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Fig. 8 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 8 | Total microbial diversity across vertebrate hindguts and within multi- plebodysites of fish. a Hindgutmicrobiotasamplesfrom 569 speciesof vertebrates were rarified to 5000 reads and unique or shared ASVs determined for each class. b The percentage of unique ASVs only found in a given class (not shared in other classes) as compared to the total ASVs within that class. c Rarefaction of cumulative gamma diversity as a function of unique vertebrate species. Included is a single fish species, S. japonicus, sampled over 3 years "black dots" and the unrarefied FMP samples which had detectable bacteria in all four body sites (gill, skin, midgut, and hindgut).d Gammadiversity of 68 fishspeciesacrossfour bodysites.e Percentageof unique ASVs associatedwitha given bodysiteacrossthe 68 fish species.f Rarefaction curve of increasing gamma diversity (inclusive of four body sites) as a function of increasing fish species. ASV amplified sequence variant, 5k 5000.

Fig. 7 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 7 | Microbial source tracking analysis. a Microbial sources of 60 sea water (blue circle) samples taken from 30 unique sampling stations from two timepoints are distributed on a 10 km transect from Torrey Pines beach to Mission Bay. Microbial sources of 108 marine sediment samples (red stars) from San Diego coastalenvironmentincludes 60 paired samples (samelocationsas seawater) from the same 10 km transect along with 58 samples from the various reef habitats near La Jolla. Geographic data presented using ArcGIS. b Sourcetracker2 analysis of likely sources for the four body sites of the fish comparing contributions of beach sand, marine sediment, sea water, and "unknown". Unknown refers to microbes from an unknown source which could include diet and other animals or locations not sampled. c Specific microbial contributions of sea water to the four mucosal body sites and d specific microbial contributions of marine sediment to the four mucosalbodysites b–d: distributionisin medianand interquartilerange.Statistical differences determined using non-parametrictesting Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. e Proportion of microbes (distribution is in median and interquartile range) likely originating from the sea water vs. sediment for each unique body site (sea water vs. sediment pairwise comparison for each body site using Mann–Whitney test p <0.05).f Spearmanrho valuesfromcomparisons ofthe ratio of sea water "SW" and marine sediment "SED" against various continuous fish life history metadata variables for each unique body site (Spearman correlation p <0.05). g Comparison of the SW:SED ratios across the habitats from which the fish live. Comparisons performed on each unique body site (Kruskal–Wallis test, p <0.05). *p <0.05, **p <0.01, ***p <0.001, ****p <0.0001, ASV amplified sequence variant ~unique sub-Operational Taxonomic Unit, SD standard deviation, MG midgut, HG hindgut, KW Kruskal–Wallis test statistic "H", IQR inter quartile range, SW sea water.

Fig. 5 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 5 | Biological and life history drivers of mucosal microbiota in diverse sampling of marine fish from Southern California. a Multivariate analysis of biological and life history parameters evaluated using unweighted and weighted normalized UniFrac distances. Statistical significance (PERMANOVA p = 0.001) indicated by yellow blocks (left) and effect size (right). All samples compared together (all) along with individual sample types (gill, skin, midgut, hindgut). b Impact of trophic level on similarity between midgut and hindgut (within a species) (linear model:p p value, mslope,dottedlinesare 95% confidence interval). F-Stat test statistic used in PERMANOVA analysis, all row names in a are metadata column names used in the analysis, MG midgut, HG hindgut, Gen. Weighted UniFrac generalized weighted UniFrac.

Fig. 6 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 6 | Evidence forphylosymbiosis across fishbody sites. Effectof evolutionary distance (low divergence time indicates a short branch length or similar fish species) of all fish compared to a skin unweighted UniFrac distance, b gill generalized weighted UniFrac distance, c hindgut generalized weighted UniFrac distance. Comparisons performed using Mantel test with multiple testing by FDR. Divergence time between fish species calculated using timetree.org. Gen. Weighted UniFrac generalized weighted UniFrac.

Fig. 3 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 3 | Alpha diversity and biomass comparisons across ecological and biolo- gical gradients in marine fish. Comparison of microbial diversity a "Chao1", b "Faith's Phylogenetic Diversity", c "Shannon", or d microbial biomassacrossbody site (gill, skin, midgut, and hindgut). Distributions in "red" are median with inter- quartile range. Statistical differences determined using non-parametric testing Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. Further testing computed for each unique body site for a variety of biological and ecological metadata categories. Metadata whichis e categorical istestedusing Kruskal–Wallis f whereas numeric metadata tested using Spearmancorrelation. Onlysignificant associations are represented in e (Kruskal–Wallis p <0.05) and f (Spearman p <0.05). KW or KW stat "H" test statistic from Kruskal–Wallis test, MG midgut, HG hindgut, Faith PD Faith's Phylogenetic Diversity metric, GI:TL gastrointestinal length to fish total length "ratio", TL total length of fish.

Fig. 4 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 4 | Associations between fishmass and collection location as measuredby and d distance from shore with gill microbial biomass. e Comparison of fish mass distancefrom shorewith fishgill microbialbiomassand alphadiversity. Subset and f distancefromshorewithalpha diversitymetrics (Chao1).g Comparison of fish of fish from EPO and Atlantic (n = 54) collected from ocean (excludes bay and mass and h distance from shore with alpha diversity metric: Faith's PD. estuary samples) and from the neritic zone (<200 m depth). a Correlation matrix c–h (Confidenceintervalsof 95% aredisplayedasdotted lines). habita- between sample metadata where values are rho and significance indicated by t_act_collection refers to the metadata column name from where this habitat clas- *p <0.05, **p <0.01, ***p <0.001, ****p <0.0001 (Spearmancorrelation). sification can be found…, SZsurf zone, RIT rocky intertidal, RST rocky subtidal, IS b Comparison of gill microbial biomass (log cells per gram) across habitat types inner shelf, KBRF kelp bed rocky reef, MDRF mid depth rocky reef, CP coastal from which the fish were collected. Distribution is in median and interquartile pelagic, P pelagic. Mass_g_log = log 10 (mass of the fish in grams), dis- range. Statistical differences determined using non-parametric testing tance_from_shore_m_log = log 10 (distance from nearest point on shore in meters Kruskal–Wallistest with 0.05 FDRBenjamini–Hochberg. c Comparison of fish mass from where the fish was caught).

Fig. 2 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 2 | Limit of detection, sample exclusion, and microbial biomassestimation for FMP101 dataset. a Application of KatharoSeq formula to calculate limit of detection of microbiota platesusing the Bacillus/Paracoccus mock community (1150 reads at 90%). b Limit of detection based on cell counts of Bacillus/ Paracoccus mock community (~16 cells into extraction at 90%). c Model fit of the log(sequencing read counts) of positive extraction controls vs. the log cell counts of those positive extraction controls (empirically determined using plate counts. The linear regression of the line is indicative of the quality of method to estimate microbial biomass from sequencing read counts. Con- fidenceintervalsof 95% aredisplayedas dottedlines. Thismethodissimilar to a qPCR curvewherethe log (Ct) would beequivalent to the log(read counts).This equation is then used to estimate the number of "microbial density" of the existing samples which is then further normalized by the volume of the DNA extraction,biomass of materialgoinginto theextraction and finallynormalized to at estimated microbial cells per gram of tissue. d Community analysis comparison and validation of compositionality of controls of twosets of mock community controls (section 1 = Bacillus/Paracoccus mock community; section 2 = zymo mock community). Putative contaminant g__Geobacillus identified (presentin 93% of negatives and higherrelative abundance ascompared to positives and samples). e Number of samples successful across the four body sites collected from the broad fish microbiota dataset. QC quality control, g__ refers to a genus of bacteria, HM mock homemade mock or human made mixture of bacteria to use as a control whereas zymo mock = mock microbial community created by a company "Zymo".

Fig. 1 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 1 | Samplingdesignof 116 speciesof marine fish. a Using ArcGIS todepictthe general area from which fish were sampled: black dots indicate the locations of the 101 unique species of marine fish sampled from the California Current Ecosystem in the Eastern Pacific Ocean primarily in the waters of San Diego CA. Red circles depict the locations of an additional 17 species of fish (15 unique species with 2 species duplicates) collected from the Western Atlantic primarily in the waters of New York. When multiple species of fish were caught in the same location, a single circle is used to indicate the location. b Fish were sampled across a gradient of depth and distances from shore. c Biometric measurements taken for nearly all fish include total length, fork length, mass, gape, and GI length. Various ratios from these lengths were also calculated. Microbiota samples from the gill were primarily whole tissue specimens from the entire left second gill arch or a section of the top middle and bottom of the entire filament. Skin mucus samples were taken by scraping using a razor blade. Midgut digesta material was collected from directly posterior of the stomach or if stomach was absent, the beginning of the GI tract. Hindgut digesta samples were taken from near the anus. Image from phylopic. MG midgut, HG hindgut, GI gastrointestinal tract, m meters.

opencc-by-4.0Oct 2023View details →

Distribution System Environmental and Sequencing Datasets for Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System

The dataset of environmental parameters and sequence fastqs used to create figures and do analysis in the paper Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System  submitted to Environmental Science & Technology

opencc-zeroDec 2017View details →

Data from: Ecology of sleeping: the microbial and arthropod associates of chimpanzee beds

The indoor environment created by the construction of homes and other buildings is often considered to be uniquely different from other environments. It is composed of organisms that are less diverse than those of the outdoors and strongly sourced by, or dependent upon, human bodies. Yet, no one has ever compared the composition of species found in contemporary human homes to that of other structures built by mammals, including those of non-human primates. Here we consider the microbes and arthropods found in chimpanzee beds, relative to the surrounding environment (n = 41 and 15 beds, respectively). Based on the study of human homes, we hypothesized that the microbes found in chimpanzee beds would be less diverse than those on nearby branches and leaves and that their beds would be primarily composed of body-associated organisms. However, we found that differences between wet and dry seasons and elevation above sea level explained nearly all of the observed variation in microbial diversity and community structure. While we can identify the presence of a chimpanzee based on the assemblage of bacteria, the dominant signal is that of environmental microbes. We found just four ectoparasitic arthropod specimens, none of which appears to be specialized on chimpanzees or their structures. These results suggest that the life to which chimpanzees are exposed while in their beds is predominately the same as that of the surrounding environment.

opencc-zeroApr 2022View details →

Synthetic microbial consortia with programmable ecological interactions

1. Central to the composition, structure and function of any microbial community is the complex species interaction web. But understanding the overwhelming complexity of ecological interaction webs has been challenging, owing at least partly to the lack of efficient tools for disentangling species interactions in natural or artificial microbial communities. 2. In this study, we developed a microbial experimental system which allows for rapidly generating microbial consortia with programmable ecological interactions. We engineered the model organism Escherichia coli to construct metabolism- and quorum sensing-based modules. The two engineered modules were used to create synthetic microbial consortia of synergy, competition and exploitation. 3. We showed that each of synthetic microbial consortia displayed the unique mode of population dynamics under certain initial inoculation conditions. We also demonstrated that the transitions between exploitation and the types of competition or synergy based on the same paired strains were plausible by tuning the two engineered modules. We lastly derived mathematical models to quantitatively capture the experimentally observed population dynamics of these synthetic microbial consortia. 4. This approach offers a fresh angle to engineering microbial systems for experimentally testing ecological questions with a much greater control and manipulation.

opencc-by-4.0Apr 2022View details →

Urban Stream Environmental and Sequencing Datasets for Exploring the Impacts of Full-Scale Distribution System Orthophosphate Corrosion Control Implementation on the Microbial Ecology of Hydrologically Connected Urban Streams

The dataset of environmental parameters and sequence fastqs used to create figures and do analysis in the paper Exploring the Impacts of Full-Scale Distribution System Orthophosphate Corrosion Control Implementation on the Microbial Ecology of Hydrologically Connected Urban Streams submitted to Applied and Environmental Microbiology.

opencc-by-4.0Apr 2019View details →

Data from: Are drivers of microbial diatom distributions context dependent in human impacted and pristine environments? in Ecological Applicatios (2019)

Species occurrence (0/1) and environmental data from research article "Are drivers of microbial diatom distributions context dependent in human impacted and pristine environments?" in Ecological Applications (2019).  Please see more details in the readme-file and the original article.

opencc-by-4.0Aug 2021View details →

2017 Microbial Ecology of the Surface Ocean - SCOPE, Water Column Data

This dataset consists of samples taken at various depths in the water column from niskin bottles attached to a CTD rosette on the MESO-SCOPE cruise in 2017. Mesoscale eddies affect the variability of ocean ecosystems through different dynamics, but an exhaustive description of their influence on the plankton community is still missing, partly due to the complex physical-biological interaction taking place inside the eddies. One of the most well known effects of mesoscale eddies is the vertical displacement of water in their interior due to the geostrophic balance with the eddy circular motion. Since eddies of different polarity displace water in opposite directions, their impact on pelagic ecosystems is postulated to be very different. Based on this information, one of the objectives of the MESO-SCOPE expedition was to study a mesoscale dipole, i.e. a system composed of adjacent cyclonic and anticyclonic eddies. The MESO-SCOPE (Microbial Ecology of the Surface Ocean - Simons Collaboration on Ocean Processes and Ecology) expedition collected oceanographic observations in the region north of the Hawaiian Islands with the aim of identifying the impact of mesoscale eddies on the ecosystem of the North Pacific Subtropical Gyre. The expedition was funded by the Simons Collaboration on Ocean Processes and Ecology (SCOPE) and hosted on the research vessel Kilo Moana between June 26 and July 15 2017, leaving from and returning to the port of Honolulu.  Scientists from eight countries, representing eleven SCOPE laboratories, participated in the MESO-SCOPE expedition and contributed their expertise in research areas as ocean biogeochemistry, molecular biology, bio-optics, plankton taxonomy, microbiology, and ecology.

opencc-by-4.0Oct 2021View details →

2017 Microbial Ecology of the Surface Ocean - SCOPE, Downcast CTD Data

This dataset consists of measurements taken at various depths in the water column from CTD sensors attached to a CTD rosette on the MESO-SCOPE cruise in 2017. Mesoscale eddies affect the variability of ocean ecosystems through different dynamics, but an exhaustive description of their influence on the plankton community is still missing, partly due to the complex physical-biological interaction taking place inside the eddies. One of the most well known effects of mesoscale eddies is the vertical displacement of water in their interior due to the geostrophic balance with the eddy circular motion. Since eddies of different polarity displace water in opposite directions, their impact on pelagic ecosystems is postulated to be very different. Based on this information, one of the objectives of the MESO-SCOPE expedition was to study a mesoscale dipole, i.e. a system composed of adjacent cyclonic and anticyclonic eddies. The MESO-SCOPE (Microbial Ecology of the Surface Ocean - Simons Collaboration on Ocean Processes and Ecology) expedition collected oceanographic observations in the region north of the Hawaiian Islands with the aim of identifying the impact of mesoscale eddies on the ecosystem of the North Pacific Subtropical Gyre. The expedition was funded by the Simons Collaboration on Ocean Processes and Ecology (SCOPE) and hosted on the research vessel Kilo Moana between June 26 and July 15 2017, leaving from and returning to the port of Honolulu.  Scientists from eight countries, representing eleven SCOPE laboratories, participated in the MESO-SCOPE expedition and contributed their expertise in research areas as ocean biogeochemistry, molecular biology, bio-optics, plankton taxonomy, microbiology, and ecology.

ClinicalTrials.gov36/100

Clear Aligner Cleaning: Brushing, Chlorhexidine, and BlueM Effects on Oral Microbial Ecology and Periodontal Indices

ClinicalTrials.gov study NCT07307716. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

publicApr 2018View details →

Data from: Ecology of sleeping: the microbial and arthropod associates of chimpanzee beds

Open the record for dataset details and reuse information.

publicApr 2022View details →

Synthetic microbial consortia with programmable ecological interactions

Open the record for dataset details and reuse information.