deletions | additions       diff --git a/Introduction.tex b/Introduction.tex  index 391066b..87c28b1 100644  --- a/Introduction.tex  +++ b/Introduction.tex 
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how resource amendments influence planktonic communities has the potential to  influence the formation of microbial biofilms during community assembly.   % Fakesubsubsection  In a crude sense, biofilm and planktonic microbial communities can be broken  into two key groups: phototrophic eukaryotes (hereafter algae) and  heterotrophic bacteria and archaea. This dichotomy, while somewhat artificial, 
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across ecosystems of varying trophic state \citep{Cotner_2002}. Heterotrophic  bacteria meet some to all of their organic carbon (C) requirements from algal  produced C while simultaneously competing with algae for limiting nutrients  such as phosphorous (P). The presence of external C inputs, such as terrigenous C leaching from the watershed \citep{Jansson_2008, Karlsson_2012} or C exudates derived from macrophytes \citep{Stets_2008, Stets_2008b}, can alleviate bacterioplankton reliance on algal derived C and shift the relationship from commensal and competitive to strictly competitive \citep[see][Figure~\ref{fig:conceptual}]{Stets_2008}. Under this mechanism  increased C supply should increase the resource space available to the bacteria and lead to increased competition for P, decreasing P available for algae {\textendash} assuming that bacteria are superior competitors for P as has been observed \citep[see][Figure~\ref{fig:conceptual}]{COTNER_1992}. These dynamics should result in the increase in bacterial biomass relative to the algal biomass along a gradient of increasing labile C inputs. % Fakesubsubsection  While these gross level dynamics have been discussed conceptually  \citep{Cotner_2002} and to some extent demonstrated empirically  \citep{Stets_2008}, the effect that these shifts in the bulk biomass pool have  diff --git a/Results.tex b/Results.tex  index 8351617..bd521c4 100644  --- a/Results.tex  +++ b/Results.tex 
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the experiment.   This increase in DOC in the higher C:P treatments was associated with decreases  in planktonic Chl \textit{a} in each treament treatment  (Figure~\ref{fig:pool_size}a), however there was no significant difference in biofilm Chl \textit{a} among  treatments (Figure~\ref{fig:pool_size}b). In combination with the decrease in  planktonic Chl \textit{a} on the 6th day of the experiment the highest C:P 
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communities to be more sharply skewed in both the algal and bacterial datasets  (Figure~\ref{fig:rank_abund_shape}).   To investigate differences in the community's strucure structure  and membership between the bacterial biofilm and overlying planktonic communities we identified thethe  most disproportinately disproportionately  enriched OTUs in biofilm compared to the planktonic communities and vice versa. When relative mean of OTU abundance were calculated  between plantonic planktonic  versus biofilm lifestyles the most enriched OTUs were consistently in planktonic samples (with respect to biofilm)  (Figure~\ref{fig:l2fc}). This is consistent with the higher alpha diversity in  biofilm communities compared to planktonic communities and evidence that  sequence counts were spread across a greater diversty diversity  of taxa in the biofilm libraries compared to the planktonic libraries (i.e. biofilm communities had  higher evenness than planktonic communities). Of the top five enriched bacterial OTUs  between the two lifestyles (biofilm or plankton), one is annotated as \textit{Bacteroidetes}, two  \textit{Gammaproteobacteria}, one \textit{Betaproteobacteria} and one  \textit{Alphaproteobacteria} and all five were enriched in the planktonic  liraries libraries  relative to biofilm (Table~\ref{Tab:01}). Of the top 25 enriched OTUs among lifestyles only five bacterial OTU centroid sequences shared high  sequence identity (\textgreater= 97\%) with cultured isolates (Table~\ref{Tab:01}).  
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the top 25 OTUs were enriched in the biofilm and 16 were enriched in the  planktonic samples. Eight of these 9 biofilm enriched OTUs were  \textit{Stramenopiles} of class \textit{Bacillarophyta}, the remaining OTU was  classified as a member of the \textit{Rhodophyta}. The 16 plantonic planktonic  enriched OTUs (above) were distributed into the \textit{Viridiplantae} (5 OTUs),  \textit{Cryptophyta} (4 OTUs), \textit{Haptophyceae} (4 OTUs), and  \textit{Stramenopiles} (3 OTUs). Similar to differences among bacterial 
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the bacterial and algal libraries (p-value 0.006 and 0.001, respectively). The  lifestyle category represents 18\% and 36\% of variance for pairwise sample  distances in bacterial and algal libraries, respectively. The Adonis result is  also consistent with lifestyle (biofilm versus plantonic) planktonic)  clustering along the first principal component for the algal libraries but not for the bacterial  libraries (Figure~\ref{fig:pcoa}).  \subsubsection{Community membership high C}  Although community membership was predominately driven by lifestyle we also  innvestigated investigated  how resource amendments affected community membership and structure. To do this we calculated differential abundance values for OTUs  between a high C and low C sample class. Because the abiotic (e.g. DOC) and all  biomass indicators (e.g. biomass pool size) were only significantly different  diff --git a/Sequence_Quality_Control_and_Analysis.tex b/Sequence_Quality_Control_and_Analysis.tex  index 22a24c7..7629e5a 100644  --- a/Sequence_Quality_Control_and_Analysis.tex  +++ b/Sequence_Quality_Control_and_Analysis.tex 
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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 {\textquotedblleft}denovo{\textquotedblright} ''denovo''  mode \citep{21700674} via the Mothur UChime wrapper. \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" ''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{Clustering}  Reads were clustered into OTUs following the UParse pipeline. Specifically 
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statistically different from its proportion mean in another. This differential abundance  could mark an enrichment of the OTU in either sample class and the direction of  the enrichment is apparent in the sign (positive or negative) of the metric  used to summarize the proportion mean difference. Here we use log2 log$_{2}$  of the proportion mean ratio (means are derived from OTU proportions for all samples  in each given class) as our differential abundance metric. It is also important  to note that the DESeq2 R package we are using to calculate the differential