Functional diversity of microbial communities in the mixed boreal plain forest of central Canada

Soil Biology & Biochemistry 37 (2005) 1359–1372 www.elsevier.com/locate/soilbio

Functional diversity of microbial communities in the mixed boreal plain forest of central Canada Christopher White, Jacques C. Tardif*, Anne Adkins, Richard Staniforth Centre for Forest Interdisciplinary Research (C-FIR) and Department of Biology, University of Winnipeg, 515 Portage Avenue, Winnipeg, MB, Canada R3B 2E9 Received 31 May 2004; received in revised form 10 December 2004; accepted 21 December 2004

Abstract This study was designed to examine whether or not specific tree species (Picea glauca, Picea mariana, Pinus banksiana, Populus tremuloides), their post-fire stand age, or their position in a successional pathway had any significant effect on the functional diversity of associated soil microbial communities in a typical mixed boreal forest ecosystem (Duck Mountain Provincial Forest, Manitoba, Canada). Multivariate analyses designed to identify significant biotic and/or abiotic variables associated with patterns of organic substrate utilization (assessed using the BIOLOGe System) revealed the overall similarity in substrate utilization by the soil microbial communities. The five clusters identified differed mainly by their substrate-utilization value rather than by specific substrate utilization. Variability in community functional diversity was not strongly associated to tree species or post-fire stand age; however, redundancy analysis indicated a stronger association between substrate utilization and successional pathway and soil pH. For example, microbial communities associated with the relatively high pH soils of the P. tremuloides–P. glauca successional pathway, exhibited a greater degree of substrate utilization than those associated with the P. banksiana–P. mariana successional pathway and more acidic soils. Differences in functional diversity specific to tree species were not observed and this may have reflected the mixed nature of the forest stands and of their heterogeneous forest floor. In a densely treed, mixed boreal forest ecosystem, great overlap in tree and understory species occur making it difficult to assign a definitive microbial community to any particular tree species. The presence of P. tremuloides in all stand types and post fire stand ages has probably contributed to the large amount of overlap in utilization profiles among soil samples. q 2005 Elsevier Ltd. All rights reserved. Keywords: BIOLOGe; Boreal forest succession; Functional diversity; Microbial communities; Partial-PCA/RDA; Picea glauca; Picea mariana; Pinus banksiana; Populus tremuloides; Gap

1. Introduction Differences in the structure and function of heterotrophic microbial communities in forest soils have been linked primarily to the quantities and qualities of soil organic materials (Saetre and Ba˚a˚th, 2000). These, in turn, are influenced by the availability and biochemical composition of the litter derived from the dominant tree species (Johansson, 1995) and their associated understory vegetation (Priha et al., 2001), as well as from root exudates (Grayston and Campbell, 1996). A patchy distribution of organic litter may result in a high degree biochemical * Corresponding author. Tel.: C 1 204 786 9475; fax: C 1 204 774 4134. E-mail address: [email protected] (C. White). 0038-0717/$ - see front matter q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.soilbio.2004.12.007

compartmentalization (Bauhus et al., 1998; Priha and Smolander, 1999; Coˆte´ et al., 2000; Priha et al., 2001) and may ultimately cause a spatial aggregation of the forest soil microbiota (Saetre and Ba˚a˚th, 2000). Thus a post-fire successional sequence from deciduous to coniferous species will have a significant impact on the morphological and physiological profiles of soil microbial communities (Bormann and Sidle, 1990; Bending et al., 2002; Merila¨ et al., 2002). This may be due to differences in the structure and composition of leaf and needle litter, which alter nutrient availability in the soil (Priha and Smolander, 1997) and cause shifts in microbial populations as the community adapts to new environmental conditions. The assessment of the composition and/or function of soil microbial communities presents a number of

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challenges. Conventional methods, such as the most probable number (MPN) and plate-counting techniques, were designed to determine the total numbers and/or species of microorganisms in a particular soil. However, these methods provide only limited information regarding the functional diversity of the microbial community. In recent years, a rapid method of fingerprinting the metabolic potentials of bacterial communities has supplanted traditional methodology. In spite of various limitations (see reviews by Konopka et al., 1998; Garland, 1999; PrestonMafham et al., 2002), the BIOLOGe System has been used to characterize microbial communities associated with various crop types (Garland and Mills, 1991; Garland, 1996b), agricultural soils (Adkins and Burton, unpublished data), grasslands (Zak et al., 1994), tree species (Grayston and Campbell, 1996), boreal forest soils (Staddon et al., 1998b; Adkins et al., 2001), and plant rhizospheres (Grayston et al., 1998). In addition, Staddon et al. (1998a) differentiated between soil microbial communities along a climatic gradient, and Winding (1994) was able to discriminate between bacterial communities of different soil types. Whereas the functional link between plant and soil microbial communities has now been clearly established (Broughton and Gross, 2000), there are some aspects of this connection that are not well understood. For instance, the influences of tree stand composition, their post-fire ages, and stages of forest succession on the activities of soil microbial communities are far from clear. We hypothesize that soils sampled from beneath four tree species: jack pine (Pinus banksiana Lamb.), black spruce (Picea mariana [Mill.] B.S.P.), trembling aspen (Populus tremuloides Michx.), and white spruce (Picea glauca [Moench] Voss), will show differences in patterns of microbially-mediated biochemical activities when compared amongst themselves or with soils from forest gaps (without tree cover). In addition, we also expect to demonstrate that changes in the functional diversity of microbial communities will parallel the successional changes found in the above-ground postfire communities.

2. Materials and methods 2.1. Study area The study area is part of the Duck Mountain Provincial Forest (DMPF; 51839 0 N; 100854 0 W) in western Manitoba, Canada. The DMPF is a prominent topographical feature rising above the Manitoba lowlands by 369–523 m, to an altitude of 839 m above sea level (Manitoba Provincial Parks Branch, 1973). The complex physiography of this region strongly influences its vegetation, soils, groundwater hydrology, and mesoclimate, creating a diverse assemblage of forest communities (Hamel and Kenkel, 2001).

Vegetation in the DMPF is composed primarily of mixed forest with trembling aspen, balsam poplar (Populus balsamifera L.), white spruce, and balsam fir (Abies balsamea [L.] Mill.) forming the dominant overstory associations. Jack pine and black spruce are dominant on sandy uplands and black spruce is dominant in poorly drained areas (Klenner and Kroeker, 1990). Many lakes of glacial origin have been filled in by vegetation and now form black spruce and tamarack (Larix laricina [Du Roi] K. Koch) bogs (Sauchyn and Hadwen, 2001). The nearest meteorological station is located at Swan River (52803 0 N; 101813 0 W). For the reference period of 1971–2000, the daily mean January and July temperatures were K18.2 and C18.1 8C, respectively, and the average annual precipitation was 530.4 mm, 65.5% of which fell as rain between May and September (Environment Canada, 2002). 2.2. Soil sampling Soil samples were collected in June 2002, from forest stands representing different post fire ages and successional pathways. Three fire ages (1890s, 1930s, and 1960s) and two successional pathways (P. banksiana to P. mariana and P. tremuloides to P. glauca) were selected. Post-fire age was determined in each of the selected stands, during previous dendroecological studies (Tardif, 2004). In total, 167 soil samples were collected from 18 forest stands, nine representing each successional pathway. In each successional pathway, three site replicates were selected from within each post-fire aged stand (1890s, 1930s, and 1960s). Ideally, each stand replicate had a different pre-disturbance age, to minimize the effect of prefire composition and fire intensity (see Pare´ et al., 1993). In each of the 18 forest stands, eight dominant trees were selected and sampled, four from each of the species characterizing the successional pathway. A total of 36 samples were obtained from each of the four tree species, resulting in a total of 144 soil samples. In addition, attempts were made to select two gaps at each forest stand; however, only 23 of the desired 36 gaps were located. Four cylindrical soil samples, 5 cm deep and 10 cm in diameter, were collected at compass points (N–E–S–W) midway between the stem center and the periphery of its crown, for each of the selected trees. Soil samples from the 23 forest gaps were collected at compass points at 1 m from the center of the gap. In each case, the litter horizon (L) was removed and the resulting soil samples contained both fibrous (F) and humus (H) horizons. Some samples contained the upper mineral horizon (Ae), depending on thickness of the F, and H horizons. The four samples from each tree or gap were combined in the field and placed in a plastic bag, labeled, and stored in a portable cooler at 4 8C in the dark. Upon return from the field, each soil sample was passed through a 6.35 mm sieve to remove rocks and large

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debris. The samples were then stored in the dark at 4 8C until preparation of the inoculums. 2.3. Site characterization

CIi Z

4 X ðDj =Di Þ=Distij

the following formula: Moisture content ð%Þ Z ½ðWfms K Wods Þ=Wfms  !100%

A competition index was calculated for each selected tree to characterize the canopy influence on each soil-sampling site. This was accomplished by measuring the diameter at breast height (dbh) of each selected tree, the distance from the selected tree to the nearest competitor in each compass quadrant, and the dbh of each competitor. From this information, a competition index was calculated using the following formula (Avery and Burkhard, 2002) (1)

jZ1

where CIi is the competition index, Dj is dbh of jth competitor, Di is dbh of subject tree, and Distij is distance between subject tree i and the jth competitor. We characterized the sampling sites by measuring and averaging the LFH thickness and taking densiometer readings at each of the four soil collection points. The attributes of litter associated with each sampling point were classified according to one of 18 categories of litter and debris. Sampling points containing large pieces of wood were not sampled, and the sampling location was moved accordingly (Pare´ et al., 1993). Soil pH was determined by mixing approximately 10 g of sieved soil with 20 mL of 0.01 M CaCl2. The suspension was stirred for 30 min and allowed to settle for 20 min at which time the pH was read using an Acumet Model 230 pH/ion meter (Fisher Scientific Co., Montreal, Que.). Soil conductivity was determined using the 1:2 extract method (Bower and Wilcox, 1965; Scott, 1998).

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(2)

where Wfms is the weight (g) of field moist sample and Wods is the weight (g) of oven-dried sample. The method used for inoculum preparation was adapted from Zak et al. (1994), Staddon et al. (1998b), and Lupwayi et al. (1998). Field-moist, sieved soil samples equivalent to 10 g dry weight (10 g Wods) were added to 90 mL of sterile sodium pyrophosphate buffer (0.1% w/v Na4P2O710H2O) and 3 g of 3 mm glass beads in a 250 mL Erlenmeyer flask. The flask was shaken on a rotary shaker (200–250 rpm) at 25 8C, 1 h. The flask was left to stand at 25 8C for 15 min, and approximately 30 mL of the cleared suspension was transferred into a sterile centrifuge tube and centrifuged (700!g, 10 min, RT) to pellet soil particles remaining in the suspension. The resulting supernatant was decanted and filtered through two layers of sterile gauze, to remove any remaining organic debris. The filtrate was centrifuged at 8000!g for 10 min and the resulting pellet containing the representative bacterial community was resuspended in sterile physiological saline (0.85% NaCl, w/v). Inoculum density can affect rate of color formation and variation in density among samples can result in classification based on density of microorganisms rather than metabolic profile (Haack et al., 1995; Garland, 1996a). Thus, each sample inoculum was standardized to an optical density (OD590) of 0.1, considered the maximum density exhibited by all samples. Standardized inoculum was added in 125 mL aliquots to each well of the GN BIOLOGe plate, which was then incubated in the dark at 25 8C for up to 96 h (BIOLOG, 1993).

2.4. Preparation of BIOLOGe inoculum and inoculation

2.5. Image acquisition and determination of color development

Samples were processed and inoculated onto Gramnegative (GN) microplates as soon as possible to reduce impacts of prolonged soil storage. The mean time between soil collection and inoculation of the BIOLOG plate was 47 daysG14 (nZ167). This storage time compared to that of other studies (Ba˚a˚th et al., 1995; Lupwayi et al., 1998; Bending et al., 2002; Baath and Anderson, 2003). Processing of the samples was done in a way that ensured that samples from each successional pathway were prepared daily to limit methodological effects on substrate utilization profiles. The moisture content (%MC) of each field-moist sieved soil sample was determined using a method described by Scott (1998), which ensured that a constant dry-weightequivalent of soil was used for preparation of BIOLOGe inoculum. A 10–15 g sub-sample was oven dried (110 8C) to constant weight and its moisture content (amount of water present at time of processing) was determined using

A digital image of each GN BIOLOGe plate was obtained every 24 h, over the 96 h incubation period (see Grayston et al., 1998). To assess the degree of substrate utilization in each well, each image was analyzed by measuring the intensity of color development caused by incorporation of tetrazolium dye into the respiring bacterial community (Zak et al., 1994). Examination of color development in each plate over the 96 h time period displayed a pattern of metabolic activity representative of the functional attribute of the inoculated bacterial community (Bochner, 1989). The images of each plate were obtained using a high resolution digitizing video scanner. Image analysis was performed using WinCell 2001 Pro (Regent Instruments, 2001), which assessed the color intensity of each well at 24, 48, 72, and 96 h after inoculation. A numerical value was assigned to each well based on a semi-quantitative scale varying from 0 to 2. Prior to analysis a range of color

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intensities was assigned to each value (0–2), where a value of zero corresponded to wells with no color change (intensity comparable to that of the control well), and values of one and two corresponded to low and high color intensities, respectively. This was comparable to the semiquantitative scale used by Shishido and Chanway (1998), except that we used an image analysis system to avoid biases associated with visual estimation. The use of a semiquantitative scale and the associated reduction in number of comparisons to be made among samples, is less sensitive to biases resulting from soil storage or inoculum preparation. As stressed by Konopka et al. (1998), the use of quantitative data compared to presence-absence data provides a wider spectrum of response (more capacity to compare different samples) but puts more demand on data standardization. In most studies using BIOLOG plates, a single incubation period was used to derive and analyze the data. The use of a fixed time comparison of the community patterns has been criticized (Garland, 1999; PrestonMafham et al., 2002). In this study, rather than presenting four analyses (24, 48, 72 and 96 h) or choosing a single period, we derived a simple index that reflected the kinetic of color development and its intensity (integral of color development, Garland, 1999). To calculate this index, the numerical values assigned to each well over the 96 h incubation period were transformed into a percent (%) value that took into account both speed and intensity of color formation (Table 1). A percent value of 0 indicated no color development after 96 h and a value of 100 indicated that maximum color development occurred after 24 h (Table 1). Because of the nature of our index, we did not transform our data using the average well color development (AWCD) method. This transformation was meant as a correction for the initial differences in inoculum density (Garland, 1999) Table 1 Percent value assigned to each well based on the rate and intensity of substrate usage 24 h

48 h

72 h

96 h

%

0 0 0 0 0 0 0 0 1 0 1 0 1 1 2

0 0 0 0 0 1 0 1 1 1 1 2 1 2 2

0 0 0 1 1 1 2 1 1 2 1 2 2 2 2

0 1 2 1 2 1 2 2 1 2 2 2 2 2 2

0 12.5 25.0 25.0 37.5 37.5 50.0 50.0 50.0 62.5 62.5 75.0 75.0 87.5 100.0

Values of 0–2 are semi-quantitative and indicate intensity of substrate utilization, based on intensity of color development at different time period. A value of 0 indicates no detectable color change, a value of 1 indicates light color development and a value of 2 indicates dark color development.

and its usage has also been criticized (Preston-Mafham et al., 2002). In our study, we attempted to reduce the impact of inoculum density by initially standardizing each sample inoculum to an optical density (OD590) of 0.1 before inoculation. Further, we believe that our semi-quantitative analysis of color development was more robust (less sensitive) to variation in inoculum density. 2.6. Statistical analysis The percent values obtained for each of the 95 substrates for all 167 soil samples were analyzed using multivariate techniques to differentiate among microbial communities based on substrate utilization profiles (Garland and Mills, 1991; Winding, 1994; Zak et al., 1994; Grayston and Campbell, 1996; Garland, 1996a,b). These methods were also used to assess the association between substrate utilization profiles and environmental factors. Two complementary approaches were followed. First, both principal components analysis (PCA) and redundancy analysis (RDA) were calculated using the percent values for each of the 95 substrates from the 167 soil samples. In addition, we calculated one partial-PCA and one partial-RDA using the number of days between soil sample collection and BIOLOG plate inoculation for each soil sample (storage period) as a covariable. The results from the partial-PCA (-RDA) were then compared to that of the original PCA (RDA). The rational for this analysis was to test if differences in functional diversity observed between soil samples could be attributed to transformations in soil properties occurring during the storage period (Preston-Mafham et al., 2002). In other words, we wanted to assure that our findings (or lack of) were not reflecting storage time but actual differences in community signature that could be attributed to tree species, stand age, etc. If results from the partial-PCA and the original PCA (partialRDA and RDA) were not statistically different, then it could be concluded that storage time had a negligible influence on our results and their interpretation. For all these analyses, calculations were done using a covariance matrix, because our descriptors (i.e. utilization of substrates) were of the same kind, shared the same order of magnitude and were measured in the same units (Legendre and Legendre, 1998). Note that RDA, the canonical form of PCA, is a multivariate ‘direct’ gradient analysis intended to display the main trends in variation of a multidimensional data set in a reduced space of few linearly independent dimensions (Legendre and Legendre, 1998). In RDA, the ordination components are constrained to be linear combinations of supplied environmental variables (ter Braak and Prentice, 1988; ter Braak, 1994; ter Braak et al., 1998). In RDA, significant environmental variables (P!0.05) were selected using a forward selection followed by a Monte Carlo permutation test based on 999 random permutations (Manly, 1998). All ordinations were computed using the program CANOCO

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(Version 4.0) and scaling of the ordination components was done using a correlation biplot (ter Braak, 1994). Second, the results from the ordination analyses were paired with those derived from classification (TWINSPAN: Two-Way Indicator Species Analysis; Hill, 1979) and multivariate discriminant analysis (MDA). A polythetic divisive classification was calculated with the program TWINSPAN to classify soil samples based on their substrate utilization profile. This classification method used a chi-squared measure of distance and contrasted with PCA–RDA methods, which are based on Euclidian distances. For a more detailed presentation of these methods, we refer the reader to Legendre and Legendre (1998). Calculations were done using PCORD 4.0 for Windows (McCune and Mefford, 1999). All parameters were left to the default setting except for the pseudo-species cut levels, which were set as follows: 0, 12.5, 25, 37.5, 50, 62.5, 75 and 87.5% to retain the semi-quantitative nature of our data (Table 1). Following grouping of samples, multivariate discriminant analysis (MDA) was used to identify the best subset of (i) substrates and (ii) environment variables responsible for discrimination among these groups. A stepwise forward procedure was used to identify discriminatory factors and the probability to enter and to be removed from the model was fixed to a probability value of alpha equal to 0.05. MDA was also used to predict group membership based on (i) substrate utilization and (ii) environmental factors. All MDAs were calculated using program SYSTAT v 9.1 (SYSTAT, 1998). The environmental data matrix used in both RDA and MDA was constructed using both abiotic and biotic site factors that were assumed to influence microbial substrate utilization. In this matrix, all classes of the qualitative environmental variables were transformed into dummy binary variables (Legendre and Legendre, 1998). The environmental factors included the following: tree species (four qualitative variables), gap (one qualitative variable), stand age (three qualitative variables), species-gap/age combination (15 qualitative variables), successional pathway (one qualitative variable), conductivity, pH, subject tree height (m), subject tree dbh (cm), mid-canopy distance (cm), LFH thickness (cm), densiometer reading (%), and a competition index (seven tree species). In addition, litter type (15 variables: moss, spruce needle, pine needle, bark, deciduous leaf, spruce cone, branches, grass, pine cone, down woody debris, spruce bought, pine bought, lichen, charcoal and animal waste) was assessed at each point of sample collection. Percent values for litter categories were then assigned as follows: 100% if the type of litter was found at all four sampling points, 75% at three sampling points, 50% at two, 25% if found at one, and 0% if not found at any. Litter types observed in less than five of the 167 sampled sites were excluded because of their rarity.

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3. Results A substrate utilization matrix (Table 2) showed at least some utilization of all 95 carbon-substrates by microorganisms in the soil samples. All soil samples catabolized 44 of the substrates, stressing the overall similarity among the samples. Only six substrates were catabolized by less than 50% of the soils (lowest to highest relative frequency: 92, 40, 53, 63, 27 and 82). In terms of reaction intensity, 20 substrates registered an overall mean utilization value above 70% (lowest to highest utilization mean value: 32, 41, 49, 84, 18, 54, 69, 29, 85, 70, 38, 74, 81, 45, 57, 68, 43, 42, 58 and 39). Eleven substrates recorded an overall mean value of less than 20% (lowest to highest value: 92, 40, 53, 63, 82, 51, 46, 71, 21, 27 and 77). For example, substrates 92 (2,3butanediol), 40 (formic acid), 53 (a-keto valeric acid), and 63 (glucuronamide) occurred least frequently and had the lowest reaction value (Table 2). 3.1. Principal components analysis (PCA) Both the partial-PCA calculated using soil storage days as a covariable and the original PCA provided very similar results (figures not presented). The sum of all eigenvalues after fitting the covariable went from 1.000 to 0.964. Only a slight decrease in the overall variance expressed by the first two components was noticed (partial-PCA: 0.3636 and 0.0651, respectively; original PCA: 0.3941 and 0.0652, respectively), their loadings for the 95 wells and the 167 soil samples were significantly correlated (95 wells: PC-1: 0.992, p!0.0001; PC2: 1.000, p!0.0001 and 167 soil samples: PC1: 0.960, p!0.0001; PC2: 0.999, p!0.0001). This indicated that ‘storage time’ had a negligible influence on our ability to compare soil samples and their respective communities. Therefore, only results from the original PCA are presented. The PCA results showed that the first principal components expressed a large portion (39.4%) of the total variance when compared to the subsequent three components (6.5, 4.23, and 3.3%, respectively). Overall, the first two components explained 45.9% of the total variance. The positive value of the 95 substrates indicated that all substrates were positively correlated the first principal component (Fig. 1), and stressed the commonality in substrate utilization among all samples, varying basically only in intensity and speed of reaction (arrow length). The substrates with scores close to the origin represented those for which the average utilization value (U%) was most similar among samples. Substrates with long vectors were those with maximum variation in U%. The representation of the mean value and standard deviation (contour) of the ordination scores for each of the species-gap/age nominal classes further indicated that substrate utilization was not strongly associated with either stand age or with the tree species under which the soil samples were taken (Fig. 2). In general, there was high

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Table 2 Relative frequency of utilization (F%) and average utilization value (U%) for each Biolog substrate for the five community types (clusters) identified by Twinspan Well

Carbon substrate

Cluster (number of samples) 1 (nZ40)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57

Water a-Cyclodextrin Dextrin Glycogen Tween 40 Tween 80 N-acetyl-D-Galactosamine N-acetyl-D-Glucosamine Adonitol L-Arabinose D-Arabitol Cellobiose i-erythritol D-Fructose L-Fucose D-Galactose Gentiobiose a-D-Glucose m-Inositol a-D-Lactose Lactulose Maltose D-Mannitol D-Mannose D-Melibiose b-Methyl D-glucoside D-Psicose D-Raffinose L-Rhamnose D-Sorbitol Sucrose D-Trehalose Turanose Xylitol Methylpyruvate Mono-methyl succinate Acetic acid cis-Aconitic acid Citric acid Formic acid D-Galactonic acid lactone D-Galacturonic acid D-Gluconic acid D-Glucosaminic acid D-Glucuronic acid a-Hydroxybutyric acid b-Hydroxybutyric acid g-Hydroxybutyric acid p-Hydroxy phenylacetic acid Itaconic acid a-Keto butyric acid a-Keto glutaric acid a-Keto valeric acid D,L-Lactic acid Malonic acid Propionic acid Quinic acid

2 (nZ28)

3 (nZ34)

4 (nZ27)

5 (nZ38)

NZ167

F%

U%

F%

U%

F%

U%

F%

U%

F%

U%

F%

U%

0.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 75.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 97.5 100.0 100.0 50.0 100.0 100.0 100.0 100.0 100.0 92.5 100.0 100.0 100.0

0.0 57.8 62.2 60.3 63.4 71.6 55.9 74.4 57.2 64.7 65.0 65.9 41.3 68.1 64.4 72.5 63.4 72.2 66.9 48.1 26.6 65.6 73.8 70.6 63.1 72.2 32.8 65.3 75.3 73.4 73.1 75.3 59.1 51.6 70.0 38.8 36.3 75.3 79.7 7.8 74.1 79.4 76.3 62.5 75.3 21.9 69.7 61.9 74.4

0.0 96.4 100.0 100.0 100.0 100.0 96.4 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 96.4 100.0 100.0 100.0 96.4 96.4 100.0 100.0 100.0 100.0 100.0 67.9 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 96.4 100.0 100.0 100.0 25.0 100.0 100.0 100.0 100.0 100.0 71.4 100.0 100.0 100.0

0.0 46.9 60.3 57.1 61.2 67.9 37.5 70.5 50.0 62.5 63.4 52.7 39.7 63.4 60.3 65.2 60.7 71.4 63.8 42.9 21.9 62.9 71.9 65.6 58.9 62.1 28.6 61.6 73.7 68.8 68.3 70.5 48.7 45.1 65.2 33.5 36.2 75.9 77.2 3.6 71.9 76.8 75.4 62.5 75.9 17.0 67.4 61.6 73.7

0.0 97.1 97.1 100.0 100.0 100.0 97.1 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 94.1 79.4 100.0 100.0 100.0 100.0 100.0 17.6 100.0 100.0 100.0 100.0 100.0 97.1 100.0 100.0 88.2 97.1 100.0 100.0 2.9 100.0 100.0 100.0 100.0 100.0 58.8 100.0 97.1 100.0

0.0 48.9 53.3 52.2 59.6 66.5 29.4 64.3 43.4 61.0 61.4 41.2 38.2 62.9 60.3 64.3 52.2 73.2 63.6 19.1 11.0 54.0 69.1 65.1 45.2 51.8 4.8 37.5 73.5 66.5 67.3 66.9 32.4 40.1 65.1 27.6 32.7 75.0 76.8 0.7 69.5 75.7 75.0 61.4 75.0 10.7 66.5 55.5 71.3

0.0 96.3 100.0 96.3 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 92.6 100.0 100.0 100.0 96.3 100.0 25.9 100.0 100.0 100.0 100.0 100.0 92.6 100.0 100.0 81.5 96.3 100.0 100.0 3.7 100.0 100.0 100.0 100.0 100.0 44.4 100.0 92.6 100.0

0.0 40.7 61.6 46.3 60.6 63.9 50.9 75.0 45.4 62.5 62.0 55.6 39.8 64.4 61.6 66.7 57.9 72.7 65.3 16.2 12.0 63.0 70.8 69.9 57.9 71.3 9.7 58.3 74.5 68.5 70.4 74.1 56.5 43.5 65.7 27.8 35.6 74.1 75.0 0.5 73.1 75.0 75.0 62.5 75.0 7.4 66.7 43.5 74.1

0.0 73.7 86.8 84.2 100.0 100.0 57.9 100.0 100.0 100.0 100.0 78.9 94.7 100.0 94.7 100.0 89.5 100.0 100.0 42.1 23.7 92.1 100.0 100.0 65.8 81.6 15.8 76.3 92.1 100.0 97.4 100.0 36.8 97.4 100.0 63.2 68.4 100.0 100.0 0.0 100.0 100.0 100.0 97.4 100.0 13.2 100.0 81.6 97.4

0.0 18.8 26.0 25.0 59.5 62.5 14.5 56.3 38.2 52.0 56.3 20.4 31.6 50.0 54.3 63.8 26.6 69.1 62.5 5.6 3.0 32.6 64.8 58.2 16.8 27.0 5.3 24.3 66.1 62.8 59.5 64.8 12.2 36.8 58.6 14.5 22.4 68.1 74.0 0.0 65.1 72.4 74.3 59.2 70.7 2.3 61.8 36.5 64.8

0.0 92.2 96.4 95.8 100.0 100.0 88.6 100.0 100.0 100.0 100.0 95.2 98.8 100.0 98.8 99.4 97.6 100.0 100.0 85.0 76.6 98.2 100.0 100.0 91.6 95.8 40.7 94.6 98.2 100.0 99.4 100.0 83.8 99.4 100.0 85.6 91.0 100.0 100.0 17.4 100.0 100.0 100.0 99.4 100.0 56.3 100.0 94.0 99.4

0.0 42.5 51.7 47.8 60.9 66.6 37.2 67.7 46.9 60.3 61.5 46.6 37.9 61.5 60.1 66.7 51.4 71.6 64.4 26.5 14.9 54.9 70.0 65.7 47.4 55.9 16.4 48.6 72.5 68.0 67.6 70.2 40.8 43.5 64.9 28.3 32.3 73.5 76.6 2.7 70.6 75.9 75.2 61.5 74.3 12.0 66.4 51.8 71.4

100.0 95.0 100.0 67.5 100.0 100.0 100.0 100.0

63.1 19.7 71.6 10.9 75.0 46.3 42.5 75.3

100.0 78.6 100.0 35.7 100.0 100.0 100.0 100.0

63.8 15.6 67.9 5.8 74.6 41.5 41.5 74.6

100.0 61.8 97.1 23.5 100.0 100.0 100.0 100.0

62.5 11.4 65.8 3.3 70.2 38.2 39.0 73.9

100.0 51.9 100.0 7.4 100.0 100.0 100.0 100.0

62.0 8.3 69.0 0.9 71.8 41.2 39.8 75.0

100.0 18.4 100.0 2.6 100.0 97.4 92.1 100.0

60.9 100.0 62.4 3.0 61.1 11.7 67.1 99.4 68.3 0.7 28.7 4.6 67.4 100.0 71.7 32.2 99.4 39.8 29.3 98.2 38.2 74.0 100.0 74.6 (continued on next page)

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Table 2 (continued) Well

Carbon substrate

Cluster (number of samples) 1 (nZ40)

58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

D-Saccharic

acid Sebacic acid Succinic acid Bromo succinic acid Succinamic acid Glucuronamide Alaninamide D-Alanine L-Alanine L-Alanyl-glycine L-Asparagine L-Aspartic acid L-Glutamic acid Glycyl-L-aspartic acid Glycyl-L-glutamic acid L-Histidine Hydroxy L-proline L-Leucine L-Ornithine L-Phenylalanine L-Proline L-Pyroglutamic acid D-Serine L-Serine L-Threonine D,L-Carnitine g-Amino butyric acid Urocanic acid Inosine Uridine Thymidine Phenyl ethylamine Putrescine 2-Amino ethanol 2,3-Butanediol Glycerol D,L-a-Glycerol phosphate Glucose-1-phosphate Glucose-6-phosphate

2 (nZ28)

3 (nZ34)

4 (nZ27)

5 (nZ38)

NZ167

F%

U%

F%

U%

F%

U%

F%

U%

F%

U%

F%

U%

100.0 100.0 100.0 100.0 100.0 62.5 100.0 100.0 100.0 100.0 100.0 100.0 100.0 87.5 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 82.5 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 15.0 100.0 100.0 100.0 100.0

80.6 34.4 38.8 38.4 67.5 10.0 47.8 62.5 62.8 62.8 75.6 75.3 75.9 25.0 45.0 67.8 75.3 44.4 57.8 26.3 62.8 69.4 69.7 75.3 12.5 62.2 73.4 74.7 65.9 60.0 60.6 60.3 57.2 62.2 3.4 72.8 39.1 60.3 70.9

100.0 85.7 100.0 92.9 100.0 42.9 100.0 100.0 100.0 100.0 100.0 100.0 100.0 57.1 100.0 100.0 100.0 100.0 100.0 92.9 100.0 100.0 92.9 100.0 67.9 100.0 100.0 100.0 100.0 100.0 89.3 96.4 100.0 100.0 7.1 100.0 67.9 92.9 100.0

76.8 25.0 37.5 34.8 65.2 7.6 39.7 62.1 61.6 60.3 75.0 73.2 75.4 10.7 39.7 68.8 73.7 36.6 52.7 18.3 62.1 67.9 58.0 74.6 9.4 55.8 73.7 73.2 61.6 44.2 25.9 57.6 60.7 61.6 2.2 64.7 23.7 45.1 62.9

100.0 94.1 100.0 100.0 100.0 23.5 97.1 100.0 100.0 100.0 100.0 100.0 100.0 47.1 100.0 100.0 100.0 97.1 100.0 97.1 100.0 100.0 97.1 100.0 44.1 100.0 100.0 100.0 100.0 100.0 85.3 100.0 97.1 100.0 8.8 100.0 70.6 79.4 100.0

76.1 30.5 37.5 35.3 68.0 2.9 43.8 62.5 64.0 59.6 75.0 72.8 73.2 10.3 31.3 68.8 74.6 34.9 52.2 19.5 62.5 66.9 60.7 75.0 5.9 59.2 72.1 74.3 61.4 41.2 23.9 59.2 53.3 60.3 2.2 65.4 21.0 28.3 47.8

100.0 85.2 100.0 96.3 100.0 18.5 92.6 100.0 100.0 100.0 100.0 100.0 100.0 63.0 100.0 100.0 100.0 100.0 100.0 92.6 100.0 100.0 100.0 100.0 44.4 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 0.0 100.0 96.3 100.0 100.0

75.0 16.2 37.5 34.3 62.0 2.8 35.2 62.5 62.5 61.6 75.0 72.2 74.1 17.1 42.1 68.1 75.0 33.8 52.8 17.1 61.1 66.7 64.8 75.0 5.6 59.7 71.8 74.5 62.5 57.4 52.3 61.1 55.6 61.6 0.0 71.3 37.0 53.7 73.6

100.0 36.8 100.0 86.8 92.1 7.9 63.2 100.0 100.0 100.0 100.0 100.0 100.0 13.2 92.1 100.0 100.0 100.0 97.4 42.1 100.0 100.0 71.1 100.0 7.9 100.0 100.0 100.0 100.0 100.0 71.1 97.4 100.0 100.0 0.0 100.0 44.7 36.8 89.5

74.0 8.6 37.5 28.6 51.6 1.3 18.4 55.3 60.5 55.3 73.4 66.4 67.8 3.3 28.3 64.1 71.4 28.6 42.4 6.6 54.6 62.5 40.8 71.7 1.0 49.0 67.1 68.4 56.6 32.6 18.1 49.3 55.9 54.6 0.0 63.5 14.1 12.2 32.9

100.0 79.6 100.0 95.2 98.2 31.7 89.8 100.0 100.0 100.0 100.0 100.0 100.0 53.3 98.2 100.0 100.0 99.4 99.4 83.8 100.0 100.0 91.6 100.0 49.1 100.0 100.0 100.0 100.0 100.0 88.6 98.8 99.4 100.0 6.6 100.0 75.4 80.2 97.6

76.6 23.2 37.8 34.3 62.7 5.0 36.9 60.8 62.3 59.8 74.8 71.9 73.1 13.4 37.1 67.4 74.0 35.9 51.5 17.6 60.5 66.6 58.5 74.3 6.9 57.1 71.5 72.9 61.6 46.9 36.3 57.3 56.4 59.9 1.6 67.6 26.8 39.2 56.7

variability among soil samples collected from under a given tree species and/or a given stand age. However, soil samples collected from under P. banksiana (PBA) and, to a lesser extent, from under P. mariana (PMA) tended to occupy the upper left portion of the ordination plan, while soil samples collected from under P. tremuloides (PTR) and P. glauca (PGL) tended to occupy the lower right portion of the ordination plan. This reflects the lower substrate utilization values of the P. banksiana (PBA)–P. mariana (PMA) successional pathway. Soil samples collected from gaps tended to occupy the lower left and central portions of the ordination plan. Despite the great variability from one soil sample to the other, the observed patterns indicated that substrate utilization profiles were influenced by existing ecological conditions under tree species.

3.2. Redundancy analysis (RDA) Comparison of the partial-RDA and the original RDA also revealed great similarities in these analyses (figures not presented). Again, the loadings for the 95 wells and the 167 soil samples were significantly correlated (95 wells: RDA-1: 0.992, p!0.0001; RDA-2: 1.000, p!0.0001 and 167 soil samples: RDA-1: 0.960, p!0.0001; RDA-2: 0.999, p! 0.0001) indicating that ‘storage time’ had a negligible incidence on our ability to identify environmental factors influencing BIOLOG utilization profiles. We expected a slightly lower correlation to occur here because site positions were constrained by the environmental variables that were found significant after 999 Monte Carlo iterations. The environmental variables selected were almost identical indicating that our results were robust. Therefore, only

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Fig. 1. PCA-ordination biplot of the 167 soil samples according to their substrate utilization profile. In PCA, all species (95 substrates) should be indicated by arrows but for clarity only two were drawn. Longer arrows indicate a greater change in substrate utilization value. In PCA, arrows with narrow angles are strongly correlated, arrows that are perpendicular show no correlation (as indicated by vectors for substrates 59 and 94) and arrows in opposite directions indicate negative correlation. More confidence characterizes comparisons between variables with longer arrows, as inferences made from variables located near the center of the diagram are often imprecise. In PCA, objects (soil samples) can also be projected at right angles into the descriptor-arrows (substrates) to approximate their value along the descriptors. The five clusters identified in TWINSPAN (Fig. 4) are also illustrated: Cluster I, empty circles; Cluster II, pale gray triangles; Cluster III, dark gray triangles; Cluster IV, empty triangles; Cluster V, filled squares.

results from the original-RDA are presented. In addition, the sample scores for the first two components of the PCA (Fig. 1) and the RDA (not presented) were highly significantly correlated (rZ0.996 and 0.862, respectively; p!0.0001; nZ167), indicating that the environmental variables constraining the ordination expressed a portion of the initial variance in the data. The first two components of the RDA explained 14.1 and 2.0% of the total variance, respectively, and speciesenvironment correlation values were 0.62 and 0.55, respectively. Significant environmental factors were as follows: soil pH, P. glauca competition (PGLc), conductivity (cond), deciduous leaves (dl), P. mariana of 1890 stands (pma90), and P. banksiana of 1960 stands (pba60). Soil pH and pglc dominated the first component of the RDA (Fig. 3). Separation along the second component was mainly related to soil conductivity, deciduous leaves, pma90, and pba60. Similar to PCA, in RDA the cosine of the angle between two arrows provides an approximation of their correlation. It can be inferred from Fig. 3, that higher (more alkaline) soil pH values prevail under P. glauca and P. tremuloides, with soil litter composed of deciduous leave. On the other hand, P. banksiana and P. mariana were associated with more acidic soil conditions. RDA further stressed the higher substrate utilization values observed in less acidic conditions for most substrates and the positive association between microbial substrate utilization and pH.

Fig. 2. PCA-ordination biplot of the 167 soil samples illustrating the mean and contour (one standard deviation) of their scores as derived for each of the 12 species/stand age nominal variables. Pba, Pinus banksiana; Pma, Picea mariana; Ptr, Populus tremuloides; and Pgl, Picea glauca. The 3 gap/stand age nominal variables are also presented by successional pathways. Gap1 refers to gaps pertaining to the P. banksiana–P. mariana pathway whereas Gap2 refers to the P. tremuloides–P. glauca) pathway. The vertical hatched circle (light gray) represents the samples taken in stands that originated in the 1890s, the horizontal hatched circle (dark gray) represents samples collected in stands that originated in the 1930s and the black lined circle represents sample that were collected in stands that originated in the 1960s.

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Fig. 3. RDA-ordination biplot of the 95 substrates and environmental variables. As in PCA, all species (substrates) should be represented by arrows but for clarity they were not drawn. The significant (p!0.05) environmental variables after 999 Monte Carlo iterations are represented by black lined arrows. The dark-gray arrows represent variables made passive in the RDA but characterized by long arrows. The addition of these variables facilitated the interpretation of the ordination diagram without affecting the analysis. Like in PCA (Fig. 1), the interpretation of the arrows follows the same rules but also extends to the environmental variables. Pba, P. banksiana; pbac, competition from P. banksiana; pba60, P. banksiana originating from the 1960s; pma, P. mariana; pma90, P. mariana originating from the 1890s; ptr, P. tremuloides; pgl, P. glauca; pglc, competition from P. glauca; dl, deciduous leaves, cond, conductivity; gras, grasses; need, P. banksiana needles.

3.3. Twinspan analysis The TWINSPAN classification of the 167 soil samples identified five clusters, which we interpreted as five distinctive microbial communities (Fig. 4). The mean number of substrate utilized was highest in cluster 1 (92.25G1.65) and lowest in cluster 5 (77.63G5.98) with clusters 2–4 having intermediate values (respectively, 89.43G3.08, 87.44G3.16 and 87.63G2.88). Cluster I (40 samples) was also characterized as having the highest average substrate-utilization value (U%) for 81 of the 95 substrates (Table 2). Conversely, Cluster V (38 samples) had the lowest U% for all 95 substrates, except D-psicose (27), -keto glutaric acid (52), quinic acid (57), succinic acid (60), and putrescine (90; Table 2). Fewer differences were observed among the three other clusters (Fig. 4, Table 2).

Fig. 4. TWINSPAN classification of the 167 soil samples based on their substrate utilization profiles showing five distinctive clusters of heterotrophic microbial communities.

For example, Cluster II (28 samples) and Cluster III (34 samples) had the highest U% for substrates 38, 45, 50, 84, 90, and 18, 62, 66, respectively (Table 2). Cluster III also had the lowest U% for substrates 27, 52, 57 and 90. Cluster IV (27 samples) had the highest U% for substrates 8, 89, and 96. Furthermore, the projection of each soil sample (in its respective cluster) onto each substrate vector in the PCA confirmed that Cluster I had an higher utilization value for most carbon-substrates, while the opposite was observed for samples from Cluster V (Fig. 1). 3.4. Multiple discriminant analysis (MDA) The MDA results indicated that substrate 3, 17, 20, 24, 25, 27, 40, 48, 77, 87, 88, and 96 had the strongest discriminant power (p!0.05, not shown). A jack knife classification of the 167 soil samples using the discriminant function found that 88% of the samples had been correctly classified in Cluster I, 71% in Cluster II, 76% in Cluster III, 85% in Cluster IV, and 89% in cluster V. These results further stressed differences among clusters in terms of substrate utilization. In contrast to substrate utilization, the surveyed environmental factors provided little discriminating power; only pH, PGL competition, and PMA 1890 were significant at p!0.05 (not shown). The mean pH value of soil samples in each cluster revealed increasing acidity from Cluster I to V (Table 3). Competition by P. glauca (PGL competition) was highest in Cluster I. Overall, PGL competition was observed in 67 instances and especially (62%) from samples collected

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Table 3 Relative frequencies (F%) and mean values of selected abiotic and biotic factors for the five community types (clusters) identified by TWINSPAN Variables

Cluster (number of samples) 1 (nZ40)

Soil pH Soil conductivity LFH (cm) Densiometer (%) PBA competitiona PMA competitiona PTR competitiona PGL competitiona PTR PGL PBA PMA GAP AGE 1890 AGE 1930 AGE 1960 Moss Pine Needles Deciduous leaves Grasses

2 (nZ28)

3 (nZ34)

4 (nZ27)

5 (nZ38)

F%

Mean

F%

Mean

F%

Mean

F%

Mean

F%

Mean

ND ND ND ND 17.1 16.4 27.9 33.6 25.0 35.0 10.0 17.5 12.5 42.5 27.5 30.0 1.3 30.6 90.6 24.4

5.4 0.7 12.5 10.3 3.8 3.5 9.0 6.8 ND ND ND ND ND ND ND ND ND ND ND ND

ND ND ND ND 25.0 16.3 42.4 7.6 28.6 10.7 14.3 28.6 17.9 25.0 39.3 35.7 0.9 37.5 89.3 21.4

5.0 0.6 13.5 10.5 5.3 6.1 11.0 1.2 ND ND ND ND ND ND ND ND ND ND ND ND

ND ND ND ND 22.7 25.0 41.4 5.5 23.5 26.5 26.5 17.7 5.9 32.4 26.5 41.2 1.5 36.8 89.7 24.3

4.8 0.6 12.2 8.0 6.4 5.7 22.6 1.2 ND ND ND ND ND ND ND ND ND ND ND ND

ND ND ND ND 25.0 21.2 28.9 18.3 18.5 22.2 33.3 22.2 3.7 40.7 18.5 40.7 0.0 56.5 88.9 15.7

4.5 0.6 10.2 7.5 8.7 2.5 7.1 3.1 ND ND ND ND ND ND ND ND ND ND ND ND

ND ND ND ND 40.2 23.2 25.0 6.3 13.2 10.5 26.3 23.7 26.3 23.7 52.6 23.7 6.6 55.3 79.0 24.3

4.2 0.6 11.0 10.9 15.7 3.7 8.7 0.9 ND ND ND ND ND ND ND ND ND ND ND ND

a For cluster 1 (nZ35), cluster 2 (nZ23), cluster 3 (nZ32), cluster 4 (nZ26) and cluster 5 (nZ28) due to the exclusion of gap samples from competition evaluations.

from under P. tremuloides and P. glauca trees. A jack knife classification of the 167 soil samples showed that: 40% were correctly classified in Cluster I, 61% in Cluster II, 15% in Cluster III, 43% in Cluster IV, and 41% in Cluster V. The low discriminant power of environmental factors indicates that the variability encountered in substrate utilization among the five clusters was not strongly associated to our measured variables.

4. Discussion The BIOLOGe system was successful in differentiating among soil samples based on patterns of substrate utilization. Both partial-PCA and partial-RDA indicated that the identified patterns were not artifacts resulting from variable storage/processing time of the soil samples. Patterns obtained from samples that were stored for 3–4 weeks were comparable to those obtained from samples stored for 6–7 weeks. Despite the disturbance that occurs during soil sample collection and processing, soil samples were classified into groups according to differences in the functional attributes of associated microbial communities. Soil pH was the most influential environmental factor responsible for discrimination among these groups. Values of substrate utilization were significantly higher in soils of higher pH. Similarly, Grayston et al. (2003) related variation in microbial diversity to differences in soil pH. Soil acidity has been linked to a decrease in the availability

of carbon to microbial communities (Anderson and Domsch, 1993; Ba˚a˚th et al., 1995), and to slower bacterial growth rates (Ba˚a˚th, 1998). However, studies have also shown optimum pH for growth of bacterial communities to be correlated with soil pH from which the communities were extracted, indicating that different bacterial communities are adapted to different pH values (Ba˚a˚th, 1996; Andersson and Nilsson, 2001). Bacterial communities associated with coniferous forest soils may contain larger proportions of Gram-positive bacteria adapted to the acidifying environment; in contrast, increases in soil pH may result in larger proportions of Gram-negative bacteria (Frostega˚rd et al., 1993; Pennanen, 2001). Differences in microbial community structure, such as this, may result in differences in the substrate-utilization profile of the community (Anderson and Joergensen, 1997). Thus, the lower values of substrate utilization observed in this study may be due to differences in microbial community structure induced by soil pH. Additionally, numbers of bacteria have been shown to decrease in acidified soils (Ba˚a˚th et al., 1980), possibly because bacteria are less adapted to acidic conditions in soil, compared to fungi (Matthies et al., 1997). The methodology of our study favored rapidly growing Gram-negative bacteria, and may not have been sensitive to fungal contributions to metabolic profiles. Though this study provided no direct evidence concerning the identity of microorganisms responsible for substrate utilization, it was possible that lower values of utilization in acidic soils were due to the insensitivity of GN BIOLOGe plates to

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the contribution of Gram-positive bacteria and fungi in the catabolism of carbon substrates. Variations in substrate utilization were not strongly associated with specific tree species from under which samples were obtained. This was unexpected since previous studies have shown that coniferous and deciduous tree species differentially affect the chemical and microbial properties of soil (Saetre et al., 1997; Priha et al., 1999; Saetre, 1999), and since tree species have varying soil fertility requirements (Sims et al., 1990). However, all of our soil samples were obtained from mixed forest stands, and the heterogeneous nature of these stands may account for the variability in substrateutilization profiles associated with a single tree species. Surrounding trees of different species as well as associated shrub and herb layers influenced carbon input and the microbial properties found in soil under the canopy of a sampled tree. Saetre and Ba˚a˚th (2000) found that Norway spruce trees exerted a much stronger influence on microbial community structure than birch trees in a mixed stand. Further, trees in mixed forests have been shown to cause soil resource heterogeneity (Kleb and Wilson, 1997) and spatial patterns of microbial activity related to the arrangement of trees within a forest stand (Saetre, 1999; Saetre and Ba˚a˚th, 2000). Thus, the spatial influence of surrounding tree and other plant species will affect the microbial community found under the canopy of sample trees to varying degrees depending on stand composition and arrangement of trees within a stand. When substrate-utilization profiles were examined at the stand level rather than at the tree species level, variation in patterns of substrate utilization could be attributed to the effects of dominant tree species (both sample tree species and competitors) on soil properties. High substrate-utilization values were observed in soils associated in the P. tremuloides–P. glauca successional pathway; in contrast, those of the P. banksiana–P. mariana successional pathway were significantly lower. The occurrence of P. glauca was a significant environmental component responsible for discrimination among soil sample groups. Picea glauca is a nutrient-demanding species, and is not as tolerant of low fertility and acidic soil conditions as the other species in this study (Sims et al., 1990). Consequently, its distribution in our stands is limited to soils of higher pH and nutrient status. Conversely, P. banksiana has low requirements for soil fertility and can tolerate more acidic soil conditions (Sims et al., 1990). It was apparent that samples associated primarily with P. glauca (Cluster I) had significantly higher values of substrate utilization compared to those associated with P. banksiana (Cluster V). These two species were representative of their respective associated successional pathways because both P. tremuloides and P. mariana are widely distributed and were present over a broad range of soil and climatic conditions (Sims et al., 1990). This may have resulted in the large amount of

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overlap in utilization profiles between successional pathways. The higher than average utilization values associated with the P. tremuloides–P. glauca successional pathway may be due to the higher soil fertility requirements of P. glauca (such as soil pH) and/or the soil-improving nature of P. tremuloides. The availability of nutrients in forest soils has been shown to be higher in aspen stands than in coniferous stands (Pare´ et al., 1993; Pare´ and Bergeron, 1996). Deciduous litter may be more favorable for microbial decomposers than coniferous litter (Mikola, 1985), and the greater quality of deciduous species litter may have a positive effect on the functional diversity of soil bacteria (Insam et al., 1996; Sharma et al., 1998). In addition, the greater diversity of litter associated with mixed deciduousconiferous stands may have a positive effect on substrate utilization (Stephan et al., 2000). We also expected that the patterns of microbial substrate-utilization profiles in soils collected from different aged forest stands would be altered as a result of successional changes within these stands. Because of a general decline in nutrient availability and soil pH associated with succession proceeding towards establishment of coniferous species (Brais et al., 1995; Pare´ et al., 1993; van Cleve et al., 1983), it had been expected that any variations to the biochemical profiles would be most pronounced in the P. tremuloides–P. glauca successional pathway. The results of this study, however, failed to exhibit any significant changes in substrate- utilization patterns; however, the approximate 110-year chronosequence of this study may have been too short for the development of measurable transformations in the microbial communities associated with greater dominance by white spruce. The absence of young stands originating after the 1960s may also be an important contributor. Priha and Smolander (1997) found no differential effects on soil characteristics under Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies [L.] Karst.), and silver birch (Betula pendula L.) planted on similar field sites 23–24 years earlier. However, soil properties were found to be different under birch and spruce after 60 years (Priha and Smolander, 1999). Brais et al. (1995) noted that representation of trembling aspen remained high in stands for up to 121 years, but declined thereafter, while that of white spruce was highest in stands aged 121–168 years. Even though microbial communities responded quickly to changes in soil properties, the length of time that white spruce was present as a dominant overstory species may have been too short to have significantly altered the soil properties and to have affected any measurable changes in the functional diversity of microbial communities. Similarly, Pennanen et al. (1999) found no strong differences in microbial communities under Scots pine (P. sylvestris L.) and Norway spruce (P. abies L. Karst.) forests of different ages.

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5. Conclusion

References

Soil pH is an important factor influencing the functioning of soil microbial communities in boreal forest soils, and may influence rates of substrate utilization. Tree species from under which soil samples were collected were not strongly associated with patterns of microbial-substrate utilization; however, successional pathway (stand composition) was found to be associated. It is possible that the influence of surrounding tree species on soil properties in mixed stands may have masked species-specific influences on the functional diversity of soil microorganisms. We suggest that observed differences in substrate utilization between successional pathways may be due to differences in soil fertility requirements between P. glauca and P. banksiana, the influence of P. tremuloides on soil properties, and the difference in litter quality between the two pathways. In order to determine species-specific effects on the functional diversity of associated microbial communities, it would be beneficial to investigate soil samples obtained from pure stands (or tree plantation), in addition to the soil samples obtained in this study. This would minimize the effects of tree-species competition and determine with greater certainty whether species- specific effects on patterns of substrate utilization exist. Also, it would have been beneficial to obtain phospholipid fatty acid (PLFA) profiles of the sampled soils, in addition to the communitylevel physiological profiles. This would have identified changes in microbial community structure related to variations in soil pHs. Compared to substrate–utilization profiles, PLFA profiles have been shown to be more sensitive to changes in microbial communities, as they measure changes in the entire community and not only their functional attributes (Priha et al., 1999; Waldrop et al., 2000; Grayston et al., 2003). It is possible that each tree species supported its own unique microbial-community, but that the techniques used in this study were not sufficiently sensitive to discern subtle differences between tree species. DNA profiling, in addition to biochemical fingerprinting, would provide more detailed information as to the composition of microbial communities in forest soils.

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Acknowledgements We thank Alanna Sutton and Monika Thiessen for their help during the field work, Geraldine Damiani, Dan Bailey and David Havixbeck for their laboratory support, and Martin-Philippe Girardin for his help in data analysis. We also thank Manitoba Conservation and Louisiana Pacific Inc. for their logistic support. This study was funded by a SFMN and NSERC grant to Jacques Tardif and a University of Winnipeg Major Grant to Anne Adkins. During this study, Christopher White benefited from an NSERC-USRA. We thank all the above institutions for their support.

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