Oikos
OIK-03781
Schrieber, K., Wolf, S., Wypior, C., Höhlig, D.,
Hensen, I. and Lachmuth, S. 2016. Adaptive and nonadaptive evolution of trait means and genetic trait
correlations for herbivory resistance and performance
in an invasive plant. – Oikos doi: 10.1111/oik.03781
Appendix 1
Information on the study populations
Figure A1. Overview of the geographic location of the sampled native (right) and invasive (left) S.
latifolia populations.
Table A1. Overview of the geographic location, population size, and population genetic
characteristics of the sampled native and invasive S. latifolia populations.
Range
City (state)
ID
N
E
Native
Caresana (IT)
ca
44.07702
9.96384
Native
Cecina (IT)
ce
43.313769
Native
Gilching (GE)
gi
Native
Jethe (GE)
Native
Population
Ho
Ar
Q
262
0.357
9.8
0.081
10.51195
149
0.348
10.9
0.143
48.105634
11.253771
35
0.415
6.9
0.067
je
51.6899
14.59683
485
0.396
7.8
0.962
Montpellier (FR)
mp
43.653567
3.893247
341
0.376
9.9
0.141
Native
Monteneau (FR)
mt
47.848117
3.552508
19
0.349
8.3
0.033
Native
Nackenheim (GE)
nh
49.925119
8.342758
132
0.39
11.6
0.372
Native
Nijmengen (GE)
nj
51.883074
5.85185
46
0.298
6.3
0.019
Invasive
Crumpler (NC)
ac
36.52576
-81.41558
60
0.441
9
0.687
Invasive
Bennington (VT)
be
42.894717
-73.294919
31
0.343
10.1
0.809
Invasive
Hillsdale (NY)
cv
42.234825
-73.506433
350
0.34
10.5
0.887
Invasive
Bushkill (PA)
es
41.14009
-74.9294
900
0.396
9.7
0.144
Invasive
Harrisonburg (VA)
hg
38.49079
-78.97762
70
0.322
7.8
0.072
Invasive
Lewisburg (PA)
lb
40.98179
-76.93041
184
0.376
7.8
0.676
Invasive
Washington Boro (PA)
ma
39.99635
-76.47243
1100
0.394
9.6
0.092
Invasive
Grantsville (MD)
ng
39.637308
-79.099481
1000
0.363
11.2
0.774
size
Population genetic characteristics were determined based on the SSR-data. We determined observed
Hererozygosity (Ho) and Allelic richness (Ar) with 'Arlequin 3.5.1.3' (Excoffier and Lischer 2010)
to estimate genetic diversity. In addition, we studied the genetic structure by employing the
Bayesian assignment analysis implemented in 'Structure 2.3.4' (Pritchard et al. 2000). The most
likely cluster partitioning according to Evanno et al. (Evanno et al. 2005) revealed an optimal
number of two genetic clusters (I and II) in the sampled populations, which correspond to the two
clusters identified in former studies on S. latifolia (Taylor and Keller 2007, Keller et al. 2012). We
inferred the population mean posterior assignment probabilities to cluster I (Q) with 'Clumpp 1.1.2'
(Jakobsson and Rosenberg 2007) to assess the degree of admixture between the two clusters in each
population.
References
Evanno, G. et al. 2005. Detecting the number of clusters of individuals using the software structure:
a simulation study. – Mol. Ecol. 14: 2611–2620.
Excoffier, L. and Lischer, H. E. L. 2010. Arlequin suite ver 3.5: a new series of programs to
perform population genetics analyses under Linux and Windows. – Mol. Ecol. Resour. 10:
564–567.
Jakobsson, M. and Rosenberg, N. A. 2007. CLUMPP: a cluster matching and permutation program
for dealing with label switching and multimodality in analysis of population structure. –
Bioinformatics 23: 1801–1806.
Keller, S. R. et al. 2012. Bayesian inference of a complex invasion history revealed by nuclear and
chloroplast genetic diversity in the colonizing plant, Silene latifolia. – Mol. Ecol. 21: 4721–
4734.
Pritchard, J. K. et al. 2000. Inference of population structure using multilocus genotype data. –
Genetics 155: 945–959.
Taylor, D. R. and Keller, S. R. 2007. Historical range expansion determines the phylogenetic
diversity introduced during contemporary species invasion. – Evolution 61: 334–345.
Appendix 2
Design enemy exclusion / inclusion experiment
The enemy exclusion / inclusion experiment was initially designed to investigate inbreeding x
environment interactions in native and invasive study populations and thus not solely included
experimentally outbred plants, but also experimentally inbred plants. The 16 experimental plots
established in the common garden (Fig. B1b) were thus planted with inbred and outbred individuals
from the F2-generation based on the following design: each plot included all native and invasive
populations represented by two to three seed families. As such, the five seed families within each
population were split between two plots (plot pair), which together comprised all of the FPCs. Each
of the plot pairs and consequently all FPCs were replicated an additional seven times. While
populations and seed families were planted randomly within the plots, the ranges and breeding
treatments were uniformly distributed (Fig. B1a) in order to reduce plot edge effects. Plots within
pairs and plot pair repetitions were randomly distributed across the experimental area. The distance
between individuals within plots was on average 0.65 m.
Figure A2.1. (a) Overview of the organization of plants within the experimental plots with respect
to range (native = black, invasive = gray) and breeding treatment (filled = outcrossed, squared =
inbred). (b) Overview of the experimental manipulation of enemy attack. The figure illustrates the
non-vegetated areas (light gray faces) with the experimental plots (white faces) and the vegetated
areas (structured dark gray faces) from which natural enemies colonized the plots. Either the enemy
exclusion (bold black frames) or the enemy inclusion (thin black frames) treatment was applied to
each eight uniformly distributed plots.
Appendix 3
Results from the genetic correlation analyses
Table A3.1. Results from the single predictor pedigree mixed models for analyses of genetic correlations
among performance traits (aboveground boimass, number of flowers, number of fruits) and resistance traits
(leaf-, flower-, and fruit-feeding-resistance) in native and invasive populations of Silene latifolia. The table
provides standardized regression coefficients for each resistance fixed effect, which correspond to correlation
coefficients, as well as χ2- and p-values for each model. The results of these analyses are illustrated in Fig. 4
of the manuscript.
Leaf-feeding-resistance
Estimate
p
χ2
Flower-feeding-resistance
Estimate
p
χ2
Fruit-feeding-resistance
Estimate
p
χ2
Aboveground native
biomass invasive
0.22
1.6
0.21
0.17
1.06
0.30
-0.19
0.73
0.39
0.36
3.71
0.06
0.11
0.48
0.49
0.31
3.59
0.06
Number of native
flowers invasive
-0.11
0.91
0.34
-0.05
0.16
0.69
0.14
0.61
0.44
0.44
5.12
0.02
0.22
1.08
0.18
0.03
0.05
0.82
Number of native
fruits invasive
-0.32
3.08
0.08
-0.25
1.99
0.16
-0.14
0.35
0.55
0.02
0.01
0.92
-0.20
1.75
0.19
0.16
0.85
0.36
Table A3.2. Results from the pedigree mixed models testing for divergence in the strength and direction of
genetic performance x resistance correlations between native and invasive populations of Silene latifolia. The
table provides standardized regression coefficients for the interaction between range and the respective
resistance trait for each performance response variable, as well as χ2- and p-values for the regarding
interaction. The results of these analyses are illustrated in Fig. 4 of the manuscript.
Leaf-feeding-resistance
Estimate
p
χ2
Flower-feeding-resistance
Estimate
p
χ2
Fruit-feeding resistance
Estimate
p
χ2
Aboveground
biomass
0.10
0.16
0.69
-0.07
0.1
0.76
0.46
2.75
0.10
Number of
flowers
0.47
4.34
0.04
0.22
1.18
0.28
-0.04
0.03
0.87
Number of
fruits
0.27
1.01
0.30
0.05
0.05
0.81
0.29
1.09
0.29