Promoter: Prof. dr. Ron de Goede

Co-promoter: dr. ir. Gerard Heuvelink

Israel Onoja Ikoyi

Spatial distribution of nematodes in a heavy metal-contaminated nature

reserve

Academic year 2012 – 2013

Wageningen University

Department of Soil Quality

Soil Biology and Biological Soil

Quality Group

&

Wageningen University

Department of Soil Geography and

Landscape

Thesis submitted to obtain the degree of European Master of Science in Nematology

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Spatial distribution of nematodes in a heavy

metal-contaminated nature reserve

Israel O. IKOYI

Department of Soil Quality, Department of Soil Geography and Landscape, Wageningen

University, Droevendaalsesteeg 4, 6708PB, Wageningen, The Netherlands

Department of Biology, Nematology Section, Ghent University, K.L. Ledeganckstraat 35, B-

9000 Ghent, Belgium

Summary- Soil organisms are often distributed in patches indicating spatial aggregation in the

populations of these organisms. Studies have shown that field distribution of nematodes is aggregated indicating that nematodes also do not occur at random but exhibit spatial dependency. Nematodes are

seen as promising indicators of soil quality and health due to their high diversity and diversity of

ecological roles. This research was set up to evaluate the spatial structure in the distribution of

nematode community indices in a heavy metal-contaminated nature reserve. Also, regression models were built to establish relationships between nematode community indices (maturity index, structure

index, diversity indices, abundances of life history and feeding groups) and a selection of

environmental factors (pH, clay, moisture, organic matter, cadmium and zinc concentrations) to contribute to a better understanding of the possible drivers behind the spatial arrangement of the

nematode communities. Results obtained indicated that about 41 to 86% (relative structure) of the

variation in the distribution of the nematode community indices in the study area was not random but exhibited spatial patterns over distances ranging from 80 to 180 m. The regression models obtained

were significant indicating that the spatial structure in the environmental factors reflected in the spatial

structure of the nematode data. These models explained about 21-50% of the variation in the nematode

data. The nematode community indices showed significant negative correlation with most of the environmental factors and the regression models indicated that soil pH was almost always (significant

in 11 out of the 13 models) a significant factor affecting the distribution of the nematode community

indices in the study area. Detailed analysis of the result showed that factors such as pH and moisture content were the possible drivers behind the spatial structure of the nematode community indices

rather than heavy metal pollution. The regression models were used in predicting and mapping the

spatial distribution of the nematode community indices in a regression kriging approach. The results indicated that the selected environmental factors could not explain all the spatially related variation in

the nematode data (as shown by the spatial structure in the residual data of the regression models of

some of the nematode community indices). This suggests that inclusion of factors other than the ones

considered in the current study could further improve the explanation of the spatial structure of the nematode community indices. There is the need to validate the accuracy of the resulting maps obtained

in this study. Further studies could also explore the use of other forms of non-linear models such as

random forests to see if such models better explain the relationship between nematode community indices and environmental factors.

Keywords –kriging, regression models, spatial heterogeneity, nematode community structure,

cadmium, zinc.

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In terrestrial ecosystems, the soil biodiversity consists mainly of microorganisms and small

invertebrates (Lavelle & Spain, 2001). Studies have shown that the distribution of these

organisms is not random but display rather spatial patterns which are aggregated over scales

ranging from square millimeters to hectares (Gorres et al., 1998; Stoyan et al., 2000; Ettema

et al., 2000). Competition, configuration of the habitat and historical effects have been

identified as possible factors influencing the spatial structure of communities of micro- and

macro organisms (Martiny et al., 2006; Horner-Devine et al., 2007; Vos & Velicer, 2008). As

a result, populations become isolated and therefore differentiations occur at local spatial

scales. Spatial patterns of soil biota are vertical, horizontal or both. Nematodes are among the

small invertebrates living in the soil.

Nematodes have been reported to be the most abundant metazoans on the Earth‟s surface with

about 108 individuals distributed in a square meter (Lambshed, 2004; Decraemer & Hunt,

2006). They occur in all environments, in every type of soil, under every climatic condition

and in all ecological niches, varying from undisturbed to disturbed ones (Bongers & Ferris,

1999). Different nematode taxa utilize specific food sources and shifts in these feeding groups

often reflect changes in the soil foodweb (Ferris et al., 2001; Yeates et al., 2009). For

instance, members of the Plectidae and Cephalobidae families feed on bacteria but never on

higher plants or fungi. Their transparent body allows easy observation of the internal

structures and determination of feeding habit. They have varying lifespan ranging from a few

days to years. In addition, nematodes have differential sensitivities to different forms of stress

and disturbance including heavy metal contamination (Korthals et al., 1996; Georgieva et al.,

2002; Tenuta & Ferris, 2004; Sanchez-Moreno & Navas, 2007). The combinations of feeding

habit, life span and sensitivity to environmental disturbance have allowed the classification of

nematode taxa with similar response characteristics into functional guilds (Bongers &

Bongers, 1998; Ferris et al., 2001; Ferris & Bongers, 2006). Because of their high diversity

and ecological roles, nematodes are seen as promising bioindicators of environmental health,

soil quality and ecosystem resilience (Bongers 1990; Ferris et al., 2001; Neher, 2001; Hoss &

Traunspurger, 2003; Yeates, 2003; Mulder et al., 2005; Schratzberger et al., 2006; Heininger

et al., 2007). Several studies have reported the effects of soil disturbance on nematode faunal

assemblage and diversity (Hanel, 2003; Kardol et al., 2005; Korenko & Schmidt, 2006).

Recently, lower nematode abundance and trophic diversity (a measure of the relative

abundance and evenness of the occurrence of nematode trophic groups) have been reported

from heavy metal contaminated sites (Park et al., 2011; Salamun et al., 2012).

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Field distribution of nematode taxa has been described as aggregated indicating that nematode

data are spatially dependent. Considerable variability in the spatial patterns of species

composition and abundance of plant parasitic nematodes has been reported from agricultural

fields (which are perceived to have relatively homogenous soils) (Robertson & Freckman,

1995). Previous studies showed that nematode distribution and soil properties often are

correlated and can have spatial patterns (Ettema et al., 1998; Liang et al., 2005; Monroy et al.,

2012; Park, 2012). There is however limited information on models that relate nematode

distribution with environmental variables. Such models are needed for a better understanding

of the spatial patterns of soil biota such as nematodes in natural ecosystems which are far less

homogenous than agricultural soils. These spatial patterns can have important effects on the

patterns of above ground plant community structure (Ettema & Wardle, 2002). The reverse

relationship is also true. In addition, data from sites with useful gradients in natural and

anthropogenic soil characteristics presents possibilities to study „natural‟ relationships

between nematodes and their environment and also the effects of artificially introduced

stresses.

The floodplains of the „Dommel‟ river (tributary of the river Meuse) in the south of The

Netherlands are heavily contaminated by heavy metals such as cadmium and zinc, due to

former upstream mining activities. The highest topsoil Cd concentrations (up to 100 mg/kg) in

The Netherlands are found in the Malpiebeemden nature reserve. The distribution of the

contamination in the floodplains is very heterogeneous, resulting in large gradients of heavy

metals (Bleeker & Gestel, 2007). The river Dommel is a rainfed river being almost always

flooded after spring rains. Upstream, close to the Dutch-Belgian border, is a factory that

produces zinc and cadmium. Since 1888 waste discharge into the Dommel resulted in serious

heavy metal contamination in the downstream area. In the mid-1990s, about 1-3 kg cadmium

and 50-200 kg zinc were transported daily by the river. At present the concentrations in the

river water are lower, but concentrations in the flood plains are still elevated (Postma, 1995;

Bleeker & Gestel, 2007). In 2008, soil moisture, pH, SOM, Total and available Cd and Zn

concentrations, Earthworm number and Biomass were measured from a spatial grid of 100

sampling points but no data on nematodes were considered.

Against this background, there is therefore the need to study the spatial distribution of

nematodes in relation to environmental properties (including heavy metal-contamination) as

this will make sampling for bioindication surveys more efficient.

This study was designed to achieve the following objectives:

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1. To evaluate the spatial structure of the nematode faunal assemblage along the Dommel

river.

2. To establish the relationships between nematode community indices and

environmental factors including heavy metal concentrations.

3. Use these relationships in mapping the spatial distribution of the nematodes in a

regression kriging approach.

The research questions addressed in this study include: a) Does spatial heterogeneity exist in

the distribution of soil nematodes in the contaminated site? b) How are the nematode

distribution and other environmental factors including soil heavy metal content related? c)

Can geostatistical models be developed and used to predict the spatial distribution of

nematodes at unsampled locations in the Malpiebeemden area?

The hypotheses considered in this study include:

1. Spatial heterogeneities exist in the distribution of nematode taxa in the contaminated

site and this will be associated with specific patterns in their life-history

characteristics. The c-p2 nematodes will aggregate and be dominant even in spots with

higher heavy metal concentration, while persisters (K-selected species) will have

lower incidences and relative abundances in such spots as a result of their sensitivity to

disturbances, such as heavy metal contamination.

2. An increased complexity of the nematode assemblage (as indicated by taxa richness

and diversity) will be associated with spots with low levels of disturbance, because

stressed conditions (heavy metal pollution, moisture and oxygen stress) favour mainly

the tolerant taxa (c-p2).

3. The selected abiotic environmental (soil) properties that were measured in the

Malpiebeemden area are sufficient to explain the spatial structure of the nematode

fauna. The remaining, i.e. unexplained, residual variation will have no spatial

component.

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Materials and methods:

STUDY AREA DESCRIPTION

This study was conducted in the Malpiebeemden nature reserve located in the south of The

Netherlands, in the province of North-Brabant, in between the municipality of Valkenswaard

and the Dutch-Belgian border. The area of 122 ha comprises grassland, bog, forested bog with

several ponds, and is bordered at the east by the river Dommel. The area is owned by

Staatsbosbeheer (National Forest Conservation Organisation), but it is managed by

Natuurmonumenten (Organization for the Conservation of Nature Reserves). Figure 1a is the

map of the Netherlands showing the location of Wageningen and the study area.

The Malpiebeemden area was selected for this research because not only do the spatial

distribution of Cd and Zn differ, but also other environmental properties such as soil texture,

organic matter content, pH, moisture and elevation are spatially variable (Bleeker & Gestel,

2007). This research focused on a relative small part within the Malpiebeemden (5o27‟EL;

51o18‟NB), comprising grassland and small forest patches (Figure 1b) south of Groot Malpie-

ven and West of the river Dommel. The size of the studied area is about 23 hectares. The area

is characterized by four different soil types (Figure 1c).

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Figure 1. Map of a) the Netherlands indicating Wageningen and the study area, b) the study

area and the forest patches marked with yellow and green outline respectively, c) the study

area showing the different soil types (world classification system) according to De Bakker,

(1979).

SOIL SAMPLING

The sampling design was a stratified random sampling because this yields a fairly uniform

spread over the area while still having data for doing sound validation using sampling theory

from statistics (De Gruijter et al., 2006). It also yields short-distance comparisons useful for

variogram fitting. The area was divided into eight strata of equal size using the spcosa

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package (in the R statistical software). The spcosa-package provides algorithms for spatial

coverage sampling and for random sampling from compact geographical strata (De Gruijter et

al., 2006, Walvoort et al., 2010). Prior to stratification, the forested patches (Figure 1b) were

masked in order to have soil samples from uniform vegetation (grassland). Ten random soil

sampling points were chosen from each stratum making a total of 80 soil sampling points

distributed over the entire study area (Figure 2). Also, before going to the field, five extra

random soil sampling points per stratum were selected as a precaution in case any of the

selected main sampling points turns out to be in an unsuitable location (i.e. inside the forest

patches) in the field. These five additional soil sampling points had a ranking order in such a

way that the rank is taken into consideration when any of the main sampling points is to be

replaced. In the field, the positions of the soil sampling points were located with a

Geographical Positioning System (GPS) based on the beforehand selected coordinates. At

each of the sampling locations, one soil sample was taken from 0-20 cm soil depth using a 4

cm diameter soil corer.

NEMATODE EXTRACTION AND IDENTIFICATION

Nematodes were extracted from 100 g fresh soil samples using the Oostenbrink elutriator

(Oostenbrink, 1960). They were counted (10% of the total extracted volume) using a high

magnification microscope at 100-400 times magnification. The nematodes were heat killed

and fixed in 4% formaldehyde and identified to the genus or family based on morphological

features and identification keys (Bongers, 1988; Andrassy, 1984; Siddiqi, 1986; Jairajpuri &

Ahmad, 1992). This was the highest achievable taxonomic resolution and is typically the

finest resolution used in studies that report composition of soil nematode communities.

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Figure 2. Study area showing sampling points.

CHARACTERIZATION OF NEMATODE FAUNAL ASSEMBLAGE

The nematodes were categorized into the various feeding groups (Yeates et al., 1993) and c-p

groups (Bongers & Bongers, 1998). The total nematode abundance (individuals/100g soil),

abundance of feeding groups (individuals/100g soil), abundance of c-p groups

(individuals/100g soil), Genera richness (GR), Shannon-Weiner (H'), maturity, enrichment,

channel and structure indices were calculated for each of the 80 samples. Genera richness and

Shannon-Weiner index were selected as diversity indices based on their wide usage in most

ecological studies (Neher & Darby, 2009).

Maturity index was calculated (for the free-living taxa) as:

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∑

(1)

Where vi is the colonizer-persister (c-p) value assigned to the family i, fi is the frequency of

family i in the sample and n is the total number of individuals of the free-living taxa in the

sample.

Food web indices were calculated following Ferris et al. (2001) and Ferris & Matute (2003).

All nematode genera were assigned to functional guilds associated with a specific weight for

each group. Basal (b), enrichment (e) and the structure (s) component of the nematode

assemblage were calculated as:

( ) (2)

( ) ( ) (3)

( ) ( ) ( ) ( ) ( ) (4)

where Ba1&2, Fu2, Ca2, Can and Omn are bacterial-feeding, fungal-feeding, carnivorous and

omnivorous nematodes belonging to 1, 2 and n c-p groups, respectively; n is the sum of the

c-p groups 3, 4 and 5; W1=3.2, W2=0.8, W3=1.8, W4=3.2 and W5=5.0. In this way, enrichment

and structure indices were calculated as:

(

) (5)

(

) (6)

Channel index ( ) was calculated as the proportion of fungal-feeding nematodes in c-p2

(Fu2) within the opportunistic decomposer guilds i.e. fungal-feeding nematodes in c-p2 and

the bacterial-feeding nematodes in c-p1 (Ba1). Taking into consideration the specific weights

of each guild, CI was calculated as:

(

) (7)

These calculations were done for each of the 80 samples. Pictorial presentation of the faunal

composition was summarized in a food web diagram (Ferris et al., 2001). For the food web

diagram, the 80 sample were divided into 3 pollution gradients (based on their total zinc

concentrations): group A (unpolluted samples with total Zinc concentration less than or equal

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to the Dutch target value of 140 mg/kg), group B (moderately polluted with total Zinc

concentration above the target value but less than the Dutch intervention value of 720 mg/kg)

and group C (heavily polluted with total Zinc concentration above the Dutch intervention

value of 720 mg/kg).

KRIGING SOIL PHYSICAL AND CHEMICAL PROPERTIES

Kriging is a geostatistical interpolation or mapping approach where the value of a random

variable at one or more unsampled locations is predicted by calculating some weighted

average of the observations (Webster & Oliver, 2007; Hengl et al., 2007). The general kriging

formula is given as:

( ) ∑ ( )

( )

where ( ) is the predicted value of the target variable at an unvisited location given its

map coordinates, the sample data ( ) ( )...., ( ) and their coordinates. The weights ( )

are selected in such a way that the error associated with the prediction variance is minimized

producing weights that depend on the spatial autocorrelation structure of the variable. This

type of interpolation approach is often referred to as ordinary kriging (OK) in most literature.

The expected error associated with the prediction is given as:

( ( ) ( )). (9)

For each kriged estimate, there is a variance associated with the kriging which is calculated

as:

( ) { ( ( ) ( ))}

2 . (10)

The standard deviation is calculated by taking the square root of the variance and used in

plotting the kriging standard deviation map. A crucial step in kriging is the calculation and

fitting of the semivariogram. This involves calculating the semivariance (half of the squared

difference between values at paired locations) for all pairs of locations separated by distance

(h).

The general formula for calculating the semivariance as given in Ettema & Wardle (2002) is:

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( )

( ) ∑[ ( ) ( )]

( )

where N(h) is the number of pairs observations separated by distance h, z(si) is the value of

the variable of interest at location si, and z(si+h) is its value at a location at distance h from si.

This is often presented as a graph of the averaged semivariances (plotted against distance)

known as the semivariogram. The semivariogram presents a picture of the spatial dependency

of the data based on the principle of geography that states: closer things are more similar than

distant things (Ettema & Wardle, 2002). A hypothetical semivariogram is shown in Figure 3.

Figure 3. Generalized semivariogram showing the various properties.

The range is the distance at which the data exhibit spatial dependency and beyond which the

data is independent. The Sill (C) is an estimate of the total population variance. The nugget

(intercept Co) is the variance due to sampling error or spatial heterogeneity at scales not

sampled, while the difference between the sill and the nugget gives the partial sill (spatially

structured variance). In a situation where there are no spatial relationships between samples

(i.e. the association is entirely random), it is called a “pure nugget effect”. In such cases, the

fitted model (the solid line) will be a straight horizontal line as the semivariances will be

scattered just around the total sample variance (the sill).

In 2008, soil samples collected from 100 sampling points in the study area were analyzed for

pH, organic matter, clay and moisture contents, and total (extraction in 2 M HNO3) and

available (extraction in 0.01 M CaCl2) concentrations of Cadmium and Zinc. The various soil

physical and chemical properties were mapped using ordinary kriging (OK). The results of the

OK predictions of soil physical and chemical properties at the 80 locations for our study were

obtained by overlaying these locations on the OK prediction maps. All this was done in the R

statistical software. A sample R script used for this analysis is attached in Appendix 1.

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STATISTICAL ANALYSES

Estimation of spatial heterogeneity in the distribution of nematode community indices

Geostatistical tools in R (R Development Core Team, 2013) were used to calculate and fit the

semivariograms of the nematode data to test for spatial heterogeneity. The semivariances

were calculated according to equation 11. The relative structure (C/C+Co) of the

semivariograms i.e. the proportion of the sample variance that is spatially autocorrelated was

calculated for each of the nematode parameters that showed spatial heterogeneity (Robertson

& Freckman, 1995). To meet the statistical assumptions of normality, the nematode count

data were log-transformed prior to the semivariogram fitting. Nematode data that showed no

spatial structure (Pure nugget effect) were excluded from further analyses (modelling and

mapping). The R scripts for these analyses are presented in Appendix 2.

Modelling relationships between the nematode community indices and soil properties

Multiple linear regression models were used to establish the relationship between the

dimensionless nematode indices i.e. maturity index, structure index, Shannon index and

genera richness (as dependent variables) and soil properties (pH, organic matter, Moisture,

clay content, Cd and Zn concentrations) as explanatory variables. As a result of a nearly

perfect positive correlation between the total and available concentrations of Cd and Zn,

models were built using only the total concentrations of the metals and the other soil

properties as explanatory variables. The regression was done in a stepwise manner to select

the best predictors based on the Akaike Information Criterion (AIC). The AIC is a method of

selecting a model from a set of candidate models in which the chosen model is the one that

minimizes the Kullback-Leibler distance (a measure of the difference between two probability

distributions) between the model and the truth (Burnham & Anderson, 2002). In this way,

over-fitting of the model is avoided. Models with the lowest AIC are considered to be the

best. The goodness of fit of the models was examined by a plot of the observed values against

the fitted values. Also, the normality plot of the residuals was examined as the residuals of a

multiple linear regression model are assumed to have a normal distribution (See Appendix 3

for sample R script). The general form of a multiple linear regression model is:

(12)

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Where Y is the dependent variable, is the intercept, are the coefficients of

the predictors (explanatory variables) and while is the residual.

Poisson regression is often used for modelling the relationship between environmental

properties and counts of organisms because the statistical distributions of the counts of

organisms are generally skewed, and hence not normally distributed. The assumption of the

Poisson distribution is that the variance is equal to the mean. However, it is often observed

that the amount of variation for each sampling unit is typically higher than that expected by a

pure Poisson process. This situation is referred to as over-dispersion (where the conditional

variance is greater than the conditional mean) making estimates from such models to be

erroneously interpreted. For our data, over-dispersion was observed in the Poisson regression

model. Although there are various recommendations for modelling over-dispersed data, a

more formal way to accommodate over-dispersion in a count data regression model is to use a

negative binomial model (Zeileis et al., 2008). The negative binomial regression analysis was

used to model the relationship between counts of nematode abundance (total abundance,

abundance of feeding groups and c-p groups) and environmental factors. This was done in R

using the glm.nb() function in the MASS package (Venables & Ripley, 2002). Unlike the

Poisson regression model, the negative binomial model assumes that the conditional mean are

not equal to the conditional variance and captures this by estimating a dispersion parameter.

One advantage of the negative binomial model over other methods recommended for

modelling over-dispersed count data is that it allows for stepwise variable selection based on

information criteria such as the AIC. The negative binomial regression can be seen as a form

of Poisson regression in which the log of the dependent variable is predicted with a linear

combination of the variables. The general form of the negative binomial regression model is:

( ) (13)

Where ( ) is the log of predicted counts, is the intercept while are the

coefficients of the predictors (explanatory variables) and . The output of the negative

binomial regression analyses also includes a null deviance (the amount of variation in the

model using only the intercept as predictor) and a Residual deviance (the amount of variation

in the model predictions when other predictors are added to the model). It is often expected

that the addition of other predictors to the null model (intercept-only) should reduce the null

deviance if the predictors are statistically significant to be included in the model.

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The goodness of fit of all models was examined by plotting the observed values against the

fitted values. The R scripts used for these determinations are provided in Appendix 4.

kriging of the nematode community indices

Geostatistical kriging of the nematode community indices was done in a regression kriging

approach. The generic model for the regression kriging prediction is given as:

(14)

Where is the dependent variable, is the outcome of the regression analyses (trend)

and is the difference between the measured and predicted values (residual). The residuals

are checked for possible spatial dependency. If the residuals are spatially dependent, they are

then added to the regression models for improved explanation of the variation in the

dependent variable.

Geostatistical tools in R (R Development Core Team, 2013) were used to calculate and fit the

semivariograms of the residuals from the regression model to test for spatial autocorrelation.

The relative structure (C/C+Co) of the semivariograms i.e. the proportion of the sample

variance that is spatially autocorrelated was calculated for each of the residuals that showed

spatial autocorrelation (Robertson & Freckman, 1995). For the count data of nematode

abundances, the residuals were square root transformed prior to the semivariogram fitting

since they did not meet the assumptions of normality. The properties of the semivariograms

were observed for spatial dependency and where this was true; interpolation of the residuals

over the entire study area was done using a simple kriging approach (like ordinary kriging but

here the mean is assumed to be constant and fixed at 0). The interpolated values of the

residuals were then back-transformed and added to the predicted values from the regression

models. The summed predictions were then used in mapping the nematode spatial

distributions. This approach is referred to as the regression kriging method (Odeh et al., 1994;

1995). The algorithm and steps involved in regression kriging are described in Hengl et al.

(2007). A summary of the regression kriging steps is given below:

1. Select explanatory variables and fit regression model (estimate regression

coefficients).

2. Compute residuals (by subtracting the fitted trend from the observations) at

observation locations and compute from them a semivariogram.

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3. Apply the regression model to all unobserved locations.

4. Krige the residuals (mostly done using simple kriging).

5. Add up the results of steps 3 and 4.

The kriging standard deviations (the square root of the kriging variance) were also computed.

The ratio of the kriging standard deviations to the predicted values indicates the coefficient of

variation which was used as a measure of the accuracy of predictions.

Results

NEMATODE FAUNAL ASSEMBLAGE

A total of 69 genera belonging to 33 families were identified from the area. The total number

of nematodes varied from 75 individuals to about 10,000 individuals per 100 g of fresh soil.

The most prevalent genera were Plectus (occuring in 96% of the sample), Aphelenchoides and

Eucephalobus (94%). These three genera belong to the c-p2 nematode grouping. The

nematode maturity index ranged between 1.3 and 2.8. Figure 4 shows box plots of the various

nematode community indices. The Shanon index ranged from 1.1 to 3.0 while the number of

genera per sample (genera richness) was between 8 and 32. The structure, enrichment and

channel indices ranged from 0 to 88, 4 to 93 and 0 to 100% respectively. For the nematode

life-history groups, the c-p2 nematodes were present in all samples and constituted about 65%

of the total nematode abundance. The relative proportions of c-pl, c-p3, c-p4 and c-p5 were

about 10, 20, 4 and 1% respectively. Among the feeding group distributions, the relative

proportion of bacterial feeders was highest (about 50% of the total nematode abundance). The

omnivores and predators were the least abundant with a relative proportion of about 3% of the

total nematode abundance.

The division of the samples based on the heavy metal pollution gradient showed that 70% of

the samples could be considered unpolluted (group A with total Zn concentration <140

mg/kg- the Dutch target value for Zn in soils), 21% moderately polluted (group B with total

Zn concentrations greater than the Dutch target value but less than the Dutch intervention

value for Zn in soils-720 mg/kg) and the remaining 9% as heavily polluted (group C having

Zn concentrations 720 mg/kg-the Dutch intervention value for Zn concentration in soils). As

can be seen from the foodweb analysis diagram (Figure 5), groups A and B were found in

each of the 4 quadrants. The heavily polluted samples (although there were few points) had

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SI< 60%. Also, some of the samples showed some kind of eutrophication having relatively

high levels enrichment.

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Figure 4. Box Plots of (a) maturity index (MI) (b) Shannon index (H'), (c) genera richness

(GR), (d) structure index (SI), enrichment index (EI) and Channel index (CI), (e) total

nematode abundance (TNEM), abundance of c-p groups and feeding groups (BF=bacterial

FF=fungal feeders, PF=plant feeders, OMV=omnivores, PRED=predators,

Ectop=ectoparasite subgroup of plant feeders, RHF=epidermal cell and root hair feeders

(subgroup of the plant parasites).

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Figure 5. Food web diagnostic diagram: A=unpolluted, B=moderately polluted and

C=heavily polluted.

SEMIVARIOGRAMS OF THE DISTRIBUTION OF NEMATODE COMMUNITY INDICES

Semivariograms were computed and plotted to test for spatial heterogeneity in the distribution

of the nematode community indices. The results are shown in Figures 6-8. Most of the

nematode community indices showed spatial heterogeneities as evidenced from the

semivariograms. Table 2 summarizes the semivariogram characteristics. About 43-86% of the

distribution of nematode community indices was spatially dependent ranging over distances

between 80-180 m. The semivariograms of the log-transformed abundances of c-p1 (Figure

6b) nematodes, the root hair feeders (Figure 7g), and the enrichment and channel indices

(Figures. 8e&f) showed a pure nugget effect.

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Figure 6. Semivariograms of the distributions of (a) total nematode abundance, (b) c-p1

nematodes, (c)c-p2 nematodes and (d)c-p3-5 nematodes.

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Figure 7. Semivariograms of distributions of feeding groups (a) bacterial feeders, (b) fungal

feeders, (c) plant feeders, (d) omnivores, (e) predators and (f) ectoparasites (g) root hair

feeders.

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Figure 8. Semivariograms of the distributions of (a) maturity index, (b) structure index, (c)

Shannon index, (d) Genera richness, (e) enrichment index and (f) channel index.

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Table 1. Semivariogram characteristics of the distribution of the nematode community

indices.

Nematode

parameter

Model Co (nugget) C(partial sill) C/C+Co

(relative

structure)

Range

Total

nematode

abundance

Spherical 0.4 0.7 0.64 180

c-p1 Pure Nugget

c-p2 Spherical 0.6 1.0 0.63 165

c-p3-5 Spherical 1.2 2.0 0.63 140

Bacterial

feeders

Spherical 0.8 1.3 0.62 160

Fungal

feeders

Spherical 0.3 1.1 0.79 99

Plant feeders Spherical 0.5 0.35 0.41 100

Omnivores Spherical 1.6 1.8 0.53 80

Predators Spherical 0.6 2.3 0.79 90

Ectoparasites Spherical 1.2 3.0 0.71 130

Root hair

feeders

Pure Nugget

Maturity

index

Spherical 0.04 0.03 0.43 100

Enrichment

index

Pure Nugget

Structure

index

Spherical 158.0 402.0 0.72 158

Channel

index

Pure Nugget

Shannon

index

Spherical 0.05 0.09 0.64 140

Genera

richness

Spherical 3.0 19.0 0.86 130

23 | P a g e

KRIGED SOIL PHYSICAL AND CHEMICAL PROPERTIES

The results of the kriged soil physical and chemical properties used in this study and the

summary statistics is shown in Table 2. The pH was in the acidic range.

Table 2. Descriptive statistics of soil physical and chemical properties used in this study.

pH Moisture

(%dwt)

Clay

(%)

Organic

matter

(%dwt)

Cd total

(mg/kg)

Cd

available

(mg/kg)

Zn total

(mg/kg)

Zn

available

(mg/kg)

Min. 3.7 11.92 0.53 2.10 0.07 0.07 5.3 3.4

1st Qu. 4.3 18.43 0.65 3.69 0.44 0.22 19.9 11.7

Median 4.5 35.16 1.19 6.39 1.66 0.40 66.4 23.7

Mean 4.4 38.12 2.47 7.12 10.29 2.35 295.1 60.5

3rd Qu. 4.6 52.76 2.54 10.33 13.53 2.57 298.7 77.9

Max. 4.9 79.27 10.53 16.20 68.90 14.14 1329.0 328.5

The semivariograms of the soil properties (Figure 9) indicated that the soil properties were

spatially dependent. The kriged maps of the soil physical and chemical properties that were

selected for this study are shown in Figure 10. The highest values of most of the soil

properties are located at spots closer to the river (Eastern part of the maps).

24 | P a g e

Figure 9. Semivariograms of the soil properties.

25 | P a g e

Figure 10. Maps of the distribution of the soil properties used as predictors.

26 | P a g e

CORRELATION ANALYSES OUTPUT

Most of the soil properties (especially the total and available concentrations of Cd and Zn)

were strongly, positively cross-correlated (Figure 11). Most of the nematode parameters were

significantly (P<0.05) negatively correlated with soil properties (Table 3). The total number

of nematodes (log transformed), Maturity index, structure index and Shannon-Weiner Index,

genera richness, the log-transformed abundances of c-p3-5, plant feeders, omnivores and

predators were significantly negatively correlated with all the soil properties. The

bacterivorous nematodes were significantly negatively correlated with all the soil properties

with the exception of the clay content where no significant correlation was found. The log-

transformed abundances of c-p2, fungivorous and ectoparasitic nematodes were significantly

negatively correlated with only soil organic matter, moisture and pH. The enrichment index

and channel Index, the log-transformed abundances of c-p1 nematodes and root hair feeders

did not show any statistically significant correlation with the soil properties. As a result of

this, they were excluded from modelling and regression kriging predictions analyses.

27 | P a g e

Figure 11. Scatter plot-correlation matrix between soil properties.

28 | P a g e

Table 3. Pearson‟s correlation coefficients between the nematode community indices and soil

properties.

Clay SOM Cdtot Zntot Moisture pH

Total numbers -0.23 -0.45 -0.29 -0.30 -0.50 -0.61

Maturity Index (MI) -0.29 -0.42 -0.28 -0.33 -0.42 -0.44

Structure Index (SI) -0.38 -0.46 -0.36 -0.40 -0.45 -0.48 Enrichment Index (EI) 0.01 0.07 0.01 0.04 0.04 0.03

Channel Index (CI) 0.10 0.10 0.07 0.06 0.16 0.23

Shannon-Weiner Index (H') -0.31 -0.43 -0.26 -0.30 -0.46 -0.46 Genera richness -0.35 -0.48 -0.33 -0.34 -0.49 -0.51

log c-p1 -0.08 0.10 -0.08 -0.02 -0.02 -0.03

log c-p2 -0.14 -0.36 -0.21 -0.21 -0.42 -0.52

log c-p3-5 -0.38 -0.56 -0.37 -0.40 -0.59 -0.63 logFungivores(FF) -0.08 -0.30 -0.14 -0.13 -0.33 -0.37

logBacterivores(BF) -0.17 -0.39 -0.22 -0.23 -0.45 -0.56 logPlant-Feeders (PF) -0.33 -0.41 -0.34 -0.37 -0.41 -0.45

Omnivores -0.31 -0.39 -0.35 -0.35 -0.45 -0.40

Predators -0.27 -0.45 -0.29 -0.32 -0.49 -0.58

logRoot-hair feeders(RHF) -0.09 -0.01 -0.03 -0.03 -0.04 0.05

logEctoparasites -0.17 -0.39 -0.20 -0.24 -0.44 -0.46 *Values in bold shows significant correlation (p<0.05); SOM=soil organic matter content; Cdtot, and Zntot =total Cd and Zn concentrations

RESULTS OF REGRESSION MODELLING ANALYSIS

All the nematode data that showed spatial autocorrelation and significant correlations with the

soil properties were included in the regression modelling analyses. A stepwise regression

analyses was performed to model the relationship between the nematode parameters and soil

properties (Clay, Organic matter, Moisture, pH, total concentrations of Cd and Zn) as

predictors. The summary of the final model output is given in Tables 4&5. The estimated

model for the MI had pH and the total concentrations of the heavy metal (Cd and Zn) as its

predictors. This model explained about 21% of the variation in MI. The overall relationship

between MI and the predictors was significant (F3,76=7.80, p<0.0005). The estimated model

for the SI (with Organic matter, moisture and pH as predictors) explained about 25% of the

variation in the SI and the overall model was significant (F3,76=9.91, p<0.0005). For the

diversity indices, the Shannon index (H') had pH and moisture content of the soils as its

predictors with about 22% of the variation in the H‟ explained by this model (a significant

model with F2,77=12.14, p<0.0005) while the genera richness (GR) model (having pH, clay

and total Zn concentration as its predictors) explained about 27% of the total variation in the

GR (a significant model with F3,76=10.52, p<0.0005). Soil pH was a significant predictor in

all these 4 models.

29 | P a g e

As shown in Table 4 (given the values for the estimated regression coefficient of the

predictors), the estimated models relating the dimensionless nematode indices with the soil

properties are:

(15)

(16)

(17)

(18)

Table 4. Summary of multiple linear regression analysis for the dimensionless nematode

indices.

MI=maturity index, SI=structure index, H'=Shannon index, GR=genera richness, Cdtot=total cadmium concentration, OrgMat=soil organic

matter content, Zntot=total zinc concentration, Std. error= standard error of coefficients and Pr(>|t|)=probability value associated with the t

test.

The estimated negative binomial regression model (Table 5) for the total number of

nematodes observed in the study had pH, Organic matter and Clay contents as its predictors.

This model was significant as it reduced the null deviance (intercept-only model) by a factor

of 67.17 (about 44% reduction in the variance). For the log counts of c-p2, c-p3-5, bacterial

feeders, fungal feeders, omnivores and ectoparasites, there were significant reductions in the

null deviance due to the addition of the predictors by 38, 50, 40, 26, 24, 31, 40 and 27%

respectively (these percentages are the difference between the null deviance and the residual

Indices Model

parameters

Estimate Std.

error

t value Pr(>|t|) Adjusted

R2

MI Intercept 3.66 0.56 6.53 <0.001 0.2053

pH

Cdtot

-0.31

0.02

0.001

0.01

-2.39

1.85

0.02

0.07

Zntots -0.001 0.001 -1.99 0.05

SI Intercept 216.7 54.21 4.00 <0.001 0.2527

pH

OrgMat

-36.80

-5.72

13.60

3.14

-2.71

-1.82

0.008

0.07

Moisture 0.99 0.69 1.43 0.14

H' Intercept 4.10 0.87 4.73 <0.001 0.2199

pH

Moisture

-0.36

-0.01

0.22

0.003

-1.66

-1.66

0.10

0.09

GR Intercept 58.13 8.78 6.62 <0.001 0.2655

pH

Clay

-8.57

-0.79

2.01

0.40

-4.25

-1.96

<0.001

0.05

Zntot 0.01 0.004 1.54 0.13

30 | P a g e

deviance). Soil pH was an important predictor in 7 out of the 9 models. As shown in Table 5

(the estimated coefficients of the predictors), the estimated negative binomial regression

models relating the count of nematode abundances with soil properties are:

( ) (19)

( ) (20)

( ) (21)

( ) (22)

( )

(23)

( ) (24)

( ) (25)

( ) (26)

( ) (27)

In general, soil pH was selected as a significant predictor in 11 out of the 13 models.

31 | P a g e

Table 5. Summary of negative binomial regression output for the nematode counts

BF=bacterial feeders, FF=fungal feeders, PF=Plant feeders, Cdtot=total cadmium concentration, OrgMat=soil organic matter content,

Zntot=total zinc concentration, Std. error= standard error of coefficients and Pr(>|z|)=probability value associated with the significance of

predictors.

Indices Model

parameters

Estimate Std.

error

t

value

Pr(>|z|) Null

deviance

Residual

deviance

Total

nematode

number

Intercept 14.91 1.69 8.83 <0.001 154.08 86.91

pH -1.61 0.42 -3.82 <0.001

OrgMat -0.11 0.05 -2.33 0.02

Clay 0.12 0.06 2.09 0.04

CP2 Intercept 12.50 2.31 5.40 <0.001 145.96 89.92

pH -1.23 0.59 -2.11 0.03

Moisture -0.04 0.01 -2.98 0.003

Clay 0.19 0.06 3.10 0.002

CP3-5 Intercept 15.71 2.52 6.25 <0.001 190.65 95.07

pH -2.01 0.63 -3.22 0.001

OrgMat -0.26 0.07 -3.58 <0.001

Clay 0.16 0.08 1.88 0.06

BF Intercept 14.51 2.46 5.90 <0.001 151.52 91.15

pH -1.61 0.62 -2.59 0.01

Moisture -0.03 0.01 -2.64 0.008

Clay 0.18 0.07 2.75 0.006

FF Intercept 10.14 2.64 3.85 <0.001 123.31 91.56

pH -1.17 0.65 -1.80 0.07

OrgMat -0.15 0.07 -2.28 0.02

Cdtot -0.09 0.04 -2.47 0.01

Zntot 0.004 0.002 1.81 0.07

Clay 0.27 0.13 2.17 0.03

PF Intercept 11.12 1.79 6.22 <0.001 115.83 88.11

pH -0.98 0.44 -2.22 0.03

OrgMat -0.06 0.03 -1.84 0.07

Omnivores Intercept 5.01 0.68 7.41 <0.001 102.51 70.77

OrgMat 0.98 0.37 2.70 0.007

Cdtot -0.09 0.03 -2.83 0.005

Moisture -0.22 0.07 -3.22 0.001

Predators Intercept 15.10 6.09 2.48 0.01 93.68 56.04

pH -2.44 1.50 -1.62 0.10

OrgMat -0.40 0.12 -3.21 0.001

Ectoparasites Intercept 7.10 0.42 17.08 <0.001 133.27 97.71

Cdtot 0.14 0.06 2.58 0.01

Zntot -0.01 0.003 -2.45 0.01

Moisture -0.07 0.01 -5.18 <0.001

Clay 0.29 0.16 1.87 0.06

32 | P a g e

ANALYSIS OF SPATIAL STRUCTURE IN RESIDUALS

The semivariograms of the residuals from the regression modelling analyses of the nematode

community indices are shown in Figures 12-14. Most of the semivariogram of the residuals

exhibited a pure nugget effect (having their semivariances scattered around the total sample

variance-the sill) except for structure index (Figure 12b), Genera richness (Figure 12d), c-p2

nematodes (Figure 13b), bacterial feeders (Figure 14a) and fungal feeders (Figure 14b). The

characteristics of the semivariogram of residuals of all the nematode parameters that showed

spatial autocorrelation are shown in Table 6. The relative structure (C/C+Co) is the proportion

of sample variance that is spatially autocorrelated. For these five models of the nematode

community indices, 63-95% of the residual variance was still spatially dependent over

distances ranging from 60-150 m.

Table 6. Semivariogram characteristics of the regression model residuals that showed spatial

autocorrelation.

Nematode

parameter

Model Co (nugget) C(partial sill) C/C+Co

(relative

structure)

Range

SI Matheron 23.14 413.51 0.95 87

GR Matheron 3.00 14.00 0.82 80

c-p2 Spherical 0.05 0.15 0.75 150

BF Matheron 0.06 0.18 0.75 60

FF Spherical 0.07 0.12 0.63 100 SI=structure index, GR=Genera richness, BF=bacterial feeders, FF=fungal feeders

33 | P a g e

Figure 12. Semivariograms of residuals from the regression models for (a) Maturity index,

(b) Structure index, (c) Shannon-Weiner index and (d) Genera richness.

34 | P a g e

Figure 13. Semivariograms for residuals of the regression models of (a) Total nematode

number, (b) c-p2 nematodes and (c) c-p3-5 nematodes.

35 | P a g e

Figure 14. Semivariograms for residuals of regression models of (a) Bacterivorous (b)

Fungivorous (c) Plant feeding (d)Omnivorous (e) Predatory and (f) Ectoparasitic nematodes

REGRESSION KRIGING AND ASSOCIATED STANDARD DEVIATION MAPS OF PREDICTIONS OF

DISTRIBUTION OF NEMATODE COMMUNITY INDICES

Dimensionless nematode indices

The semivariograms of the residuals from the multiple linear regression analyses of maturity

index and Shannon index were found to be spatially independent (Figures 12a&c) and so

predictions of these indices were done with the regression model alone. For the structure

index and Genera richness, the semivariograms were found to show spatial autocorrelation

(Figures 12b&d). These residual semivariograms were interpolated over the entire study area

(Figures 16b and 18b) and then summed up with the regression model predictions in a

regression kriging approach to map the distribution of SI (Figure 16c) and GR (Figure 18c)

36 | P a g e

over the study area. A visual comparison of the maps of the SI and GR from the regression

model only with the regression kriging maps (regression plus the interpolated residuals)

showed that the addition of the maps of the interpolated residuals further improved the maps

(Figures 16a and 18a) as patterns that were not initially visible became differentiated and

visible. In general, the lowest MI predictions (<2.0) were obtained at locations closest to the

river while MI predictions higher than 2.4 were obtained at locations farther from the river. A

similar trend was observed for the SI where predictions higher than 60% were obtained

farther away from the river. The standard deviation maps (a measure of the error associated

with the regression predictions) was also obtained. For the maturity index, using the standard

deviation map (Figure 15b), the coefficient of variation (CV) associated with the regression

predictions ranged from 10-11% while that for the structure index ranged from 31 to 46%.

The blue spots seen in the standard deviation maps are overlapping with areas with a

relatively high density of observation points where the kriging variance is small. The

interpolation maps (Figures 17a&18c) showed a higher nematode diversity (H'>2.6) and

number of taxa (GR>25) at locations farther away from the river as compared to lower

diversity (H'<2.0) and number of taxa (GR<15) obtained at spots closest to the river. The

coefficient of variation for the predictions of the Shannon index and Genera richness (Figures.

16b & 17d) ranged from 12-15 and 11-21 %, respectively. In general, there was a high

variability associated with the prediction of the structure index compared to all the other

nematode parameters.

37 | P a g e

Figure 15. Maps of (a) predictions of maturity index from the regression model, (b) standard

deviations of the predictions (c) maturity index values at observation points.

38 | P a g e

Figure 16. Maps of (a) predictions of Structure index from regression model-only, (b)

interpolated residuals of the regression, (c) regression + residuals {regression-kriging} and (d)

regression-kriging standard deviations. This is to illustrate how regression kriging works.

39 | P a g e

Figure 17. Maps of (a) Shannon index predictions from the regression model, (b) standard

deviations associated with predictions and (c) values of Shannon index at sample locations.

40 | P a g e

Figure 18. Maps of (a) Genera richness predictions from the regression model (b) the

residuals of the regression model (c) the combination of the regression model predictions with

the residuals and (d) regression-kriging standard deviations.

Total nematode abundance and abundance of life history groups

The total nematode abundance as predicted by the regression kriging ranged from <1000 to

about 7000 with highest abundances (indicated with pink and yellow pattern) obtained at the

western parts of the study area (Figure 19a) which correspond to locations farther away from

the river. The standard deviation associated with this prediction ranged from 0.24 to 0.36

(Figure 18b). A visual look at the maps of the abundances of c-p2 (Figure 20c) and c-p3-5

(Figure 21a), lower abundances (<500) were predicted at locations closest to the river. The

standard deviations for the c-p2 abundance predictions ranged from 0.26 to 0.44 (Figure 20b)

while that for the c-p3-5 ranged from 0.43 to 0.47 (Figure 21b).

41 | P a g e

Figure 19. Maps of (a) the total nematode abundance predictions from the regression model

(b) standard deviations associated with the prediction and (c) plot of the observed values of

total nematode abundance.

42 | P a g e

Figure 20. Maps of (a) c-p2 nematode abundance predictions from regression-kriging and (b)

regression-kriging standard deviations

Figure 21. Maps of (a) c-p3-5 nematode abundance predictions from the regression model

and (b) standard deviations.

Abundance of feeding groups

The maps of the prediction of the abundances of the various trophic groups and the associated

standard deviations are shown in Figures 22-27. Generally, it can be seen from these maps

that bacterial feeding nematodes had the highest abundance in the study area ranging from

43 | P a g e

<1000 to 5000 (Figure 22a), with most of the highest abundances recorded at points farther

away from the river. For the fungal feeding nematodes, the abundance was between 0 and

350 (Figure 23a). This was far much lower compared with the abundance of the bacterial

feeders. The abundance of the plant feeding nematodes ranged from 200 to about 1600

(Figure 24a), with the highest abundances occurring farther from the river (Western part of

the area). It can be seen from Figures 25a and 26a that the omnivorous and predatory

nematodes had the least abundance in the entire area with the abundances ranging from 0 to

350 (Figure 25a) and 0 to 120 (Figure 26a) for the omnivorous and predatory nematodes,

respectively. For the ectoparasitic nematodes, a higher abundance was recorded at locations

farther from the river (Figure 27a).

Figure 22. Maps of (a) bacterial-feeding nematode abundance predictions from regression-

kriging (i.e. combined prediction from the regression model and the residual) and (b)

regression-kriging standard deviations.

44 | P a g e

Figure 23. Maps of (a) fungal-feeding nematode abundance predictions from regression-

kriging and (b) regression-kriging standard deviations.

Figure 24. Maps of (a) plant-feeding nematode abundance predictions the regression model

and (b) standard deviations.

45 | P a g e

Figure 25. Maps of (a) omnivorous nematode abundance predictions from the regression

model and (b) standard deviations.

Figure 26. Maps of (a) predatory nematode abundance predictions from the regression model

and (b) standard deviations.

46 | P a g e

Figure 27. Maps of (a) ectoparasitic nematode abundance predictions from the regression

models (log scale) and (b) standard deviations.

Discussion

The present study was focused on the spatial distribution of soil nematode communities; their

dependence on and relationship with environmental factors including heavy metal

concentrations. The first hypothesis of this study states that spatial heterogeneity exists in the

distribution of nematode community indices (maturity index, structure index, Shannon index,

Genera richness, abundance of feeding and life history groups) in the contaminated site. To

test this hypothesis, semivariograms were computed for the nematode data obtained from the

area. The results showed that the distributions of the nematode community indices were not

random but exhibited spatial patterns over distances ranging from 80 to 234 m (Figures 6-8).

These patterns of nematode distribution could be attributed to patterns in their life history

characteristics and response to environmental factors. This finding is in agreement with the

findings of Monroy et al. (2012), who found that spatial aggregation was a characteristic

feature of both bacteria and nematode communities. Other studies also showed that at various

sampling scales, nematode populations are spatially patterned (Ettema et al, 1998; Liang et al.

2005; Park, 2012). Even in an agricultural soil which is considered relatively homogenous,

Robertson & Freckman (1995) detected spatial patterns in the distribution of nematode

groups. The semivariograms of the log-transformed abundances of c-p1 (Figure 6b)

nematodes, the root hair feeders (Figure 7g), and the enrichment and channel indices (Figures

8e&f) showed a pure nugget effect indicating that the distribution of these nematode

community indices was either random or that their distribution was not spatially structured at

47 | P a g e

the scales studied. These indices also did not show any significant relationship with the

environmental properties (Table 2). For the c-p1 nematodes, this could be attributed to their

low proportion recorded in the study area (about 10% of the total nematode abundance). The

c-p1 nematodes are bacterivores that increase significantly in abundance in response to

eutrophication events. In the study site, the food web diagram shows signs of eutrophication

in a few of the samples only. The EI is calculated based on the proportion of c-p1 nematode

group (consisting of bacterial feeders) that increase rapidly upon an increase in microbial

activity as a result of organic input (Ferris et al., 2001). The low abundance of the c-p1

nematodes could have resulted in the high variability in the EI (Figure. 4d). The finding of

this study supports the findings of Bert et al. (2009), who also did not find significant

relationships between c-p1 nematode groups and the historical pollution.

To establish the relationships between the nematode community indices and environmental

factors, stepwise regression analysis was performed in line with the second objective of this

study. About 21-50% of the variation in these indices was explained by the soil properties;

although different predictors tended to predict the different indices (Tables 4&5). Soil pH was

almost always a significant predictor in all models. In most of the models were pH and

moisture were significant predictors, they had negative relationship with the nematode

community indices. The heavy metals were selected as predictors in 5 out of the 13 models.

This however does not mean that the other environmental factors that were not selected in

some models were not affecting the nematode distribution but that the strong cross-

correlations between the environmental factors (Figure 11) could have led to the selection of

other cross-correlated factors. Also, Soil nematodes may become stressed by a number of

environmental factors such as pH, salinity, moisture, redox potentials etc. thereby masking the

impacts of other factors such as heavy metal contamination (Sochova et al., 2006). The

Pearson‟s correlation analysis between the nematode community indices and the

environmental factors (Table 2) showed that the total nematode abundance, maturity index,

structure index, Shannon index, genera richness, the abundances of CP3-5, plant feeders,

omnivores and predators were significantly negatively correlated with all the soil properties.

This negative relationship was expected for the heavy metals as toxic effects of these metals

on total nematode abundance are often reported in literature (Korthals et al., 1996; Yeates et

al., 2003; Nagy et al., 2004; Zhang et al., 2007, Shao et al. 2008). Moreover, Van Vliet & De

Goede (2008) reported a significant negative correlation of MI, trophic diversity, proportion

of bacterial and fungal feeders with clay, organic matter, Cd and Zn contents. Correlations of

48 | P a g e

these nematode parameters with pH were however not reported in their study. The negative

correlation with pH is quite surprising as it was contrary to the general expectation that

increase in pH will have a positive impact on these nematode parameters. This observation

could be partly explained by the short range of pH found in the study area (between 3.7 and

4.9), which was also in the acidic range. The observation of the negative relationship could

also be due to the interaction between pH and other environmental variables. Shukurov et al.

(2005) for instance, reported negative correlation between total nematode abundance and pH.

The finding of this study is however in contrast to the findings of De Goede & Bongers

(1994) who reported a significant positive correlation between pH and the proportion of taxa

that were sensitive to environmental disturbance.

Although, the regression models were all significant (p<0.0005) indicating the importance of

the selected predictors in predicting the response variables, a large portion of the variation in

the nematode community indices still remained unexplained. An examination of the

semivariograms of the regression model residuals of the nematode community indices showed

that the residuals were mostly spatially randomly distributed, thereby adding no further spatial

explanation to the model. Thus, the third hypothesis that the selected abiotic properties that

were measured in the Malpiebeemden area were sufficient to explain the spatial structure of

the nematode fauna was partially true. For the few cases where the regression model residuals

showed spatial dependency (structure index, Genera richness, c-p2 nematodes, bacterial and

fungal feeders), the spatial autocorrelation of the residuals indicated that these nematode

parameters were affected by other factors not measured in this study. The other factors could

include other biotic parameters such as plant species identity and intrinsic population

processes such as competition for resources and reproduction (Ettema & Wardle, 2002), but

only if these factors are spatially dependent. For instance, De Deyn et al. (2004) and Viketoft

et al. (2005) reported that plant species identity can influence soil nematode assemblage

composition.

The third objective of this study was to include the relationship between the nematode

community indices and environmental variables (regression models) in mapping the spatial

distribution of the nematode community indices. This was done in a regression kriging

approach. The resulting maps are presented in Figures 15-27. For the maturity and structure

indices, the lowest values were generally obtained at locations closest to the river (Eastern

part of the study area). These locations correspond to where the highest values of the

environmental factors (pH, organic matter, clay, moisture Cd and Zinc contents) were

49 | P a g e

measured (see Figure 10 for the maps of the distribution of soil properties). The MI and SI

increased with increasing distance from the river and the highest values were obtained at the

farthest distances and highest elevations in the Malpiebeemden area. Combining the

correlation with a visual analysis of the map, it is however unclear if these patterns were

causally related to the pollution stress alone. To obtain a better understanding of the effects of

the heavy metal pollution, the samples were divided into 3 groups based on pollution

gradients (unpolluted, moderately polluted and heavily polluted samples). Correlation and

regression analyses were performed on the unpolluted samples which constituted about 70%

of the total number of samples. The results indicated that pH, organic matter and moisture

seemed to be the major factors affecting the nematode assemblage and were in all cases

showing negative relationships. The soil moisture distribution could be a possible explanation

for the trend observed in the distribution of the nematode community indices. Though

nematodes can survive under low oxygen conditions in saturated soils (Poinar, 1983), the

population growth of many taxa may be affected by accumulation of products of anaerobic

metabolism. This might be the case for this study as most of the locations nearest to the river

were in saturated conditions. All nematode groups were least abundant in these areas with

high moisture. Similar effect of high moisture on nematode taxa was reported by Ettema et al.

(1998). Higher MI and SI indicate that soils are in a stable condition and not stressed

(Bongers 1990, Ferris et al, 2001; Vitekoft et al., 2011). Similar trends were observed for the

diversity indices (Figures 17 & 18) and abundances of the nematode groups. Regarding the

second hypothesis of an increased diversity being associated with low levels of disturbance,

the observations in this study seemed to suggest that the nematode diversity is affected by

stress conditions like high moisture content rather than the heavy metal concentrations. This

claim is supported by the grouping of the samples based on levels of pollution and the

resulting foodweb diagram (Figure 5). Interestingly, in all models where moisture was

selected as a significant predictor, it had a negative relationship with the nematode

community indices. To further investigate the effect of moisture, samples were divided into

two moisture content classes: (Group A = those with moisture content at field capacity i.e.

moisture content less or equal to 25% while Group B = those samples with moisture content

at saturation point i.e. moisture content greater than 25%). The food web diagram that resulted

from this classification is shown in Appendix 5. The result indicated that most of the samples

having moisture content at saturation point had SI<60%.

50 | P a g e

The maps obtained in this study revealed similar abundances in both c-p2 (Figure 20) and c-

p3-5 (Figure 21) nematode groups at some locations even though the c-p3-5 group are

expected to be relatively more sensitive to most forms of stress (Zhang et al, 2007; Shao et

al., 2008). A possible explanation for this could be due to the dominance of c-p3 bacterial

feeders belonging to the family Teratocephalidae and Prismatolaimidae in the c-p3-5 group at

such locations and not the abundance of the stronger K-selected species (c-p4 and 5). Further

support is given to this explanation as the abundance of omnivores and predators (mainly

classified as c-p4 or c-p5) were relatively low (0-350 and 0-120, respectively, as seen in

Figures 25 and 26). For the feeding group distributions, bacterial feeding nematodes were the

most abundant. This corroborates the results of several other studies in European grasslands

(De Goede & Bongers, 1994; Wasilewska, 1994; Hanel, 1996; Ekschmitt et al., 2001; Zolda,

2006). Fungal feeders made up a smaller proportion compared to plant feeders similar to the

reports of Ekschmitt et al. (2001) and Hanel (2003). Decomposition pathways in grasslands

are mostly mediated by bacteria, and bacterial feeding nematodes are to be expected as

dominant secondary decomposers (McSorley and Frederick, 2000; Rues, 2003).

The standard deviation maps give some level of confidence on the accuracy of the regression

kriging maps. The highest coefficient of variation was obtained for the prediction of the

structure index (46%). In general, most of these maps showed that the coefficients of variation

associated with the predictions of nematode parameters at unsampled locations are within the

acceptable range for field experiments (Patel et al., 2001). For instance, for the maturity

index, the coefficient of variation ranged between 9 and 11 %.

Conclusions

This study was designed with the aim to evaluate the spatial structure of nematode community

indices, and examine the relationships between nematode community indices and

environmental factors including effects of heavy metal pollution. Also, another objective was

to use these relationships in mapping the spatial distribution of the nematode community

indices in a regression kriging approach. The results of this study indicated that spatial

heterogeneity exists in the distribution of nematodes in the Malpiebeemden study area. The

patterns obtained from the semivariograms of the observations and the interpolation maps of

the regression models support the claim that the distribution of nematodes in this study site

was not random but exhibited some spatial patterns. Despite its exploratory nature, this study

offers some insights into the relationships between nematode community indices and

51 | P a g e

environmental factors. Geostatistical models relating the nematode community indices with

environmental factors were developed and used in producing interpolation maps at unsampled

locations. Regression kriging approach seemed to be a valid technique in mapping spatial

distributions of nematode community indices only in a few cases where the residuals from the

regression models exhibited spatial dependency.

Although this study could not adequately explain any direct influence of heavy metals (Cd

and Zn) on the nematode faunal assemblage and distribution, studies such as this that

measures soil properties including heavy metals in polluted sites are a holistic approach on

factors that affect nematode faunal structure rather than reporting data on heavy metals alone.

It is also possible that the effects of the heavy metal contamination were not evident as only a

few samples were heavily polluted (heavy metal concentrations above the Dutch Intervention

value for Zn concentration in soils- 720 mg/kg).

There is need to validate the performance of the interpolated maps using interpolation and

validation based on random sample extraction technique. In fact, this was an objective for this

study but was not executed due to time constraints. The validation could be obtained by

removing two sample points from each stratum (for the eight strata=16). Regression models

could then be built and maps developed based on the remaining 64 sampling points.

Repeating this e.g. five times gives a total of 80 randomly distributed validation points. True

prediction accuracy can then be evaluated by comparing estimated values with actual

observations at validation points to assess systematic error, calculated as mean prediction

error; and accuracy of prediction, calculated as root mean square prediction error. The set-up

of the sampling design used in this study completely suits such a validation. There is also the

need for further investigation on other forms of models that could relate nematode community

indices with environmental factors. Suggestion could be the use of random forests or other

forms of non-linear models to see if such models could better explain the spatial variations in

these nematode community indices.

Acknowledgements

I would like to thank the European commission for the Erasmus Mundus grant,

EUMAINE Consortium, Soil Quality Research Group of Wageningen University (The

Netherlands) for accepting me to conduct my thesis research and Mr. Tamas Salanki for

helping with the field sampling and other technical assistance. Many thanks to my promoters:

Professors Ron de Goede and Gerard Heuvelink for their guidance, help, valuable advice and

52 | P a g e

for patiently supervising me throughout this work. Finally, I am grateful to my family for the

relentless prayers and unending support.

References

Andrassy, I. (1984). Klasse Nematoda (Ordungen Monhysterida, Desmoscolecida,

Areolaimida, Chromadorida, Rhabditida). Akademie-Verlag, Berlin, 288 pp.

Bert, W., Manhout, J., Van Colen, C., Borgonie, G. & Decraemer, W. (2009). Nematode

assemblages in a nature reserve with historical pollution. Belgian Journal of Zoology

139, 1-15.

Bleeker, E. A. J. & Van Gestel, C. A. M. (2007). Effects of spatial and temporal variation in

metal availability on earthworms in floodplain soils of the river Dommel, The

Netherlands. Environmental Pollution 148, 824-832.

Bongers, T. & Bongers, M. (1998). Functional diversity of nematodes. Applied Soil Ecology

10, 239–251.

Bongers, T. & Ferris, H. (1999). Nematode community structure as a bioindicator in

environmental monitoring. Trends in Ecology & Evolution 14, 224-228.

Bongers, T. (1988). De Nematoden van Nederland. KNNV Bibliotheek Uitgeverij Pirola,

Schoorl, 408pp.

Bongers, T. (1990). The maturity index: an ecological measure of environmental disturbance

based on nematode species composition. Oecologia 83, 14-19.

Burnham, K. P., & Anderson D. R. (2002). Model selection and multimodel inference: a

practical information-theoretic approach. Springer, New York.

De Bakker, H. ( 1979). Major soils and soil regions in the Netherlands. Junk , The Hague and

PUDOC, Wageningen, published. 203pp.

De Deyn, G.B., Raaijmakers, C.E., van Ruijven, J., Berendse, F. & Van der Putten, W.H.

(2004). Plant species identity and diversity effects on different trophic levels of

nematodes in the soil food web. Oikos 106, 576–586

De Goede, R.G.M. & Bongers, T. (1994). Nematode community structure in relation to soil

and vegetation characteristics. Applied Soil Ecology 1, 29-44.

De Gruijter, J., Brus, D., Bierkens, M. & Knotters, M. (2006). Sampling for Natural Resource

Monitoring. Springer, Berlin.

Decraemer, W. & Hunt, D. (2006). Structure and classification. In: Perry, R. & Moens, M.

(Eds). Plant Nematology. Wallingford, UK, CABI publishing. pp. 1-33.

Ekschmitt, K., Bakonyi, G., Bongers, M., Bongers, T., Bostrom, S., Dogan, H., Harrison, A.,

Nagy, P., O‟Donnel, A.G., Papatheodorou, E.M., Sohlenius, B., Stamou, G.P. &

53 | P a g e

Wolters, V. (2001). Nematode community structure as indicator of soil functioning in

European grassland soils. European Journal of Soil Biology 37, 263–268.

Ettema, C. H. & Wardle, D. A. (2002). Spatial soil ecology. Trends in Ecology & Evolution

17, 177-183.

Ettema, C. H., Coleman, D. C., Vellidis, G., Lowrance, R. & Rathbun, S. L. (1998).

Spatiotemporal distributions of bacterivorous nematodes and soil resources in a

restored riparian wetland. Ecology 79, 2721-2734.

Ettema, C. H., Rathbun, S. L. & Coleman, D. C. (2000). On spatiotemporal patchiness and the

coexistence of five species of Chronogaster (Nematoda: Chronogasteridae) in a

riparian wetland. Oecologia 125, 444-452.

Ferris, H. & Bongers, T. (2006). Nematode indicators of organic enrichment. Journal of

Nematology 38, 3-12.

Ferris, H., & Matute, M. M. (2003). Structural and functional succession in the nematode

fauna of a soil food web. Applied Soil Ecology 23, 93–110.

Ferris, H., Bongers, T., & De Goede, R.G.M. (2001). A framework for soil food web

diagnostics: extension of the nematode faunal analysis concept. Applied Soil Ecology

18, 13–29.

Ferris, H., Venette, R.C. & Scow, K.M. (2004). Soil management to enhance bacterivore and

fungivore nematode populations and their nitrogen mineralisation function. Applied

Soil Ecology 25, 19–35.

Georgieva, S. S., Mcgrath, S. P., Hooper, D. J. & Chambers, B. S. (2002). Nematode

communities under stress: the long-term effects of heavy metals in soil treated with

sewage sludge. Applied Soil Ecology 20, 27-42.

Gorres, J. H., Dichiaro, M. J., Lyons, J. B. & Amador, J. A. (1998). Spatial and temporal

patterns of soil biological activity in a forest and an old field. Soil Biology and

Biochemistry 30, 219-230.

Hanel, L. (1996). Composition and seasonal changes of soil nematode community in a south

Bohemian meadow. Acta Societatis Zoologicae Bohemicae 60, 103–114.

Hanel, L. (2003). Recovery of soil nematode populations from cropping stress by natural

secondary succession to meadow land. Applied Soil Ecology 22, 255-270.

Heininger, P., Höss, S., Claus, E., Pelzer, J. & Traunspurger, W. (2007). Nematode

communities in contaminated river sediments. Environmental Pollution 146, 64–76

Hengl, T., Heuvelink, G.B.M., & Rossiter, D.G. (2007). Hengl, T., Heuvelink, G.B.M., and

Rossiter, D.G. (2007). About regression kriging: From equations to case studies.

Computers and Geosciences 33, 1301-1315.

54 | P a g e

Horner-Devine, M. C., Silver, J. M., Leibold, M. A., Bohannan, B. J. M., Colwell, R. K.,

Fuhrman, J. A., Green, J. L., Kuske, C. R., Martiny, J. B. H. & Muyzer, G. (2007). A

comparison of taxon co-occurrence patterns for macro-and microorganisms. Ecology

88, 1345-1353.

Hoss, S. & Traunspurger, W. (2003). Nematodes. In: Markert, B.A., Breure, A.M. &

Zechmeister, H.G. (Eds.), Bioindicators and Biomonitors. Elsevier, Oxford, pp. 529–

554.

Jairajpuri, M. S. & Ahmad, W. (1992). Dorylaimida. Free-living, Predaceous and Plant-

parasitic Nematodes. E.J. Brill, Leiden, 458 pp.

Kardol, P., Bezemer, T., Van Der Wal, A. & Van Der Putten, W. (2005). Successional

trajectories of soil nematode and plant communities in a chronosequence of ex-arable

lands. Biological Conservation 126, 317-327.

Korenko, V. & Schmidt, C. (2006). Effects of agricultural practices in the rice crop system on

nematode communities in Uruguay. Nematologia Mediterranea 34, 151-159.

Korthals, G. W., Ende, A., Megen, H., Lexmond, T. M., Kammenga, J. E. & Bongers, T.

(1996). Short-term effects of cadmium, copper, nickel and zinc on soil nematodes

from different feeding and life-history strategy groups. Applied Soil Ecology 4, 107-

117.

Lambshead, P.J.D. (2004). Marine Nematode Biodiversity. In: Chen, Z.X., Chen, S.Y. &

Dickson, D.W. (Eds). Nematology: Advances and Perspectives. Wallingford, CABI

Publishing, pp.439-468.

Lavelle, P. & Spain, A. (2001). Soil ecology. Springer, pp. 203-213.

Liang, W., Jiang, Y., Li, Q., Liu, Y. & Wen, D. (2005). Spatial distribution of bacterivorous

nematodes in a Chinese Ecosystem Research Network (CERN) site. Ecological

Research 20, 481-486.

Martiny, J. B. H., Bohannan, B. J. M., Brown, J. H., Colwell, R. K., Fuhrman, J. A., Green, J.

L., Horner-Devine, M. C., Kane, M., Krumins, J. A. & Kuske, C. R. (2006). Microbial

biogeography: putting microorganisms on the map. Nature Reviews Microbiology 4,

102-112.

McSorley, R. & Frederick, J.J. (2000). Short-term effects of cattle grazing on nematode

communities in Florida pastures. Nematropica 30, 211–221.

Monroy, F., Van Der Putten, W. H., Yergeau, E., Mortimer, S. R., Duyts, H. & Bezemer, T.

M. (2012). Community patterns of soil bacteria and nematodes in relation to

geographic distance. Soil Biology and Biochemistry 45, 1-7

55 | P a g e

Mulder, C., Schoutena, A.J., Hund-Rinkeb, K., & Breurea, A.M. (2005). The use of

nematodes in ecological soil classification and assessment concepts. Ecotoxicology

and Environmental Safety 62, 278–289.

Nagy, P., Bakonyi, G., Bongers, T., Kadar, I., Fabian, M. & Kiss, I. (2004). Effects of

microelements on soil nematode assemblages seven years after contaminating an

agricultural field. Science of the total environment 320, 131-143.

Neher, D. A. (2001). Role of nematodes in soil health and their use as indicators. Journal of

Nematology 33, 161-168.

Neher, D.A. & Darby, B.J. (2009). General community indices that can be used for analysis

of nematode assemblages. In: Nematodes as Environmental Indicators. Wallingford:

CAB International, pp. 110-123.

Odeh, I., McBratney, A. & Chittleborough, D. (1994). Spatial prediction of soil properties

from landform attributes derived from a digital elevation model. Geoderma 63, 197–

214.

Odeh, I., McBratney, A. & Chittleborough, D. (1995). Further results on prediction of soil

properties from terrain attributes: heterotopic cokriging and regression-kriging.

Geoderma 67, 215– 226.

Oostenbrink, M. (1960). Estimating nematode populations by some selected methods. In:

Nematology (J.N. Sasser, W.R. Jenkins, eds.), pp. 85-102. The University of North

Carolina Press, Chapel Hill.

Park, B. Y., Lee, J. K., Ro, H. M. & Kim, Y. H. (2011). Effects of heavy metal contamination

from an abandoned mine on nematode community structure as an indicator of soil

ecosystem health. Applied Soil Ecology 51, 17-24.

Park, J. J. (2012). Spatial pattern analysis of entomopathogenic and other free‐living

nematodes at landscape scales. Entomological Research 42, 104-110.

Patel, J.K., Patel, N.M. & Shiyani, R.L. (2001). Coefficient of variation in field experiments

and yardstick thereof – An empirical study. Current Science 81, 1163-1164.

Poinar, G. O., Jr. (1983). The natural history of nematodes. Prentice-Hall, Englewood Cliffs,

New Jersey, USA.

Postma, J.F. (1995). Adaptation to metals in the midge Chironomus riparius. PhD thesis,

University of Amsterdam, Amsterdam, The Netherlands.

R Development Core Team (2013). R: A language and environment for statistical computing.

R Foundation for Statistical Computing, Vienna, Austria.

Robertson, G. P., & Freckman, D. W. (1995). The spatial distribution of nematode trophic

groups across a cultivated ecosystem. Ecology 76,1425–1432.

56 | P a g e

Ruess, L., (2003). Nematode soil faunal analysis of decomposition pathways in different

ecosystems. Nematology 5, 179–181.

Šalamún, P., Renčo, M., Kucanová, E., Brázová, T., Papajová, I., Miklisová, D. & Hanzelová,

V. (2012). Nematodes as bioindicators of soil degradation due to heavy metals.

Ecotoxicology 21, 2319-2330.

Sánchez-Moreno, S. & Navas, A. (2007). Nematode diversity and food web condition in

heavy metal polluted soils in a river basin in southern Spain. European Journal of Soil

Biology 43, 166-179.

Schratzberger, M., Bolam, S., Whomersley, P. & Warr, K. (2006) Differential response of

nematode colonist communities to the intertidal placement of dredged material.

Journal of Experimental Marine Biology and Ecology 334, 244–255.

Shao, Y., Zhang, W., Shen, J., Zhou, L., Xia, H., Shu, W., Ferris, H. & Fu, S. (2008).

Nematodes as indicators of soil recovery in tailings of a lead/zinc mine. Soil Biology

& Biochemistry 40, 2040–2046.

Shukurov, N., Pen-Mouratov, S. & Steinberger, Y. (2005). The impact of Almalyk industrial

complex on soil chemical and biological properties. Environmental Pollution 136, 331

– 340.

Siddiqi, M.R. (1986). Tylenchida. Parasites of Plants and Insects. Commonwealth

Agricultural Bureaux, Farnham Royal, Slough, 645 pp.

Sochova, I., Hofman, J. & Holoubek, I. (2006). Using nematodes in soil ecotoxicology.

Environment International 32, 374–383.

Stoyan, H., De-Polli, H., Bohm, S., Robertson, G. P. & Paul, E. A. (2000). Spatial

heterogeneity of soil respiration and related properties at the plant scale. Plant and

Soil 222, 203-214.

Tenuta, M. & Ferris, H. (2004). Relationship between nematode life-history classification and

sensitivity to stressors: ionic and osmotic effects of nitrogenous solutions. Journal of

Nematology 36, 85–94.

Van Vliet, P.C.J. & De Goede, R.G.M. (2008). Nematode-based risk assessment of mixture

toxicity in a moderately polluted river floodplain in The Netherlands. Science of the

Total Environment 406, 449-454.

Venables W.N. and Ripley B.D. (2002). Modern Applied Statistics with S. Springer-Verlag,

New York, 4th edition.

Viketoft, M., Palmborg, C., Sohlenius, B., Huss-Danell, K. & Bengtsson, J. (2005) Plant

species effects on soil nematode communities in experimental grasslands. Applied Soil

Ecology 30, 90–103.

57 | P a g e

Viketoft, M., Sohlenius, B., Boström, S., Palmborg, C., Bengtsson, J., Berg, M.P. & Huss-

Danell, K. (2011). Temporal dynamics of soil nematode communities in a grassland

plant diversity experiment. Soil Biology & Biochemistry 43, 1063-1070.

Vos, M. & Velicer, G. J. (2008). Isolation by Distance in the Spore-Forming Soil Bacterium

Myxococcus xanthus. Current Biology 18, 386-391.

Walvoort, D. J. J., Brus, D. J., & De Gruijter, J. J. (2010). An R package for spatial coverage

sampling and random sampling from compact geographical strata by k-means.

Computers & Geosciences 36, 1261-1267.

Wasilewska, L. (1994). The effect of age of meadows on succession and diversity in soil

nematode communities. Pedobiologia 38, 1–11.

Webster, R. & Oliver, M. A. (2007). Geostatistics for Environmental Scientists, Second

Edition. John Wiley & Sons, Chichester, 307pp.

Yeates, G.W., Bongers, T., De Goede, R.G.M., Freckman, D.W. & Georgieva, S.S. (1993).

Feeding habits in soil nematode families and genera – an outline for soil ecologists.

Journal of Nematology 25, 315–331.

Yeates, G.W., Ferris, H., Moens, T. & Van Der Putten, W.H. (2009). The role of nematodes

in ecosystems. In: Nematodes as Environmental Indicators. Wallingford: CAB

International, pp. 1-44.

Yeates, G.W., Percival, H.J. & Parshotam, A. (2003). Soil nematode responses to year-to-year

variation of low levels of heavy metals. Australian Journal of Soil Research 41, 613–

625.

Zeileis, A.; Kleiber, C. & Jackman, S. (2008). Regresssion models for count data in R.

Journal of Statistical Software 27, 1-25.

Zhang, W., Wang, X., Li, Q., Jiang, Y. & Liang, W. (2007). Soil nematode responses to

heavy metal stress. Helminthologia 44, 87-91.

Zolda, P. (2006). Nematode communities of grazed and ungrazed semi-natural steppe

grasslands in Eastern Austria. Pedobiologia 50, 11-22.

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Appendix 1. Sample R script for ordinary kriging predictions of soil physical properties

from 2008 measurements

# Extrapolation of soil properties from 2008 measurements

rm(list = ls()) # clean memory

graphics.off() # close graphic windows

# load libraries:

library(foreign)

library(RColorBrewer)

library(rgdal) library(sp)

library(maptools)

library(gstat)

# read data file:

malpie = read.table("malpie_data.txt", header = TRUE)

class(malpie)

names(malpie)

# translation to get locations at the right spot:

malpie$Coord.X = malpie$Coord.X + 255 malpie$Coord.Y = malpie$Coord.Y + 95

# make spatial:

coordinates(malpie)=~Coord.X+Coord.Y

# read boundary of study area:

border = readShapePoly("border.shp")

# plot observations:

# windows(width=5, height=8)

spplot(malpie, zcol="Cdtot.s", scales=list(draw=T), cex=1.4, main="Total Cadmium concentration topsoil",

col.regions=brewer.pal(4, "Oranges"),

sp.layout=list("sp.polygons", border))

#log transformation to remove skewness

malpie$logCdtot.s = log(malpie$Cdtot.s + 1, 10)

spplot(malpie, zcol="logCdtot.s", scales=list(draw=T), cex=1.4,

main="10log of total Cadmium concentration topsoil",

col.regions=brewer.pal(4, "Oranges"),

sp.layout=list("sp.polygons", border))

hist(malpie$logCdtot.s)

hist(malpie$Cdtot.s)

# read and plot DEM:

grid = readGDAL("ahn_malpie.asc")

grid$elev = grid$band1

grid$band1 = NULL

windows(width = 6, height = 6)

spplot(grid, zcol = "elev", col.regions = bpy.colors(), scales=list(draw=T))

# read and plot distance to river:

temp = readGDAL("dist_to_river.asc")

grid$dist = temp$band1 rm(temp)

windows(width = 6, height = 6)

spplot(grid, zcol = "dist", col.regions = bpy.colors(), scales=list(draw=T))

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# read and display mask:

temp = readGDAL("mask.asc")

grid$mask = temp$band1

rm(temp)

windows(width = 6, height = 6)

spplot(grid, zcol = "mask", col.regions = bpy.colors(), scales=list(draw=T))

names(grid)

# read and plot rasterized soil map: temp = readGDAL("bod50rast.asc")

grid$soil = temp$band1

rm(temp)

table(grid$soil)

class(grid$soil)

windows(width = 6, height = 6)

spplot(grid, zcol = "soil", col.regions=brewer.pal(6, "Set2"), scales=list(draw=T),

at = seq(from = -0.5, to = 5.5, by = 1))

soilrastdbf = read.dbf("bod50rast.dbf")

soilrastdbf

# zoom in on mask:

zgrid = grid[33:198,99:191]

# masking all maps:

zgrid$elev = 0*zgrid$mask + zgrid$elev

zgrid$soil = 0*zgrid$mask + zgrid$soil

zgrid$dist = 0*zgrid$mask + zgrid$dist

# plotting zoomed maps with data points:

pts = list("sp.points", malpie, pch = 3, lwd = 1.5, col = "grey")

# windows(width = 5, height = 7) spplot(zgrid, zcol = "elev", col.regions = bpy.colors(),

scales=list(draw=T), sp.layout = list(pts),

xlim = c(159275,159740), ylim = c(368440,369275))

# windows(width = 5, height = 7)

spplot(zgrid, zcol = "dist", col.regions = bpy.colors(),

scales=list(draw=T), sp.layout = list(pts),

xlim = c(159275,159740), ylim = c(368440,369275))

table(zgrid$soil)

# windows(width = 5, height = 7)

spplot(zgrid, zcol = "soil", col.regions = brewer.pal(4, "Set2"), scales=list(draw=T), sp.layout = list(pts),

at = seq(from = -0.5, to = 3.5, by = 1),

xlim = c(159275,159740), ylim = c(368440,369275))

# image(zgrid,4)

# points(159400,368800)

# extending dataset with spatial properties:

temp <- over(geometry(malpie),grid)

malpie$elev = temp$elev

malpie$dist = temp$dist

malpie$soil = temp$soil

names(malpie)

table(malpie$soil)

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# relationships with elev and dist:

windows(width = 6, height = 6)

plot(malpie$elev, log(malpie$Cdtot.s)+1)

windows(width = 6, height = 6)

plot(malpie$dist, log(malpie$Cdtot.s)+1)

# geostatistics for Cdtot.s :

# define gstat object and compute variogram:

# malpie = subset(malpie, !is.na(malpie$logCdtot.s)) # needed if missing values

g = gstat(id = c("logCdtot.s"), formula = logCdtot.s~1, data = malpie)

vg = variogram(g, cutoff=500)

vg = variogram(g, boundaries = c(30,70,120,200,300,450))

windows(width = 8, height = 6)

plot(vg, plot.numbers = TRUE)

# choose initial variogram model and plot:

vgm = vgm(nugget=0, psill=0.5, range=140, model="Mat", kappa = 0.9)

plot(vg, vgm)

# fit variogram model:

vgm = fit.variogram(vg, vgm)

windows(width = 8, height = 6)

plot(vg, vgm, main = "Variogram 10logCdtot.s")

vgm

# ordinary kriging:

malpie.krig = krige(logCdtot.s~1, malpie, newdata = zgrid, vgm)

names(malpie.krig)[1] = "ok.pred"

names(malpie.krig)[2] = "ok.var"

# plot ordinary kriging map:

min(malpie.krig$ok.pred, na.rm = TRUE)

max(malpie.krig$ok.pred, na.rm = TRUE)

windows(width = 6, height = 9)

spplot(malpie.krig, zcol = "ok.pred", col.regions = bpy.colors(),

main = " Ordinary kriging logCdtot.s",

scales=list(draw=T), at = seq(from = -0.02, to = 2.2, by = 0.1))

savePlot(filename="logCdtot_okpred",type="png")

savePlot(filename="logCdtot_okpred",type="pdf")

malpie.krig$Cdtot.mean <- 10^(malpie.krig$ok.pred + 0.5*malpie.krig$ok.var) - 1

writeAsciiGrid(malpie.krig, attr="Cdtot.mean", "Cdtot.asc") writeAsciiGrid(malpie.krig, "logCdtot_okpred.asc", "ok.pred")

#Back transformation of the log transformed values

malpie.krig$Cdtot.median <- 10^(malpie.krig$ok.pred) - 1

malpie.krig$Cdtot.mean <- 10^(malpie.krig$ok.pred + 0.5*malpie.krig$ok.var) - 1

spplot(malpie.krig, zcol = "Cdtot.median", col.regions = bpy.colors())

spplot(malpie.krig, zcol = c("Cdtot.median","Cdtot.mean"), col.regions = bpy.colors())

names(malpie.krig)

# ordinary kriging to point locations and back transformations to mean and median:

cdtots = read.table("israelocs.csv", sep=",",header = TRUE) cdtots

class(cdtots)

cdtots.sp = cdtots

coordinates(cdtots.sp)=~Coord.X+Coord.Y

cdtots.krig = krige(logCdtot.s~1, malpie, newdata = cdtots.sp, vgm)

cdtots.krig$var1.sd = sqrt(cdtots.krig$var1.var)

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cdtots.krig$Cdtot.median <- 10^(cdtots.krig$var1.pred) - 1

cdtots.krig$Cdtot.mean <- 10^(cdtots.krig$var1.pred + 0.5*cdtots.krig$var1.var) - 1

cdtots.krig

write.table(cdtots.krig, file = "cdtots.csv", sep = ",", row.names=F)

Appendix 2. Sample R script for geostatistical computation of semivariograms of

observed data (Total nematode abundance)

# plot of observations and semivariograms of nematode data

rm(list = ls()) # clean memory

graphics.off() # close graphic windows

# load libraries:

library(foreign)

library(RColorBrewer)

library(rgdal)

library(sp) library(maptools)

library(gstat)

# read data file:

malpie = read.table("malpie_data.txt", header = TRUE)

class(malpie)

names(malpie)

# make spatial:

coordinates(malpie)=~Coord.X+Coord.Y

# read boundary of study area:

border = readShapePoly("border.shp")

#log transformation of nematode counts to remove skewness

malpie$logTNEM=log(malpie$TNEM+1)

# plot observations for:

# windows(width=5, height=8)

spplot(malpie, zcol="TNEM", scales=list(draw=T), cex=1.4, cuts=c(0,500,1000,2000,5000,10000),

main="Total nematode abundance",

col.regions=brewer.pal(4, "Oranges"), sp.layout=list("sp.polygons", border))

savePlot(filename="obsptsTNEM", type="png")

#manipulate variogram plot characteristics

trellis.par.get("fontsize")->fontsize

fontsize$default<-16

fontsize$points<-16

fontsize$text<-20

trellis.par.set("fontsize",fontsize)

trellis.par.get("fontsize")

# geostatistics for log TNEM :

# define gstat object and compute variogram:

# malpie = subset(malpie, !is.na(malpie$logTNEM)) # needed if missing values

g = gstat(id = c("logTNEM"), formula = logTNEM~1, data = malpie)

vg = variogram(g, cutoff=500)

vg = variogram(g, boundaries = c(30,70,120,200,300,450))

windows(width = 8, height = 6)

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plot(vg, plot.numbers = TRUE)

# choose initial variogram model and plot:

vgm = vgm(nugget=0.6, psill=0.5, range=200, model="Sph")

plot(vg, vgm)

# fit variogram model:

vgm = fit.variogram(vg, vgm)

windows(width = 8, height = 6) plot(vg, vgm, ylim=c(0,1.4), pch=19, lwd=2)

vgm

savePlot(filename="semivarTNEM", type="png")

Appendix 3. R script for multiple linear regression analyses

#Modelling relationships using multiple linear regression

#Modelling relationship between MI and soil properties in a stepwise selection manner

rm(list = ls()) # clean memory

graphics.off() # close graphic windows

#Read the txt table containing the dataset

malpie = read.table("malpie_data.txt", header = TRUE)

dim(malpie)

names(malpie)

summary(malpie)

library(MASS)

attach(malpie)

#regression model of MI using the explanatory variables (only total heavy metal conc) modell1MI<-lm(MI~Clay+OrgMat+Moisture+Cdtot+Zntots+pH)

step <- stepAIC(modell1MI, direction="backward")

step$anova

#using the final model (best model) after the stepwise regression

fit1MI<- lm(MI ~ Cdtot + Zntots + pH)

summary(fit1MI)

#Assessing Goodness of fit using 4 plots

par(mfrow=c(2,2))

plot(fit1MI)

hist(fit1MI$resid)

limits = c(min(MI, fitted(fit1MI)), max(MI, fitted(fit1MI))) windows(width = 5, height = 5)

plot(MI, fitted(fit1MI), xlab = "Observed", ylab = "Fitted",

xlim = limits, ylim = limits, col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2, main = "Maturity Index")

abline(a=0, b=1, col = "turquoise3", lwd = 2)

plot(fitted(fit1MI), residuals(fit1MI), xlab = "Fitted",

ylab = "Residuals", col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2)

abline(h=0)

#Modelling relationship between SI and soil properties in a stepwise selection manner

modell1SI<-lm(SI~Clay+OrgMat+Moisture+Cdtot+Zntots+pH)

step <- stepAIC(modell1SI, direction="backward")

step$anova

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#using the final model (best predictors) after the stepwise regression

attach(malpie)

fit1SI<- lm(SI ~ OrgMat+Moisture + pH)

summary(fit1SI)

#Assessing Goodness of fit using 4 plots

par(mfrow=c(2,2))

plot(fit1SI)

hist(fit1SI$resid)

limits = c(min(SI, fitted(fit1SI)), max(SI, fitted(fit1SI)))

windows(width = 5, height = 5)

plot(SI, fitted(fit1SI), xlab = "Observed", ylab = "Fitted",

xlim = limits, ylim = limits, col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2, main = "Structure Index")

abline(a=0, b=1, col = "turquoise3", lwd = 2)

plot(fitted(fit1SI), residuals(fit1SI), xlab = "Fitted",

ylab = "Residuals", col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2)

abline(h=0)

#regression model of SI using the explanatory variables (only available heavy metal conc)

modell2SI<-lm(SI~Clay+OrgMat+Moisture+Cdav+Znav+pH)

step <- stepAIC(modell2SI, direction="backward")

step$anova

#using the final model (best model) after the stepwise regression

fit2SI<- lm(SI ~ OrgMat+Moisture+pH)

summary(fit2SI)

#Assessing Goodness of fit using 4 plots

par(mfrow=c(2,2)) plot(fit2SI)

hist(fit2SI$resid)

limits = c(min(SI, fitted(fit1SI)), max(SI, fitted(fit1SI)))

windows(width = 5, height = 5)

plot(MI, fitted(fit1SI), xlab = "Observed", ylab = "Fitted",

xlim = limits, ylim = limits, col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2, main = "Structure Index")

abline(a=0, b=1, col = "turquoise3", lwd = 2)

plot(fitted(fit1SI), residuals(fit1MI), xlab = "Fitted",

ylab = "Residuals", col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2)

abline(h=0)

#Modelling relationship between HI and soil properties in a stepwise selection manner

modell1HI<-lm(HI~Clay+OrgMat+Moisture+pH+Cdtot+Zntots)

step <- stepAIC(modell1HI, direction="backward")

step$anova

#using the final model (best predictors) after the stepwise ##regression

attach(malpie)

fit1HI<- lm(HI ~ Moisture + pH) summary(fit1HI)

#assessing the fitness of the model

par(mfrow=c(2,2))

plot(fit1HI)

hist(fit1HI$resid)

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limits = c(min(HI, fitted(fit1HI)), max(HI, fitted(fit1HI)))

windows(width = 5, height = 5)

plot(HI, fitted(fit1HI), xlab = "Observed", ylab = "Fitted",

xlim = limits, ylim = limits, col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2, main = "Shannon Index")

abline(a=0, b=1, col = "turquoise3", lwd = 2)

plot(fitted(fit1HI), residuals(fit1HI), xlab = "Fitted",

ylab = "Residuals", col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2) abline(h=0)

#Modelling relationship between GR and soil properties in a stepwise selection manner

modell1GR<-lm(GR~Clay+OrgMat+Moisture+Cdtot+Zntots+pH)

step <- stepAIC(modell1GR, direction="backward")

step$anova

#using the final model (best predictors) after the stepwise regression

fit1GR<- lm(GR ~ Clay + Zntots + pH)

summary(fit1GR) #assessing the fitness of the model

par(mfrow=c(2,2))

plot(fit1GR)

hist(fit1GR$resid)

limits = c(min(GR, fitted(fit1GR)), max(GR, fitted(fit1GR)))

windows(width = 5, height = 5)

plot(GR, fitted(fit1GR), xlab = "Observed", ylab = "Fitted",

xlim = limits, ylim = limits, col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2, main = "Genera Richness")

abline(a=0, b=1, col = "turquoise3", lwd = 2) plot(fitted(fit1GR), residuals(fit1GR), xlab = "Fitted",

ylab = "Residuals", col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2)

abline(h=0)

Appendix 4. R script for negative binomial regression analyses

#poisson regression modelling(for count data)

rm(list = ls()) # clean memory

graphics.off() # close graphic windows

#Read the txt table containing the dataset

malpie = read.table("malpie_data.txt", header = TRUE)

dim(malpie)

names(malpie)

summary(malpie)

#Total Nematode abundance (TNEM)

attach(malpie)

modellTNEM <- glm(TNEM~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie,

family=poisson())

step <- stepAIC(modellTNEM, direction="backward")

step$anova ##All predictors were found to be important

attach(malpie)

modellTNEM <- glm(TNEM~pH + OrgMat +Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

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summary(modellTNEM)

##Negative binomial regression recommended for overdispersed data

attach(malpie)

modellTNEM <- glm.nb(TNEM~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

step <- stepAIC(modellTNEM, direction="backward")

step$anova

#Negative binomial modelling for best predictors

attach(malpie) modellTNEM <- glm.nb(TNEM~pH + OrgMat + Clay, data=malpie)

summary(modellTNEM)

##Assesing of goodness of fit

limits = c(min(TNEM, fitted(modellTNEM)), max(TNEM, fitted(modellTNEM)))

windows(width = 5, height = 5)

plot(TNEM, fitted(modellTNEM), xlab = "Observed", ylab = "Fitted",

xlim = limits, ylim = limits, col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2, main = "Total nematode count")

abline(a=0, b=1, col = "turquoise3", lwd = 2)

plot(fitted(modellTNEM), residuals(modellTNEM), xlab = "Fitted",

ylab = "Residuals", col = "green", pch = 21, bg = "blue", cex = 1.2, cex.lab = 1.2, cex.axis = 1.2)

abline(h=0)

#CP2

attach(malpie)

modellCP2 <- glm(CP2~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

step <- stepAIC(modellCP2, direction="backward")

step$anova

## All predictors were found to be important

attach(malpie) modellCP2 <- glm(CP2~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

summary(modellCP2)

##Negative binomial regression also recommended for overdispersed data

attach(malpie)

modellCP2 <- glm.nb(CP2~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

step <- stepAIC(modellCP2, direction="backward")

step$anova

## Negative binomial modelling for best predictors

##Final Model: CP2 ~ pH + Moisture + Clay

attach(malpie) modellCP2 <- glm.nb(CP2~pH+Moisture+Clay, data=malpie)

summary(modellCP2)

##Assesing of goodness of fit

limits = c(min(CP2, fitted(modellCP2)), max(CP2, fitted(modellCP2)))

windows(width = 5, height = 5)

plot(CP2, fitted(modellCP2), xlab = "Observed", ylab = "Fitted",

xlim = limits, ylim = limits, col = "green", pch = 21, bg = "blue",

cex = 1.2, cex.lab = 1.2, cex.axis = 1.2, main = "CP2 nematode number")

abline(a=0, b=1, col = "turquoise3", lwd = 2)

plot(fitted(modellCP2), residuals(modellCP2), xlab = "Fitted",

ylab = "Residuals", col = "green", pch = 21, bg = "blue", cex = 1.2, cex.lab = 1.2, cex.axis = 1.2)

abline(h=0)

#CP3-5

attach(malpie)

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modellCP35 <- glm(CP35~pH + OrgMat+Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

step <- stepAIC(modellCP35, direction="backward")

step$anova

## All predictors were found to be important

attach(malpie)

modellCP35 <- glm(CP35~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

summary(modellCP35)

##overdispersion was observed ##Negative binomial regression also recommended for overdispersed data

attach(malpie)

modellCP35 <- glm.nb(CP35~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

step <- stepAIC(modellCP35, direction="backward")

step$anova

# Negative binomial modelling for best predictors

#Final Model:CP35 ~ pH + OrgMat + Clay

attach(malpie)

modellCP35 <- glm.nb(CP35~pH+OrgMat+Clay, data=malpie) summary(modellCP35)

#FF

attach(malpie)

modellFF <- glm(FF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

step <- stepAIC(modellFF, direction="backward")

step$anova

## All predictors were found to be important

attach(malpie)

modellFF <- glm(FF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

summary(modellFF)

##overdispersion was observed

##Negative binomial regression also recommended for overdispersed data

attach(malpie)

modellFF<- glm.nb(FF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

step <- stepAIC(modellFF, direction="backward")

step$anova

## Negative binomial modelling for best predictors

## Final Model:FF ~ pH + OrgMat + Cdtot + Zntots + Clay

attach(malpie) modellFF <-glm.nb(FF~pH+OrgMat+Cdtot+Zntots+Clay, data=malpie)

summary(modellFF)

###BF

attach(malpie)

modellBF <- glm(BF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

step <- stepAIC(modellBF, direction="backwards")

step$anova

## All predictors were found to be important

attach(malpie)

modellBF <- glm(BF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson()) summary(modellBF)

#overdispersion was observed

#Negative binomial regression also recommended for overdispersed count data

attach(malpie)

modellBF<- glm.nb(BF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

67 | P a g e

step <- stepAIC(modellBF, direction="backward")

step$anova

# Negative binomial modelling for best predictors

#Final Model: BF ~ pH + Moisture + Clay

attach(malpie)

modellBF <-glm.nb(BF~pH + Moisture + Clay, data=malpie)

summary(modellBF)

###PF

attach(malpie)

modellPF <- glm(PF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

step <- stepAIC(modellPF, direction="backward")

step$anova

## All predictors were found to be important

attach(malpie)

modellPF <- glm(PF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

summary(modellPF)

##overdispersion was observed

##Negative binomial regression also recommended for overdispersed count data attach(malpie)

modellPF<- glm.nb(PF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

step <- stepAIC(modellPF, direction="backward")

step$anova

## Negative binomial modelling for best predictors

## Final Model:PF ~ pH + OrgMat

attach(malpie)

modellPF <-glm.nb(PF ~ pH + OrgMat, data=malpie)

summary(modellPF)

###RHF attach(malpie)

modellRHF <- glm(RHF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

step <- stepAIC(modellRHF, direction="backward")

step$anova

## All predictors were found to be important

attach(malpie)

modellRHF <- glm(RHF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie,

family=quasipoisson())

summary(modellRHF)

##overdispersion was observed

##Negative binomial regression recommended for overdispersed count data attach(malpie)

modellRHF<- glm.nb(RHF~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

step <- stepAIC(modellRHF, direction="backward")

step$anova

## Negative binomial modelling for some predictors

## Final Model:RHF ~ 1

attach(malpie)

modellRHF <-glm.nb(RHF~ pH+OrgMat+Moisture, data=malpie)

summary(modellRHF)

##OMV

attach(malpie)

modellOMV <- glm(OMV~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

step <- stepAIC(modellOMV, direction="backward")

step$anova

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## All predictors were found to be important

attach(malpie)

modellOMV <- glm(OMV~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

summary(modellOMV)

##overdispersion was observed

modellOMV<- glm.nb(OMV~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

step <- stepAIC(modellOMV, direction="backward")

step$anova ## Negative binomial modelling for best predictors

## Final Model:OMV ~ OrgMat+Cdtot+Moisture

attach(malpie)

modellOMV <-glm.nb(OMV~OrgMat+Cdtot+Moisture, data=malpie)

summary(modellOMV)

###PRED

attach(malpie)

modellPRED <- glm(PRED~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

step <- stepAIC(modellPRED, direction="backward")

step$anova ## All predictors were found to be important

attach(malpie)

modellPRED <- glm(PRED~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

summary(modellPRED)

##overdispersion was observed

##Negative binomial regression recommended for overdispersed count data

attach(malpie)

modellPRED<- glm.nb(PRED~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

step <- stepAIC(modellPRED, direction="backward")

step$anova

## Negative binomial modelling for best predictors

## Final Model: PRED ~ pH + OrgMat attach(malpie)

modellPRED <-glm.nb(PRED ~ pH + OrgMat, data=malpie)

summary(modellPRED)

###ECTOP

attach(malpie)

modellEctop <- glm(Ectop~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

step <- stepAIC(modellEctop, direction="backward")

step$anova ## All Predictors were found to be important

attach(malpie)

modellEctop <- glm(Ectop~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie, family=poisson())

summary(modellEctop)

##overdispersion was observed

##Negative binomial regression also recommended for overdispersed count data

attach(malpie)

modellEctop<- glm.nb(Ectop~pH + OrgMat + Cdtot + Zntots + Moisture + Clay, data=malpie)

step <- stepAIC(modellEctop, direction="backward")

step$anova

## Negative binomial modelling for best Predictors

## Final Model: Ectop ~ Cdtot + Zntots + Moisture + Clay

attach(malpie)

modellEctop <-glm.nb(Ectop ~ Cdtot + Zntots + Moisture + Clay, data=malpie)

summary(modellEctop)

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Appendix 5. Food web analysis graph based on moisture gradient classifications

(A=moisture content at field capacity, B=moisture content at saturation point)