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74 results for “microbial biomass”
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.
Microbial biomass and soil properties data, global scale,1970s-2010s
The data for Microbial Data System. We will collect data for soil and microbial characteristics in terrestrial ecosystems and update regularly. The date of publications used spans from the late 1970s to 2012. The data were aggregated into 11 major biome types. These data points were collected exclusively for surface soils, primarily 0–15 cm depth with some 0–30 cm. We obtained 3259 data points with geographical information.
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Off Plot Soil Incubation By Depth I - Soil Properties and Final Microbial Biomass 2013-2014
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. We investigated C and nitrogen (N) mineralization within the soil profile by incubating soil cores collected adjacent to (but not within) the CiPEHR site. These soil cores spanned the entire active layer and approximately 30 cm of permafrost. Soil cores were separated into 10 cm depth intervals and incubated for 241 days at 15 degC and field moisture was maintained with water additions. This dataset contains data on the bulk soil %C, bulk soil %N, soil bulk density, initial gravimetric water content measured prior to the incubation. Microbial biomass was measured at the end of the incubation and is presented here as well.
Microbial biomass at the Coweeta Hydrologic Laboratory in 1994
All of the data originated in the riparian watershed (WS 55) at Coweeta. Thus far, all soil data occurred in 1994 - ie. before the rhododendron removal. The summary C and N files are saved by date (year, month,and day). Within each file are data for Microbial carbon and nitrogen. The sample names (ex. C1-1, 0-5) indicate control plot, 1 meter distance from the stream, sample number 1, at the 0-5 cm soil depth, and so on. T would indicate treatment plot and 5-10 indicates the 5-10 cm soil depth. Within each plot (C and T), we established three transects, 10 meters in length, at 1, 5, and 15 meter distances upslope from a stream. Along each transect, 4 samples were collected and each sample consisted of two bulked soil cores, separated by depth (0-5 cm and 5-10 cm). All sampling dates were in 1994, beginning in March. Sample dates were selected to correspond with seasonal changes and occurred March 25, June 16, August 16, November 1, and December 16. Microbial C data is listed as microgram C per gram dry weight soil, followed by an average of C (measured by distance from the stream) and standard deviation corresponding to that average. ND listed in the Project indicates that the sample needs to be reanalyzed or was missing. The december Project needs more work, but I have included it anyway. Microbial nitrogen is listed as microgram of total persulfate N per gram dry weight soil. This is again followed by an average N value by distance from the stream and depth of the soil and the corresponding standard deviation.
Microbial Biomass Dynamics at the Kellogg Biological Station, Hickory Corners, MI (1989 to 1996)
Dataset Abstract The purpose of these studies is to determine patterns of microbial biomass dynamics in crop, successional and forest communities. This project consists of two studies involving microbial biomass determination in soil samples taken from the Main Cropping System Experiment (KBS004): Chloroform Fumigation-Incubation; Soil Microbial Biomass C and N, and Epifluorescence Microscopy and Image Analysis; Soil Microbial Biovolume and C. The data for both studies are generated from subsets of the same soil samples, collected from treatments 1 – 8, CF, DF, and SF. See the descriptor (KBS004) for treatment descriptions. Samples are collected approximately monthly (during ice-free conditions) or annually (varies by year) from 5 sampling stations in each of 6 replicate plots per treatment; exceptions are treatment 8 (four reps), and treatments CF, DF, and SF (three reps each). For experiment descriptions, protocols, and data, see individual datasets. Samples were collected by K. Klingensmith in 1989 and by S. Halstead in other years. D. Harris performed lab analysis, data entry, data analysis, and data submission. Original data files not available; contact the LTER data manager with questions. The data currently available was derived from web-based files which were last reviewed by D. Harris on 10-01-1997 and may be available from archives. original data source http://lter.kbs.msu.edu/datasets/14
McMurdo Dry Valleys LTER: Microbial mat biomass and Normalized Difference Vegetation Index (NDVI) values from Lake Fryxell Basin, Antarctica, January 2018
This package contains data collected from microbial mat surveys (i.e., percent cover, ash-free dry mass (AFDM), and pigment concentrations – chlorophyll-a, scytonemin, and carotenoids) associated with satellite-derived Normalized Difference Vegetation Index (NDVI) values from the Lake Fryxell Basin of Taylor Valley, located in the McMurdo Dry Valleys of Antarctica. The purpose of this study was to quantitatively compare key microbial mat characteristics to NDVI. Data were collected at seven plot locations within the Canada Glacier Antarctic Specially Protected Area (ASPA) near Canada Stream, as well as alongside Green Creek and McKnight Creek. NDVI values were derived from a WorldView-2 multispectral satellite image taken of the Lake Fryxell Basin on January 19, 2018, while biological ground surveying and sampling were conducted during the 2nd and 4th weeks of January 2018.
Data from: Functional diversity enhances, but exploitative traits reduce tree mixture effects on microbial biomass
1. Soil microorganisms play key roles in terrestrial biodiversity and ecosystem functions. Despite recent progress in elucidating the association between plant diversity and soil microorganisms, it remains unclear whether the functional properties of plant mixtures might alter this association. 2. We examined whether the effects of tree species mixtures on soil microbial biomass were impacted by the functional diversity (FD) and community-weighted-mean (CWM) of tree mixtures, by conducting a global meta-analysis involving 123 paired observations of tree mixtures and the corresponding monocultures from 38 studies in forests. 3. We found that the tree mixture effect on microbial biomass increased with the FD of specific leaf area (SLA), leaf N and P content, as well as the FD based on all of these traits plus leaf dry matter content. Meanwhile, the responses of microbial biomass to tree mixtures decreased with the CWM of SLA, leaf N, and P content. The effects of FD and CWM remained consistent, despite variable tree species richness, stand age and climatic factors. 4. Our results provide a new insight that the functional properties of plants may alter the magnitude of the association between plant diversity and soil microorganisms.
Trophic regulation of soil microbial biomass under nitrogen enrichment: A global meta-analysis
<p>Eutrophication, including nitrogen (N) enrichment, can affect soil microbial communities through changes in trophic interactions. However, a knowledge gap still exists about how plant resources ('bottom-up effects') and microbial predators ('top-down effects') regulate the impacts of N enrichment on microbial biomass at the global scale.</p> <p>To address this knowledge gap, we conducted a global meta-analysis using 2885 paired observations from 217 publications to evaluate the regulatory effects of plant biomass and soil nematodes on soil microbial biomass under N enrichment across terrestrial ecosystems.</p> <p>We found that the effects of N enrichment on soil microbial biomass strongly varied across ecosystems. N enrichment decreased the soil microbial biomass of natural grasslands and forests due to soil acidification and the subsequent losses of predatory and microbivorous nematodes stimulating microbial growth. By contrast, N enrichment increased the microbial biomass of managed croplands mainly via increasing plant biomass production. The short-term of N enrichment (experimental duration ≤ 5 years) could reduce microbial biomass via decreasing nematode abundance across diverse ecosystems, whereas the long-term of N enrichment (experimental duration > 5 years) mainly promoted microbial biomass via increasing plant biomass.</p> <p>These findings highlight the critical roles of microbial predators and plant input in shaping microbial responses to N enrichment, which are highly dependent on ecosystem type and the period of N enrichment. Earth system models that predict soil microbial biomass and their linkages to soil functioning should consider the variations in plant biomass and soil nematodes under future scenarios of N deposition.</p>
Dataset on soil and soil microbial biomass carbon, nitrogen, and phosphorus stoichiometry
<p>Dataset on soil and soil microbial biomass carbon, nitrogen, and phosphorus stoichiometry. This dataset is compiled for for the scientific paper entitled "Interpreting stoichiometric homeostasis and flexibility of soil microbial biomass carbon, nitrogen, and phosphorus" (doi: 10.1016/j.ecolmodel.2022.110018).</p>
Soil property, microbial abundance, and plant and invertebrate biomass data across a natural soil temperature gradient in Iceland from August 2018
<p><span>This is a dataset of soil physiochemical properties, bacterial and fungal abundance, and above and belowground plant and invertebrate biomass, sampled at 40 plots in the Hengill geothermal valley, Iceland, from 15<sup>th</sup> to 22<sup>nd</sup> August 2018. The plots span a temperature gradient of 10</span><span>-35 °C over the sampling period, and this temperature gradient is consistent over time. The dataset also includes data on the decomposition rate of soil organic matter, which was sampled at 60 plots in the Hengill valley from May to July 2015.</span></p>
Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale
<p>R code, raw datasets, and predicted global maps of soil microbial biomass C, N, and P and their stoichiometric ratios at 0-30 cm depth.</p> <p>When using any of these layers, please cite: Gao et al., Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale (2022). Global Change Biology. DOI: 10.1111/gcb.16374</p>
Abiotic and biotic drivers of tree trait effects on soil microbial biomass and soil carbon concentration
<p>Forests are critical ecosystems to understand the global carbon budget, due to their carbon sequestration potential in both above- and belowground compartments, especially in species-rich forests. Soil carbon sequestration is strongly linked to soil microbial communities, and this link is mediated by the tree community, likely due to modifications of micro-environmental conditions (i.e., biotic conditions, soil properties, and microclimate). We studied soil carbon concentration and the soil microbial biomass of 180 local neighborhoods along a gradient of tree species richness ranging from 1 to 16 tree species per plot in a Chinese subtropical forest experiment (BEF-China). Tree productivity and different tree functional traits were measured at the neighborhood level. We tested the effects of tree productivity, functional trait identity and dissimilarity on soil carbon concentrations, and their mediation by the soil microbial biomass and micro-environmental conditions. Our analyses showed a strong positive correlation between soil microbial biomass and soil carbon concentrations. Besides, soil carbon concentration increased with tree productivity and tree root diameter while it decreased with litterfall C:N content. Moreover, tree productivity and tree functional traits (e.g. root fungal association and litterfall C:N ratio) modulated micro-environmental conditions with substantial consequences for soil microbial biomass. We also showed that soil history and topography should be considered in future experiments and tree plantations, as soil carbon concentrations were higher where historical (i.e., at the beginning of the experiment) carbon concentrations were high, themselves being strongly affected by the topography. Altogether, these results imply that the quantification of the different soil carbon pools is critical for understanding microbial community–soil carbon stock relationships and their dependence on tree diversity and micro-environmental conditions.</p>
Tree diversity effects on soil microbial biomass and respiration are context-dependent across forest diversity experiments
<p><b>Aim</b></p> <p>Soil microorganisms are essential for the functioning of terrestrial ecosystems. Although soil microbial communities and functions may be linked to tree species composition and diversity, there has been no comprehensive study of how general these potential relationships are, or if they are context-dependent. Here, we examine tree diversity–soil microbial biomass and respiration relationships across environmental gradients using a global network of tree diversity experiments.</p> <p><b>Location</b></p> <p>Global</p> <p><b>Time Period</b></p> <p>2013</p> <p><b>Major Taxa Studied</b></p> <p>Soil microorganisms</p> <p><b>Methods</b></p> <p>Soil samples collected from eleven tree diversity experiments in four biomes were used to measure microbial respiration, biomass, and respiratory quotient using the substrate-induced respiration method. All samples were measured using the same analytical device, method, and procedure to reduce measurement bias. We used linear mixed-effects models and PCA to examine the effects of tree diversity (taxonomic and phylogenetic), environmental conditions, and interactions on soil microbial properties.</p> <p><b>Results</b></p> <p>Abiotic drivers, mainly soil water content, but also soil carbon and soil pH, significantly increased soil microbial biomass and respiration. Optimal soil water content reduced the importance of other abiotic drivers. Tree diversity alone had no effect on the soil microbial properties, but interactions with phylogenetic diversity indicated that diversity effects are context-dependent and stronger in drier soils. Similar results were found for soil carbon and soil pH.</p> <p><b>Main conclusions</b></p> <p>Our results point to the importance of abiotic variables and especially soil water content for maintaining high levels of soil microbial functions and modulating the effects of other environmental drivers. Planting tree species with diverse water-use strategies and structurally complex canopies and high leaf area may crucial for maintaining high soil microbial biomass and respiration. Since higher phylogenetic distance alleviated unfavorable soil water conditions, reforestation efforts accounting for traits improving soil water content or choosing more phylogenetically distant species may assist in increasing soil microbial functions.</p>
Impact of multiple soil microbial inoculants on biomass and biomass allocation of the legume crop field pea (Fabaceae: Pisum sativum L.)
<p>Food production is a global challenge and consequently, there is considerable interest in manipulating the rhizobiome using microbial inoculants (MI) to support sustainable agriculture. We investigated how three commercially-available types of plant growth-promoting MI, alone and in combination (B5: five species of <em>Bacillus</em> bacteria, GP: four species of <em>Trichoderma</em> fungi, N2: <em>Paenibacillus polymyxa</em> bacteria) impacted field pea (Fabales: Fabaceae, <em>Pisum sativum</em> L.) in the greenhouse and a two-year field experiment in the United States, North Dakota, NDSU Field Research station at Prosper ND. CON indicates controls that did not receive any MI and FC is the fertilizer control in the field experiment which also did not receive any MI. The dataset consists of data plant data from a 2-wk greenhouse experiment (GH 2wk), a 4-wk greenhouse experiment (GH 4wk), and a two-year field experiment (field). In the greenhouse, we found that effects of MI on plant performance varied, with positive effects of MI only apparent when plants were grown in the winter and likely under greater stress because they lacked nodules. Plants grown in the summer had nodules, and two-week-old MI plants had less root biomass and total plant weight than non-inoculated controls, but weight of four-week-old MI plants was similar to or greater than controls. In the field, the root-to-shoot biomass ratio was highest in non-inoculated controls, and positive effects of N2 on shoots and B5 on shoots and pod densities didn't translate into differences in pod weight or total plant weight. In most cases, plants inoculated with all three inoculants performed similarly to those receiving a single inoculant, while root colonization by arbuscular mycorrhizal fungi (AMF) was higher for B5 plants than plants in the other treatments. This research underscores the need to consider microbial and environmental context when evaluating MI. </p>
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OpenNeuro
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