15 Bioindicators and Sensors of Soil Health and the Application of Geostatistics Ken Killham University of Aberdeen, Aberdeen, Scotland William J. Staddon Eastern Kentucky University, Richmond, Kentucky I. INTRODUCTION We require the soil to perform a variety of key functions. It must provide the food, fuel, and fiber needs of the world’s burgeoning population and must also regulate the quality of the air we breathe and the water we drink. We also require the soil to act as a sink for the many pollutants generated by human domestic, agricultural, and industrial activities. Because of the conflicting pressures increasingly applied to the soil resource, there is a crucial need for the capacity to assess and monitor the health or quality of soil. In 1996 the Soil Science Society of America (1) defined soil health as ‘‘the continued capacity of a specific kind of soil to function as a vital living system, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, to maintain or enhance the quality of air and water environments, and to support human health and habitation.’’ The definition offered by the society provides a useful basis for considering the relevance of bioindicators and sensors for the assessment of soil health. It is clear from the definition that relevant indicators and sensors must contribute to measurement of the functional integrity of soil in order to assess whether it can sustain its key roles. As dis- cussed in later sections in this chapter, it is unlikely that any one property or process (and therefore a single bioindicator or biosensor) is sufficient to provide a reliable measure of soil health. It is much more likely that indicators and sensors will be used in a battery of tests in which enzymes of plant, microbial, and animal origin play a part. As well as exercising care in terms of overreliance on single bioindicators and bio- sensors, it has been pointed out (2) that whereas scientists select indicators for the link with functions of soil quality, others such as agriculturalists may just as validly characterize soil health by using descriptive properties such as tilth with a direct value judgment. This chapter reviews the main biological properties/systems that can be used as indicators and sensors of soil health and the application of geostatistics for describing the spatial variability of these properties. Copyright © 2002 Marcel Dekker, Inc. II. BIOINDICATORS OF SOIL HEALTH A. Definition A bioindicator is defined as ‘‘an organism, part of an organism, product of an organism (e.g., enzyme), collection of organisms or biological process which can be used to obtain information on the quality of all or part of the environment.’’ A number of bioindicators have been suggested for monitoring soil health, and these are briefly considered. B. Soil Microbial Biomass Jenkinson and Rayner (3) defined the soil microbial biomass as the ‘‘eye of the needle’’ through which all organic matter in soil must eventually pass. It is therefore the key driver of ecosystem productivity and, despite the fact that the microbial biomass typically repre- sents about 5 tons per hectare of a temperate grassland ecosystem compared to biomass of the vegetation an order of magnitude greater (4), most of the carbon/energy and nutrient flow is through the soil microbial biomass. In the 1970s and 1980s a considerable number of methods for determining soil microbial biomass were developed. These methods have been reviewed by Sparling and Ross (5) and are dominated by two techniques: chloroform fumigation and substrate- induced respiration. The fumigation techniques are based on the susceptibility of microbial biomass to chloroform vapor. The chloroform-labile carbon mobilized is determined either by measuring the CO 2 released by mineralization when the fumigated soil is incubated or by measuring the C that can be extracted from the fumigated soil. Biomass N, P, and S can also be determined after extraction from fumigated soil. Substrate-induced respiration techniques are physiologically based; they involve providing the soil microbial biomass with a saturating concentration of a readily mineralizable substrate (usually glucose) and monitoring the respiration over a short incubation period. The substrate saturation rate of respiration represents maximal reaction velocity and is therefore proportional to the bio- mass. Conversion factors are available to convert the V max respiration and the C, N, and P extracted from fumigated soil into a biomass value. Because the soil microbial biomass is the main processing unit for organic matter, its size tends to be roughly proportional to the total organic matter pool. Skeletal montane soils, for example, have low organic matter and a correspondingly low microbial biomass. Deciduous woodland soils, on the other hand, have much higher organic matter status and a higher microbial biomass. Typical biomass values for a range of soils are reported in a 1997 review by Sparling (2). Because the microbial biomass is generally related to the organic matter content of the host soil, it is not the absolute size of the biomass that indicates soil health but changes in biomass size (other than those that result from seasonal and other natural factors). The soil microbial biomass can therefore be seen as a barometer, with reductions in biomass related to either a reduction in the carbon inputs that sustain it or a toxic impact of some kind (6). A change in biomass size then heralds a later change in soil organic matter status. The predictive value of measuring soil microbial biomass as a bioindicator of soil fertility has been suggested by a number of researchers (7,8). Although the soil microbial biomass can, as mentioned, be affected by toxic impacts, there are numerous soil contaminants that can adversely affect the biological functioning of the soil but that do not affect the size of the biomass itself. Some of these contaminants affect the respiratory quotient of the biomass (i.e., the rate of soil microbial respiration Copyright © 2002 Marcel Dekker, Inc. is a function of biomass size) rather than biomass alone (9), but others are more subtle and require other bioindicators in order for their impact on soil health to be evaluated. C. Carbon and Nutrient Cycling Mineralization reactions are vital both for the turnover of organic residue inputs to soil and for the release of bound nutrients to plants. The mineralization reactions are carried out by both soil animals and microbes. The former group may not rival the microbes in terms of total carbon/energy, nutrient flow, and breadth of their enzymatic activities, but they have a key role in comminuting organic debris and sometimes acting as vectors in inoculating the newly exposed surfaces with microbial degraders. The measurement of rates of mineralization of organic C and associated nutrients (e.g., N, P, and S) probably targets the best overall bioindication of soil health. So many organisms are involved in these processes, however, that such measurements are unlikely to identify effects on individual species that may themselves still be of importance to soil health. Carbon mineralization has generally been measured by loss of substrate (e.g., the traditional litter-bag techniques) or by respiration of CO 2 . Measurement of carbon mineral- ization rates can be defined by use of C isotopes. This technique enables all mineralized C to be assessed and allows quantification of the partitioning of carbon into biomass and into cell maintenance. Various quotients can then be determined, and these can indicate stress to the microbial biomass as well as rates of C mineralization. This is because the degree to which soil microorganisms partition carbon into biomass versus maintenance of cell integrity is largely a function of environmental stress (10). Nitrogen mineralization measurements can be made both aerobically and anaerobi- cally. The advantage of the latter is that it precludes many of the problems of reimmobilisa- tion of N due to microbial processing of C during cell growth and synthesis (11). As with C mineralization, the use of isotopic techniques has done a great deal to facilitate N mineralization determinations. Isotope dilution techniques involving 15 N enable gross mineralization to be reliably measured (12). In contrast to the mineralization processes, nitrification is a soil N cycle flux involv- ing very few species (Table 1). The simplified reactions illustrated indicate how potentially sensitive a bioindicator the nitrification process can be. Because the process is the domain Table 1 Soil Microorganisms Involved in Mineralization and Nitrification N Fluxes. N flux Soil microorganisms involved N mineralization Most of the heterotrophs, which domi- Organic N → NH 3 /NH 4 ϩ nate the soil microbial biomass. Nitrification In most soils, the first nitrification step NH 4 ϩ O 2 ϩ H ϩ ϩ 2e Ϫ → NO 2 Ϫ ϩ 5H ϩ ϩ 4e Ϫ is dominated by the genera Nitrosolo- N 2 O Ϫ ϩ H 2 O → NO 3 Ϫ ϩ 2H bus, Nitrosospira, and to a lesser ex- tent Nitrosomonas; the second by the genus Nitrobacter; in acid forest soils, these autotrophs are replaced by a range of heterotrophs (mainly fungi). Copyright © 2002 Marcel Dekker, Inc. of a very few specialist, chemoautotrophic bacteria, any factor that adversely affects these ‘‘keystone’’ species dramatically affects the process (and hence the release of the most plant-available form of mineral N in soils, nitrate). It is for this reason that screening tests for pesticides and other agrochemicals always include assessment of impact on nitrification and why environmental risk assessments of soil pollutant also include nitrification (13). However, in many cases, good soil health does not require a high supply of available nutrients through processes such as nitrification (2). D. Soil Enzymes Although enzymes contribute to the part played by the other bioindicators considered in this review, it is particularly important to appreciate the invaluable integrative role of a suite of enzymes in assessing soil health. This is because of the massive array of enzyme assays that can readily be applied to soil, encompassing the hydrolases (e.g., phosphatases, sulfatases, urease, proteases, peptidases, deaminases, cellulases), the oxidoreductases (e.g., dehydrogenases, phenol oxidases, peroxidases, catalases), the lyases, and the transferases. Many soil enzymes have a functional location that is outside the cell, and the significance of these and other enzymes in soil microbial ecology has been reviewed (14). These extra- cellular enzymes are often relatively stable and can persist for extended periods, thereby providing a longer-term perspective than measurements involving extant soil organisms alone. The impact of pollutants on soil health has been addressed through the measurement of enzyme activity. Such an approach offers a useful soil management tool as soil enzyme activity should relate to key soil functions such as biogeochemical cycling, plant growth, and degradation of organic contaminants (15). Enzymes that catalyze a wide range of soil biological processes offer a useful assess- ment of soil ‘‘function’’ (14), and common enzymes, such as dehydrogenase, urease, and phosphatase, fit into this category. Metabolic stains such as fluorescein diacetate (FDA) also provide a useful functional indicator (16). The assay works on the principle that the FDA molecule is taken by active cells and hydrolyzed by a range of enzymes, including proteases, lipases, and esterases. This releases the fluorochrome fluorescein so that enzy- matically active cells can easily be distinguished with the aid of a fluorescence microscope with an ultraviolet (UV) source. Enzymes that catalyze a narrow range of soil biological activity are useful when sensitive indicators of change, such as may result from a pollution event, are sought. Enzymes catalyzing the degradation of certain organoxenobiotics (e.g., polyaromatic hy- drocarbons [PAHs], polychlorinated biphenyls [PCBs], dioxins) fall into this category. Knowledge of a reduction in a soil’s capacity to act as a fully functional mineralization medium for pollutants is critical in overall soil health assessment, but particularly in waste management (17) and as an indicator of the successful bioremediation of contaminated land (16). E. Community Structure and Biodiversity In recent years, a great deal of research has been devoted to developing and optimizing methods to assess the structure of the soil microbial community in terms of taxonomy and in terms of function. Developments in molecular biology have now provided soil biologists with ‘‘off the shelf’’ methods for assessing microbial diversity. This has represented nothing short Copyright © 2002 Marcel Dekker, Inc. of a revolution, allowing the genetic and functional diversity of the whole community, rather than just the very small percentage that can be cultured in the laboratory, to be measured for the first time. The molecular and other methods available for analysis of microbial community structure was reviewed in 1997 by White and McNaughton (18) and are briefly discussed in relation to soil health in the following section. The genetic diversity of the soil microbial community can now be assessed by using broad screening methods as well as methods with a narrow focus. The broad screening methods, such as deoxyribonucleic acid (DNA) reanealling kinetics (i.e., the rate at which melted, single-standard DNA reaneals on cooling depends on the genetic diversity), and denaturation gradient gel electrophoresis/thermal gradient gel electrophoresis (DGGE/ TGGE), methods that aim to quantify genetic diversity by exploring banding patterns of soil microbial DNA by gel electrophoresis, may have a future contributory role in soil health assessment, but probably in combination with more focused probing at the genus and the specific level. The latter gene probes use DNA and ribonucleic acid (RNA) tech- niques and can be linked to polymerase chain reaction (PCR) methodologies for increased sensitivity of detection. 16S-Ribosomal RNA probes are now particularly well developed for the better characterized groups of soil bacteria (19) and have contributed considerably to our understanding of genetic diversity in soil. DNA probes linked to enzymes with specific functions provide a more activity-based assessment and have, for example, been used to assess the presence of xenobiotic degraders (20) and denitrifiers (21) in soil. Mes- senger RNA, with its very short turnover, can be probed to provide ‘‘real-time’’ functional assessment. When such probes are linked to fluorescent tags, they can also provide spatial information on genetic/functional diversity. The RNA probes now represent a standard ecological tool that will increase in power of resolution as more and more systems are developed. This particularly applies to the soil fungi (both free-living and symbiotic), for which molecular techniques are still in their infancy; to the less well characterized bacteria; and to the microfauna. Development of molecular probes to assess functional diversity has partly been driven by the limitations of techniques that rely on the culturability of soil microbes. Of these techniques, the most widely used is probably the Biolog system. This system is based on physiological profiling—the range and number of carbonaceous sole substrates utilized by the enzymatic activity of microbial communities or by individual soil microor- ganisms—and the data generated can be interrogated by principal component analysis to differentiate between soils or to assess changes in soil health (22). F. Soil Animals Because of the fundamental importance of soil animals in carbon and nutrient cycling, their abundance and diversity have been used to provide a key contribution to the overall assessment of soil health (23). There are a number of relatively simple methods for ex- tracting the micro- and mesofauna from soil (24), although identification beyond genus level without considerable experience is difficult. 1. Microfauna and Soil Health Numerous workers have established the potential of using protozoa and nematodes as indicators of soil health because of their tremendous abundance, their production of a wide range of enzymes for roles ranging from plant pathogenicity to mineralization of soil organic matter, and their scope for culturing the former for use in linked bioassays Copyright © 2002 Marcel Dekker, Inc. (25). The diversity and abundance of soil protozoa (26) and nematodes (27) can be signifi- cantly reduced by the impact, for example, of air-borne pollutants and by heavy metal– contaminated wastes. Because of the trophic interactions that link the activity of the soil protozoa and the nematodes both to plants and to the bacteria and the fungi (4), such reductions in microfaunal abundance and diversity can have a profound effect on soil health. 2. Mesofauna and Macrofauna and Soil Health That mesofaunal groups, such as the arthropods, and their associated enzymatic activities have long been used to assess ecosystem impacts of pollution suggests that they represent important bioindications of soil health. The contrasting ecophysiological characteristics of many of the soil arthropods provide the key to their value as bioindicators. For example, comparisons of the median pH preference of soil arthropods have identified the strength of the indicator value of individual arthropods with respect to this soil parameter (28). Presumably, this approach can be applied to other soil parameters such as organic matter quantity and quality, and ultimately to soil health. The earthworms represent the most studied group of soil animals and links between earthworms and soil health have been suggested for centuries. In 1997, these links were more reliably quantified in agroecosystems with a reasonably strong correlation between the yield of a cereal crop and the biomass of earthworms in the soil supporting the crop (29). Earthworm bioindication of pollutant impacts on soil health has considerable merit and addresses pollutant bioavailability rather than total concentrations. Furthermore, it has been pointed out (30) that the different ecophysiological strategies of the earthworms provide scope for differentiating certain pollutant effects—the epigeic (surface dwelling) species tend to be directly affected by surface-deposited pollutants, whereas the endogeic (soil-dwelling) species tend to experience more chronic exposure through ingestion of soil contaminated with ‘‘aged’’ pollutants. There are numerous advantages to the use of earthworms as bioindicators of soil health. They are relatively easy to sample and enumerate and, with some experience and care, can be readily identified. Their relatively long generation times compared to those of many other soil invertebrates also allow sampling to identify changes in soil health to be done somewhat less frequently. The use of earthworms as well as other soil animals as bioindicators of soil health must be considered carefully for soils where management has uncoupled the natural linkage between soil faunal activity and the soil’s capacity to sustain crop growth as well as other soil functions. The use of pesticides and fertilizers may have this effect, for example, massively reducing the population density of the earth- worms, and yet the farmer would describe the soil as fit for purpose and in good health. It has been concluded therefore that the high variability of earthworm abundance is deter- mined by factors other than those that most influence soil health and crop yield (31). G. Plants The importance of plants as bioindicators of soil health has been known since ancient times (32) where the presence of a particular ‘‘natural’’ plant species or the condition of a ‘‘crop’’ species is diagnostic of soil conditions, be they physical, chemical, and/or even biological. Where a high degree of diagnostic sensitivity is required, production of particu- lar chemicals or ‘‘biomarkers’’ by certain plant species can be used (33). These biomarkers include a range of primary and secondary metabolites, the former including the amino Copyright © 2002 Marcel Dekker, Inc. acid proline (34) and the latter including polyamines such as spermidine and putrescine (35). The activities of certain plant enzymes, such as peroxidases and catalases, can also be used as biomarkers, particularly for assessing pollutant impacts (36). Plants can serve as bioindicators of toxic pollutant effects on soil health through three means: either pollutant accumulation in tissues, absence or presence of key plant species in a vegetation community, or physiological and biochemical changes to the plant. Plants that provide useful bioindicators in this regard have been proposed for different classes of pollutant. Plant response to metals is particularly well documented (plants are either metal accumulators, metal excluders, or metal indicators, depending on whether their tissue concentrations indicate accumulation or exclusion, or reflect soil concentra- tions, respectively) (37). This background knowledge of plant response greatly facilitates selection of plant species and the means of bioindication. Plants have a number of major advantages as bioindicators of soil health. They are relatively cessile, they are generally easy to identify and analyze, and their root systems can integrate over space and time. This last named property is of great importance when many of the chemical and physical properties of soil are heterogeneous in distribution and can change at the microscale. III. BIOSENSORS OF SOIL HEALTH A. Definitions A biosensor is ‘‘any biological material which, when exposed to an analyte (e.g. air, soil, water), provides an information linked response via a suitable transducer’’ (38). The biological material used in a biosensor can comprise plants (whole plants, or- gans, or cells), vertebrates, invertebrates, microorganisms), microbial tissue, enzymes, nu- cleic acid probes, antibodies, as well as other kinds of biological receptor. In using biosen- sors to test for soil health, the analyte is the soil or soil constituents, although it may be exposed to the sensor in a number of ways. Soils may be extracted with a range of solvents and the extract used with the solid phase present, either intact as a slurry or in a procedure that more closely defines the contact with either the liquid or the solid phase of the soil (13). The type of transducer involved in biosensing for soil quality can vary, and electrical, conductivity, acoustic, and optical transducers can be used. In Sec. III.C the emphasis is on optical transducers since the sensors being considered are light-emitting. B. Whole Cell/Organism Sensors and Reporter Genes Recent advances in molecular biology have allowed the introduction of reporter genes into a wide variety of soil microorganisms. These genes can provide real-time reporting on the function of the host; the nature of the function is determined by the gene promoter downstream of which the reporter gene(s) is placed in the genome. If a suitable general promoter is used, then the genes can give a signal that reports on the overall metabolic health/status of the host. The introduction of enzyme-linked lux, luc, gfp, lac, and xyl reporter genes into bacteria and fungi (39) has generated a wide range of ecologically relevant whole cell reporter systems that can be used to assess soil health. Recently (C Lagido, personal com- munication), luc genes have been cloned into nematodes so that soil animals can also Copyright © 2002 Marcel Dekker, Inc. provide real-time reporting of soil health. The movement and greater surface area contact between a nematode and the soil environment, coupled with the key role of the soil animals in nutrient cycling, make this a particularly useful development. C. lux Biosensors The lux genes encode for bioluminescence in naturally luminescing marine bacteria such as Vibrio fischeri, Vibrio harveyi, and Photobacterium phosphoreum, and light output is expressed via the enzyme luciferase (39). lux genes have now been cloned into a wide range of microorganisms so that biolu- minescence reports on the metabolic status of each of these whole cell biosensors can be used for ecologically relevant and rapid assessment of soil health (40). Examples of these biosensors and the ecological niche they represent are provided in Fig. 1. In addition to the ‘‘metabolic health’’ sensors illustrated in Fig. 1, reporter genes can be placed under the control of catabolic promoters so that catabolic activities can be monitored by the particular reporter system (luminescence, fluorescence etc.) (40). This is a particularly valuable tool in the study of the enzymological characteristics of degrada- tion of both xenobiotics and natural soil organic constituents. Biosensors can be used in a variety of ways to assess soil health (40,41). Probably the most useful approach involves solid-phase soil health testing, although tests involving soil extracts are also used. In all cases, bioluminescence is assayed after varying periods of exposure to the soil. Acute and chronic exposures both provide important information that can contribute decision support for soil/land management (41). It has been reported (41) that lux bacterial biosensors may be used as a decision support tool in the management of bioremediation of a large industri- ally contaminated site. The sensors were used to assess whether soil health was adequate Figure 1 Examples of lux bacterial biosensors and the information they can provide for assess- ment of soil health. Copyright © 2002 Marcel Dekker, Inc. forintrinsicbioremediationand,wherethiswasnotthecase,whatmeasureswererequired torestoresoilhealth. IV.GEOSTATISTICS A.Introduction Sincethespatialvariabilityofmicrobialcommunitiesandprocessesexistsatseveral scales,includingmicrosite,plot,andlandscapelevels(43),understandingtheirspatial structureiscriticaltounderstandingsoilecologicalprocessesandsoilconservationefforts (44).Thespatialvariabilityofsoilenzymeactivitieshasbeenexaminedbyusingclassical statisticalapproaches(45,46).However,geostatistics,whichhaditsoriginsinthemining industry,isbecomingincreasinglypopularamongsoilscientistsforassessingspatialvari- ability,andthereareseveralexcellentreviewsoftheprocess(47–50).Severalstudies haveusedthisapproachtocharacterizethespatialvariationinsoilenzymeactivities(51– 54).Thefollowingisabriefdescriptionofgeostatisticsandinsightsintosoilenzyme ecologicalfeaturesithasprovided.Clearly,thespatialvariationofallpotentialbioindica- torsmustbebetterunderstoodforimplementationofsuccessfulmonitoringprograms. B.Definitions Geostatisticscharacterizesthespatialdependenceorindependenceofsoilparameters takenatdifferentsamplinglocations.Itwouldbeaxiomatictostatethatwhensoilsamples aretakenclosetogetherthevariation(orrelativelackthereof)betweenmeasuredvalues reflectstheircloseproximity.Suchsamplesaresaidtobespatiallydependentorautocor- relatedsincetheirvariationreflectslocalizedconditions.Assamplesaretakenatincreas- ingdistances,thevariationbetweenthemalsoincreases.Whenthedistancesbecomelarge enough,thesamplesareindependentofeachother. Geostatisticscomprisestwocomponents:(1)modelingthespatialvariationtocreate thesemivariogram(Fig.2)and(2)krigingtoproducemaps(Fig.3).Thesestudiesbegin byestablishingsamplinggridswithinaplot(Fig.4).Samplesaretakenateachpointand parameters measured. Differences in parameter values are then compared for all points. Semivariograms (Fig. 2) describe the semivariance (a measure of parameter variance) Figure 2 Example of a semivariogram. Semivariance is plotted for each log distance and a model is fitted to the points. The verge is the distance over which samples are spatially dependent. The sill represents the maximal variation in the plot. A nugget occurs when the model does not intercept at the origin and is indicative of sampling error or spatial structure between the sampling locations. Structural variance represents the proportion of the variance resulting from spatial structure. Copyright © 2002 Marcel Dekker, Inc. Figure 3 Map created from Kriging data. As with other interpolation techniques, the contour lines represent predicted values for a particular location. However, the values predicted by Kriging were determined by using a semivariogram, which allows errors associated with each prediction to be determined. between sampling locations at different lag distances (Fig. 4). As one would expect, if the distance between sampling locations increases, the semivariance also increases (Fig. 2). At a certain distance, known as the range, the semivariance ceases to increase. The maxi- mal semivariance is referred to as the sill. Soil properties that lie within the range are spatially dependent and are said to be autocorrelated. Soil samples that lie beyond the range are spatially independent. The range is important because it provides the researcher with an estimate of the area for which a sample is representative. Further, as samples taken within the range are spatially dependent, the use of classical statistics is precluded, Figure 4 Grids are established in a plot and samples are taken from every point. The parameter for each sampling point is compared with those of all other sampling points. All the pairs of a given distance (known as the lag distance) are pooled together to give a measure of semivariance for that lag distance. Pairs that are separated by a distance that does not correspond to one of the established lag distances are assigned to the closest lag distance. Copyright © 2002 Marcel Dekker, Inc. [...]... interpreting results of such studies Copyright © 2002 Marcel Dekker, Inc In conclusion, this chapter reviews the exciting and rapidly developing field of bioindicators and biosensors of soil health and identifies a key role for geostatistics to help overcome the challenges of spatial heterogeneity in applying these indicators and sensors REFERENCES 1 JW Doran, TB Parkin Defining and assessing soil quality In: ... independence This information is valuable in the design of sampling strategies for bioindicators as an understanding of the representativeness of samples of a larger area is critical The third important feature is the nugget Theoretically, when the lag distance is zero (samples taken at the same point) there should be no variance Often the semivariogram intercepts along the Y axis, not at the origin,... amount of variance arising from the underlying spatial structure The greater the ratio, the more spatially dependent the soil parameter is Information generated in the variogram is then used for kriging Kriging allows maps (Fig 3) that predict parameter values at unsampled locations to be drawn What separates this approach from other interpolation techniques is that confidence in the predicted value can... area in which much could be learned from geostatistical approaches and bioindicators of soil health would be very important for assessing bioremediation potential and success Spatial analysis could be useful for predicting contaminant concentrations as well as developing appropriate sampling (and then treatment) strategies Potential studies could include examining the spatial variability of contaminant... not always the case In a comparison of areas within a riparian zone that varied in drainage (51) a spatial relationship between organic matter and phosphatase was found in a moderately well drained area, but no relationship between these two parameters was noted in a poorly drained area Again these relationships do not necessarily represent causal interactions but do enhance our understanding of soil... patterns of community-level-sole-carbon-source utilization Appl Environ Microb 57:2351–2359, 1991 23 CE Pankhurst Biodiversity of soil organisms as an indicator of soil health In: CE Pankhurst, BM Doube, VVSR Gupta, eds Biological Indicators of Soil Health Wallingford: CAB International, 1997, pp 297–324 24 F Schinner, R Ohlinger, E Kandeler, R Margesin, eds Methods in Soil Biology Berlin: Springer, 1996,... gained increasing popularity in the soil sciences Many studies have described the spatial variation of soil properties This interest has, at least in part, been driven by the desire to develop high-precision agricultural practices Such technologies depend on an understanding of the spatial distribution of soil properties such as nutrients and organic matter However, soil scientists have recognized the. .. depends on the enzyme and localized conditions Dehydrogenase activity was found to be moderately spatially dependent (spatial structure 37%) in a no-till field with a range that exceeded 200 m (56) In contrast, others reported that urease activity was autocorrelated over distances of Ͻ1 to 15 m, depending on the field examined (51,54) In contrasting ranges between studies, the size of the areas examined must... origin, and this is known as the nugget effect Presence of a nugget indicates either measurement error or spatial structure over distances shorter than the intervals between sampling locations Structural variance is the fourth property characterized by the semivariogram This value, which is often expressed as a ratio between the variance not explained by the nugget and the total variance, quantifies the. .. Ecology and Relationships with Soils and Land Use Sydney: Academic Press, 1985 31 CE Pankhurst, BM Doube, VVSR Gupta Biological indicators of soil health-a synthesis In: CE Pankhurst, BM Doube, VVSR Gupta, eds Biological Indicators of Soil Health Wallingford: CAB International, 1997, pp 419–436 32 WHO Ernst Geobotanical and biogeochemical prospecting for heavy metal deposits in Europe and Afirca In: B . Marcel Dekker, Inc. acid proline (34) and the latter including polyamines such as spermidine and putrescine (35). The activities of certain plant enzymes, such as peroxidases and catalases, can. Inc. assuchanalysesassumesampleindependence.Thisinformationisvaluableinthedesign ofsamplingstrategiesforbioindicatorsasanunderstandingoftherepresentativenessof samplesofalargerareaiscritical.Thethirdimportantfeatureisthenugget.Theoretically, whenthelagdistanceiszero(samplestakenatthesamepoint)thereshouldbenovariance. OftenthesemivariograminterceptsalongtheYaxis,notattheorigin,andthisisknown asthenuggeteffect.Presenceofanuggetindicateseithermeasurementerrororspatial structureoverdistancesshorterthantheintervalsbetweensamplinglocations.Structural varianceisthefourthpropertycharacterizedbythesemivariogram.Thisvalue,whichis oftenexpressedasaratiobetweenthevariancenotexplainedbythenuggetandthetotal variance,quantifiestheamountofvariancearisingfromtheunderlyingspatialstructure. Thegreatertheratio,themorespatiallydependentthesoilparameteris.Informationgener- atedinthevariogramisthenusedforkriging.Krigingallowsmaps(Fig.3)thatpredict parameter. be assessed and allows quantification of the partitioning of carbon into biomass and into cell maintenance. Various quotients can then be determined, and these can indicate stress to the microbial