Draft manuscript – do not circulate without permission
Supplemental Methods:
Molecular Identification:
To determine the proportion of yeast species present we extracted DNA from yeast
colonies (12 for two species treatments and 2 for single species treatments) and
used restriction fragment length polymorphism (RFLP) fingerprinting to determine
species identity. DNA was extracted using the REDExract-N-Amp Tissue Kit (SigmaAldrich MO, USA) according to manufacturers instructions except with 10 l of
extraction solution and 10 l of dilution solution. The nuclear LSU was amplified
using the polymerase chain reaction (PCR) with the primer set NL1-NL4 (31).
Based on in silico digests using the program Geneious Pro v. 5.1.7 (Biomatters,
Auckland, New Zealand) we selected two restriction enzyme combinations that
could distinguish between all six species (HaeIII + MspI and HaeIII + MvaI).
Digestions were performed in 10 l reactions with 5 l of PCR product, 1 l of 10 X
buffer, 0.125 l of each FastDigest enzyme (Fermentas, MD USA), and 3.75 l H2O in
a thermocycler at 37 C for 15 minutes. Digested PCR products were loaded onto
2% Agarose gels, run at 200V for 25 min in a sodium boric acid buffer (33), stained
with EtBr and visualized with ultraviolet light. RFLP patterns were manually
compared with in silico digests to determine the species identity of each DNA
extract. In total, PCR-RFLP typing was carried out on ~1,891 colonies (some two
species treatments had < 12 colonies).
Nectar chemistry
Amino acid analysis
Sample preparation
The commercially available Phenomenex EZ:faast amino acid analysis kit was
used for preparation of the nectar samples. The procedure consisted of the solid
phase extraction (SPE) with pipette tips for the extraction of the amino acids from
the nectar matrix followed by derivatization of the amino acids, liquid-liquid
extraction and analysis by LC–MS/MS.
An aliquot of 2µl of nectar was transferred to a vial and diluted 198 µl of
0.1% methanol (1:100). After that 100 µl of Internal Standard mix (from Ez:faast
kit) was combined with 100 µl of 1:100 diluted sample and filtered through 0.45 µm
PTFE syringe filter to remove residual yeast cells. Further sample preparation
procedure was performed according to the Ez:faast kit manual.
LC–MS/MS
The chromatographic separation was performed on an EZ:faast AAA-MS
column, 250 mm ×3 mm ID, 4 µm particles (Phenomenex, Torrance, CA, USA) using
an Agilent 1100 HPLC (Santa Clara, CA, USA) with integrated autosampler and
sample chiller set at a temperature of 15°C. Separation of the amino acid derivatives
1
Draft manuscript – do not circulate without permission
was achieved by gradient elution using the following gradient: 0 min 70% B, 13 min
85% B, 13.01 min 70% B and 17 min 70% B at a flow of 0.4 mL/min at a column
temperature of 25°C. Eluent A consisted of 10 mM ammonium formate in water and
B consisted of 10 mM ammonium formate in methanol. The sample injection volume
was 10µL.
A Quattro Premier triple quadrupole mass spectrometer (Waters, Millford,
MA, USA) equipped with an electrospray ionization (ESI) probe was employed for
detection of the amino acid derivates. The mass spectrometer was operated with a
capillary voltage of 3 kV, source temperature of 110°C, desolvation temperature of
350°C, and collision gas (Argon) pressure of 2.1x10-3 mbar. Positive ion selected
reaction monitor mode (SRM) was used for monitoring the transitions of ions for
each analyte. Instrumentation control and data analyses were accomplished using
MassLynx 4.1 and QuanLynx 4.1 software. The lower limit of quantitation (LLOQ)
for the analytes, defined as signal-to-noise ratio of 10:1 for the analytes varied.
Standard calibration curves were obtained over the concentration range 0.005 – 20
nmol/mL. Homoarginine, homopheneylalanine and methionine-d3 were spiked into
each sample, and calibration curve solutions at 50nmol/mL served as Internal
Standards.
We excluded from statistical analyses 11 of the 24 amino acids because the
average concentration of the negative controls (e.g. uncolonized nectar) was below
the detection threshold of the instrument. Four samples were also rejected as clear
outliers with measured values orders of magnitude higher than any other sample.
Sugar analysis
Sample preparation:
The nectar samples, which were diluted 1:100 times for the amino acid
analysis, were further diluted with 1% methanol resulting in 1:10000 diluted nectar
samples. Fifty µl of each sample was spiked with 10 µl of 50 µg/mL of Internal
Standard spiking solution containing 13C6-glucose, 13C6-fructose and 13C6sucrose. Samples were further diluted with 390 µl of acetonitrile.
LC-MS/MS:
An Agilent 1100 HPLC system was used for solvent delivery and sample
introduction. Samples (10 μl) were injected onto a Luna NH2 column, 150 x 2.0 mm
ID, 3µm particle size (Phenomenex, Torrance, CA). Components were eluted at 25°C
at a flow rate of 0.4 mL/min. Separation of the sugars was achieved by gradient
elution using the following gradient: 0 min 90% B, 1 min 90% B, 3.5 min 60% B, 5
min 60% B, 5.5 min 90% B and 10 min 90%B. Eluent A was water and B was
acetonitrile.
Detection of the sugars was carried out on a Quattro Premier triple
quadrupole mass spectrometer (Waters, Millford, MA, USA) equipped with a
standard electrospray ion source operating in the negative mode. The MS was
operated with a capillary voltage of -2.5kV, source temperature of 120°C,
desolvation temperature of 350°C, and collision gas pressure of 2.5x10-3 mbar.
Negative ion selected reaction monitor mode (SRM) was used for monitoring the
2
Draft manuscript – do not circulate without permission
transitions of ions at m/z 178.9 > 88.7 for glucose and fructose (12V cone voltage,
8eV collision energy) and m/z 341.1 > 178.8 (19V cone voltage, 14eV collision
energy) for sucrose. The SRM transition for Internal Standards was as follows: m/z
184.8 > 91.7 for 13C labeled glucose and fructose and m/z 347.1 > 178.9 for 13C
labeled sucrose. The cone voltage and collision energy were the same as for the
corresponding unlabeled analytes. Since the glucose and fructose are structural
isomers and are indistinguishable by the MS, the baseline separation of the
chromatographic peaks was necessary to enable quantitative analysis of the
analytes. The retention times for the glucose, fructose and sucrose were 5.3, 4.8 and
5.8 min, respectively. The limit of quantitation (LOQ) for glucose, fructose and
sucrose was found to be 10 ng for sucrose and glucose and 20ng for fructose.
Standard calibration curves were obtained over the concentration range 0.1 – 50
µg/mL. Instrumentation control and data analyses were accomplished using
MassLynx 4.1 and QuanLynx 4.1 software (Waters).
Growth Rates
To assess yeast growth capabilities in different environments, we measured the
population growth rates of each yeast species in four different types of media: 20%
sugar solutions (sucrose, glucose, fructose) amended with 0.2% Difco Yeast
Nitrogen Base (Becton Dickinson, Sparks, MD USA) and a liquid version of the
standard yeast media used for culturing. Overnight cultures in liquid yeast media
were used to inoculate 3 replicates of each species into 200 l of each media (N = 3
replicates x 4 media types x 6 species = 72 replicates in total) using a BD-Falcon 96well tissue culture plate (BD Biosciences, Franklin Lakes, NJ USA). To monitor
growth we measured OD600 using a TECAN Infinite M200 microplate reader (Tecan
Systems, San Jose, CA, USA). The microplate reader was set to shake orbitally with
an amplitude of 1 mm and measure OD600 every 15 minutes for 23 hours.
We used the R package grofit (34) to estimate two parameters used to
compare population growth rates: the length of the lag phase () and maximum
growth rate (m). Growth over time was modeled as Ln(OD/ODinitial) and cell
doubling time was calculated as Ln(2) / m (35). We used the non-parametric spline
model available in grofit, although in a few cases where the spline fit was not
appropriate we used values from the best-fit parametric model instead (see Kahm et
al. 2010 for details).
3
Draft manuscript – do not circulate without permission
Supplemental Tables:
Table S1: Results from linear mixed effects models predicting the effects of
phylogenetic distance and ecological similarity on the strength of priority effects,
presented with corresponding R codes.
Model 1a: Phylogenetic distance (all species)
Formula:
lme(invasion.score~phylo.dist, random = ~1 | Treatment, data= peyn,
na.action="na.omit", method="REML")
Linear mixed-effects model fit by REML
Data: peyn
AIC
483.2388
BIC
495.2276
Random effects:
Formula: ~1 | Treatment
(Intercept)
StdDev:
2.019628
logLik
-237.6194
Residual
0.8603868
Fixed effects: invasion.score ~ phylo.dist
Value
Std.Error
DF
(Intercept) -6.142464
0.9538186 120
phylo.dist
13.423543 2.9517053 28
Correlation:
phylo.dist
t-value
-6.439866
4.547725
p-value
0e+00
1e-04
(Intr)
-0.919
4
Draft manuscript – do not circulate without permission
Model 1b: Phylogenetic distance (subset)
Formula:
lme(invasion.score ~ phylo.dist, random = ~1 | Treatment, data = peyn.sub,
na.action="na.omit", method="REML")
Linear mixed-effects model fit by REML
Data: peyn.sub
AIC
BIC
150.1368 158.3786
logLik
-71.06839
Random effects:
Formula: ~1 | Treat.Lab
(Intercept)
StdDev:
1.720427
Residual
0.5850963
Fixed effects: invasion.score ~ phylo.dist
Value
Std.Error
DF
(Intercept) -11.26881
2.25340
48
phylo.dist
36.11857
12.13641
10
t-value
-5.000804
2.976050
p-value
0.0000
0.0139
Correlation:
(Intr)
phylo.dist -0.975
5
Draft manuscript – do not circulate without permission
Model 2a: Ecological distance (all species)
Formula:
lme(invasion.score ~ total.ecoDist, random = ~1 | Treatment, data=peyn,
na.action="na.omit", method="REML")
Linear mixed-effects model fit by REML
Data: peyn
AIC
BIC
logLik
496.3384 508.3272
-244.1692
Random effects:
Formula: ~1 | Treatment
(Intercept)
StdDev:
2.368495
Residual
0.8603868
Fixed effects: invasion.score ~ total.ecoDist
Value Std.Error
DF
(Intercept) -7.295414
1.8991703 120
total.ecoDist 1.038019
0.3731458 28
t-value
-3.841369
2.781805
p-value
0.0002
0.0096
Correlation:
(Intr)
total.ecoDist -0.973
6
Draft manuscript – do not circulate without permission
Model 2b: Ecological distance (subset)
Formula:
lme(invasion.score ~ total.ecoDist, random = ~1 | Treatment, data = peyn.sub,
na.action="na.omit", method="REML")
Linear mixed-effects model fit by REML
Data: peyn.sub
AIC
BIC
logLik
159.5330 167.7748
-75.76652
Random effects:
Formula: ~1 | Treatment
(Intercept) Residual
StdDev: 2.029504 0.5850963
Fixed effects: invasion.score ~ total.ecoDist
Value
Std.Error
DF
(Intercept) 0.1060214
2.6042319 48
total.ecoDist -1.2536171 0.6573018 10
t-value
0.0407112
-1.9072170
p-value
0.9677
0.0856
Correlation:
(Intr)
total.ecoDist -0.974
7
Draft manuscript – do not circulate without permission
Supplemental Figure Legends
Fig S1. Effect of storage time on nectar sucrose concentration. Nectar from a single
aliquot was split into 0.2 ml tubes with 8ul of nectar and stored -80 C until %
sucrose concentration could be measured using a refractometer. Bars show mean
of three replicates +/- 1 standard deviation. Over one month nectar concentration
changed less than 0.5%.
Fig S2. Correlation between different measures of yeast density across all single
species treatments. Y-axis shows yeast density expressed as cells per ul measured
using hemocytometry and the X-axis shows yeast density expressed as colony
forming units (CFUs) measured by colony counts of yeast cultures. The regression
shows an ~1:1 relationship between the two measures (r2 = 0.76, slope = 1.02).
Colors indicate species identity of each datapoint.
Fig S3. Reconstructed phylogeny of the Saccharomycetales showing placement of
taxa used for competion experiments in this study. The phylogeny was built using
sequence data from the large subunit of the nuclear ribosomal RNA operon.
Sequences are primarily derived from an environmental study of nectar yeasts (24)
and a recent phylogeny of the Saccharomycetales (38). Sequences were aligned
using the program MAFFT (39) and phylogeny estimated using maximum likelihood
with the program PhyML (40). Numbers correspond to the 12 major yeast clades
identified by Suh et al. (2006).
8
Draft manuscript – do not circulate without permission
Fig S4. Density of colony forming units (CFUs) for six species of yeast across
competitive treatments. Bars show logarithmic mean of 5 replicates +/- one
standard error. Dashed line shows initial inoculation density and black bars show
final CFU densities when the focal species was introduced on Day 0 and grey bars
when introduced on Day 2. Treatment labels show the identity of the competitor
species. Results of two way ANOVA are shown in the upper left, O = Order, S =
Species Identity, O x S = Interaction between Order and Species main effects
9
Draft manuscript – do not circulate without permission
Figure S1. Effect of storage time on nectar sucrose concentration
10
Draft manuscript – do not circulate without permission
Figure S2. Correlation between CFU and cell density across yeast species
11
Draft manuscript – do not circulate without permission
Figure S3: Phylogenetic placement of taxa from this study.
12
Draft manuscript – do not circulate without permission
Figure S4: CFU density for six species of yeast across competitive treatments
13