spatial variation in autumn leaf color matt hinckley edtep 586 autumn 2003

Spatial variation in autumn leaf color

Matt Hinckley

EDTEP 586

Autumn 2003

Preview

Introduction Background Initial model

Methods Results

Data, maps, graph Discussion

Evidence for claim Revision of model

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

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

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

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

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

?

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, temperaturetemperature, precipitation

?

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, temperaturetemperature, precipitationDefinitely changes by altitude in the Cascades

?

Introduction: initial model

Leaf color

When leaves fall off

Spatialvariability

Introduction: initial model

Leaf color

When leaves fall off

Spatialvariability

Temp.

Precip.

Correlation

Causal

Introduction: initial model

Leaf color

When leaves fall off

Spatialvariability

?Temp.

Precip.

Light

Correlation

Causal

Adiabatic cooling

Adiabatic cooling

Introduction: initial model

Leaf color

When leaves fall off

Spatialvariability

?Temp.

Precip.

Light

Correlation

Causal

Elevation Adiabatic cooling

Adiabatic cooling

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

Methods

Driving the Puget Sound area Digital photography Image analysis

Quantification of color GIS analysis of quantitative data

Mapping Spatial interpolation

Methods

Driving the Puget Sound area Digital photography Image analysis

Quantification of color GIS analysis of quantitative data

Mapping Spatial interpolation

Study area – drivingStudy area – driving

Digital photos

Methods

Driving the Puget Sound area Digital photography

Digital photos

Methods

Driving the Puget Sound area Digital photography

Digital photos

Methods

Driving the Puget Sound area Digital photography

Methods

Hue

Driving the Puget Sound area Digital photography Image analysis

Quantification of color GIS analysis of quantitative data

Mapping Spatial interpolation

Methods

Driving the Puget Sound area Digital photography Image analysis

Quantification of color GIS analysis of quantitative data

Mapping Spatial interpolation

Results

The data

Sample Number Color Elevation Location

2 103 303 66 30

4 70 70 SR 18 interchange5 47 70 SR 18 interchange

6 67 507 48 50

8 68 509 71 80

10 41 100 SR 167 Puyallup curve11 76 100

12 46 3013 55 30 Puyallup River

14 69 100 South Hill17 75 500 S.I.R.

18 77 500 S.I.R.19 69 600 NW Trek

20 50 70021 31 800

22 69 125023 70 1200 Almost Elbe

24 53 1200 Almost Elbe25 37 1250

26 39 125027 45 1300

28 53 150029 69 1700

30 69 1800 Past Ashford31 44 1850

32 37 190033 70 2100 In MRNP

34 19 3200 Cougar Rock35 10 3400

36 19 345037 19 3500 Christine Falls

38 12 3900 Past Nisqually bridge39 17 4200 Snow zone

40 14 420044 0 6000

Results

The data How to interpret it?

Sample Number Color Elevation Location

2 103 303 66 30

4 70 70 SR 18 interchange5 47 70 SR 18 interchange

6 67 507 48 50

8 68 509 71 80

10 41 100 SR 167 Puyallup curve11 76 100

12 46 3013 55 30 Puyallup River

14 69 100 South Hill17 75 500 S.I.R.

18 77 500 S.I.R.19 69 600 NW Trek

20 50 70021 31 800

22 69 125023 70 1200 Almost Elbe

24 53 1200 Almost Elbe25 37 1250

26 39 125027 45 1300

28 53 150029 69 1700

30 69 1800 Past Ashford31 44 1850

32 37 190033 70 2100 In MRNP

34 19 3200 Cougar Rock35 10 3400

36 19 345037 19 3500 Christine Falls

38 12 3900 Past Nisqually bridge39 17 4200 Snow zone

40 14 420044 0 6000

Results: mappingResults: mapping

Results: mappingResults: mapping

Results: mappingResults: mapping

Results: mappingResults: mapping

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Sample Number

Hue

0

1000

2000

3000

4000

5000

6000

7000

Feet

Color Elevation

Leaf color and elevation

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Sample Number

Hue

0

1000

2000

3000

4000

5000

6000

7000

Feet

Color Elevation

Leaf color and elevation

Freezing level ?

Spatial interpolationSpatial interpolation

Spatial interpolationSpatial interpolationSpatial interpolationSpatial interpolation

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

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

Initial revised model

Leaf color

When leaves fall off

Spatialvariability

?Temp.

Precip.

Light

Correlation

Causal

Elevation Adiabatic cooling

Adiabatic cooling

Final model

Leaf color

When leaves fall off

Temp.

Precip.

Light

Correlation

Causal

Elevation Adiabatic cooling

Latitude

Otherfactors

Hard to test locally

More easily tested

?

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!