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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-zeroDec 2022View details →

Ecological signal in the size and shape of marine amniote teeth – 3D models and landmarks

Amniotes have been a major component of marine trophic chains from the beginning of the Triassic to present day, with hundreds of species. However, inferences of their (palaeo)ecology have mostly been qualitative, making it difficult to track how dietary niches have changed through time and across clades. Here, we tackle this issue by applying a novel geometric morphometric protocol to 3D models of tooth crowns across a wide range of raptorial marine amniotes. Our results highlight the phenomenon of dental simplification and widespread convergence in marine amniotes, implying strong functional constraints which limit the range of tooth crown morphologies. Importantly, we quantitatively demonstrate that tooth crown form (shape plus size) is strongly associated with diet, whereas crown surface complexity is not. The maximal range of tooth shapes in both mammals and reptiles is seen in medium-sized taxa; large crowns are simple and restricted to a fraction of the morphospace. We recognise four principle raptorial guilds within toothed marine amniotes (durophages, generalists, meat cutters, and flesh piercers). Moreover, even though all these feeding guilds have been convergently colonised over the last 200 million years, a series of dental morphologies are unique to the Mesozoic period, probably reflecting a distinct ecosystem structure.

publicJul 2024View details →

Data from: Ecological forensic testing: Using multiple primers for eDNA detection of marine vertebrates in an estuarine lagoon subject to anthropogenic influences

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publicNov 2025View details →

Food limitation erodes the thermal tolerance of larvae in an ecologically influential marine herbivore

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publicDec 2022View details →

Ecological signal in the size and shape of marine amniote teeth – 3D models and landmarks

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publicNov 2023View details →

Ecological forecasts for marine resource management during climate extremes

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dryad36/100

Data from: Form–function relationships in a marine foundation species depend on scale: a shoot to global perspective from a distributed ecological experiment

Form-function relationships in plants underlie their ecosystem roles in supporting higher trophic levels through primary production, detrital pathways, and habitat provision. For widespread, phenotypically-variable plants, productivity may differ not only across abiotic conditions, but also from distinct morphological or demographic traits. A single foundation species, eelgrass (Zostera marina), typically dominates north temperate seagrass meadows, which we studied across 14 sites spanning 32-61° N latitude and two ocean basins. Body size varied by nearly two orders of magnitude through this range, and was largest at mid-latitudes and in the Pacific Ocean. At the global scale, neither latitude, site-level environmental conditions, nor body size helped predict productivity (relative growth rate 1-2% d-1 at most sites), suggesting a remarkable capacity of Z. marina to achieve similar productivity in summer. Furthermore, among a suite of stressors applied within sites, only ambient leaf damage reduced productivity; grazer reduction and nutrient addition had no effect on eelgrass size or growth. Scale-dependence was evident in different allometric relationships within and across sites for productivity and for modules (leaf count) relative to size. Z. marina provides a range of ecosystem functions related to both body size (habitat provision, water flow) and growth rates (food, carbon dynamics). Our observed decoupling of body size and maximum production suggests that geographic variation in these ecosystem functions may be independent, with a future need to resolve how local adaptation or plasticity of body size might actually enable more consistent peak productivity across disparate environmental conditions.

opencc-zeroDec 2016View details →

opennotspecifiedAug 2024View details →

marine ecology literature: marine ecology lit v5

A collection, from the literature, of traits relating to trophic guild, habitat and host relationships of marine invertebrates

opencc-by-4.0Jun 2024View details →

Statistical Decision Trees for Marine Ecological Data

Both decision trees can be used to choose an appropriate statistical analysis when dealing with ecological (marine) data. For each analysis, an example publication using/explaining the method, some of the R commands required and a tutorial are listed. Please note that (1) these statistical analyses should be seen as suggestions and other approaches are available, (2) papers listed are only provided as examples but appropriate referencing of the method is expected and (3) some analyses have specific assumptions that the users should check before analysing their data.

opencc-by-4.0Feb 2013View details →

Fig. 4 in The nematode assemblage as a tool for the assessment of marine ecological quality status: a case-study in the Central Adriatic Sea

Fig. 4: nMDS plot of the nematode assemblages (fourth root transformed).

opencc-by-4.0Feb 2013View details →

Fig. 1 in The nematode assemblage as a tool for the assessment of marine ecological quality status: a case-study in the Central Adriatic Sea

Fig. 1: Geographical position of the study area.