Measuring respiration profiles of soil microbial communities across Europe using MicroResp™ method

Applied Soil Ecology 97 (2016) 36–43

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Measuring respiration profiles of soil microbial communities across Europe using MicroRespTM method R.E. Creamera,* , D. Stonea,b , P. Berrya , I. Kuiperc,d a

Teagasc, Johnstown Castle Research Centre, Ireland Faculty of Biological Sciences, Miall Building, Leeds University, Leeds LS2 9JT, UK Department of Soil Quality, Wageningen University, P.O. Box 47, Droevendaalsesteeg 4, Wageningen AA 6700, The Netherlands d Agrifirm Plant, P.O. Box 20000, Landgoedlaan 20, Apeldoorn 7302 HA, The Netherlands b c

A R T I C L E I N F O

A B S T R A C T

Article history: Received 25 February 2015 Received in revised form 30 July 2015 Accepted 11 August 2015 Available online 29 August 2015

A European “transect” was established to assess soil microbial activity, using the MicroRespTM method, as part of a larger project looking at soil biodiversity and function across Europe. 81 sites were sampled across five biogeographical zones described and mapped in the EEA report (EEA, 2012) and included the following classes; Boreal, Atlantic, Continental, Mediterranean and Alpine, three land-use types (Arable, Grass and Forest) incorporating a wide range of soil pH, soil organic carbon (org C) and texture. Seven carbon substrates were used to determine multiple substrate induced respiration (MSIR), incorporating; acids, bases, sugars and amino acids. Substrates included: D-(+)-galactose, L-malic acid, gamma amino butyric acid, n-acetyl glucosamine, D-(+)-glucose, alpha ketogluterate, citric acid and water. MicroRespTM results showed discrimination of land-use type over a large spatial scale and response to soil pH and soil organic carbon. Substrates behaved differently depending upon combinations of land-use and soil properties specifically the greater utilisation of carboxylic acid based substrates in arable sites. ã 2015 Elsevier B.V. All rights reserved.

Keywords: MicroRespTM Soil microbial activity Multiple substrate induced respiration (MSIR) Monitoring Biological indicators Soil quality

1. Introduction As we move further into the 21st century, the assessment of soil quality has never been more critical. The recent withdrawal of the Soil Framework Directive (EU (European Union), 2006) has come at a time when there is increased concern about soil quality and food security, with proposed policy initiatives by the European Union to enhance sustainable use of soil in relation to the food production sector (COM(2011) 571). However, the assessment and monitoring requirements of soil quality have been debated for many years at both European and Global scales (Doran and Zeiss, 2000; Van Bruggen and Semenov, 2000; Schloter et al., 2003). Currently, monitoring frameworks exist across Europe and at national level within Member States, including the LUCAS-soil survey (Toth et al., 2013) and GEMAS (Reimann et al., 2014) at European scale, both of which assess physical/chemical aspects of soil quality monitoring, but as yet do not include a soil biological component. At Member State level there have been numerous surveys and monitoring frameworks in place in the last twenty years which in part include some biological measures of soil quality. Key examples of these include: the French National Soil Quality Monitoring Network

* Corresponding author. E-mail address: [email protected] (R.E. Creamer). http://dx.doi.org/10.1016/j.apsoil.2015.08.004 0929-1393/ ã 2015 Elsevier B.V. All rights reserved.

“Reseau de Mesures de la Qualite des Sols” (Saby et al., 2009; Dequiedt et al., 2011), National Soil Inventory (NSI) of England and Wales (Bellamy et al., 2005) and a separate inventory for Scotland (Lilly et al., 2010; Yao et al., 2013), Countryside Survey UK (Black et al., 2003) and the Dutch Monitoring Network (Rutgers et al., 2009). The Soil Framework Directive (SFD) (EU (European Union), 2006) however recognised that the loss of soil biodiversity is of critical importance, highlighting that soil biodiversity is essential for ecosystem functioning and contributes to soil functions. These functions include; support of primary productivity, cycling of nutrients through mineralisation, enhancing water infiltration, creation of soil aggregates contributing to the stability of soil structure and as a food source within a dynamic food web network of soil organisms. As well as emphasising the role of soil biodiversity for soil quality and associated ecosystem functions, Haygarth and Ritz (2009) also highlighted the need for further research and the imperative for the identification of robust biological indicators of soil quality. However, as the SFD highlighted in 2006, not enough information is available across Europe to quantitatively provide baseline information, from which changes in soil biodiversity could be measured (EU (European Union), 2006). This is due to a lack of data collection and consistency of methods applied to soil biological data across Europe. Thus, the major challenge set to soil biologists

R.E. Creamer et al. / Applied Soil Ecology 97 (2016) 36–43

within Europe, was to provide baseline data for soil biodiversity across Europe and to demonstrate the feasibility of a Europeanwide biodiversity monitoring network. To meet this challenge, the EcoFINDERS project developed a unique sampling campaign named “the transect” to address this issue. It included the site selection of 81 sites across Europe based on five key variables; land-use, biogeographical zone (reflecting climate), soil pH, soil textural class and soil organic carbon content. Details of the site selection and sampling protocols can be found in Stone et al. (2016). The microbial community is considered the driver of many of the soil functions (Bååth and Anderson, 2003), playing key roles in aggregate formation, nutrient mineralisation (Schmidt and Waldron, 2015), plant health and is considered the basis of food webs in soils (de Ruiter et al., 1994). There are currently several microbial assays that can be applied to measure the activity of the microbial community including; the measurement of microbial community respiration, microbial biomass, enzyme activity, BiologTM and MicroRespTM method for community level physiological profiling (CLPP). Microbial assays provide a rigorous means of comparing soil samples/sites under laboratory controlled conditions. Due to the manipulation of the soils during the procedure, they do not necessarily reflect real soil activity conditions, but rather allow the assessment of potential activity for a given site.

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The MicroRespTM method (Campbell et al., 2003) provides a way to measure microbial respiration rates induced by a range of carbon sources (Chapman et al., 2007), defined as Multiple Substrate Induced Respiration (MSIR). The amount of carbon utilised indicates the abundance of the microbial biomass able to utilise a specific carbon source. It is hypothesised that the greater the diversity of the microbial community the wider the range of carbon source utilisation (Creamer et al., 2009). Soil microbial biomass or diversity is often associated with available soil organic carbon content, with lowest biomass associated with dry conditions and low organic matter (desert) or wet conditions with extremely high organic matter (Boreal) (Fierer et al., 2009). It is therefore proposed that soils from Mediterranean and Boreal climates would be expected to have the lowest microbial biomass and therefore reduced carbon utilisation, compared to Atlantic, continental or alpine conditions. In this study we employ the MicroRespTM method to assess the potential microbial activity of 81 sites of varying physiochemical parameters, located across a range of contrasting biogeographical (climatic) zones and land-uses across Europe. The objective of this study was twofold; firstly, to determine the range of microbial respiration activity for European soils, how this varies according to climate (biogeographical zone), land-use and is influenced by key

Fig. 1. Map of transect sites, based upon the Biogeographical zones of Europe (EEA, 2012).

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soil properties. Secondly, to determine the suitability of MSIR as an indicator of microbial activity in a European-wide monitoring network. 2. Methods 81 sites were sampled across Europe, as part of the EcoFINDERS project; this study is known as the European “transect” (Fig. 1). Sites were selected from within European Union countries using a spatial random sampling model, weighed to derive a spectrum of sites representative of the range of soil properties; (organic carbon, textural class (representing by% clay) and pH), land-use and biogreographical zones across Europe. Data used to spatially derive potential locations for sampling were based on the European Food Safety Authority (EFSA) database, the Corine landcover map and soils database (Gardi et al., 2011) and the European Environment Agency map of biogeographical zones (EEA, 2012). Full details of the development of the site selection model and sampling can be found in Stone et al. (2016). To assess the climatic parameter, data were derived from the biogeographical zones of Europe map (EEA, 2012). The final sites incorporated all major climatic zones, three landuses (arable, grassland, forestry) and a spectrum of soil physical and chemical properties that varied in organic carbon, texture and pH (Table 1). Each site was sampled between September and November 2012 following a pre-agreed standard operating protocol (SOP). Soil was taken from the top 5 cm of the profile using plastic cores hammered into the ground and then dug out. The cores were transported at 4  C to a central handling station, employing the use of polystyrene boxes and freezer blocks to maintain temperature. Upon immediate receipt of samples, soil was from removed from the cores and hand mixed using the ‘cone and quarter’ technique (Massey et al., 2014). The mixed soil was sieved to 2 mm and sieved soil stored at 4  C. Dry matter content was determined for each soil (dried at 105  C for 24 h) and water holding capacity (WHC) was measured by saturating soil in a Buchman funnel for 30 min, allowing the soil to drain for 16 h and then taking the dry matter content of the saturated Table 1 Chemical, physical and biogeographical parameter groupings of 81 soils sampled across Europe. Category Bio zone Alpine Atlantic Boreal Continental Mediterranean

Forestry sites Grassland sites Arable sites Total 2 7 4 5 1

9 13 0 11 2

1 13 0 11 2

12 33 4 27 5

pH <5 5–7 >7

11 6 2

1 21 13

14 13

12 41 28

Organic carbon <2% 2–15% >15%

1 12 6

6 28 1

15 12

22 52 7

4

5

6

15

3

13

8

24

6 6

16 1

13

35 7

Texture Coarse <18% clay Medium 18% < clay < 35% Fine >35% Clay No texture (Organic soils)

soil. Soil clay content was measured as part of the particle size pipette method (ISO 11277:1998). Total carbon (C), nitrogen (N) following (ISO 10694:1995) and organic carbon (org C) were determined by the LECO elemental analysis, which was conducted on 0.25 mm milled dry soil sub-samples (Massey et al., 2014). Cation exchange capacity (CEC) was measured using BaCl2 extraction method (ISO 11260:1994). pH was measured in a 1:2.5 soil in water suspension using a glass electrode (van Reeuwijk, 2002). Details of all methods can be found in Massey et al. (2014). Soils were incubated for 7 days at 20  C between 30 and 60% of their WHC, to allow the microbial community to settle after initial disturbance by sampling and sieving (Bloem et al., 2005). Soils which were wetter than 60% of their water holding capacity were air dried in large trays at 15  C until they reached 60% WHC and then incubated. MSIR rates were produced for each of the 81 soils using the MicroRespTM method described by Creamer et al. (2009) and Campbell et al. (2003). Colorimetric gel detector plates were created using cresol red indicator solution to be read on a Modulus Microplate reader (Turner Biosystems, CA, USA) at 570 nm. A spectrum of seven substrates, plus water was selected from the 15 suggested in Campbell et al. (2003), incorporating acids, bases, sugars and amino acids. These were: D-(+)-galactose, L-malic acid, gamma amino butyric acid, n-acetyl glucosamine, D-(+)-glucose, alpha ketogluterate, citric acid and finally, water for basal respiration measurements. Six of the substrates used had previously been tested by Creamer et al. (2009). This study replaced Larginine with D-(+)-galactose as responses to L-arginine have been found to skew final conclusions (Creamer et al., 2009). The seven substrates were prepared to a concentration of 30 mg g 1 soil water and twelve replicate 25 ml aliquots were dispensed in a randomised block design of (three replicates in each block randomised over four blocks) to avoid any edge effects on the 96 well microtiter plate. Soil was added to the substrate plates following the Campbell method (2003) and the plate was left open for a period of 30 min to allow for the release of any carbonates present in the soils associated with adding acid based substrates. Initial colorimetry values were read from the indicator plate at 570 nm before the system was sealed and incubated at 20  C for 6 h. Following the 6 h incubation, the colorimetric detector plate was re-read on the plate reader at 570 nm to provide the final absorbance data. Respiration rates (mg CO2-C g 1 h 1) were calculated from adsorption data, minus the blank sample (average values for each plate calculated from initial colorimetric values). All substrate absorbance data was normalised and natural log transformed before analysis. Any outliers due to leakage of the micro-plate, were identified and removed prior to data analysis. The plate set-up had been designed to allow for this. Environmental variables such as org C content (%), clay content (%), CEC (cmol kg 1) and total N content (%) were all natural log transformed. Multiple backward regression was applied to explain the respiration of each substrate as a function of biogeographical zone (categorical variable) land-use (categorical variable) and soil properties; pH, org C, CEC, clay, total N (log normalised). Given the large number of assessments we used a strict criterion of p < 0.01 for variables to stay in the model. To assess the parameters in the model, CANOCO (Version 4.5; Microcomputer Power Inc., Ithaca, NY) was used to calculate partial redundancy analysis (RDA) sample scores, for which a Monte Carlo Permutation test using 499 permutations was applied to evaluate the statistical significance of the environmental variables and conditional affects analysed. When high correlation between the environmental variables occurred, only one of these variables has been used within the redundancy analysis. Biogeographical zones (categorical) and land-use (categorical) were

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describing the median of the data, the lower and upper quartiles (25% and 75%) and the minimum and maximum values of the seven substrates and water across the three land-use categories. All of the box and whisker plots with the exception of citric acid display a significant difference between arable and grassland systems at p < 0.01 significance. The relationship is strongly defined for basal (water), galactose, gamma amino butyric acid (GABA), n-acetyl glucosamine (NAGA) and glucose. This is the same behaviour as explained within the backward multiple regression model, with the exception of water. Partial redundancy analysis (RDA) was used to describe the MSIR of the carbon substrate utilisation. The sum of all eigenvalues described 55% of the total variance using the land/ soil parameters described above, of this 55% RDA 1 and RDA 2 accounted for 51.7% of that variance described. Table 3 describes the conditional effects of the model, following forward selection using Monte Carlo analysis with 499 permutations with the main land/soil parameters, again biogeographical zone and land-use were treated as categorical variables in this analysis, significance of the model was set at p < 0.05. Table 3 highlights the importance of pH, grass land-use (compared to arable) and Org_C as the main drivers of substrate utilisation (respiration) in soils across Europe. All other parameters were not significant (p > 0.05) in describing the behaviour of substrate utilisation in these 81 soil samples. Fig. 3 depicts the ordination of MSIR for the 81 sites sampled across Europe and shows the response of the substrates to the various land/soil parameters. We have included parameters that were considered significant in the RDA analysis only. RDA 1 was described by pH, Log Org C and grass land-use category, (p < 0.01), (Fig. 3). The effect of pH is negative on basal respiration and substrate utilisation of GABA, galactose and NAGA, whilst showing a positive response with the carboxylic acid group of substrates; citric acid, malic acid and AKGA. RD2 only accounts for 5.5% of the variation and can be described by Org_C alone.

presented as individual parameters, using binary scores and set against a baseline of arable, Atlantic conditions, to ensure that a full categorical matrix was not applied. All other soil properties were natural log transformed in excel before use in CANOCO. 3. Results 81 sites were sampled across five biogeographical zones (Alpine, Atlantic, Boreal, Continental and Mediterranean) throughout this field campaign (Fig. 1), representing three land-use categories (arable, forestry and grassland) with a broad spectrum of soil properties; pH, org C content, clay content (representing soil textural class), CEC and total N (Table 1). pH, org C and clay content were categorised to show the number of sites representing the range of soil properties. Table 2 summarises backward multiple regression analysis to explain the substrate utilisation (respiration) of the seven substrates and basal respiration (measured as water) as a function of the land/soil parameters. A strict value criterion of p < 0.01 was applied for variables to stay in the model and therefore all significant results described below are within this p (<0.01) value. Biogeographical zone and land-use have been coded as categorical variables, using Atlantic arable systems as a baseline, from which to compare the substrate utilisation. There is no effect of biogeographical zone on the substrate utilisation of any of the substrates, when compared to a baseline of Atlantic climatic conditions, suggesting a lack of climatic effect on respiration potential. Soils derived from grassland sites behaved significantly differently to arable sites in the case of substrates; galactose, gamma amino butyric acid (GABA), n-acetyl glucosamine (NAGA), and glucose. Forestry sites did not display any significant differences in substrate utilisation compared to arable sites. There was a significant effect of soil properties; pH, organic carbon content (Org_C), total nitrogen (TN) and cation exchange capacity (CEC), on substrate utilisation. pH shows a significant negative correlation with basal respiration (water), gamma amino butyric acid (GABA) and n-acetyl glucosamine (NAGA), suggesting a lower substrate utilisation for these substrates in more alkaline soils. Org_C significantly affected most substrate types with a positive response for all substrates with the exception of a-ketoglutaric acid (AKGA). The most significant coefficients were seen for basal (water), galactose and glucose, suggesting that soils high in Org C had a greater capacity for basal and simple carbon substrate utilisation. TN had only had a significant effect on a-ketoglutaric acid (AKGA) this was the only land/soil property to explain the behaviour of AKGA. CEC had a positive effect on two of the carboxylic acid substrates; malic acid and citric acid. Clay had no significant effect on any of the substrates. Fig. 2 shows simple box and whisker plots

4. Discussion The application of the MicroRespTM method to assess community level profiling of microbial activity across a range of soils, has been successful in showing the potential response of the microbial community to a range of land-uses and soil properties across Europe. This is the first pan-European study to apply the MicroRespTM technique and shows the validity of MSIR as a biological indicator for consideration within a European soil monitoring scheme. This method was deployed in the first instance to assess if climatic parameters, summarised by biogeographical zones, had a significant influence on the potential activity of microbial communities in soils. This paper shows that no significant effect was found as a result of climatic biogeographical

Table 2 Backward multiple regression analysis to explain the respiration of each substrate as a function of biogeographical zone (categorical variable) land-use (categorical variable) and soil properties. Given the large number of assessments we used a strict criterion of p < 0.01 for variables to stay in the model. Abbreviations of substrates are as follows: GABA: g-aminobutyric acid; NAGA: n-acetyl glucosamine; AKGA: a-ketoglutaric acid. Ln_water Continental Medit Boreal Alpine Grass Forestry pH Ln_Org C Ln_TN Ln_CEC Ln_Clay R2 of model

Ln_galactose

Ln_malic acid

0.275 0.32 0.69

0.68

0.642

Ln_GABA

Ln_NAGA

Ln_glucose

0.259

0.313

0.292

0.37 0.592

0.71 0.633

0.668

Ln_AKGA

Ln_citric acid

0.359 0.703

0.285 0.65

0.63

0.71

0.496 0.73

0.63

0.63

0.49

0.58

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Fig. 2. Box and whisker plot to describe the range of carbon substrate utilisation across land-use categories for the MSIR substrate types.

zone differences. This may be reflected by the low number of sites in the Boreal and Mediterranean (n = 4 and 5, respectively), but clearly shows no significant difference between the remaining

three zones (Atlantic, continental and alpine). However, this represents a major finding, as it illustrates that MSIR is a robust biological indicator which is not sensitive to local climatic

R.E. Creamer et al. / Applied Soil Ecology 97 (2016) 36–43 Table 3 Conditional effects obtained from the summary of forward selection using Monte Carlo permutation tests. Conditional effects Variable

Lambda A

P

F

pH Grass Ln_Org C Mediterranean Ln_Clay Boreal Ln_TN Continental Forestry Ln_CEC

0.41 0.05 0.04 0.01 0.01 0.01 0.01 0.01 0 0

0.002 0.002 0.008 0.126 0.284 0.304 0.19 0.288 0.764 0.792

54.32 7.69 6.28 1.67 1.35 1.28 1.53 1.17 0.4 0.32

Fig. 3. Redundancy analysis using CANOCO to calculate the variance in carbon substrate utilisation of the MicroRespTM method. Sites are coded according to landuse by symbols. Substrate loadings of redundancy analysis are included in the ordination.

conditions, but rather is controlled by land management practices and local soil properties, principally pH and Org C content This is in contrast to Oren and Steinberger (2008) who assessed the response of soil microbial (particularly fungal) communities to a climatic-geographical gradient of increasing aridity across Israel, finding a complex relationship of greatest respiration activity taking place in the semi-arid environment, followed by both the Mediterranean based sites and finally lowest activity present in the arid zone. However, this respiratory response may have been influenced by rainfall flushes on the soil microbial community, as the semi-arid site had received greater than average rainfall in the year of study. Nielsen et al., (2010) recognised a potential climate relationship with microbial activity in soils at the landscape scale, but attribute the majority of the discrimination between sites to soil properties, rather than vegetation or climate parameters. This is in agreement with the study presented in this paper, where landuse and soil properties were of significance, rather than climate parameters.

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Land-use type was significant in the discrimination of microbial activity of soils, both for the sum of microbial activity (RD1 and RD2) and for individual substrates, where grassland sites showed significantly greater substrate utilisation compared to arable sites. Forestry sites did not show any significant difference for any of the substrates compared to arable sites. Soil sampled from arable sites displayed lower carbon utilisation and therefore microbial activity when compared with grassland soils. Even at such a large spatial scale, as across the European continent (and associated islands) the impact of management practice in arable systems reduces the catabolic functional capacity of the microbial community. Rutgers et al. (2016) applied BiologTM to the same set of sites and included an additional study from the Dutch soil monitoring network (NSMN) where they found a clear separation of response between grass and arable sites, suggesting from that study that land management is more important than soil texture. This finding is confirmed by the results presented in this paper. Soil organic carbon (org C) content was of significant value in describing the relationship (RDA 1 and RDA 2) with the microbial respiration activity of soils across Europe. SOC is significantly correlated with land-use, with greater org C concentrations found in grassland and forest soils compared to arable soils. All carbon substrates were positively correlated to SOC (with the exception of AKGA), suggesting that the presence of higher org C results in a greater utilisation of carbon substrates added in these soils. Lagomarsino et al. (2012) assessed the relationship between SOC and microbial diversity and found that all substrates were utilised to a greater extent in bulk soil samples from pasture and forest soils compared to vineyard and hayland (arable) soils. Murugan et al. (2014) used the MicroRespTM method to assess differences in the catabolic functioning of distinct land-uses in Germany, showing a clear discrimination of MSIR between grassland and monoculture maize trials. They showed a significantly lower org C content, biomass C and residue C in the monoculture maize compared to the grassland treatments and suggested that higher labile C present in the grassland systems promoted bacterial diversity. pH was also shown to be a significant soil property driving the composition of MSIR rates across all sites. This was previously observed in a range of recent smaller scale studies (Wakelin et al., 2008; Sradnick et al., 2013; Andruschkewitsch et al., 2014). pH had a significant negative correlation with substrates; water (basal respiration) and galactose, gamma amino butyric acid (GABA) and n-acetyl glucosamine (NAGA). Wakelin et al. (2008) assessed the potential catabolic function of the microbial community, using the MicroResp method at seven agricultural field sites in Australia, finding the soil properties, particularly pH, rather than land management practice was the dominant driver of MSIR. The carboxylic acids (citric acid, malic acid and a-Ketoglutaric acid (AKGA)) showed an affiliation to increasing pH in the RDA plot, suggesting that these substrates were optimised in higher pH soils. However, there was no apparent effect of land-use for this group of substrates, even though arable sites are associated with higher pH, however there was no clear correlation between arable sites and the carboxylic acid substrates. Rutgers et al. (2016) who applied the BiologTM method to the same sites described in this paper and found that arable sites were most strongly associated with utilisation of carboxylic acids. However, Sradnick et al. (2013) suggested that arable soils with higher pH resulted in a higher utilisation of amino acid and carbohydrate based substrates. Lauber et al. (2009) characterised bacterial communities in 88 soils across North and South America and found that soil pH was the dominant predictor of bacterial community composition. The major findings of this study are that pH, organic C and landuse are the drivers of microbial carbon substrate utilisation (MSIR) in soils across Europe. While this is the first study to describe this at such a large scale, there are numerous studies which have

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suggested these parameters as the major drivers of soil microbial activity in soils. Grayston et al., (2004) found that changes in CLPP (measured as BiologTM) across a range of ten upland grassland sites in the U.K. was significantly influenced by pH and soil organic matter. Rutgers et al. (2016) proposed that soil management was more important than soil texture where comparing BiologyTM across a range of grassland sites in the Netherlands and Europe and that using the same European dataset, as described in this paper, they found pH to be the dominant factor affecting CLPP in European soils. Fierer et al. (2009) studying the global patterns in microbial communities, agrees with this finding, stating that pH and C:N ratios in soil are key to predicting the structure of belowground microbial communities. 5. Conclusion Sampling of 81 sites across Europe has demonstrated that microbial activity depends primarily on land-use, pH and soil organic matter. Sites were selected to represent a spectrum of key soil properties across each of the land-uses with a range in pH from <5 to >7, org C contents extending from <2% to >15% and soil textures from <15% clay to soils with greater than 35% clay. The utilisation of the MicroRespTM method as a measure of MSIR to assess baseline soil respiration activity across Europe has been successful. This method allowed for the discrimination of land-use type over a large spatial scale and allowed us to understand what are the major soil properties affecting soil microbial respiration activity across Europe. The lack of effect of climate (biogeographical zone) is considered to be of great importance, showing that at a European scale, local climatic conditions do not directly have an effect on MSIR as a biological indicator (there may be indirect, as a result of changes in other soil properties, such as an increased SOC in Boreal soils, compared to Mediterranean soils, as a result of climate). The main drivers of MSIR in European soils is pH, organic carbon content and land-use (grassland versus arable). This dataset has shown the applicability of MicroRespTM, as a measure of MSIR for use in large scale monitoring systems, to assess microbial functional capacity in C cycling as a component of soil quality. Acknowledgements This works was funded as part of the FP7 Ecofinders project (FP7-264465). Additional support was given to this research from the laboratory staff at Johnstown Castle, Teagasc. References Andruschkewitsch, M., Wachendorf, C., Sradnick, A., Hensgen, F., Joergensen, R.G., Wachendorf, M., 2014. Soil substrate utilization pattern and relation of functional evenness of plant groups and soil microbial community in five low mountain Natura 2000. Plant Soil 383 (1–2), 275–289. Bååth, E., Anderson, T.H., 2003. Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biol. Biochem. 35 (7), 955–963. Bellamy, P.H., Loveland, P.J., Bradley, R.I., Lark, R.M., Kirk, G.J., 2005. Carbon losses from all soils across England and Wales 1978–2003. Nature 437 (7056), 245–248. Black, H.I.J., Parekh, N.R., Chaplow, J.S., Monson, F., Watkins, J., Creamer, R., Potter, E. D., Poskitt, J.M., Rowland, P., Ainsworth, G., Hornung, M., 2003. Assessing soil biodiversity across Great Britain: national trends in the occurrence of heterotrophic bacteria and invertebrates in soil. J. Environ. Manag. 67, 255–266. Bloem, J., Hopkins, D.W., Benedetti, A. (Eds.), 2005. Microbiological methods for assessing soil quality. CABI. Campbell, C.D., Chapman, S.J., Cameron, C.M., Davidson, M.S., Potts, J.M., 2003. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69 (6), 3593–3599. Chapman, S.J., Campbell, C.D., Artz, R.R., 2007. Assessing CLPPs using MicroRespTM. Soils Sedim. 7 (6), 406–410. COM, 2011. 571. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee

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