Supplemental Methods
Methods for Feigl-Anger cyanogenesis assays
We used modified Feigl-Anger assays to determine the cyanotype of each collected individual. We
collected triplicate samples of leaf tissue from a single individual in 48 well Costar polystyrene plates
(Corning). We froze leaf tissue at -80°C for at least 30 minutes to cause cell rupture before thawing the
tissue. We disrupted tissue with a pipette tip and then used Feigl-Anger test paper (which turns blue upon
exposure to HCN) to test leaf samples for HCN release under each of three conditions: leaf tissue alone
(to identify AcLi plants), tissue with exogenously added cyanogenic glucosides (to identify acLi plants),
and tissue with exogenously added linamarase (to identify Acli plants). Samples were incubated at 37° C
for 4 hours before recording results. Leaf samples that showed no HCN release under any of the test
conditions were inferred to be acli cyanotypes. Any individuals with ambiguous assay results were rephenotyped up to three times. In previous studies we have observed that the presence/absence of
cyanogenic components as detected in the Feigl-Anger assay shows a near perfect correlation with gene
presence/absence polymorphisms at the CYP79D15 and Li loci (>95% accuracy; Kooyers and Olsen,
2012).
DNA Extraction and microsatellite amplification procedures
DNA was extracted with DNeasy Plant Mini kits (Qiagen, Valencia, CA) following the manufacturer’s
protocol, using 50 mg of young leaf tissue from each plant. Amplification conditions and the genotyping
protocol for each marker are described in Kooyers & Olsen (2012). All genotyping was performed on an
ABI 3130 sequencer at Washington University in St. Louis. We hand-checked every allele call and coded
any sample with more than two strong peaks as missing data. Microsatellite reactions that did not yield a
product were repeated up to three times before being coded as missing data. All loci had less than 5%
missing data, with the exception of loci 4G02 and 2B11, which had 7.3% and 13.4% missing data,
respectively, in the New Zealand samples. We tested for evidence of null alleles using the program
Micro-Checker (Van Oosterhout et al., 2004). There was evidence for null alleles at three loci (5E05,
2B11, and 4G02; Table S3), but all conclusions were similar if these loci were thrown out of the dataset;
results are reported for the complete dataset.
Details on analyses for identifying potential agents of selection
We extracted 46 different environmental factors from raster data sets obtained from the BIOCLIM
(Hijmans et al., 2005) and CGIAR (Trabucco and Zomar, 2009) databases and extracted values for each
variable for each subpopulation in ArcMap 9.3.1 (ESRI, Redlands, CA). We examined correlations
between environmental variables and the frequency of either Ac, Li or AcLi plants using general linear
models in R 2.11.1. Significance of a linear regression indicated clinal variation with respect to an
environmental variable. A two-step process was used to determine which models to use in a multiple
regression framework. First, we determined which individual variables accounted for the most spatial
variation across transects in frequencies of cyanogenesis genes and cyanotypes (via r2 values for each
target in each cline). Second, we generated a covariance matrix containing seven of these variables and
selected three — annual precipitation (AP), minimum winter temperature (MWT) and potential
evapotranspiration (Apet) — that showed little covariance with each other, but strong covariance with the
other four variables. Aridity index, which is calculated as the quotient of AP divided by Apet, was also
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subsequently used in some analyses. Multiple regression analysis was performed using lm () in R 2.11.1
with every combination of AP, MWT and Apet, as well as interactions among these variables. Models
were compared using AIC values and adjusted r2 values; AIC values for each model were extracted using
the extractAIC call. To determine whether all clines can be described by the same set of environmental
variables, we combined all subpopulations from all transects into one dataset and re-ran multiple
regression analysis to create a model that best predicts population variation in frequency of cyanogenesis
genes and the cyanogenic phenotype.
Cline shape analysis
We assessed the parameter
for the Ac allele, the Li allele and the AcLi cyanotype in each cline
where z is the logit (allele frequency) or logit (cyanotype frequency) of a subpopulation and x is the
minimum winter temperature (MWT) of a subpopulation. The parameter
is inversely proportional
to cline width and directly proportional to the slope of either the selection gradient or the selection
coefficient (depending on whether a gradient or ecotone model is assumed) (Slatkin, 1973; Endler, 1977;
Barton and Hewitt, 1985; Barton & Gale, 1993). We used the logit transformation of either allele
frequency or cyanotype frequency because we leave the tail of the central U.S. cline in the analysis (i.e.,
the Wisconsin subpopulations; see Table S2). If no transformation is used, the non-linearity of this tail
causes poor fit of the model and substantially changes the relationship between cline shape in the central
U.S. and the cline shapes in New Zealand and Mt. Baker. This is the same transformation used in several
previous studies of clines (e.g., Barton and Hewitt, 1989; Szymura and Barton, 1991). Since logit
transformations cannot be performed on fixed allele frequencies, we used values of -5 and 5 in cases
where frequencies were 0 or 1 respectively (see Szymura and Barton, 1991). The role of epistasis on
cline shape at a single locus is difficult to predict without context (i.e., information on founding
population allele frequencies), but cline shape should still be near linear on a logit scale as is empirically
shown in snail shell band and color clines (Johnson, 2012). MWT was selected as the independent
variable because it has previously been linked to herbivore abundance as the proposed agent of selection
in the cyanogenesis system. It is a better measure than geographic distance because cyanogenesis clines
are typically thought of as dispersal-independent clines – where populations in the middle of transects can
adapt to local conditions because the ratio of gene flow to selection is low (Barton and Hewitt, 1985). In
dispersal-independent clines, clines over similar gradients will generate similar cline shapes regardless of
the geographic length of the transect over which an environmental gradient occurs; the cyanogenesis
clines appear to fit model this well (Daday, 1958; see also results within the main text).
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