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Can microbial functional traits predict the response and resilience of decomposition to global change?

Can microbial functional traits predict the response and resilience of decomposition to global change? . Steve Allison UC Irvine Ecology and Evolutionary Biology Earth System Science allisons@uci.edu. Project goals.

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Can microbial functional traits predict the response and resilience of decomposition to global change?

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  1. Can microbial functional traits predict the response and resilience of decomposition to global change? Steve Allison UC Irvine Ecology and Evolutionary Biology Earth System Science allisons@uci.edu

  2. Project goals • Determine how microbial taxa respond to reduced precipitation and increased N • Determine the distribution of enzyme genes among taxa • Predict enzyme function and litter decomp based on first two goals • Test if microbial communities are resilient to environmental change

  3. Project design

  4. Litter origin Plot A Ambient A N A N Nitrogen experiment Nitrogen enriched A N A N Precip reduced Mic. comm. origin A P A Ambient Precip experiment N Nitrogen enriched A P P Precip reduced A P B A P inoculation 2013 composition samples 2012 2011 additional samples June Feb June Dec Dec Feb Feb Dec

  5. Allison lab responsibilities • Litter mass remaining • Fungal and bacterial counts • Microscopy (fungi), flow cytometer (bacteria) • Extracellular enzyme activities • Litterbag and plot-level • Litter chemistry • nIR, C/N analysis • Decomposition model

  6. Litter mass remaining: Drought • Microbes from reduced water leave more mass remaining (6-12 months) • Less mass loss in reduced water plots (6 months)

  7. Litter mass remaining: N addition • Significant plot by litter interactions that differ at 6 vs. 12 months

  8. Fungal counts: Drought • More fungi in reduced water plots (3-6 months) • Significant and contradictory microbial origin effects

  9. Bacterial counts: Drought • Strong negative effects of reduced water; microbial origin effect disappears by 6 months

  10. Bacterial counts: N addition • Positive effect of N in litter origin at 6 months

  11. Enzymes: Drought • Higher activities of all hydrolytic enzymes except LAP

  12. Enzymes: N addition • Higher LAP in fertilized litter; other effects are weak

  13. Initial litter chemistry • Similar for litter from control and added N plots • Litter from reduced water plots has more lignin, protein, labile compounds; less cellulose and hemicellulose • Some differences are maintained after 3 months:

  14. Litter chemistry: Drought • 3-6 months: relatively more labile constituents remaining in reduced water plots

  15. Litter chemistry: N addition • Greater lignin loss in litter from N plots (6 months)

  16. Data summary • Reduced water effects generally stronger than N effects • Direct effects of plot on decomposition generally stronger than indirect effects on plants and microbes • Reduced water favors fungi over bacteria, slows decomposition, and allows enzymes and labile substrates to accumulate

  17. Project goal: model integration • Incorporate disturbance responses and gene distributions into a model • Predict response of litter decomposition to treatments • Validate model with reciprocal transplant results

  18. Approaches to modeling decomposition Exponential decay (Olson 1963) Schimel and Weintraub (2003) Moorhead and Sinsabaugh (2006) “Guild decomposition model” (functional groups)

  19. What is a “trait-based” model? • Organisms are represented explicitly (biomass, physiology, etc.) • Each taxon possesses a specific set of trait values • Trait values can be randomly chosen and/or empirically derived • Community composition is an emergent property www.brooklyn.cuny.edu

  20. Represented traits • Extracellular enzymes and uptake proteins: • Gene presence/absence • Vmax, Km • Specificity • Production and maintenance costs • Carbon use efficiency • Cellular stoichiometry • Dispersal distance www-news.uchicago.edu

  21. Model structure

  22. Example question and application • Under what conditions are generalist versus specialist strategies favored? • Generalist = broad range of enzymes produced Specialist Generalist

  23. Model set-up • 100 taxa, 100 x 100 grid • Taxa may possess 0 to 20 enzymes • 12 chemical substrates (approximates fresh litter) • Each degraded by at least 1 enzyme Enzymes Substrates Vmax values Taxa Enzymes

  24. Model set-up • Equivalent uptake across taxa • Could also implement uptake matrices Transporters Monomers Vmax values Taxa Transporters

  25. Model experiments • Simulate leaf litter decomposition (no inputs) • Test effect of tradeoffs in enzyme traits • Increase litter N or lignin • Model validation with Hawaiian litter

  26. Model results • Taxa vary in density over time (succession)

  27. Model results • Should be selection to link uptake with enzymes Enzymes and uptake correlated No correlation

  28. Model results • Species interactions are present but vary by taxon and model conditions

  29. Model validation • Fits are better for decomposition than enzymes R2 = 0.35 P < 0.001 R2 = 0.81 P < 0.001 Slope = 1.7±0.2

  30. Model summary • Enzyme genes and uptake proteins should be correlated • Species interactions may be important • Empirical and genomic data can tell us about tradeoffs, trait correlations, and trait distributions

  31. Thank you! NSF ATB, DOE BER, audience

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