Is current classification relevant to soil function and soil diversity?

1. Defra Project SP1602 code

2. Project title Is current relevant to soil function and soil diversity?

3. Contractor Centre for Ecology and organisation(s) and Bangor University (subcontractor)

54. Total Defra project costs £ 34,943 (agreed fixed price)

5. Project: start date ...... 01/09/2009

end date ...... 31/08/2010

1 Prediction and inter-dependence of , function and diversity at a national scale

Paul Simfukwea, Rob I. Griffithsc, Bridget A. Emmettb, David L. Jonesa*, Paul W. Hilla, David M. Cooperb and Robert T.E. Millsb. Ed Roweb, David Spurgeonc, Brian Reynoldsb.

aSchool of the Environment and Natural Resources, Bangor University, Gwynedd LL57 2UW, UK. bCentre for Ecology and Hydrology, Bangor, Environment Centre Wales, Bangor, Gwynedd, LL57 2UW, UK cCentre for Ecology & Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB

Corresponding author: D. L. Jones Corresponding author address: School of the Environment and Natural Resources Bangor University Bangor, Gwynedd. LL57 2UW. UK

Corresponding author Tel: +44 1248 382579 Corresponding author Fax: +44 1248 354997 Corresponding author E-mail: [email protected]

2 Abstract Quantifying and understanding the underlying controls of soil functions such as carbon (C) and nitrogen (N) mineralisation across a wide range of ecosystems are critical to the development of future monitoring and modelling activities for tracking and predicting the impacts of future changes of climate and . may provide one variable for better predicting functions and response of as may the quantification of relevant elements of . To test the relationship between soil type and function and diversity, soils (0-15cm) were sampled from across the UK using a stratified random approach of landcover types as part of an integrated, national monitoring programme. Soils were characterised for a wide range of physico-chemical properties, basal respiration, high (HMW) and low (LMW) 14C molecular-weight substrate-induced respiration, potential N mineralisation, bacterial and invertebrate diversity indices. Discriminant analysis indicated samples were most divergent in the order physico-chemical variables > function > diversity. Soil type was found to explain only 2% of topsoil invertebate diversity and 12% of bacterial diversity, and between 1 and 17% of various measures of C and N mineralisation. Vegetation type explained on average double the variation in topsoil physico-chemical properties, function and diversity relative to soil type. Key physico-chemical variables which determined topsoil physico-chemical cluster associations were total C and bulk density > at field capacity whilst soil function clusters were driven by HMW substrate-induced respiration > and bacterial diversity. Other factors tested included phenolics, Ca/Al ratios, and nutrient status. Indices of bacterial and invertebrate diversity, whilst generally following a broad soil pH/carbon gradient, were no better predictors of soil C function than the physico-chemical variables. Potential soil N mineralisation was poorly associated with any fundamental physico-chemical or biodiversity topsoil property although there were differences among vegetation types. Combinations of physico-chemical variables using step- wise regression explained 40-50% of basal respiration, HMW substrate-induced respiration, N mineralisation and bacterial diversity. Variation in LMW substrate-induced efficiency, microbial N mineralisation efficiency and basal respiration efficiency between soils were broadly similar across soils suggesting these fundamental processes are remarkably consistent at a national scale under controlled conditions. The results suggest that soil type, and topsoil diversity are poor predictors of topsoil function and do not enhance predictive capacity of physico-chemical measurements. In conclusion, the functioning of UK may be more dependent on the quality and quantity of present-day organic matter inputs above and below- ground and/or in situ climatic conditions than soil type and biological diversity.

Key words: , soil classification, microbial respiration, soil microbial activity, soil diversity, soil properties, nitrogen mineralisation,

3 1 Introduction With anticipated changes in global climate and land use, there is growing interest in understanding how these perturbations affect soil microbial processes and the consequences this will have on terrestrial ecosystem functioning and resilience (Palm et al., 2007). This knowledge can be used to better predict future ecosystem responses via mathematical models. For example, models such as DNDC and Century have been used to facilitate calculation of country-specific greenhouse gas emissions to meet the IPCC Tier II reporting requirements (Smith et al., 2010; Zhang et al., 2010). The underlying data used to drive these models is typically derived from national soil inventories and associated maps (Brown et al., 2002). It is therefore vital that these maps accurately reflect the temporal and spatial variability in soil processes. Currently, however, the relationship between microbial diversity and function within and between a wide range of soil types is poorly understood (Wall et al., 2005). One of the most important purposes of a soil classification scheme is for the prediction of soil properties across a range of geographical scales. This is of particular interest to policymakers (e.g. in implementing agri-environment schemes, climate change mitigation, protection of water quality). For this approach to be successful requires that the general tenet that soils in different locations but with the same classification will respond in the same way, holds true. There are a number of assumptions that need to be critically evaluated before accepting this statement such as the consideration that some national classifications were carried out more than 50 years ago when the land use regime, vegetation cover and climatic variables (e.g. rainfall, N and S deposition) may have been significantly different (Vitharana et al., 2008). Furthermore, the scale and accuracy to which soils have been mapped at the landscape level will also be a critical determinant of the reliability of soil maps (Borujeni et al., 2009; Butler, 1980; Vitharana et al., 2008). Specifically, this relates to the potential for abrupt transitions in soil type, which are unrealistic in landscapes where lateral changes in soil are gradual, and that maps essentially ignore spatial variation in soil properties within mapping units (Kempen et al., 2010). This is exemplified by Vitharana et al. (2008) who reported that traditional soil maps were poor predictors of soil chemical properties. Similarly, Jones et al. (2009) found few differences in soil function in relation to organic N cycling over a global latitudinal gradient that encompassed a huge variation in soil type. They ascribed this lack of difference to the large amount of functional redundancy in soil microbial communities suggesting that only some soil processes are highly soil type dependent. Two key soil processes which remain fundamental in providing many ecosystem services are the cycling of carbon and nitrogen. Most current knowledge about the biotic and abiotic controls on C and N cycling has been gathered from discrete local studies on a particular soil type or ecosystem, hampering large scale syntheses across landscapes. Furthermore, the high spatial variability of greenhouse gas emissions from soil even at local scales suggests that defining values for individual soil types may prove difficult. Through changes in soil C and N cycling, below-ground biodiversity has been assumed to influence, ecosystem stability, productivity and resilience towards stress and disturbance (Bengtsson, 1998; Torsvik and Ovreas, 2002; Nannipieri et al., 2003), yet the explicit relationships

4 between microbial diversity and soil function are largely unknown (Wall et al., 2005). For these reasons there have been many studies using both traditional and modern molecular microbiological techniques attempting to characterise the soil biota, in an attempt to map key microbial groups to specific soil functions. Despite a scarcity of evidence suggesting any clear correlations, there has also been much interest in using measures of soil biodiversity to estimate soil quality or health. Again, a major reason for the lack of synthesis on this subject relates to the spatial or temporally defined nature of much of the work carried out preventing comparison across multiple soil ecosystems. Additionally, synthesis by meta-analyses is often hampered by the lack of standard methods of measurement. At the broadest level there is therefore a fundamental need to link measures of soil function with abiotic and biotic variables across multiple soil ecosystems. Concerted efforts using standard methodologies on multiple soil types offer the best opportunity to interrogate the broad scale abiotic and biotic controls of soil functioning across landscape scales. Whilst incorporating fine scale spatial and temporal variability into models is likely to be problematic, it is probable that specific soil ecosystem types will show different ranges of natural functional variability. The recognition and subsequent experimental manipulation of these soil ―biomes‖ (defined by function) may be the first step in improving the parameterisation of global climate models. This approach has recently been developed in the UK as part of a national integrated monitoring programme (Carey et al. 2008) which has delivered for the first time spatial and temporal change in soil physic-chemical, diversity and functional properties (Emmett et al. 2010) linked to changes in land use, vegetation composition and water quality at a national scale. The aim of this study was therefore to use this globally unique dataset of abiotic and biotic soil characteristics at a national scale to explore the relationships between soil function and diversity in relation to soil type and vegetation. Using a range of statistical techniques we investigated: (1) whether soil type or vegetation is a good predictor of soil microbial function and diversity (2) whether inclusion of soil biodiversity as explanatory variables increases the predictive capacity of soil function (3) which individual soil parameters best predict and classify soil function, and (4) the relative discrimination of physical-chemical, function and diversity between soils.

2 Materials and methods

2.1 Description of the study area and sample sites

Soil samples were collected throughout the UK as part of the CEH Countryside Survey (CS) 2007 (Emmet et al., 2010) with sites representing the main types of landscape and soil groups. Samples were collected from locations included in the original (1978) CS, which was a stratified random sample of 1 km squares at gridpoints on a 15 km grid using the Institute of Terrestrial Ecology (ITE) Land Classification as the basis of the stratification (Scott, 2008). The general climate of the region is temperate with mean annual temperature and rainfall

5 ranging from 7.5 oC and 1700 mm in the north to 10.6 oC and 650 mm in the south-east. The mean annual soil temperature at 10 cm depth is approximately 10 oC (unpublished, Matthew, P., 2006). Figure 1 shows the distribution of the samples across the study area.

Figure 1. Map showing locations of soil samples across the UK (a subset of the CS locations).

2.1.1 Sample classification - soils type and vegetation types

The soils were classified during the 1978 and 1990 surveys using the British soil classification system of Avery (1973 and 1980). In 1978, pits were dug at each site of the selected plots in the 1 km squares; soils were sampled and classified to the sub group level. In 1990, the soils of GB were surveyed and mapped at 1:25 000 using the Soil Survey Technical Monograph No.14 (Avery, 1980) by the Macaulay Land Use Research Institute in Scotland and the Land Research Centre, now NSRI in England and Wales. The major soil groups used here were a product of a rigorous comparison process between 1978 data and 1990 maps. The description was derived and allocated manually and therefore the product was a more accurate classification than either the 1978 or the 1990 classifications taken in isolation.

6 Table 1 shows the seven major soil classes defined by the CS amongst soil samples collected and their corresponding approximate World Reference Base (WRB) soil class. Surface-water Gley (SWG) and Ground-water Gley (GWG) are differentiated according to the source of waterlogging (Avery, 1990). British soil Abbreviation Percentage World Reference Base classification classification of total Brown soils Brown 27 , and some Luvisols, Lithomorphic Lithomorphic 6 and some soil Gley soils GWG and 9 , , and some , SWG Luvisols Podzolic soils Podzolic 10 soils Peat 6 Pelosols soil Pelosol 2

Table 1. Soil classification of CS samples, their percentage of the total and their equivalents in the WRB soil classification system.

Across all land uses and soil types, CS defines eight aggregate vegetation classes (VEG). These were derived by cluster analysis of the mean DECORANA scores for 100 smaller classes obtained by TWINSPAN analysis of plant species data from the original CS sample plots (Bunce et al., 1999; Smart et al., 2003). A brief description of each vegetation type is shown in the Table 2 below.

7

Aggregate % Description vegetation classes (VEG) Crops/weeds 16 Weedy communities of cultivated and disturbed ground, including species poor arable and horticultural crops. Tall 5 Less intensively managed tall herbaceous vegetation typical of grassland/herb field edges, roadside verges, stream sides and hedge bottoms. Fertile grass 21 Improved or semi improved grasslands. Often intensively managed agricultural swards with moderate to high abundance of perennial rye grass. Infertile grass 24 Less productive unimproved and often species rich grasslands in a wide range of wet to dry and acidic to basic situations. Lowland 3 Vegetation dominated by shrubs and trees in neutral or basic wooded situations, generally in lowland Britain. Includes many hedgerows. Upland wooded 3 Vegetation of broad leafed and conifer woodland often in more acidic situations, generally in upland Britain. Moorland 10 Extensive, often unenclosed and sheep grazed hill pastures. grass/mosaic Heath/bog 14 Vegetation dominated by heathers. Included drier heaths as well as bog. Mostly in the uplands.

Table 2. Summary descriptions of the eight aggregate vegetation classes (VEG) represented and their percentage coverage (% of the total samples). Table adapted from Smart et al. (2003).

2.2 Sampling protocol

A total of 699 soil samples were used, collected from selected 1 km squares defined by the Institute of Terrestrial Ecology (ITE) Land Classification, located at the intersection of a 15 km square grid across the region (Scott, 2008). Sampling was carried out in 242 of the 256 1 km squares that were included in the 1978 CS, due to access restrictions. Up to 5 permanently marked vegetation plots were randomly located within each 1 km square. Four soil samples were collected from the centre of each plot, 10 cm apart, using plastic tubes hammered into the soil. One core sampled 0-8 cm soil and was used to analyse invertebrate diversity (named ―core F‖, see Emmett et al., 2010). Three subsequent cores sampled 0-15cm soil, and were used for analysing: a) pH, loss-on-ignition (LOI), Bulk density, %C, %N and Olsen P (―core C‖ )

8 b) Mineralisable-N, exchangeable Al3+ and Ca2+, and leachate measurements (―core N‖) c) Microbial diversity (―core P‖). After collection, cores were stored in a coolbox until they could be sent for analysis. On arrival at the laboratory, all soil cores were logged in, the core removed from the plastic tube, a digital photograph taken and basic core dimensions (e.g. profile length in the case of shallow soils) taken before processing began. All soils were stored at 4°C when not being used for analyses. For full information on methodology and Standard Operating Procedures for all variables see the CS Soils Technical Manual (Emmett et al., 2008).

2.3 Pre-conditioning of soil from core N Soil moisture was firstly standardised to field capacity by flushing with a weak salt solution (a ―UK artificial rainfall minus N‖). Additionally this step served to minimise variations in N content due to accumulation of mineral N since the last rainfall and during transportation of samples to the laboratory (e.g. differences in transit times), factors known to increase errors in the calculation of net flux (Emmett et al., 2010; Rowe et al., in prep). The artificial rainfall solution (125 μM NaCl; 15.7 μM CaCl2; 1.3 μM CaSO4; 15.3 μM MgSO4; 12.3 μM H2SO4) was applied to each soil core in a 10°C room until 150 ml of leachate had been collected, according to the protocol described by Emmett et al. (2008).The soils were then incubated at 10 °C for 28 days in a sealed, gas-permeable plastic bag prior to the determination of potential N mineralisation. Additionally, the collected leachate was stored frozen at -18 °C (to prevent biologically-mediated changes in solution chemistry) to await analysis (see below).

2.4 Leachate analysis Soluble phenolics in leachates from ―core N‖ were determined using the method of Box (1983) and Ohno and First (1998) but adapted for small solution volumes (<300 µl). Soluble humic substances in leachates were estimated by measuring the absorbance of the leachate solutions at 254 (Ab254) and 400 (Ab400) nm using a PowerWave XS scanning microplate spectrophotometer (BioTek® Instrument, Winooski, VT). Amino acids in soil leachates were determined fluorometrically by the OPAME procedure of Jones et al. (2002). Total soluble organic C and N in leachates were determined using a TOC-VCSH/CSN analyzer (Shimadzu, Kyoto, Japan). Ammonium and nitrate in the leachates were determined using a Skalar SAN++ segmented-flow autoanalyzer (Skalar, Brede, Netherlands). Leachate pH was measured using a HI-209 pH meter (Orion Research, Boston, MA, USA). The soil leachate was analysed because soil solution contains a wide range of anions, cations and uncharged compounds at various concentrations depending on soil acidity, organic matter content and land use (Westergaard et al., 1998). The most abundant are chlorides, nitrates, sulphates, calcium, aluminium (in acidic soils), DOC (including low molecular weight compounds), humic substances and phenolics (Westergaard et al., 1998). The soil solution bathes and nourishes microbes and roots, while different soil solutes differ in their bioavailability (Mulder and Cresser, 1994).

9 2.5 Soil respiration (intact cores) Basal soil respiration (SR) was determined on the 15 cm long, 2.5 cm diameter intact ―core N‖ after 14 d incubation at field capacity at 10 °C. The cores were placed in a sealed chamber with a 1250 cm3 head space. The soils were incubated at 10°C (average UK air temperature) for 1 h (at which linearity was known to be established following testing on selected cores which covered the range of soil types sampled; n > 30). Subsequently, the head space gas was removed with a gas-tight syringe and analysed for CO2 concentration within 48 h using a Clarus 500 Gas Chromatograph (Perkin Elmer Corp., Beverley, MA). SR was determined as the change in CO2 concentration before and after incubation corrected for soil dry weight (SR) and content (SRLOI).

2.6 Potential mineralisable-N and substrate-induced respiration After 28 d incubation at 10 °C (see 2.3), ―core N‖ samples were broken-up, roots and stones removed and the soil thoroughly mixed. A 10 g subsample of moist soil was extracted with 100 mL of 1 M KCl and ammonium and nitrate concentrations determined as a measurement of mineralisable N (following full protocol as per Emmett et al., (2008)). Potential N mineralisation was calculated as the amount of extractable-N per gram dry weight of soil (NMIN) and per gram organic matter content (NMINLOI). The microbial efficiency of nitrogen mineralisation was also calculated as the ratio of nitrogen mineralisation to basal respiration (NMINEFF). A subsample of the mixed ―core N‖ soil was used to quantify substrate-induced respiration (SIR) using two contrasting C substrates. A simple and a complex 14C-isotopically labelled C substrate were used to estimate C mineralisation rates in soil. The simple C substrate was chosen to reflect low molecular weight (LMW; < 1000 MW) root exudates and comprised a solution of 14C-glucose (50 mM), 14C-fructose (5 mM), 14C-sucrose (5 mM), 14C- citrate (10 mM), 14C-malate (5 mM) and 14C-succinate (2 mM) and possessed a specific activity of 8.4 Bq µmol-1 C. The complex, high molecular weight (HMW; >1000 MW) C substrate consisted of 14C-labelled shoots of Lolium perenne with a specific activity of 12.3 kBq g-1. The 14C-enrichment of Lolium perenne plant material was by pulse labelling with 14 CO2 at a constant specific activity according to the procedure of Hill et al. (2006). To characterise the 14C label in the plant material, a sequential chemical fractionation was performed according to Jones and Darrah (1994). To assess the mineralisation of the 14C-labelled substrates, 10 cm3 of each soil was placed into a sterile 50 cm3 polypropylene container. Either 0.5 ml of the 14C-labelled simple C substrate (artificial root exudates) or 100 mg of the 14C-labelled complex C substrate (Lolium perenne shoots) was then added to the soil. A further 0.5 ml of distilled water was added to the soil receiving the complex C substrate to ensure the same moisture content in both treatments. A vial containing 1 M NaOH (1 ml) was then placed above the soil and the 14 polypropylene containers hermetically sealed. The CO2 capture efficiency of the NaOH traps was >95%. The soils were then placed in the dark in a climate-controlled room (10 °C) 14 and the NaOH traps exchanged after 0.5 h, 1 d, 7 d, 14 d, 28 d and 90 d. The CO2 in the NaOH traps was determined by liquid scintillation counting as described above. While

10 exponential or other decay models may be fitted to incubation data such as these, this introduces uncertainty, particularly when fitting to as few as 6 data points. In view of this we selected only one individual time point as a response variable which exhibited the maximum variability across all samples. For the HMW substrate this time point was determined to be 28 d, whereas for the LMW substrates this was 1 d following substrate addition.

2.7 Basic soil physico-chemical measurements Soil total C and N content were determined on homogenised soil from ―core C‖ following the removal of roots and stones. This analyses was performed on an Elementar Vario-EL elemental analyser (Elementaranalysensysteme GmbH, Hanau, Germany) using the CEH Lancaster UKAS accredited method SOP3102 described in Emmett et al. (2008). Bulk density was calculated as core mass/volume. Loss-on-ignition (LOI) was measured on a 10 g soil subsample after first drying the soil at 105 °C (24 h) followed by ashing at 375 °C for 16 h. Soil phosphorous was also assessed on ―core C‖ using the Olsen P method. Briefly, a 5 g air-dried and sieved soil sample was extracted with 100 ml of 0.5 M sodium bicarbonate at pH 8.5. The phosphorus in the extract was determined colorimetrically using a continuous flow analyser. The analyser method used molybdenum blue at 880 nm with the addition of a dialysis step to overcome the effect of the Olsen‘s reagent. Soil pH was measured in a water extract using soil from ―core C‖. Briefly, 10 g of field-moist soil was placed in a 50 ml plastic beaker to which 25 ml of deionised water was added giving a ratio of soil to water of 1:2.5 (w/v). The suspension was stirred thoroughly and left to stand for 30 minutes after which time the pH electrode was inserted into the suspension and a reading taken after a further 30 s. Moisture content of field-moist soil was determined gravimetrically by weight loss after oven drying 10 g soil at 105 °C for 16 h. Soil moisture at field capacity was estimated by saturating the soil followed by measuring the retained at -33 kPa suction pressure. Relative soil moisture content was calculated as the ratio of field moisture content to moisture content at approximately field capacity, measured as described above. All the assays described above are covered in more detail in Emmett et al. (2008). To determine exchangeable calcium (Ca) and aluminium (Al), 5 g of soil from ―core N‖ (following incubation at 10 °C for 28 d) was extracted with 25 ml of 1.0 M ammonium chloride according to the Society of America (SSSA) standard extraction method (Sparks, 1996). The sample was centrifuged (5000 g, 10 min), the supernatant removed and its Ca and total monomeric Al content determined by atomic absorption spectroscopy (AAS) and pyrocatechol violet methods respectively. Ca in the extracts was determined by atomic absorption spectrometry at 422.7 nm, using an air-acetylene flame and hollow cathode lamp on a Perkin Elmer Analyst 400 Atomic Absorption Spectrometer (PerkinElmer Life and Analytical Sciences, Shelton, USA). The extracts were diluted with

0.5 % w/v LaCl3 as a chemical interference suppressant. Deuterium background correction was deployed to reduce background interference. Al content in the extracts was determined using the modified method of Dougan and Wilson (1974). The volumes of reagents were

11 adjusted to microplate level, where 210 µl extract was mixed with 2.4 µl 35% HCl, 12 µl 0.0375 % catechol violet solution, 6 µl 0.1 % 1,10-phenanthroline solution and 60 µl 30 % hexamine buffer in a 350 µl microplate well. The absorbance of the solution was measured at 580 nm using a PowerWave XS scanning microplate spectrophotometer (BioTek® Instrument, Winooski, VT). Table 3 summarises the soil physico-chemical properties measured.

Variable Units Abbreviation Soil variables 1 pH pH

2 Soil carbon content %dry weight CTOT

3 Soil nitrogen content %dry weight NTOT 4 C/N ratio Ratio of 3 to 4 C/N 5 Soil Olsen P mg kg-1 dry weight P 6 Soil loss-on-ignition (LOI) %dry weight LOI 7 Soil bulk density g cm-3 BD 2+ -1 8 Exchangeable Ca mg kg soil Caexc 3+ -1 9 Exchangeable Al mg kg soil Alexc 10 Ca2+/Al3+ ratio Ca/Al

11 Soil moisture content at field capacity % dry weight ΘFC

12 Relative soil moisture content: Ratio ΘMS:FCfw Ratio of soil moisture content at time of

sampling to/ ΘFC Leachate variables -1 1 Dissolved organic carbon mg l DOC(L) -1 2 Total dissolved nitrogen mg l TDN(L) -1 - 3 Nitrate in leachate mg l NO3 (L) -1 + 4 Ammonium in leachate mg l NH4 (L) -1 5 Phenols in leachate mg l Phenolics(L) -1 6 Amino acids in leachate mg l AminoA(L)

7 Absorbance at 254 nm Ab254

Table 3. A summary of the soil physico-chemical variables selected to be used in the analysis and abbreviations.

2.8 Bacterial and invertebrate biodiversity

Soil bacterial biodiversity was determined using molecular profiles of total bacterial communities following DNA extraction from ―core P‖ (Griffiths et al., 2000). Soils were firstly defrosted and then homogenised by hand under sterile conditions. Total nucleic acids were extracted from 0.25 g of soil using a previously described method (Lane, 1991),

12 modified to include a 30 min hexadecyltrimethylammonium bromide (CTAB) freeze-thaw, soft-lysis stage. TRFLP analysis of 16S rRNA genes was performed using forward primer 63F 5‘-CAGGCCTAACACATGCAAGTC-3‘ labelled at the 5‘ end with 6FAM fluorescent dye (Sigma Genosys) and reverse primer, 519R (Lane, 1991) 5‘- GTATTACCGCGGCTGCTG – 3‘ (MWG operon) modified as detailed by Thomson et al. (2010). Amplicons were purified using the PureLink PCR purification kit (Invitrogen), then digested using restriction endonuclease MspI (Promega). Fragment analysis was performed using a 3730 DNA analyser (Applied Biosystems) and individual TRFs were binned manually using Genemarker software (SoftGenetics). Prior to statistical analyses, the intensity of each fragment was converted to a proportional abundance by dividing by the total intensity of all detected fragments within MS Excel. The Simpsons index of diversity was then calculated for each sample, as where pi is the relative abundance of each TRFLP peak within each sample. Soil invertebrates were extracted from ―core F‖ as soon as possible after removal from the field using a dry Tullgren extraction method (for a full protocol see Emmett et al. (2008)). The soil cores were placed onto the sieve balanced above the funnel to extract soil fauna over a period of 5 d. A 40 W light bulb was suspended above the sieve (containing the soil samples) and was used to provide heat to drive the soil fauna from the soil cores and into the collection bottles filled with a 70% ethanol preservative, placed at the end of the funnel. Once collected, the soil invertebrates were identified to major taxa (Taxonomic level 1) and counted. Further identification of collembola and mites to morphotype level was then carried out. The Shannon index was calculated using H = - Σ pi ln (pi ) where pi is the relative abundance of each taxon within each sample. Table 4 is a summary list of the function and diversity variables measured on soil samples.

13 Variable/parameter Units/description Abbreviation 1 Soil respiration (SR) µgC gsoil-1 hr-1 SR 2 Soil respiration /LOI µgC gLOI-1 hr-1 SR(LOI) 3 N mineralisation mgN g-1 NMIN -1 4 N mineralisation/LOI mgN gLOI NMIN(LOI) -1 5 N mineralisation/SR mgN mgC NMIN(EFF) 14 6 C remaining in plant % of total added SIR(HMW) substrate after 28 days incubation 14 7 C remaining in labile % of total added SIR(LMW) substrate after 1 day incubation

8 Bacterial Simpson index Biodiv(bact) entropy

9 Invertebrate Shannon index Biodiv(invert) entropy

Table 4. A summary list of the soil function and diversity variables measured and abbreviations.

3 Results

3.1 Relationship between soil function and diversity in relation to soil and vegetation type

The box plots presented in Figures 2-4 show the spread of each measured soil property for each soil and vegetation type. The boundary of the box closest to zero indicates the 25th percentile, the line within the box marks the median (50th percentile), and the boundary of the box farthest from zero indicates the 75th percentile. Whiskers below and above the box indicate the 10th and 90th percentiles while the filled circles below and above represent outlier values.

14 9 9

8 8

7 7

6 6

Soil pH Soil

Soil pH Soil

5 5

4 4

3 3 1600 1600 1200 1200

1000 1000

800 750

600 500 400

250 200

Moisture content at field capacity (%) content at field capacity Moisture

Moisture content at field capacity (%) content at field capacity Moisture 0 0 6.0 2.5 4.0 2.0 2.0 1.5 1.5

(PFW)

(PFW)

1.0 1.0

Ratio MCS:MCFC Ratio Ratio MCS:MCFC Ratio 0.5 0.5

0.0 0.0

) ) 1.5 1.5

-3

-3

1.0 1.0

Bulk density (g cm density Bulk Bulk density (g cm density Bulk 0.5 0.5

0.0 0.0

80 80

(%)

(%)

LOI

LOI 60 60

40 40

Soil organicSoil matter Soil organicSoil matter 20 20

0 0 2.00 2.00 1.00 1.00

0.75 0.75

0.50 0.50

0.25 0.25

Solution absorbance at 254 nm

Solution absorbance at 254 nm

0.00 0.00

Peat GWG SWG Brown Pelosol

Podzolics

Lithomorphics Heath and bog Upland wooded Fertile grassland Lowland wooded Crops and weeds Infertile grassland

Tall grass and herbs Moorland grass mosaics

Figure 2. Box plots for selected basic soil physico-chemical properties categorised by soil and vegetation types.

15

Figure 3. Box plots for selected basic soil nutrient and potential N mineralisation properties categorised by soil and vegetation types

16 20 20 15 15 10 10

) )

-1 -1

h h

-1 -1

6 6

4 4

2 2

Soil respiration (g C core Soil respiration (g C core Soil

0 0

) )

-1 -1 35 35

hr hr

-1 -1

30 30

25 25

20 20

15 15

10 10

5 5

Soil repiration per LOI (g C g LOI (g C repiration per LOI Soil g LOI (g C repiration per LOI Soil 0 0

90 90

80 80

(LMW) (LMW)

SIR 70 SIR 70

60 60

50 50

80 80

60 60

(HMW) (HMW)

SIR 40 SIR 40

20 20

0 0 0 1 2 3 4 5 6 7 8

0.95 0.95

0.90 0.90

0.85 0.85

Bacterial biodiversty Bacterial

Bacterial biodiversity Bacterial 0.80 0.80

0.75 0.75

Heath and bog 1.5 1.5 Upland wooded Fertile grassland Lowland wooded Crops and weeds Infertile grassland

Tall grass and herbs

Moorland grass mosaics

1.0 1.0

0.5 0.5

Invertebrate biodiversity Invertebrate biodiversity Invertebrate

0.0 0.0

Peat GWG SWG Brown Pelosol Podzolic

Lithomorphic Heath and bog Upland wooded Fertile grassland Lowland wooded Crops and weeds Infertile grassland

Tall grass and herbs Moorland grass mosaics

Figure 4. Box plots for selected soil carbon rate functions and biodiversity variables categorised by soil and vegetation types

17

A number of clear trends are apparent in the spread of data for selected basic soil quality indicators categorised by soil and vegetation type. The pH and BD were generally higher in the mineral soils than in the organic soils following the order: Pelosol > Brown > GWG > SWG > Podzolic > Lithomorphic > Peat. The trend was in the opposite order with respect to ΘFC, ΘMS:FCfw, SOM and Ab254. The ΘMS:FCfw may indicate the speed and capacity of soil to drain. Similarly, pH and BD were generally higher in managed habitats than in the natural habitats in the order: crop and weeds > tall grass and herbs > fertile grassland > infertile grassland > lowland wooded > upland wooded > moorland grass mosaics > heath and bogs. Again the trend was in the reverse order with respect to ΘFC, ΘMS:FCfw, SOM and

Ab254. The selected basic soil nutrient indicator variables showed noticeable differences with respect to Caexc and C:N and weak contrast in Alexc and in effective respiration (mineralisable N per soil respiration) among the soil types. The Caexc was highest in Pelosol and lowest in Podzolics and Peat while all other soil types were intermediate. The C:N ratio was lower in mineral than in organic soils. Both the median and the inter-quartile ranges increased from Pelosol < Brown < GWG < SWG < Lithomorphic < Podzolic

lowland wooded > moorland grass mosaics > upland wooded > heath and bogs. The selected soil C rate functions and biodiversity variables were more or less similar between both soil types and vegetation types (Fig. 4). The difference in the C rate functions and biodiversity is evident only with respect to Pelosols versus Peat and/or Podzolic soil for soil types. In the vegetation types the difference is between the intensively managed versus natural habitats.

3.2 Relationship between traditional soil chemical and physical quality indicators in relation to soil classification The descriptive statistics presented in the preceeding section identified some consistent patterns in certain soil characteristics reflecting gradients of soil type and vegetation. Before proceeding to formally test which of the classification methods were better predictors of soil reponses (function & diversity), we wished to generate a new classification based on the soil physical chemical characteristics alone. This would provide us with a new categorical variable, based specifically on measured topsoil physical chemical properties. In order to generate this new soil classification we used k-means clustering of the scaled dataset. This clustering method potentially can generate a large number of clusters, which may not represent meaningful classifications of the data variables. Additionally, repeated iterations of the algorithm can yield different clusters (ie unstable), particularly when large numbers of clusters are assigned. Ideally we want a minimal number of

18 classifications which effectively and stably represents broad groupings of soil samples based on their physical chemical characteristics. Therefore, to identify the appropriate number of clusters we used the clustergram visualisation tool in R (Schonlau, 2002; 2004) to determine a stable number of clusters, with optimally spaced centroids (average multivariate score of each soil per cluster) along the first principal component axis. We eventually identified three clusters based on the physico-chemical variables listed in Table 3. Cluster 1 consisted mainly Brown soils with high inclusions from SWG, GWG and Pelosol soils and a few inclusions from Lithomorphic soils (mineral soils). Cluster 2 was mainly Podzolic soils with high inclusions from Lithomorphic, SWG and Peat soils and a few from GWG soils (organo- mineral- intermediate soils). Cluster 3 consisted mainly of with high inclusions from SWGs, Podzolic and Lithomorphics and a few from Browns and GWG soils (organic soils). The box plots presented in Figure 5 also show the differences in the three clusters with respect to the selected variables.

19

Figure 5. The box plot of the function and diversity variables grouped by the three clusters created by the selected physico-chemical variables.

To identify thresholds which categorize soils for soil C rate functions and all soil variables (physico-chemical, function and diversity), a regression tree approach was applied in S-PLUS (TIBCO Software Inc.) using a tree routine. For this analysis all samples were used, regardless of C content. Regression trees (RT) use successive partitioning of the explanatory variables to subdivide the samples into subsets, on the basis of either a classification or values of the response variable. Samples in each subset are either given the same classification, or the same simulated value of the response variable. Results of a

20 classification RT analysis are presented in Figure 6. The complete sample set is successively split into binary subsets based on a single explanatory variable. Depending on the value with respect to the criterion (< or >), the sample is partitioned into left and right binary subsets. Figure 6 shows the RT classification into soil types and the vegetation types based on the basic soil quality indicators or the C rate function. The fraction below the node shows the number of correctly classified samples to the named soil or vegetation type out of the total samples at the respective node. For example, Figure 6 Panel (a) shows one split based on soil C rate function

(SIR(HMW)) producing two terminal nodes (boxes) separating soil types into mineral soils (predominantly Brown soils (189/324)) with high carbon turnover rates from organic soil (Peats; 54/72) with lower rates. The numbers 253/396 below the ovals/circle labelled Brown show that the dominant soil type was the Brown soil type with 253 classified as such out of 396 samples. Panel (b) shows that the soil C rate functions splits the vegetation types into those with high turnover rates on the left (infertile grassland and crop and weeds) against those with lower turnover rates on the right (infertile grassland and heath and bogs). Those on the left were further split based on bacterial diversity, with infertile grasslands having lower bacterial diversity (Simpson diversity index < 0.955) against the crop and weeds with greater diversity (> 0.955). The habitats on the right were split based on basal soil respiration (SR) separating the infertile grasslands with lower SR (<1.3 µg Cg-1 hr-1) from the heath and bogs with higher SR (>1.3 µg Cg-1 hr-1).

Panel (c) shows two splits based on total soil C (CTOT) and moisture content at field capacity (ѲFC) producing three terminal nodes (boxes nodes) separating Brown, Podzolic and Peat soils. The samples on the left with C content less than 10 % were mostly Brown soils (152 out of 282). The samples on the right with carbon content greater than 10 % were mostly Peat soils (88/114). A further split of this node yielded two nodes separating Podzolic soil (49/68) from Peat soil (24/46). Panel (d) shows that the basic SQI split the habitats into two major groups based on bulk density. Those on the left with bulk density less that 0.705 g cm-3 having high moisture content at field capacity (around 245 %; Mooland grassland mosaic and Heath and bogs) against those on the right with high bulk density and less moisture content (around 36%; Crop and weeds and infertile grasslands).

21 (a) RT on soil carbon rate function vs soil (b) RT on soil carbon rate function vs Veg. types types

Infertile Browns grassland 253/396 296/396

SIR(HMW) < 75% SIR(HMW) > 75% SIR(HMW) < 80% SIR(HMW) > 80%

Crops & Heath & Browns Peat weeds bog 130/191 157/205 189/324 54/72 Biodiv(bact) Biodiv(bact) SR< 1.3 SR > 1.3 <0.955 <0.955

Infertile Crops & Infertile Heath & grassland weeds grassland bog 43/80 58/111 98/138 31/67

(c) RT on basic Soil quality indicators vs soil (d) RT on basic Soil quality indicators vs types Veg. types Infertile Browns grassland 253/396 296/396

CTOT <10% CTOT >10% BD < 0.705 BD > 0.705

Browns Peat Heath & Fertile bog grassland 152/282 88/114 83/130 184/266

ΘFC < 300% ΘFC > 300% ΘFC < 245 ΘFC > 245 ΘFC < 36 ΘFC > 36

Podzol Peat Heath & Crops & Infertile Moorland bog weeds grassland 49/68 24/46 48/71 17/59 30/91 101/175

Figure 6. Regression trees splitting soil types and the vegetation types in relation to the C rate function (panels a and b). Panels c and d splits soil types and the vegetation types in relation to basic physico-chemical variables. Ovals and boxes are labelled with the predominant type.

22 3.3 Relationship between all variables

An analyses of variance was carried out to explore which of the three classifications (Soil Type, Vegetation, or the new classification) best predicted the functional and biodiversity responses (Table 5a). Vegetation type is clearly the better predictor of variation observed in function and diversity showing significant relationships with NMIN,SR,

SIR(HMW) and Biodiv(bact).

Factor NMIN NMIN(EFF) SR SR(LOI) SIR(LMW) SIR(HMW) Biodiv(bact) Biodiv(invert) Soil Type 0.166 0.066 0.145 0.041 0.009 0.141 0.124 0.022 Vegetation type 0.32 0.094 0.226 0.075 0.007 0.335 0.350 0.060 New classification 0.102 0.031 0.071 0.032 0.000 0.149 0.153 0.020

Table 5a. R2 values from Analysis of Variance predicting soil function and diversity measures from soil type, vegetation and the new classification based on topsoil physical chemical parameters.

We then used stepwise regression analysis (Splus routine stepAIC in package MASS) to examine whether individual or combinations of soil characteristics could better predict functions and diversity (Table 5b). Each column represents t-values of parameters associated with row-variables with selection using a BIC stopping criterion. While the results of any individual analysis should not be over-interpreted, such an analysis can add support to scientific conceptualisation and understanding. We have used stepwise regression on log transformed data, with a Bayesian Information Criterion (BIC) rule for final selection.

23 Variable NMIN NMIN(EFF) SR SR(LOI) SIR(LMW) SIR(HMW) Biodiv(bact) Biodiv(invert) Soil pH -4.18 2.59 14.99 Moisture content at field -4.74 15.37 3.09 3.14 capacity ΘFC

Percentage C (CTOT) -2.76 5.35 -3.36

Percentage N (NTOT) 4.14 -6.26 -3.22 C:N ratio (C/N) Olsen P (P) 3.88 -4.12 Loss-on-ignition (LOI) -4.10 -4.82

- NO3 (L) 10.24 9.55 -3.95 2.76

+ NH4 (L) 2.96 2.63 -4.08 4.55

Phenolics(L)

AminoA(L) -3.41 Absorbance at 254 nm Bulk density (BD) -3.72 Relative moisture content -2.83

Calcium (Caexc) -2.51

Aluminium (Alexc) 3.77 3.56 5.22 Ca: Al ratio 3.46 2.68 4.41

Biodiv(bact) N/A N/A

Biodiv(invert) -2.71 N/A N/A R2 0.53 0.27 0.42 0.15 0.13 0.44 0.57 0.08

Table 5b. Results of stepwise regression for soil function and diversity measures versus physico-chemical variables and overall R2 values. N/A denotes that we did not attempt to predict diversity using diversity as a predictor, and blank boxes indicate that a variable was thrown out during the stepwise regression.

The dominant explanatory variable for each function/diversity metric response - variable with t-value >5 were: ΘFC versus SR (15.37); pH versus Biodiv(bact) (14.98); NO3 (L) versus NMIN (10.24) and NMINI(EFF) (9.55); NTOT versus SIR(LMW) (6.26); SOM versus

SR(LOI) (-4.82); CTOT versus SIR(LMW) (5.35) and Alexc versus SIR(HMW) (5.22). Both the stepwise regression and regression tree approaches highlight the covariation of several predictors, inferring that functional attributes may be more related to general gradients in soil abiotic and biotic properties. In order to directly visualise the correlation between the response variables and individual physico-chemical variables we performed an unconstrained ordination of the scaled soil parameters, and then fitted the response variables to this ordination using vector fitting methods in the R vegan package (Figure 7). This

24 method allows visualisation of the relationships of a number of vectors (here functional and diversity responses) with an ordination plot (here representing soil parameters). The length of the arrows signifies the relative strength of the relationship, and the direction indicates the gradient in ordination space towards which the vectors change most rapidly.

25

Relative moisture 0.4 SIR(HMW)

C% N% 0.2 LOI C/N Ratio Moisture at SR

0.0 field Biodiv(invert) SIR(LMW) capacity SR(LOI) Al3+ Biodiv(bact) Ca/Al pH

NMIN(EFF)

0.2 - PC2 Bulk density

Ca2+ 0.4 - Olsen P NMIN Leachate Amino acids NMIN(LOI)

0.6 Leachate Phenols - Absorbance 254

Leachate C Leachate NO3 0.8 - Leachate NH4 Leachate N

-1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 PC1 PC1

Figure 7. PCA biplots showing (a) principal components analysis of the standardised soil physic-chemical variables; and (b) the soil diversity and function properties fitted using the Envfit routines in the Vegan package.

26

The first axis of the PCA extracted 35% of the variation in the soil chemical properties, and represented a broad gradient of soil organic matter, moisture and pH. The second axis was mainly related to the chemical properties of the soil leachate and soil phosphorous (Olsen P). This suggest that axis-1 was predominantly a mineral soils (BD, ΘFC) versus organic soils, (high LOI, %C and N) gradient. In contrast, axis 2 can be identified as a

‗nutrient‘ gradient as indicated by TDNL and Olsen-P. The response variables were then fitted to this ordination, in order to examine the inter-relationships between soil physico-chemical parameters and the response variables. Soil respiration (SR) was generally closely related to the dominant soil-physico-chemical gradient (r2 = 0.3232, p<0.001), indicative of high respiration rates in the moist organic-rich soils. However, correcting for the amount of organic matter reduced the correlation with the soil physico-chemical parameters (r2 = 0.0667, p<0.001), whilst still remaining significant. With regards to the specific mineralisation data, only the HMW substrates showed any significant correlation with the soil variables (r2 = 0.346, p<0.001). Greater amounts of plant substrate mineralisation broadly followed the increases in pH, Ca/Al ratio and bulk density, though closely related to the dominating organic/moisture gradient. Potential soil N mineralisation was, perhaps unsurprisingly, a good predictor of leachate N (Table 5b), but was not itself clearly related to other physico-chemical or biodiversity measures, as shown by the PCA (Figure 7). Potential soil N mineralisation did however differ among vegetation classes (Figure 3), implying that it may be controlled by vegetation-specific factors that were not measured, such as litter quality. Bacterial diversity was generally well correlated with the soil variables (r2 = 0.30, p<0.001), whereas the measures of invertebrate communities (total numbers) were only weakly correlated (r2 = 0.07, p<0.001). The direction of bacterial and invertebrate correlations, as visualised from the biplot, was broadly in line with the first axis, again highlighting the close links with soil parameters and diversity.

Response r2 p NMIN 0.1811 0.00099

NMIN(LOI) 0.3537 0.00099 NMIN(EFF) 0.0389 0.00299 SR 0.3232 0.00099

SR(LOI) 0.0667 0.00099 SIR(HMW) 0.3406 0.00099 SIR(LMW) 0.0023 0.61938 Biodiv(bact) 0.3029 0.00099 Biodiv(invert) 0.0748 0.00099

Table 6. Summary of relationships between the response variables and the first axis of the PCA ordination of soil physico-chemical variables; r2 values obtained by linear fitting linear regression, significance (p) determined by permutation (nperm = 999).

27

To identify if different relationships were statistically significant between low and high C content soils, stepwise regression was performed on the subsamples with low and high carbon (less than 10% total C or greater). Of particular interest were relationships which differed fundamentally between soils rather than just lower statistical power. For example, + the best correlate of SIR(HMW) in low carbon soils was with NH4 (L) (4.36) and Alexc (4.15) and yet in high carbon soils it was pH (-5.88). This was most likely due to the fact that the high carbon soils were predominately peat and podzolic soil types which generally possess a low pH and almost all Alexc is complexed and therefore bio-unavailable. The SIR(LMW) correlated best with NTOT in low carbon samples while for high carbon it was ΘFC. Again in low carbon samples the available NTOT is indicative of available N which is dependent on the amount of SOM while in the high carbon soil samples there is an abundance of N and therefore is not limiting parameter. Apart from the moisture availability, the ΘFC is reflective of the physical characteristics (texture and pore space) of the soil which may be more important for the SR. The XY of plots of the few significant relationships from the stepwise regression are shown in Figure 8. Low and high carbon soil samples are shown by open and filled circles respectively. As discussed, in some cases there is a clear difference between the low and high carbon samples in relation to the effect of an explanatory variable on the soil function or biodiversity variable. When biodiversity was treated as explanatory variables rather than response variables, in the stepwise regression, only SIR(LMW) comes out significant with the invertebrate biodiversity with t-value of -2.71. However, this result is not indicative of a major effect of invertebrate biodiversity on the SIR(LMW) variable. This is also seen in the indeterminate relationship in Figure 8 panel (xi) scatter plot. The X-Y scatter plots of selected variables were produced using SigmaPlot version 10 (SPSS Inc., Chicago).

28

0.16 1200 50 A B C

)

-1 1000 40

)

0.12 -1

)

h

-1 800

-1 30

0.08 600

20 400

(mg N mg C mg N (mg 0.04 (mg C kg SOM kg C (mg 10 Mineralisable N per SR SR per N Mineralisable 200

N mineralization (mg N g N (mg mineralization N

Soil respiration (SOM basis) (SOM respiration Soil

0.00 0 0 0 5 10 15 20 25 0 5 10 15 20 25 30 0 20 40 60 80 100 Nitrate in leachate (mg N l-1) Nitrate in leachate (mg N l-1) Loss-on-ignition (%)

20 2.5 1.00 D E F

16 2.0 0.95

)

-1

12 1.5 0.90

soil h

-1

8 1.0 0.85

Soil respiration respiration Soil

(Shannon index) (Shannon

(Simpson index) (Simpson

(mg C kg C (mg 4 0.5 biodiversity Bacterial 0.80

Invertebrate biodiversity Invertebrate

0 0.0 0.75 0 300 600 900 1200 1500 1800 0 10 20 30 40 50 60 3 4 5 6 7 8 9 Moisture content at field capacity (%) Total soil carbon (%) Soil pH

Figure 8. Selected explanatory variables plotted against function and biodiversity variables. Low carbon soil samples (<10% total C) are shown by filled circles while high carbon soil samples (≥ 10% total C) are shown by open circles.

29

100 1.00 100 G H I 0.95 10

90 )

-1

0.90 1

soil h

80 -1

(% remaining) (% 0.85 0.1

Soil respiration

(LMW)

(Simpson index)

70 (mg kg C Bacterial biodiversity 0.80 0.01

SIR

60 0.75 0.001 0.0 0.5 1.0 1.5 2.0 2.5 3.0 -1 0 1 2 3 4 5 -1 0 1 2 3 4 5 Total soil N (%) Log exchangeable Ca:Al ratio 10 Log10 exchangeable Ca:Al ratio 100 2.0 J K Legend 1.5 80 Low C content soils High C content soils

1.0

(% remaining) (% 60

(HMW) (Shannon index) (Shannon 0.5

SIR 40 biodiversity Invertebrate

0.0 0.001 0.01 0.1 1 10 100 1000 60 70 80 90 100 14 -1 SIR (% C remaining) Exchangeable Al (mg kg ) (LMW) Figure 8 (cont). Selected explanatory variables plotted against function and biodiversity variables. Low carbon soil samples (<10% total C) are shown by filled circles while high carbon soil samples (≥ 10% total C) are shown by open circles.

30 3.4 Discriminant analysis of soil and vegetation types based on physico-chemical, function and diversity data The DISCRIMINATE procedure of Genstat version 8.0 (VSN International, Oxford, UK) was used to investigate the nature of the discrimination among soil types and vegetation types. The discriminant analysis of soil types based soil physical chemical parameters suggests a good discrimination in the 95% confidence circles around the group means of Peat soils, and Podzolic soils from the rest of the soil types (Fig. 9a). An examiniation of the individual variable contributions highlighted pH, moisture content at field capacity, BD and exchangeable Ca as the best physico-chemical variables splitting the soil types. The function variables separated the Peats from the rest of all the soils (Fig. 9b), with SR, SR(LOI) and SIR(HMW) as the best discriminating variables. The biodiversity measures showed the poorest discrimination between the soil types (Fig. 9c). Similarly, the vegetation types were best discriminated based on physico-chemical variables > function variables > biodiversity variables. The respective variables important in the discrimination were: BD > LOI > soil pH > C:N; SIR(HMW) > NMIN(LOI) > NMIN; Biodiv(invert) > Biodiv(bact). The first axis in Fig 9d separates the 95% confidence circles around the group means of heath and bogs, moorland grass mosaics and the upland wooded on the right from the rest of the habitats on the left. The greatest difference is between the heath and bogs versus crop and weeds. The second axis separates heath and bogs and crop and weeds on one end from the upland and lowland wooded on the other. The rest of the habitats were intermediate. In Fig 9e, only axis 1 was the most important in discriminating habitats, however, the 95% confidence circles around the means were large showing more uncertainty in the location of the means. Most of the habitats could not be separated in axis 2. In relation to the biodiversity variables Fig 9f indicate similarity in the means as seen from the big size and overlaps of the confidence circles around the means.

31 (a) Groups discriminated by physico-chemimical variables (b) Groups discriminated by function variables (c) Groups discriminated by Biodiv(bact) and Biodiv(invert) variables

-1.0 Scores[2] Scores[2] 2 Scores[2] -1.0 -0.5 Lithomorphic Podzo 1 -0.5 SWGs Podzol 0.0 Browns SWGs SWGs Browns Peat GWGs 0.0 GWGs Pelosol GWGs 0 Lithomorphic 0.5 Browns Lithomorphic Peat

0.5 Pelosol Pelosol 1.0 -1 Peat 1.0

1.5 1.5 1.0 0.5 0.0 -0.5 -1.0 -2 1.5 Scores[1] -1 0 1 2 3 -0.5 0.0 0.5 1.0 1.5 2.0 Scores[1] Scores[1] (f) Groups discriminated by biodiv(Bact) and Biodiv(invert) (d) Groups discriminated by physico-chemical (e) Groups discriminated by function variables variables variables -2 -3 -1.5 Scores[2] Scores[2] Scores[2]

-2 Lowland -1 Infertile grassland -1.0 wooded Upland Lowland -1 Fertile wooded wooded grassland Tall grass Fertile grassland Moorland grass Infertile mosaics -0.5 0 and herb Upland Tall0 grass grassland Moorland grass and herb mosaics Upland Tall grass Fertile grassland wooded Lowland wooded Heath and 1 Crops and 0.0 and herb Heath and bog Crops and wooded bog 1 Infertile weeds Heath and weeds Crops and grassland Moorland grass 2 bog 0.5 weeds mosaics

3 2 1.0 1.5 1.0 0.5 0.0 -0.5 -1.0 -3 -2 -1 0 1 2 3 4 2 1 0 -1 -2 Scores[1] Scores[1] Scores[1]

Figure 9. 95 % confidence circles around the soil and vegetation type group means defined by (a) physico-chemical, (b) function and (c) biodiversity variables using discriminant analysis. The combined-groups creates an all-groups scatterplot of the first two linear discriminants (Scores 1 and Scores 2). Analysis uses the DISCRIMINATE procedure of Genstat (VSN International, Oxford, UK).

32 4 Discussion

4.1 How well do traditional soil chemical and physical quality indicators predict soil function?

The traditional soil classification (soil types), the topsoil physico-chemical classification by cluster analysis, and the individual chemico-physical quality indicators were investigated for predictive relationships with various measures of soil C and N mineralisation. The box plots and regression tree analysis have shown broad differences in soil types and soil quality indicator based clusters, based on soil respiration and soil moisture at field capacity only, weakly separating mineral soils from organic soils with the organo- mineral soils as intermediate soils. The low predictive value of soil types may be related to fact that soil function are a product of the short term (<1 y) soil mineral–soil biota interaction (Sanchez, 2003) which are mainly dependent on nutrient availability and environmental factors rather than soil types (Florinsky et al., 2004; Cavigelli et al., 2005; Kurola, 2006; Kemmitt et al., 2006), and which are a product of long term (usually >1000 y) soil forming processes (Jenny, 1994). Our results also suggest a second alternative which is the potential role of vegetation. This may include effects on temperature and moisture through differences in canopy structure, interception and water use efficiencies, variability in nutrient uptake and quantity and quality of organic matter inputs entering from both above and below-ground. In relation to the selected traditional soil chemical and physical quality indicators, the results from the stepwise regression show that the function variables were correlated to specific soil properties such as pH, nutrient, availability, and moisture content (Kemmitt et al., 2006; Aciego-Pietri and Brookes, 2008; Rousk et al., 2009). As expected, mineralisable N - and mineralisable N efficiency were highly correlated with NO3 (L) (t value = 10.11, and 9.55 respectively); soil respiration (SR) with ΘFC (t value = 15.37); SIRHMW with Alexc (t value = + 5.22) and NH4 (L) (t value = 4.55) and SIRLMW with CTOT (t value = 5.35) and NTOT (t value = 6.26). Notably, in the low carbon soil samples, SIRHMW highly correlated with Alexc while in the high carbon soil samples it correlated best with pH as most of the Al is complexed with OM (Kaiser and Guggenberger, 2000; Mayer and Xing, 2001). This shift in significance between soils of low and high carbon content, demonstrates the effect of pH and Al on the rate of OM decomposition in mineral and organic soils respectively. Soil pH is known to have a direct control of biomass composition of fungi and bacteria and microbial biodiversity (Rousk et al., 2009). Low pH is also known to reduce C turnover rates by reducing microbial activity and nutrient turnover in the soil (Dalal, 2001). Furthermore, enzyme denaturation may also occur at extreme pHs and soluble forms of Al at low pH, may form Al-OM complexes and other organomineral aggregates which can prevent access by enzymes and consequently reduce C turnover in the soil (Zunino et al., 1982; Kaiser and Guggenberger, 2000; Mayer and Xing, 2001). From the XY plot presented in Figure 8d, the best positive predictive relation between 2 soil respiration was with ΘFC with the regression equation SR=0.0048(ΘFC) + 0.35; R =0.46.

33 Fitting the best line of fit to all other graphs presented in Figure 8 yielded a poor correlation with R2 values <0.04. In some variables, the correlation between the function and the explanatory variables improved when the values of one or both variables in a pair were log transformed, but their predictive relationships were still poor and difficult to describe. However, given such a wide range of soil types from a similarly wide range of ecosystems which were investigated, the observed variability in soil function explained by the explanatory variables with R2 >0.15 (from stepwise regression) was significant. Therefore this study confirms that soil carbon rate function is mainly dependent on substrate type and amount (Simfukwe et al., unpublished), nutrient availability, pH, moisture content and temperature (Stotzky, 1997; Florinsky et al., 2004; Kurola, 2006; Kemmitt et al., 2006; Aciego-Pietri and Brookes, 2008; Rousk et al., 2009) but this still only explains ca. 50% of variability observed indicating new methods need to be introduced if predictive capability is to be improved to levels required for future scenario testing.

4.2 How well do traditional soil chemical and physical quality indicators predict soil biodiversity? One of the primary soil functions as identified by Defra (2005) is the support of ecological habitats and biodiversity. Consequently, it is of importance to recognise how variation in soil abiotic parameters affects the diversity of soil organisms, both as a prelude to determining possible affects on soil functioning; and also if we are to value diversity, in itself as a conservational feature of soil ecosystems. As part of the 2007 Countryside Survey, data were collected on both the molecular diversity of soil bacterial communities, and also counts of broad groups of invertebrate taxa (specifically the mesofauna). For simplicity, both these multivariate datasets were summarised using a diversity index (Shannon‘s or Simpson‘s), which is reflective of the number and relative abundances of taxa within each soil sample. Both groups of organisms responded in a similar manner to the broad gradient of chemical and physical parameters manifest within British soils, with typically less diversity in organic acidic soil habitat types (e.g. Fig. 4). Bacterial community diversity exhibited a strong correlation with soil pH, and significant correlations with other covarying chemical and physical parameters (e.g. bulk density, LOI, C:N ratio; Griffiths et al., In Prep). These patterns are not limited to UK soils, as the overarching relationship between bacterial diversity and pH has recently been reported in a number of other geographically dispersed ecosystems (Fierer and Jackson, 2006; Hartman et al., 2008; Lauber et al., 2009; Dimitriu and Grayston, 2010; Rousk et al., 2010), and is routinely attributed to an increased abundance of Acidobacterial taxa at low pH. Overall levels of variation explained was ca. 60% indicating there remains unexplained sources of variability. The exact nature of the relationships between soil chemical and physical parameters and invertebrate diversity were less clearly defined. Though moorland, heath and bog habitats tended to have less overall diversity (Fig. 4) in this survey and a previous survey (CS 2000; Emmett et al., unpublished), general relationships between diversity and the soil physical and chemical parameters were weak. It is likely this is in part due to the mobility and dynamic nature of the invertebrate community which is as yet poorly defined.

34

4.3 How well does soil biodiversity predict soil function? One of the primary aims of this study was to determine whether the inclusion of biotic variables enhances the predictability of soil functions. Overall, we found that including biotic variables as predictors in the stepwise regression did not improve the models based on combinations of soil abiotic variables. Therefore, for these soils, measurements of biodiversity did not improve the predictability of soil function based on soil physico-chemical parameters alone. However, there were still some significant relationships between the measures of soil diversity and function. In general, the bacterial diversity measurements were generally more predictive of soil functions than measures of invertebrate diversity. However, with the exception of the SIR(HMW) responses, these correlations were still notably weak. Bacterial diversity is most likely a better predictor than invertebrate diversity due to the stronger relationships between bacterial diversity and the dominant abiotic gradient which is largely correlated with the major soil processes relating to C and N cycling. A significant and novel finding is that bacterial diversity is highly correlated with the mineralisation of high molecular weight substrates. However, this effect is confounded, or perhaps primarily driven, by the correlation between this functional response and soil pH and soluble Al concentrations. Therefore high pH soils, comprising greater bacterial diversity, mineralise plant material at a faster rate than low diversity soils. It is not possible, however, to elucidate whether it is the physiological effect of the soil abiotic environment, or altered diversity which is regulating rates of decomposition. The lack of any strong relationships between microbial diversity and the other soil functions is perhaps surprising, given that much of these processes are thought to be microbially mediated (Ritz et al., 2009). However, we only examined one component of the soil microbial community with a methodology which discriminates at the level of dominant broad groups of taxa. We therefore cannot exclude the possibility that, if monitored, other microbial groups such as fungi (including mycorrhizas) or Archaea, or specific microbial sub populations would show stronger relationship with the assessed functions. This is particularly true for N mineralisation, where several studies have reported the bacterial-to-fungal ratio as well as the diversity of specific nitrifier and denitrifier populations are important predictors (Hallin et al., 2009). With regard to the basal soil respiration and LMW C mineralisation, it is possible that most of the microbial community is active in the decomposition of simple carbon compounds. Therefore diversity is less useful as a predictor for such generalist soil processes, where a degree of functional redundancy may exist within the microbial community (Rousk et al., 2009).

4.4 Do soil and vegetation types differ for physico-chemical, function and diversity The discriminant analysis of the soil and vegetation types using physico-chemical, function and diversity variables was performed to investigate the differences in the groups. The discriminant plots of the group means and the 95% confidence circles around the means are shown in Figure 9. The soil physico-chemical variables discriminated three groups; the

35 pelosol, brown, GWG, SWG and the lithomorphic in one group; the podzolic in the second and the peat in the third group, corresponding to mineral, organo-mineral and organic soils. The function variables discriminated two groups of the mineral soils against organic (peat) soils. The discrimination among the vegetation types was better than among the soil types with respect to both physico-chemical and function variables, observing from the size and separation of the 95% confidence circles around the means. There were more vegetation classes that were resolved than soil types using the selected physico-chemical and function variables. The discrimination of both soil types and vegetation using biodiversity variables was ambiguous as the 95% confidence circles around the group means greatly overlapped with each other. The better correlation of physico-chemical and function variables to vegetation classes than soil classes may be due to fact that the variable measurement were done on the topsoil (15 cm depth) which may be related more to surface and habitat features than to traditional soil types which are based on variables measured in the subsurface (e.g. 50 cm) (Sanchez et al., 2003). Alternatively, it may be an indication that there may be soil characteristics which we do not understand or measure which determines soil function and biodiversity.

4.6. Conclusions

Soil type was found not to be a good predictor or either topsoil function or bacterial or invertebrate diversity. Likewise bacterial and invertebrate diversity were not in themselves, or when combined with physico-chemical parameters, good predictors of topsoil function. Vegetation type was the single best predictor suggesting recent soil forming processes associated with vegetation may be most critical for determining soil function. This is likely to include quantity and quality of organic matter supply, variability in nutrient uptake and environmental conditions associated with different canopy structures and water use efficiencies.

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