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Department for Environment, and Rural Affairs Research project final report

Project title Review and initial assessment of what makes some more resilient to change and how this resilience can be conferred to other soils.

Sub-project D of Defra Project SP1605: Studies to support future Policy

Defra project code SP1605

Contractor SKM Enviros organisations Cranfield University Rothamsted Research

Report authors Karl Ritz ([email protected]), Ron Corstanje, Lynda Deeks, Andy Whitmore

Project start date November 2009 Project end date April 2010

Review and initial assessment of what makes some soils more resilient to change and how this resilience can be conferred to other soils.

Sub-project D of Defra Project SP1605: Studies to support future Soil Policy

EXECUTIVE SUMMARY

The Soil Strategy for England, 'Safeguarding our Soils', was published by Defra in 2009 with a vision that by 2030 all England's soils will be managed sustainably and degradation threats tackled successfully. Achieving this aim will improve the quality of England's soils and safeguard their ability to provide essential services, including food production, for the future. The maintenance of the fertility of our soils will become increasingly important as pressures on food and supplies increase. The Soil Strategy for England aimed to put in place measures to protect and enhance soils, but there is also recognition that there are evidence gaps that need to be examined and addressed.

Soils are constantly subjected to stresses and pressures and change is likely to increase these. Soils need to be resilient to be able to adapt in order to continue to deliver the functions required of them, particularly in relation to production. A better understanding of resilience may lead to improved management of poorer soils to enhance their resilience. The stability of an is comprised of a resistance factor (degree of change) coupled with resilience factor (rate and extent of subsequent recovery) from a disturbance.

Resilience is apparently affected by soil physico-chemical factors (e.g. texture, , ), biology, topology and use. Data to date suggest a strong context- dependency to the factors which affect resilience phenomena. Modelling of factors was carried out on data from 38 English and Scottish soils. This showed that , parent material and appear dominant in determining soil resilience in general and aspects of the soil microbial community were also pertinent. It is notable that and organic matter content, which are commonly hypothesised to be influential, ranked amongst the lowest significant factors. However, these conclusions are based upon the very limited coherent datasets which are available.

There is still much to learn about resilience and the inter-relationships between potentially governing factors, such as biology and parent material. Whilst we cannot really change parent material and soil texture, we can modify land use for example via cropping practises, organic matter management, and . Whilst direct evidence for organic matter playing an important role in resistance and resilience is currently equivocal, it may be more pertinent via complex interactions with the soil biota. The key implication of the apparent context dependency of resistance and resilience phenomena is that management of them is likely to be possible, but not via a single or direct approach. Rather, it may require specific approaches in particular circumstances, and this will need a greater understanding of the phenomena based upon coherent research as rehearsed above. Furthermore, it is possible that the system-level configuration of the soil which is of greatest consequence as opposed to individual factors, and hence a systems-level approach to management of soils is likely to be the most effective strategy.

1. Introduction Soils must be able to recover from the stresses and disturbances to which they are exposed otherwise they would not persistently function in an effective manner, i.e. they must be resilient. Such pressures can arise from natural phenomena or be anthropogenically imposed. The range of such factors is wide and includes: environmental variation (e.g. wet/dry, freeze/thaw cycles), compaction (stock trampling, machinery trafficking), physical disruption (cultivation practices), erosion (wind, water), contamination (pollutants), salinisation and urban sealing. can exacerbate such pressures, particularly if it is rapid or results in extreme events. Hence cannot be deemed sustainable unless there is adequate recovery of soil properties and functions following the application of such practises. In order to develop strategies to protect soils from degradation as a consequence of their management or changing conditions, it is necessary to understand the natural resilience of different soil types to the impacts of change, and the mechanistic bases of such properties. Such knowledge can then assist in the development of practices to be implemented to protect soils that are less resilient to change. This report aims to review the evidence of what makes soils resilient to change and to provide an initial assessment of the extent to which resilience can be conferred to soils. Whilst the fundamental mechanisms and relationships will be considered in a generic sense, specific consideration will be given to the soils of England in the context of the Soil Strategy.

1.1. Concepts of soil resilience The term resilience derives from the Latin resilire which means to “rebound” or “spring back”, and is nowadays adopted in a wide variety of disciplines ranging across physics, ecology, psychology and sociology. In ecology, the term was first applied to refer to the amplitude of ecological changes induced by disturbance, and the dynamics of post-disturbance recovery, particularly in the context of species’ invasions (Elton 1958). Subsequently, Holling (1973) proposed that the concept be extended to involve two features, resilience and stability, where resilience defines the persistence of relationships within a system, and stability the ability of the system to return to an equilibrium after a temporary disturbance. Pimm (1984) proposed that ecological stability comprises two components: resistance, which is the initial deviation from the initial state following perturbation; and resilience which is then the speed and extent of subsequent recovery to the pre-disturbance level. A number of studies have since endorsed these concepts in the specific context of soil systems (e.g. Greenland & Szabolcs 1994; Seybold et al. 1999; Griffiths et al. 2000; Orwin & Wardle 2004; Kuan et al. 2007). Both these components are time-dependent responses, but typically the resistance component is manifest more rapidly. It is arguable that ‘recovery’ is a more precise term for the return phenomenon, but it tends not to be adopted at present, at least in . Thus the terms ecosystem stability, resistance, recovery and resilience are prone to some confusion, particularly since resilience is also used in a more general sense, notably in policy contexts, to essentially encompass all four terms to some extent. In psychological and sociological disciplines, resilience generally carries a connotation of positivity, i.e. to recover from, or to achieve better than expected outcomes, following adversity, disruption, stress or trauma. Hence resilience is often perceived as an inherently desirable trait of systems. However, in ecological systems resilience is not necessarily a positive attribute if it prevents change to a desired state being attained (Botton et al. 2006). For example, a highly polluted system can be very resilient to change, making remediation difficult to achieve, or resilient systems may curtail the development of successional sequences to desired ecosystem types. Whilst an overarching concept of soil resilience is outwardly straightforward, any detailed consideration soon becomes complicated since it is apparent that different properties pertaining to the soil system may be more or less resilient depending upon the nature of the perturbation, intrinsic and extrinsic soil factors and the timescale over which effects are considered. The issue is akin to that of defining ‘’ where it acknowledged that soils need to be healthy but beneath the headline there are many sub-clauses to be considered.

1.2. Derivation of resilience indices The components involved in ecosystem resilience phenomena are typically: (i) the system property being considered, i.e. the response variable; (ii) the nature of the perturbation1; (iii) the time-course of the response variable both prior to and following the perturbation; and

1 Noting that stress can be related to presence of agents that compromise, or the absence of agents that facilitate processes, organisms, communities, etc. (iv) the factor or factors which are hypothesised to affect resilience. The relationship between these components for a hypothetical system is shown in Figure D1.

Perturbation Control (not perturbed)

C0 Cx |D|2 1− 0 )1....(...... D Resistance = x C + 00 |)D|(

D0 Px |2 |D Perturbed Resilience = 0 − )2(...... 1

Property 0 |D(| + x |)D|

P0

t0 tx Time

Figure 1. Generalised concept of resistance and resilience. Graphical representation of trajectory of resistance and resilience in perturbed systems. Broken line shows timecourse of response variable in unperturbed (control) systems, solid line shows response following perturbation. Resistance is measured as the degree of impairment of response relative to control; resilience as the rate and extent of recovery, as denoted by Equations (1) and (2). Note that recovery may be

incomplete within the measured timescale. t0 = time at end of perturbation ; tx = time at which resilience is calculated (arbitrary, and can denote any number of such occasions); C = numerical value of property for control sample; P = numerical value of property for perturbed; D = absolute difference in numerical value of property between perturbed and control samples; subscripts denote times to which parameters apply as defined for t. After Ritz et al. 2004 and Orwin and Wardle (2004).

Studies into resilience consider these components more or less formally, and in the latter case a variety of indices which quantify resistance and recovery (resilience) have been derived. These have been reviewed by Orwin & Wardle (2004), who discussed the principal requirements for effective indicators and consider that they should be monotonic, scaled for both resistance and resilience, bounded, and standardised relative to unperturbed controls. Given that many of the published indices do not meet all these criteria, they proposed the following derivations which do so:

|D|2 Resistance = 1− 0 )1....(...... C + 00 |)D|(

|D |2 |D Resilience = 0 − )2(...... 1 |)D| |D(| + |)D| 0 x where:

D0 = the absolute change in the numerical value of the property between perturbed and control samples after t0, the time at which this point is taken

C0 = the numerical value of the property at t0

Dx = the absolute change in the numerical value of the property between perturbed and control samples after tx, the time at which this point is taken. It is notable that these indices, whilst satisfying the above criteria, are based upon a prescribed time point following the perturbation and do not then synthesise the temporal trajectory of recovery. This emphasises that there is a need to define the time over which resilience is to be considered, which may be confounded by the relationship between the perturbed system and the control. Over long time periods, the pertinence of the control may decline as the two systems being compared may diverge to such an extent that they are no longer comparable, particular if environmental conditions which the systems are subjected to also change.

2. Factors that affect soil resilience and the mechanistic bases for resistance and resilience phenomena Resilience of soil properties and functions are founded upon the processes which govern recovery from perturbation. These in turn fundamentally relate to factors which make such processes resistant to perturbation in the first place, and factors which facilitate their action to impart recovery to ascribed levels. Such factors include those which are intrinsic to the soil system, which have been predominantly considered in soil resilience studies, as well as extrinsic ones which have received rather less attention.

2.1. Intrinsic factors 2.1.1 Physico-chemical properties Soil texture has been shown to be influential in terms of both physical and biological resilience. For example, Gregory et al. (2007) showed that soil texture had a significant effect on the resilience of soils to compaction, with sandy and sandy clay soils showing little resilience, but a clay soil was resilient. They attributed this to a buoyancy effect of within the clay soil. Clays also play an important role in underpinning the structural integrity of soils via their aggregating properties, and hence would be expected to provide a resilience mechanism by this route. An example of this is in the case of self-mulching clays which are essentially structurally resilient systems which are rarely described in such terms. Clays also influence the buffering power of soils via cation exchange processes, and it is reasonable to hypothesise that there is a relationship between buffering capacity and resilience of processes affected by components prone to attenuation by absorption. There has been little consideration of resilience in relation to the cation exchange capacity of soils. Soil organic matter (SOM) is often considered to play an important role in underpinning resilience phenomena. In a study of 26 contrasting soils, Kuan et al. (2007) found that soil organic carbon content correlated strongly with resilience after biological and physical stresses, particularly resistance to stress induced by the presence of copper and recovery from compression. Fujino et al. (2008) demonstrated that organic amendments increased the resistance and resilience of decomposition following application of a broad-spectrum biocide. Girvan et al. (2005) found that both microbial community composition and function in an organo- soil was more resilient to copper- and benzene-induced stresses than a mineral soil. The mechanistic basis of such effects are likely to founded on direct and indirect effects. For physical attributes such as compressive recovery, OM may act as a physical spring, and for chemical-based stresses OM will impart a buffering capacity, particularly for organic pollutants (Girvan et al. 2005; Griffiths et al. 2008). Fujino et al. (2008) showed that destructuring a soil by grinding it removed the resistance of cellulose-decomposing ability that was manifest when the soil was structurally intact. Indirect effects include interactions between OM and soil texture, which strongly influences the nature and function of the soil biota, manifest via the architecture2 of the soil.

2 Soil architecture is defined as the physical arrangement of the all of the non-living and living components in a soil, a concept which emphasises that functional soils are reliant on the appropriate interaction between organisms and the physical structure of their environment. 2.1.2 Biological factors Given the pivotal role that the soil biota plays in governing (Bardgett 2005; Defra 2010: Report SP1601-A), much more attention has apparently been paid to interactions between biology and the resilience of soil systems than other edaphic factors. However, the relationships between biological factors and resilience, akin to the more general concept of relations between and ecosystem function, are remarkably complex. A number of mechanisms apparently underlie the relationships between soil communities and resistance and resilience, including: (i) Biomass: It can be hypothesised that total biomass may play a role in resilience via a form of buffering capacity – the greater the biomass the more potential for recovery. However, direct evidence for this is rather sparse. Banning & Murphy (2008) found resilience of induced by the addition of malic acid was positively related to microbial biomass but not community structure. (ii) Repertoire: for a biologically-mediated process to occur, organisms (or arguably, genes) that carry out that process must be present. A diverse system will inherently carry a wider suite of potential abilities that will underwrite a wider range of functions, which will be less prone to disruption if perturbed. Degens et al. (2001) found evidence for reduced resistance and resilience to a range of stresses including acidification, salinity and metal contamination was associated with a decline in the functional diversity of the microbial community. Griffiths et al. (2004b) concluded that the functional stability of decomposition was more related to specific components of the microbial community than diversity per se. (iii) Diversity: the greater the range of organisms that are present, and which can carry out a function in a particular soil, the more likely it is that if some are incapacitated or removed the process will remain unaffected; those that remain may fill the gap. This is related to concepts of redundancy, and one of the consequences of the generally extreme levels of biodiversity found in soil systems is that there are often high levels of functional redundancy, particularly for broad ecological processes such as decomposition of SOM (Schimel 2005; Botton et al. 2006; Chaer et al. 2009). Despite this, there is contrasting evidence for direct relationships between genetic diversity and resistance and resilience. For example, positive relationships have been reported by Girvan et al.(2005), for bacterial diversity in relation to resistance to benzene addition and for general microbial diversity resistance and resilience to heat stress (Griffiths et al. 2000). Banning & Murphy (2008) found a significant relationship between the resistance of malate-induced respiration and the genetic and phenotypic bacterial community structure in a soil subjected to heat stress. However, there are also reports of no distinct relationships between microbial diversity and resistance or resilience to copper stress (Griffiths et al. 2000; Griffiths et al. 2004a). It is reasonable to hypothesise that ecologically-narrow processes, i.e. where there is an inherently low diversity of organisms capable of carrying out such functions, may be more prone to a reduction in resilience where diversity is reduced. However, in a study by Wertz et al. (2007) where the diversity of denitrifiers and nitrite oxidisers in soils was experimentally reduced, this was shown not to be the case for a heat stress, so long as the cell abundance for these groups was equitable. (iv) Interactions: most soil organisms have the capacity to directly or indirectly influence other organisms, either positively or negatively. A greater diversity of organisms offers a greater potential for interactions, and a more complex network of interactions may be more adaptive to change and resilient to perturbation. In a microcosm experiment, Maraun et al. (1998) showed that the presence of oribatid mites strongly influenced the recovery of the microbial community in a forest soil following freezing and heating perturbations. They attributed this to an acceleration of the colonisation of litter material by fungi by increased spore dispersal by mite activity, plus the stimulation of microbial populations by mite grazing. This example of the role of trophic connectivity upon resilience also and suggests that spatial connectivity, here manifest via faunal movement, can also play a role. (v) Community conditioning: It is well established that populations and communities can become conditioned to stresses and perturbations if they are repeatedly applied. This is an intrinsic part of adaptive and evolutionary processes. As such, community resilience can putatively be induced by such circumstances, in response to both related and unrelated stresses. For example, Bressan et al.( 2008) showed that subjecting soil to heat, copper and atrazine stresses resulted in increased ancillary resilience to severe mercury stress. Calderon et al. (2000) demonstrated that short- term responses to tillage were less pronounced in soils with a long history of cultivation, which may be due to the development of a resilient community to such perturbations. Girvan et al. (2005) found evidence for adaptation to copper pollution by surviving communities. Tobor-Kaplon et al. (2005) found evidence that long-term copper and low-pH stresses on soils can result in supplementary stresses having stronger impacts in terms of reduced resistance and resilience than in unstressed systems. However, in a subsequent study, it was found that such phenomena were context dependent and that the stability of a particular process could vary significantly depending upon the nature of the perturbation (Tobor-Kaplon et al. 2006). Furthermore, Kuan et al. (2007) found complex interactions occurred where soils were treated with sequential stresses, and resilience varied according tot the type and duration of stress applied, microbial activity, soil characteristics and treatment regimes.

There are also likely to be interactions between soil OM and the biotic bases of resilience. This is because OM represents the primary source for the soil biota, and hence fuels the biological activity which can impart recovery. In an experiment studying the dynamics of free amino acids and other dissolved organic compounds in the solution of a vineyard soil, Avramides et al. (2009) determined that such pools were rapidly depleted, but readily replenished, during the period when -derived inputs were low. They postulated that the soil contained intrinsic reserves of labile C that were capable of supporting the soil microbial community during periods of reduced plant inputs. These likely originate from SOM, which is duly mineralised by the soil biomass, a process regulated a wide range of factors. Griffiths et al. (2008) studied the combined effects of soil type and microbial composition upon resilience and found them to be inter-related. The resistance of the bacterium Pseudomonas fluorescens to decompose added plant residues depended significantly on which of 26 sterile soils it was inoculated into. In a subsequent experiment, communities from each of two soils of contrasting texture (sandy and clay-loam) were cross-inoculated into sterile instances of each and it was found that the resulting community structure depended upon soil type rather than the inoculum source (Griffiths et al. 2008). Furthermore, the resistance and resilience of decomposition was similarly governed by the soil and not the inoculum source, suggesting that this particular functional resilience was governed by the physico-chemical constitution of the soil, mediated by its effect upon the microbial community composition and associated physiology.

2.2. Extrinsic factors Extrinsic factors such as topology would be hypothesised to affect resilience, but the mechanisms underlying such effects will be principally via their effects on intrinsic properties. Banning & Murphy (2008) found that the resilience of substrate-induced respiration (using malic acid) was significantly affected by the micro-topography (mound or furrow) of a forest soil. They postulated that this could be due to the microenvironment within mound soils (lower organic matter accumulation and nutrient availability) which may select for microbial populations that are more tolerant of environmental fluctuations in comparison to those in furrows. This could be due to such populations being conditioned to lower SOM and nutrient availability. However, topological factors have apparently been rarely considered in this context. Land use is essentially an extrinsic factor, and has been shown to affect resilience in complex ways. This would be expected given that affects many intrinsic soil properties in diverse ways, particularly in relation to the . For example, Chaer et al. (2009) studied how deforestation followed by long-term cultivation changed microbial community composition and found that the two land uses had differential effects on microbial functional stability. For example, microbial biomass was reduced up to 25% in both soils 3 days after a heat perturbation, but general microbial hydrolytic activity was less affected (more resistance) and cellulose and laccase activities recovered more rapidly (more resilience) in the forest soil relative to the agricultural soil (Chaer et al. 2009). Carter et al. (2009) showed how particular conservation and crop rotational practises resulted in the restoration of many soil properties including soil C and N fractions, water-stable macro aggregates, microbial biomass and Collembola abundance, which had declined as result of a potato crop in the rotation. Orwin et al. (2006) found a high degree of context-dependency in the relationships between resistance and resilience in three primary plant chronosequences in New Zealand and Hawaii. Consistent trends were found within each chronosequence but the relationships between stability of soil biological response variables and ecosystem development depended on the chronosequence.Griffiths et al. (2001) found that organically- farmed soils were more resilient to Cu stress than conventionally farmed ones, but no difference between such soils with respect to heat stress. Kuan et al. 2007, in a study encompassing 26 Scottish soils from a range of land-uses and found the statistical significance for effects of land-use and FAO soil class classification for resistance and resilience in relation to heat, Cu, compression and physical overburden stresses, but were not able to detect any distinct trends relating land-use to such properties. Likewise, Gregory et al. (2009) found differential land-use effects upon resistance and resilience which were not straightforward to account for. The potential role of rotations in affecting resilience phenomena was demonstrated by Miethling & Tebbe (2004), who showed differential effects of prevailing plant type upon the recovery of populations of the legume symbiont Sinorhizobium meliloti, notably that the absence of a host plant resulted in low resilience of this particular bacterium to population recovery. These studies collectively support the conclusion that such effects are highly context-dependent.

3. Modelling the basis of resilience in order to evaluate the likely resilience of soils in England Studies carried out to date investigating resilience phenomena in soil systems are wide- ranging in their approaches, and results are varied in terms of confirming that the principal factors which are hypothesised to affect the resilience of soil systems, described above actually do so. It is difficult to draw general conclusions from the disparate published data via a simple reviewing process, due to the variety of such studies, associated results and conclusions, which are sometimes contradictory. What is notably lacking are coherent datasets of sufficient breadth and scope in terms of a range of soils studied, where resistance and resilience response variables are adequately measured and covariate data (representing putative factors which affect resilience), is also available. Such data would allow a more formal statistical modelling approach to evaluate factors which govern resilience phenomena in soils, and allow wider evaluation of soil in this respect. The only published precedent for this sort of approach to date is that of Debeljak et al. (2009), who used a classification and regression tree (CART) technique to demonstrate the potential of the approach and produce maps of the notional resilience of Scottish soils. However, this was notably based upon a limited data set, and as the authors’ explain, did not involve any independent validation (Debeljak et al. 2009). This method has the advantage that it is insensitive to non-normal data, can handle categorical (e.g. , soil parent material) as well as continuous data (e.g. pH, organic matter) and non-linear relationships. However it tends to over-fit to data, has poor predictive power and is not robust for extrapolation, particularly where a small number of observations are involved (Khoshgoftaar & Allen 2001). We here investigated whether a basic model could be created which would robustly relate soil properties to resilience, and thence be used to produce a directory of the resilience of the soils of England, based on national-scale data extant in soil inventories, and the potential to map these.

3.1 Approaches Following a comprehensive search of extant and available data, only two apparently suitable datasets were identified which were suitable to attempt such an approach, namely those of Kuan et al. (2007), which involved a range of Scottish soils, and Gregory et al. (2009), which utilised a suite of soils from central England. In these studies, resistance and resilience of physical (void ratio) and biological (respiration) soil functions were consistently quantified with respect to compression-, heat- and Cu-based perturbations. These datasets were procured and organised into two coherent frames: (i) English data, based upon 12 soils for which 22 covariates were available3 (ii) Combined English and Scottish data, based upon 38 soils for which 15 covariates were available. There are inevitably limitations with each individual dataset, and hence limitations to the modelling that result from combining the two datasets. The English dataset covered a limited geographical extent confined to Hertfordshire, Bedfordshire and Devon; samples were derived from a range of fields (and hence management practices) but only on four sites, of which three were located in close proximity. The range of soil series was therefore restricted and in the context of the diversity of soil series in England, not overtly representative at a national scale. However, a wide range of covariate soil properties data was available for these soils. The Scottish dataset contained more observations, based upon 26 samples from as many distinct sites, but this dataset contained less covariate soils data. Various indices of resilience and resistance were reviewed, as described by Gregory et al. (2009) and Orwin & Wardle (2004), and the latter were identified (i.e. Equations 1 and 2 in Section 1.2 above) as most appropriate for this exercise. Two approaches to modelling soil resistance and resilience with these data were then taken: (i) Via the application of a classification and regression tree (CART) approach (R Development Core Team 2008). In the current study, this approach was applied to both the English and the combined datasets. (ii) Via the application of a Bayesian network approach, involving the combining and incorporation of knowledge derived from the literature relating to factors influencing resilience, utilising the English dataset. Such models are based on a Bayesian inference system (Spiegelhalter et al. 1993) in which the networks can be informed both by the existing quantitative data as well as more qualitative forms, thereby possibly increasing the predictive ability of these models and circumventing some of the earlier described limitations.

3.2. Modelling soil resilience using classification and regression trees In this exercise, soil resistance and resilience was modelled individually. In all cases, the models performed well when predicting data on which the models were developed, with R2 > 0.8 in all cases. The caveat is that is insufficient data were available for independent validation and this performance likely represents some degree of over-fitting, but it can be taken that it is reasonably robust (such a high degree of fit is by no means assured even with small data sets). Interpretation of the output is based upon the feature that each regression tree contains within it a mapping of which variables are key in discriminating between levels of resistance or resilience. For example, for soil resilience to heat for the English soils (Figure D2), texture is identified as a critical driver, with a clay loam texture being associated with lower resilience. Moving on to the next level, content (again, a texture-related property) becomes important, where soils with sand content of >56% being less resilient; and so on down the tree. There is a hierarchy to this ordering, with factors at the apex of the tree being more significant than those lower down. The full suite of CART trees are presented in Annex D1 for the English Set(i) soils and Annex D2 for the Combined Set(ii) respectively. A range of factors were identified as being of primary importance in being associated with resistance and resilience, with no factors overarchingly dominant, however there are some general inferences which can be made:

3 Three soils reported in Gregory et al. (2009) were omitted due to incomplete covariate data. These were those encoded as Sy A, Sy A+M and Sy L-A in this study (viz. Stackyards arable, Stackyards arable+manure, and Stackyards ley-arable). (i) In the general models, based on the combined dataset, variables such as soil type and parent material appear dominant and relatively frequently, indicating that they play an important role in determining soil resilience. At the next level down, forms of soil texture are common to all models. (ii) In the models derived specifically for the English soils, the same pair of factors play a dominant role. However, in this dataset, variables relating to aspects of the soil microbial community were also present and hence there is a suggestion that they play an important role in determining soil resilience. It is notable that microbial data was not available for the Scottish data and hence the absence of such factors in the combined dataset cannot be used to infer that microbial factors are unimportant in this context.

ID=1 N=12

Mu=0.421333 Var=0.054597

Texture = clay loam, ... = Other(s) ID=2 N=5 ID=3 N=7

Mu=0.191400 Mu=0.585571 Var=0.016755 Var=0.016889

Bb A+M Sand (2000-63 æm) Wa A <= 0.557000 > 0.557000 Bt A ID=4 N=6 ID=5 N=1 Bt G Cm A Mu=0.631667 Mu=0.309000 Var=0.004830 Var=0.000000

Clay (<2 æm) DB A <= 0.207000 > 0.207000 ID=6 N=1 ID=7 N=5

Mu=0.758000 Mu=0.606400 Var=0.000000 Var=0.001966

DB G Bb W Hf A Hf F Hf G Wa G

Figure D2. Example of classification and regression tree (CART) output for English soil dataset as relating to resilience of decomposition of added plant material to heat stress. Values in each box represent resistance values, mu is the average resistance N soils, Var is the observed variation of resistance [based on decomposition of added plant material, high value relates to greater resistance, according to definitions as explained in texts]. Predictor variables are named under the box, with critical values at the extension of each arm towards the next box. Soil origins are identified under each box, labeled according to Whitmore et al. (2009): Bb A+M= Broadbalk Arable + FYM; Bb W= Broadbalk ; Hf A= Highfield Arable; Hf F = Highfield Fallow; Hf G = Highfield Grass; Wa A=Warren Arable; Wa G=Grass; Bt A=Boot Arable; Bt G=Boot Grass; Cm A=Cashmore Arable; DB A=De Bathe Arable; DB G= De Bathe Grass)

3.3 Modelling resilience using Bayesian networks Bayesian inference is based on a set of prior probabilities that can be updated as new information becomes available. In this case, some knowledge is available on what may affect soil resilience from the previous analysis of the datasets via CART, and there is another source of understanding from the literature. The aim here was to develop a method with which combine these two. In this case, two forms of inference model based upon the combined dataset were developed, using Netica software (Norsys Software Corp, Vancouver, BC): (i) A ‘naïve net’, which is not informed by the literature and involves universal connections between all dependent variables and factors (Figure D3a); (ii) An ‘informed net’, where information relating to factors affecting resilience gleaned from a literature review is consciously mapped via prescribed connections (Figure D3b). The source material drawn from the literature – targeting heat, copper and compression stresses - is presented in Annex D3. Connections are then only made within the net where there is evidence according to the literature for such associations. (a)

(b)

Figure D3. Schematic representation of Baysian network models. (a) naïve net; (b) informed net. Boxes (nodes) represent observed or modeled drivers and responses. The bar chart within each box represents the relative probability that this variable may take a value in that given range. Arrows represent links or causality between variables, direction of the arrow denotes dependency from the dependent to the independent (with the exception of soil resilience; as a latent variable the dependency is reversed). N = nitrogen; PD = particle density; CtoN = C:N nitrogen ratio; SOM is soil organic matter; EC is electrical conductivity; 1/CC = physical resilience to compression.

These types of networks allow the modelling of variables that are not - or can not - be observed directly. These are known as latent variables4. In this case, the factor to be modelled is prescribed, i.e. soil resilience in general terms. Here, this is a composite of the three measures of soil resilience for which we have observations, pertaining to Cu, heat and compression, determined by the network model. The network model thus consists of those variables that the previous CART models identified as drivers, which predict the measurable forms of soil resilience, which in turn predict the underlying generic factor ‘soil resilience’. In the case of these networks, three categorical states of soil resilience are prescribed, notionally ‘high’, ‘medium’ or ‘low’. Within the modelling framework, elements of the network can be disconnected, since connections are entirely operator defined. The topology of the network is therefore prescribed. In both cases, validation is difficult as there are explicit no measures of ‘soil resilience’ with which to validate these predictions. The performance and credibility of these networks was assessed in two ways: 1. Based on cross-validation of the predictions versus observed values of each of the measured forms of soil resilience. In the case of the naïve net, this performed well, with an error rate of < 3 % for each of the resilience measures (Cu = 2.4 %; heat = 2.8 % and compression = 2.1%). The informed net performed substantially poorer; with misclassification rates of 32%, 29% and 32% for Cu, heat and compression resilience respectively. 2. Based on allowing the networks to produce a set of 1000 simulated cases, with simulated predictor and predicted values, which are subsequently treated as observations and used in further prediction. This provides a measure of how robust each net is if it were deployed within a wider context, for example to geographically map resilience based upon soil properties. Here, the performance of the naïve net was superior, with a 50% misclassification rate. The corresponding misclassification rate of the literature informed net was 62%. Both performance results suggest that there are factors that are were not taken into consideration that are important drivers of soil resilience. The overall performance of the naïve net is considerably better than that of the literature net, and main differences between the two networks is that the naïve network incorporates information typically not considered by many of the researchers in the literature such as parent material, soil type, etc. Taking the naïve net outputs as most reliable, the hierarchical ordering of sensitivity to the factors that were included are shown in Table D2. This reveals that soil series and parent material are the most significant, supporting the conclusions of the CART approach, followed by two physically-related properties of particle density and liquid limit.5 It is notable that land use and organic matter content, which would be hypothesised to be influential, ranked amongst the lowest factors. When considering the relevance and impact of the results from this modelling exercise, there are two limitations to bear in mind. The first is that these models were based on a very small dataset and therefore the results from this modelling must be treated as exploratory not confirmatory of those factors which play an important role in determining soil resilience. The second limitation is when the modelling was extended to include the literature in an effort to mitigate for the first limitation, this lead to a considerable deterioration of the modelling performance. This may be a reflection of how complex the basis of soil resilience is.

4 For example, the most common example of ‘latent variables’ are the underlying factors extracted via principal components analysis. 5 The liquid limit (LL) is the water content where a soil changes from plastic to liquid behaviour.

Table D2. Sensitivity analysis of the naïve Bayesian network6. Variance of Node Mutual Infoa Beliefsb Soil series 0.0275 0.00445 Parent material 0.02001 0.002133 Particle density 0.01107 0.000527 Liquid limit 0.01026 0.001344 pH 0.00997 0.00072 Sand 0.00965 0.000788 Soil_order 0.0088 0.000718 Electrical conductivity 0.00868 0.001953 Nitrogen 0.00857 0.000498 C:N ratio 0.00823 0.000715 0.00806 0.000597 Clay 0.00744 0.000898 Plasticity index 0.00725 0.000626 Linear shrinkage 0.00676 0.00055 Land use 0.00676 0.000778 Plastic limit 0.00414 0.000924 SOM 0.00319 0.000546

aA measure of entropy, or noise reduction. b The expected reduction of real variance

3.4 Scoping the mapping of the resilience of soils of England and Wales In terms of the modelling part of this study, the only realistic objective was to analyze the limited, existing data to identify which factors are important in soil resilience and resistance and to set the stage, as new data is obtained, for a more comprehensive model which eventually could be deployed to determine and map soil resilience in England and Wales. The modelling exercises above demonstrate that the approaches are feasible and provide tentative evidence that soil resilience is potentially a deeply imbued property related to the fundamental pedological and geological origin of the soils themselves. However, this is with the caveat that there is extremely limited data currently available, which compromises the robustness of the approach, and due caution must be attached to any such conclusions. One consequence of this is that soil pedological maps in themselves may represent resilience per se, in that if parent material or texture, for example, are primary governors of the resilience phenomenon, then maps of such properties alone could be taken as resilience maps. More complex relationships between resilience and soil properties would require model-based derivations for which pedological maps would not be simple surrogates. To reiterate, resistance and resilience properties have not been measured across a sufficiently wide range of soil types yet to assess this. Extant datasets are not adequate to enable a wide-ranging assessment of the resilience of English soils, and it is certainly not yet appropriate to attempt to map such functions. Research needs then could be based upon an appropriately-designed spatial sampling regime for soil resilience and resistance across England and Wales. This sampling effort need not need to be excessively intensive as it can be stratified using the main factors identified in this study. Such new data could then also inform and improve the Bayesian model and a robust version of which then be deployed for mapping purposes. It is also clear from the English dataset that soil biology does play an important role in soil resilience/resistance. This dataset was not used in informing the current modelling efforts as

6 Biological data absent since not incorporated in analysis due to non-availability its size was too small. It is important that future efforts at determining soil resilience should incorporate measures pertaining to the soil biology as the preliminary results in this study suggest this is important. A final point to made is that many of the factors identified in this modelling exercise are ‘intrinsic’ soil properties and there needs to be greater consideration of extrinsic factors such as position in the (location on a slope, valley bottom, etc.). It is noteworthy that the main factors considered in the literature as putatively key to soil resilience/resistance, such as soil organic matter, did not surface as important in the modelling part of this study. This may be due to the small sample size on which these models were built, although all other studies were made on similar or smaller sample sizes. It may be that many of the ‘intrinsic’ soil properties contain some of this information (there is correlation in certain properties, e.g. OM is one of the criteria in the scheme). However, we found that when excluding soil parent material, soil order, soil class, etc. in the naïve network, performance deteriorated considerably. These results would suggest that the scope of ‘intrinsic’ factors for soil resilience and resistance needs to be widened.

4. Conclusions 4.1 Generation and management of soil resilience In the context of soil management and environmental protection, is important to consider if resilience can be effectively managed, and if it is possible to impart such properties to soils that are otherwise not stable. This presupposes that resilience is actually a desired trait in any particular system under consideration, and this needs due consideration in any policy application of resilience concepts. The evidence, as reviewed above, is currently equivocal. It is however very clear that there is a high degree of context dependency in soil resilience phenomena, and these contexts pertain across the spectrum of response variables, stressors involved, and intrinsic and extrinsic soil factors. It is also apparent that much is not yet understood about the fundamental mechanisms by which resistance and resilience are conferred, notwithstanding that conceptually sound hypotheses for such mechanisms can be formulated. These relate to features which make soil systems resistant to perturbation in the first place, and factors which facilitate processes which impart recovery to ascribed levels. They principally revolve around soil factors that relate to chemical buffering capacity and the nature of the biological systems in the soil. What has not been considered in any detail hitherto is the possibility that generic resilience phenomena in soils are fundamentally linked to the geological origin of soils and are actually deeply inherent in their pedological bases. If this is the case, the implications are that resilience is actually rather bounded and could only be modified to a limited extent. Whilst intrinsic soil factors such as parent material and texture cannot be readily modified, it is possible to manage other intrinsic factors to some extent, particularly in production systems via appropriate crop rotations, organic residue and material management, liming, etc. Soil organic matter does appear to play an important role in affecting resistance and resilience, but in complex ways, and particularly through its effects upon the soil biota. These are not yet readily definable, as revealed by the attempt to model such functions and paucity of data. With respect to soil communities, the important feature appears to be not related to an inherent biodiversity per se, but to the functional properties of the biota and hence an appropriate community configuration. This is in turn defined by the edaphic context of the soil (not manageable) and the prevailing land-use (manageable). The key implication of the apparent context dependency of resistance and resilience phenomena is that management of them is likely to be possible, but not via a single or direct approach. Rather, it may require specific approaches in particular circumstances, and this will need a greater understanding of the phenomena based upon coherent research as rehearsed above. Furthermore, it is possible that the system-level configuration of the soil which is of greatest consequence as opposed to individual factors, and hence a systems-level approach to management of soils is likely to be the most effective strategy. This conclusion arguably applies to the majority of other aspects of managing soils sustainably.

4.2 The context of the Soil Strategy for England. The Soil Strategy for England aimed to put in place measures to protect and enhance soils. A number of specific topics in the Strategy have a direct bearing on the resilience of soil, and particularly in relation to others considering climate change, soil protection, best practice for management and soil monitoring. The ability of soils to provide functions required of them in these contexts is fundamentally related to their resilience. The limited evidence available to date suggests that inherent soil resilience is strongly related to fundamental soil properties which may not be readily amendable to manipulation, suggesting that certain soils may be inherently less resilient in relation to certain functions. However, there is some evidence that resilience of some functions is also affected by interactions between soil organic matter and biota, which are amenable to management but rather complex and context-dependent. This further demonstrates the overarching importance of soil organic matter and a certain need for appropriate SOM management to be a core part of any strategy to protect soils. Such context- dependency suggests that there are no straightforward overarching protection measures for soils, and that location-specific measures are likely to be required for most effectiveness. Fundamentally, this requires the development of spatially-explicit decision-support tools, tuned to the variety of strategic goals. Within the Strategy there is recognition that there are evidence gaps that need to be examined and addressed. This project has highlighted a key evidence gap is data relating to soil resilience, and particularly in a form conducive to allow more robust identification of the factors which govern resilience phenomena. We suggest that in order to establish the fundamental basis of resilience in soil systems, some form of quantitative resilience assay is applied in a coherent manner to a representative range of English soils for which a comprehensive data relating to other properties are available. This could be based on extant assays, notwithstanding such assays have not been developed for this purpose and they all have their caveats. The absence of such data is a significant knowledge gap, particularly in the context of the limited available data suggesting both an inherent resilience in soils plus a context-dependency in relation to other factors which govern such resilience. It would quantitatively establish the extent to which English soils are resilient and enable the formulation of a model which could be used to devise potential management practices which may imbue or enhance such resilience, and which soils may be more or less amendable to such management. Such measures could be effectively realised by their incorporation into any future soil monitoring programmes and lead to the development of both better understanding and more targeted policy.

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Statistical Science 8, 219-283. Tobor-Kaplon, M. A., Bloem, J. & de Ruiter, P. C. 2006 Functional stability of microbial communities from long-term stressed soils to additional disturbance. Environ. Toxicol. Chem. 25, 1993-1999. Tobor-Kaplon, M. A., Bloem, J., Romkens, P. F. A. M. & de Ruiter, P. C. 2005 Functional stability of microbial communities in contaminated soils. Oikos 111, 119-129. Wertz, S., Degrange, V., Prosser, J. I., Poly, F., Commeaux, C., Guillaumaud, N. & Le Roux, X. 2007 Decline of soil microbial diversity does not influence the resistance and resilience of key soil microbial functional groups following a model disturbance. Environ. Microbiol. 9, 2211-2219. Annex D1: Classification and regression tree (CART) outputs for Set(i) English soils. Values in each box represent resistance values, mu is the average resistance N soils, Var is the observed variation of resistance. Predictor variables are named under the box, with critical values at the extension of each arm towards the next box. Soil origins are identified under each box, labeled according to Whitmore et al. (2009): Bb A+M= Broadbalk Arable + FYM; Bb W= Broadbalk Wilderness; Hf A= Highfield Arable; Hf F = Highfield Fallow; Hf G = Highfield Grass; Wa A=Warren Arable; Wa G=Grass; Bt A=Boot Arable; Bt G=Boot Grass; Cm A=Cashmore Arable; DB A=De Bathe Arable; DB G= De Bathe Grass).

(a) Physical resilience to compression

ID=1 N=12 Mu=1.854917 Var=1.612516 Linear shrinkage <= 17.650000 > 17.650000 ID=2 N=10 ID=3 N=2 Mu=1.326400 Mu=4.497500 Var=0.250676 Var=0.041820 Soil group Bt A = Brown sand... = Other(s) Bt G ID=4 N=1 ID=5 N=9 Mu=2.512000 Mu=1.194667 Var=0.000000 Var=0.104992 Fungal ELFA markers Cm A <= 14.760000 > 14.760000 ID=6 N=7 ID=7 N=2 Mu=1.063714 Mu=1.653000 Var=0.057804 Var=0.000064 Bacterial:Fungal ELFA markers <= 1.730000 > 1.730000 Hf G ID=8 N=6 ID=9 N=1 DB A Mu=0.989000 Mu=1.512000 Var=0.028362 Var=0.000000 Plastic limit Bb A+M <= 0.216500 > 0.216500 ID=10 N=1 ID=11 N=5 Mu=0.677000 Mu=1.051400 Var=0.000000 Var=0.010672

Hf F Bb W Hf A Wa A Wa G DB G

(b) Resistance to heat ID=1 N=12 Mu=1.854917 Var=1.612516 Linear shrinkage <= 17.650000 > 17.650000 ID=2 N=10 ID=3 N=2 Mu=1.326400 Mu=4.497500 Var=0.250676 Var=0.041820 Soil group Bt A = Brown sand... = Other(s) Bt G ID=4 N=1 ID=5 N=9 Mu=2.512000 Mu=1.194667 Var=0.000000 Var=0.104992 Fungal ELFA markers Cm A <= 14.760000 > 14.760000 ID=6 N=7 ID=7 N=2 Mu=1.063714 Mu=1.653000 Var=0.057804 Var=0.000064 Bacterial:Fungal ELFA markers <= 1.730000 > 1.730000 Hf G ID=8 N=6 ID=9 N=1 DB A Mu=0.989000 Mu=1.512000 Var=0.028362 Var=0.000000 Plastic limit Bb A+M <= 0.216500 > 0.216500 ID=10 N=1 ID=11 N=5 Mu=0.677000 Mu=1.051400 Var=0.000000 Var=0.010672

Hf F Bb W Hf A Wa A Wa G DB G

(c) Resilience to heat

ID=1 N=12

Mu=0.421333 Var=0.054597

Texture = clay loam, ... = Other(s) ID=2 N=5 ID=3 N=7

Mu=0.191400 Mu=0.585571 Var=0.016755 Var=0.016889

Bb A+M Sand (2000-63 æm) Wa A <= 0.557000 > 0.557000 Bt A ID=4 N=6 ID=5 N=1 Bt G Cm A Mu=0.631667 Mu=0.309000 Var=0.004830 Var=0.000000

Clay (<2 æm) DB A <= 0.207000 > 0.207000 ID=6 N=1 ID=7 N=5

Mu=0.758000 Mu=0.606400 Var=0.000000 Var=0.001966

DB G Bb W Hf A Hf F Hf G Wa G

(d) Resistance to copper

ID=1 N=12

Mu=0.159083 Var=0.195856

Basal respiration <= 1.250000 > 1.250000 ID=2 N=10 ID=3 N=2

Mu=0.295000 Mu=-0.520500 Var=0.112619 Var=0.057840

SOM Wa G <= 0.028500 > 0.028500 Bt G ID=4 N=3 ID=5 N=7

Mu=-0.161333 Mu=0.490571 Var=0.026551 Var=0.022011

Actinomycetes ELFA markers Hf A Hf F <= 2.270000 > 2.270000 DB A ID=6 N=4 ID=7 N=3

Mu=0.603250 Mu=0.340333 Var=0.006990 Var=0.002539

Bb A+M Bb W Wa A Hf G Bt A Cm A DB G

(e) Resilience to copper

ID=1 N=12

Mu=0.317000 Var=0.038211

C <= 0.052500 > 0.052500 ID=2 N=9 ID=3 N=3

Mu=0.414111 Mu=0.025667 Var=0.012975 Var=0.000750

C:N Bb W <= 9.600000 > 9.600000 Hf G Wa G ID=4 N=3 ID=5 N=6

Mu=0.530000 Mu=0.356167 Var=0.000536 Var=0.009122

Hf F Texture Bt A = clay loam, ... = Other(s) DB A ID=6 N=2 ID=7 N=4

Mu=0.236500 Mu=0.416000 Var=0.001892 Var=0.001996

Bb A+M Hf A DB G Wa A Bt G Cm A

Annex D2: Classification and regression tree (CART) outputs for Set(ii) Combined soils. Values in each box represent resistance values, mu is the average resistance N soils, Var is the observed variation of resistance. Predictor variables are named under the box, with critical values at the extension of each arm towards the next box. Soil origins are identified under each box, labeled : A-Z = Scottish soils according to Kuan et al. (2007). Others according to Whitmore et al. (2009): Bb A+M= Broadbalk Arable + FYM; Bb W= Broadbalk Wilderness; Hf A= Highfield Arable; Hf F = Highfield Fallow; Hf G = Highfield Grass; Wa A=Warren Arable; Wa G=Grass; Bt A=Boot Arable; Bt G=Boot Grass; Cm A=Cashmore Arable; DB A=De Bathe Arable; DB G= De Bathe Grass).

(a) Physical resilience to compression

ID=1 N=33 Mu=1.149303 Var=0.900315 Sand (2000-63 æm) <= 0.136500 > 0.136500 ID=2 N=2 ID=3 N=31 Mu=4.497500 Mu=0.933290 Var=0.041820 Var=0.185787 Soil order Bt A = Arenosol... = Other(s) Bt G ID=4 N=1 ID=5 N=30 Mu=2.512000 Mu=0.880667 Var=0.000000 Var=0.106133 Linear shrinkage Cm A <= 15.005000 > 15.005000 ID=6 N=24 ID=7 N=6 Mu=0.990792 Mu=0.440167 Var=0.068116 Var=0.015652 Soil unit N = Chromic Luvisol, ... = Other(s) <= 0.520000 > 0.520000 ID=8 N=8 ID=9 N=16 ID=18 N=3 ID=19 N=3 Mu=1.236500 Mu=0.867938 Mu=0.544333 Mu=0.336000 Var=0.102366 Var=0.005711 Var=0.005951 Var=0.003653 Silt (63-2 æm) Sand (2000-63 æm) <= 0.500500 > 0.500500 <= 0.526000 > 0.526000 I H ID=10 N=5 ID=11 N=3 ID=12 N=12 ID=13 N=4 K T Mu=1.409400 Mu=0.948333 Mu=0.827250 Mu=0.990000 Var=0.061519 Var=0.037582 Var=0.000912 Var=0.000241 W V Silt (63-2 æm) Bb A+M Bb W <= 0.226000 > 0.226000 A Hf G Hf A ID=14 N=2 ID=15 N=10 Y Mu=0.879000 Mu=0.816900 Wa A Hf F Var=0.000361 Var=0.000380 O DB A Sand (2000-63 æm) Z DB G Bt A <= 0.439500 > 0.439500 Bt G ID=16 N=5 ID=17 N=5 Mu=0.803800 Mu=0.830000 Var=0.000272 Var=0.000144

B F C M G P J R S U (b) Resistance to heat

ID=1 N=33

Mu=0.592636 Var=0.087925

Parent material

= Fluvoglacial sand, ... = Other(s) ID=2 N=6 ID=3 N=27

Mu=0.266333 Mu=0.665148 Var=0.148476 Var=0.045551

Sand (2000-63 æm) Soil order

<= 0.746000 > 0.746000 = , ... = Other(s) ID=4 N=5 ID=5 N=1 ID=6 N=14 ID=7 N=13

Mu=0.141400 Mu=0.891000 Mu=0.554286 Mu=0.784538 Var=0.084521 Var=0.000000 Var=0.046123 Var=0.017445

Sand (2000-63 æm) pH

A Z <= 0.557500 > 0.557500 <= 5.990000 > 5.990000 B ID=8 N=13 ID=9 N=1 ID=16 N=10 ID=17 N=3

C Mu=0.522154 Mu=0.972000 Mu=0.837000 Mu=0.609667 T Var=0.035216 Var=0.000000 Var=0.005757 Var=0.016650

Y Plastic limit Particle density

<= 0.485000 > 0.485000 DB AB<= 2.530000 > 2.530000 b A+M ID=10 N=10 ID=11 N=3 ID=18 N=5 ID=19 N=5 Bb W

Mu=0.455000 Mu=0.746000 Mu=0.894000 Mu=0.780000 Hf G Var=0.024193 Var=0.006822 Var=0.003364 Var=0.001653

pH <= 5.360000 > 5.360000 Bt G Wa G Hf A ID=12 N=4 ID=13 N=6 K H Hf F Mu=0.569500 Mu=0.378667 P U Cm A Var=0.013233 Var=0.016933 V I N W J DB G <= 0.225000 > 0.225000 F ID=14 N=4 ID=15 N=2 L Mu=0.461750 Mu=0.212500 M Var=0.004585 Var=0.000210

Wa A G Bt A O R S

(c) Resilience to heat

ID=1 N=33

Mu=0.486909 Var=0.057325

Parent material

= Recent clay-with-flints over London Clay (Eocene), ... = Other(s) ID=2 N=16 ID=3 N=17

Mu=0.644687 Mu=0.338412 Var=0.035597 Var=0.032294

Linear shrinkage Liquid limit

<= 8.150000 > 8.150000 <= 0.390000 > 0.390000 ID=4 N=2 ID=5 N=14 ID=12 N=3 ID=13 N=14

Mu=0.283000 Mu=0.696357 Mu=0.080333 Mu=0.393714 Var=0.000676 Var=0.019228 Var=0.002860 Var=0.021271

Soil unit EC 5:1 Bb A+M = Chromic Luvisol, ... = Other(s) Cm A <= 186.150000 > 186.150000 DB A ID=6 N=9 ID=7 N=5 A ID=14 N=12 ID=15 N=2 Mu=0.613667 Mu=0.845200 Z Mu=0.444167 Mu=0.091000 Var=0.008727 Var=0.003666 Var=0.006978 Var=0.000121

Sand (2000-63 æm) Liquid limit <= 0.518500 > 0.518500 DB G <= 0.670500 > 0.670500 Bt A ID=8 N=8 ID=9 N=1 B ID=16 N=10 ID=17 N=2 Bt G Mu=0.585000 Mu=0.843000 C Mu=0.413300 Mu=0.598500 Var=0.002422 Var=0.000000 V Var=0.002517 Var=0.000702 pH W Liquid limit <= 4.765000 > 4.765000 H <= 0.405000 > 0.405000 Wa G ID=10 N=4 ID=11 N=4 ID=18 N=1 ID=19 N=9 K Mu=0.542250 Mu=0.627750 Mu=0.287000 Mu=0.427333 Var=0.000246 Var=0.000943 Var=0.000000 Var=0.000827

Plasticity index Hf F Bb W R <= 0.120000 > 0.120000 I Hf A ID=20 N=2 ID=21 N=7 J Hf G Mu=0.467000 Mu=0.416000 U T Var=0.000144 Var=0.000444 EC 5:1 O <= 161.700000 > 161.700000 Y ID=22 N=6 ID=23 N=1 Mu=0.423167 Mu=0.373000 Var=0.000159 Var=0.000000

Linear shrinkage M <= 11.770000 > 11.770000 ID=24 N=4 ID=25 N=2

Mu=0.415250 Mu=0.439000 Var=0.000046 Var=0.000009

Wa A, F G,L P, S

(d) Resistance to copper ID=1 N=33 Mu=0.178970 Var=0.119910

Soil unit = Gleyic Luvisol, ... = Other(s) ID=2 N=3 ID=3 N=30

Mu=-0.386000 Mu=0.235467 Var=0.072673 Var=0.089523

Clay (<2 æm) Wa G <= 0.161500 > 0.161500 K ID=4 N=5 ID=5 N=25 T Mu=-0.094400 Mu=0.301440 Var=0.037983 Var=0.073716

Clay (<2 æm) DB A <= 0.269000 > 0.269000 H ID=6 N=17 ID=7 N=8 W Mu=0.396765 Mu=0.098875 Y Var=0.044183 Var=0.076132 Z Soil unit pH = Chromic Luvisol, ... = Other(s) <= 6.795000 > 6.795000 ID=8 N=7 ID=9 N=10 ID=16 N=7 ID=17 N=1

Mu=0.560571 Mu=0.282100 Mu=0.021143 Mu=0.643000 Var=0.017511 Var=0.030923 Var=0.038670 Var=0.000000

Clay (<2 æm) Plasticity index C:N <= 0.250000 > 0.250000 <= 0.115000 > 0.115000 <= 11.025000 > 11.025000 Bt A ID=10 N=4 ID=11 N=3 ID=12 N=1 ID=13 N=9 ID=18 N=3 ID=19 N=4

Mu=0.473250 Mu=0.677000 Mu=-0.122000 Mu=0.327000 Mu=-0.131667 Mu=0.135750 Var=0.002605 Var=0.013664 Var=0.000000 Var=0.014198 Var=0.015990 Var=0.025033

Clay (<2 æm) Bb W Hf A Hf G Bb A+M O <= 0.215500 > 0.215500 DB G Hf F G Wa A ID=14 N=5 ID=15 N=4 B Bt G L V Mu=0.414600 Mu=0.217500 U Var=0.007731 Var=0.000700 S

Cm A I A J C M F R P

(e) Resilience to copper

ID=1 N=33

Mu=0.541939 Var=0.096477

Soil unit = Chromic Luvisol, ... = Other(s) ID=2 N=17 ID=3 N=16

Mu=0.349294 Mu=0.746625 Var=0.031841 Var=0.083824

Soil unit SOM = Chromic Luvisol, ... = Other(s) <= 0.024000 > 0.024000 ID=4 N=7 ID=5 N=10 ID=10 N=1 ID=11 N=15

Mu=0.217143 Mu=0.441800 Mu=-0.207000 Mu=0.810200 Var=0.039673 Var=0.005576 Var=0.000000 Var=0.024744

Particle density Parent material N <= 2.540500 > 2.540500 = Recent drift over, ... = Other(s) A <= 1.110000 > 1.110000 ID=6 N=5 ID=7 N=2 ID=8 N=5 ID=9 N=5 ID=12 N=14 ID=13 N=1

Mu=0.106200 Mu=0.494500 Mu=0.390400 Mu=0.493200 Mu=0.843929 Mu=0.338000 Var=0.010641 Var=0.004556 Var=0.005448 Var=0.000421 Var=0.009447 Var=0.000000

Silt (63-2 æm) Bb A+M Hf A Wa A Bt A <= 0.243500 > 0.243500 H Bb W Hf f Cm A Bt G ID=14 N=4 ID=15 N=10

Hf G DB A B Mu=0.970500 Mu=0.793300 Wa G DB G K Var=0.001928 Var=0.003484 V T W Silt (63-2 æm) L <= 0.383000 > 0.383000 O ID=16 N=9 ID=17 N=1 Y Mu=0.809222 Mu=0.650000 Var=0.001336 Var=0.000000 Z Parent material = Mixed metamorphic, igneous... = Other(s) I ID=18 N=1 ID=19 N=8

Mu=0.721000 Mu=0.820250 Var=0.000000 Var=0.000408

Sand (2000-63 æm) C <= 0.422000 > 0.422000 ID=20 N=3 ID=21 N=5

Mu=0.804000 Mu=0.830000 Var=0.000425 Var=0.000144

G F J M S P R U Annex D3. Summary data drawn from literature review used to inform Bayesian model. In relation to (a) copper (b) heat and (c) compression stresses.

(a) Copper stress

Reference: Deng et al. 2009 Function: Factor: Mode: Resolution: Biological resistance and Cu contamination for 1 year. Relative Qualitative resilience Features pertinent to model: • High Cu and low pH in soil pore water coincides with a reduced resistance and resilience. • Formation of a tolerant community after Cu pollution, secondary perturbation and Cu aging may contribute to resistance and resilience. • The microbial community structure of the control soil was both resistant and resilient to 400 mg kg-1 Cu perturbation. • Substrate induced respiration (SIR) was reduced in the Cu polluted soil.

Reference: Girven et al. 2005 Function: Factor: Mode: Resolution: Bacterial community diversity Relative Features pertinent to model: • Large reduction in bacterial numbers and biomass. • Only small differences in bacterial community diversity and structure were observed • Functionality, measured by mineralization rates, remained unchanged. A suggestion of non- selective pressure and a degree of genetic and functional resistance to copper perturbation, despite a significant reduction in bacterial numbers and biomass • Apparent adaptation by the surviving community.

Reference: Gregory et al. 2009 Function: Factor: Mode: Resolution: Substrate-induced respiration SOM Absolute Features pertinent to model: • Resistance to Cu in grassland soils may also be enhanced by greater OM, as well as microbial diversity • Reduced respiration rates in arable soils by up to 85% but only up to 30% in grassland soils. Difference was ascribed to the greater microbial biomass and diversity in grassland soils and buffering by the OM

Reference: Griffiths et al. 2005 Function: Factor: Mode: Resolution: Respiration Features pertinent to model: • Cu contamination increased basal respiration over that of uncontaminated sludge. • Soils receiving sludge contaminated at 150 mk kg-1 were less resilient than either the uncontaminated sludge or sludge contaminated with other Cu levels (may be an anomalous result). • Did not detect any significant effects of ether sludge addition or sludge contamination on the soil microbial biomass. This was somewhat unexpected as there have been consistent decreases in the soil microbial biomass resulting from metal-contaminated sludge’s • The extent of biological resilience to copper stress fell within the range determined for other agricultural soils, 95-23% of original function. • Lack of any recovery in decomposition following perturbation with Cu is consistent with previous studies. • Increase in resistance to Cu stress in soils amended with uncontaminated sludge could be attributed to the increased soil C, as metals are known to be chelated and rendered biologically unavailable by OM in soils. • Contamination by Cu did not precondition the soil to a subsequent Cu stress. • Zn and Cd contamination and hydrocarbon-polluted soil was more resilient to a Cu stress than an uncontaminated control soil.

Reference: Griffiths et al. 2008 Function: Factor: Mode: Resolution: Community composition on Respiration microbial resistance and resilience Features pertinent to model: • Significant correlation between resistance and soil organic carbon content for Cu (R2 = 0.46). • Resistance and resilience of microbes to Cu in clay-loam was greater than in the sandy soil. This is consistent with the clay-loam having greater OM content and particle surface area • With a single species, there was no recovery and a gradual decline in function compared to the unstressed control, whilst with a community, there was recovery over time (resilience). = functional redundancy.

Reference: Kuan et al. 2007 Function: Factor: Mode: Resolution: Respiration Relative Features pertinent to model: • Soil organic C content correlated strongly with resilience after Cu stress (r = 0.72). • The negative correlation of pH with stability to Cu perturbation was inconsistent with increasing solubility of Cu at low pH. • The soils most resilient to Cu were those with a low pH, and large SOC and dissolved organic C content.

Reference: Tobor-Kaplon et al. 2005 Function: Factor: Mode: Resolution: Respiration Features pertinent to model: • Soils not contaminated with Cu were more resistant to Pb than those with Cu. • The most vulnerable, to addition of lead, were communities from the acid soils with high Cu level that showed a reduction in respiration of 47% • Without copper, bacterial growth rates were more reduced in the acid soils than in the neutral soils. • Long term exposure of soil microbial communities to stresses such as Cu reduce the functional stability of the soil ecosystem. • In general the highest resistances and/or resilience were found in the least contaminated soils with neutral pH and/or no Cu load.

Reference: Bressan et al. 2008 Function: Factor: Mode: Resolution: Genetic structure modification Formal Features pertinent to model: • Change in community structure more intense and expressed for a longer time period than physical stress (heat) before recovering completer resilience. • Selection of metal resistant bacterial leading to higher resistance of the community to mercury.

Reference: Griffiths et al., 2000 Function: Factor: Mode: Resolution: Decomposition of added grass Microbial diversity Relative Formal substrate Features pertinent to model: • Soils were not resilient to persistent stress, no recovery in decomposition rate over time, but the soils with the highest biodiversity were more resistant to stress

Reference: Griffiths et al. 2001 Function: Factor: Mode: Resolution: Decomposition of added grass Relative Formal substrate Features pertinent to model: • Both grassland soils and the organically managed agricultural soil had the greatest resistance to copper. • Industrial sols had the least resistance to Cu • After adding Cu both the grassland soils showed no recovery over 2 weeks. • Polluted industrial soil showed a marked recovery after the addition of Cu. • The agricultural soils showed no recovery following the addition of Cu

Reference: Griffiths et al. 2004 Function: Factor: Mode: Resolution: Decomposition of plant Microbial community structure Relative Formal residues Features pertinent to model: • Clear correlation between altered microbial community structure and functional stability. • Stability related to specific components of the microbial community. • Changes in microbial community structure were associated with reduced functional stability to copper • Over the short period of the incubation, Cu did not change microbial community structure.

Reference: Kuan et al. 2006 Function: Factor: Mode: Resolution: Decomposition of plant residues Features pertinent to model: • Resilience to Cu was totally absent in all treatments, viz. untreated (control); biocide application, N and lime application; sewage sludge application; and reseeding.

(b) Heat stress Reference: Griffiths et al 2008 Function: Factor: Mode: Resolution: Soil properties and a single Decomposition biological indicator Relative Quantitative (Pseudomonas fluorescens) Features pertinent to model: • Resilience of the clay-loam to heat stress did not depend on the water content of the soil at the time of stress, although the physical condition of the soil when decomposition was measured did affect the outcome. • The absolute respiration rates for heat-stressed P. fluorescens in the sandy soil were only significantly different from the unstressed control on day 1, but were significantly lower in the clay-loam soil on days 1, 3, 7 and 14. • Soils maintained at a constant matric potential (-5 and -50 kPa) before and after heating were resilient and showed a recovery in function over time, whilst those at -0.5 kPa and air-dried soil were resistant to the heat stress. Re-equilibration of the soil to -5 kPa after the heat stress significantly increased the decomposition rate but also affected the functional resilience of the wet soil. • Significant correlation between resistance and soil organic carbon content for heat stress. • Sandy soil more resilient than the clay-loam soil. Recovery of function of the cells may have been aided by the lower water content of the sandy soil allowing better diffusion than the clay-loam soil. A similar response was also seen with a more complex microbial community • Applying heat stress to clay-loam soil at non-optimal water contents showed a better resistance to heat stress, although the absolute rates of decomposition were less than at optimal water contents. • The response to heat showed that the effects of the stress was not dependent on the thermal capacity or actual water content of the soil, but was controlled by the amount of air-filled pores and water potential of the soil when respiration was measured

Reference: Kuan et al 2007 Function: Factor: Mode: Resolution: Short term decomposition Relative Quantitative (cumulative CO2 cumulative Soil organic carbon (SOC) over 24h Features pertinent to model: • There were significant treatment effects of heat on resistance, measured as CO2 evolution rate 1 day after stress. Recovery from heat stress was on average almost complete at 28 days. • A progressive recovery in short-term decomposition after heat stress was found. • Resilience to heat stress was not related to any measured soil parameters (such as pH, texture and carbon and nitrogen contents) and neither was there a relationship between resistance and recovery. • Broad classifications of the experimental soils, such as their land use or soil class, were quite useful in distinguishing the responses of the soils to heat stress. Soils differed significantly in both resistance to and recovery after heat stress and land use was useful as a descriptor.

Reference: Chaer et al., 2009 Function: Factor: Mode: Resolution: Soil microbial community Relative Qualitative structure Forest and deforested area now agricultural soils Respiration Features pertinent to model: • Heat shocks affected short-term respiration rates in both soils. Showing increases in respiration above th unheated control soil • Microbial biomass was reduced up to 25% in both soils 3 days after the heat shocks. • Fluorescein diacetate (FDA) hydrolysis activity was less affected (more resistance) and cellulose and laccase activities recovered more rapidly (more resilience) in the forest (FST) soil relative to the agricultural (AGR) soil. • In the AGR soil, lacase activity did not show resilience to any heat shock level. • Within each soil type, the microbial community composition did not differ between heat shock and control samples at day 3. However, at day 30, FST soil samples treated at 60°C and 70°C contained a microbial community significantly different from the control and with lower biomass regardless of high enzyme resilience. • Deforestation followed by long-term cultivation changed microbial community composition and had differential effects on microbial functional stability. • The resilience of the substrate-specific activities of laccase and cellulose were lower in AGR soils, indicating a less diverse community of microorganisms capable of producing these enzymes and confirming that specific microbial functions are more sensitive measurements for evaluating change in the ecological stability of soils. • The reduced resilience of laccase and cellulose in the AGR soil probably the result of a limited diversity of microorganisums capable of producing these enzymes.

Reference: Gregory et al., 2009 Function: Factor: Mode: Resolution: Substrate-induced respiration Organic matter Absolute Features pertinent to model: • Resistance to heat in grassland soils may also be enhanced by greater OM, as well as microbial diversity

Reference: Griffiths et al. 2008 Function: Factor: Mode: Resolution: Community composition on microbial resistance and resilience Features pertinent to model: • Significant correlation between resistance and soil organic carbon content for heat stress (R2 = 0.44). • After transient heat stress, the sandy soil was more resilient than the clay-loam soil for a single microbe. • Resistance and resilience to heat also followed the same trend in the clay-loam and sandy soils with a complex microbial community • At non-optimal water contents, there was better resistance to the heat stress, although the absolute rates of decomposition were less than at optimal water contents. • The response to heat showed that the effects of the stress was not dependent on the thermal capacity or actual water content of the soil, but was probably controlled by the amount of air- filled pores and water potential of the soil when respiration was measured.

Reference: Kuan et al., 2007 Function: Factor: Mode: Resolution: Respiration Relative Features pertinent to model: • No strong correlation between resilience following heat and any measured soil characteristics, including SOC

Reference: Tobor-Kaplon et al, 2006 Function: Factor: Mode: Resolution: Functional stability, community tolerance. Lead, salt, zinc and cadmium. Respiration and bacterial growth rate Features pertinent to model: • With regard to respiration the most polluted soils have the lowest stability to heat (disturbance) i.e. more stressed systems have less energy to cope with additional stress or disturbance • With regard to bacterial growth, the least polluted soils were the least stable to increased temperatures i.e. more stressed soils are more stable to additional stress/disturbance due to properties they gained when exposed to the first stress. • Agricultural soils which are exposed to higher variation in temperatures may be more tolerant to heat stress.

Reference: Banning and Murphy 2008 Function: Factor: Mode: Resolution: Soil microbial biomass, Relative Formal microbial activity and microbial community structure Features pertinent to model: • Resulted in a significant loss of microbial biomass that did not recover in 28 days. • However, resistance of malate-induced respiration following heat-disturbance was greater in the non-burnt soils. • Resistance of malate-induced respiration significantly correlated with bacterial community structure • Resilience of malate-induced respiration: only non-burnt furrow soil recovered to the respiration rate of the non-disturbed control. • Resilience was positively related to the size of the microbial biomass • Resilience not related to the microbial community structure.

Reference: Bressan et al., 2008 Function: Factor: Mode: Resolution: Genetic structure modification Features pertinent to model: • Initial impact showed change in community structure followed by complete recovery (complete resilience). • Second impact did not induce significant modification (complete resistance) suggesting pre- adaptation process leads to an increased tolerance and higher stability.

Reference: Griffiths et al., 2000 Function: Factor: Mode: Resolution: Microbial diversity Features pertinent to model: • Decomposition rates were initially depressed by the transient stress and then recovered over time. • Resilience was reduced in the soils with decreasing biodiversity

Reference: Griffiths et al., 2001 Function: Factor: Mode: Resolution: Functional stability of biological community Features pertinent to model: • Both grassland soils and the organically managed agricultural soil, had the greatest resistance to copper • Industrial sols had the least resistance to heat • Grassland soils recovered decomposition activity to control soil levels 2 months later. • Industrial soils recovered some degree of decomposition . • Decomposition activity in the organically managed agricultural soil was not significantly affected by heat. • Intensively managed agricultural soil recovered the majority of its decomposition activity 2 weeks after heat stress

Reference: Griffiths et al., 2004 Function: Factor: Mode: Resolution: Microbial community structure Functional stability – Upland pasture decomposition of plant Fumigation residues Features pertinent to model: • Clear correlation between altered microbial community structure and functional stability. • Stability related to specific components of the microbial community. • Changes in microbial community structure were associated with reduced functional stability to heat • Heat perturbation did cause a shift in the composition of the active community in some soils. • Some soils showed resilience to heat.

Reference: Kuan et a., 2006 Function: Factor: Mode: Resolution: Functional resilience of Relative Formal microbial community – decomposition Features pertinent to model: • Soil samples from treated plots were less resilient to heat stress than soil from control plots • Functional response to heat perturbation differed according to the land management practices applied • Resistance to heat was highest in the control soil and lowest in the reseeded soils • Sewage-amended soils were least resilient to heat over 28-days

Reference: Maraun et al., 1998 Function: Factor: Mode: Resolution: Microbial community Features pertinent to model: • The fungal community was strongly disturbed by freezing and heating and did not recover within the 6 week duration

Reference: Wertz et al., 2007 Function: Factor: Mode: Resolution: Community function Relative Formal Features pertinent to model: • A reduction in the diversity of communities did not affect their resistance and resilience following heat disturbance, provided that cell abundance recovered

(c) Compaction stress

Reference: Gregory et al., 2009 Function: Factor: Mode: Resolution: Void ratio OM Absolute Features pertinent to model: • Greater particulate OM probably enhanced elastic recovery from the physical compression stress. Quicker recovery of void ratio

Reference: Griffiths et al., 2005 Function: Factor: Mode: Resolution: Void ratio Features pertinent to model: • Stability (resistance) was taken to be the compression index. Resilience was taken to be the expansion index. • Greater stability against compression for soils amended with digested sewage sludge. • Cu contamination reduces rebound and differentially affecting compression.

Reference: Kuan et al., 2007 Function: Factor: Mode: Resolution: Void ratio Relative Features pertinent to model: • SOC content correlated strongly with recovery from compression (r = 0.67). • Resistance to compression was negatively correlated with SOC (r = -0.63) • Compressible soils were characterized by greatest pH, water content, SOC and DOC and had the greatest recovery due to the elastic properties of the carbon compounds. • The mineral soils were more resistant to the compression stress than the organic soils because of their smaller initial void ratio and the larger compressibility of particulate carbon. • Compaction from agricultural activity could increase aggregate density and therefore increase resistance to subsequent stress.

Reference: Zhang et al., 2005 Function: Factor: Mode: Resolution: Resistance and resilience of the soil pore structure to OM mechanical stresses Features pertinent to model: • Although may decrease the resistance to compression, the initial soil pore structure and the resilience to this mechanical stress was greatly improved. • To amend with OM will improve recovery from vehicle traffic damage. • OM increases compression resilience • Compression increases with peat amendment because of greater initial porosity of the soil and the greater compressibility of peat. • The increase in rebound with higher peat amendment indicates that the resilience to compressive stresses is greater.

Reference: Gregory et al., 2007 Function: Factor: Mode: Resolution: Soil strength Soil texture Features pertinent to model: • Compaction generally did not affect microbial communities, presumably because they occupy pores smaller than those affected by compaction. • Clay soil was deemed to be more resilient to the compaction stress than the sandy loam and sandy clay loam soils. • In sandy loam and sandy clay loam, compaction resulted in a persistent increase in soil strength and severely decreased crop growth. Sandy loam and sandy clay loam were not resilient to compaction stress. Clay soil was resilient to compaction stress.

Reference: Shestak and Busse 2005 Function: Factor: Mode: Resolution: Microbial community response Sandy loam and clay loam soil textures Relative Features pertinent to model: • Habitable sized pores for micororganisms increased at least 40% in both soils with compaction. • Microbial measures were either unaffected by compaction or shoed inconsistent increases across sampling periods and soil types. • Broad tolerance of microbial communities from contrasting soil textures to compaction