Supporting Information Methods S1, Tables S1–S6, Figs S1 & S2
Methods S1 Phylogenetic regression and phylogenetic signal statistics with branchlength transforms and measurement error
Here we present briefly the statistical model that we used to perform phylogenetic regressions
with the inclusion of within-species variation (measurement error). The approach is a merger of
phylogenetic regression with branch-length transforms as implemented by REGRESSIONv2.m
(Lavin et al. 2008) and phylogenetic regression with measurement error that assumes a Brownian
motion model of evolution (Ives et al. 2007). These references provide further background and
place the analyses into the context of the broader literature. Here we focus on model structure.
The Matlab code, MERegPHYSIGv2.m, is available from Ted Garland
(theodore.garland@ucr.edu) upon request.
The statistical model for dependent variable y measured with error, independent variable
x measured with error, and independent variable u measured without error is
X* = ax + x
Y* = a0 + a1U + bX* + y
(S1)
X = X* + x
Y = Y* + y
where X* and Y* are N 
N species, and X and Y are the values observed with measurement errors x and y. Values of
trait u measured without error are contained in the N 
U. Because values of the
independent trait x will likely be phylogenetically correlated, we assume x has covariance
matrix Vx, and because residual variance in Y* given by y is also likely to be phylogenetically
correlated, we assume y has covariance matrix Vy() where  is a parameter that changes the
1
phylogenetic correlation matrix Vy according to a specific branch-length transformation (see next
paragraph). Because values of U are fixed, their distribution does not affect the analysis.
Following standard regression, we assume that variation in x and y are independent. We also
assume that measurement error may be correlated for traits x and y within species. If there is no
measurement error (Mx = My = 0), then this problem reduces to phylogenetic regression under a
branch-length transform (as implemented in REGRESSIONv2.M program; Lavin et al., 2008). If
the parameter  is not estimated but instead defined by the user, then this model reduces to a
phylogenetic measurement error model (as implemented in MERegPHYSIG.m; Ives et al.,
2007). Although we present this model for single independent variables with and without
measurement error (trait x and trait u, respectively), the model expands naturally to include p
independent variables measured with error and q independent variables measured without error.
In model S1, we implement two different branch-length transforms to the residual
covariance matrix Vy(): the Ornstein-Uhlenbeck (OU) model of evolution and Pagel's . The
OU model of evolution (Felsenstein 1988, Martins and Hansen 1997) assumes that there is
stabilizing selection around a trait optimum, with the strength of stabilizing selection given by
the parameter d (Blomberg et al. 2003). Our particular model of OU evolution assumes that the
basal node of the phylogenetic tree is at this optimum, in contrast to other variants of the OU
model (Hansen 1997). This assumption means that the case of Brownian motion (BM) evolution
is recovered when d = 1. When there is stabilizing selection, "phylogenetic memory" is lost, so
the strength of phylogenetic signal decreases. At the limit of very strong selection, all taxa (tips
of the phylogenetic tree) are independent, which corresponds to d = 0. Disruptive selection can
lead to stronger-than-BM signal in which recently separated taxa have stronger trait correlations
than predicted by the BM model; these cases are given by d > 1.
2
Pagel's  changes the phylogenetic correlations among taxa by adding branch length to
the tips of the phylogenetic tree. In our implementation, when  = 1 the added branch lengths are
zero, returning the original "starter" phylogenetic tree. As  approaches zero, the proportional
length of the tip branches increases to one, thereby implying no phylogenetic signal. By adding
uncorrelated variation to the tip taxa of a phylogenetic tree, Pagel's  is structurally similar to
measurement error in the residual variation (or dependent variables) of a regression, since
measurement errors will also give uncorrelated variation in the residuals. Thus, Pagel's  in some
contexts is assumed to contain measurement error (e.g., Housworth et al. 2004). The important
distinction here, however, is that we assume measurement error is measured from multiple
samples from the same taxa. This means that there is no confounding between the lengthening of
tip branches due to measurement error (which is estimated separately) and lengthening using
Pagel's  to account for less-than-BM phylogenetic signal. Finally, unlike the OU transform,
greater-than-BM phylogenetic signal cannot be modeled using Pagel's .
Although the independent variables may contain phylogenetic signal, it is more
appropriate to estimate this signal independently. Thus, for example, model S1 could be applied
by treating the independent variable as the dependent variable (and including no independent
variable) to estimate Vx(), with this estimate then used in S1 to regress y on x.
Specifying the branch-length transforms mathematical, for the OU transformation with d
> 0, the elements of Vy(d) are
d
vij =
(cii +c jj -2cij )
(1- d )
2cij
(S2)
(1- d )
2
where cij are the shared branch lengths between taxa i and j obtained from the "starter"
phylogeny that is derived under the assumption of Brownian motion evolution (Blomberg et al.,
3
2003). In the absence of phylogenetic signal (d = 0), the matrix Vy(0) has all zeros on the offdiagonals. For Pagel's 
vij = cij
(S3)
for off-diagonal elements, while the diagonal elements remain unchanged.
The regression model can be written
W=A++
(S4)
where W is the 2N 
X and Y, and A is the 2N 
first N elements are ax and second N elements are given by a0 + a1U. The resulting covariance
matrix E{(W–A)(W–A)’} = 2  is
æ
2
s x2 Vx + s mx
Mx
bs x2 Vx + rms mxs my M xy
ç
  =ç
2
ç bs x2 Vx + rms mxs my M xy ' b2s x2 Vx + s y2 Vy (q ) + s my
My
è
2
ö
÷
÷÷ .
ø
(S5)
The terms involving measurement error are derived from the covariance matrix given by
æ
2
s mx
Mx
rms mxs my M xy
ç
E{'} = ç
2
rs s M '
s my
My
è m mx my xy
ö
÷
÷
ø
(S6)
x on top of y, mx2Mx and my2My are
where  is the 2N 
matrices containing the measurement error variances, and rmmxmyMxy is the matrix containing
covariances in measurement errors between traits for each species. If measurement errors for
each trait are independent among species, then Mx, My, and Mxy will be diagonal matrices (i.e.,
all off-diagonal elements will be zero). If measurement errors for the two traits within species are
correlated (e.g., the measurements of traits x and y for a given species tend to err either high or
low in unison), then this correlation is given by rmMxy. These terms are all estimated
independently from data using multiple measurements on the same taxa; these within-species
4
variances include both true measurement error and variation among individuals or populations
within taxa.
We estimated parameter values using Restricted Maximum Likelihood (REML)
estimation. The log-likelihood function from which variance parameters are estimated is
(Harville 1974):
(S7)
For regression with p independent variables measured with error and q independent variables
measured without error, â GLS is the p + q + 1)  vector containing the "mean" components of
the model consisting of ax (p values, one for each trait x measured with error) and ai (i = 0, …,
q). The p + 1)N p + q + 1) matrix Z contains ones in the first p columns identifying rows
corresponding to the p traits x, and (q + 1) columns containing in the last N rows the values of
trait u corresponding to values of trait y, with the first of these columns containing ones. To
speed the numerical maximization, the likelihood function is concentrated by using the GLS
estimates â GLS and 2 conditioned on the remaining parameters.
In addition to standard asymptotic approaches for computing standard errors and
confidence intervals, MERegPHYSIGv2.m also performs parametric bootstrapping. This is the
only reliable way to obtain confidence in estimates of d and . Furthermore, parametric
bootstrapping is useful in the estimation of the other parameters because it identifies bias in the
estimates. Parametric bootstrapping for a given model is performed by first estimating
parameters and then using the model fitted with these estimates to simulate a large number of
data sets. For each of the simulated data sets, the parameters of the model are re-estimated. The
5
resulting sets of parameter estimates approximate the distribution of the parameter estimators
under the assumption that the model (with its fitted parameter values) is correct. Thus, the 95%
inclusion intervals for the estimates give the approximate 95% confidence intervals for the
parameter estimates. Bias in the estimates is revealed if the mean of the simulated parameter
estimates differs substantially from the parameter values estimated from the data and used to
construct the simulated data sets.
References
Blomberg, S. P., T. Garland, Jr., and A. R. Ives. 2003. Testing for phylogenetic signal in
comparative data: behavioral traits are more labile. Evolution 57:717-745.
Felsenstein, J. 1988. Phylogenies and quantitative characters. Annual Review of Ecology and
Systematics 19:445-471.
Hansen, T. F. 1997. Stabilizing selection and the comparative analysis of adaptation. Evolution
51:1341–1351.
Harville, D. A. 1974. Bayesian inference for variance components using only error contrasts.
Biometrika 61:383-385.
Housworth, E. A., E. P. Martins, and M. Lynch. 2004. The phylogenetic mixed model.
American Naturalist 163:84-96.
Ives, A. R., P. E. Midford, and T. Garland, Jr. 2007. Within-species variation and
measurement error in phylogenetic comparative methods. Systematic Biology 56:252270.
Lavin, S. R., W. H. Karasov, A. R. Ives, K. M. Middleton, and T. Garland, Jr. 2008.
Morphometrics of the avian small intestine, compared with non-flying mammals: a
phylogenetic approach. Physiological and Biochemical Zoology 81:526-550.
Martins, E. P. and T. F. Hansen. 1997. Phylogenies and the comparative method: A general
approach to incorporating phylogenetic information into the analysis of interspecific data.
American Naturalist 149:646–667. Erratum 153:448.
6
Supporting Information Tables S1–S6
Table S1 Summary of the common phenolics identified from leaf samples by UPLC-MS
across 26 Oenothera species. The table gives peak names, retention times (Rt),
molecular weights (Mw) and assignments of peaks measured during this study. There
were many less abundant and/or rare phenolics that contributed to our quantification of
phenolic diversity and total concentrations of the three main phenolic types which are
not included here.
Peak Name
Rt (min)
Peak ID
Mw (g/mol)
ET1
2.90
oenothein B
1568
ET2
3.23
oenothein A
2352
ET3
2.69
oxidized oenothein A
2366
2.48
caffeoyl tartaric acid
312
3.23
feruloyl tartaric acid
326
3.81
3.90
3.99
4.08
4.21
4.21
4.25
4.27
4.28
4.28
4.35
4.40
4.40
4.51
4.61
4.62
4.62
4.66
4.76
4.78
quercetin glycoside
quercetin glycoside
flavonoid glycoside
quercetin glycoside
kaempferol glycoside
kaempferol glycoside
kaempferol glycoside
quercetin glycoside
flavonoid glycoside
quercetin glycoside
kaempferol glycoside
kaempferol glycoside
quercetin glycoside
flavonoid glycoside
flavonoid glycoside
flavonoid glycoside
myricetin glycoside
kaempferol glycoside
quercetin glycoside
kaempferol glycoside
616
610
464
478
448
594
600
630
550
434
448
462
448
492
614
432
506
534
564
432
Ellagitannins (ET)
Caffeic acid derivatives (CA)
CA1
CA2
Flavonoid glycosides (FG)
FG1
FG2
FG3
FG4
FG5
FG6
FG7
FG8
FG9
FG10
FG11
FG12
FG13
FG14
FG15
FG16
FG17
FG18
FG19
FG20
7
Table S2 Eigenvalues (A) and eigenvectors (B) from the principal components analysis
of phenolic chemistry variation among species. Values in bold show the traits with the
two highest and two lowest loadings on PC 1 and PC 2.
A) Eigenvalues
Eigenvalue
Proportion of total variance
Cumulative variance
explained
B) Eigenvectors
peaks 280 nm
peaks 349 nm
oenothein B
oenothein A
ox-oenothein A
total ET
total CA
FG4 - quercetin
total kaempferol
total quercetin
other flavonoids
total FG
PC1
3.08
0.26
PC2
2.50
0.21
PC3
1.32
0.11
PC4
1.17
0.10
PC5
1.00
0.08
0.26
PC1
-0.36
-0.35
0.32
-0.02
0.34
0.43
0.22
0.09
-0.07
0.05
-0.29
-0.44
0.47
PC2
-0.09
0.09
-0.15
0.11
0.25
0.21
-0.35
-0.53
0.32
-0.57
0.02
-0.13
0.58
0.67
0.76
8
Table S3 Eigenvalues (A) and eigenvectors (B) from the principal components analysis
of all traits combined among species. Values in bold show the traits with the two highest
and two lowest loadings on PC 1 and PC 2.
A) Eigenvalues
Eigenvalue
Proportion of total variance
Cumulative variance explained
B) Eigenvectors
peaks 280 nm
peaks 349 nm
oenothein B
oenothein A
ox-oenothein A
total ET
total CA
FG4 - quercetin
total kaempferol
total quercetin
other flavonoids
total FG
toughness
% water
Specific leaf area
tannins
trichomes
PC1
2.08
0.25
0.25
PC1
-0.22
-0.05
0.03
0.23
0.32
0.34
-0.10
-0.21
0.12
-0.27
-0.07
-0.25
0.32
-0.34
-0.31
0.39
-0.04
PC2
1.83
0.20
0.45
PC2
0.24
0.33
-0.34
0.11
-0.14
-0.27
-0.34
-0.28
0.21
-0.28
0.24
0.30
0.12
-0.12
-0.26
-0.19
-0.16
9
PC3
1.33
0.10
0.56
PC4
1.20
0.08
0.64
PC5
1.13
0.08
0.71
Table S4 Species-level correlations between traits. Correlations were performed while taking intraspecific variation into
account (see Methods), and 95% CI were calculated using parametric bootstrapping with 1000 iterations. We show
correlation coefficients in bold when their 95% CI did not overlap 0.
peaks
280
peaks
349
oeno
B
oeno
A
oxoeno
A
total
ET
total
CA
FG 4querc
total
querc
total
kaemp
other
FG
total
FG
0.39
peaks
349
-0.19
-0.08
-0.56
-0.54
-0.17
0.04
-0.07
-0.01
0.24
0.18
-0.22
-0.25
-0.43
-0.14
-0.12
-0.02
0.01
0.54
0.25
0.3
0.16
0.17
-0.11
0.05
0.53
total
ET
0.04
-0.03
total
CA
-0.21
oeno
B
-0.13
oeno
A
0.06
oxoeno
A
toughness
% H20
SLA
PPC
trichomes
0.31
0.03
0.45
0.19
-0.45
-0.27
0.36
0.6
-0.06
-0.17
-0.18
-0.17
-0.16
-0.05
-0.29
-0.34
-0.17
0.08
0.15
0.29
0.23
-0.13
0.12
0.08
-0.17
0.59
-0.62
-0.63
0.58
-0.06
-0.21
-0.16
0.05
-0.2
-0.5
0.45
-0.41
-0.24
0.53
-0.09
-0.03
-0.23
0.02
-0.4
-0.62
0.29
-0.29
-0.18
0.88
0.05
0.26
FG 4querc
0.42
-0.3
-0.08
-0.25
-0.38
-0.04
0.5
0.12
0.27
0.86
total
querc
-0.33
-0.19
0.09
-0.19
0.43
0.22
-0.06
0.16
-0.39
total
kaemp
-0.13
0.28
-0.33
0.39
0.35
-0.23
0.42
-0.24
other
FG
0.22
0.3
-0.19
-0.38
-0.11
-0.01
0.34
total
FG
-0.11
-0.3
-0.11
-0.17
-0.05
-0.31
0.07
-0.08
-0.65
0.2
-0.32
-0.66
0.44
-0.18
% H20
0.63
-0.6
-0.05
SLA
-0.36
-0.1
PPC
0.03
toughness
10
Table S5 Associations between herbivore performance and variation in plant traits. The
measures of herbivore performance are shown as subheaders in the left-most column,
and bivariate regressions with each of the 17 traits follows. The regression statistics
shown correspond to the phylogenetic generalized least-squares (PGLS) OrnsteinUhlenbeck model of stabilizing selection. When the best fitting model had a slope
different from 0 at P<0.1, we show the slope, t-value and P-value in bold. We chose
P<0.1 here not as a hard line of statistical significance testing, but to indicate traits of
potential interest because the non-measurement error PGLS models show lower
statistical power. We also performed these analyses while incorporating measurement
error, and although there were many more significant relationships, a large fraction of
the models failed to run and so for consistency we only show the non-measurement
error models here. For completeness, we underline those traits that were also
significant (P<0.05) according to non-phylogenetic ordinary least-squares regression. Pvalues are not corrected for multiple comparisons.
Response/Predictor
Caterpillar herbivory
No. peaks at 280nm
No. peaks at 345nm
Oenothein B
Oenothein A
Ox-oenothein A
Total ET
Total CA
FG 4-quercetin
Total kaempferol
Total quercetin
Other flavonoids
Total FG
Toughness
% water
SLA
PPC
Trichomes
Caterpillar mass
No. peaks at 280nm
No. peaks at 345nm
Oenothein B
Oenothein A
Ox-oenothein A
Total ET
Total CA
FG 4-quercetin
Intercept
Slope
t
P
R2
d
-0.1476
0.0131
0.0544
0.0549
0.0483
0.0849
0.0410
0.0431
0.0464
0.0517
0.0409
0.0240
0.0667
0.0036
-0.0068
0.0870
0.0621
0.0024
0.0025
-0.0002
-0.0009
0.0000
-0.0005
0.0013
0.0052
0.0006
-0.0016
0.0043
0.0022
-0.0002
0.0006
0.0003
-0.0190
-0.0002
1.19
0.98
0.62
1.47
0.11
1.93
0.71
1.51
0.14
0.50
0.98
0.77
0.98
0.28
1.22
1.53
1.75
0.245
0.339
0.543
0.154
0.912
0.066
0.484
0.144
0.893
0.622
0.338
0.447
0.335
0.785
0.236
0.139
0.093
0.06
0.04
0.02
0.09
0.00
0.14
0.02
0.09
0.00
0.01
0.04
0.03
0.04
0.00
0.06
0.09
0.12
0.11
0.03
0.07
0.08
0.18
0.02
0.21
0.48
0.19
0.12
0.09
0.11
0.09
0.22
0.18
0.11
0.12
-0.5840
0.0809
0.2830
0.2680
0.2620
0.4270
0.2210
0.2230
0.0100
0.0116
-0.0012
-0.0033
-0.0010
-0.0025
0.0044
0.0148
1.13
0.93
0.85
1.10
0.53
2.01
0.51
0.88
0.270
0.363
0.402
0.282
0.601
0.056
0.612
0.388
0.05
0.03
0.03
0.05
0.01
0.14
0.01
0.03
0.35
0.34
0.36
0.36
0.40
0.34
0.41
0.50
11
Total kaempferol
Total quercetin
Other flavonoids
Total FG
Toughness
% water
SLA
PPC
Trichomes
Caterpillar survival
No. peaks at 280nm
No. peaks at 345nm
Oenothein B
Oenothein A
Ox-oenothein A
Total ET
Total CA
FG 4-quercetin
Total kaempferol
Total quercetin
Other flavonoids
Total FG
Toughness
% water
SLA
PPC
Trichomes
Mite eggs
No. peaks at 280nm
No. peaks at 345nm
Oenothein B
Oenothein A
Ox-oenothein A
Total ET
Total CA
FG 4-quercetin
Total kaempferol
Total quercetin
Other flavonoids
Total FG
Toughness
% water
SLA
0.2360
0.2380
0.2600
0.0551
0.3148
-0.7945
-0.0196
0.4651
0.2839
0.0029
0.0014
-0.0102
0.0181
-0.0006
0.0134
0.0013
-0.1096
-0.0005
0.12
0.09
0.43
1.35
0.83
1.27
1.16
1.82
1.26
0.904
0.928
0.668
0.190
0.412
0.216
0.256
0.081
0.220
0.00
0.00
0.01
0.07
0.03
0.06
0.05
0.12
0.06
0.40
0.40
0.43
0.35
0.37
0.48
0.41
0.35
0.37
-1.2900
0.3610
0.4210
0.3930
0.3970
0.5660
0.3660
0.3390
0.3230
0.4080
0.2910
0.1030
0.4630
-0.5775
0.2292
0.6084
0.4383
0.0200
-0.0005
-0.0020
-0.0046
-0.0018
-0.0028
-0.0023
0.0324
0.0203
-0.0176
0.0440
0.0243
-0.0010
0.0123
0.0006
-0.1222
-0.0008
1.99
0.04
1.13
1.38
0.82
2.18
0.24
1.74
0.81
1.05
2.00
1.61
1.00
1.05
0.50
1.99
1.73
0.058
0.972
0.270
0.180
0.419
0.039
0.813
0.095
0.427
0.304
0.057
0.120
0.327
0.303
0.625
0.058
0.097
0.14
0.00
0.05
0.07
0.03
0.17
0.00
0.11
0.03
0.04
0.14
0.10
0.04
0.04
0.01
0.14
0.11
0.20
0.14
0.00
0.05
0.20
0.00
0.13
0.50
0.16
0.03
0.02
0.01
0.00
0.31
0.12
0.00
0.18
-0.5050
0.6960
0.5240
0.4580
0.4670
0.5680
0.4140
0.4860
0.2970
0.4990
0.3580
0.4820
0.3830
2.7932
0.6997
0.0118
-0.0173
-0.0015
0.0017
0.0002
-0.0013
0.0122
-0.0085
0.0789
-0.0116
0.0681
-0.0011
0.0008
-0.0299
-0.0012
0.93
0.93
0.69
0.36
0.08
0.62
0.97
0.32
2.14
0.50
1.94
0.05
0.68
1.86
0.64
0.361
0.360
0.496
0.724
0.934
0.540
0.343
0.749
0.043
0.624
0.064
0.959
0.503
0.075
0.529
0.03
0.04
0.02
0.01
0.00
0.02
0.04
0.00
0.16
0.01
0.14
0.00
0.02
0.13
0.02
0.56
0.70
0.56
0.59
0.57
0.55
0.63
0.56
0.82
0.58
0.67
0.58
0.60
0.61
0.58
12
PPC
Trichomes
Mite survival
No. peaks at 280nm
No. peaks at 345nm
Oenothein B
Oenothein A
Ox-oenothein A
Total ET
Total CA
FG 4-quercetin
Total kaempferol
Total quercetin
Other flavonoids
Total FG
Toughness
% water
SLA
PPC
Trichomes
Beetle herbivory
No. peaks at 280nm
No. peaks at 345nm
Oenothein B
Oenothein A
Ox-oenothein A
Total ET
Total CA
FG 4-quercetin
Total kaempferol
Total quercetin
Other flavonoids
Total FG
Toughness
% water
SLA
PPC
Trichomes
Field herbivory
No. peaks at 280nm
No. peaks at 345nm
Oenothein B
Oenothein A
0.4684
0.4800
0.0016
-0.0001
0.02
0.16
0.987
0.872
0.00
0.00
0.58
0.56
-0.1340
0.3080
0.6500
0.6330
0.6390
0.6230
0.6370
0.6110
0.6720
0.6370
0.6260
0.5020
0.6956
0.6827
0.6098
0.5891
0.6309
0.0094
0.0243
-0.0002
0.0007
0.0000
0.0002
0.0005
0.0121
-0.0186
0.0009
0.0098
0.0139
-0.0005
-0.0006
0.0001
0.0224
0.0001
1.35
3.06
0.20
0.29
0.02
0.18
0.07
0.99
1.14
0.07
0.60
1.36
0.74
0.08
0.17
0.48
0.30
0.190
0.005
0.843
0.778
0.985
0.858
0.941
0.332
0.266
0.941
0.554
0.186
0.467
0.940
0.867
0.637
0.764
0.07
0.28
0.00
0.00
0.00
0.00
0.00
0.04
0.05
0.00
0.01
0.07
0.02
0.00
0.00
0.01
0.00
0.15
0.35
0.18
0.21
0.22
0.26
0.22
0.27
0.19
0.22
0.19
0.14
0.16
0.24
0.24
0.28
0.19
0.2247
0.1653
0.0845
0.0931
0.1006
0.0752
0.0816
0.1002
0.0816
0.1016
0.1041
0.1431
0.0918
0.6766
0.1900
0.0496
0.0595
-0.0015
-0.0053
0.0004
0.0013
0.0000
0.0003
0.0029
0.0002
0.0209
-0.0004
-0.0019
-0.0048
0.0001
-0.0075
-0.0004
0.0231
0.0006
0.59
1.77
1.19
1.79
0.01
0.87
1.22
0.04
2.40
0.09
0.30
1.48
0.37
3.39
1.31
1.36
2.63
0.560
0.094
0.250
0.091
0.996
0.399
0.238
0.971
0.028
0.927
0.765
0.156
0.713
0.004
0.206
0.192
0.018
0.02
0.16
0.08
0.16
0.00
0.04
0.08
0.00
0.25
0.00
0.01
0.11
0.01
0.40
0.09
0.10
0.29
0.55
0.83
0.57
0.47
0.58
0.56
0.65
0.58
0.52
0.59
0.59
0.71
0.55
0.61
0.47
0.54
0.54
0.6689
0.2164
0.1577
0.1249
-0.0062
-0.0044
0.0000
0.0031
0.93
0.56
0.00
2.00
0.366
0.586
0.998
0.061
0.05
0.02
0.00
0.19
0.01
0.06
0.04
0.01
13
Ox-oenothein A
Total ET
Total CA
FG 4-quercetin
Total kaempferol
Total quercetin
Other flavonoids
Total FG
Toughness
% water
SLA
PPC
Trichomes
0.1183
0.1118
0.0809
0.1429
0.1062
0.1435
0.1781
0.2127
0.1153
0.9871
0.3093
0.0064
0.1087
0.0017
0.0006
0.0112
0.0058
0.0429
0.0044
-0.0131
-0.0055
0.0004
-0.0108
-0.0008
0.0657
0.0006
14
1.76
0.72
2.18
0.54
1.80
0.39
1.09
0.64
0.63
2.09
1.21
1.90
1.06
0.097
0.483
0.044
0.599
0.089
0.701
0.292
0.531
0.536
0.052
0.245
0.074
0.303
0.15
0.03
0.22
0.02
0.16
0.01
0.07
0.02
0.02
0.20
0.08
0.18
0.06
0.00
0.02
0.30
0.08
0.00
0.06
0.03
0.03
0.01
0.02
0.01
0.00
0.05
Table S6 Results from phylogenetic regressions of herbivore performance on principal
component summaries of variation in chemical traits (PCchemistry) and chemical and nonchemical traits (PCall traits). When the best fitting model had a slope different from 0 at
P<0.1, we show the slope, t-value and P-value in bold. We chose P<0.1 here not as a
hard line of statistical significance testing, but to indicate traits of potential interest
because the non-measurement error PGLS models show lower statistical power. For
completeness, we underline those traits that were also significant (P<0.05) according to
non-phylogenetic ordinary least-squares regression. P-values are not corrected for
multiple comparisons.
Response/Predictor
Caterpillar herbivory
PC 1 - chemistry
PC 2 - chemistry
PC 1 - all traits
PC 2 - all traits
Caterpillar mass
PC 1 - chemistry
PC 2 - chemistry
PC 1 - all traits
PC 2 - all traits
Caterpillar survival
PC 1 - chemistry
PC 2 - chemistry
PC 1 - all traits
PC 2 - all traits
Mite eggs
PC 1 - chemistry
PC 2 - chemistry
PC 1 - all traits
PC 2 - all traits
Mite survival
PC 1 - chemistry
PC 2 - chemistry
PC 1 - all traits
PC 2 - all traits
Beetle herbivory
PC 1 - chemistry
PC 2 - chemistry
PC 1 - all traits
PC 2 - all traits
Field herbivory
PC 1 - chemistry
Intercept
Slope
t
P
R2
d
0.0458
0.0499
0.0476
0.0460
-0.0089
-0.0042
-0.0059
0.0061
1.53
0.69
1.28
1.13
0.140
0.499
0.215
0.271
0.09
0.02
0.07
0.05
0.04
0.29
0.21
0.04
0.2190
0.2554
0.2418
0.2246
-0.0522
-0.0269
-0.0382
0.0279
1.76
0.90
1.75
0.99
0.091
0.379
0.093
0.330
0.11
0.03
0.11
0.04
0.29
0.47
0.42
0.32
0.3406
0.3564
0.3562
0.3425
-0.0635
-0.0030
-0.0340
0.0509
2.23
0.09
1.34
1.87
0.036
0.929
0.192
0.073
0.17
0.00
0.07
0.13
0.01
0.16
0.19
0.01
0.4614
0.4654
0.4715
0.4524
-0.0375
0.0153
0.0133
0.0441
0.78
0.33
0.38
1.01
0.443
0.747
0.706
0.322
0.02
0.00
0.01
0.04
0.53
0.57
0.59
0.53
0.6380
0.6412
0.6396
0.6374
-0.0319
-0.0128
-0.0107
0.0118
1.52
0.58
0.63
0.58
0.143
0.566
0.535
0.570
0.09
0.01
0.02
0.01
0.09
0.20
0.19
0.19
0.1000
0.1004
0.1026
0.0992
0.0126
0.0018
0.0084
-0.0094
1.61
0.22
1.49
1.09
0.126
0.830
0.155
0.290
0.13
0.00
0.12
0.07
0.64
0.58
0.52
0.65
0.1597
0.0216
1.33
0.201
0.09
0.02
15
PC 2 - chemistry
PC 1 - all traits
PC 2 - all traits
0.1577
0.1528
0.1568
0.0007
0.0229
-0.0171
16
0.04
1.87
0.91
0.972
0.079
0.374
0.00
0.17
0.05
0.04
0.00
0.10
Supporting Information Figs S1 & S2
Fig. S1 Representative UV chromatograms and spectra showing quantitative and
qualitative variation in polyphenolic profiles among Oenothera species. A1-A6 show
chromatograms of six species at 280 nm (highlighting ellagitannin diversity and
abundance) and 349 nm (highlighting caffeic acid derivative and flavonoid diversity and
abundance). Species were selected to illustrate differences in polyphenolic profiles
identified in principle component and hierarchical cluster analyses. A1-2: Plants with
high levels of total ellagitannins and high levels of the oxidized oenothein A (ET3). A3-4:
Plants with high levels of oenothein B (ET1), caffeic acid derivatives (CA1 and CA2),
and quercetin glycosides (particularly peak FL4, a quercetin glycoside). A5-6: Plants
with high levels of total flavonoids and high diversity of polyphenolics (number of peaks
at 280 and 349 nm). Figure inset shows the chromatogram between 0.95 and 2 minutes
to illustrate smaller peaks observed at 280 nm. B: representative UV spectra of different
classes of polyphenolics measured in this study, including: ellagitannins, caffeic acid
derivatives, and flavonoid glycosides.
17
A
A1
1,3e+6
280 nm
O. perennis
ET3
1,0e+6
7,8e+5
5,2e+5
2,6e+5
1
8,0e+4
CA1
0,0
6,0e+4
4,0e+4
2,0e+4
0,0
FL4
4,0e+4
0,0
0
1
4
5
0
O. grandis
7,2e+5
7,2e+5
4,8e+5
4,8e+5
2,4e+5
2,4e+5
0,0
0,0
FL4
CA2
5,0e+4
0,0
0,0
5,0e+5
1
2
3
4
O. heterophylla
4,0e+5
1
2
3
4
5
Retention Time
280 nm
O. triangulata
4,8e+4
ET3
3,6e+4
3,0e+5
2,4e+4
2,0e+5
1,2e+4
1,0e+5
0,0
0,0
6,0e+4
4,0e+4
2,0e+4
0,0
349 nm
9,0e+4
FL14
FL4
349 nm
6,0e+4
3,0e+4
0,0
0
1
2
3
4
5
0
1
2
Retention Time
B
ET3 - Oxidized oenothein A
1,8e+6
Absorbance (au)
FL4
CA2
A6
6,0e+4
280 nm
5
O. humifusa
349 nm
0
5
4
ET1
Retention Time
A5
3
1,0e+5
6,0e+4
0
2
Retention Time
280 nm
1,5e+5
349 nm
1,2e+5
1
FL4
9,6e+5
ET1
1,8e+5
CA1
A4
1,2e+6
280 nm
9,6e+5
Absorbance (au)
3
349 nm
Retention Time
A3
1,2e+6
2
ET1
5,0e+5
2
349 nm
ET3
1,0e+6
0,0
1,2e+5
O. longtituba
1,5e+6
**
***
** **
280 nm
2,0e+6
ET1
*
*
A2
2,5e+6
CA1 - Caffoyl tartaric acid
1,5e+6
1,0e+6
1,0e+6
6,0e+5
5,0e+5
5,0e+5
0,0
0,0
1,5e+6
300
400
ET1 Oenothein B
3e+6
300
400
CA2 - Feruloyl tartaric acid
200
1,5e+6
2e+6
1,0e+6
5,0e+5
1e+6
5,0e+5
0,0
0
300
400
5
0,0
200
1,0e+6
200
4
FL4 - Quercetin glycoside
1,5e+6
1,2e+6
200
3
Retention Time
300
400
FL14 - Flavonoid glycoside
0,0
200
300
Wavelength
18
400
200
300
400
Fig. S2 Ordinations from principal components analysis. Shown are the (A) species
scores on PC 1 and PC 2 from the PCA on chemical traits, and (B) the trait loadings on
these same axes. We also performed PCA on all chemical and non-chemical traits
combined, where (C) species’ scores and (D) trait loadings are similarly displayed. The
trait clusters identified by hierarchical cluster analysis (see Fig. 4), and the
corresponding species with these traits, are shown by “i”, “ii”, and “iii” in C and D. To
show the loadings on a comparable scale to that of the scores we multiplied the vectors
by 5.5 and 9.5 in B and D, respectively.
33
33
acaulis
acaulis
acutissima
acutissima
serrulata
serrulata
elata
elata
313
1
PC 2
202
0
triangulata
triangulata
sufffulta
ulta
suf
11
-1
-1
PC 2
00
-2
-2
triangulata
triangulata
suf
sufffulta
ulta
-1
-1
-3
-3
-2
-2
-4
-4
-3
-3
ruticosa
ffruticosa
acaulis
acaulis
longituba
longituba
speciosa
rhombipetala
speciosa
rhombipetala
biennis
biennis
grandif
lora
grandif
lora
ersicolor
vversicolor
heterophylla
lla
heterophy
gaura
gaura perennis
perennis
af
f
inis
af
f
inis
acutissima
acutissima
serrulata
serrulata
elata
elata
berlandieri
berlandieri
rosea
rosea
ffruticosa
villaricae
illaricae
sandiana
vruticosa
sandiana
clelandii
clelandii
speciosa
speciosa
rhombipetala
rhombipetala
biennis
biennis
grandif
grandif
lora
vvlora
ersicolor
ersicolor
heterophy
heterophylla
lla
perennis
perennis
af
afdrummondii
ffinis
inis
drummondii
laciniata
laciniata
berlandieri
berlandieri
rosea
rosea
vvillaricae
illaricae
sandiana
sandiana
clelandii
clelandii
humifusa
usa
humif
drummondii
drummondii
grandis
grandis
laciniata
laciniata
-4
-4
-2
-2
B
gaura
gaura
humif
humif
usa
00usa
-2
-2
11
-1
-1
00
-2
-2
-1
-1
-3
-3
-3
-3
22
00
22
-2 total querc
-2
00 FG 4-quercetin
22
-4
-4
PC 11
PC
-4
-4
-2
-2
00
22
PC
PC 11
C
D
iiii
44
total FG
ii
-2
-2
00
-4
-4
-2
-2
serrulata
serrulata
clelandii
clelandii
elata ggrandiflora
elata
randiflora
biennis
biennis
gaura
gaura
acaulis
acaulis
heterophylla
heterophylla
speciosa
speciosa
acutissima
acutissima
affinis
affinis
rhombipetala
rhombipetala
versicolor
versicolor
rosea
rosea
fruticosa
fruticosa
berlandieri
berlandieri
serrulata
serrulata
humifusa
laciniata clelandii
humifusa
laciniata
villaricae
villaricae
clelandii
elata
elata ggrandiflora
drummondii
drummondii
randiflora
biennis
biennis
gaura
gaura
grandis
grandis
perennis
perennis
affinis
affinis
sandiana
sandiana
versicolor
versicolor
rosea
rosea
fruticosa
fruticosa
berlandieri
berlandieri
humifusa
humifusa
laciniata
laciniata
villaricae
villaricae
drummondii
drummondii
grandis
grandis
perennis
perennis
iii
-4
-4
-4
-4
iii -2-2
sandiana
sandiana
00
22
44
PC 2
22
44
ii
longituba
longituba
PC 2
triangulata
triangulata
suffulta
suffulta
00
22
peaks 349nm
acaulis
acaulis
heterophylla
speciosa
heterophylla
speciosa
acutissima
acutissima
rhombipetala
rhombipetala
22
44
iiii
44
triangulata
triangulata
suffulta
suffulta
PC 2
oeno B
total CA
PC
PC 11
PC 2
ox oeno A
total ET
oeno A
peaks 349nm
total other FGs
peaks 280nm
total FG
-4
-4
PC 11
PC
-4
-4
total kaemp
202
0
-2
-2
-4
-4
grandis
grandis
-4
-4
22
313
1
PC 2
22
PC 2
A
longituba
longituba
i
-2
-2
22
44
-4
-4
-2
-2
66
i
sla
total querc FG 4
oeno B
total CA
total ET
iii
-4
-4
iii
-2
-2
00
22
44
66
22
44
66
PC 11
PC
-2
-2
00
PC
PC 11
19
ox oeno A
PPC
trichomes
-4
-4
PC
PC 11
oeno A
toughness
% water
-2
-2
00
longituba
longituba
66
ii
total kaemp
i
-4
-4
00
other FGs
00
22
PC 11
PC
-4
-4
peaks 349nm