemical, biological)

  19. Brief Genomic Tutorial Proteins/ Enzymes mRNA DNA -Proteomics -Physiological Rates -“Phenotype” -Diversity (DNA, rRNA) -Metabolic pathways -Genetic potential “genotype” -Diversity (DNA, rRNA) -Metabolic pathways -Genetic potential “genotype” -Gene Expression -Response to environmental forcing -Gene Expression -Response to environmental forcing

  20. Molecular/genomic data • -validation • -new metabolism • -why are some pathways turned on • -improved

  21. Current generation models • -many aspects empirically based • -aggregate trophic levels • -geochemical functional groups • Increased biological complexity • -biological “sub-grid-scale” parameterization • Molecular/genomic data • -validation • -new metabolism • -why are some pathways turned on • -improved

  22. -Microbes dominate marine ecology/biogeochem. -Regulation of atm. CO2 & other radiative gases -Responses & feedbacks to warming, dust, … -Predictions require mechanistic understanding

  23. Regional changes in ecosystem boundaries, productivity & community structure • Warming & stratification • Increased N2 fixation Reduced dust?? • Increased N2 fixation Boyd and Doney (2002)

  24. Phytoplankton Fixed C/N/P, Variable Fe/C, Chl/C, Si/C Diazotrophs (C, Chl, Fe) Picoplankton / Coccolithophores (C, Chl, Fe, CaCO3) Zooplankton (C) Diatoms (C, Chl, Fe, Si) Nutrients Ammonium Nitrate Phosphate Dissolved Organic Material (C, N, P, Fe) Silicate Sinking Particulate Material (C, (N, P), Fe, Si, CaCO3, Dust) Iron ECO-C3 Marine Ecosystem Component

  25. Ecosystem Modeling -Population dynamics based -Multiple limiting nutrients -Plankton community structure (size, geochemical functionality) -Embed in full global 3-D physics model

  26. Brief Genomic Tutorial Proteins/ Enzymes mRNA DNA -Proteomics -Physiological Rates -“Phenotype” -Diversity (DNA, rRNA) -Metabolic pathways -Genetic potential “genotype” -Diversity (DNA, rRNA) -Metabolic pathways -Genetic potential “genotype” -Gene Expression -Response to environmental forcing -Gene Expression -Response to environmental forcing

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