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

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 1…20 1010 …000 100100 1…12 102.50 …001.2 201.700 Enzymes Taxa Enzymes Substrates V max values

24. Model set-up Equivalent uptake across taxa Could also implement uptake matrices 1…20 1010 …000 100100 1…14 102.50 …001.2 201.700 Transporters Taxa Transporters Monomers V max values

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 R 2 = 0.35 P < 0.001 R 2 = 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