Functional substrate biodiversity of cultivated and uncultivated A horizons of vertisols in NW New South Wales

Geoderma 96 Ž2000. 321–343

Functional substrate biodiversity of cultivated and uncultivated A horizons of vertisols in NW New South Wales F. Yan a , A.B. McBratney a,b, L. Copeland a,) a

Department of Agricultural Chemistry and Soil Science, Ross St Building A03, The UniÕersity of Sydney, Sydney, NSW 2006, Australia b Australian Cotton CooperatiÕe Research Centre, Department of Agricultural Chemistry and Soil Science, Ross St Building A03, The UniÕersity of Sydney, Sydney, NSW 2006, Australia Received 30 November 1998; received in revised form 16 December 1999; accepted 31 January 2000

Abstract Concern over the effects of anthropogenic activities on soil ‘quality’ has fuelled efforts to identify and measure those factors that affect soil quality. Soil microbial diversity is one of many possible factors. Our objective was to compare the functional diversity of microbial communities in the A horizons of cultivated and uncultivated vertisols in NW New South Wales. Samples from two cultivated and two uncultivated sites were tested using the community level physiological profiles ŽCLPP. method. Substrate richness, the rate of substrate use and the diversity of substrate use, as measured by the Shannon index, were significantly larger in the uncultivated sites than in the cultivated sites. The CLPP also indicated a higher rate of substrate use in the uncultivated sites, although this may have been due to greater initial inoculum densities. When diversity values for each site were compared with several soil physical and chemical properties, a relationship between organic carbon and functional diversity was apparent. The fit to a broken-stick model showed that diversity increased up to 1.76% organic carbon and remained constant above that value. The implications of these results for soil quality will depend upon future investigations on the significance of soil microbial diversity as a component of soil quality. q 2000 Elsevier Science B.V. All rights reserved. Keywords: community level physiological profile ŽCLPP.; BIOLOG plates; functional diversity; soil biodiversity; soil microbial communities; substrate utilisation

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Corresponding author. E-mail address: [email protected] ŽL. Copeland..

0016-7061r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 6 - 7 0 6 1 Ž 0 0 . 0 0 0 1 8 - 5

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1. Introduction The potential importance of the contribution of soil microbial communities to soil quality has prompted research on soil microbial parameters: their measurement ŽKunc, 1994. , their usefulness as indicators of soil properties, such as organic matter content ŽCarter, 1986; Powlson et al., 1987; Sparling, 1992. , and their ecological significance as soil properties themselves. One of the least quantified aspects of soil microbial communities is their diversity. Diversity may arguably be defined as the number of groups Ž richness. and the relative abundance of individuals within each group Ž evenness. Ž Magurran, 1988.. In describing biological diversity, or biodiversity, groups may be taxonomically based, trophically based or functionally based Ž functional group diversity.. Functional groups are groups whose membership denotes the ability to perform a certain function, e.g., metabolise a certain substance or fix nitrogen ŽBeare et al., 1997; Setala ¨ ¨ et al., 1998.. General hypotheses concerning biodiversity provide a good background to microbial diversity studies. In the past, most biodiversity research has involved macro- and meso-biota, and a number of hypotheses have been developed on the importance of biodiversity in ecosystems. The traditional view was that ‘diversity begets stability’ ŽElton, 1958. . To take this view to an extreme would be to say that the progressive loss of species steadily damages ecosystem stability. More recent theories modified this view to include the concept of species redundancy. The ‘species-redundancy view’ argues that since many species in the ecosystem perform the same function, ecosystem function is unimpaired as long as the major functional groups are present and the proportional biomass of primary producers, consumers and decomposers is unchanged Ž Lawton and Brown, 1994; Tilman and Downing, 1994. . Arguments against this view are that species in the same functional group are unlikely to all have the same growth range, and thus not all will be active under a particular set of environmental conditions. Ehrlich and Ehrlich Ž1981, as quoted in Ehrlich, 1994. proposed the ‘rivet hypothesis’, which likened species to rivets and the ecosystem to an aircraft: the aircraft can lose a certain number of rivets without loss of function, but the loss of one more rivet could tip the balance and have serious consequences. This model emphasises that although there may be some initial degree of species redundancy, a threshold point will eventually be reached, beyond which a reduction in diversity will compromise ecosystem functioning. A modification of the rivet hypothesis is that all species are not equal, that the loss of some species is more important than that of others, and that species loss may be tolerated up to some critical threshold Ž Ehrlich and Wilson, 1991. . However, it is not clear that general theories on biodiversity can be applied directly to soil microbial diversity. Most microbial communities differ from other biological communities because of the immense number of species involved, and because of their rapid generation time Ž e.g., a few hours for many

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species of bacteria.. These factors suggest that if there is a change in environmental conditions that eliminates existing microbial niches and creates new ones, then it is probable that previously dormant species in the population will be able to take advantage of the new niches created. It is also probable that these newly active populations will be able to increase and use the niche to its full capacity in a relatively short period of time. The great variety and complexity of microbial species, niches and interactions observed in a freshwater pond led Finlay et al. Ž1997. to suggest that microbial diversity in general would never decrease to a level where the ability of a microbial community to carry out biogeochemical cycling would be adversely affected. However, whether this view can be automatically projected from freshwater pond communities into terrestrial ecosystems needs to be investigated. Advances in technology have improved the accuracy and comprehensiveness with which soil microbial communities can be characterised. Molecular techniques avoid problems related to difficulties in culturing many microbial species and are being increasingly applied in microbial ecology. Examples include DNA cross-hybridisation, DNA reassociation ŽGriffiths et al., 1997. and rRNA targeted fluorescent molecular probes ŽGottschal et al., 1997. . Other methods such as phospholipid fatty acid analysis ŽPLFA. measure markers derived from cellular structures ŽSteinberger et al., 1999. , whereas community level physiological profiles Ž CLPP. measures the functional Ž phenetic. diversity of microbial communities. The CLPP method tests the ability of a microbial community to utilise different C substrates in a microplate. The pattern of substrate utilisation can be used to estimate the functional diversity of the community. Originally intended for easy identification of isolates in microbiology laboratories, CLPP plates were first introduced as a method of characterising microbial communities in ecological situations in 1991 Ž Garland and Mills, 1991. . The CLPP method using Biolog plates has been used to differentiate between microbial activities in soil samples subject to different environmental influences, such as differences in vegetation type and climatic zones ŽZak et al., 1994., rhizosphere chemistry Ž Garland, 1996. , temporal changes Ž Garland and Mills, 1994., C inputs and flooding ŽBossio and Scow, 1995. , pollution levels Ž Fritze et al., 1997., conventional and organic farming practices Ž Fließbach and Mader, ¨ 1997. and length of fallow ŽDhillion, 1997. . Diversity indices based on data from Biolog plates was first reported by Zak et al. Ž 1994. , and later by others ŽDhillion, 1997; Fließbach and Mader, 1997; Kreitz and Anderson, 1997; ¨ Sharma et al., 1997. . In all of these reports, the functional microbial diversity of the sites was calculated using the Shannon index, which has the form: H X s yÝ pi Ž ln pi . where H X is substrate diversity and pi is the ratio of the activity on a particular substrate to the sum of activities on all substrates.

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The objective of this study was to compare the functional microbial diversity of cultivated and uncultivated vertisol A horizons, using CLPP. These cultivated vertisols are used for intensive arable production and are subject to different, and possibly harsher, environmental stresses than the uncultivated vertisols.

2. Methods 2.1. Sampling sites The sampling sites were located in the lower Namoi Valley near Narrabri in northwest New South Wales, one of Australia’s principal cotton-growing areas. Cotton is generally grown on flood-irrigated vertisols. During the growing season, crops are sprayed with insecticides about nine times per season, and receive about 150 kg N hay1 as anhydrous ammonia. There were four sites, grouped into two pairs. One pair was located on ‘Doreen’ Ž 30800X S and 149817X E. , a farm 37 km northwest of the Australian Cotton Research Institute on the Spring Plain Rd. The two sites on ‘Doreen’ were a field of faba bean, in crop, and an adjacent field of remnant vegetation Ž sparsely populated Coolabah Ž Eucalyptus coolabah. timber, Mitchell grass Ž Astrebla spp.., Medic Ž Medicago spp.. , Coolar grass Ž Panicum coloratum., Turnip Weed Ž Rapistrum rugosum. and other weeds Ž Lytton-Hitchins, 1998.. . The other pair of sites was located approximately 2 km east of the Australian Cotton Research Institute Ž 30813X S 149847X E. on the Wee Waa Rd. One site was a section of a travelling stock route on one side of the road, and the other site was a cotton field, in fallow, on the other side of the road. The stock route is a non-cultivated area of land with some remnant vegetation, subject to periodic heavy grazing. Thus, each pair of sites consisted of a cultivated and uncultivated site. The cultivated sites have been under arable production for an estimated 20 years or longer. The soil profile at all four sites was a self-mulching greyrblack cracking clay, a pellustert ŽSoil Survey Staff, 1975. or greyrblack vertisol ŽIsbell, 1996.. Five soil samples were taken along a transect at each site. For the remnant vegetation and faba bean sites, the first soil samples were taken 50 m in from the side of the field. For the stock route and cotton sites, the first soil samples were taken 25 m in from the side of the road or field. Neighbouring samples in all four sites were taken 25 m apart. Soil samples were taken from the top 10 cm, avoiding rhizosphere zones as much as possible, and sealed in plastic bags. Samples taken from the faba bean and remnant vegetation fields were allowed to dry slightly from their field moisture condition for 16 h after collection, as the samples were near saturation. The bagged and sealed samples were refrigerated at 48C within 16 h of collection.

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2.2. Physical and chemical properties The soil samples were analysed for organic C ŽWalkley and Black, 1934. and particle-size distribution Ž Gee and Bauder, 1986. as described. Dry bulk density was measured in 47.5 mm diameter and 40 mm high cylindrical cores of undisturbed soil, and pH and electrical conductivity ŽEC. were measured in 1:5 soil:water mixtures. 2.3. Biolog plates Gram negative ŽGN. microplates were obtained from Biolog ŽHayward, CA. . Soil suspensions for inoculation were prepared by suspending a mass of soil Ž at field moisture. equivalent to 1 g of soil solids, in 100 ml of sterile 0.85% sodium chloride solution. The suspensions were shaken end-over-end for 3 h. One ml of the shaken suspension was diluted to 10 ml using the sodium chloride solution. The resulting suspension was shaken manually for 10 s and a 120-ml aliquot added to each well in the Biolog plate. One plate was used for each soil sample. The plates were incubated at 308C, and colour development was monitored visually at 6-h intervals until 42 h, and every 24 h thereafter up to 116 h. Colour development was recorded as either the absence Ž0. or presence Ž1. of a colour change above that of the control well. 2.4. Statistical analysis The results were recorded and analysed with JMP software Ž SAS Institute, 1995.. The operational formula for the Shannon diversity index became: H X s yÝ pi ln pi , where pi s

if pi ) 0 or 0,

response of well i S

if pi s 0

§ Ž1 or 0.

Logistic curves were fitted to both the richness vs. time curves and the diversity vs. time curves. The logistic model used was: ps

u1 1 q u 2 eyu 3 t

or

HX

u1 1 q u 2 ey u 3 t

Ž1.

where u 1, u 2 , u 3 are parameters and t is time in hours after inoculation. The rate of change of the logistic curves was calculated by taking the derivative. A biplot analysis ŽGabriel, 1971. was used to detect differences between soil samples and sites based on their pattern of substrate use.

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A one-way analysis of variance was performed on the averaged site response of the four vertisols for each substrate Ž a s 0.10. in order to identify those substrates which significantly differentiated the sites. A linear model, and a broken-stick model of the form: HXs

½

a q b Ž organic C % . , if organic C - c a q bc, otherwise

Ž2.

were fitted to the organic C vs. diversity data, where c is the %C at the discontinuity. In order to assess the suitability of each model, the Akaike Information Criterion Ž AIC; Akaike, 1974. of each model was calculated using the formula: AIC s n lnSSEq 2 p where n is the number of data points, p is the number of parameters in the model Ž p s 2 for the linear model, p s 3 for the broken-stick model. and SSE is the sum of squared errors, or the residual sum of squares.

3. Results 3.1. Soil properties Analyses of the soil samples for various physical and chemical properties indicated several dissimilarities in soil characteristics between the four sites ŽTable 1.. The soil from the stock route had lower pH values than from the other sites. The stock route and remnant vegetation sites had higher organic C and also larger sand fractions, which might explain their lower bulk density values. Trends between organic C and pH, and organic C and bulk density, are shown in Figs. 1 and 2. 3.2. Substrate richness Substrate richness Ž S . was determined as the number of wells with colour development, or simply the sum of well responses, for each plate. The proportional substrate richness Ž P . was given by Sr95. The stock route and remnant vegetation samples showed earlier commencement of colour development ŽFig. 3., needed a shorter time period in which to reach their maximum P value, and had a significantly higher P value at 116 h Ž a s 0.05., than the cotton and bean samples ŽTable 2. . Values of P at the final time point Ž 116 h. ranged from a maximum of 1.00 for samples S1, S2 and R5, to 0.71 for sample B2. At 116 h, colour development in the slowest developing microplates had reached a plateau; moreover, colour had developed in the control wells in 7 of the 20 plates.

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Table 1 Physical and chemical properties measured for the 20 soil samples Site

Sample

Wee Waa Rd Stock route S1 S2 S3 S4 S5 Mean S.D. Significant difference Cotton C1 C2 C3 C4 C5 Mean S.D. Significant difference

Organic pH C Ž%.

% clay

% silt % sand E.C. Bulk ŽdSrcm. density ŽMg my3 .

4.22 3.51 4.35 4.58 2.96 3.92 0.671 R, B, C

6.42 6.90 6.44 6.62 6.77 6.63 0.208 R, B, C

36.9 26.7 37.1 43.5 43.0 37.4 6.77 B, C

38.6 28.3 33.8 29.6 25.4 31.1 5.15 R, B

24.5 45.0 29.1 26.9 31.6 31.4 8.03 R

0.10 0.09 0.07 0.10 0.07 0.09 0.016 B, R

1.64 1.57 – 1.73 1.60 1.68 0.120 B, C

1.07 1.18 1.11 1.03 1.08 1.09 0.056 S, R

8.25 8.27 8.26 8.08 7.93 8.26 0.150 S, R

55.1 40.0 54.7 47.7 56.3 50.8 6.90 S, B

17.7 36.0 27.3 26.0 20.0 25.4 7.16 R, B

27.1 24.0 18.0 26.2 23.7 23.8 2.87 R

0.09 0.08 0.12 0.08 0.09 0.09 0.018 R

1.29 – 1.28 1.31 1.27 1.28 0.016 S, R

Doreen Remnant vegetation

R1

2.2

7.78

50.9

12.7

36.4

0.10

–

1.54 1.6 1.76 2.03 1.83 0.282 S, B, C

8.13 8.21 6.49 7.67 7.76 0.690 S, C

34.9 41.4 38.7 54.9 44.2 8.43 B

13.9 15.9 8.9 12.4 12.8 2.56 S, C

51.2 42.6 52.3 32.7 43.0 8.71 S, B, C

0.16 0.15 0.07 0.19 0.13 0.049 S, C

1.56 1.57 1.59 1.40 1.60 0.181 B, C

Bean

R2 R3 R4 R5 Mean S.D. Significant difference B1 B2 B3 B4 B5 Mean S.D. Significant difference

0.75 0.69 0.75 0.69 0.65 0.71 0.043 S, R

8.20 8.22 8.19 7.98 8.12 8.14 0.098 S

62.3 64.3 64.2 59.6 51.3 60.3 5.40 S, R, C

11.9 13.5 17.0 16.2 23.2 16.4 4.34 S, C

25.9 22.2 18.9 24.2 25.5 23.3 2.87 R

0.12 0.11 0.10 0.15 0.15 0.13 0.023 S

1.41 1.33 1.33 1.33 1.38 1.36 0.036 S, R

These differences in rate and maximum value became more apparent after further data analysis. A logistic curve Ž Eq. Ž 1.. was fitted to the P vs. time curve for each site Ž parameter values are shown in Table 3. .

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Fig. 1. Relationship between organic C and pH at the four sites sampled Ž R 2 s 0.764..

The rate of change of the fitted equation was calculated and became the estimate of the rate of change of proportional richness over time. This procedure clearly showed the differences in the maximum rates of richness development, and the times at which the maximum rate occurred Ž Fig. 4. . Approximate values for the maximum rates and corresponding times were 0.85 at 16 h for the stock route, 0.22 at 30 h for the cotton field, 0.48 at 21 h for the remnant vegetation field, and 0.19 at 39 h for the bean field. 3.3. DiÕersity of substrate utilisation At the final time point Ž 116 h. , values of HX ranged from a maximum of 4.55 for samples S1, S2 and R5, to 4.20 for sample F2. Analysis of the site means at 116 h showed that the stock route and remnant vegetation sites had significantly higher diversity Ž a s 0.05. than the cotton and bean sites ŽTable 4..

Fig. 2. Relationship between organic C and bulk density at the four sites sampled Ž R 2 s 0.600..

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Fig. 3. Measured and fitted curves displaying proportional substrate richness Ž P . of soil samples after inoculation into the Biolog CLPP plates.

Plotting H X over time ŽFig. 5. showed differences in the rate of development of diversity. As was done for the richness curves, a logistic model Ž Eq. Ž 1.. was fitted to the diversity curves ŽTable 5., and the rate of change of the fitted diversity curves was calculated. Again, this gave the maximum rate of change of diversity, and the time at which it occurred Ž Fig. 6. . The approximate maximum rates and the times after inoculation at which they occurred were 0.048 at 25 h for the stock route, 0.018 at 42 h for the cotton field, 0.056 at 30 h for the remnant vegetation field, and 0.022 at 43 h for the bean field. 3.4. Site similarities and substrate redundancy Using a biplot analysis ŽGabriel, 1971. of the primary data, the samples and sites were differentiated on the basis of their pattern of substrate use. The data

Table 2 Final proportional richness Ž P . values for the vertisol sites Level

Number

Mean

Standard deviation

Site is significantly different to sites

Stock route Cotton Remnant vegetation Bean

5 5 5 5

0.97 0.85 0.97 0.84

0.973 0.067 0.019 0.109

B, C R, S B, C R, S

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Table 3 Values of parameters a, b and c, and RMSE values, for the fitted logistic model Site

u1

u2

u3

RMSE

Stock route Cotton Remnant vegetation Bean

0.932 0.807 0.923 0.770

110 108 637 67.9

0.207 0.108 0.240 0.101

0.052 0.049 0.053 0.056

matrix consisted of 20 rows representing the samples, and 95 columns representing the substrates. The ordinates of the first two principal components, which accounted for 27.3% and 13.6% of the variance, were plotted ŽFig. 7.. The averages of the sites were also plotted in the biplot space. The separation of samples and site averages indicate a difference in the variety, andror number, of substrates used. The proximity in the plane of the first two principal components of the remnant vegetation and stock route sites is due to the similarity of the substrates used by both sites. In fact, the microbial communities in most samples from both sites were able to use all substrates in the plate, with the exception of 2,3-butanediol Ž this substrate was used by only two samples from the stock route site, and one sample from the remnant vegetation site.. Hence, any difference between these two sites and other sites can be attributed to a decrease in the number of substrates used by the other site, not from the use of different kinds of substrates. Differences between the cotton

Fig. 4. Rate of change of proportional richness Žd Prd t . over time.

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Table 4 Diversity values for the sites Site

Number of samples

Mean

Standard deviation

Site is significantly different to sites

Stock route Cotton Remnant vegetation Bean

5 5 5 5

4.53 4.39 4.53 4.37

0.04 0.08 0.02 0.13

B, C S, R B, C S, R

and bean, sites, however, are due to differences in both the number and type of substrates used. The biplot rays representing the substrates have been expanded and shown in Fig. 8. The rays are labeled with the numbers 2 to 96, which correspond to the 95 substrates. Vectors of substrates which were positively correlated pointed in one direction, whereas negatively correlated ones pointed in the opposite direction, and uncorrelated ones were orthogonal to one another Ž Gabriel, 1971. . The direction of the rays can be used as an indication of which substrates were instrumental in differentiating between the sites and samples. In this case, substrates whose rays extend to the far right were not always metabolised by the microbial communities in samples of those sites which plotted out to the far left. Substrates that were utilised by all samples are located at the origin of the graph. A one-way analysis of variance was performed on the averaged site response of the four vertisols for each substrate Ž a s 0.10. in order to identify those

Fig. 5. Measured and fitted curves for substrate diversity Ž H X . over time.

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Table 5 Values of parameters a, b and c, and RMSE values, for the fitted logistic model Site

u1

u2

u3

RMSE

Stock route Cotton Remnant vegetation Bean

4.360 4.199 4.392 4.172

2 069 988 668 46 168 207

0.861 0.215 0.515 0.172

0.194 0.246 0.124 0.167

substrates which significantly differentiated the sites. Forty-five of the 95 substrates were utilised by the microbial communities in all samples, and so provided no differentiating power. Of the remaining 50 substrates, 19 were useful in detecting significant differences between the four sites Ž Table 6. . Also given in Table 6 are the average well responses Ži.e., 1.0 if the substrate was utilised by all of the samples, 0.2 if the substrate was utilised by 1 sample, etc.. and sites which differed significantly from the listed site in the utilisation of a particular substrate. For example, microbial communities in samples from the stock route and remnant vegetation sites had significantly more potential for utilising thymidine than the cotton and bean sites. The discriminating substrates listed in Table 6 include 5 carboxylic acids out of the 24 on the plate, 5 out of 20 amino acids, 3 of 6 aminesramides, 1 of 5 polysaccharides, both hexose phosphates, and glycerol phosphate, thymidine and 2,3-butanediol. None of the 30 carbohydrate substrates on the plate was significant in differentiating the four vertisol sites, since all of these carbohydrates were utilised readily by the microbial communities in all of the samples.

Fig. 6. Rate of change of diversity over time.

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Fig. 7. Biplot showing the position of 95 substrates and the samples in the plane of the first two principle components. The two components explain 40.9% of the variance in the data. The samples are denoted by the letters S Žstock route., C Žcotton., R Žremnant vegetation. and B Žbean., and the numbers 1 to 5. The site averages have also been added to the biplot.

Fig. 8. Component rays of biplot in Fig. 7. Substrate labels are given in Table 6.

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Table 6 Substrates that were used to a significantly different extent among sites, average response of individual samples to the substrate, and sites that differed significantly from the listed site in the use of that substrate, as determined by one-way analysis of variance Ž a s 0.10. Substrate

Label a

Remnant vegetation R

Stock route S

Cotton C

Bean B

a-Cyclodextrin Formic acid a-Hydroxybutric acid Itaconic acid a-Keto butyric acid Sebacic acid Succinamic acid Alaninamide Glycyl-L-aspartic acid Glycyl-L-glutamic acid L-Phenylalanine D-Serine D,L-Carnitine Thymidine Phenyl ethylamine 2,3-Butanediol D,L-a-Glycerol phosphate Glucose-1-phosphate Glucose-6-phosphate

2 40 46 50 51 59 62 64 71 72 77 80 83 88 89 92 94 95 96

1.0 B 1.0 B, C 1.0 B, C 1.0 C 1.0 B 1.0 C 1.0 C 1.0 B 1.0 B 1.0 B 1.0 C 0.8 1.0 B 1.0 B, C 1.0 B, C 0.2 0.8 B, C 1.0 B 1.0 B

1.0 B 1.0 B, C 0.6 1.0 C 1.0 B 0.8 C 1.0 C 1.0 B 1.0 B 1.0 B 0.8 1.0 B, C 1.0 B 1.0 B, C 1.0 B, C 0.4 B, C 1.0 B, C 1.0 B 1.0 B

0.8 0.6 R, S, B 0.4 R 0.4 R, S, B 0.6 0.2 R, S, B 0.6 R, S, B 0.8 B 0.6 1.0 B 0.4 R 0.4 S 1.0 B 0.4 R, S 0.4 R, S 0.0 S 0.2 S, R 0.8 0.6

0.6 R, S 0.2 R, S, C 0.2 R 0.8 C 0.4 R, S 0.8 C 1.0 C 0.4 R, S, C 0.4 R, S 0.6 R, S, C 0.6 0.4 S 0.6 R, S, C 0.4 R, S 0.2 R, S 0.0 S 0.2 S, R 0.6 R, S 0.4 R, S

a

Labels correspond to the substrates in the Biolog plate.

3.5. Relationship between organic C and functional diÕersity The physical and chemical soil characteristics reported in Table 1 were plotted against the measured diversity, for the four vertisol sites. No apparent

Fig. 9. Diversity vs. organic C Ž%..

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Fig. 10. Diversity vs. pH.

relationship was observed between diversity and % clay, or diversity and EC. However, a relationship appears to exist between organic C and diversity Ž Fig. 9.. A broken-stick model ŽEq. Ž 2.. was fitted to the data Ž RMSEs 0.0789. , and the values of parameters a, b and c were estimated to be 4.24, 0.163 and 1.76, respectively. The AIC of the broken-stick model Žy38.9. was smaller than the AIC of the best-fitting linear model Žy32.7., verifying that the broken-stick model was more suitable than the linear model. The trend shown in Fig. 1 between organic C and pH density is again apparent if pH is plotted against functional diversity ŽFig. 10. .

4. Discussion 4.1. DiÕersity of substrate utilisation The functional diversity of microbial communities was significantly smaller, and also more variable, in the cultivated sites than in the uncultivated sites. This finding is in contrast to that of Kennedy and Smith Ž 1995. , who found greater diversity of substrate utilisation and stress resilience in cultivated wheat soils than in uncultivated prairie soils. It is unclear why the cotton and bean fields had smaller and more variable diversity levels. Smaller organic C contents in the cultivated fields may be a major factor Ž the relationship between organic C and microbial diversity is discussed later. . Soil pH may also be a factor, although the average pH values of the soils of the remnant vegetation and bean fields were not significantly different. Bulk density is unlikely to be a factor as the

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cultivated sites had a lower bulk density, suggesting better aeration, which would favour the kind of microorganisms able to grow in the microplate. The greater spatial variation in functional diversity of the cultivated sites cannot be explained by soil properties such as organic C, pH, and bulk density, which were more uniform in the cultivated fields. The increased homogeneity of soil properties in the cultivated fields is probably due to tillage Ž Cattle et al., 1994. . Non-edaphic factors could also influence soil microbial diversity, including the history of chemical applications to a field, in the form of fertiliser, insecticides and herbicides, and phytodiversity, which is higher in uncultivated sites. It has been suggested that a diverse plant community favours effective soil microbial communities by decreasing their energy demand Ž Fließbach and Mader, 1997. . ¨ 4.2. Rates of substrate use The higher rates at which microbial richness and diversity became apparent in the uncultivated soils may be attributed to higher initial inoculum densities, considering that the uncultivated sites had significantly higher levels of organic C than the cultivated soils, and that organic C is generally correlated to microbial biomass Ž Schnurer et al., 1985.. However, the parameters u 1, u 2 and u 3 in the logistic model fitted to the richness and diversity curves ŽEq. Ž1.. are independent of the inoculum density, and can be used as distinguishing characteristics of the microbial communities. The logistic model is very similar to the model Lindstrom et al. Ž1998. fitted to the colour development curve obtained from measuring absorbance values over time. Their model had the form: y s optical density Ž absorbance. value s Kr Ž 1 q eyr Ž tys. . to describe a population of individuals Ž N Ž t .., where K, r and s are the variable parameters and t is the time following inoculation of the microplate. K and r are independent of the inoculum density. The parameters in both logistic models are related as follows:

u1 sK u3sr u 2 s e r s s e u 3 s or s s Ž ln u 2 . ru 3 u 1Ž K . represents the asymptote of the curve, and the actual measured values of u 1 have been reported as the final diversity values obtained Žat 116 h.. u 1 is independent of the initial inoculum density. u 3Ž r . determines the shape of the curve, and according to Lindstrom et al. Ž 1998. is also independent of initial inoculum density. If so, u 3 represents that part of the rate of colour change that is determined by microbial community characteristics, not by inoculum density. Differences in community structure Ž special and physiological. would be respon-

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sible for differences in the rate of development of richness or diversity. For example, different species in the same functional group may be able to utilise the same substrate, but may do so at different rates. Interspecial interactions are dependent on community structure, so the activity of a species population may vary from one community to another. 4.3. Relationship between organic C and functional diÕersity Microbial biomass and activity have been shown to be highly correlated with organic matter content Ž Schnurer et al., 1985; Carter, 1986; Powlson et al., 1987; Sparling, 1992. . However, the correlation between organic matter and microbial diversity has not been investigated as thoroughly and the relationship is not as clear. Our results suggest that there is a positive relationship between organic C and microbial diversity, below a certain level, and no relationship above this level. Dhillion Ž1997. reported that organic matter, microbial biomass and Biolog substrate richness values increased with increasing length of the fallow period. In a study of functional diversity of soil microbial communities from four different climatic regions in Ethiopia, Sharma et al. Ž 1997. reported a positive relationship between functional diversity and microbial biomass C, which is generally correlated to organic matter Ž Schnurer et al., 1985. , and thus organic C. Sharma et al. Ž 1997. fitted a linear relationship between microbial C and diversity Žtheir Fig. 2, r s 0.536, P s 0.001.. However, this linear fit seemed to ignore two data points on their Fig. 2, which also did not appear in their table of data points Žtheir Table 4. , at approximately 40 mg microbial C gy1 of soil. Accordingly, a re-analysis of their data was performed, using all the data points in their Table 4 Žwith the exclusion of the data point from the Awassa forest soil, assumed to be an outlier. , and using the two omitted points in their Fig. 2 Žvalues were taken from the figure.. A broken-stick model was fitted to the new data ŽSSE s 0.0679. ŽFig. 11., and the resulting parameter values obtained Ž to 5 significant figures. : a s 1.2893, b s 0.0048910, c s 105.593. The critical level of microbial biomass C was 105.6 mg gy1 soil. As the AIC for the linear model Žy47. was greater than the AIC of the broken-stick model Žy49., it was determined that the broken-stick model was more suitable than the linear model. In order to relate this critical level of microbial C to organic C, values of microbial C were plotted against organic C Ždata taken from their Table 2. and a linear relationship fitted. The resulting linear equation was obtained: C org s 0.9422 q 0.00707 C micro

Ž r s 0.5 .

Using this equation, the critical threshold level of organic C was determined to be 1.69%. It is interesting to note that the critical level calculated from the data of Sharma et al. was relatively close to our value Ž1.76% organic C., especially

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Fig. 11. Relationship between microbial C and diversity, using data from Sharma et al. Ž1997..

as two of their four soil types were vertisols, the other two being a nitosol and a cambisol ŽSharma et al., 1997. . It is generally argued that, as most soil microorganisms rely on external nutrients, they respond promptly to a decrease in non-living organic nutrient matter in soil ŽPotter and Meyer, 1990.. The findings of this and other reports could lead to the hypothesis that decreasing amounts of organic C increasingly limit substrate availability to microorganisms, and that below a certain level of organic C, decreases in organic C will affect functional microbial diversity. The CLPP approach measures the maximum potential activity of the microbial community, not the in situ activity, so measured differences in substrate utilisation indicate differences in the maximum potential activity, not the actual activity at the time of sampling. It is generally argued that organic C is important for soil structural quality ŽMcGarry, 1996.; the prevention of a decline in functional microbial diversity may be another reason for maintaining organic C levels in soil. 4.4. Limitations of the method The limitations of the CLPP method have been thoroughly discussed by many researchers Žfor example, Zak et al., 1994; Haack et al., 1995. . The main limitations are that: Ø the rate of colour development is largely determined by inoculum density Žhowever, various data analysis methods have been used to overcome this problem, as in Garland and Mills, 1991; Lindstrom et al., 1998..

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Ø the method only tests for a subset of the total microbial community. The activity of species that cannot grow Ž or only very slowly. in the artificial environment of the microplate will not be registered. Fast-growing aerobic bacteria would dominate microbial activity in the microplate. The importance of fungi in the community are largely ignored. The particular method used in this study had other limitations. The absorbance values of the wells could not be measured, and this affected the results in two ways: first, the binary results could not be objectively measured without a plate reader; and second, the binary data underestimated the differences between samples from the cultivated and uncultivated sites. This is because positive well responses in the plates for uncultivated sites often tended to have greater colour density than corresponding positive well responses from the cultivated sites. 4.5. Implications for soil quality and suggestions for further research There is growing evidence in general biodiversity studies to support the hypothesis that decreasing biodiversity will, at some point, begin to erode the capacity of the ecosystem to carry out certain functions. Soil quality may be defined as ‘the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation.’ ŽKarlen et al., 1997. . We may ask the question: Does a reduced ability to metabolise substrates reduce soil quality as defined above? At present, there is not enough evidence available to enable us to answer this question. Investigation is needed in the type of research that studies the dynamics of functional diversity and measured aspects of soil quality, in relation to dynamics in the soil environment Žmanagement-induced or otherwise. . The type of research into which this report falls may be useful for characterising and differentiating between the communities, and for comparing the functional diversity of different communities, but does not give us much of an indication about the sensitivity of functional diversity to environmental changes, or about the range of fluctuations in diversity that might be considered ‘normal’ Ž e.g., seasonal fluctuations. . Other researchers have measured functional diversity before and during changes in the soil environment, e.g., Fritze et al. Ž 1997. , who measured microbial community differences after the addition of fertiliser to soil along a pollution gradient, and Gorlenko et al. Ž1997., who measured community differences as a result of selected disturbances to the soil chemistry. In a way, such studies do assess the resiliency or stability of the soil community, but do not assess changes in soil quality, as defined above. An example of the kind of research required might be, using the sites studied in this report, to let a section of the

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cultivated fields revert to an unmanaged state, and to measure any changes in functional diversity, other soil properties, and selected ecological processes that are influenced by soil microbes. It may be that such studies are unable to isolate changes in functional diversity from changes in certain other soil properties, such as organic matter or phytodiversity. There may be a very good reason for this — perhaps functional microbial diversity is not a primary determinant of certain aspects of soil quality. It is possible that it acts only as a ‘link’ between, for example, organic matter and other soil and ecosystem components, or it may be little more than a ‘passenger’ on the back of organic matter. At the least, it may be useful as an indicator of, for example, organic matter, especially if the relationship that we have observed between organic C and functional diversity below a certain level of organic C, does exist.

5. Conclusions Ž1. Analysis of Biolog results generated two parameters that were characteristic of each microbial community’s diversity or richness: the maximum diversity of substrate utilisation, calculated using the Shannon diversity index, or estimated by the parameter u 1 in the logistic model fitted to the diversity development curves; the maximum richness, measured at the final time point when the richness development curves had plateaued, or estimated by the parameter u 1 in the logistic model fitted to the richness development curves; and the inoculumindependent rate of substrate utilisation or diversity development, estimated by the parameter u 3 in the logistic model fitted to the richness or diversity curves, respectively. Ž2. The functional diversity of soil microbial communities, as measured by the CLPP method, was significantly smaller and more variable in cultivated than uncultivated vertisol A horizons. The decrease in substrate diversity in the cultivated sites resulted from the decreased use of a number of substrate classes Že.g., carboxylic acids, amino acids and aminesramides., not from a decrease in only one or two substrate classes. Ž3. Microbial communities from the uncultivated sites were able to utilise substrates at a faster rate than samples from the cultivated sites, which indicated a structural difference between the microbial communities of the cultivated and uncultivated sites. Ž4. The results suggest that soil organic C and functional microbial diversity may be positively related below a certain level of organic C, and are not related above that level. Ž5. The impact of functional microbial diversity on soil quality and ecosystem functioning remains to be established.

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Acknowledgements We should like to thank Dr. James Lytton-Hitchins, CRC for Sustainable Cotton Production, for locating the vertisol sites.

References Kaike, H., 1974. Factor analysis and AIC. Psychometrica 52, 317–332. Beare, M.H., Reddy, M.V., Tian, G., Srivastava, S.C., 1997. Agricultural intensification and agroecosystem function in the tropics: the role of decomposer biota. Appl. Soil Ecol. 6, 87–108. Bossio, D.A., Scow, K.M., 1995. Impact of carbon and flooding on the metabolic diversity of microbial communities in soils. Appl. Environ. Microbiol. 61, 4043–4050. Carter, M.R., 1986. Microbial biomass as an index for tillage-induced changes in soil biological properties. Soil Tillage Res. 7, 29–40. Cattle, S.R., Koppi, A.J., McBratney, A.B., 1994. The effect of cultivation on the properties of a Rhodoxeralf from the wheatrsheep belt of New South Wales. Geoderma 63, 215–225. Dhillion, S.S., 1997. Fallow age influences microbial functional abilities, soil properties and plant functional groups. In: Insam, H., Rangger, A. ŽEds.., Microbial Communities. Functional versus Structural Approaches. Springer-Verlag, Berlin, pp. 140–148. Ehrlich, P.R., 1994. Foreword. Biodiversity and ecosystem function: need we know more? In: Schulze, E.D., Mooney, H.A. ŽEds.., Biodiversity and Ecosystem Function. Springer, Berlin, pp. vii–xi. Ehrlich, P.R., Wilson, E.O., 1991. Biodiversity studies: science and policy. Science 253, 758–762. Elton, C.S., 1958. The Ecology of Invasion by Animals and Plants. Chapman & Hall, London. Fließbach, A., Mader, P., 1997. Carbon source utilization by microbial communities in soils under ¨ organic and conventional farming practice. In: Insam, H., Rangger, A. ŽEds.., Microbial Communities. Functional versus Structural Approaches. Springer, Berlin, pp. 109–120. Finlay, B.J., Maberly, S.C., Cooper, J.I., 1997. Microbial diversity and ecosystem function. Oikos 80, 209–213. Fritze, H., Pennanen, T., Vanhala, P., 1997. Impact of fertilizers on the humus layer microbial community of Scots Pine stands growing along a gradient of heavy metal pollution. In: Insam, H., Rangger, A. ŽEds.., Microbial Communities. Functional versus Structural Approaches. Springer, Berlin, pp. 68–83. Gabriel, K.R., 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika 58, 453–467. Garland, J.L., 1996. Patterns of potential C source utilization by rhizosphere communities. Soil Biol. Biochem. 28, 223–230. Garland, J.L., Mills, A.L., 1991. Classification and characterization of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source utilization. Appl. Environ. Microbiol. 57, 2351–2359. Garland, J.L., Mills, A.L., 1994. A community-level physiological approach for studying microbial communities. In: Ritz, K., Dighton, J., Giller, K.E. ŽEds.., Beyond the Biomass: Compositional and Functional Analysis of Soil Microbial Communities. Wiley, Chichester, pp. 77–84. Gee, G.W., Bauder, J.W., 1986. Particle size analysis. In: Methods of Soil Analysis. 2nd edn. Klute, A. ŽEd.., Agron Monogr. 9 ASA and SSSA, Madsison, WI, pp. 383–411, Part 1. Gorlenko, M.V., Majorova, T.N., Kozhevin, P.A., 1997. Disturbances and their influence on

342

F. Yan et al.r Geoderma 96 (2000) 321–343

substrate utilisation patterns in soil microbial communities. In: Insam, H., Rangger, A. ŽEds.., Microbial Communities. Functional versus Structural Approaches. Springer, Berlin, pp. 84–93. Gottschal, J.C., Meijer, W.G., Oda, Y., 1997. Use of molecular probing to assess microbial activities in natural ecosystems. In: Insam, H., Rangger, A. ŽEds.., Microbial Communities. Functional versus Structural Approaches. Springer, Berlin, pp. 10–18. Griffiths, B.S., Ritz, K., Wheatly, R.E., 1997. Relationship between functional diversity and genetic diversity in complex microbial communities. In: Insam, H., Rangger, A. ŽEds.., Microbial Communities. Functional versus Structural Approaches. Springer, Berlin, pp. 1–9. Haack, S.K., Garchow, G., Klug, M.J., Forney, L.J., 1995. Analysis of factors affecting the accuracy, reproducibility, and interpretation of microbial community carbon source utilisation patterns. Appl. Environ. Microbiol. 61, 1458–1468. Isbell, R.F., 1996. The Australian Soil Classification. CSIRO Publishing, Australia. Karlen, D.L., Mausbach, M.J., Doran, J.W., Cline, R.G., Harris, R.F., Schuman, G.E., 1997. Soil quality: a concept, definition, and framework for evaluation Ža guest editorial.. Soil Sci. Soc. Am. J. 61, 4–10. Kennedy, A.C., Smith, K.L., 1995. Soil microbial diversity and the sustainability of agricultural soils. Plant and Soil 170, 75–86. Kreitze, S., Anderson, T.-H., 1997. Substrate utilisation patterns of extractable and non-extractable bacterial fractions in neutral and acidic beech forest soils. In: Insam, H., Rangger, A. ŽEds.., Microbial Communities. Functional versus Structural Approaches. Springer, Berlin, pp. 149– 160. Kunc, F., 1994. Methods for the analysis of soil microbial communities. In: Ritz, K., Dighton, J., Giller, K.E. ŽEds.., Beyond the Biomass: Compositional and Functional Analysis of Soil Microbial Communities. Wiley, Chichester, pp. 23–28. Lawton, J.H., Brown, V.K., 1994. Redundancy in ecosystems. In: Schulze, E.D., Mooney, H.A. ŽEds.., Biodiversity and Ecosystem Function. Springer, Berlin, pp. 255–270. Lindstrom, J.E., Barry, R.P., Braddock, J.F., 1998. Microbial community analysis: a kinetic approach to constructing potential C source utilization patterns. Soil Biol. Biochem. 30, 231–239. Lytton-Hitchins, J.A., 1998. Seasonal abundance and diversity of soil fauna in the principal cotton growing valleys of NSW. PhD Thesis. The University of Sydney. Magurran, A.E., 1988. Ecological Diversity and Its Measurement. Chapman & Hall, London. McGarry, D., 1996. The structure and grain size distribution of vertisols. In: Ahmad, N., Mermut, A. ŽEds.., Vertisols and Technologies For Their Management. Elsevier, Amsterdam, pp. 231–259. Potter, C.S., Meyer, R.E., 1990. The role of soil biodiversity in sustainable dryland farming systems. Adv. Soil Sci. 13, 241–251. Powlson, D.S., Brookes, P.C., Christensen, B.T., 1987. Measurement of soil microbial biomass provides an early indication of changes in total soil organic matter due to straw incorporation. Soil Biol. Biochem. 19, 159–164. SAS Institute, 1995. JMP Statistics and Graphics Guide, Version 3.1. SAS Institute, Cary. Schnurer, J., Clarholm, M., Rosswall, T., 1985. Microbial biomass and activity in an agricultural soil with different organic matter contents. Soil Biol. Biochem. 17, 611–618. Setala, H., Laakso, J., Mikola, J., Huhta, V., 1998. Functional diversity of decomposer organisms in relation to primary production. Appl. Soil Ecol. 9, 25–31. Sharma, S., Piccolo, A., Insam, H., 1997. Different carbon source utilization profiles of four tropical soils from Ethiopia. In: Insam, H., Rangger, A. ŽEds.., Microbial Communities. Functional versus Structural Approaches. Springer, Berlin, pp. 132–139. Soil Survey Staff, 1975. Soil Taxonomy. Agricultural Handbook 436. US Government Printing Office, Washington, DC.

F. Yan et al.r Geoderma 96 (2000) 321–343

343

Sparling, G.P., 1992. Ratio of microbial biomass carbon to soil organic carbon as a sensitive indicator of changes in soil organic matter. Aust. J. Soil Res. 30, 195–207. Steinberger, Y., Zelles, L., Bai, Q.Y., von Lutzow, M., Munch, J.C., 1999. Phospholipid fatty acid profiles as indicators of the community structure in soils along a climatic transect in the Judean Desert. Biology and Fertility of Soils 28, 292–300. Tilman, D., Downing, J.A., 1994. Biodiversity and stability in grasslands. Nature 367, 363–365. Zak, J.C., Willig, M.R., Moorhead, D.L., Wildman, H.G., 1994. Functional diversity of microbial communities: a quantitative approach. Soil Biol. Biochem. 26, 1101–1108.