Motive

1. Motive • Konza: understanding disease, since there is no apparent reason to manage native pathogens of native plants • Also have background information in the event that a new pathogen is introduced • Also background comparison for disease severity compared to severity for potential newly introduced host species • Xoo: develop durable resistance in the short run and developing mechanistic understanding of what produces durable resistance to make its development easier in the future in other systems • Root SIR: figure out infection risks and how to manage disease

2. Motive in your system?

3. Hypotheses to be tested or parameters to be estimated? • Konza: • Disease incidence is substantially lower in dryer environments, such as upland sites • Burning reduces disease incidence • Pathogen reproduction is host frequency dependent • Xoo: The abundance of virulent isolates will remain low if they experience a substantial cost of virulence • Root SIR: What is the transmission rate between roots of different ages?

4. Hypotheses to be tested or parameters to be estimated?

5. Need for long-term data • Konza • Surprising result that pathogen populations did not bounce back quickly after drought • Could have very different view of “importance of disease” depending on when disease is sampled • Also different ideas about how direct the effect of, for example, moisture availability is for disease risk • Background information for evaluating new diseases • Xoo: by definition, durability of resistance must be studied over long time periods • Root SIR: Especially for perennial plants, responses may change greatly over time; annual environmental variability can be studied

6. Advantages of long-term data in your system?

7. What is the inference space? • Konza: All sampling within KPBS, some within small experiments. We can suggest that the Flint Hills will be similar to Konza… perhaps even tallgrass prairie, in general? • Xoo: All sampling at one experimental site in the Philippines. We can suggest that it is representative of at least the Philippines • Root SIR: Experiment would need to be performed in a controlled environment. Inference outside that environment…?

8. What is the inference space in your system?

9. Experimental unit • Konza: For some analyses, individual plants; for other analyses, plots in which treatments have been imposed • Xoo: Individual bacterial isolates • Root SIR: Individual roots? Individual plants if treatments applied at that scale? • In plants, the definition of individuals is more flexible… leaves, genets, clones

10. Your experimental unit?

11. How do pathogens enter and leave your study system?

12. What are the response variables? • Konza: Disease incidence (per quadrat) • Xoo: Lesion length on resistant and susceptible host plants (per isolate) • Root SIR: Number of roots susceptible, number of roots infectious, and number of roots resistant

13. What are your response variables?

14. What are the predictor variables? • Konza: Topographic position, Precipitation rate, Grazing (+/-), Burn return time,… • Xoo: Host plant genotype,… • Root SIR: Perhaps Plant age

15. What are your predictor variables?

16. What are appropriate statistical methods for estimation of effects of predictor variables? • Konza: Analysis of variance with repeated measures • Xoo: Analysis of variance • Root SIR: Perhaps analysis of variance for evaluation of environmental effects, etc., or other maximum likelihood methods

17. What are appropriate statistical methods for estimation of effects of predictor variables?

18. What are potential sources of bias? • Konza: some plant species were selected because of observed disease levels – therefore, questions about typical pathogen loads across plant species could encounter bias • Xoo: isolates would tend to be collected in the field when lesions are readily visible – therefore, questions about isolates may not be approachable on a “per lesion” basis • Root SIR: larger roots might maintain their integrity during infection to a greater extent and so be more readily sampled

19. Potential sources of bias in your system?

20. What desirable data are not available?

21. Are there widely accepted models for these systems available? • Konza: Can apply some models from agricultural systems as initial hypotheses • Xoo: Can modify Leonard’s parameterization of the cost of virulence to incorporate changes in plant resistance with temperature • Root SIR: use of SIR model allows comparison to many systems

22. Are there widely accepted models for your system available?

23. What form of sensitivity analysis might be useful? • Konza: Given disease incidence data, combine with models of disease effects on plant productivity to look at range of possible effects of disease on plant community • Xoo: Given pathogen responses to host genotype, consider the possible effects of pathogen bottlenecks during non-conducive weather • Root SIR: Determine potential importance of plant age at beginning of epidemic, for example

24. What form of sensitivity analysis might be useful?

25. What forms of model validation might be useful? • Konza: Use of replicate watersheds in a formal statistical analyses functions as a validation tool; with a longer time series, could also see whether models based on earlier years worked in later years • Xoo: Perhaps not relevant here • Root SIR: Validating predictions based on controlled environment analyses in the field would be useful

26. What forms of model validation might be useful?