1. Spatial variation in autumn leaf color Matt Hinckley EDTEP 586 Autumn 2003

2. Preview Introduction Background Initial model Methods Results Data, maps, graph Discussion Evidence for claim Revision of model

3. Introduction: background Leaves change color in the fall when they lose their chlorophyll Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case? Trees “know” when it’s fall

4. Introduction: background Leaves change color in the fall when they lose their chlorophyll Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case? Trees “know” when it’s fall

5. Introduction: background Leaves change color in the fall when they stop making chlorophyll Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case? Trees “know” when it’s fall

6. Introduction: background Leaves change color in the fall when they stop making chlorophyll Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case? Trees “know” when it’s fall Factors: Light, temperature, precipitation

7. Introduction: background Leaves change color in the fall when they stop making chlorophyll Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case? Trees “know” when it’s fall Factors: Light, temperature, precipitation ?

8. Introduction: background Leaves change color in the fall when they stop making chlorophyll Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case? Trees “know” when it’s fall Factors: temperature Light, temperature, precipitation ?

9. Introduction: background Leaves change color in the fall when they stop making chlorophyll Altitudinal succession mirrors latitudinal succession Does this principle hold true in this case? Trees “know” when it’s fall Factors: temperature Light, temperature, precipitation Definitely changes by altitude in the Cascades ?

10. Introduction: initial model Leaf color When leaves fall off Spatial variability

11. Introduction: initial model Leaf color When leaves fall off Spatial variability Temp. Precip. Correlation Causal

12. Introduction: initial model Leaf color When leaves fall off Spatial variability ? Temp. Precip. Light Correlation Causal Adiabatic cooling

13. Introduction: initial model Leaf color When leaves fall off Spatial variability ? Temp. Precip. Light Correlation Causal Elevation Adiabatic cooling

14. Introduction: assumptions Trees across the sample area will have leaves that can be observed on them Most problematic assumption: high elevation deciduous trees had lost all leaves Conducting observations ≥ 1 week apart would be OK It was not – leaves change fast, so only one observation was conducted I would be able to control for tree species

15. Methods Driving the Puget Sound area Digital photography Image analysis Quantification of color GIS analysis of quantitative data Mapping Spatial interpolation

16. Methods Driving the Puget Sound area Digital photography Image analysis Quantification of color GIS analysis of quantitative data Mapping Spatial interpolation

17. Study area – driving

18. Digital photos

19. Methods Driving the Puget Sound area Digital photography Digital photos

20. Methods Driving the Puget Sound area Digital photography Digital photos

21. Methods Driving the Puget Sound area Digital photography

22. Methods Hue Driving the Puget Sound area Digital photography Image analysis Quantification of color GIS analysis of quantitative data Mapping Spatial interpolation

23. Methods Driving the Puget Sound area Digital photography Image analysis Quantification of color GIS analysis of quantitative data Mapping Spatial interpolation

24. Results The data

25. Results The data How to interpret it?

26. Results: mapping

30. Leaf color and elevation

31. Freezing level ?

32. Spatial interpolation

34. Data limitations Image analysis problems Differences in lighting Selecting a tree to sample in each picture Tree species loosely controlled Limited sample size Snapshot in time and on Earth Therefore, claims may not be widely applicable

35. Final Claim Generally, leaf color hue decreases along the visible spectrum as elevation increases Shown by data Temperature drops as altitude increases Known principle, observable in Cascades Therefore, lower temperature = more intense leaf color

36. Initial revised model Leaf color When leaves fall off Spatial variability ? Temp. Precip. Light Correlation Causal Elevation Adiabatic cooling

37. Final model Leaf color When leaves fall off Temp. Precip. Light Correlation Causal Elevation Adiabatic cooling Latitude Other factors Hard to test locally More easily tested ?

38. Conclusions Data shows: Lower temperature = more intense leaf color We know that: Altitudinal succession = latitudinal succession Remains unclear whether these two principles can be applied together on a larger scale Regional/local limitation Further research: road trip to Alaska Control for tree species!