Bioinformatics and analysis
Demultiplexing resulted in 1,536 individual fastq files (192 samples
across four cycle treatments with forward and reverse reads). We used
zgrep in bash to identify sequence motifs unique to each of the four
species, combining forward and reverse read counts (Supplemental
information).
We generated a standard curve by combining results from across the two
sets (Figure S2). As discussed, the critical number to determine
relative abundance in qPCR is the Ct value. Because qAMPseq data do not
directly yield amplicon counts at the end of every cycle, we did not
have direct knowledge of the exact shape of the PCR curve – the
important step in determining relative abundance. To determine the cycle
when samples crossed a Ct value required using a different approach:
first, we log-transformed read counts associated with each of the
four-cycle points for which we quantified amplicons. Log-transformation
of a PCR sinusoidal curve theoretically results in a linear relationship
of cycle and amplicon number. We took advantage of this by determining
the slope of the amplification line, i.e.\(\frac{\ log(amplicon\ count)}{\ \text{cycle\ number}}\). We set our Ct
number as log(10,000 reads), and used it in a simple equation to
determine the cycle that corresponded to Ct for each amplification curve
of each species in each sample. We henceforth call this number the cycle
count. If after 35 cycles a species in a sample had fewer than 10,000
reads, it was assigned a cycle count value of zero. We calculated cycle
numbers and performed all of the following analyses in R version 3.5.2
(R Core Team, 2018).
Ecologically speaking, our goals were to quantify the amount of eachClarkia species’ pollen in each pollen ball to determine if bees
(1) were inconstant pollen foragers (2) exhibited preference for certain
species of Clarkia and (3) used Clarkia species pollen in
ways that were not apparent from observations of floral visitation. We
also wanted to compare results yielded by the new approach, qAMPseq, to
the results from relative read abundance from the plateau phase of PCR.
To determine if bees were inconstant while foraging for pollen, we asked
which Clarkia species were present or absent in pollen balls
using both the qAMPseq method as well as the RRA method. Inconstant
pollinators will have more than one species in their pollen balls. In
qAMPseq, we determined presence/absence of Clarkia in our samples
by asking simply if the cycle values were nonzero (present) or zero
(absent) for each species of Clarkia . In contrast, RRA does not
yield a cycle count but instead yields relative reads after full
amplification (35 cycles). To determine presence/absence ofClarkia with RRA, we used three different sample proportion
cutoffs to determine the presence of a species in samples: 0%, where
presence was defined as any nonzero read count; and 5% and 10 %
cutoffs, where presence was defined as anything above 5% or 10%,
respectively. We counted the proportion of pollen balls that had more
than one species of Clarkia using our four different metrics (raw
relative read abundance (RRA), and relative read abundance with 5% and
10% sample proportion cutoffs (RRA5 and RRA10)).
To further understand constancy, we compared the number of species in
bee pollen balls to the number of flowering species where they were
captured. When we sampled bees in Clarkia communities, the
communities contained one to four species of flowering Clarkia .
If bees are completely inconstant, then we expect their pollen balls to
contain the same number of species as the communities they were captured
in. To test this, we tallied the number of bees caught in communities
with one, two, three, and four species of flowering, as well as the
number of Clarkia in each of their pollen balls. We ran a
Pearson’s Chi-squared test to determine if the proportion of samples
containing one to four species of Clarkia pollen matched the
proportion of bees caught in communities with one to four species of
flowering Clarkia. If bees are inconstant when pollen foraging,
these proportions would be the same, and the test would return a
non-significant result.
Preference for different Clarkia species was estimated as the
difference between the relative amount of a species’ pollen in a sample
and the relative amount of that species’ floral abundance in a surveyedClarkia community where the bee was captured (as in James 2020).
This measure of preference can only be calculated for communities with
more than one Clarkia species, because there is not an available
‘choice’ to make between plants in single-species communities; as such,
we only calculate preference using the pollinator visits/pollen balls
from communities with more than one Clarkia species. The
calculation yields a value between -1 and 1 for each Clarkiaspecies in each pollen ball. Negative values indicate avoidance,
positive values indicate preference, and values of zero indicate that
bees do not preferentially forage for any species.
We calculated preference using values generated by both RRA with a 0%
sample proportion cutoff and qAMPseq. To calculate preference using
qAMPseq values, we used the cycle count for a given species divided by
the sum of cycle counts in a sample to estimate relative abundance of
each species in each sample. We used a paired t-test to determine if
there was a significant difference in estimates of preference using
qAMPseq versus RRA. We then ran an ANOVA, using Tukey’s honest
significant difference test to determine if pollinator preference forClarkia species were significantly different, and t-tests to
determine if pollinator preferences were significantly different from
zero.
For a complete picture of pollen use by pollinators, we also
incorporated Clarkia abundance in communities and in pollen balls
into our analyses. First, we compared the average flowering abundance ofClarkia species to compare the amount of floral resources the
different Clarkia provided to pollinators when flowering. To do
so, we log-transformed all non-zero values of flowering abundance and
ran an ANOVA with Tukey’s honest significant difference test toClarkia species. There was one ‘zero’ value that we retained in
the analysis: the four-Clarkia community Kingsnake only had three
flowering Clarkia species during one sampling period, and as
such, the species with no open flowers (C. xantiana ) was assigned
a zero. The second way we incorporated abundance into our analyses was
to use the data we generated with the qAMPseq method. For each bee taxon
X Clarkia species combination, we summed the number of bees
carrying pollen from that Clarkia species, weighted by the
proportion the Clarkia species was represented in the pollen
ball. This weighted value tells us not only the presence/absence ofClarkia pollen on the bee, but the extent to which the bee
species used that particular pollen resource.
Finally, we compared pollinator visitation to Clarkia andClarkia pollen use by constructing two networks of plants and
pollinators: one network with observations of the Clarkia species
bees were caught on, and the other with the Clarkia pollen that
we identified in bees’ pollen balls. In the case of the visitation
network, the dataset consists of the number of times each pollinator was
caught visiting each of the Clarkia species. The pollen-use
dataset consists of proportions of Clarkia pollens in each
sample, rather than a single plant-pollinator connection or the
presence/absence of Clarkia species in a pollen ball. To address
this and build the dataset for the pollen network, we multiplied the
proportion of each Clarkia species in each pollen sample by 100,
and rounded to the nearest whole number.
We measure and compare networks’ network-level specialization, H2’, to
understand if pollinators use pollen in ways similar to their floral
visitation. We use H2’ because it is robust to differences in the number
of interactions (Blüthgen, Menzel & Blüthgen 2006). Values of network
specialization, H2’, are between 0 and 1, where higher H2’ values
indicate that a network is comprised of more specialized relationships
between plants and pollinators, and lower values indicate the network
has more generalized relationships. Network specialization should be the
same between the two networks if pollinators carry Clarkia pollen
at the same rates that they visit Clarkia . Bipartite networks
were built and H2’ was calculated using the package bipartite (Dorrman
et al. 2020).