High throughput phenotyping at the cottonwood common gardens
Chris Doughty and Eleanor Thomson
Asner et al 2016
R E F L E C T A N C E S I G N A T U R E S
• Many leaf traits, including photosynthesis (Amax), can be predicted via leaf spectral properties.
Using leaf spectroscopy to predict leaf traits
Doughty et al 2011
Can we predict other non-leaf traits?
Amax-sunlit
predicted
0 2 4 6 8 10 12 14
me
asure
d
0
5
10
15
20
25
Amax
X Data
400 600 800 1000
-3e-5
-2e-5
-1e-5
0
1e-5
2e-5
3e-5
we
ighting
s
-0.0002
-0.0001
0.0000
0.0001
0.0002
1st PC
2nd PC
r2 = 0.28
RMSE = 2.9
RMSE/mean = 0.46
Asat-sunlit
X Data
0 5 10 15 20 25
me
asure
d
0
5
10
15
20
25
30
35
Asat
X Data
400 600 800 1000
-4e-5
-2e-5
0
2e-5
4e-5
we
ighting
s
-0.0004
-0.0002
0.0000
0.0002
0.0004
1st PC
2nd PC
Leaf Veination-sunlit
X Data
0 5 10 15 20 25
me
asure
d
0
5
10
15
20
25
30Leaf Veination
X Data
400 600 800 1000
-0.00010
-0.00005
0.00000
0.00005
0.00010
we
ighting
s
-0.010
-0.005
0.000
0.005
0.010
1st PC
2nd PC
r2 =0.53
RMSE=3.8
RMSE/mean = 0.31
Leaf wettness-sunlit
predicted
30 40 50 60 70 80 90 100 110
me
asure
d
30
40
50
60
70
80
90
100
110
Leaf wettness
spectra (nm)
400 600 800 1000
we
ighting
s
-0.002
-0.001
0.000
0.001
0.002
0.003
1st PC
2nd PC
r2 = 0.83
RMSE = 9.6
RMSE/mean = 0.15
r2=0.19
RMSE=5.0
RMSE/mean = 0.46
• We can predict traits like photosynthesis, wood density, veination and leaf wetness with spectroscopy
Doughty et al 2017
A two-environment reaction norm showing the components of phenotypic variation of four genotypes: G = trait variation due to
population genetics within a single environment, E = trait variation due to change in environment (plasticity), GxE = the variation in plasticity among genotypes. Phenotypic variation (VP) = VG + VE + VGxE. From
Cooper et al. 2018 Global Change Biology.
Genetics-based
responses to
climate change
Plasticity
Genetic Variation
in Plasticity
Genetic
Reciprocal common gardens show finer scale local adaptation
within the Sonoran desert ecotype
Reciprocal
Common
Gardens
TNC
Dugout Ranch, Canyonlands
Cooler Garden – MAT 10.7 °C
AZG&F
Horseshoe Ranch
Intermediate – MAT 17.2 °C
BLM
Mittry Lake, Yuma
Hot Garden – MAT 22.8 °C
(Cooper et al. 2018 Global Change Biology)
Preliminary Methods
• Hierarchical cluster analysis
• Partial least squares regression
• Need more traits for comparison!
Hierarchical cluster analysis applied to the average spectra of each population sampled in each garden.
Strong predictions of both genotypes and plant
height
Ind
ividu
al traits
Ecosystem properties
Co
mm
un
itie
s
Remote sensing
Genes-to-Ecosystems: Scaling from Critical Plant Species to Global Ecosystems for Precision Conservation & Security of Natural Resources
NEEDS DESCRIPTION
• We have demonstrated that individual leaf spectroscopy predicts key plant traits.
• A suite of technologies is needed to scale trait detection to landscape and global levels.
• We can succeed by combining Raytheon’s expertise and capacities in hyperspectral remote sensing with NAU’s infrastructure and expertise in genetics, ecology and remote sensing.
• We can integrate genetics and environment to develop predictive models of landscape scale shifts as a function of the environment.
• Hyperspectral remote sensing and lidar predicts tree genetics and phenotype, enabling prediction of future plant traits that promote drought tolerance needed in a warmer world.
• Predict vulnerable environmental regions before an environmental catastrophe leads to famine, instability and migration.
• Customers: DoD, DHS, FEMA
• Climate change and invasive species are altering ecosystems at an unprecedented rate, leading to massive mortality of plants that provide key ecosystem services including food, fiber, fuel, and clean water.
• By combining high performing plant genotypes with microbes that promote stress tolerance, we will restore ecosystem services to degraded lands, promoting global health and security.
deep Shallow roots
roots
Water table
Populus fremontii
field trial Field Trials
Early Warning System Remote measurement of genotype
• Infer gene by environment interactions to predict vulnerability to climate change.
• Customers: USFS, NPS, USDA, BLM, BOR
MISSION
WIN STRATEGY/KEY TECHNOLOGIES
END USER/CUSTOMER Shallow roots