380 likes | 509 Views
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.
E N D
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 stopmaking 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 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
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 ?
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 ?
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 ?
Introduction: initial model Spatial variability Leaf color When leaves fall off
Introduction: initial model Correlation Causal Spatial variability Leaf color Temp. When leaves fall off Precip.
Introduction: initial model Correlation Causal Spatial variability Adiabatic cooling Leaf color Temp. ? When leaves fall off Precip. Light Adiabatic cooling
Introduction: initial model Correlation Causal Elevation Spatial variability Adiabatic cooling Leaf color Temp. ? When leaves fall off Precip. Light 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
Methods Digital photos • Driving the Puget Sound area • Digital photography
Methods Digital photos • Driving the Puget Sound area • Digital photography
Methods • Driving the Puget Sound area • Digital photography
Methods • Driving the Puget Sound area • Digital photography • Image analysis • Quantification of color • GIS analysis of quantitative data • Mapping • Spatial interpolation Hue
Methods • Driving the Puget Sound area • Digital photography • Image analysis • Quantification of color • GIS analysis of quantitative data • Mapping • Spatial interpolation
Results • The data
Results • The data • How to interpret it?
Leaf color and elevation Freezing level ?
Spatial interpolation Spatial 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 Correlation Causal Elevation Spatial variability Adiabatic cooling Leaf color Temp. ? When leaves fall off Precip. Light Adiabatic cooling
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
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!