Biodiversity and Conservation 10: 1933–1947, 2001. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.
Functional diversity and community structure of microorganisms in rhizosphere and non-rhizosphere Canadian arctic soils L. TAM1 , A.M. DERRY2 , P.G. KEVAN1 and J.T. TREVORS1,∗ 1 Department of Environmental Biology, University of Guelph, Ontario, Canada, N1G 2WL; 2 CW405, Department of Biological Sciences, University of Alberta, Edmonton, Canada, AB T6F 2E9; ∗ Author for correspondence (e-mail:
[email protected]; fax: +1-519-837-0442)
Received 12 July 2000; accepted in revised form 3 January 2001
Abstract. Functional diversities of microorganisms in arctic soil samples at three incubation temperatures were assessed using sole-carbon-source-utilization (SCSU). Soil samples from four sites were collected from the rhizosphere and non-rhizosphere soils. Microorganisms were extracted from samples and inoculated into ECO-Biolog plates and incubated at 4, 10 and 28 ◦ C. Calculations of Shannon–Weaver diversity and Shannon–Weaver evenness were based on the substrate utilization in the Biolog plates. Shannon–Weaver diversities (H ) in rhizosphere samples were significantly greater (x¯H = 3.023 ± 0.197; P < 0.005) than in non-rhizosphere samples (x¯H = 2.770 ± 0.154). Similarly, the evenness (E) of the inoculated microbial cells exhibited significant differences (P < 0.005) between the rhizosphere and non-rhizosphere soil samples (x¯E = 0.880 ± 0.057 for soils with rhizosphere; x¯E = 0.807 ± 0.044 for non-rhizosphere samples). Higher microbial diversity and evenness were observed in samples incubated at 4 ◦ C than at 28 ◦ C [least significant difference (lsd) = 0.29], and evenness indices were higher in rhizosphere samples than in non-rhizosphere soils incubated at all three temperatures (lsd = 0.02). Principal component analysis (PCA) of the multivariate data set differentiated the soil samples on the relatively gross scale of microbial communities isolated from rhizosphere and non-rhizosphere soils at all three temperatures. Key words: Arctic, biodiversity, Biolog, Canada, community structure, microorganisms, non-rhizosphere soil, rhizosphere soil
Introduction Soil microbial diversity is essential for long-term ecosystem sustainability (Staddon et al. 1998a,b). Soil microorganisms fulfil essential ecosystem functions, such as biogeochemical cycling, decomposition of plant, animal and microbial wastes, and of pollutants; in addition they participate in symbiotic relationships with higher organisms (Kunc 1994; Staddon et al. 1997; Marilley et al. 1998). A number of studies have investigated changes to structure and functional diversities of microbial communities from a variety of soil environments such as the differences between rhizosphere and non-rhizosphere zones (Bachmann and Kinzel 1992; Tedla and Stanghellini 1992; Young et al. 1995; Thompson et al. 1992; Kim et al. 1999) and functional microbial diversity in arctic soils (Derry et al. 1999a). Soil microbial populations can respond to the
1934 release of organic nutrients near plant roots (Marilley et al. 1998; Jjiemba and Alexander 1999). This may be significant in arctic regions where plant-enriched areas may have higher microbial diversity compared to mineral-rich areas (Derry et al. 1999a,b). Differences in microbial community composition among soil samples have been noted elsewhere (Zak et al. 1994; Staddon et al. 1997; Derry et al. 1999a) (Table 1). There are several factors hypothesized to influence biodiversity: the age of a particular area for species colonization, spatial heterogeneity, competition, predation, climatic variability, productivity and disturbance (Staddon et al. 1997; Pianka 1966). Other variables such as soil texture and chemical contamination/pollution can contribute to a lower functional microbial diversity and slower decomposition rates at high latitudes, which may result in poorer nutrient quality of soils (Staddon et al. 1998a; Pianka 1966). Table 1. Comparisons of reported Shannon–Weaver diversity and Shannon evenness indices between different ecosystems to high arctic tundra soil samples (standard deviation in parentheses). References
Ecosystem
Communities
Shannon diversity
Shannon evenness
Zak et al. (1994)
Chihuahuan desert High arctic tundra Chihuahuan desert Pine forest High arctic tundra High arctic tundra Pine forest High arctic tundra Pine forest High arctic tundra Pine forest
Creosotebush bajada Sporobolus grassland Raised sand beach
0.05 1.40 1.72 (0.23)
N/A N/A 0.50 (0.019)
Herbaceous bajada
1.99
N/A
Scarified, mineral forest soil Sand dune
2.65 (0.41) 2.74 (0.10)
0.73 (0.10) 0.80 (0.01)
Non-rhizosphere soil
2.77 (0.15)c
0.88 (0.06)
Timber, mineral forest soil Rhizosphere soil
2.97 (0.21) 3.02 (0.20)d
0.74 (0.04) 0.81 (0.04)
Burned, mineral forest soil Unenriched mineral tundra soil
3.47 (0.49) 3.62 (0.73)
0.80 (0.08) 0.84 (0.001)
Clear-cut, mineral forest soil Scarified organic forest soil Anthropocentrically enriched organic tundra soil Organic tundra soil fertilized from animal activity Clear-cut, organic forest soil Burned organic forest soil Timber, organic forest soil
3.75 (0.31) 3.92 (0.10) 4.04 (0.17)
0.86 (0.04) 0.88 (0.02) 0.91 (0.0004)e
4.12 (0.06)
0.92 (0.0002)e
4.21 (0.13) 4.26 (0.13) 4.32 (0.11)
0.94 (0.03) 0.93 (0.02) 0.95 (0.03)
Tam (2000)a Zak et al. (1994) Staddon et al. (1996) Tam (2000)b This study Staddon et al. (1996) This study Staddon et al. (1996) Derry et al. (1997) Staddon et al. (1996) Derry et al. (1997) Derry et al. (1997) Staddon et al. (1996)
High arctic tundra High arctic tundra Pine forest
a Unpublished data. Sample collected from sandy dune site at top of Cape Martyr (74◦ 42 N, 95◦ 02 W,
elev. 166 m), Nunavut, Canada, 1998. Unpublished data. Sample collected from sandy dune on Melleville Island, Nunavut, Canada, 1998. Average of data from four rhizosphere sites at all three temperatures. Average of data from four non-rhizosphere sites at all three temperatures. Unpublished data.
b c d e
1935 There are few experimental approaches for assessing the diversity of microbial communities. Most methods are based on isolation and culture techniques that are restrictive because only a minority of microorganisms are selected (Kunc 1994; Krebs 1994). These methods involve physiological tests, fatty acid profiles, protein analysis or DNA fingerprinting, and metabolic reactions to reflect microbial activity in soil (Kunc 1994). Recently, molecular methodology has been used to determine bacterial diversity in bulk and rhizosphere soil fractions by denaturing gradient gel electrophoresis (DGGE) and PCR restriction analysis of 16S rDNA (Muyzer et al. 1993; Marilley et al. 1998; Kim et al. 1999). There are some procedural concerns in the extractability of DNA from a heterogenous suspension of cells, DNA amplificability, and biases during ligation (Kim et al. 1999) that are inherent to these methods. An alternative method for describing microbial diversity is to use sole-carbon-sourceutilization (SCSU) patterns obtained on Biolog micro-plates (Biolog Inc., Hayward, California). SCSU is a rapid method to assess the immediate carbon source utilization of samples by avoiding the isolation and culturing of microbes (Garland and Mills 1991; Staddon et al. 1997; Derry et al. 1998). In addition to identifying bacterial isolates, Biolog plates have been useful in characterizing bacterial communities from various environments including soil (Garland and Mills 1991; Staddon et al. 1997), fresh water (Garland and Mills 1991), sediments (Fredrickson et al. 1991) and activated sludge (Guckert et al. 1996). ECO-Biolog type plates were used for our analyses. These plates differ from the GN-Biolog plates used in earlier studies, where the carbon substrates in the GN plates are designed to differentiate and identify gram-negative isolates (Guckert et al. 1996; Staddon et al. 1997; Derry et al. 1998). Rather than 95 different carbon substrates on each plate, one ECO-Biolog plate contains three replications of 31 different substrates that are most useful for environmental community analysis (Table 2). The relevancy of the 31 substrates from the ECO-Biolog plate in the soil environment still requires additional research. However, both types of plates have the capacity to discriminate among the heterotrophic microbial communities (Choi and Dobbs 1999). The assay of substrate utilization patterns provides substantial phenotypic information of microbes and analysis of natural samples allows discrimination between habitats (Konopka et al. 1998). The primary objective of our study was to determine if differences exist in microbial functional diversity and community structure between rhizosphere and non-rhizosphere arctic soils. Our second objective was to determine the effect of temperature on patterns of microbial activities on the ECO-Biolog plates using arctic soil samples. A paucity of information exists in this area of arctic environmental microbiology. An examination of microbial diversity in an extreme arctic environment is imperative to our understanding of microbial activities by providing valuable information on microbial communities and their role in biogeochemical processes (Kim et al. 1999; Krebs 1994).
1936 Table 2. ECO-Biolog microplate carbon substrates used to calculate Shannon–Weaver diversity and Shannon evenness values. Water Pyruvic acid methyl ester Tween 40 Tween 80 α-Cyclodextrin Glycogen D -Cellobiose α-D-Lactose β-Methyl-D-glucoside D -Xylose I-Erythritol D -Mannitol N-Acetyl-D-glucosamine D -Glucosaminic acid Glucose-1-phosphate D , L -α-Glycerol phosphate
D -Galactonic
acid γ -lactone acid 2-Hydroxy benzoic acid 4-Hydroxy benzoic acid γ -Hydrozy butyric acid Itaconic acid α-Keto butyric acid D -Malic acid L -Arginine L -Asparagine L -Phenylalanine L -Serine L -Threonine Glycyl-L-glutamic acid Phenylethylamine Putrescine D -Galacturonic
Materials and methods Study sites and soil sampling Two soil samples, about 500 g each, were collected from each of four different sites near Resolute Bay on Cornwallis Island, Nunavut, Canada, in August 1998. At each of the four sites, one sample of rhizosphere and another of non-rhizosphere soil were taken within 1 m of each other. Rhizosphere samples were collected from areas with vascular plants (dominant vegetation at: site 1 (74◦ 46 N, 95◦ 05 W) Dryas integrifolia, site 2 (74◦ 46 N, 94◦ 05 W) Salix arctica, site 3 (74◦ 46 N, 95◦ 03 W) Carex spp., and site 4 (74◦ 45 N, 95◦ 02 W) moss where soil on and immediately next to the plant roots were collected. Sampling was done in this manner to not damage or destroy the natural vegetation which was left on the site. Non-rhizosphere samples (mineral soil) were collected from the same areas 1 m from the vegetation where no roots existed. Samples were placed in separate zip-lock bags and kept cold under ambient conditions until transport to the University of Guelph where they were immediately frozen at −40 ◦ C until use. Prior to experimentation, samples were thawed over four days by gradually increasing the temperature (day 1 at −20 ◦ C, days 2 and 3 at −10 ◦ C, day 4 at 4 ◦ C) to prevent thermal shock and cell lysis. Extraction of microbial cells from soil samples Microbial cells were extracted from the soil samples so that differential utilization of the carbon sources in the ECO-Biolog plates could be assessed. Methods for the
1937 extraction of microbial cells from samples were according to the protocol of Derry et al. (1999a). Soil samples of 25 g were suspended in sterile 0.1% (w/v) sodium pyrophosphate (adjusted to pH 7.0) with 4 g of 2–3 mm diameter sterile glass beads in a 125 ml stoppered flask. The flasks containing the soil solutions were shaken at 140 rpm for 1 h at 22 ◦ C. The suspension from each flask was decanted into a sterile 250 ml Nalgene polypropylene centrifuge bottle (Fisher Scientific, Toronto, Canada) and centrifuged at 2000 × g for 10 min at 4 ◦ C. This procedure was completed three times so a total volume of 35 ml of 0.1% (w/v) sodium pyrophosphate was used. Previous studies have used a 1:10 (w/w) dilution in suspending cells extracted from soil (Staddon et al. 1997; Derry et al. 1999a). However, it has been observed that the 1:10 dilution contains 105 –107 cells ml−1 . A density of 108 cells ml−1 is needed for colour development on Biolog microplates (Konopka et al. 1998). The 35 ml of soil extract was centrifuged at 2000 × g for 10 min at 4 ◦ C. The top of 10 ml of the supernatant fluid was removed and placed into a sterile 50 ml centrifuge tube. The removed 10 ml volume was replaced with sterile 0.1% (w/v) sodium pyrophosphate (pH 7.0) and the mixture shaken for 1 h at 140 rpm, followed by centrifugation at 2000 × g at 4 ◦ C to remove soil debris yet leave the cells in suspension. It was necessary to use at least 2000 × g centrifugation to pellet the soil debris. Removal of the supernatant containing the cells and centrifugation was repeated until there was 30 ml of pooled supernatant, containing the extracted microbial cells. This sample was centrifuged again at 2000 × g for 10 min at 4 ◦ C and the pooled supernatant fluid transferred to a second sterile 50 ml centrifuge tube that was centrifuged at 8000 × g for 10 min at 4 ◦ C to pellet the cells. The resulting pellet contained the extracted microorganisms. The supernatant fluid was replaced with 30 ml of sterile, double-distilled water and the microbial cell pellet was washed three times by centrifugation (8000 × g for 10 min at 4 ◦ C). The pellet of microbial cells was resuspended in 30 ml of sterile 0.85% (w/v) NaCl, vortexed and transferred to a sterile test tube and capped. An aliquot of each supernatant sample was measured at 600 nm using a BioRAD micro plate reader (Model 3350-UV) with 0.85% (w/v) NaCl as the blank (Staddon et al. 1997; Derry et al. 1998) to determine the turbidity. The extract from each soil sample was used to inoculate three ECO-Biolog plates with 100 µl per well. Each ECO-Biolog plate contained three replicates of 32 different substrate wells, with the exception of the reference well. One ECO-Biolog plate was inoculated with one sample from each site and was incubated at 28, 10 or 4 ◦ C. Each plate was scanned every 24 h at 595 nm for 28 days to take into account lag, exponential, and stationary phases associated with growth on the Biolog substrates (Staddon et al. 1997; Derry et al. 1999a). Data analysis Differences in microbial diversity between the soil types across the temperature gradient were statistically analyzed with a randomized complete block design
1938 (RCBD-ANOVA). Comparisons were made between the soil types within a given temperature treatment using mean Shannon–Weaver indices, which represented maximum growth of microorganisms isolated from rhizosphere and non-rhizosphere soil samples (Figures 1a–c). The Shannon–Weaver diversity index was calculated for each soil sample using the formulae provided in Table 3 (Staddon et al. 1997; Derry et al. 1999a) and the least significant difference (lsd) test was used to detect differences between means in diversity (Steel et al. 1997). The Shannon–Weaver diversity index takes into account both the richness and evenness of substrate utilization. Richness is defined as the number of different groups of microorganisms found occurring together and evenness is the expected distribution of microbial groups within the community (Atlas 1984). This measure of diversity is limited as different communities can have the same index of diversity. Consequently, principal component analysis (PCA) was used to distinguish between microbial communities (Derry et al. 1999a). PCA of the data set differentiates samples on the scale of microbial habitat type as it allows for comparisons of microbial samples on the basis of differences in the pattern of SCSU (Garland and Mills 1991). The ability to distinguish among samples within similar habitats was a much more powerful test of the resolving power of the assay, allowing for the examination of structuring agents within specific types of microbial communities (Garland and Mills 1991) as well as changes over time (succession) in communities exposed to both natural (root exudates) and anthropogenic (industrial wastes) sources. Systat (Systat v.8.0, SPSS Inc., Chicago, Illinois) and Sigmastat (Sigmaplot v.5.0, Jandel Scientific, San Rafael, California) were used to conduct statistical analyses.
Results The analysis of microbial communities by ECO-Biolog microplates produced a large data set as each of the 96-wells per plate yielded absorbance measurements taken Table 3. Formulae for Shannon–Weaver diversity and Shannon evenness. Index
Definition
Shannon diversity
Measure of richness and evenness
Formula H = − pi ln pi
H = C/N (N ln N − ni ln ni )
Shannon evenness
Evenness calculated from Shannon Index
E = H/ln S
Definitions pi = proportional colour development of the ith well over total colour development of all wells of a plate N = sum of positive optical densities on a Biolog plate ni = zero or positive optical density of a test well on a Biolog plate C = 2.3 H = Shannon index of diversity S = number of wells with colour development
1939 over a 7-, 14-, or 28-day incubation time period. As a result, 72 curves needed to be analyzed. Figures 1a–3b represents average Shannon–Weaver diversity data for rhizosphere and non-rhizosphere soil from each of the four sampling sites. Diversity differences between soil types according to Shannon–Weaver index Mean values for Shannon–Weaver diversities of samples of rhizosphere soils were significantly greater (x¯H = 3.02 ± 0.20, P < 0.005) than non-rhizosphere soil samples
Figure 1. Shannon diversities of microorganisms isolated from arctic (a) rhizosphere and (b) non-rhizosphere soil samples and incubated at 4 ◦ C for 28 days (each data point is average of three replicates).
1940
Figure 2. Shannon diversities of microorganisms isolated from arctic (a) rhizosphere and (b) non-rhizosphere soil samples and incubated at 10 ◦ C for 14 days (each data point is average of three replicates).
(x¯H = 2.77 ± 0.15, P < 0.005) at all three temperatures (F1,15 = 39.444) (Figures 1a–3b). Differences in diversity between similar soil types at all three incubation temperatures were also detected (F2,15 = 24.818, P < 0.005, RCBD-ANOVA) where diversity was higher in samples incubated at 4 ◦ C (Figures 1a,b) compared to those at 28 ◦ C (lsd = 0.29) (Figure 3a,b). Soil type and incubation temperature acted independently on the diversity of microorganisms in the samples (P > 0.10), as determined by RCBD-ANOVA.
1941
Figure 3. Shannon diversities of microorganisms isolated from arctic (a) rhizosphere and (b) non-rhizosphere soil samples and incubated at 28 ◦ C for 7 days (each data point is average of three replicates).
Differences in Shannon–Weaver evenness values between soil samples at a given incubation temperature It is interesting to examine the Shannon–Weaver diversity curves of the non-rhizosphere soil samples at 28 ◦ C compared to those at 4 and 10 ◦ C (Figures 1–3b). The duration of incubation between the temperatures clearly indicate differences in microbial growth as the 28 ◦ C curve did not reach an asymptote as the 4 and 10 ◦ C
1942 incubations. It is recommended soil samples at 28 ◦ C should be incubated for at least 14 days to ensure the Shannon–Weaver diversity curve reaches an asymptote. It is also important to note the decrease in the Shannon–Weaver diversity curve of the non-rhizosphere Dryas site at 28 ◦ C. It is possible this soil sample attained maximum diversity within the first 24 hours after inoculation before the first absorbance reading as the inoculum may have contained a high density of active microbes, thus able to immediately utilize the carbon source while the other inocula remained in lag phase of growth. The Shannon–Weaver evenness values of the rhizosphere soil samples (x¯E = 0.88 ± 0.06, P < 0.005) were significantly higher than non-rhizosphere soils (x¯E = 0.82 ± 0.04, P < 0.005, RCBD-ANOVA). Differences in evenness in the distribution of substrate utilization also existed between incubation temperatures (F2,15 = 24.819, P < 0.005, RCBD-ANOVA) for similar soil types where samples incubated at 28 ◦ C had lower evenness than samples at 10 and 4 ◦ C (lsd = 0.02). According to PCA, microbial communities isolated from rhizosphere soil samples differentiated from microorganisms isolated from non-rhizosphere soil samples at all three temperatures (Figures 4–6). We also qualitatively observed microbial differentiation between the sampling sites according to the predominant vegetation type at each incubation temperature. The greatest resolution between soil types was observed at 10 ◦ C while samples at 28 ◦ C were the least resolved (Figures 4–6). There was clear clustering of diversity indices at each vegetated soil site as seen at the 10 ◦ C incubation temperature (Figure 5).
Figure 4. PCA of SCSU results for arctic rhizosphere and non-rhizosphere soil samples after 28 days incubation at 4 ◦ C on ECO-Biolog plates for all four sites (each data point is average of three replicates).
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Figure 5. PCA of SCSU results for arctic rhizosphere and non-rhizosphere soil samples after 14 days incubation at 10 ◦ C on ECO-Biolog plates for all four sites (each data point is average of three replicates).
Figure 6. PCA of SCSU results for arctic rhizosphere and non-rhizosphere soil samples after 7 days incubation at 28 ◦ C on ECO-Biolog plates for all four sites (each data point is average of three replicates).
Discussion Plant material is known to be the dominant factor determining microbial biodiversity for most rhizosphere systems (Denton et al. 1999; Bowen 1980). It is known that higher levels of soil nutrients can result in higher growth rates of arctic plants which leads
1944 to enhanced rates of nutrient cycling (Bachmann and Kinzel 1992; Hobbie 1995). The diversity of microorganisms was greater in rhizosphere soil samples than in non-rhizosphere soils. The observed differences in microbial diversity between soil types may reflect variation in the presence of carbon substrates as well as differential use of the substrates by microorganisms. The lower diversity of microorganisms in non-rhizosphere soil samples may reflect the nutrient-poor status of the arctic soil samples used in this study. Furthermore, the Shannon–Weaver evenness values associated with both rhizosphere (x¯E = 0.88 ± 0.06) and non-rhizosphere soil samples (x¯E = 0.82 ± 0.04) indicated that substrate utilization was not uniform in the soil samples. This implies that microorganisms are not evenly distributed in the soil sample. Nutrient cycling and availability are likely to be greater in warmer climates because of greater microbial activity than in the cold arctic environment (Berg et al. 1995). This would affect community evenness between the soil types because rhizosphere samples contain a greater concentration and variety of organic substrates compared to non-rhizosphere soil sites. Psychrophilic bacteria have an optimum growth temperature ≤ 15 ◦ C with an upper limit for growth at 20 ◦ C and a minimal growth temperature at 0 ◦ C or below (Brock et al. 2000). It is possible that the dominant groups of bacteria were psychrophiles and psychrotrophs, capable of growth with an optima of > 20 ◦ C. It is uncertain if the psychrotolerants can grow as fast as the mesophiles. As reported by Derry et al. (1999b), lower functional microbial diversity at 28 ◦ C compared to 4 ◦ C suggests that incubation of arctic microbes in ECO-Biolog plates at the lower temperature provides conditions more representative of the environment from which the microbes were extracted (Brady and Weil 1996; Derry et al. 1999b). There are sources of variation inherent to the SCSU methodology that may confound our interpretation of substrate utilization. Garland and Mills (1991) suggested that the application of the community-level approach to assays of microbial function would provide a more sensitive and ecologically meaningful measure of heterotrophic microbial community structure. Substrate utilization patterns has generally been used in two ways: as a tool to characterize individual strains and as a means to characterize microbial community diversity (Konopka et al. 1998). Several procedural issues, however, must be considered in using SCSU in characterizing microbial diversity. Is the inoculum preparation representative of the numbers and diversity of microbes present in the environment? Garland and Mills (1991) suggested that bacterial cell densities in the inoculum could affect the length of the lag period prior to colour development in the ECO-Biolog wells. This reflects the time required for the microbial cells to grow and reach a density at which colour development is observed (a density of 108 ml−1 is needed) (Konopka et al. 1998). Soils suspensions prepared at a 1:10 (w/w) dilution in water contain 105 –107 cells ml−1 (Konopka et al. 1998). Colour development may be dominated by species that are present at high numbers or have high growth rates. Garland and Mills (1991) recommends repeated temporal monitoring of the colour in each of the wells and calculation of
1945 average well colour development (AWCD). To compensate for unequal cell densities, incubation periods for temperature treatments were prolonged and data was transformed by division by the AWCD of each plate (Garland and Mills 1991; Derry et al. 1999b). Other researchers have discussed disadvantages of the Biolog method due to the differential growth of microorganisms that occurs in the individual microplate wells (Guckert et al. 1996; Konopka et al. 1998). If the objective of microbial community analysis is to characterize functional diversity then the carbon substrates in Biolog plates should reflect the diversity in the environment (Konopka et al. 1998). Since the types and concentrations of carbon sources available in ecosystems are likely to differ among samples, substrate utilization on Biolog microplates may not represent information about the actual dominant members of the microbial community, but rather a pseudo-community that is a product of selective enrichment. Konopka et al. (1998) argued that the SCSU approach is best used to determine if environmental samples differ in their carbon response patterns. Nevertheless, studies using the SCSU approach have examined gross differences between microbial communities such as post-disturbance forest soils (Staddon et al. 1997) and creosote-contaminated versus uncontaminated soils (Derry et al. 1998). Despite limitations of the SCSU methodology, it is still a useful tool in providing phenotypic information on microbial diversity in the Arctic environment. As one of the first studies in Arctic soil microbial biodiversity, our research illustrates the relationship between diversity and the presence of plants in arctic soils. Considering the vulnerability of the Arctic ecosystem to pollution precipitants from the industrial south, biodiversity research that shows significant impact of the rhizosphere on microorganisms in the biogeochemical cycling in Arctic soils has important implications in monitoring long-term global impacts of pollution and climatic change.
Acknowledgements This research was supported by Natural Science and Engineering Research Council Canada operating grants to J.T. Trevors and to P.G. Kevan.
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