704 resultados para Marine heterotrophic bacteria

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The HCMR_SES_LAGRANGIAN_GR2_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during October 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Heterotrophic Nanoflagellate abundance: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6?m and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Ciliate abundance: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Heterotrophic bacteria, Synechococcus, Prochlorococcus bacteria: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Abundance data were converted into C biomass using 250 fgC cell-1 (Kana & Glibert 1987) for Synechococcus, 50 fgC cell-1 (Campbell et al. 1994) for Prochlorococcus and 20fgC cell-1 (Lee & Fuhrman 1987) for heterotrophic bacteria. Heterotrophic Nanoflagellate biomass: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Abundance data were converted into C biomass using 183 fgC µm**3 (Caron et al. 1995). Ciliate biomass: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Ciliate cell sizes were measured and converted into cell volumes using appropriate geometric formulae using image analysis. For biomass estimation, the conversion factor 190 fgC µm**3 was used (Putt and Stoecker 1989).

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The HCMR_SES_LAGRANGIAN_GR1_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during April 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Heterotrophic Nanoflagellate abundance: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Ciliate abundance: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Heterotrophic bacteria, Synechococcus, Prochlorococcus biomass: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Abundance data were converted into C biomass using 250 fgC cell-1 (Kana & Glibert 1987) for Synechococcus, 50 fgC cell-1 (Campbell et al. 1994) for Prochlorococcus and 20fgC cell-1 (Lee & Fuhrman 1987) for heterotrophic bacteria. Heterotrophic Nanoflagellate biomass: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Abundance data were converted into C biomass using 183 fgC µm**3 (Caron et al. 1995). Ciliate biomass: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Ciliate cell sizes were measured and converted into cell volumes using appropriate geometric formulae using image analysis. For biomass estimation, the conversion factor 190 fgC µm**3 was used (Putt and Stoecker 1989).

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The dataset is based on samples collected in the framework of the project SESAME, in the Ionian, Libyan and Aegean Sea during March- April 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson).

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Diversity of endolithic Dry Valley rock microorganisms was studied by evaluating the presence of morphotypes in enrichments. Storage of rock samples for 16 h over dry ice affected the diversity of endolithic organisms, especially that of algae and fungi. Diversity in various samples depended on rock location and exposure, on the rock type, and to some extent on the pH of the pulverized rock samples. In most cases sandstone contained more morphotypes than dolerite or granite. Presence of many different phototrophs resulted in greater diversity of the heterotrophs in the enrichments. Samples from Linnaeus Terrace and Battleship Promontory had higher morphotype (MT) numbers than those from more exposed sites such as New Mountain, University Valley, Dais, or Mt. Fleming. Beacon sandstone (13 samples) from Linnaeus Terrace varied greatly with respect to MT numbers, although the pH values ranged only from 4.2-5.3. The highest MT number of 24 per sample was obtained from the upper surface of a flat boulder tilted to the North. Only two MT's were found in a hard sandstone sample from the wind-exposed and more shaded east side of the Terrace. 15 sandstone samples from Battleship Promontory contained more diverse populations: there occurred a total of 131 different MT's in these samples as compared to only 68 in Linnaeus Terrace samples. Cysts of colorless flagellates were found in some Battleship Promontory samples; rnost samples were populated with a wealth of different cyanobacteria. Studies on the distribution of actinomycete morphotypes in Linnaeus Terrace sandstone revealed great differences between individual boulders. Identification tests and lipid analyses made with representative strains of the isolated 1500 pure cultures led to genus names such as Caulobacter, Blastobacter, Hyphomicrobium, Micrococcus, Arthrobacter, Brevibacterium, Corynebacterium, Bifidobacterium, Mycobacterium, Nocardia (Amycolata), Micromonospora, Streptomyces, Blastococcus, and Deinococcus. Our data demonstrate the great diversity of Antarctic endolithic microbial populations.

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With the accumulation of anthropogenic carbon dioxide (CO2), a proceeding decline in seawater pH has been induced that is referred to as ocean acidification. The ocean's capacity for CO2 storage is strongly affected by biological processes, whose feedback potential is difficult to evaluate. The main source of CO2 in the ocean is the decomposition and subsequent respiration of organic molecules by heterotrophic bacteria. However, very little is known about potential effects of ocean acidification on bacterial degradation activity. This study reveals that the degradation of polysaccharides, a major component of marine organic matter, by bacterial extracellular enzymes was significantly accelerated during experimental simulation of ocean acidification. Results were obtained from pH perturbation experiments, where rates of extracellular alpha- and beta-glucosidase were measured and the loss of neutral and acidic sugars from phytoplankton-derived polysaccharides was determined. Our study suggests that a faster bacterial turnover of polysaccharides at lowered ocean pH has the potential to reduce carbon export and to enhance the respiratory CO2 production in the future ocean.

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Standing stocks and production rates for phytoplankton and heterotrophic bacteria were examined during four expeditions in the western Arctic Ocean (Chukchi Sea and Canada Basin) in the spring and summer of 2002 and 2004. Rates of primary production (PP) and bacterial production (BP) were higher in the summer than in spring and in shelf waters than in the basin. Most surprisingly, PP was 3-fold higher in 2004 than in 2002; ice-corrected rates were 1581 and 458 mg C/m**2/d respectively, for the entire region. The difference between years was mainly due to low ice coverage in the summer of 2004. The spatial and temporal variation in PP led to comparable variation in BP. Although temperature explained as much variability in BP as did PP or phytoplankton biomass, there was no relationship between temperature and bacterial growth rates above about 0°C. The average ratio of BP to PP was 0.06 and 0.79 when ice-corrected PP rates were greater than and less than 100 mg C/m**2/d, respectively; the overall average was 0.34. Bacteria accounted for a highly variable fraction of total respiration, from 3% to over 60% with a mean of 25%. Likewise, the fraction of PP consumed by bacterial respiration, when calculated from growth efficiency (average of 6.9%) and BP estimates, varied greatly over time and space (7% to >500%). The apparent uncoupling between respiration and PP has several implications for carbon export and storage in the western Arctic Ocean.

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The HCMR_SES_LAGRANGIAN_GR2_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during October 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Bacterial production was estimated by the 3H-leucine method (Kirchman et al. 1986, Kirchman 1993). At each depth, duplicate samples and a control were incubated with 20 nM L-[4,5 3H]-leucine. Samples were incubated in the dark, at in situ temperature.

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The dataset is based on samples collected in the framework of the project SESAME, in the Ionian, Libyan and Aegean Sea during March- April 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column. Bacterial production was estimated by the 3H-leucine method (Kirchman et al. 1986, Kirchman 1993). At each depth, duplicate samples and a control were incubated with 20 nM L-[4,5 3H]-leucine. Samples were incubated in the dark, at in situ temperature.