Magnimaribacterales marine bacteria genetically partition across the nearshore to open-ocean in the tropical Pacific Ocean

Magnimaribacterales marine bacteria genetically partition across the nearshore to open-ocean in the tropical Pacific Ocean | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Magnimaribacterales marine bacteria genetically partition across the nearshore to open-ocean in the tropical Pacific Ocean Oscar Ramfelt , Sarah J. Tucker , Kelle C. Freel , View ORCID Profile A. Murat Eren , View ORCID Profile Michael S. Rappé doi: https://doi.org/10.1101/2025.06.17.660167 Oscar Ramfelt 1 Department of Oceanography, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa , Honolulu, Hawai‘i, United States 2 Hawai‘i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa , Kāneʻohe, Hawai‘i, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah J. Tucker 2 Hawai‘i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa , Kāneʻohe, Hawai‘i, United States 3 Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory , Woods Hole, MA 02543, USA 4 Helmholtz Institute for Functional Marine Biodiversity , 26129 Oldenburg, Germany 5 Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research , 27570 Bremerhaven, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kelle C. Freel 2 Hawai‘i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa , Kāneʻohe, Hawai‘i, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site A. Murat Eren 3 Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory , Woods Hole, MA 02543, USA 4 Helmholtz Institute for Functional Marine Biodiversity , 26129 Oldenburg, Germany 5 Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research , 27570 Bremerhaven, Germany 6 Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg , 26129 Oldenburg, Germany 7 Max Planck Institute for Marine Microbiology , 28359 Bremen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for A. Murat Eren Michael S. Rappé 2 Hawai‘i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa , Kāneʻohe, Hawai‘i, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael S. Rappé For correspondence: rappe{at}hawaii.edu Abstract Full Text Info/History Metrics Preview PDF Abstract The bacterial order Magnimaribacterales , previously known as the SAR86 lineage, is among the most abundant groups of planktonic bacteria inhabiting the global surface ocean. Despite their prevalence, our understanding of how this genetically diverse lineage partitions into units with coherent ecology and evolution remains limited. Here we surveyed multiple stations in the tropical Pacific Ocean using shotgun metagenomes and 16S rRNA gene amplicons to resolve distinct habitat preferences for Magnimaribacterales lineages across nearshore, offshore, and open-ocean environments. The comprehensive collection of genomes that captured a large fraction of the known evolutionary breadth of Magnimaribacterales , revealed patterns of ecotypic differentiation manifested primarily among genus-level clusters with specific clear preferences for distinct marine habitats. Enrichment analyses identified several functional genes associated with genomes from genera abundant in the nearshore environment, including those associated with sugar metabolism, peptide transport, and glycerophospholipid biosynthesis. Such metabolic adaptations likely facilitate the predominance of specific Magnimaribacterales genera in nearshore environments, promoting ecological partitioning across marine habitats. Importance Understanding the nature by which abundant, genetically-diverse planktonic marine bacteria organize into evolutionarily related and functionally coherent units remains an important question for scientists interested in the ecology of the global ocean. The bacterial order Magnimaribacterales (formerly SAR86) is one of the most prevalent clades in the global surface ocean, yet its ecological differentiation remains poorly resolved. By integrating metagenomic and amplicon analyses across a nearshore-to-open-ocean gradient in the tropical Pacific, this study reveals distinct habitat preferences within this lineage. The study also highlights how differences in distribution appear are primarily found between genus-level groupings. Our findings also highlight key functional traits, including sugar metabolism and glycerophospholipid biosynthesis, that were associated with the partitioning of nearshore and open-ocean Magnimaribacterales . This work enhances our understanding of the evolutionary processes shaping the diversity of one of the ocean’s most abundant bacterial clades. Introduction The Magnimaribacterales , formerly known as the SAR86 clade ( 1 ), is an order-level lineage of marine bacteria within the Gammaproteobacteria that is ubiquitous within planktonic microbial communities of the global surface ocean ( 2 , 3 ). Similar to the abundant Pelagibacterales (SAR11) lineage of Alphaproteobacteria , cells of the Magnimaribacterales have long been assumed to play an important role in the global carbon cycle because of their high cellular abundance in seawater ( 2 , 4 ). However, despite sharing similar coarse features such as small cell size, small genomes and low GC content, analyses of gene repertoires for transporters and metabolic pathways reveal that Magnimaribacterales bacteria appear to avoid direct competition with co-occurring populations of Pelagibacterales by utilizing a distinct subset of the seawater organic carbon pool for heterotrophic growth. This includes a variety of fatty acid-containing molecules such as lipids, steroid-like polycyclic rings, and aromatic compounds ( 2 , 3 , 5 ). The Pelagibacterales , on the other hand, are known to generally degrade low-molecular weight compounds of the dissolved organic matter (DOM) pool that experience rapid turnover times ( 6 ). In the case of the Pelagibacterales , integrative ’omics approaches have yielded a range of ecological and evolutionary insights. These include evidence of genetic differentiation into distinct clusters across ocean depths, along with genetic changes likely supporting this divergence ( 7 – 9 ). Additional distinct patterns of differentiation have also been observed across broad oceanic provinces, where comparative analyses reveal genetic variation among geographically separated populations ( 10 , 11 ). Notably, differentiation is also apparent over finer spatial scales including between nearshore and open-ocean environments where putative functional differences between populations have been observed ( 12 , 13 ). Temporal differentiation has also been reported, including seasonal shifts among closely related clades at the Bermuda Atlantic Time-series Study (BATS) site, suggesting a dynamic response to seasonality in the open-ocean ( 14 , 15 ). In contrast, studies of Magnimaribacterales ecology have primarily relied on whole-genome analyses and phylogenetic markers like the 16S rRNA gene ( 3 , 5 , 16 – 18 ). However, the limited phylogenetic resolution of the 16S rRNA gene, combined with sparse spatiotemporal coverage of available metagenomes, has left much of the ecology of Magnimaribacterales and the functional determinants of its niche partitioning explored. For instance, while some studies have suggested genetic differences between coastal and open-ocean Magnimaribacterales based on 16S rRNA sequences, these comparisons remain limited in scope ( 16 , 19 ). Here we take advantage of the Kāneʻohe Bay Time-series (KByT), a long-term study established in 2017 along the windward coast of the Hawaiian island of Oʻahu, to explore how Magnimaribacterales populations genetically and functionally differentiate across a stark nearshore to open-ocean environmental gradient ( 12 , 20 ). KByT leverages easy access from nearshore to open-ocean waters in the tropical Pacific, serving as a natural laboratory to investigate the ecotypic differentiation of marine microorganisms ( 12 ). Sampled near-monthly, KByT spans a marine Synechococcus -dominated nearshore to Prochlorococcus -dominated offshore gradient ( 12 , 20 ). In addition, the proximity of KByT to Station ALOHA, located ∼144 km to the north of Kāneʻohe Bay, allows us to directly link our observations with those of the Hawaiʻi Ocean Time-series (HOT) program within the North Pacific Subtropical Gyre ( 21 , 22 ). Our findings are supported by leveraging extensive DNA sequence and environmental datasets from the surface ocean of both KByT and HOT, including 16S rRNA gene amplicons and shotgun metagenomes, providing a detailed view of how the Magnimaribacterales partitions across this gradient. Materials and Methods Sample description Seawater samples were collected as part of KByT as previously described ( 12 , 20 ). Briefly, starting in August 2017, approximately monthly samples were collected from a depth of two meters across ten stations spanning the nearshore environment within Kāneʻohe Bay on the island of Oʻahu, Hawaiʻi, to the offshore environment outside of the bay ( Figure 1 ). In addition to samples filtered for analyses of microbial nucleic acids and phytoplankton pigments, a variety of other measures used to characterize the environment were obtained (Table S1). Thirty-six metagenomes were sequenced from samples collected between 2017-2021 (Table S1; 20, 23). Download figure Open in new tab Figure 1. Map of Kāneʻohe Bay Time-series (KByT) sampling stations on the windward side of the Hawaiian island of Oʻahu. Background shading of station markers indicates environmental category (yellow - coastal; grey - transition; blue - offshore). The inset shows the location of Station ALOHA relative to Oʻahu. Previously published metagenomes from surface ocean (0 - 75 m) samples collected as part of the Hawaii Ocean Time-series (HOT) at Station ALOHA in the North Pacific Subtropical Gyre (NPSG) were also included in these analyses (Table S1; 24). In addition to metagenome sequence data, we also re-analyzed a previously published 16S rRNA gene amplicon time-series survey from KByT samples, also from a depth of 2 m, spanning August 2017 to June 2019 (BioProject Accession PRJNA706753; 12). Ribosomal RNA gene amplicon analysis Forward amplicon reads were imported into Qiime 2 v2022.11.1 ( 25 ), trimmed, and grouped into amplicon sequence variants (ASVs) using DADA2 v1.26.0 ( 26 ). ASVs found in only one sample or that had less than 10 reads across all samples in aggregate were excluded from further analyses. The taxonomy of each ASV was determined using the SILVA 99% OTUs full-length sequences dataset v138 ( 27 ) with the feature-classifier plugin ( 28 ). Features classified as part of the order “SAR86_clade” from the feature-classifier analysis were subsequently assumed to be a part of the Magnimaribacterales order. A phylogenetic tree was constructed with the Magnimaribacterales ASV sequences and 16S rRNA genes extracted from the Magnimaribacterales reference genomes used in this study (n=64). Sina v1.7.2 ( 29 ) was used to align the Magnimaribacterales sequences with the Silva SSU Ref NR 99 v138.1 dataset and subsequently used to infer a phylogeny using RAxML v8.2.12 ( 30 ) with the GTRGAMMA model, 100 bootstraps, and a sequence alignment of 245 nucleotides. This tree was used to infer the family-level taxonomic classifications of each ASV ( Figure S1 ). We replaced all ASV family classifications for f_SAR86_clade with the more recent family-level classifications from ( 3 ) using the “tools import” command in Qiime. These updated family classifications for Magnimaribacterales ASVs were used all downstream analyses with amplicons. Relative abundance estimates for each ASV were calculated by first filtering out any sequences classified within the domain Eukaryote, domain Unassigned, and the Order Chloroplast. Then, any remaining ASVs had their relative abundance calculated using the ‘feature-table relative-frequency’ command from Qiime 2. To calculate family-level relative abundances the ‘taxa collapse’ command was used with a ‘–p-level’ of 5. This collapsed table was then used as the input to ‘feature-table relative-frequency’. The filtered ASV table was used to estimate the cellular abundance of Magnimaribacterales by using the cellular abundances of non-chlorophyll containing cells presumed to be predominantly heterotrophic bacteria and archaea determined via flow cytometry from corresponding subsamples of the same seawater collection used for 16S rRNA gene amplicons (Table S1; 12). First, the dataset was further filtered by excluding any amplicon sequences related to the phylum Cyanobacteria from the previously filtered ASV table. The relative abundance of Magnimaribacterales within the reduced amplicon dataset was then multiplied by the cellular abundance of non-chlorophyll containing cells presumed to be predominantly heterotrophic bacteria and archaea ( 12 ). Metagenome assembly and binning Five different co-assemblies were used to construct metagenome assembled genomes (MAGs) from shotgun sequenced bacterioplankton samples collected from KByT (Table S1). Three co-assemblies used metagenomes from samples collected between 2017-2019 and were grouped into nearshore, transition, and offshore categories based on previous analyses (KByT_1_Nearshore, KByT_1_Transition, and KByT_1_Offshore in Table S1; 12). Metagenomes collected between 2020-2021 were assembled separately and were split into nearshore and offshore groups based on the location of collection and co-assembled separately (KByT_2_Nearshore and KByT_2_Offshore in Table S1). All metagenomes were quality filtered using ‘iu-filter-quality-minoche’ v2.12 (Table S2; 31). Quality filtered reads were assembled into contigs using MEGAHIT v1.2.9 ( 32 ), and contigs below 2500 base pairs in length were filtered out using ‘anvi-script-reformat-fasta’ v7.1 ( 33 ). Reads were mapped back to the filtered contigs using Bowtie2 (v2.3.5) ( 34 ) and were sorted and indexed using SAMTools v1.9 ( 35 ). The filtered contigs and mapping results were used as input to create genome bins using the programs MaxBin v2.2.7 ( 36 ), MetaBAT v2.15 ( 37 ), and CONCOCT v1.1.0 ( 38 ). The program DAS Tool v1.1.4 ( 39 ) was then used to parse the results of the binning approaches, resulting in the bins used for downstream analyses. Following binning, filtered contigs were used to create anvi’o contigs databases, and later converted to anvi’o profiles using ‘anvi-profile’. Coassemblies with multiple individual profiles were merged using ‘anvi-merge’. Bins identified from DAS Tool were imported into their respective profiles using ‘anvi-import-collection’. Dereplication of genomes was accomplished with dRep v3.3.0 with the -comp 50 option ( 39 ) and run twice; once with the 2017-2019 KByT genome bins as input and once with the 2020-2021 KByT genome bins as input. Genome bins were identified as Magnimaribacterales (order “SAR86” in GTDB-Tk) from the de-replicated genomes using the ‘classify_wf’ command of GTDB-Tk v1.7.0 ( 40 ) and were manually refined using the command ‘anvi-refine’ ( 41 ). Post-refinement, the Magnimaribacterales genome bins were exported and quality checked using ‘taxonomy_wf’ within Checkm v1.2.0 ( 42 ) and a Magnimaribacterales -specific marker gene set ( 3 ). MAGs with a completeness 7.5% were excluded from downstream analyses. Phylogenomics A phylogenomic tree was constructed from an alignment of the Magnimaribacterales MAGs generated in this study with an exhaustive database of representative Magnimaribacterales genomes, including the genome of type strain Magnimaribacter mokuoloeensis str. HIMB1674 ( 3 ). The commands ‘identify’ and ‘align’ in GTDB-Tk v1.7.0 ( 40 ) were used to identify GTDB marker genes and create a multiple sequence alignment (MSA). Briefly, the ‘identify’ command used Prodigal v2.6.3 ( 43 ) to call genes and then HMMER v3.1b2 ( 44 ) identified the marker genes from the BAC120 gene set used by GTDB ( 45 ). The ‘align’ command used the output from ‘identify’ as input to create the MSA, which was then used as input by IQ-Tree v2.1.2 ( 46 ) for a maximum likelihood phylogenomic analysis using the LG + F + R7 model, which has been used previously for Magnimaribacterales phylogenomic analyses ( 3 ), and 1000 ultrafast bootstrap replicates (UFBoot) ( 47 ). A previously constructed taxonomic framework for the Magnimaribacterales that includes family and genus designations ( 3 ) was used as a scaffold from which to infer the taxonomy of new MAGs generated as part of this study. Visualizations of the phylogenetic tree alongside external data were constructed with ggtree v3.6.2 ( 48 ). Average nucleotide identity (ANI) was calculated for all genome pairs using fastANI v1.32 ( 49 ), and subsequently used to group newly constructed MAGs into species clusters using previously described methods ( 50 ) and an ANI value of 93% previously used for the Magnimaribacterales ( 3 ). Representative genomes were selected from each species cluster using the following approach. If the species cluster contained a genome classified in ( 3 ) then that genome was used. If the species cluster consisted only of new MAGs assembled as part of this study then the MAG with the highest completion metric was used. This approach established 81 representative genomes that were used for read recruitment (“Representative” column in Table S3). Metagenomic read recruitment The program Bowtie2 was used to map sequence reads from quality filtered KByT and Station ALOHA metagenomes to the representative genome set. SAMtools was used to sort and index the output sam files. Mapping metrics were calculated by importing bam files into anvi’o profiles using ‘anvi-profile’ and merged into a single profile with ‘anvi-merge’. Once merged, metrics for the merged profiles were exported using ‘anvi-summarize’. The detection metric from anvi’o, which represents the proportion of a genome covered by reads, was used to help identify the presence of Magnimaribacterales genomes within metagenomes. Based on previous studies that have found detection values to follow a bimodal distribution ( 51 ), a detection value of 0.25 or greater was selected in line with other studies to identify the presence of a genome and avoiding false-positive signals ( 52 ). For any metagenomes where a genome had a detection level below the cutoff, its relative abundance in that metagenome was modified to zero. This was done to accurately reflect the absence of genomes from metagenomes where less than 25% of the genome recruited reads. This gave a matrix of presence/absence for each representative genome in each metagenome used in this study. Using the matrix of the presence of each representative genome in each metagenome, it we then sought to identify the prevalence of each genome within the wider environments of nearshore, offshore, and open-ocean (Station ALOHA). The presence of a genome in an environment was measured as the proportion of metagenomes from each environmental grouping that the genome was found to be present in. As an example, if a genome was present in 4 out of 10 metagenomes from the nearshore environment it would be present in 40% of metagenomes. To determine a suitable cutoff for whether a genome was present in an environment the ‘geom_density’ function within ggplot v3.4.2 ( 53 ) was used to make a smooth histogram for each environment. This showed that the offshore and open-ocean environment followed a bimodal distribution pattern with most genomes present in almost all metagenomes (near a 100%) or almost none (a value near 0%) in these two environments ( Figure S2 ). In contrast, the nearshore environment, while displaying two peaks at 0% and 100%, showed two more subtle peaks at just over 75% and at around 60% ( Figure S2 ). Based on these peaks the value of 50% was selected for a genome to be present in any environment meaning that if a genome was found in more than half of all metagenomes from an environment it would be considered present in that environment. Metagenomes originating from the transition environment of KByT were excluded from this analysis. Pangenomic and enrichment analysis A pangenome was constructed from the Magnimaribacterales genome dataset using the anvi’o pangenomic pipeline ( 54 ). Anvi’o v8 ( 33 ) contig databases were generated for each genome using ‘anvi-gen-contigs-database’, which identified genes using Prodigal v2.6.3 ( 43 ). Genes were then annotated using clusters of orthologous genes (COGs) ( 55 ) and Kyoto’s Encyclopedia of Genes and Genome ( 56 ) via the commands ‘anvi-run-ncbi-cogs’ and ‘anvi-run-kegg-kofams’, respectively. Furthermore, EGGNOG annotations (database: v5.0.2) ( 57 ) were also determined by using the identified genes as input for the ‘emapper.py’ program v2.1.6 ( 58 ). These annotations were then imported to their respective contig databases using the ‘anvi-script-run-eggnog-mapper’ function of anvi’o. A genome-storage database was subsequently created using ‘anvi-gen-genome-storage’ and then the pangenomic analysis was run using ‘anvi-pan-genome’ with a mcl inflation value of 10 and a minbit value of 0.5. The ‘anvi-pan-genome’ command used DIAMOND v2.1.8 ( 59 ) to compare amino acid sequences between all genes found within the input genomes, and the Basic Local ALignment Search Tool (BLAST) v2.14.1 ( 60 ) from National Center for Biotechnology Information (NCBI) to identify gene clusters based on minbit values. The results of the pangenomic analysis were exported into a tabular format using ‘anvi-summarize’ for manual inspection. To help identify functional enrichment among closely related genomes, the ‘anvi-compute-functional-enrichment-across-genomes’ pipeline was used ( 61 ). The pipeline was run twice for the Magnimaribacterales order using genus classifications as the grouping variable. The first run used KOfam annotations while the second run used annotations from COG. The results of the enrichment analysis were used to help identify differential gene distribution across environments. Results Suzuki family dominates the relative abundance of Magnimaribacterales 16S rRNA gene amplicons across KByT Ribosomal RNA gene amplicon data showed that Magnimaribacterales constituted 2.0% to 4.3% of all amplicons, excluding those associated with Eukaryotes, chloroplasts, or unassigned sequences, across the three environments sampled through KByT ( Figure 2A , Table S4). The Suzuki family was the most abundant of the four family-level lineages across all three environments, ranging from an average of 3.0% in the nearshore environment to 1.3% in the transition environment ( Figure 2B , Table S4). Changes in cellular abundances were more pronounced, ranging from an average of 7.02 x10 4 to 1.39 x10 4 cells mL -1 in the nearshore and transition zones, respectively ( Figure S3 , Table S4). In addition to Suzuki, the CHAB-I-7 family also peaked in both average relative abundance and estimated absolute cellular abundance within the nearshore environment ( Figure 2B , Figure S3 , Table S4). While the RedeBAC7D11 and Magnimaribacteraceae families peaked in average relative abundance in the offshore environment ( Figure 2B ), the estimated absolute cellular abundance of Magnimaribacteraceae was actually higher in the nearshore environment compared to the offshore ( Figure S3 , Table S4). Download figure Open in new tab Figure 2. Relative abundance of (A) the order Magnimaribacterales , (B) families within the Magnimaribacterales , and (C) ASVs within the Magnimaribacterales across KByT, based on 16S rRNA gene amplicons. All ASVs that did not exceed 1% relative abundance are grouped as “other”. For (A) and (B), the line in the box displays the median value, hinges indicate the 25th and 75th quartiles, and whiskers indicate the lowest and largest value within a range of 1.5x the interquartile range. Dots indicate outliers. Of 168 ASVs identified as part of the Magnimaribacterales order ( Figure S1 ), only 38 were found in more than one sample and had more than 10 reads associated with them (Table S5). Of the 38, five were frequently responsible for a majority of the Magnimaribacterales relative abundance, especially in the nearshore environment ( Figure S4 ). These include three Suzuki, one CHAB-I-7, and one RedeBAC7D11 family ASVs (Table S5). The five abundant ASVs were not uniformly distributed across the KByT study area ( Figure 2C , Figure S4 , Table S5). Over the two years of this amplicon dataset, ASV10 and ASV72 from Suzuki and ASV84 from CHAB-I-7 were more abundant in the nearshore environment compared to the offshore, while ASV76 from Suzuki and ASV117 from RedeBAC7D11 were more abundant in the offshore environment ( Figure 2C , Figure S4 , Table S5). One particular ASV from the Suzuki family (ASV10) dominated the nearshore Magnimaribacterales community, peaking at over 8% average relative abundance among nearshore samples collected in February 2019 ( Figure 2C ). ASV10 was largely responsible for the temporal variability in relative abundance of Magnimaribacterales among nearshore samples ( Figure S4 ). Magnimaribacterales read recruitment across KByT and the surrounding open-ocean is dominated by genomes of the family Suzuki Nine of 12 initial Magnimaribacterales KByT MAGs passed quality control (>50% completion and <7.5% contamination; Table S6) and were included in a phylogenomic analysis with a previously assembled database of global Magnimaribacterales genomes ( 3 ). All nine MAGs branched within one of the previously delineated families and genera of this order ( Figure 3 , Table S6). Download figure Open in new tab Figure 3. Phylogenomics of the order Magnimaribacterales based on a concatenated alignment of 120 single copy core genes. Solid circles indicate bootstrap values greater than 95%, while open circles indicate values over 80%, from 1000 replicates. Four genomes from the gammaproteobacterial orders Burkholderiales and Pseudomonadales (GCF_000305785.2, GCF_003574215.1, GCF_003752585.1, GCF_006980785.1) were used as an outgroup. Within each family, genera are indicated with the prefix “G”. Filled symbols in the Nearshore, Offshore, and Open-Ocean columns indicate the presence of a genome within each environment based on read recruitment. Genomes without shapes were not recruited since they were too closely related to another genome. For metabolic genes, filled circles indicate the presence of a gene within each genome. Genes prefixed by ”c-” indicate the copy number of those genes for the respective genome. ED - Entner-Doudoroff; PG/CL – phosphatidylglycerol/cardiolipin; PE - phosphatidylethanolamine; PC - phosphatidylcholine. Consistent with 16S rRNA gene amplicon sequence data, metagenome read recruitment to Magnimaribacterales genomes revealed that the vast majority of reads recruited to members of the Suzuki family ( Figure 4A , Table S7). Relative to the Magnimaribacterales community, families RedeBAC7D11 and Magnimaribacteraceae increased in abundance in the offshore and open-ocean environments, with RedeBAC7D11 almost completely absent from the nearshore environment. In contrast, CHAB-I-7 genomes increased in abundance relative to the total Magnimaribacterales community in the nearshore environment ( Figure 4A , Figure S5 ) Download figure Open in new tab Figure 4. (A) Proportion of the four families to the total Magnimaribacterales read recruitment from metagenomes sequenced from KByT and Station ALOHA, (B) Heatmap indicating the proportion of each representative Magnimaribacterales genome to the total Magnimaribacterales read recruitment within each metagenome. A small subset of Magnimaribacterales genera were responsible for the majority of recruited reads. These genera largely originated from the Suzuki family, where five (G02, G05, G07, G09, and G10) were responsible for >75% of reads recruited to Suzuki and over half of the reads recruited to the Magnimaribacterales as a whole ( Figure 4 , Figure S5 ). These five Suzuki genera, two (G07 and G09) dominated the recruitment of metagenomes from the nearshore environment, recruiting an average of roughly half of all Magnimaribacterales reads ( Figure S5 ). Meanwhile, two other genera (G05 and G10) dominated the recruitment of offshore KByT and open-ocean Station ALOHA metagenomes that together recruited an average of over 40% of total reads ( Figure 4 , Figure S5 ). While the genomes within Suzuki genus G02 were most abundant in the nearshore, they did not significantly decline in relative abundance in the offshore to open-ocean environments ( Figure S5 , Table S8). Similar to the distribution of 16S rRNA gene amplicons, genomes within the RedeBAC7D11 family were almost entirely limited to offshore and open-ocean environments ( Figure 4 , Figure S5 ). However, one genus (G2) was most abundant in the offshore environment immediately outside of Kāneʻohe Bay and declined at open-ocean Station ALOHA, while two others (G3 and G4) were most abundant in the open-ocean environment ( Figure S5 , Table S8). Read recruitment to the CHAB-I-7 family revealed genera with distinct nearshore or offshore and open-ocean patterns of distribution. Within the Magnimaribacteraceae , members of the genus Magnimaribacter were the only genomes consistently detected across all environments, peaking in the open-ocean at Station ALOHA ( Figure 4 , Figure S5 , Table S8). While the closely related genomes within individual genus-level genome clusters usually followed a similar pattern of distribution, exceptions were evident. At the genus level, CHAB-I-7 genus G1, Suzuki genus G03, and the Magnimaribacter genus were broadly distributed across the nearshore to open-ocean environments ( Figure 4 , Figure S5 ). However, read recruitment to individual genomes within each of these genera revealed some genomes that were limited to the nearshore environment with closely related neighbour genomes limited to the offshore and open-ocean environments ( Figure 4 ). Read recruitment also revealed temporal differences between the closely related genomes kbyte_2_ns_01 and GCA_902559885 from Suzuki genus G07 ( Figure 4B ) with the former genome peaking early in the year, while the latter often peaking in September. Finally, read recruitment was used to categorize each of the 81 genomes as present or absent from each of the nearshore, offshore, and open-ocean environments ( Figure 3 ). Of 42 genomes categorized as “present” in at least one of the environments of this system, 34 were limited in distribution to either the nearshore or offshore and open-ocean environments, five were present across all environment types, and three were present only in the open-ocean environment ( Figure 3 , Table S3). The five genomes present in all three environments originated from the Suzuki family and primarily from a single genus, Suzuki G02. The Entner-Doudoroff pathway is associated with abundant nearshore Magnimaribacterales In general, all families of the Magnimaribacterales order contained a complete or near complete Embeden-Meyeroff-Parnas (EMP) pathway for glycolysis except for RedeBAC7D11, which consistently lacked a putative pyruvate kinase gene as has been previously described (Table S9; 3). In contrast to the wide distribution of the EMP pathway among Magnimaribacterales , we observed that the full complement of genes for the Entner-Doudoroff (ED) pathway for glycolysis ( zwf , pgl , edd , eda ) was restricted to genomes of a limited number of nearshore-associated lineages within the Suzuki family (G02, G03, G07, G09) ( Figure 3 , Figure 5 ). Download figure Open in new tab Figure 5. Presence of genes within (A) the Entner-Doudoroff (ED) pathway in genomes of the Suzuki family, and (B) glycerophospholipid biosynthesis pathways in genera of the Suzuki and RedeBAC7D11 families, as well as the families CHAB-I-7 and the Magnimaribacteraceae . Genes associated with the ED pathway are based in part on ( 62 ), while genes for the synthesis of glycerophospholipids are based on ( 66 ). Gene names are indicated in Table S9. Because the presence of the ED pathway is frequently associated with the metabolism of glucose and various sugar acids, Magnimaribacterales genomes were manually inspected for genes related to these functions. Three genomes from Suzuki genus G09 encoded a glucuronate isomerases ( uxaC ), with two of these genomes also encoding a putative 2-dehydro-3-deoxygluconokinase ( kdgk ). These genes function in a pathway that converts the sugar acid D-glucuronate into 2-keto-3-deoxy-6-phosphogluconate (KDPG) ( 62 ), which can be subsequently metabolized by the ED pathway ( Figure 5 ). Among the genomes encoding the ED pathway, two potential sources of D-glucuronate were identified: alginate and beta-D-glucuronosides. A small subset of genomes encode putative oligo-alginate lyases ( alg17C ) that are capable of converting alginate into sugar acids ( 63 ). Additionally, putative beta-galactosidase/beta-glucuronidases ( lacZ/uidA ) capable of converting beta-D-glucuronosides to D-glucuronate were also present in ED-associated Suzuki genomes ( Figure 3 ). However, three CHAB-I-7 genomes were also found to encode putative copies of this gene. As the CHAB-I-7 family entirely lacked the pathway ED genes, the putative role of this gene as a funnel into the ED pathway is uncertain. Differences associated with nitrogen metabolism distinguish closely related phylotypes within the RedeBAC7D11 family Since genera of the family RedeBAC7D11 differ in their distributions between offshore and open-ocean environments ( Figure 4 ), we next sought to compare the genomes of the four RedeBAC7D11 genera to identify functions associated with these distinct spatial patterns. Enrichment analysis revealed that two oligopeptide transport system genes, oppA and oppC , distinguished the two offshore RedeBAC7D11 genera (G1 and G2), from the two that peak in abundance in the open-ocean (G3 and G4; Figure 3 ). An expanded search for the other three opp related genes ( oppB , oppD , and oppF ; 64, 65) found no homologs in RedeBAC7D11 genomes, though oppB along with oppA and oppC were present in other Magnimaribacterales family genomes. Inspection of the genomic region surrounding the putative opp system genes oppA and oppC within the RedeBAC7D11 genera G1 and G2 revealed these genes to flank a putative peptide/nickel transport system permease gene (K02033). As RedeBAC7D11 genera G1 and G2 appeared to lack a oppB , this gene may support the functions carried out by oppB . To assess whether the absence of the opp system in RedeBAC7D11 G3 and G4 was not simply due to missing contigs, a comparative analysis was performed focusing on the genomic region containing the opp system in RedeBAC7D11 G1 and G2. A putative peptidoglycan lytic transglycosylase D ( mltD ) gene, located near the oppA and oppC genes, was identified as a consistent feature across all RedeBAC7D11 genomes, each encoding a single copy. This gene was subsequently used to anchor the region of interest. The resulting comparison revealed a homologous genomic segment across all four RedeBAC7D11 genera, displaying a high degree of concordance in both functional annotations and gene synteny ( Figure S6 ). The transport of oligopeptides suggested by the presence of the opp system in RedeBAC7D11 genera G1 and G2 was associated with differences in the gene repertoire responsible for ammonia transport. Most of the genomes within RedeBAC7D11 genus G2 contained two copies of the ammonia transporter amtB , while the RedeBAC7D11 genera G3 and G4 contained only a single copy ( Figure 3 , Table S9). Of two genomes within RedeBAC7D11 genus G1, one contained two amtB homologs while the other lacked genome sequence data that corresponds to this genomic region. In addition to two amtB homologs and the opp oligopeptide transport system, the genomes of RedeBAC7D11 genera G1 and G2 were also enriched in genes encoding for putative amidases (Table S9). While amidases are present across almost all RedeBAC7D11 genomes, the genera G1 and G2 encoded up to two and four homologs respectively, while RedeBAC7D11 genera G3 and G4 generally contain a single copy ( Figure 3 ). Pathways for glycerophospholipid biosynthesis distinguish the four Magnimaribacterales families The Suzuki, RedeBAC7D11, and Magnimaribacteraceae families broadly encode pathways necessary to biosynthesize the glycerophospholipids phosphatidylglycerol (PG) and cardiolipin (CL) ( Figure 5 , Table S9). In contrast, the CHAB-I-7 family appears to entirely lack the ability to synthesize glycerophospholipids de novo . Additionally, the family lacks many of the genes required to synthesize CDP-diacylglycerol, a key glycerophospholipid precursor ( Figure 3 ). Genes associated with the biosynthesis of a third glycerophospholipid, phosphatidylethanolamine (PE) ( 66 ), were more limited. These genes were present in the Magnimaribacteraceae family and a subset of genera within the Suzuki and RedeBAC7D11 families ( Figure 3 , Figure 5 , Table S9). The Suzuki genera capable of synthesizing PE broadly overlap with those that are abundant in the nearshore environment (genera G03, G06, G07, and G09; Figure 3 ). Within the RedeBAC7D11 family, the pathway for PE biosynthesis was limited to RedeBAC7D11 genus G2, that is more abundant in the offshore compared to the open-ocean environment ( Figure 3 , Figure S5 , Table S8). The gene, pcs , supporting the synthesis of a fourth glycerophospholipid, phosphatidylcholine (PC), were only present in genomes of the Magnimaribacteraceae family. Discussion Despite three decades of documenting the prevalence of Magnimaribacterales bacteria across the global surface ocean ( 1 – 3 , 5 , 67 ), the nature and extent of ecotypic differentiation within this genetically diverse order-level group remains poorly understood. Building upon our previous work that established a highly curated phylogenomic framework for the Magnimaribacterales ( 3 ), we leveraged environmental, genomic, and metagenomic data from the geographically constrained Kāneʻohe Bay Time-series (KByT) study system and adjacent Hawaiʻi Ocean Time-series (HOT) study site at Station ALOHA in the North Pacific Subtropical Gyre (NPSG) to uncover how ecotypic differentiation shapes the diversity of this globally abundant order. The results presented here show that Magnimaribacterales families, genera, and individual genomes partition across the nearshore to open-ocean waters of the tropical Pacific, with ecotypic differentiation emerging episodically throughout the evolutionary history of this lineage. Magnimaribacterales genomes present in this system are largely divided into nearshore and open-ocean specialists, with only a small number of generalist genomes present throughout the entire gradient. Putative functional traits of genomes reflected these spatial differences, as independent lineages occupying the same spatial distribution showed overlap in enriched genes. These findings underscore the importance of ecotypic differentiation in understanding the evolution and diversity of abundant marine bacteria like the Magnimaribacterales . The paraphyletic distribution of nearshore-associated genera across the Magnimaribacterales phylogeny, where these genera are interspersed with other differentially distributed genera, may be indicative of a reduced capacity for more closely related genera to coexist within this environment. Indeed, this pattern aligns with recent reports of overdispersion within other abundant marine such as Pelagibacterales ( 11 ), where lineages more distantly related to one another are more likely to be able to coexist ( 68 ). The drivers of the observed overdispersion remain unknown. However, two potential explanations include the competitive exclusion of closely related lineages, or the convergence of function among more distantly related lineages to a similar niche ( 69 ). Convergence on function is an unlikely explanation for the phylogenetic overdispersion examined here because, while this study identified several enriched genes, these genes were limited in distribution to only within the Suzuki family. Instead, the disparate distribution of the nearshore-associated lineages across the Magnimaribacterales coupled with the limited overlap in enriched functions across these lineages point towards competitive exclusion as a more likely driver of overdispersion for the broad scale patterns of Magnimaribacterales we observe within this system. The overlap in enriched functions among nearshore-associated Suzuki genera may provide evidence of how environmental drivers shape the community of Suzuki. The association between the nearshore environment and the suite of genes for the synthesis of different glycerophospholipids within Suzuki genomes suggests that these genes may offer some advantage in the nearshore environment and are thus selected for within nearshore-distributed Suzuki genomes. The capacity to vary the lipid composition of the cell has been associated with genome streamlining and the efficient allocation of limited phosphorus and nitrogen available to marine bacterial cells ( 70 – 72 ). The limited diversity of glycerophospholipids that open-ocean-associated genera from the Suzuki family appear able to produce would be indicative of this effect. Conversely, abilities of nearshore-associated Suzuki lineages to biosynthesize PE provide evidence of differentiation in response to an environment where the presence of PE-containing lipids provides a selective advantage. The presence of PE has been shown to play a key role in supporting the function of membrane proteins involved in sugar transport ( 73 ). Although this remains speculative, the co-occurrence of the ED pathway, specialized in sugar metabolism, and PE, which may facilitate efficient sugar transport, suggests a linkage between these pathways. These two sets of genes might therefore provide improved fitness in the nearshore environment. Furthermore, the absence of PE has been found in other bacteria to lead to greater sensitivity to osmotic and oxidative stress indicative of the diverse set of functions it influences ( 74 ). Assuming that Magnimaribacterales cells remain viable despite an inability to synthesize PE, as has been previous found for other bacteria ( 73 ), it does raise intriguing questions regarding the ability of marine bacteria to adapt to the absence of crucial glycerophospholipids like PE. However, the testing of hypotheses related to the presence and absence of PE would greatly benefit from cultivated isolates that could validate the putative functions of the enriched genes, and the impact of their gain or loss on the fitness of cells. Complete genomes from cultivated isolates would also potentially help understand the evolutionary origin of the enriched genes, be it ancestral to the Magnimaribacterales order or the Suzuki family or alternatively if they have were acquired through some form of gene acquisition. This study also provides ample evidence for the ecotypic differentiation of genomes at finer scales than the genome clusters we refer to as genera, indicating likely species-level discrimination within the Magnimaribacterales . The temporal variation observed within genera such as Suzuki genus G07, along with spatial differences within the genera Magnimaribacter and Suzuki G03, suggest that even subtle genomic distinctions have led to adaptations to specific spatial or temporal niches, differentiating them from closely related genomes. This partitioning of distributions among closely related genomes mirrors patterns seen in other abundant marine bacteria including Prochlorococcus ( 75 ) and the Pelagibacterales ( 11 ). A particularly striking example of spatial differentiation was observed in genus G2 of the RedeBAC7D11 family, where several closely related genomes each displayed distinctive patterns of distribution that were often dissimilar to that of their closest genome relative. Similar phenomena have been observed in other marine bacteria, such as comparative analyses of closely related genomes of Prochlorococcus and Pelagibacterales ( 13 , 54 , 76 ). For example, comparisons of Prochlorococcus and Pelagibacterales populations between the NPSG and the North Atlantic have revealed biogeographical differences among closely related populations that appear to be associated with only a small set of genes ( 76 ). These genes appear heavily driven by environmental variability, including phosphate availability. Other analyses comparing closely related Prochlorococcus genomes have found differentiating genes to be located within genomic islands ( 54 ). Additionally, recent investigations into Pelagibacterales have revealed similar ideas with only a small set of genes appearing to drive distribution differences ( 13 ). However, in this case the genes driving these patterns were instead associated within the genomic backbone. Our findings indicate that, similar to other abundant marine bacterial clades, even minimal genetic differentiation can drive the formation of distinct ecotypes within even closely related genome clusters of the order Magnimaribacterales . Coupled with recent studies that delineated ecotypes of Pelagibacterales that partition across the nearshore and offshore waters of KByT ( 11 – 13 ), our findings suggest that the evolutionary trajectories of oligotrophic marine bacteria are shaped, at least in part, by the nearshore-offshore environmental gradient and that environmental selection likely plays a key role in partitioning, maintaining, and generating diversity. The insights presented here highlight the value of leveraging time-series data spanning natural environmental gradients to investigate ecotypic differentiation and the evolution of marine bacteria. Incorporating both temporal and spatial dimensions of the data allow for a more comprehensive view of the evolution of Magnimaribacterales in this system and permits us to elucidate ecotypes using both time and space. However, we acknowledge that analyses examining a longer span of time would help further disentangle transient events and seasonal trends. Coupled with more cultivated isolates and complete genomes from these isolates would support the investigation of finer-scale patterns that may be observed. These efforts would help to further extend the understanding of the evolutionary forces that have driven the diversity of Magnimaribacterales observed here, and more generally how these drivers shape the diversity observed in marine microbial communities. Supplementary Table Legends Table S1. Summary of samples used in this study. Table S2. Results of quality filtering on metagenomes used in this study. Table S3. Relative abundance of Magnimaribacterales genomes across the metagenomes used in this study, as a proportion of the total Magnimaribacterales metagenome read recruitment. Table S4. Average, maximum and minimum relative abundances of Magnimaribacterales and associated families across nearshore, transition, and offshore environments of KByT, based on 16S rRNA gene amplicon analyses. Also included are estimated average cellular abundances for Magnimaribacterales families, including the standard deviation for each mean in parentheses. Table S5. Average relative abundance of Magnimaribacterales ASVs across KByT based on 16S rRNA gene amplicon analyses. ASV categories as in Figure 2C . Table S6. Characteristics of Magnimaribacterales MAGs generated in this study. Table S7. Average relative abundance of families within the Magnimaribacterales based on read recruitment to each metagenome used in this study. Relative abundances are as a proportion of total Magnimaribacterales metagenome read recruitment. Table S8. Average relative abundance of Magnimaribacterales genera as a proportion of the total Magnimaribacterales community across the 10 KByT stations and three different depths at Station ALOHA in the open-ocean (25, 45, and 75 m). Values within the brackets indicate maximum and minimum relative abundances. Table S9. Table of information regarding enriched and associated genes investigated in this study. Columns ending with average indicate the average number of gene copies found within that genus and columns ending with proportion indicate the proportion of genomes within each Magnimaribacterales genus that contained at least one copy of the associated gene. Supplementary Figures Download figure Open in new tab Figure S1. Phylogeny of 16S rRNA gene amplicon ASVs generated from KByT data and 16S rRNA genes from reference Magnimaribacterales genomes. The ribosomal RNA genes from four references (GCF_000305785.2, GCF_003574215.1, GCF_003752585.1, GCF_006980785.1) were used as an outgroup. Download figure Open in new tab Figure S2. Smoothed histogram showing the distribution of the proportion of metagenomes each genome was detected, separated based on, nearshore, offshore, and open-ocean environments. Download figure Open in new tab Figure S3. Boxplot showing the estimated cellular abundance of Magnimaribacterales families based on the relative abundance of Magnimaribacterales to the overall heterotrophic bacteria community and direct cell counts. Download figure Open in new tab Figure S4. (A) Bar plots of the relative abundance of Magnimaribacterales ASVs within each sample, and (B) Average relative abundance of ASVs at each KByT station. Download figure Open in new tab Figure S5. Average relative abundance as a proportion of total Magnimaribacterales metagenome read recruitment to genomes of all Magnimaribacterales genera across KByT and Station ALOHA in the nearby open-ocean. Download figure Open in new tab Figure S6. Phylogeny and associated genomic region surrounding the mltD gene in genomes from the RedeBAC7D11 family. Colored arrows indicate genes of interest, including putative: peptidoglycan lytic transglycosylase D ( mltD ), glucokinase ( glk ), and the two oligopeptide transport system genes oppA and oppC , and a peptide/nickel transport system permease related gene (K02033). Acknowledgements We thank J. Jones, C. Ratum, H. Mochimaru, A. Boettiger, A. Deck, J. Hunckler, C. Spotkaeff, C. Sullivan, E. Monaghan, E. Kiefl, J. Fuessel, and E. Freel for their assistance collecting samples, G. Robertson for assistance with sonde calibration, and K. Selph for flow cytometry measurements. This research was supported by funding from the National Science Foundation to MSR (grants OCE-1538628 and OCE-2149128), a Data Science Graduate Fellowship to OR (grant OAR-2118222 to G. Jacobs), a Denise B. Evans Fellowship to OR, and the Colonel Willys E. Lord, DVM & Sandina L. Lord Scholarship to OR, and the J. Watumull Merit Scholarship to OR. This is SOEST contribution xxx and HIMB contribution xxx. References 1. ↵ Mullins TD , Britschgi TB , Krest RL , Giovannoni SJ . 1995 . Genetic comparisons reveal the same unknown bacterial lineages in Atlantic and Pacific bacterioplankton communities . Limnology and Oceanography 40 : 148 – 158 . OpenUrl CrossRef 2. ↵ Dupont CL , Rusch DB , Yooseph S , Lombardo M-J , Alexander Richter R , Valas R , Novotny M , Yee-Greenbaum J , Selengut JD , Haft DH , Halpern AL , Lasken RS , Nealson K , Friedman R , Craig Venter J . 2012 . Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage . The ISME Journal 6 : 1186 – 1199 . OpenUrl CrossRef PubMed 3. ↵ Ramfelt O , Freel KC , Tucker SJ , Nigro OD , Rappé MS . 2024 . Isolate-anchored comparisons reveal evolutionary and functional differentiation across SAR86 marine bacteria . The ISME Journal 18 :wrae227. 4. ↵ Giovannoni SJ , Stingl U . 2005 . Molecular diversity and ecology of microbial plankton . 7057 . Nature 437 : 343 – 348 . OpenUrl CrossRef PubMed Web of Science 5. ↵ Roda-Garcia JJ , Haro-Moreno JM , Rodriguez-Valera F , Almagro-Moreno S , López-Pérez M . 2023 . Single-amplified genomes reveal most streamlined free-living marine bacteria . Environmental Microbiology 25 : 1136 – 1154 . OpenUrl CrossRef 6. ↵ Giovannoni SJ . 2017 . SAR11 Bacteria: The Most Abundant Plankton in the Oceans . Annual Review of Marine Science 9 : 231 – 255 . OpenUrl CrossRef PubMed 7. ↵ Field KG , Gordon D , Wright T , Rappé M , Urback E , Vergin K , Giovannoni SJ . 1997 . Diversity and depth-specific distribution of SAR11 cluster rRNA genes from marine planktonic bacteria . Applied and Environmental Microbiology 63 : 63 – 70 . OpenUrl Abstract / FREE Full Text 8. Thrash JC , Temperton B , Swan BK , Landry ZC , Woyke T , DeLong EF , Stepanauskas R , Giovannoni SJ . 2014 . Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype . 7 . ISME J 8 : 1440 – 1451 . OpenUrl CrossRef 9. ↵ Ruiz-Perez CA , Bertagnolli AD , Tsementzi D , Woyke T , Stewart FJ , Konstantinidis KT . 2021 . Description of Candidatus Mesopelagibacter carboxydoxydans and Candidatus Anoxipelagibacter denitrificans: Nitrate-reducing SAR11 genera that dominate mesopelagic and anoxic marine zones . Systematic and Applied Microbiology 44 : 126185 . OpenUrl CrossRef 10. ↵ Haro-Moreno JM , Rodriguez-Valera F , Rosselli R , Martinez-Hernandez F , Roda-Garcia JJ , Gomez ML , Fornas O , Martinez-Garcia M , López-Pérez M . 2020 . Ecogenomics of the SAR11 clade . Environmental Microbiology 22 : 1748 – 1763 . OpenUrl CrossRef 11. ↵ Freel KC , Tucker SJ , Freel EB , Giovannoni SJ , Eren AM , Rappé MS . 2025 . New isolate genomes and global marine metagenomes resolve ecologically relevant units of SAR11 . bioRxiv doi: 10.1101/2024.12.24.630191 . OpenUrl Abstract / FREE Full Text 12. ↵ Tucker SJ , Freel KC , Monaghan EA , Sullivan CES , Ramfelt O , Rii YM , Rappé MS . 2021 . Spatial and temporal dynamics of SAR11 marine bacteria across a nearshore to offshore transect in the tropical Pacific Ocean . PeerJ 9 : e12274 . OpenUrl CrossRef PubMed 13. ↵ Tucker SJ , Freel KC , Eren AM , Rappé MS . 2024 . Habitat-specificity in SAR11 is associated with a handful of genes under high selection . bioRxiv doi: 10.1101/2024.12.23.630198 . OpenUrl Abstract / FREE Full Text 14. ↵ Carlson CA , Morris R , Parsons R , Treusch AH , Giovannoni SJ , Vergin K . 2009 . Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea . 3 . ISME J 3 : 283 – 295 . OpenUrl CrossRef PubMed Web of Science 15. ↵ Giovannoni SJ , Vergin KL . 2012 . Seasonality in Ocean Microbial Communities . Science 335 : 671 – 676 . OpenUrl Abstract / FREE Full Text 16. ↵ Suzuki MT , Béjà O , Taylor LT , DeLong EF . 2001 . Phylogenetic analysis of ribosomal RNA operons from uncultivated coastal marine bacterioplankton . Environmental Microbiology 3 : 323 – 331 . OpenUrl CrossRef PubMed Web of Science 17. Kan J , Evans S , Chen F , Suzuki M . 2008 . Novel estuarine bacterioplankton in rRNA operon libraries from the Chesapeake Bay . Aquat Microb Ecol 51 : 55 – 66 . OpenUrl CrossRef 18. ↵ Hoarfrost A , Nayfach S , Ladau J , Yooseph S , Arnosti C , Dupont CL , Pollard KS . 2020 . Global ecotypes in the ubiquitous marine clade SAR86 . The ISME Journal 14 : 178 – 188 . OpenUrl CrossRef PubMed 19. ↵ Rappé MS , Vergin K , Giovannoni SJ . 2000 . Phylogenetic comparisons of a coastal bacterioplankton community with its counterparts in open ocean and freshwater systems . FEMS Microbiology Ecology 33 : 219 – 232 . OpenUrl CrossRef PubMed 20. ↵ Tucker SJ , Rii YM , Freel KC , Kotubetey K , Kawelo AH , Winter KB , Rappé MS . 2025 . Seasonal and spatial transitions in phytoplankton assemblages spanning estuarine to open ocean waters of the tropical Pacific . Limnol Oceanogr doi: 10.1002/lno.70075 . OpenUrl CrossRef 21. ↵ Karl DM , Lukas R . 1996 . The Hawaii Ocean Time-series (HOT) program: Background, rationale and field implementation . Deep Sea Research Part II: Topical Studies in Oceanography 43 : 129 – 156 . OpenUrl CrossRef 22. ↵ Kavanaugh MT , Church MJ , Davis CO , Karl DM , Letelier RM , Doney SC . 2018 . ALOHA From the Edge: Reconciling Three Decades of in Situ Eulerian Observations and Geographic Variability in the North Pacific Subtropical Gyre . Frontiers in Marine Science 5 . 23. Tucker SJ , Fuessel J , Freel KC , Kiefl E , Freel EB , Ramfelt O , Sullivan CES , Gajigan AP , Mochimaru H , Rocha de Souza M , Quinn M , Ratum C , Tran LL , Miller SE , Trigodet F , Lolans K , Morrison HG , Fallon B , Huettel B , Pan T , Rappé MS , Eren AM . In review . A high-resolution diel survey of surface ocean metagenomes, metatranscriptomes, and metaepitranscriptomes . 24. Mende DR , Bryant JA , Aylward FO , Eppley JM , Nielsen T , Karl DM , DeLong EF . 2017 . Environmental drivers of a microbial genomic transition zone in the ocean’s interior . 10 . Nat Microbiol 2 : 1367 – 1373 . OpenUrl CrossRef PubMed 25. ↵ Bolyen E , Rideout JR , Dillon MR , Bokulich NA , Abnet CC , Al-Ghalith GA , Alexander H , Alm EJ , Arumugam M , Asnicar F , Bai Y , Bisanz JE , Bittinger K , Brejnrod A , Brislawn CJ , Brown CT , Callahan BJ , Caraballo-Rodríguez AM , Chase J , Cope EK , Da Silva R , Diener C , Dorrestein PC , Douglas GM , Durall DM , Duvallet C , Edwardson CF , Ernst M , Estaki M , Fouquier J , Gauglitz JM , Gibbons SM , Gibson DL , Gonzalez A , Gorlick K , Guo J , Hillmann B , Holmes S , Holste H , Huttenhower C , Huttley GA , Janssen S , Jarmusch AK , Jiang L , Kaehler BD , Kang KB , Keefe CR , Keim P , Kelley ST , Knights D , Koester I , Kosciolek T , Kreps J , Langille MGI , Lee J , Ley R , Liu Y-X , Loftfield E , Lozupone C , Maher M , Marotz C , Martin BD , McDonald D , McIver LJ , Melnik AV , Metcalf JL , Morgan SC , Morton JT , Naimey AT , Navas-Molina JA , Nothias LF , Orchanian SB , Pearson T , Peoples SL , Petras D , Preuss ML , Pruesse E , Rasmussen LB , Rivers A , Robeson MS , Rosenthal P , Segata N , Shaffer M , Shiffer A , Sinha R , Song SJ , Spear JR , Swafford AD , Thompson LR , Torres PJ , Trinh P , Tripathi A , Turnbaugh PJ , Ul-Hasan S , van der Hooft JJJ , Vargas F , Vázquez-Baeza Y , Vogtmann E , von Hippel M , Walters W , Wan Y , Wang M , Warren J , Weber KC , Williamson CHD , Willis AD , Xu ZZ , Zaneveld JR , Zhang Y , Zhu Q , Knight R , Caporaso JG. 2019 . Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. 8 . Nat Biotechnol 37 : 852 – 857 . OpenUrl CrossRef PubMed 26. ↵ Callahan BJ , McMurdie PJ , Rosen MJ , Han AW , Johnson AJA , Holmes SP . 2016 . DADA2: High-resolution sample inference from Illumina amplicon data . 7 . Nat Methods 13 : 581 – 583 . OpenUrl CrossRef PubMed 27. ↵ Quast C , Pruesse E , Yilmaz P , Gerken J , Schweer T , Yarza P , Peplies J , Glöckner FO . 2013 . The SILVA ribosomal RNA gene database project: improved data processing and web-based tools . Nucleic Acids Research 41 : D590 – D596 . OpenUrl CrossRef PubMed Web of Science 28. ↵ Bokulich NA , Kaehler BD , Rideout JR , Dillon M , Bolyen E , Knight R , Huttley GA , Gregory Caporaso J . 2018 . Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin . Microbiome 6 : 90 . OpenUrl CrossRef PubMed 29. ↵ Pruesse E , Peplies J , Glöckner FO . 2012 . SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes . Bioinformatics 28 : 1823 – 1829 . OpenUrl CrossRef PubMed Web of Science 30. ↵ Stamatakis A . 2006 . RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models . Bioinformatics 22 : 2688 – 2690 . OpenUrl CrossRef PubMed Web of Science 31. Eren AM , Vineis JH , Morrison HG , Sogin ML . 2013 . A filtering method to generate high quality short reads using illumina paired-end technology . PLOS ONE 8 : 1 – 6 . OpenUrl CrossRef PubMed 32. ↵ Li D , Liu C-M , Luo R , Sadakane K , Lam T-W . 2015 . MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph . Bioinformatics 31 : 1674 – 1676 . OpenUrl CrossRef PubMed 33. ↵ Eren AM , Kiefl E , Shaiber A , Veseli I , Miller SE , Schechter MS , Fink I , Pan JN , Yousef M , Fogarty EC , Trigodet F , Watson AR , Esen ÖC , Moore RM , Clayssen Q , Lee MD , Kivenson V , Graham ED , Merrill BD , Karkman A , Blankenberg D , Eppley JM , Sjödin A , Scott JJ , Vázquez-Campos X , McKay LJ , McDaniel EA , Stevens SLR , Anderson RE , Fuessel J , Fernandez-Guerra A , Maignien L , Delmont TO , Willis AD . 2021 . Community-led, integrated, reproducible multi-omics with anvi’o . Nature Microbiology 6 : 3 – 6 . OpenUrl PubMed 34. ↵ Langmead B , Salzberg SL . 2012 . Fast gapped-read alignment with Bowtie 2 . Nature Methods 9 : 357 – 359 . OpenUrl CrossRef PubMed 35. ↵ Danecek P , Bonfield JK , Liddle J , Marshall J , Ohan V , Pollard MO , Whitwham A , Keane T , McCarthy SA , Davies RM , Li H . 2021 . Twelve years of SAMtools and BCFtools . GigaScience 10 :giab008. 36. ↵ Wu Y-W , Simmons BA , Singer SW . 2016 . MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets . Bioinformatics 32 : 605 – 607 . OpenUrl CrossRef PubMed 37. ↵ Kang DD , Li F , Kirton E , Thomas A , Egan R , An H , Wang Z . 2019 . MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies . PeerJ 7 : e7359 . OpenUrl CrossRef PubMed 38. ↵ Alneberg J , Bjarnason BS , de Bruijn I , Schirmer M , Quick J , Ijaz UZ , Lahti L , Loman NJ , Andersson AF , Quince C. 2014 . Binning metagenomic contigs by coverage and composition . 11 . Nat Methods 11 : 1144 – 1146 . OpenUrl CrossRef PubMed Web of Science 39. ↵ Sieber CMK , Probst AJ , Sharrar A , Thomas BC , Hess M , Tringe SG , Banfield JF . 2018 . Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy . 7 . Nat Microbiol 3 : 836 – 843 . OpenUrl CrossRef PubMed 40. ↵ Chaumeil P-A , Mussig AJ , Hugenholtz P , Parks DH . 2020 . GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database . Bioinformatics 36 : 1925 – 1927 . OpenUrl CrossRef 41. ↵ Eren AM , Esen ÖC , Quince C , Vineis JH , Morrison HG , Sogin ML , Delmont TO . 2015 . Anvi’o: an advanced analysis and visualization platform for ‘omics data . PeerJ 3 : e1319 . OpenUrl CrossRef PubMed 42. ↵ Parks DH , Imelfort M , Skennerton CT , Hugenholtz P , Tyson GW . 2015 . CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes . Genome Res 25 : 1043 – 1055 . OpenUrl Abstract / FREE Full Text 43. ↵ Eddy SR . 2011 . Accelerated Profile HMM Searches . PLOS Computational Biology 7 : e1002195 . OpenUrl CrossRef PubMed 44. ↵ Hyatt D , Chen G-L , LoCascio PF , Land ML , Larimer FW , Hauser LJ . 2010 . Prodigal: prokaryotic gene recognition and translation initiation site identification . BMC Bioinformatics 11 : 119 . OpenUrl CrossRef PubMed 45. ↵ Parks DH , Chuvochina M , Waite DW , Rinke C , Skarshewski A , Chaumeil P-A , Hugenholtz P . 2018 . A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life . Nat Biotechnol 36 : 996 – 1004 . OpenUrl CrossRef PubMed 46. ↵ Minh BQ , Schmidt HA , Chernomor O , Schrempf D , Woodhams MD , von Haeseler A , Lanfear R. 2020 . IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era . Molecular Biology and Evolution 37 : 1530 – 1534 . OpenUrl CrossRef PubMed 47. ↵ Hoang DT , Chernomor O , von Haeseler A , Minh BQ , Vinh LS. 2018 . UFBoot2: Improving the Ultrafast Bootstrap Approximation . Molecular Biology and Evolution 35 : 518 – 522 . OpenUrl CrossRef PubMed 48. ↵ Yu G . 2022 . Data integration, manipulation and visualization of phylogenetic treess1st edition . Chapman and Hall/CRC . 49. ↵ Jain C , Rodriguez-R LM , Phillippy AM , Konstantinidis KT , Aluru S . 2018 . High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries . Nat Commun 9 : 5114 . OpenUrl CrossRef PubMed 50. ↵ Parks DH , Chuvochina M , Chaumeil P-A , Rinke C , Mussig AJ , Hugenholtz P . 2020 . A complete domain-to-species taxonomy for Bacteria and Archaea . Nat Biotechnol 38 : 1079 – 1086 . OpenUrl CrossRef PubMed 51. ↵ Utter DR , Borisy GG , Eren AM , Cavanaugh CM , Mark Welch JL . 2020 . Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity . Genome Biology 21 : 293 . OpenUrl CrossRef PubMed 52. ↵ Watson AR , Füssel J , Veseli I , DeLongchamp JZ , Silva M , Trigodet F , Lolans K , Shaiber A , Fogarty E , Runde JM , Quince C , Yu MK , Söylev A , Morrison HG , Lee STM , Kao D , Rubin DT , Jabri B , Louie T , Eren AM . 2023 . Metabolic independence drives gut microbial colonization and resilience in health and disease . Genome Biology 24 : 78 . OpenUrl CrossRef PubMed 53. ↵ Wickham H. 2009 . ggplot2: Elegant Graphics for Data Analysis . Springer, New York, NY . https://link.springer.com/10.1007/978-0-387-98141-3 . Retrieved 4 April 2023. 54. ↵ Delmont TO , Eren AM . 2018 . Linking pangenomes and metagenomes: the Prochlorococcus metapangenome . PeerJ 6 : e4320 . OpenUrl CrossRef PubMed 55. ↵ Galperin MY , Makarova KS , Wolf YI , Koonin EV . 2015 . Expanded microbial genome coverage and improved protein family annotation in the COG database . Nucleic Acids Research 43 : D261 – D269 . OpenUrl CrossRef PubMed 56. ↵ Ogata H , Goto S , Sato K , Fujibuchi W , Bono H , Kanehisa M . 1999 . KEGG: Kyoto encyclopedia of genes and genomes . Nucleic Acids Research 27 : 29 – 34 . OpenUrl CrossRef PubMed Web of Science 57. ↵ Huerta-Cepas J , Szklarczyk D , Heller D , Hernández-Plaza A , Forslund SK , Cook H , Mende DR , Letunic I , Rattei T , Jensen L , von Mering C , Bork P. 2019 . eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses . Nucleic Acids Research 47 : D309 – D314 . OpenUrl CrossRef PubMed 58. ↵ Cantalapiedra CP , Hernández-Plaza A , Letunic I , Bork P , Huerta-Cepas J . 2021 . eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale . Molecular Biology and Evolution 38 : 5825 – 5829 . OpenUrl CrossRef PubMed 59. ↵ Buchfink B , Xie C , Huson DH . 2015 . Fast and sensitive protein alignment using DIAMOND . Nat Methods 12 : 59 – 60 . OpenUrl CrossRef PubMed 60. ↵ Altschul SF , Gish W , Miller W , Myers EW , Lipman DJ . 1990 . Basic local alignment search tool . Journal of Molecular Biology 215 : 403 – 410 . OpenUrl CrossRef PubMed Web of Science 61. ↵ Shaiber A , Willis AD , Delmont TO , Roux S , Chen L-X , Schmid AC , Yousef M , Watson AR , Lolans K , Esen ÖC , Lee STM , Downey N , Morrison HG , Dewhirst FE , Mark Welch JL , Eren AM . 2020 . Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome . Genome Biology 21 : 292 . OpenUrl CrossRef PubMed 62. ↵ Peekhaus N , Conway T . 1998 . What’s for Dinner?: Entner-Doudoroff Metabolism inEscherichia coli . Journal of Bacteriology 180 : 3495 – 3502 . OpenUrl FREE Full Text 63. ↵ Kim HT , Chung JH , Wang D , Lee J , Woo HC , Choi I-G , Kim KH . 2012 . Depolymerization of alginate into a monomeric sugar acid using Alg17C, an exo-oligoalginate lyase cloned from Saccharophagus degradans 2-40 . Appl Microbiol Biotechnol 93 : 2233 – 2239 . OpenUrl CrossRef PubMed 64. Hogarth BG , Higgins CF . 1983 . Genetic Organization of the Oligopeptide Permease (opp) Locus of Salmonella typhimurium and Escherichia coli . Journal of Bacteriology 153 : 1548 – 1551 . OpenUrl Abstract / FREE Full Text 65. Hiles ID , Gallagher MP , Jamieson DJ , Higgins CF . 1987 . Molecular characterization of the oligopeptide permease of Salmonella typhimurium . Journal of Molecular Biology 195 : 125 – 142 . OpenUrl CrossRef PubMed Web of Science 66. ↵ Kondakova T , D’Heygère F , Feuilloley MJ , Orange N , Heipieper HJ , Duclairoir Poc C . 2015 . Glycerophospholipid synthesis and functions in Pseudomonas . Chemistry and Physics of Lipids 190 : 27 – 42 . OpenUrl CrossRef 67. ↵ Yooseph S , Nealson KH , Rusch DB , McCrow JP , Dupont CL , Kim M , Johnson J , Montgomery R , Ferriera S , Beeson K , Williamson SJ , Tovchigrechko A , Allen AE , Zeigler LA , Sutton G , Eisenstadt E , Rogers Y-H , Friedman R , Frazier M , Venter JC . 2010 . Genomic and functional adaptation in surface ocean planktonic prokaryotes . Nature 468 : 60 – 66 . OpenUrl CrossRef PubMed Web of Science 68. ↵ Davies TJ . 2006 . Evolutionary Ecology: When Relatives Cannot Live Together . Current Biology 16 : R645 – R647 . OpenUrl CrossRef PubMed 69. ↵ Webb CO , Ackerly DD , McPeek MA , Donoghue MJ . 2002 . Phylogenies and Community Ecology . Annual Review of Ecology, Evolution, and Systematics 33 : 475 – 505 . OpenUrl CrossRef 70. ↵ Van Mooy BAS , Fredricks HF , Pedler BE , Dyhrman ST , Karl DM , Koblížek M , Lomas MW , Mincer TJ , Moore LR , Moutin T , Rappé MS , Webb EA. 2009 . Phytoplankton in the ocean use non-phosphorus lipids in response to phosphorus scarcity . 7234 . Nature 458:69–72. 71. Carini P , Van Mooy BAS , Thrash JC , White A , Zhao Y , Campbell EO , Fredricks HF , Giovannoni SJ. 2015 . SAR11 lipid renovation in response to phosphate starvation . Proceedings of the National Academy of Sciences 112 : 7767 – 7772 . OpenUrl Abstract / FREE Full Text 72. ↵ Sebastián M , Smith AF , González JM , Fredricks HF , Van Mooy B , Koblížek M , Brandsma J , Koster G , Mestre M , Mostajir B , Pitta P , Postle AD , Sánchez P , Gasol JM , Scanlan DJ , Chen Y. 2016 . Lipid remodelling is a widespread strategy in marine heterotrophic bacteria upon phosphorus deficiency . 4 . ISME J 10:968–978. 73. ↵ Bogdanov M , Heacock PN , Dowhan W . 2002 . A polytopic membrane protein displays a reversible topology dependent on membrane lipid composition . The EMBO Journal 21 : 2107 – 2116 . OpenUrl Abstract / FREE Full Text 74. ↵ Rowlett VW , Mallampalli VKPS , Karlstaedt A , Dowhan W , Taegtmeyer H , Margolin W , Vitrac H . 2017 . Impact of Membrane Phospholipid Alterations in Escherichia coli on Cellular Function and Bacterial Stress Adaptation . Journal of Bacteriology 199 : 10 .1128/jb.00849–16. OpenUrl CrossRef 75. ↵ Johnson ZI , Zinser ER , Coe A , McNulty NP , Woodward EMS , Chisholm SW . 2006 . Niche Partitioning Among Prochlorococcus Ecotypes Along Ocean-Scale Environmental Gradients . Science 311 : 1737 – 1740 . OpenUrl Abstract / FREE Full Text 76. ↵ Coleman ML , Chisholm SW . 2010 . Ecosystem-specific selection pressures revealed through comparative population genomics . Proceedings of the National Academy of Sciences 107 : 18634 – 18639 . OpenUrl Abstract / FREE Full Text View the discussion thread. Back to top Previous Next Posted June 17, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Magnimaribacterales marine bacteria genetically partition across the nearshore to open-ocean in the tropical Pacific Ocean Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Magnimaribacterales marine bacteria genetically partition across the nearshore to open-ocean in the tropical Pacific Ocean Oscar Ramfelt , Sarah J. Tucker , Kelle C. Freel , A. Murat Eren , Michael S. Rappé bioRxiv 2025.06.17.660167; doi: https://doi.org/10.1101/2025.06.17.660167 Share This Article: Copy Citation Tools Magnimaribacterales marine bacteria genetically partition across the nearshore to open-ocean in the tropical Pacific Ocean Oscar Ramfelt , Sarah J. Tucker , Kelle C. Freel , A. Murat Eren , Michael S. Rappé bioRxiv 2025.06.17.660167; doi: https://doi.org/10.1101/2025.06.17.660167 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41911) Biophysics (21436) Cancer Biology (18578) Cell Biology (25482) Clinical Trials (138) Developmental Biology (13371) Ecology (19887) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15599) Genomics (22483) Immunology (17728) Microbiology (40364) Molecular Biology (17163) Neuroscience (88537) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15129) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9817) Zoology (2269)