\section{Materials and Methods} 
\subsubsection{Experimental Design} Test tube racks were placed in one smaller
(185 L, control) and 3 larger (370 L) flow-through mesocosms. All mesocosms
were fed directly with marine water from an inflow source in Great Bay, Woods
Hole, MA, approximately 200 m from the shore. Each mesocosm had an adjustable
flow rate that resulted in a residence time of approximately 12 h. Irregular
variation in inflow rate caused the actual mesocosm flow rate to vary around
the targeted flow rate throughout the day. However, regular monitoring ensured
that the entire volume of each system was flushed approximately two times per
day (i.e. maintained a residence time of $\sim$12 h). To provide a surface for
biofilm formation, we attached coverslips to glass slides using nail polish
and then attached each slide to the test tube racks using office-style binder
clips. Twice daily 10 mL of 37 mM KPO$_{4}$ and 1, 5 and 50 mL of 3.7 M glucose
were added to each of 3 mesocosms to achieve target C:P resource amendments of
10, 100 and 500. The goal of the resource amendments was to create
a gradient of labile carbon. The same amount of P was added to
each treated mesocosm to ensure that response to additions of C were not
inhibited by extreme P limitation. The control mesocosm did not receive any
C or P amendments.

\subsubsection{DOC and Chlorophyll Measurements} To assess the efficacy of the
C additions, we sampled each mesocosm twice daily during the first week of the
experiment to evaluate dissolved organic C (DOC) content. After the initiation
of the experiment we collected plankton on filters regularly to evaluate
planktonic Chl \textit{a} and bacterial abundance. Once it was clear that pool
size of each community had been altered (day 8) we filtered plankton onto 0.2
$\mu$m filters and harvested coverslips to assess bacterial and algal biofilm
community composition (16S and plastid 23S rDNA). In addition, all mesocosms
were analyzed for community composition a second time (day 17) to assess how
community composition of both the plankton and biofilm communities had changed
over time. Control samples were only analyzed for community composition on day
17.

Samples for dissolved organic C (DOC) analysis were collected in acid
washed 50 mL falcon tubes after filtration through a 0.2 $\mu$m polycarbonate
membrane filter (Millipore GTTP GTTP02500, Sigma Aldrich P9199) attached to
a 60 mL syringe. Syringes and filters were first flushed multiple times with
the control sample to prevent leaching of C from the syringe or the filter into
the sample. Samples were then frozen and analyzed for organic C
content with a Shimadzu 500 TOC analyzer \citep{Wetzel_2000}. Biomass of all
biofilm samples were measured by difference in pre-(without biofilm) and
post-(with biofilm) weighed GF/F filters after oven drying overnight at
60 $\,^{\circ}$C.

For Chl \textit{a} analysis, we collected plankton on GF/F filters (Whatman,
Sigma Aldrich Cat. \# Z242489) by filtering between 500 mL and 1L from the
water column of each mesocosm for each treatment. For biofilm samples, all
biofilm was gently removed from the complete area of each coverslip (3
coverslips for each treatment per sampling event) before being placed in a test
tube for extraction with 90-95\% acetone for $\sim$32 hours at -20 $\,^{\circ}$C
and analyzed immediately using a Turner 10-AU fluorometer \citep{Wetzel_2000}.

Bacterial abundance of the planktonic samples was analyzed using DAPI staining
and direct visualization on a Zeis Axio epifluorescence microscope after
the methods of \citet{Porter1980}. Briefly, 1-3 mL
of water was filtered from three separate water column samples through
a 0.2 $\mu$m black polycarbonate membrane filter and post stained with
a combination of DAPI and Citifluor mountant media (Ted Pella Redding, Ca)
to a final concentration of 1 $\mu$L mL$^{-1}$.  

\subsubsection{DNA extraction} For plankton, cells were collected by filtering
between 20 {\textendash} 30 mL of water onto a 0.2 $\mu$m pore-size
polycarbonate filter (Whatman Nucleopore 28417598, Sigma-Aldrich cat\#
WHA110656). For biofilm communities, biomass from the entire coverslip area of
three separate slides was collected and combined in an Eppendorf tube by gently
scraping the slip surface with an ethanol rinsed and flamed razor blade. DNA
from both the filter and the biofilm was extracted using a Mobio Power Soil DNA
isolation kit (MoBio Cat. \# 12888). 

\subsubsection{PCR} Samples were amplified for 454 sequencing using
a forward and reverse fusion primer.  The forward primer was constructed
with (5{'}-3{'}) the Roche A linker, an 8-10 bp barcode, and the forward
gene specific primer sequence. The reverse fusion primer was constructed
with (5{'}-3{'}) a biotin molecule, the Roche B linker and the reverse
gene specific primer sequence. The gene specific primer pair for bacterial
SSU rRNA genes was 27F/519R \citep{LANED.J.:1991}. The primer pair
p23SrV\_f1/p23SrV\_r1 was used to target 23S rRNA genes on plastid genomes
\citep{Sherwood_2007}. Amplifications were performed in 25 $\mu$L
reactions with Qiagen HotStar Taq master mix (Qiagen Inc, Valencia,
California), 1 $\mu$L of each 5 uM primer, and 1 $\mu$L of template.
Reactions were performed on ABI Veriti thermocyclers (Applied Biosytems,
Carlsbad, California) under the following thermal profile: 95 $^{\circ}$C
for 5 min, then 35 cycles of 94$^{\circ}$C for 30 sec, 54$^{\circ}$C for
40 sec, 72$^{\circ}$C for 1 min, followed by one cycle of 72$^{\circ}$C
for 10 min and 4 $^{\circ}$C hold. Amplification products were
visualized with eGels (Life Technologies, Grand Island, New York).
Products were then pooled equimolar and each pool was cleaned with
Diffinity RapidTip (Diffinity Genomics, West Henrietta, New York), and
size selected using Agencourt AMPure XP (BeckmanCoulter, Indianapolis,
Indiana) following Roche 454 protocols (454 Life Sciences, Branford,
Connecticut). Size selected pools were then quantified and 150 ng of DNA
was hybridized to Dynabeads M-270 (Life Technologies) to create single
stranded DNA following Roche 454 protocols (454 Life Sciences). Single
stranded DNA was diluted and used in emPCR reactions, which were performed
and subsequently enriched. Sequencing followed established manufacture
protocols (454 Life Sciences).  

\subsubsection{Sequence quality control} The 16S rRNA gene and plastid 23S
rRNA gene sequence collections were demultiplexed and sequences with
sample barcodes not matching expected barcodes were discarded. We used the
maximum expected error metric \citep{23955772} calculated from sequence
quality scores to cull poor quality sequences from the dataset.
Specifically, we discarded any sequence with a maximum expected error
count greater than 1 after truncating the sequence to 175 nt. The forward
primer and barcode was trimmed from the remaining reads. We checked that
all primer trimmed, error screened, and truncated sequences were derived
from the targeted region of the LSU or SSU rRNA gene (23S and 16S
sequences, respectively) by aligning the reads to Silva LSU or SSU rRNA
gene alignment (``Ref'' collection, release 115) with the Mothur
\citep{19801464} NAST-algorithm \citep{16845035} aligner and inspecting
the alignment coordinates. Reads falling outside the expected alignment
coordinates were culled from the dataset. Remaining reads were trimmed to
consistent alignment coordinates such that all reads began and ended at
the same position in the SSU rRNA gene and screened for chimeras with
UChime in ``denovo'' mode \citep{21700674} via the Mothur UChime wrapper.
19,978 of 56,322 16S rRNA gene sequencing reads and 44,719 or 78,695
plastid 23S rRNA gene sequencing reads passed quality control.

\subsubsection{Taxonomic annotations} Sequences were taxonomically classified
using the UClust \citep{20709691} based classifier in the QIIME package
\citep{20383131} with the Greengenes database and taxonomic nomenclature
(version ''gg\_13\_5'' provided by QIIME developers, 97\% OTU representative
sequences and corresponding taxonomic annotations, \citep{22134646}) for
16S reads or the Silva LSU database (``Ref'' set, version 115, EMBL
taxonomic annotations, \citep{23193283}) for the 23S reads as reference.
We used the default parameters for the algorithm (i.e. minimum consensus
of 51\% at any rank, minimum sequence identity for hits at 90\% and the
maximum accepted hits value was set to 3).

\subsubsection{Sequence clustering} Reads were clustered into OTUs following
the UParse pipeline. Specifically USearch (version 7.0.1001) was used to
establish cluster centroids at a 97\% sequence identity level from the
quality controlled data and to map quality controlled reads to the
centroids. The initial centroid establishment algorithm incorporates
a quality control step wherein potentially chimeric reads are not allowed
to become cluster seeds. Additionally, we discarded singleton reads.
Eighty-eight and 98\% of quality controlled reads could be mapped back to
our cluster seeds at a 97\% identity cutoff for the 16S and 23S sequences,
respectively. 

\subsubsection{Alpha and Beta diversity analyses} Alpha diversity calculations
were made using PyCogent Python bioinformatics modules \citep{17708774}. 
Rarefaction curves show average OTU counts from 25 re-samplings at 
intervals of 10 sequences for each sample. Beta
diversity analyses were made using Phyloseq \citep{24699258} and its
dependencies \citep{vegan}. A sparsity threshold of 25\% was used for
ordination of both plastid 23S and bacterial 16S libraries.  Additionally, we
discarded any OTUs from the plastid 23S rRNA gene data that could not be
annotated as belonging in the Eukaryota or cyanobacteria for differential
abundance, ordination and Adonis analyses. Cyanobacterial DNA sequences were
removed from 16S rRNA gene sequence collections for ordination, Adonis and
differential abundance analyses. We operated under the assumption that
non-cyanobacterial bacteria are predominantly heterotrophs in our mesocosm
setup and refer to non-cyanobacterial bacteria as
``heterotrophs'' in the manuscript (this abstraction is useful however there
are likely exceptions -- i.e. autotrophs among the non-cyanobacterial
bacteria). All DNA sequence based results were visualized using GGPlot2
\citep{Wickham_2009}. Adonis tests and principal coordinate ordinations were
performed using the Bray-Curtis similarity measure for pairwise library
comparisons. Adonis tests employed the default value for number of permutations
(999) ("adonis" function in Vegan R package, \citet{vegan}). Principal
coordinates of OTUs were found by averaging site principal coordinate values
for each OTU with OTU relative abundance values (within sites) as weights. The
principal coordinate OTU weighted averages were then expanded to match the
site-wise variances \citep{vegan}.

\subsubsection{Identifying enriched OTUs} We used an RNA-Seq differential
expression statistical framework to find OTUs enriched in the given sample
classes (R package DESeq2 developed by \citet{Love_2014}) (for review of
RNA-Seq differential expression statistics applied to microbiome OTU count data
see \citet{24699258}). We use the term differential abundance coined by
\citet{24699258} to denote OTUs that have different relative abundance across
sample classes. We were particularly interested in two sample classes: 1)
lifestyle (biofilm or planktonic) and, 2) high C (C:P = 500) versus not
high C (C:P = 10, C:P = 100 and C:P = control). A differentially abundant
OTU is enriched on one side of a sample class (e.g. enriched in the
biofilm versus the plankton). Differential abundance could mark an
enrichment of the OTU towards either side of the sample class and the
direction of the enrichment is apparent in the sign (positive or negative)
of the enrichment. Differential abundance was moderated (see
\citet{Love_2014}) such that the fold change OTU of an OTU across two
categories of a sample class can be used to rank the enrichment of OTUs
that span a wide range of base abundance. The DESeq2 RNA-Seq statistical
framework has been shown to improve power and specificity when identifying
differentially abundant OTUs across sample classes in microbiome
experiments \citet{24699258}.

The specific DESeq2 \citep{Love_2014} parameters we used were as
follows: All dispersion estimates from DESeq2 were calculated using a local fit
for mean-dispersion. Native DESeq2 independent filtering was disabled in
favor of explicit independent filtering by sparsity. The sparsity
thresholds that produced the maximum number of OTUs with adjusted p-values
for differential abundance below a false discovery rate of 10\% were
selected. Cook's distance filtering was also disabled
when calculating p-values with DESeq2. We used the Benjamini-Hochberg
method to adjust p-values for multiple testing \citep{citeulike:1042553}.
Identical DESeq2 methods were used to assess enriched OTUs from relative
abundances grouped into high C (C:P = 500) or low C (C:P $<$ 500 and
control) categories.

IPython Notebooks with computational methods used to create all figures and
tables as well as taking raw sequences through quality control preprocessing
are provided at the following URL:

\url{https://github.com/chuckpr/BvP_manuscript_figures}.

Version information for all R libraries is provided at the end of each 
IPython Notebook.
