FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2012017199> ?p ?o ?g. }

• W2012017199 endingPage "306" @default.
• W2012017199 startingPage "301" @default.
• W2012017199 abstract "Treatment and splitting of samples for bacteria and meiofauna biomass determinations by means of a low cost semi-automatic image analysis system is described. The technique allows enumeration, size measurement and biomass calculation of stained bacteria and rneiofauna from the same sample. An example of size spectra and b~ornass of bacteria and meiofauna around a macrofauna organism tube inhabited by Echiurus echiurus from the German Bight is presented. These results demonstrate enhanced biomass and a shift of size-class distribution of these microorganisms Inside the tube as compared to the ambient sediment. The determination of abundance, size-classes and organic carbon content of marine microorganisms is of interest to a wide range of marine scientists. The discovery and analysis of biomass spectra characteristics of benthic communities have led to considerable new insight into dynamics of these communities which are characterized by a gradient of biomass according to logarithmic size-classes (Schwinghammer 1983). Marine microbiologists expend much effort in determining numbers and biomass of marine bacteria (Meyer-Reil 1983, Bratbak 1985). Meiofauna ecologists are concerned with sizes, total counts and biomass of natural samples (Gerlach 1978, Jensen 1984). Foraminiferal population dynamicists, for example, are concerned with foraminiferal sizes in relation to physiological and physical conditions and growth rates (Lutze 1965, Altenbach 1985, Linke 1986). Following the equation of allometric growth (Bertalanffy 1960), a double logarithmic graph of test length compared to organic carbon content shows an adequate correlation, so the relation between test length and biomass can be computed (Altenbach 1987). Despite the importance of this information there is no reliable method for its rapid estimation. In the case of nematodes and foraminifera, accurate data can be obtained using an eyepiece micrometer but these measurements are time consumO Inter-Research/Pnnted in Germany ing. In the case of smaller bacteria, accurate estimation of size, biovolume and biomass is hampered by their small size (Meyer-Reil 1983, Bratbak 1985). Automated image analysis has been used since the 1950's for counting and sizing of a wide variety of objects (Pettipher & Rodriques 1982, Caldwell & Germida 1985, Siereacki et al. 1985, Bjarnsen 1986, Estep et al. 1986). However, the accurate estimation of biovolume and biomass of extremely small objects like sediment bacteria is prevented by limitations in both hardware and software. The smallest bacteria are represented by only a few screen picture elements (pixels), making their volume estimation inprecise. Systems running fully automatically are unable to differ between fluorescent stained detritus and bacteria. Image analysis systems developed by Bj~rnsen (1986) and Estep e t al. (1986) included several important hardware and software improvements, like additional magnification lenses, useful for the analysis of bacterioplankton. These systems are composed of a powerful array-processor (Ibas, Artec) with an additional host computer, a moonlight camera and an epifluorescence microscope. In this article a method for the treatment and splitting of samples is presented which enables the rapid sizedetermination of bacteria and meiofauna from the same sediment sample with a new low-cost imageanalysis system. The cost of the entire system is below that for a single component of the systems mentioned above. Treatment and splitting of samples. For bacteria and meiofauna biomass determinations, 1 cm3 of sample was preserved on board ship in cm3 of buffered 2 % formalin (filter-sterilized using 0.2 Km cellulose-nitrate membranes). A flow diagram for processing the samples is given in Fig. 1. Bacteria. In the laboratory a 1 cm3 subsample was 302 Mar Ecol. Prog. Ser 71: 301-306, 1991 SBdiment Sample in 2 10 buffered 0.2 pm filtered tormalin Replace formalin with Rose Bengal methanol solution. v Nematodes n Bacteria Replaca Rose Bengal methanol solution with Ludox. [Ludox Qnsity separation 3x1 Wash through 20 pn gauze. ... ,... Stain with Aaidine Orange (small nematodes). Moupl onto microscope slide. Ultrasonicate Icc subsample 2x5 S Dilute1 :l 0 000. Stain wim Auidine Orange. Take the remaining dilution samples back to the onginal sample. Wash the sediment with alcohol. and dry 11. [CaBrp density separation 3x1 Wash and dry extracted foraminifera. .Mount onto microscope slide To image analysis system sonicated (2 X 5 S, 40 kHz Branson sonifier 250) and diluted to final concentration of l : l 0 000. The samples were filtered onto NucIepore filters (0.2 bkm pore size, prestained in Sudan black), stained with Acridine Orange, and counted by epifluorescence microscopy (Zeiss 'Standard' fluorescence microscope). Bacteria in each sample were photographed for biovolume estimations (Kodak Ektachrome film ASA 400, colour slides). These pictures were projected onto the computer screen of the image analyser with a film-video processor (TAMRON-fotovix, 4 X magnification) and the sizes of 100 to 200 bacteria from each sample were measured. Soft meiofauna. The remaining subsamples of the dilution series were returned to the original sample. The samples were stained with Rose Bengal and the soft me~ofauna was extracted from the sed~ment by diluted Ludox-TM (b 1.21) density separation (described by de Jonge & Bouwmann 1977). The extracted meiofauna samples were washed on a 40 pm mesh sieve, sorted and taken to the image analyser for biomass estimations. Fig. 1. Flow diagram of treatment and splitting of samples Foraminifera. The remaining sediment sample was carefully washed with alcohol and dried. Another density separation with calcium bromide ( b 1.65) (described by Thomsen 1989) was carried out to separate the foraminifera from the sediment by flotation, decantation and sieving. The foraminifera samples were washed on a 20 pm mesh sieve, dried, sorted and taken to the lmage analyser for biomass estimations. The image analysis system. The system consists of an Atari 1040ST computer (68 000 CPU, lMB RAM, 760kB floppy-disk), an Atari Genlock system and a television set (Sony Tnnitron, 14) used as computer monitor. For bacteria biomass determinations the system is connected to a TAMRON film-video processor (colour slides of the bacteria). For meiofauna biomass determinations the system is connected to a dissecting microscope (Wild M8) with a Panasonic video camera. Video-pictures of the images from the microscope or the film-video processor are transferred to the computer via the Genlock system by creating a high resolution camera-like video picture of the sample on the computer monitor (Fig. 2). The software developed for Thomsen: Image analysis system 303 the biomass estimations is handled by the pulldown menus and mouse commands typical of an Atari application. The basic functions of analysing the objects on the computer screen are accomplished using 'push buttons' on the monitor with the mouse. The program is written in 'C' language and can be purchased commercially ('Biomass', Softwares finest, Fleethorn 64, W2300 Kiel 1, Germany). Measuring. After the image analysis system is switched on, the computer program starts with a manually entered calibration routine that allows the calculation of a given distance in nanometers, micrometers or millimeters, a value which is used by the program to convert measured scales to absolute distances. The Genlock system transfers the video picture of the samFig. 2. The image analysis system for meiofauna biomass determinations showing dissecting microscope with video camera (right), Genlock system (center) and Atan computer with monitor (left). For bacteria biomass determinations a film-video processor is used instead of the dissecting microscope and video camera. Images are displayed on the computer monitor via the Genlock system. The Atari computer processes the measurement, analyzes the data, stores the data on a disk and presents the statistical graphics ple onto the computer screen, where the measurements are made by drawing the lines of length and width on the image by hand, using the mouse. Based on the data, the computer calculates length, width, volume and length-to-width ratio of the measured objects. Therefore, no additional array processor is needed. The analysis of the images is done by the Atari computer. Based on the estimation of the volume from body length, both wet weight and biomass of the measured object are calculated by the computer. A problem arose that the smallest bacteria were found to occupy too few pixels to allow proper measurement. This was solved through magnification by the film-video processor. Thus a sphere of 0.5 pm diameter occupied ca 30 pixels in length on the computer screen after magnification. Table 1. Summary of conversion factors for volume and biomass estimations" @default.
• W2012017199 created "2016-06-24" @default.
• W2012017199 creator A5040357872 @default.
• W2012017199 date "1991-01-01" @default.
• W2012017199 modified "2023-09-25" @default.
• W2012017199 title "Treatment and splitting of samples for bacteria and meiofauna biomass determinations by means of a semi-automatic image analysis system" @default.
• W2012017199 cites W1969972378 @default.
• W2012017199 cites W2102036909 @default.
• W2012017199 doi "https://doi.org/10.3354/meps071301" @default.
• W2012017199 hasPublicationYear "1991" @default.
• W2012017199 type Work @default.
• W2012017199 sameAs 2012017199 @default.
• W2012017199 citedByCount "12" @default.
• W2012017199 countsByYear W20120171992015 @default.
• W2012017199 countsByYear W20120171992019 @default.
• W2012017199 countsByYear W20120171992021 @default.
• W2012017199 crossrefType "journal-article" @default.
• W2012017199 hasAuthorship W2012017199A5040357872 @default.
• W2012017199 hasBestOaLocation W20120171991 @default.
• W2012017199 hasConcept C111368507 @default.
• W2012017199 hasConcept C115540264 @default.
• W2012017199 hasConcept C127313418 @default.
• W2012017199 hasConcept C151730666 @default.
• W2012017199 hasConcept C18903297 @default.
• W2012017199 hasConcept C39432304 @default.
• W2012017199 hasConcept C43622095 @default.
• W2012017199 hasConcept C523546767 @default.
• W2012017199 hasConcept C77077793 @default.
• W2012017199 hasConcept C86803240 @default.
• W2012017199 hasConceptScore W2012017199C111368507 @default.
• W2012017199 hasConceptScore W2012017199C115540264 @default.
• W2012017199 hasConceptScore W2012017199C127313418 @default.
• W2012017199 hasConceptScore W2012017199C151730666 @default.
• W2012017199 hasConceptScore W2012017199C18903297 @default.
• W2012017199 hasConceptScore W2012017199C39432304 @default.
• W2012017199 hasConceptScore W2012017199C43622095 @default.
• W2012017199 hasConceptScore W2012017199C523546767 @default.
• W2012017199 hasConceptScore W2012017199C77077793 @default.
• W2012017199 hasConceptScore W2012017199C86803240 @default.
• W2012017199 hasLocation W20120171991 @default.
• W2012017199 hasOpenAccess W2012017199 @default.
• W2012017199 hasPrimaryLocation W20120171991 @default.
• W2012017199 hasRelatedWork W169336496 @default.
• W2012017199 hasRelatedWork W2351984954 @default.
• W2012017199 hasRelatedWork W2362426032 @default.
• W2012017199 hasRelatedWork W2363713377 @default.
• W2012017199 hasRelatedWork W2389871836 @default.
• W2012017199 hasRelatedWork W2595806310 @default.
• W2012017199 hasRelatedWork W2779452837 @default.
• W2012017199 hasRelatedWork W2901622336 @default.
• W2012017199 hasRelatedWork W2911886832 @default.
• W2012017199 hasRelatedWork W4251051526 @default.
• W2012017199 hasVolume "71" @default.
• W2012017199 isParatext "false" @default.
• W2012017199 isRetracted "false" @default.
• W2012017199 magId "2012017199" @default.
• W2012017199 workType "article" @default.