• bacterial activity;
  • bacterial diversity;
  • keystone species;
  • log-linear diversity effect;
  • plant diversity


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

1 Utilization of carbon sources by culturable soil bacteria can be assessed with BIOLOG microtiter plates (contain 31 C sources). We used this technique to investigate bacterial community structure at various levels of plant diversity. Plant diversity levels were replicated and we investigated the influence of three plant functional groups, grasses, legumes and non-leguminous herbs, as well as the influence of individual plant species.

2 Catabolic activity and catabolic diversity of culturable soil bacteria were used to estimate their density (abundance) and functional diversity, respectively. Both increased linearly with the logarithm of plant species number and with the number of plant functional groups in experimental grassland ecosystems. These effects may have been caused by an increased diversity and quantity of material and energy flows to the soil. They may also have been mediated by increased diversity of soil microhabitats via a stimulation of the soil fauna.

3 The presence of particular plant species or functional groups in the different experimental communities stimulated the activity and functional diversity of the culturable soil bacteria in addition to their contribution via plant diversity. The legume Trifolium repens had the strongest effect and may be regarded as a keystone species with regard to plant–microbial interactions in the systems studied.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The loss of biodiversity is one of the major threats to the world's ecosystems in the 21st century (Peters & Lovejoy 1992). Ecosystem processes are strongly affected by biodiversity, but the functional relationship between the two depends on the system considered (Schläpfer et al. 1999; Schläpfer & Schmid 1999). In all cases, however, processes must be maintained so that the ecosystem can continue to exist in either a constant or a changing environment. Functional redundancy of similar species may stabilize ecosystem processes during occasional species extinctions (Baskin 1994), but this ability appears to be limited (Tilman & Downing 1994). However, changes in ecosystem processes may themselves lead to a decline in biodiversity and thus to further reductions in ecosystem function, thereby starting a self-reinforcing feedback cycle.

Many experiments have been established to examine the role of biodiversity, particularly plant diversity, in maintaining ecosystem function (for a recent review see Schmid et al. 2001). One example is the European BIODEPTH project (Biodiversity and Ecological Processes in Terrestrial Herbaceous Ecosystems) in which an experiment specifically designed to assess the effects of plant species number on ecosystems, independently of the effects of species identity, has been carried out at eight sites (Diemer et al. 1997; Hector et al. 1999). Each particular level of species number was represented by a variety of species compositions.

Interactions between plant communities and soil bacteria have been reported (Grayston & Germida 1991; Grayston & Campbell 1996; Sharma et al. 1998; Staddon et al. 1998). Experiments at the Swiss BIODEPTH site therefore included an assessment of culturable soil bacteria to allow an analysis of the effects of plant species number on soil biota. We used commercial microtiter plates with multiple carbon sources (BIOLOG Ecoplates) as a sensitive but rapid screening method to measure the activity and functional diversity of a particular group of soil bacteria (Garland 1996; Smalla et al. 1998). Such bacteria can easily be cultivated and the functional diversity indicated by this technique reflects the diversity of their carbon-oxidation pathways. We asked whether bacterial activity and/or bacterial diversity increase with increasing plant diversity and investigated the influence of individual plant species and plant functional groups on both aspects of the biota.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Field site and experimental design

The Swiss site of the European BIODEPTH project is a former arable field overlaying calcareous nutrient-rich soil, situated at Lupsingen (47°27′ N, 7°41′ E, 439 m a.s.l.) in the Jura Mountains near Basel (Diemer et al. 1997). Soil analyses were carried out before establishment of the experiment (as described in Diemer et al. 1997) and yielded the following values (mean ± standard error): texture, loam; pH, 7.20 ± 0.04 (0.01-M CaCl2; pH meter 761, Knick, Berlin, Germany); total carbon, 3.74 ± 0.04 mg g−1 (CHNS-analyser; LECO-932, St. Joseph, Michigan, USA); extractable nitrate, 81.05 ± 0.77 µg g−1 (CaCl-solution 1 : 4); extractable phosphate, 1.61 ± 0.05 µg g−1 (saturated CO2-solution); extractable potassium, 5.83 ± 0.24 µg g−1 (saturated CO2-solution); extractable magnesium, 14.02 ± 0.51 µg g−1 (CO2-solution 1 : 10).

Two replicate blocks, each consisting of 32 plots, 8 × 2 m, separated by at least 1 m, were established in April 1995. A pool of 48 local grassland species belonging to 13 plant families was selected and used to assemble 32 mixtures of plant species at five diversity levels (Table 1).

Table 1.  Species pool (a) and the 32 communities assembled from them (b). PSR = log2-transformed species number, each community is identified by a number and a lower-case letter. Nomenclature follows Binz & Heitz (1990)
(a) GrassesAbbrev.LegumesAbbrev.ForbsAbbrev.
Agropyron repensAgRAnthyllis vulnerariaAVAchillea millefoliumAM
Agrostis stoloniferaAgSLathyrus pratensisLaPAjuga reptansAjR
Agrostis tenuisATLotus corniculatusLCAnthriscus sylvestrisAnS
Alopecurus pratensisAPMedicago sativaMSBellis perennisBP
Anthoxanthum odoratumAOOnobrychis viciifoliaOVCampanula patulaCP
Arrhenatherum elatiusAETrifolium pratenseTPCentaurea jaceaCJ
Cynosurus cristatusCCTrifolium repensTRCentaurea scabiosaCS
Dactylis glomerataDGVicia craccaVCCrepis biennisCB
Festuca ovinaFO  Daucus carotaDC
Festuca pratensisFP  Galium verumGV
Festuca rubraFR  Geranium pratenseGP
Holcus lanatusHL  Heracleum sphondyliumHS
Lolium perenneLoP  Knautia arvensisKA
Phleum pratensePhP  Leucanthemum vulgareLV
Poa pratensisPoP  Pimpinella majorPM
Trisetum flavescensTF  Plantago lanceolata Potentilla erecta Prunella vulgaris Ranunculus acris Salvia pratensis Sanguisorba officinalis Scabiosa columbaria Silene vulgaris Taraxacum officinalePL PE PV RA SP SO SC SV TO

Plant species number increased in geometric series from monocultures to mixtures of 32 species (Table 1). The log2-transformed plant species number is hereafter referred to as plant species richness or PSR. A number of different communities was assembled at each PSR level according to a restricted random sampling procedure from the species pool (Diemer et al. 1997; Joshi et al. 2000). All mixtures contained at least one grass, and within each level there were roughly equal numbers of mixtures containing only grasses, mixtures with one other functional group legumes or non-leguminous herbs, hereafter referred to as forbs, and where appropriate mixtures of all three functional groups. The number of functional groups (hereafter referred to as plant functional diversity, PFD) therefore ranged from 1 (in 1 to 8 species mixtures) to 3 (in 4–32 species mixtures) and was used as an additional treatment variable.

Each mixture was sown into one of the plots in each block in May 1995. Pilot germination studies were carried out and sowing densities adjusted so that for each community total seedling density was 500 m−2 and seedlings of all component species were initially present at equal frequencies.

Each plot was further subdivided into four subplots of 2 × 2 m of which two were subsequently sampled. Although one of these was subjected to trampling, disturbance had little effect and the two types of subplots were not therefore separated in the final analyses. All communities were mown twice a year as is typical for this type of extensively managed permanent grassland.

We wanted to simulate the consequences of biodiversity loss due to plant species extinctions, and therefore removed seedlings of any non-sown species as they appeared (Spehn, Joshi, Alphei, Schmid & Körner 2000; Spehn, Joshi, Schmid, Diemer & Körner 2000). Except at the highest diversity all initially sown species survived in all plots at the Swiss site, and PSR therefore remained constant. Although a few species did die out in the analysed portions of some 32-species plots, the numbers always remained closer to the initial value than to any other PSR level within the experiment (between 24 ± 1.8 in 1996 and 27 ± 1.7 species in 1997 per 2 × 2-m plot). An analysis of the effective species number (the exponential of the Shannon diversity index, see below, calculated from above-ground plant biomass proportions) within small 0.2 × 0.5 m subplots in the second year of the experiment showed a very strong congruence between designed and realized values (linear regression: R2 = 0.89, n = 64).

Plant diversity treatments affected both biomass allocation patterns (Spehn, Joshi, Schmid, Diemer & Körner 2000) and soil characteristics (Spehn, Joshi, Alphei, Schmid & Körner 2000). Above-ground biomass increased by 143% from the lowest to the highest diversity in 1997 and, although total below-ground plant biomass was not affected, that of fine roots also increased significantly. Soil moisture during the growing season was not influenced by diversity treatments, but soil temperature decreased slightly with increasing diversity. The slight increase in substrate-induced respiration suggested that soil microbial biomass may increase with increasing plant diversity, and although in-situ decomposition of cellulose and birch-wood was not affected, both numbers and biomass of earthworms were strongly positively correlated with diversity. The presence of legumes in the experimental plant communities often had significant effects on the activity of soil fauna.


Over a 4-day period in the third week of August in each of 1997 and 1998, we took two soil samples of 100 ± 20 mg bulk soil (no rhizosphere, no living or dead root parts) at a depth of 3 cm in each of the 64 plots (except that in 1997, accidentally, four plots were not sampled and four other plots were sampled twice). Preliminary sampling in three of the 64 plots had shown that the highest activity of culturable soil bacteria occurred at a depth of 2 cm and the second highest at a depth of 4 cm. In 1997, the two samples were taken in the same (undisturbed) subplot at points separated by 2 m, whereas in 1998, one sample was taken in each subplot. The soil samples were placed directly into 2.5-mL Eppendorf tubes in the field and were thereafter treated blindly.

Extraction and incubation of bacteria

Soil bacteria were extracted within 6 h after sampling. After shaking (Vortex, full speed) for 20 min in 1 mL 0.2% Tetrasodium-pyrophosphate, the samples were allowed to settle for 3 min. An aliquot (150 µL) of the supernatant was then diluted 100-fold with 0.9% NaCl before 100 µL were transferred to each well of the BIOLOG Ecoplate (BIOLOG Inc., Hayward, CA, USA).

Each of the 96 wells of an Ecoplate contained dried nutrient solutions, containing a single carbon compound, and a redox-colourant (tetrazolium violet) (three replicate wells for each of 31 C sources and no-carbon control). When culturable bacteria grow, the oxidation of the C source forms NADH which can be quantified by its reduction of the colourant. The microtiter plates were incubated at 22 °C and read with a spectrophotometer (Microplate Reader 3550, BIO-RAD, Hercules, CA, USA) at 590 nm. Absorbance values were recorded after 72 and 120 h in 1997, but as some of the latter exceeded the maximum recordable, we decided to shorten the incubation times in 1998 to 48, 72 and 96 h. We present only data after 72 h: analyses using other incubation times did not give different or further information.

Response variables

We took the mean of the three replicate wells per plate containing a particular substrate and subtracted the absorbance value for the control on the same individual plate to calculate the catabolic activity in the use of that C source (Ai). Negative Ai values were set to zero. Overall catabolic activity was calculated as the sum of activities for all 31 C sources and, if divided by the number of C sources, is equivalent to the AWCD (average well colour development) calculated by Garland (1996). For every sample, Ai values were used to calculate richness S, the Simpson index D′ (the reciprocal of the dominance index), Shannon index of diversity H′, equitability of D′ and equitability of H′. We used the formulae given in Begon et al. (1990):

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Richness simply represents a numerical measure of the diversity of C sources that are utilized by a sample. The Simpson and Shannon indices are combined measures of the ‘number’ and ‘abundance’ aspects of this diversity, but ‘number’ can be removed by dividing by richness or the natural logarithm of richness, respectively. Equitabilities therefore reflect only the ‘abundance’ aspect of the diversity in C source utilization: equitability reaches a maximum if C sources that can be used have the same Ai values, whereas low values indicate that one or a few C sources have much higher Ai values than any others.

Statistical analysis

The general linear model (GLM) approach to analysis of variance (anova) was used to analyse the data by means of the Genstat 5 software (release 3; Payne et al. 1993). The split-plot design involved a block and plot factor and treatment factors as in Table 2 (a similar approach to that used by Meyer & Schmid 1999), to allow calculation of F-values for significance tests. Block and plot effects were used to eliminate variation caused by spatial differences within the experimental site. Sample mass was used as a covariate in both years; fine root length per plot and fine root biomass per plot (data from Spehn, Joshi, Alphei, Schmid & Körner 2000) were included as further covariates in a separate anova of 1997 data (results not presented).

Table 2.  Dummy or skeleton analysis of variance for all characters measured (see text for further details)
Source of variationd.f.Mean squareVariance-ratio
  • Values for sources in italics were obtained by totalling the sum of squares and degrees of freedom of their subordinate contrasts. The deviation of the plant species and functional group diversities from the loglinear and linear contrasts, respectively, and the interaction of species and functional diversity are not shown since they had small mean squares and were omitted in all final analyses. Interactions of year with treatment factors were similarly omitted.

  • 1 For the 1997 data, we also tested fine root length and fine root biomass as covariates after sample mass (data from Spehn, Joshi, Alphei, Schmid & Körner 2000), the d.f. for the covariates was then equal to 3, the number of covariates tested.

  • 2

    We usually tested only one taxon at a time: the second taxon then falls away and the deviation of mixtures identities has d.f. 28.

  • 3

    Four plots were not sampled in 1997 and seven samples were lost due to defective BIOLOG plates, leading to smaller degrees of freedom than for a full design without missing values.

Covariate(s) (cov)1 (n)1MScovMScov/MSr
 Sample mass (w)1MSwMSw/MSr
Plot total (pt)63MSptMSpt/MSr
 Block (b)1MSbMSb/MSp
 Mixtures (m)31MSmMSm/MSp
  Diversity treatments (d)2MSdMSd/MSmi
   Species richness loglinear (s)1MSsMSs/MSmi
   No. functional groups linear (f)1MSfMSf/MSmi
  Mixture identities (mi)29MSmiMSmi/MSp
   Taxon 1 (t1)1MSt1MSt1/MSp
   Taxon 2 (t2)12MSt2MSt2/MSp
   Deviation (mid)272MSmidMSmid/MSp
 Plot (p)31MSpMSp/MSr
Year (y)1MSyMSy/
Year × plot
Residual (r)1263MSr 
Total (t)2483MSt 

Each level of PSR was represented by several communities, and the effect of PSR itself therefore could be tested against the variation among communities within PSR levels, to determine the effect of species number per se, unconfounded by any particular species composition occurring at a particular PSR level (see Hector et al. 1999). PSR levels could be ordered along a continuous axis, with a logarithmic scale giving better fits than the untransformed plant species number, allowing us to test for significant linear contrasts of PSR and the deviation from linearity. Parsimony, the small deviations from linearity and previous reports that biodiversity effects are generally linear at the logarithmic scale (Hector et al. 1999; Schmid et al. 2001) led us to prefer this method to polynomial contrasts using untransformed species number. Deviation from linearity was non-significant and small enough for it to be omitted in all final analyses of the effects of both PSR and functional diversity (PFD) within PSR (i.e. ‘eliminating PSR’, see Payne et al. 1993).

Despite allocating large amounts of time and labour to setting up and running the experiment, it was only possible to include 32 plant communities. P < 0.1 was therefore regarded as a (‘marginal’) significance level in testing the effects of PSR and PFD to reduce the risk of making type-II errors (i.e. not rejecting the null hypothesis if in fact it is false). In addition to the P-values for PSR and PFD, we report partial coefficients of determination (R2) as a measure of effect size (Cohen 1977). These coefficients measure the proportion of the variation in a response that is explained by an independent variable in a GLM. They were calculated according to Rosenthal & Rosnow (1985) as

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The variation among communities within a particular PSR and PFD level could be tested for significance at the plot level, because each particular community occurred exactly once in each of the two blocks. The factor year was used to test for temporal variation in the response variables.

The full anova was done for all response variables. Analyses of individual Ai values are not presented as these values were well reflected by the overall catabolic activity. The effect of PSR, the factor in which we are particularly interested, is presented as a linear regression of the Ai adjusted for the covariate sample mass. The analyses of individual and overall activity and of diversity values used untransformed data because residuals were normally distributed and homoscedasticity was not improved by transformation.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Influence of covariates

Sample mass was positively correlated with all response variables tested (P < 0.001) and all analyses were therefore adjusted for sample mass. Neither fine-root length nor fine-root biomass per plot, which were measured in 1997 and included in a separate analysis for that year, were significantly correlated with overall catabolic activity or any of our measures of catabolic diversity or equitability, although they have been shown to be positively correlated with PSR (Spehn, Joshi, Alphei, Schmid & Körner 2000). These additional covariates were not therefore included in any of the analyses reported here.

Effects of diversity treatments

Overall catabolic activity increased linearly with PSR (i.e. log2-transformed species number) (partial R2 = 0.28, P = 0.002, Fig. 1a) and therefore increased faster at low plant species number than at higher levels. For a given level of PSR, overall catabolic activity increased with PFD but with only marginal significance (partial R2 = 0.11, P = 0.062, Fig. 2a). Of the 31 C sources tested, 15 showed a significant increase in activity with PSR at P < 0.05, and a further 13 had positive slopes: only three had negative slopes, none of which were significant (Table 3).


Figure 1. Relationship between PSR (log2 plant species number) and (a) overall catabolic activity of culturable soil bacteria on 31 C sources (dimensionless absorbance values) and (b) the Simpson index of catabolic diversity in the use of these sources. Least-square means, adjusted for the covariate sample mass, are shown ± 1 SE for each of the 32 plant communities, and may be slightly displaced horizontally for clarity. Communities represented by squares contained legumes: hatched, Trifolium repens only; open, other legume species; filled, both T. repens and other legume species. There were eight samples for most communities (two years × two blocks × two samples per plot). The regression lines shown for PSR have partial R2 of 0.28 (a) and 0.24 (b).

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Figure 2. Relationship between PFD (number of plant functional groups) for particular levels of PSR and (a) overall catabolic activity (dimensionless absorbance values) and (b) the Simpson index of catabolic diversity. Predicted means (from a GLM including the covariate sample mass, PSR, and PFD) ± 1 SE are shown for the 10 possible combinations (of PSR and PFD). Each combination is represented by between one and four communities (see Table 1) except for monocultures (PSR = 0, n = 10). PSR = 0 (○), PSR = 1 (●), PSR = 2 (□), PSR = 3 (▪) or PSR = 5 (▴). Parallel regression lines for PFD within a PSR level give partial R2 of 0.11 in both cases. See ‘Materials and methods’ for further explanations.

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Table 3.  Catabolic activity of bulk soil bacteria on 31 C sources. Slope parameter of a linear regression model against PSR (log2-transformed plant species number) for each of 31 C sources used, plus their corresponding standard errors (SE) and the error probability (P) of the slope being different from zero. NS = not significant
C sourceMeanSlopeSEP
α-D-lactose0.99780.10100.0355< 0.01
D,l-α-glycerol phosphate0.81400.09790.0275< 0.01
D-glucosaminic acid0.73010.07900.0263< 0.01
Pyruvic acid methyl ester0.63260.07550.0207< 0.01
D-galacturonic acid1.03900.07360.0258< 0.01
β-methyl-D-glucoside0.77450.06160.0237< 0.05
α-cyclodextrin0.37280.06150.0169< 0.01
L-arginine0.99420.06100.0260< 0.05
2-hydroxy benzoic acid0.46160.05610.0181< 0.01
Phenylethylamine0.49650.05300.0219< 0.05
Glucose-1-phosphate0.49290.04930.0210< 0.05
Tween 800.92710.04830.0225< 0.05
N-acetyl-D-glucosamine0.86740.04040.0226(< 0.1)
Glycogen0.17970.03600.0111< 0.01
L-threonine0.08030.03200.0071< 0.001
Putrescine0.73490.03150.0150< 0.05
Tween 401.07360.02990.0186NS
Itaconic acid0.24650.02270.0191NS
γ-hydroxybutyric acid0.80390.02120.0260NS
D-malic acid0.33320.01730.0101NS
α-keto butyric acid0.12650.01200.0116NS
Glycyl-L-glutamic acid0.13480.01050.0038< 0.05
4-hydroxy benzoic acid0.35150.00400.0157NS
L-phenylalanine0.0829− 0.00080.0097NS
D-mannitol0.7085− 0.01580.0170NS
D-galactonic acid g-lactone0.4672− 0.01840.0120NS

Increased PSR also resulted in greater catabolic diversity as measured by richness (partial R2 = 0.14, P = 0.037), and by both Shannon (partial R2 = 0.21, P = 0.010) and Simpson indices (partial R2 = 0.24, P = 0.005, Fig. 1b). The positive effect of PFD on catabolic diversity was again rarely more than marginally significant (richness, R2 = 0.20, P = 0.013; Shannon, R2 = 0.10, P = 0.088; Simpson, R2 = 0.11, P = 0.065, Fig. 2b).

The equitabilities of the Simpson and Shannon indices also showed significant positive correlations with PSR (P = 0.006 and P = 0.013, respectively), but were not further influenced by PFD.

Effects of particular functional groups or species

Functional group identity

The effect of the presence or absence of grasses could not be tested because all communities, except some of the monocultures, contained grasses. Forbs did not have any significant effect on any response variable (P > 0.1 for all significance tests). Legumes had positive effects on overall catabolic activity (P < 0.001, Fig. 1a) as well as on all indices of catabolic diversity (Simpson: P = 0.001, Fig. 1b; Shannon: P = 0.002; richness: P = 0.021; Simpson equitability: P = 0.009; Shannon equitability: P = 0.014).

Species identity

Eighteen plant species occurred in communities at more than one PSR level and these were tested for their individual influences on overall catabolic activity and on all diversity indices (Table 4) but only one species, the legume Trifolium repens had highly significant effects. The significant or marginally significant effects found for four grass and one forb species, are close to the number expected by chance given the high number of tests (six response variables × 18 plant species tested). The separate analyses for each of the 31 C sources showed that, for more than half of them, Ai increased significantly in samples from communities with T. repens (P < 0.001 in six cases, 0.001 leqslant R: less-than-or-eq, slant P < 0.01 in six cases, 0.01 leqslant R: less-than-or-eq, slant P < 0.05 in three cases, 0.05 leqslant R: less-than-or-eq, slant P < 0.1 in two cases). Fewer significant effects were found for other species and could again have occurred by chance.

Table 4.  Significances and direction of effects particular plant species have on catabolic activity and diversity (tests were only made for species that occurred at more than one PSR-level). NS = not significant
 Overall catabolic activityRichnessSimpson-indexShannon-indexEquitability of Simpson-indexEquitability of Shannon-index
Trifolium repens+ < 0.001+ < 0.001+ < 0.001+ < 0.001+ < 0.001+ < 0.001
Poa pratensis– < 0.05NS– < 0.1– < 0.1– < 0.1– < 0.1
Dactylis glomerataNSNS+ < 0.1+ < 0.1+ < 0.1+ < 0.1
Taraxacum officinaleNSNSNSNS+ < 0.1+ < 0.05
Trisetum flavescensNSNSNSNS– < 0.1– < 0.05
Lolium perenne+ < 0.1NSNSNSNSNS
12 other plant speciesNSNSNSNSNSNS

The effect of inclusion of Trifolium repens on catabolic activities and diversities was similar to that of adding legumes as a functional group (see Fig. 2). In fact, except for the equitability indices, T. repens explained more of the variation than did legumes as a functional group, whereas no other legume species had a significant influence. When we tested legumes as a functional group before adding T. repens within legumes as a contrast to the analysis, the effect of T. repens on catabolic activity, richness and Shannon and Simpson indices was still significant at P < 0.05, although equitabilities were not significantly influenced. When T. repens was tested first, the remaining legume species only remained significant at P < 0.05 for overall catabolic activity. The effects of the functional group legumes were therefore mainly due to T. repens.

Effects of year

In 1998 overall catabolic activity (P = 0.004) and all diversity indices were significantly higher than in 1997.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Effects of plant diversity on activity and functional diversity of culturable soil bacteria

Our results show that plant species richness (PSR) and plant functional diversity (PFD) have a positive influence on overall catabolic activity and catabolic diversity of the culturable bacterial community in the bulk soil in an experimental grassland ecosystem. Although these bacteria represent only a small fraction of the taxa present in the soil, we consider them to be a useful indicator group for measuring the effects of the autotrophic plant components on the bacterial decomposers in such a system. The increased oxidation of the C sources supplied reflects an increased bacterial density (Haack et al. 1995) while the increase in catabolic diversity reflects the use of different carbon-oxidation pathways and therefore functional diversity (Insam, Amor, Renner & Crepaz 1996; Øvreås & Torsvik 1998; Sharma et al. 1998; Staddon et al. 1998). It is unlikely that a single genotype or low-level taxonomic unit could express so much plasticity in C-source utilization, and this functional diversity is therefore probably related to taxonomic diversity (Haack et al. 1995; Buyer & Drinkwater 1997; Baath et al. 1998; Ibekwe & Kennedy 1998; Øvreås & Torsvik 1998). However, plants show redundancy among taxa within functional groups (see review in Schmid et al. 2001), and a similar situation may exist within bacterial communities, so that functional diversity would provide a minimum estimate of taxonomic diversity. We therefore conclude that both activity and diversity at least of culturable soil bacteria increase with increasing plant diversity.

We were unable to detect a differential response to plant diversity in the use of a large range of carbon sources. Half of those tested, including carbohydrates, amino acids, carboxylic acids, phosphorylated compounds, polymers, esters and amines, showed the same pattern as overall catabolic activity. It has previously been suggested that carbohydrates, carboxylic acids and amino acids can be used to discriminate between the bacteria of different soil types (Grayston & Campbell 1996).

The diversities of different trophic levels may be expected to be linked if one level is limiting the other in a bottom-up (e.g. energy flow; Cody 1975; Tonn & Magnusson 1982; Brown & Southwood 1983) or top-down process. In our study, higher plant diversity may have influenced the soil bacteria by increasing the diversity of litter, the heterogeneity of soil microhabitats, or energy and material flows from the vegetation to the soil. Insam, Rangger, Henrich & Hitzl (1996) and Sharma et al. (1998) have described positive effects of litter quality on functional diversity of soil bacteria, although Wardle et al. (1997) found that manipulating litter diversity directly, rather than (as here) via the diversity of the living plant community, had no effect. The density of earthworms increased by 63% across our range of diversities (Spehn, Joshi, Alphei, Schmid & Körner 2000), suggesting that effects of plant diversity on bacterial activity and diversity were probably mediated to some extent by increased heterogeneity of soil microhabitats. Above-ground biomass increased with increasing PSR (Spehn, Joshi, Schmid, Diemer & Körner 2000) and enhanced flow to the soil may well also have contributed to the positive effect on bacteria.

Effects of individual plant species and plant functional groups on activity and functional diversity of culturable soil bacteria

The presence of particular plant species and plant functional groups (as well as their number) in the experimental communities has important effects on culturable soil bacteria. The effect of legumes as a functional group was mainly due to just one of the four species tested (Trifolium repens). Catabolic activity and diversity of soil bacteria taken from under a monoculture of this plant species was as high as in soil from the most diverse plant communities (see Fig. 1). Legumes form specific symbioses with rhizobial bacteria and it is not therefore surprising that one of them is crucial in the effect on bacterial communities. Although in plants the term ‘keystone’ species is usually used to describe the effects of a strong competitor on other plant species (Bond 1993; Troumbis et al. 2000), it can be equally well applied here, provided that expanding the use of this term is accompanied by specification of the affected process. The effects of some grass and forb species could not easily be explained and might have represented statistical type-I errors.

Individual plant species or functional groups clearly can affect the activity and diversity of culturable soil bacteria. The strong effects of these species, however, contrast with many which make a generic contribution via the overall level of plant diversity.

Effects of temporal variation

The differences between the two study years might have been caused by external factors or a successional effect. The sampling period in 1998 was, unlike that in 1997, preceded by intense rainfall, although the increased catabolic activity and diversity in 1998, the fourth year after sowing, compared with 1997, suggests successional development of the underlying soil.

Further conclusions on ecosystem functioning and conservation of biodiversity

Our results show a positive influence of plant diversity on C-source utilization patterns in soil samples and thus on the activity and functional diversity of culturable bacteria in the bulk soil (Haack et al. 1995; Insam Amor, Renner & Crepaz 1996). The relationship may be a mutual one in that plants may also profit from diverse soil bacterial communities, e.g. mediated by better nutrient mineralization, growth stimulation, and enhanced antibiosis to pathogens (Grayston & Germida 1991; Kim et al. 1998; Shah et al. 1998). Mutual relationships between plant and soil organismic diversity have also been suggested for the plant–arbuscular mycorrhizal system (Van der Heijden et al. 1998). Our results underline the importance of biodiversity and species conservation at the different trophic and taxonomic levels in grassland ecosystems.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

This project was supported by a grant from the Swiss Federal Office for Education and Science (Project EU-1311 to B.S.) to join the EU-funded BIODEPTH project and by grant no. 5001–44628 of the Swiss National Science Foundation. We would like to thank P. Breitinger for help in the field and H. Brandl, J. Joshi, M. Kéry, E. Spehn and two anonymous referees for discussion and critical comments on earlier versions of this manuscript. Once more, L. Haddon did a great job improving the final version of a manuscript written by authors for whom English is a second language.


  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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Received 30 September 1999 revision accepted 4 May 2000