Spatial variation in autumn leaf color

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 stopmaking 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 stopmaking 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 stopmaking 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 stopmaking chlorophyll • Altitudinal succession mirrors latitudinal succession • Does this principle hold true in this case? • Trees “know” when it’s fall • Factors: • Light, temperature, precipitation ?

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

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

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

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

13. Introduction: initial model Correlation Causal Elevation Spatial variability Adiabatic cooling Leaf color Temp. ? When leaves fall off Precip. Light 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 Digital photos • Driving the Puget Sound area • Digital photography

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

21. Methods • Driving the Puget Sound area • Digital photography

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

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

27. Results: mapping

28. Results: mapping

29. Results: mapping

30. Leaf color and elevation

31. Leaf color and elevation Freezing level ?

32. Spatial interpolation

33. Spatial interpolation 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 Correlation Causal Elevation Spatial variability Adiabatic cooling Leaf color Temp. ? When leaves fall off Precip. Light Adiabatic cooling

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

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!