Science of the Total Environment 415 (2012) 49–55 Contents lists available at ScienceDirect Science of the Total Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s c i t o t e n v How to calculate the spatial distribution of ecosystem services — Natural attenuation as example from The Netherlands H.J. van Wijnen a,⁎, M. Rutgers a, A.J. Schouten a, C. Mulder a, D. de Zwart a, A.M. Breure a, b a b National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands Radboud University Nijmegen, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands a r t i c l e i n f o Article history: Received 4 February 2011 Received in revised form 11 May 2011 Accepted 11 May 2011 Available online 2 July 2011 Keywords: GIS Soil quality References Digital Soil Mapping a b s t r a c t Maps play an important role during the entire process of spatial planning and bring ecosystem services to the attention of stakeholders' negotiation more easily. As example we show the quantiﬁcation of the ecosystem service ‘natural attenuation of pollutants’, which is a service necessary to keep the soil clean for production of safe food and provision of drinking water, and to provide a healthy habitat for soil organisms to support other ecosystem services. A method was developed to plot the relative measure of the natural attenuation capacity of the soil in a map. Several properties of Dutch soils were related to property-speciﬁc reference values and subsequently combined into one proxy for the natural attenuation of pollutants. This method can also be used to map other ecosystem services and to ultimately integrate suites of ecosystem services in one map. © 2011 Elsevier B.V. All rights reserved. 1. Introduction Ecosystem services are key factors for a healthy and prosperous life of humans. Examples of the products of such services are: protection against ﬂooding, provision of clean drinking water, and production of food and ﬁbers. The economic value of these services becomes even more clear (Balmford et al., 2008; Costanza et al., 1997; Millennium Ecosystem Assessment, 2005). Increasingly we see ecosystems as ‘natural resources’ to be sustainably managed and kept healthy. As in other countries, land use in The Netherlands (like elsewhere) is driven by economic, technological and demographic developments. Still, one of the major challenges remains the optimization of the land management in such a way that ecosystem services will be provided optimally, not only according to wellbeing and human activities at (most) suitable places, but also according to optimal chemical, physical and environmental characteristics. All these factors are helpful to model future land-use needs, in addition to the energy and elemental ﬂow across soil, water and environmental compartments (sensu Bateman et al., 1999; Eade and Moran, 1996; Farber et al., 2002). ⁎ Corresponding author. Tel.: + 31 302743715; fax: + 31 302744413. E-mail address: [email protected] (H.J. van Wijnen). 0048-9697/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2011.05.058 A way to express such characteristics is by indication of the extent to which an ecosystem is “ﬁt” for the performance of the desired ecosystem services. A method for analysis and assessment of ecosystem services is presented by Rutgers et al. (2012). Another tool for raising awareness and environmental assessment and planning is to express the performance of ecosystems on maps (Haygarth and Ritz, 2009). This can be done by expressing the actual, relative, or maximal possibilities (Jeffery et al., 2010). We present here a procedure to make these maps in a Geographical Information System (GIS), based on the algorithm of Rutgers et al. (2012) and on multiple regression models of existing empirical data of biotic and abiotic attributes from the Dutch national soil survey, namely the Biological indicator of Soil Quality (BiSQ; Rutgers et al., 2009). The optimal use of ecosystem services is recognized by beneﬁciaries. These are the land users in the case of the soil system. Often ecosystem services co-occur and interact in a wide range of spatial and temporal scales. The spatial distribution of ecosystem services can therefore play an important role in the process of spatial planning and bring ecosystem services on the table during negotiations (Grêt-Regamey et al., 2008; Metzger et al., 2008; Troy and Wilson, 2006). As an example we used a method to quantify and to map the ecosystem service ‘natural attenuation of pollutants’. This is a service that keeps the soil clean for, e.g. production of food and clean drinking water (National Research Council, 2000; Rittmann, 2004) and which is necessary to maintain a healthy habitat for soil organisms, which in turn support other ecosystem services (Millennium Ecosystem Assessment, 2005). 50 H.J. van Wijnen et al. / Science of the Total Environment 415 (2012) 49–55 2. Methods The US Environmental Protection Agency (EPA) deﬁnes natural attenuation as a process that includes ‘a variety of physical, chemical, or biological processes that, under favorable conditions, act without human intervention to reduce the mass, toxicity, mobility, volume, or concentration of contaminants in soil or groundwater’. These in-situ processes include biodegradation, dispersion, dilution, adsorption, volatilization, decay, stabilization, transformation, or destruction of contaminants (EPA, 1999). Several soil properties inﬂuence these processes. Modeling of these properties and comparing the outcome with a reference situation may predict in which areas the soil natural attenuation is optimal and where it is not. The natural attenuation capacity that is modeled in this study must be seen as a measure that describes the ‘biodegradation capacity’ of the soil, including biodegradation of all types of contaminants. For this, we used aspects of the ‘Digital Soil Mapping’ procedures (McBratney et al., 2003). First, we developed a model to quantify the natural attenuation capacity of the soil relative to an optimal situation. Several biotic and abiotic parameters were selected to be incorporated in the model (Section 2.1). Maps of some of these parameters are readily available in literature, but some others needed to be modeled. Therefore, measurements were needed to feed these models. In Section 2.2 we will explain how measurements in BiSQ were done and in Section 2.3 is explained how models were developed to map some of the non-available input parameters. Finally, mapping procedures are explained in Section 2.4. 2.1. Development of the preliminary model for natural attenuation The evaluation of contribution of soil organisms to ecosystem services is of conceptual and descriptive nature (Luck et al., 2009). Quantitative relations are not yet available, and best professional judgment (BPJ) was used to select the appropriate biotic indicators, like the procedure as described by Rutgers et al. (2012). We used three microbial indicators from the database as proxy indicators of the capacity of the soil to perform its natural attenuation capacity (NAC): 1) functional microbial activity (FMA), 2) potential carbon mineralization rate (PotC) and 3) potential mineralization rate of organic nitrogen (PotN). Also abiotic soil parameters may serve as indicators for ecosystem services, but again quantitative relations are not available. BPJ was used to produce a selection of abiotic indicators in the database as proxy indicators for the NAC, i.e. soil organic matter (SOM) content, pH of the soil and phosphorus content (PAL, i.e., P2O5 extracted with a solution of ammonium lactate). The level of the NAC was made a function of the ‘condition of the soil’ according to one equivalent equation as used by Rutgers et al. (2012), with the default syntax: FMA pH SOM PAL PotC PotN + log + log + log + log + log B log FMA pHref SOMref PALref PotCref PotNref ref −B @ 6 NAC = 10 0 j j j j j j j j j j j 1 jCC A ð1Þ and in few grid cells: pH PAL PotC PotN FMA SOM + log + log + log − log − log B log pH PALref PotCref PotNref FMAref SOMref ref −B @ 6 NAC = 10 0 j j j j j j j j j j j 1 jCC A: ð2Þ In the default syntax (Eq. 1), the more the observed and calculated values of these six soil properties differed from ‘reference values’, the lower the NAC. In some grid cells of The Netherlands, deviations from default syntax were necessary (Eq. 2). In fact, as soon the observed functional activity value was lower than the reference value, it was considered to be ‘better’ and therefore did not result in a lower natural attenuation capacity. The same can occur when the observed soil organic matter was higher than the reference value, as more SOM was considered to be ‘better’ for the natural attenuation capacity. Seen the opposite trends of FMA and SOM in comparison to the other four terms, these two terms were entered into the numerator of Eq. (2) with a negative sign, in contrast to the default syntax of Eq. (1). More information on this switching syntax of the equation can be found in Rutgers et al. (2012). 2.2. Measurements in BiSQ Maps of some input parameters were readily available. The soil properties pH and soil organic matter were taken from De Vries (1999) and converted to a 100 × 100 m resolution. The PAL content in the soil was derived via pedotransfer functions from Mulder et al. (2009, 2011) and converted to 100 × 100 m resolution. Maps of the other input parameters were derived by Generalized Linear Regression modeling (see Section 2.3). To feed these models, a dataset with observations from the Netherlands Soil Monitoring Network was used (BiSQ; Rutgers et al., 2009). The data with abundance and diversity for several soil biota collated in the BiSQ, together with abiotic characteristics were collected from 1999 to 2005. These data comprises information from 283 unique locations predominantly found in The Netherlands, which can be grouped in 5 soil types and 5 land-use types (Table 1). The samples are geographically identiﬁed by the latitude and longitude of the sampling locations (Dutch triangulation coordinates in meters from origin) and the date of sampling. The locations in the database are represented by farm level data, typically 5–50 ha, or less in the case of nature areas. Soil parameters included: pH, organic matter fraction and phosphor content of the topsoil (PAL, i.e., P2O5 extracted with a solution of ammonium lactate). Table 2 gives the measured variables in the physico-chemical part of the dataset that are used in the present study, together with their value distribution over the 283 sampling sites. The FMA is the amount of soil sample required to reach 50% activity in the Biolog multiwell plates (Rutgers et al., 2006, 2009). When bacterial density and diversity are low, a larger amount of soil sample will be needed to reach the 50% average coloring level in the Biolog ECOplates. Potential carbon and nitrogen mineralization processes were determined simultaneously by incubating the samples in airtight pots. Oxygen and carbon dioxide concentrations were regularly measured between week 1 and week 6 using gas chromatography (Bloem and Breure, 2003). H.J. van Wijnen et al. / Science of the Total Environment 415 (2012) 49–55 51 Table 1 Categories of soil and land use, together with their representation in the 283 sampling sites. Type Category Value range Number of sites % of sites Soil Löss Fluvial clay Peat Sand Silty loam Total Pasture Arable farming Semi-natural grassland Heathland Forest Total 0 or 1 0 or 1 0 or 1 0 or 1 Nonea 11 18 22 164 68 283 189 63 10 10 11 283 4% 6% 8% 58% 24% 100% 67% 22% 4% 4% 4% 100% Land use a 0 or 1 0 or 1 0 or 1 0 or 1 Nonea Not speciﬁed as a predictor in the regression. If 0 (zero) for all of the other categories within the same type then identiﬁed as clay or forest, respectively. Table 2 Physico-chemical variables used in the present study, with their value distribution over 283 sampling sites. Distribution centile Organic matter (% dw) pH (KCl) (pH-units) PAL (mg P2O5/100 g) 0% 5% 25% 50% 75% 95% 100% 2% 2% 5% 6% 10% 28% 60% 3.0 3.2 4.8 5.3 6.4 7.6 7.8 1 4 32 44 55 76 109 Carbon mineralization rate was calculated from the CO2 evolution (respiration) between week 1 and week 6 (mg C kg − 1 week − 1). The increase in mineral nitrogen (NH4 and NO3) in the soil during 6 weeks was used to calculate the potential nitrogen mineralization rate (mg N kg − 1 week − 1). As measurements were carried out with homogenized sieved soil at a constant temperature of 20 °C and 50% water holding capacity, the method is considered to reﬂect a potential optimum for the mineralization processes. 2.3. Modeling of some input parameters We developed models to describe soil processes that are important for the natural attenuation of pollutants. The models were restricted to the parameters from the nationwide survey, in order to enable the mapping of these properties. It makes no sense to develop mapping procedures without availability of relevant data with some kind of spatial representation. We used soil data from the previous paragraph to calibrate the models (Rutgers et al., 2009). The data of BiSQ and some other soil properties were used for modeling the FMA, PotC and PotN. Generalized Linear Regression (GLM) models of the Gaussian family (McCullagh and Nelder, 1989) are used to relate the three functional responses of the soil microbial community to land use, soil type and abiotic characteristics. The models to be calibrated by the available data were all formulated to be according to the syntax: Responses ð½FMA or ½PotC or ½PotNÞ = intercept + a ½Loss + b ½fluvial clay + c ½peat + d ½sand + e ½silt loam + f ½pasture + g ½arable fields + h ½semi natural grassland + i ½heathland + j ½longitude + k ½longitude2 + l ½latitude + m ½latitude2 + o ½OM + p ½OM2 + q ½pH + r ½pH2 + s ½PAL + t ½PAL 2 The quadratic terms for the scalar predictors in the regression formula allow for predicting non-linear response behavior as inﬂicted by optimum and minimum conditions. The regression models were calibrated using a stepwise procedure based on the Bayesian Information Criterion (BIC) (Schwarz, 1978). This is done in order to restrict the addition of terms to those that have a signiﬁcant (pb 0.05) contribution to the overall model, making the full model highly signiﬁcant. Calculations were conducted using S-Plus 2000 (MathSoft, Cambridge, MA). Subsequently, the calibrated regression formulae were used to generate a continuous map of the microbial responses by substituting continuously mapped values for the model predictors in the calibrated regression formulae. The models derived were: 4 4 4 4 FMA = 6:032•10 + −5:741•10 ½pasture + −5:494•10 ½arable farming + −5:870•10 ½semi natural grassland + −5:085•104 ½heathland + −8:885•101 ½PAL + 7:4•10−1 ½PAL2 1 1 2 PotC = 4:688•10 + −4:607•10 ½arable farming + 1:347•10 ½peat + ð6:06 ½OMÞ 1 −11 2 PotN = 2:43 + ð5:11 ½pastureÞ + ð7:10 ½semi−natural grasslandÞ + 1:511•10 ½peat + 1:73•10 ½latitude + −1:1•10−1 ½pH2 : 52 H.J. van Wijnen et al. / Science of the Total Environment 415 (2012) 49–55 The explanatory capacity of all three models is relatively high, with an explained deviance of 76, 72 and 54%, respectively. Seen the variance inﬂation factors (VIF), predictors' co-linearities are unlikely (VIF b 10). VIFs are calculated and interpreted according to Kline (1998) and O'Brian (2007). 2.4. Mapping All maps were designed at a resolution of 100 × 100 m. A map with land-use types was derived from Hazeu et al. (2010). It is a 25 × 25 m resolution map, showing 39 categories of land use. For this analysis, it was converted to a 100 × 100 m resolution map. Soil types were derived from the 1:50,000 resolution vector soil map of The Netherlands from De Vries et al. (2003) and converted to a 100 × 100 m resolution grid map. As explained earlier, the maps of the soil properties pH and soil organic matter were taken from De Vries (1999) and converted to a 100 × 100 m resolution. The PAL content in the soil was derived via pedotransfer functions from Mulder et al. (2009, 2011) and converted to 100 × 100 m resolution. Maps of the soil properties FMA, PotC and PotN were calculated from the models that are described above. Reference maps were made based on the data in Rutgers et al. (2008). The reference data represent a soil system which can be considered as the healthiest with respect to the land management within a given soil and a chosen land use. It may also be referred to as a ‘maximum ecological potential’ (MEP) within a given soil and a chosen land use (Rutgers et al., 2012). For each combination of land use and soil type, a MEP value for each soil property was taken from Rutgers et al. (2008). 3. Results and discussion The functional microbial activity (FMA) of the soil shows the amount of soil required to convert 50% of the total substrate. It is generally low in agricultural areas, especially on peat, and relatively high in nature areas (Left panel in Fig. 1). Compared to the reference ‘optimal’ situation in Fig. 1 (Right panel), it shows that in some agricultural areas the actual functional activity is higher than desired. Most grasslands are devoted to livestock (dairy and non-dairy cattle) and occur on sandy soils. Among the agricultural land-use types, the arable farms are mainly located on marine clay, whereas grasslands can be found mainly on sandy soils in the eastern part of the country. Arable farms mainly occur on soils that are suitable for arable crops. To a certain extent, even the widespread livestock farms occur on soils that are less suitable for high-productivity grasslands, as most pastures are located on nutrient-poor soils. Similar analyses were done for the other ﬁve biotic and abiotic soil properties (pH, OM, PAL, PotC and PotN). These soil properties were also compared to the reference situation (Fig. 2). The best independent predictor, soil pH, ﬂuctuates 5 orders of magnitude, followed by OM, whose content increases by a factor of 30. Within a particular soil type, its variation generally is less than 2 orders, except for maritime (Ca 2+-enriched) sand dunes, where the soil pH range is much smaller than that of inland areas, and for river clay, where its variation is over 3 orders of magnitude. In some areas, the soil property can be lower than the reference, whereas in other areas the same soil property can be equal or even higher than for the reference situation. When the deviation from the reference is high, the potential for improvement of that soil property is high as well. The natural attenuation of pollutants appears to be relatively low in nature areas and in most arable ﬁelds on sandy soils, although it is relatively high in arable ﬁelds on clay and in grasslands on sandy soils Fig. 1. Actual soil functional activity (left-hand side) and potential soil functional activity (right-hand side). H.J. van Wijnen et al. / Science of the Total Environment 415 (2012) 49–55 53 Fig. 2. Potential for improvement of six soil parameters. (Fig. 3; Table 3). Intermediate values are found in grasslands on peat and Löss. All together, less than 20% of the arable farming systems is situated on unsuitable soil, but over 40% of nature is situated on soil unsuitable for rural purposes (cf. Mulder et al., 2005). Fig. 3 shows a remarkable lack of ﬁt between the current land use and the soil properties of Fig. 2 in large parts across The Netherlands (Mulder et al., 2005). This implies that soil management regimes, historical land use and geographical situation determine soil 54 H.J. van Wijnen et al. / Science of the Total Environment 415 (2012) 49–55 Fig. 3. Ecosystem service “natural attenuation of pollutants”, based on six soil properties. properties. This situation is widely accepted in another densely populated European country, the United Kingdom, where a ﬂourishing tradition of underpinned environmental planning is rapidly growing (e.g., Haygarth and Ritz, 2009, and references therein). An improved link between current planning systems and GIS analyses like those shown here offers several possibilities to inform and eventually regulate agricultural land use in The Netherlands. Policy directed towards speciﬁc regions, which is a growing ﬁeld in Table 3 Summary of relative values (0 b proxy b 1) of natural attenuation capacity (NAC) for all occurring combinations of land-use and soil type (NO = not occurring). A value of 1 indicates that the natural attenuation capacity is equal to the reference, or MEP value. Land use Sandy soils Clay soils Peat Löss Agricultural grasslands 0.84 (0.25–1.0) 5332 km2 0.58 (0.15–0.73) 4132 km2 0.54 (0.26–0.75) 2963 km2 0.56 (0.43–0.8) 190 km2 0.95 (0.24–1.0) 415 km2 0.59 (0.19–0.86) 4003 km2 0.87 (0.33–1.0) 4358 km2 NO 0.61 (0.29–0.76) 1665 km2 NO 0.69 (0.6–0.83) 98 km2 NO NO NO NO NO NO NO NO NO Arable ﬁelds Forest Heathlands Semi-natural grasslands the environmental policy, and active regulation of key ecosystem services, offers many possibilities to integrate or to differentiate land uses, with cost-beneﬁt opportunities to match occurring soil quality (with related ecosystem services), see with the policy-demanded land use and policy-required tradeoffs (Daily et al., 2009; Egoh et al., 2008; Naidoo and Ricketts, 2006; Nelson et al., 2009; Swetnam et al., 2011). There are several uncertainties and limitations related to the use of BPJ, reference values, GLM models and the NAC model. These uncertainties need to be addressed in order to appreciate the possibilities of this methodology. Also, validation is needed for the ﬁnal data. The maps in this study must be seen as a ﬁrst attempt to show which areas have a relatively suboptimal ecosystem service capacity and which areas have an optimal capacity. The method shown in this paper to map the natural attenuation may also be used for mapping other ecosystem services. These indexes of ecosystem-service performance (EPX) can be mapped in a similar way. In addition, a map of an integration of all EPX values is possible, showing integrated soil quality (Rutgers et al., 2012). The challenge remains to select the most appropriate soil properties and develop concomitant models to estimate a realistic NAC and other ecosystem services using BPJ. This is both true for using existing data such as present in the BiSQ database as for new proxy indicators. 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