Automated image analyss method to determine fungal biomass in soils and on solid matrices

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Soil Biol. Biochem. Vol. 23, No. I, pp. 609-616, 1991 Printed in Great Britain

ACCELERATED PAPER

AUTOMATED IMAGE ANALYSIS METHOD TO DETERMINE FUNGAL BIOMASS IN SOILS AND ON SOLID MATRICES P. MORGAN,C. J. COOPER,N. S. BAI-I-ER~BY,S. A. LEE, S. T. L~wrs, T. M. MACHIN, S. C. GRAHAMand R. J. WATKINSON Shell Research Ltd, Sittingboume Research Centre, Sittingboume, Kent ME9 8AG, U.K. (Accepted 26 October 1990) Summary-A procedure has been developed for determining fungal biomass in soil and on inert surfaces by fluorescent staining and fully-automated image analysis. Soil samples were homogenised, filtered and stained with Calcofluor M2R (for total hyphal biomass measurements) or fluorescein diacctate (for viable biomass measurements). Fungi on inert surfaces were stained with Calcofluor MZR. Samples were

examined by epifluorescence microscopy and images analysed using a Teragon-Contextvision GOP-302 system. Hyphal length and biovolume were calculated in a totally automated process and novel soloftwarr routines were developed to differentiate fungal hyphae from other stained material. The principles of the software operations should be applicable to many other image analysis systems. Testing of the system against manual microscopic determination of length and known dry weights of mycelium demonstrated excellent correlation between the automated image analysis and other techniques. The method is rapid, accurate and minimises operator fatigue. Being wholly deterministic, the results obtained do not depend on the judgement of the operator. Application of the technique is illustrated with reference lo experiments studying the growth of fungi inoculated into soils and the fungal colonisation of plastics.

INTRODUCTION

Fungi are ubiquitous members of the microbial community of soil. They are involved in the decomposition of organic matter, and are therefore central to nutrient cycling, they may be associated symbiotically with plant tissue (e.g. mycorrhizas) or they may be pathogens of plants, animals or other microorganisms. However, it is extremely difficult to determine with any accuracy the size of the fungal community in the environment. This is because they are present in the environment in a variety of morphological and physiological forms and, in any case, it is difficult to distinguish an “individual” fungus in soil (Parkinson, 1982). Three approaches have been developed for measuring fungal populations in soil: isolation onto culture media (viable counts), indirect measurements and direct visual measurements. Viable count methods suffer from a number of drawbacks, primarily that the nutrient medium chosen will select solely for that portion of the community which is capable of growth upon the combination of substrates provided. Furthermore, the meaning of count data is questionable since one “individual” fungus in soil may yield numerous colonies when plated onto nutrient medium. However, viable count methods may have applications in studies of specific fungi that can be isolated on selective media. Indirect measurements of soil fungi fall into two categories: respirometric studies and the assay of cellular components. Selective inhibitors of eukaryotic and prokaryotic respiration have been used to monitor the relative

contributions of bacteria and fun@ to CO1 production in soil (Anderson and Domsch, 1973; West, 1986) but this does not indicate biomass per se. ATP, DNA and protein assays have been widely employed for estimation of total microbial biomass in soils (Holm-Hansen, 1973; Torsvik and Gokseyr, 1978; Parkinson, 1982; Macdonald, 1986) but these cannot differentiate between the bacterial and fungal contributions. Chitin and ergosterol assays are more specific to fungi and have been widely tested (Ride and Drysdale, 1972; Sharma et al., 1977; Whipps and Lewis, 1980; West et al., 1987). However, correlation of assay data to biomass concentration is extremely difficult except in monoculture experiments when calibration curves can be prepared. In addition, chitin assays with soil samples are greatly complicated by contaminating material originating from the chitinous exoskeletons of soil microfauna. Consequently, visual techniques are preferred for the determination of vegetative fungal biomass in soil (Frankland er al., 1978). Two techniques are commonly employed for sample preparation: the agar-film technique (Jones and Mollison, 1948) and membrane filtration (Hansen et ul., 1974). In the former, soil is ground and dispersed in molten agar and films of known thickness are cast upon microscope slides. In the latter, soil is suspended in buffer and known volumes are filtered so that fungal mycelia are retained on the membrane filter. Both types of preparation may then be stained and the fungal hyphae measured. Fungal concentration is measured as total hyphal length per unit of soil but approximate estimates of biovolume and biomass may be 609

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made following the determination of the appropriate conversion factors. Data obtained by the two methods have been shown to be correlated (West, 1988) but it is generally found that the membrane filter technique is simpler to perform and more flexible as regards the use of stains (B&h and Sbderstrom, 1980; West, 1988). With visual techniques it is the measurement of hyphal length that is the most time-consuming and fatiguing process. Manual measurement of hyphal length on projected images or on photomicrographs or the simpler grid-intersect techniques (Olson, 1950; West, 1988) are both slow and difficult processes. Image analysis systems are revolutionising approaches for the detection, enumeration and monitoring of microorganisms in their natural environments. They have been widely used for measuring bacterial cell size and numbers, particularly in aqueous environments (Sieracki et al., 1985; Bjornsen, 1986; Getliff and Fry, 1989), and for monitoring microbial growth on surfaces (Caldwell and Lawrence, 1989). Specific descriptions of the use of image analysis for fungi have been made (Adams and Thomas, 1988) but fully automated techniques to provide a wholly deterministic procedure are as yet rare. We describe the development, validation and application of a method for the determination of fungal hyphal length in soil that involves the membrane-filter technique coupled with an automated, computer&d image analysis system. The method has the advantage of being rapid, permitting differentiation between viable and dead hyphae and of being highly flexible enabling its wider application, for example monitoring fungal coverage of surfaces. MATERIALSAND METHODS

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until extensive sporulation was evident. Sterile fstrength Ringer’s solution was pipetted onto the plates and the spores were dislodged by gently rubbing the colony surface with a sterile glass spreader. The suspension was removed by pipette and filtered through sterile pads of washed glass wool to remove mycehal debris. The spore concentration was determined by means of haemacytometer counts. Aliquots of air-dry Keycol soil (5 f 0.05 g) were placed into sterile lOOm1 glass conical flasks. To these were added sufficient spore suspension or sterile distilled water (uninoculated controls) to adjust the water content of the soil to 70% of the field water holding capacity. The flasks were stoppered with foam bungs and incubated at 30°C in a moist atmosphere. Duplicate flasks of each treatment were taken at time 0 and periodically thereafter to determine fungal content. Assessing fungal growth on synthetic polymers Synthetic polymer surfaces were chosen to model the colonisation of solids by fungi. The assessments were made according to ASTM Standard Practice G2 l-70 (American Society for Testing and Materials, 1985) with minor modifications. Flat, 6 x 6 cm squares of nylon 6.6 and polypropylene were surface sterilised with 70% (v/v) ethanol, inoculated by dig ping into a mixed spore suspension of Aspergillus niger, Penicillium funiculosum, Chaetomium globosum, Gliocladium virens and Aureobasidium pullulans and placed on mineral salts agar (American Society for Testing and Materials, 1985) in Petri dishes. Polymer samples which had been exposed to U.V.radiation for 100 h were also used. After incubation at 30°C for 21 days, fungal growth was assessed by visual examination of the polymer squares and by epifluorescent microscopy-based image processing and analysis (see below).

Fungal strains Strain CL1 was kindly provided by Jon Wright, formerly of the Department of Biological Sciences, Portsmouth Polytechnic, Portsmouth, U.K. This organism was supplied as “Chrysosporium lignorum” (Bergman and Nilson) but its true taxonomic identity is not known. Phanerochaete chrysosporium ATCC 24725, Aspergillus niger ATCC 9642, Penicillium funiculosum ATCC 9644, Chaetomium globosum ATCC 6205, Gliocladium virens ATCC 9645 and Aureobasidium pullulans ATCC 9348 were purchased from the American Type Culture Collection. Inoculation and growth of fungi in soil For routine testing of the image analysis system, aliquots of mycelium were added to a sieved (~2 mm) sandy agricultural soil collected from Keycol. Kent, U.K. Cultures were grown in malt extract broth (Oxoid) for 72 h at 30°C with rotary agitation at 200 rev min-‘. Mycelium was harvested by centrifugation at ca. 3500 g and 20°C for 30 min, washed twice in sterile distilled water and re-pelleted. Samples of the mycelium were added to portions of soil as required. Studies were also made to monitor the growth of spores of the test fungi inoculated into soil. Spore suspensions were prepared by growing the fungi on malt extract agar plates (Oxoid) at 30°C

Extraction of fungi from soil The method employed was based on that of West (1988). 10 g soil was added to 500 ml sterile tstrength Ringer’s solution and blended in an MSE Atomix blender for 60 s. The extract was diluted as necessary in sterile tstrength Ringer’s solution and 10 ml aliquots filtered through 0.80pm pore-size polycarbonate membrane filters (Millipore). Staining procedures Solutions of Calcofluor M2R (l.Omgml-I), 8anilino-1-naphthalene sulphonic acid (3.0 mg ml-‘) and fluorescein diacetate (10 pg ml-‘) were used for staining. Filters were stained by covering their surfaces with stain solution for 2 h (Calcofluor and 8-anilino- 1-naphthalene sulphonic acid) or for 10 min (fluorescein diacetate). Suction was applied to the filters to remove the stain solution and they were washed with several volumes of distilled water. Filters were placed on microscope slides, secured at their edges with a little sticky tape and overlaid with a cover slip. With fluorescein diacetate-stained samples, viable hyphae were detected due to the production of fluorescein from this substrate by esterase activity. Samples of plastic were stained with Calcofluor M2R for 10 min, rinsed several times in distilled water and examined directly.

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Fungal biomass by automated image analysis Microscopy and image analysis Samples were examined by differential interference contrast or U.V. epifluorescence illumination using a Zeiss Axioplan microscope. For epifluorescence illumination a mode1 HBOSO high pressure Hg lamp, G365 exciter filter, FT395 chromatic beam splitter and LP420 barrier filter were used. A x 10 objective lens was used and images were captured directly into a Teragon-Contextvision GOP-302 image analysis system (Contextvision AB, Linkoeping, Sweden) via the blue channel of a JVC KY320B RGB video camera. A total of 50 0.74mm* fields were captured per filter by randomly positioning the microscope stage. The images were processed as a field of 512 x 512 pixels using a combination of inbuilt functions and software written in-house. The approach taken was developed from a method used for a deterministic procedure to detect liver cell nuclei (Graham et al., 1989). To determine hyphal length there were six main modules in the analysis operation as illustrated in Fig. 1. The initial action of this system gives a total line length on each image in pixels. Since at the magnification employed one pixel has a mean length of 1.889pm, it is possible to calculate the total hyphal length in the field and ultimately to convert this to length in the original soil sample. A similar process was employed to determine surface coverage by fungi. The hyphal length data were converted to biovolume by use of a conversion factor calculated from measurements of the mean hyphal diameter of the fungi employed. Further details on the development and operation of the image analysis software are given in the Results section. s----_---_--__ EPIFLUORESCENTIMAGE

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Fig. 1. Flow diagram of the image analysis technique developed. The main software “modules” are indicated by MI-S.

Hyphal length determination by means of a digitiser tablet Photographs of epifluorescent images were taken for automated image analysis by means of a JVC KY320B video camera. Hyphal length was determined manually by tracing the hyphae visible in the photographs with the pen of a Reichert-Jung MOPAM03 digit&r tablet (Reichert-Jung U.K. Ltd, Slough, U.K.). Traced length was calculated by the software built into the digit&r unit. Biomass dry weight determination Samples of fungal mycelium to be added to soil were taken for dry weight determination. Triplicate samples were placed in tared vessels and dried at 105°C until the residue weight was constant. RESULTS

Staining Both Calcofluor M2R and 8-anilino-I-naphthalene sulphonic acid were found to stain fungal hyphae and plant material and render them clearly visible against the background in soil and on the plastic surfaces (Fig. 2). Samples stained with 8-anilino-l-naphthalene sulphonic acid faded within a few minutes under U.V. illumination whereas those stained with Calcofluor retained their fluorescence for more than an hour. Consequently Calcofluor M2R was chosen as the stain for the image analysis technique. Staining with fluorescein diacetate resulted in strong fluorescence of active biomass [Fig. 2(E)]. This was also prone to rapid fading under U.V. illumination. Development of image analysis software Once suitable staining methods had been devised, the key difficulty with the determination of hyphal length in soil samples by image analysis proved to be differentiating between fungal material and other objects which took up the stain, particularly plant tissue. A number of approaches were taken to the image analysis until the operation summa&d in Fig. 1 was arrived at. Full details of the specific algorithms employed are given elsewhere (C. J. Cooper, S. A. Lee, N. W. Philips, S. C. Graham and P. Morgan, Binary, in preparation) but techniques may be applied equally well on other image analysis systems. There are six software stages in the analysis operation and the development of the image during their operation is illustrated in Fig. 3. Module 1 is a line enhancement operation employing a complex procedure for estimating the location and strength of the dominant line structures in the image. Module 2 is a thresholding routine for removing background from the image. The threshold is determined for each image by successively examining the number of objects in the image viewed at increasing threshold settings and choosing that value at which the number of objects detected stabilises. Module 3 excludes circular objects, which are likely to be contaminants, by a compactness operation. Compactness is defined as p2/47tA, where p is the length of the perimeter of the object and A is its area. Circular objects have a compactness of 1, whereas less circular objects

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Fig. 5. Hyphal length in uninoculated soil (0) and following inoculation with spore suspensions of “Chrysosporium lignom” (8) or Phwterochuete c~~y~o~~~~ (A). Data were obtained from SOimages of duplicate incubations. The arrow indicates where soils were dried in order to inhibit further fungal growth.

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Fig. 4. Validation of automated image analysis technique against alternative measures of fungal biomass. (a) Hyphal length measured using the image analysis system in soils spiked with different amounts of fungal biomass. Biomass was determined by means of dry weight on triplicate samples of mycelium. Hyphai kngth data were obtained br analysis of SOmicroscopic images of extracts diluted j$ in +ength Ringer’s solution. The line of best fit was calculated by linear regression and an r2 value of 1.0 was obtained. (b) Comparison of automated image analysis data with manually measured hyphal length. Data are expressed in terms of hyphal kngth per photographic image prosed. The line of best fit was calculated by linear regression and an r2 value of 0.96 was obtained.

have a compactness of < 1. Therefore by setting a threshold value, rounded objects can be excluded from the image. Module 4 thins all detected lines to one pixel width. Module 5 is a routine for excluding plant material. It was not possible to reliably differentiate between fungal hyphae and 6laments of plant origin by direct measurement of width, for two reasons. Firstly, the hyphae were only a few pixels wide and accurate, reproducible determination of width was not possible. Secondly, the line enhancement routine (module 4) reduces all lines to a single pixel width, in any case. Consequently, an alternative approach was developed to eiiminate plant material from the image. Plant material generally appeared to be more compact and more intensely stained than fungal material and therefore a thresholding process could be used to differentiate between them. A threshold value was determined which eliminated plant material from the image without affecting the hyphae and this process was incorporated into the automated routine. By constructing a binary mask of

Table 1. Fungal growth on polymer squares asset& by visual action acwrdittg to the A!STM standard method G21-70and by epifluomseent mierose~py-basedmmtttwd imgc analysis. TheA!STMacetittgaystanis based on aseigaing a rating to the otecrved growth on qximcns. Rating d&itions: no growth, 0; ttnccs of growth (C 10% coverage), 1; tight growth (IO-30%), 2; medium growth (30-60%), 3; heavy growth (60% to compktc coverage), 4 Fungal growth on polymer Polymer

Treatment

ASTM score

Hyphal length (rtm mm-‘)

Hyphal volume (umJmm-z)

Nylon 6.6 Nylon 6.6 PolypropYkne PdyproPYk= Filter paper

none U.Y. aged

1-2 2 0 0 4

4700 10,412 270 1867 No data

28,950 64,122 1662 Il,Mo No data

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WY. aged control

Fig. 2. (Seefici~gpoge.) Photomicrographs illustrating staining techniques for the visualisation of fungal hyphae in soil and on inert surfaces. (A) Differential interference contrast (D.I.C.) image of Calcofluor MZR-stained soil sample; (8) The same image under combined D.I.C. and U.V. fluorescence illumination; (C) The same image under WV. fluorescence i~umina~on; (D) Joint Calcofluor M2R and fluoresain dhtcetate-stained image under conditions for ~su~~tion of Calcofluor fluorescence; (E) The same imageunder conditions for visualisation of fluorescein diacetate fluorcscen~; (F) u.v.-aged polypropylene surface tested according to ASTM method G21-70. S indicates soil particles and H indicates fungal hyphae.

Fungal biomass by automated image analysis

the plant material from the original image and superimposing it on the partially processed image from module 4 any lines resulting from stained plant tissue are masked. This module may be eliminated if observations show that plant material is absent. Following the image preparation, the final stage of the process counts the number of pixels illuminated on screen. Since the equivalent size of one pixel at this magnification can be determined it is a simple matter to calculate the total length or volume of hyphae in the sample. The entire image analysis process for one image takes cu. 50 s and about 60% of this time is taken up by the line detection routine. By automatically sequencing the analysis of the 50 captured images from each filter it is possible to process data from a filter in under 40 min. Application of technique

When hyphal length was determined in soils supplemented with known amounts of vegetative mycelium a linear response (rz = 1) was achieved in samples containing O-30 mg dry wt added biomass [Fig. 4(a)]. To further validate the method, data obtained by automated image analysis were compared with those obtained by manual measurement of hyphal length using a digit& tablet. Very close agreement between the methods was obtained IFig. 4(b); r2 = 0.961. Note that these determinations were made independently by different operators without knowledge of results obtained by the alternative methods. The automated image analysis technique was applied to the growth of fungi inoculated into soil (Fig. 5). It was found to show a rapid increase in total hyphal length following inoculation with subsequent dying back after the soil dried. Image analysis also permitted measurement of surface colonisation by fungi and examination of the patterns of fungal growth [Fig. 2(F)]. In contrast, when growth was assessed visually according to the ASTM standard method (American Society for Testing and Materials, 1985) major underestimates of fungal colonisation resulted (Table 1). This was because much of the hyphal coverage was not visible to the naked eye.

DlSCUSSlON

The extraction, staining and automated image analysis technique developed permits relatively fast and straightforward evaluation of fungal biomass in soil and on inert surfaces. The homogenisation and filtration process is particularly simple to perform yet has been shown to produce data that are equivalent to those obtained by more laborious techniques, such as the agar film method (West, 1988). For most samples tested in our study, homogenisation of log soil in 500 ml fstrength Ringer’s solution followed by

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a further if dilution was found to give excellent images. Calcofluor was found to give the best staining of mycelium and has been shown to give results comparable with other stains (West, 1988). The biggest problem with Calcofluor is that it has a high afhnity for cellulose fibres and tends to stain plant material very strongly (Postma and Altemuller, 1990). However, the image analysis protocol developed was able to resolve hyphae even in plant-rich samples, following refinement of the algorithm for threshold determination. (C. J. Cooper et al., lot. cit.). Calcofluor has also been shown to stain effectively bacteria and fungi in soil smears (Postma and Altemtiller, 1990) and could be employed widely in studies of soil microbiology. Fluorescein diacetate (FDA) has been shown to be a reliable vita1 stain and data obtained using FDA staining and hyphal measurement are comparable with values obtained by different techniques (SBderstrGm, 1977; Ingham and Klein, 1984; Stamatiadis et al., 1990). In this study FDA was found to produce images of good quality that could be processed by the image analysis system. In conjunction with the staining techniques used, automated image analysis was found to be a rapid, simple and effective method of determining total fungal biomass (Calcofluor) or viable fungal biomass (FDA) even in “dirty” samples such as plant debrisrich soil. Excellent correlation between measured hyphal length and added biomass was obtained when soils supplemented with fungi were analysed (Fig. 4). The technique also permitted detailed evaluation of fungal growth on surfaces and yielded far more reliable data than the visual observation recommended in the standard test method employed (American Society for Testing and Materials, 1985). The subjective nature of visual assessments in the biodeterioration testing of plastics, and the difficulties in the interpretation of the assessment procedure, are well known (Seal and Pantke, 1986, 1988). Our experience suggests that image analysis techniques could lead to a better quantification of microbial growth on synthetic polymers. The principles of the image analysis routine we describe here are equally applicable to many other manufacturer’s systems. Experienced users should be able to construct similar routines for their own instruments and thereby take advantage of the benefits that the described technique offers. Various reports have demonstrated the widespread applicability of image analysis in microbial ecology but many of the described applications have relied on human intervention in the analysis operation. Our technique is largely automated and, once images have been captured, image analysis can run continuously without human intervention. This permits analysis of a much larger number of samples with minima1 operator fatigue and maximum accuracy. The data

Fig. 3. (See facing page.) Processing of stained soil sample by the image analysis system. (A) Original Calcofluor-stained cpifluorescent image; (B), Grey-scale image after line detection (software module 1); (C), (D) Thresholding of line image to eliminate background material (software module 2); (E) Removal of circular objects by means of compactness (software module 3); (F) Thinning of detected lines to single pixel width (software module 4); (G) Elimination of plant material by means of a binary mask (software module 5), (H) Processed image (green) overlaid on original image.

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can therefore be processed more rapidly than is possible for image analysis systems ~ui~ng manual selection of objects for processing. Furthermore, once the parameters for differentiating between fungal hyphae and other stained linear material in the samples have been programmed, the method is free from the subjective selection of objects for processing that is necessary in much image analysis reported to date. This eliminates operator differences, fatigueinduced bias and permits the application of constant processing within an experiment even though the images may have been obtained at different times. In addition to measurements of hyphal length in soil and, the coverage of inert surfaces as tested in our study, it would be a simple matter to adjust the analyser software or the s~ning techniques or both to investigate a variety of other topics, including the morphological structure of fungi in bioreactors (Adams and Thomas, 1988), bacterial numbers and biovolume in environmental samples (Postma and Ahemiiller, 1990) or the coionisation of biological surfaces by microorganisms (Caldwell and Lawrence, 1989). REFJ%RRNCRS

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