1. Coherence of Net Primary Productivity Estimates Keala Hagmann Mark Harmon: carbon dynamics, scalability Don Henshaw: database and people management Howard Bruner: reference stand data & NPP Suzanne Remillard: reference stand data & NPP Gody Spycher: data manipulation

2. NPP b = ∆Biomass + Mortality Net Primary Productivity of Live Tree Boles

4. Measure–ability to sequester carbon, sink or source of atmospheric carbon Ask–how scalable are the estimates? Why–refine estimates for carbon cycles; learn more about ecosystem Productivity and Scalability

5. Identify the spatial scale at which the parts represent the whole Objective

6. Reference Stand Layouts

7. Reference Stand Descriptions

8. Biomass Estimates: Plot-level Mount Rainier Reference Stands HJ Andrews Reference Stands

9. NPP Estimates: Plot-level Mount Rainier Reference Stands HJ Andrews Reference Stands

10. Min/Max r: Plot to Stand Correlation Correlation Coefficient AB08AG05AM16AO03AV02AV06AV14TO04TO11 bio r plot-0.39-0.20-0.75-0.40-0.66-0.73-0.81-0.88-0.43 bio r plot0.750.920.960.640.920.960.990.881.00 npp r plot-0.64-0.11-0.90-0.95-0.81-0.38-0.67-0.84-0.78 npp r plot0.920.970.980.950.970.990.950.980.87 RS01RS03RS21RS22RS23RS27RS28RS29RS30RS31RS34 bio r plot-0.99-0.32-0.60-0.99-0.79-0.93-0.75-0.89-0.75-0.52-0.58 bio r plot0.970.820.960.990.630.990.960.930.79 0.71 npp r plot-0.88-0.20-0.69-0.350.03-0.48-0.11-0.150.24-0.33-0.95 npp r plot0.98 0.960.830.971.000.98 0.960.97

11. # Neg Pos Sig: Plot to Stand Correlation

13. Combinations: 2, 4, 6, 8 plots MRRS, AV14: Adjacent Heterogeneity MRRS, TO11: Adjacent Homogeneity

14. Plot-level trends

15. Min/Max r: Combos to Stand Correlation

16. # Neg Pos Sig: Combos to Stand Correlation

17. Mount Rainier Reference Stands Biomass & NPP: Stand-level HJ Andrews Reference Stands Time (Year of Measurement

18. Correlate plots to each other Extend analysis to stand level Further statistical analyses to quantify coherence in trends from site to site Future

19. Potential not exploited Programmer & statistician: automate analysis & interpret results Another cross-disciplinary approach to extending comprehension of study subjects and generating insights EcoInformatics

20. Desiree Tullos, Julia Jones, Kari O’Connell and Fred Swanson for creating EISI and for hosting us. Nicole Czarmonski and Jorge Ramirez for guidance and encouragement. The numerous lecturers who gave of their time and shared their knowledge. Alan Tepley for providing an opportunity for fieldwork in incredibly beautiful forests and for all that he teaches. Thank You to: