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Soil Biology & Biochemistry 43 (2011) 2200e2207

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Soil Biology & Biochemistry

journal homepage: www.elsevier .com/locate/soi lbio

Soil fauna feeding activity in temperate grassland soils increases with legumeand grass species richness

Klaus Birkhofer a,*, Tim Diekötter a, Steffen Boch b, Markus Fischer b, Jörg Müller c,Stephanie Socher b, Volkmar Wolters a

a IFZ-Department of Animal Ecology, Justus Liebig University, Heinrich-Buff-Ring 26-32, D-35392 Giessen, Germanyb Institute of Plant Sciences, University of Bern, Altenbergrain 21, CH-3012 Bern, Switzerlandc Institute for Biochemistry and Biology, University of Potsdam, Maulbeerallee 1, 14469 Potsdam, Germany

a r t i c l e i n f o

Article history:Received 4 November 2010Received in revised form13 July 2011Accepted 14 July 2011Available online 26 July 2011

Keywords:Aboveebelowground interactionsBait laminaBiodiversity ecosystem function researchDecompositionPlant functional groupsSoil faunaSpatial scale

* Corresponding author. Tel.: þ49 (0)641 9935717;E-mail address: klaus.birkhofer@uni-giessen.de (K

0038-0717/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.soilbio.2011.07.008

a b s t r a c t

Edaphic fauna contributes to important ecosystem functions in grassland soils such as decompositionand nutrient mineralization. Since this functional role is likely to be altered by global change andassociated shifts in plant communities, a thorough understanding of large scale drivers on below-groundprocesses independent of regional differences in soil type or climate is essential. We investigated therelationship between abiotic (soil properties, management practices) and biotic (plant functional groupcomposition, vegetation characteristics, soil fauna abundance) predictors and feeding activity of soilfauna after accounting for sample year and study region. Our study was carried out over a period of twoconsecutive years in 92 agricultural grasslands in three regions of Germany, spanning a latitudinalgradient of more than 500 km. A structural equation model suggests that feeding activity of soil fauna asmeasured by the bait-lamina test was positively related to legume and grass species richness in bothyears. Most probably, a diverse vegetation promotes feeding activity of soil fauna via alterations of bothmicroclimate and resource availability. Feeding activity of soil fauna also increased with earthwormbiomass via a pathway over Collembola abundance. The effect of earthworms on the feeding activity insoil may be attributed to their important role as ecosystem engineers. As no additional effects of agri-cultural management such as fertilization, livestock density or number of cuts on bait consumption wereobserved, our results suggest that the positive effect of legume and grass species richness on the feedingactivity in soil fauna is a general one that will not be overruled by regional differences in management orenvironmental conditions. We thus suggest that agri-environment schemes aiming at the protection ofbelowground activity and associated ecosystem functions in temperate grasslands may generally focuson maintaining plant diversity, especially with regard to the potential effects of climate change on futurevegetation structure.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Soil biota play an important functional role in grasslandecosystems by affecting various processes such as decomposition ofdead organic matter (Bardgett and Cook, 1998; Wolters, 2001),nutrient mineralization (Verhoeff and Brussaard, 1990) andprimary production (Curry, 1987). Evidence suggests, however, thatthis role will be significantly affected by climate change (Wall et al.,2008; Briones et al., 2010) and associated shifts in plant commu-nities (Briones et al., 2009; Scherber et al., 2010). For being able tocounteract negative consequences for ecosystem functioning, it is

fax: þ49 (0)641 9935709.. Birkhofer).

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important to better understand the interacting abiotic and bioticdrivers of soil fauna activity at multiple spatial and temporal scales.

Investigations on the environmental factors affecting thefeeding activity of soil fauna have mostly been confined to smallspatial scales (e.g. Van Gestel et al., 2003; Van der Putten et al.,2004; Birkhofer et al., 2008). Moreover, many of these studiesonly covered short periods of time (e.g. Hobbelen et al., 2006;Hamel et al., 2007; Diekötter et al., 2010). As a consequence, therelationship between abiotic and biotic drivers such as agriculturalmanagement, soil properties or vegetation characteristics andfeeding activity of soil fauna at large spatial and temporal scalesremains largely unexplored. To fill this gap of knowledge, ourinvestigation on this relationship was carried out for a period oftwo consecutive years in 92 agricultural grassland plots of the socalled ‘Biodiversity-Exploratories’ funded by the German Research

Table 1a) General site characteristics of the three study regions in the DFG BiodiversityExploratories and b) continuous predictor variables with mean� standard error.

K. Birkhofer et al. / Soil Biology & Biochemistry 43 (2011) 2200e2207 2201

Foundation (DFG; Fischer et al., 2010), spanning a latitudinalgradient of more than 500 km.

We used the bait-lamina test after Von Törne (1990) as a stan-dardizedmethod tomeasure the feeding activity of the soil fauna insitu. Some of the assumptions underlying this approach may still bea matter of debate (Irmler, 1998; Knacker et al., 2003), as forexample the individual contribution of different soil fauna taxa tobait consumption is not easily assessed (e.g. Gongalsky et al., 2008).However, using bait consumption as a proxy for feeding activity insoil allows for complementing cumulative parameters such asdecomposition rate, nutrient mineralization or mass loss by a bio-logical parameter that provides direct insight into importantprocesses in soils. It is widely agreed that management, soilconditions and climate are the overarching drivers of invertebrateactivity in grassland soils (e.g. Curry, 1987). A major question of ourstudy thereforewas, whether the predicted effect of climate changeon the vegetation mentioned above can actually be expected tosignificantly alter the feeding activity of soil invertebrates at all. Inaddition to distance-based linear models, structural equationmodeling allowed us to detect the potential causal relationshipsamong habitat characteristics, various taxa of the soil fauna andfeeding activity of soil fauna that ultimately need to be understoodto provide informed predictions.

Upper case letters mark the variables that are included in the following indicatorgroups used in distance based linear models: 1) management, 2) plant functionalgroup composition, 3) vegetation characteristics, 4) soil properties, 5) soil fauna,6) sampling year and 7) exploratory identity.

Schwäbische Alb Hainich-Dün Schorfheide-Chorin

AEG7 HEG7 SEG7

a) General site characteristicsNumber of plots

2008/200916/12 20/12 20/12

Meadows 11 10 10Pastures 9 6 7Mown pastures 8 16 15Fertilized/unfertilized 17/11 19/13 13/19Average precipitation

(mm)938e963 750e800 520e600

Average temperature(�C)

6.5e8.0 6.5e7.5 8.0e8.4

Soil type Leptosols Cambisols, Gleysol, Histosol,

2. Material and methods

2.1. Site description

The temperate grassland sites of the ‘DFG Biodiversity-Explor-atories’ (www.biodiversity-exploratories.de) are located in threedifferent regions of Germany (Fig. 1). The region ‘Schorfheide-Chorin’ is situated in the lowlands of North-eastern Germany andsoils are dominated by glacially formed, sandy bog soils. The region‘Hainich-Dün’ is situated in Central Germany and soils containmore clay and form stagnosols with poor water penetration. Theregion ‘Schwäbische Alb’ is located in the low mountain ranges ofSouth-western Germany and soils are dominated by limestonederived Rendzina (Fischer et al., 2010). Grasslands were selected

Fig. 1. Map of the DFG biodiversity-exploratories in Germany.

according to different management types: meadows, mownpastures or pastures. Meadows and mown pastures were eitherfertilized or not, pastures were never fertilized and either grazed bycattle or sheep. Grazing intensity was converted to livestock unitsper hectare and day prior to analysis. All studied grasslands andtheir land-use types have persisted for at least 10 years. Informa-tion on average precipitation, average temperature and soil type(Table 1a) stem from the general data base of the ‘DFG Biodiversity-Exploratories’.

2.2. Sampling

In all three regions covered by the ‘DFG Biodiversity-Explor-atories’, feeding activity of soil fauna was surveyed in spring andautumn 2008 and 2009 using the bait-lamina test after Von Törne(1990). Bait-lamina sticks consisted of rigid plastic strips,6�160 mm, bearing a series of 16 holes (diameter 1.5 mm) drilledat 5 mm intervals in the lower half of the strip (terra protecta,

Stagnosols AlbeluvisolSample date,

spring 2008628.e29.04. 21.e23.04. 01.e03.05.

Sample date,autumn 20086

23.e25.09. 18.e20.09. 29.09.e01.10.

Sample date,spring 20096

04.e05.05. 27.e29.04. 22.e24.05.

Sample date,autumn 20096

21.e23.09. 02.e04.10. 22.e24.09.

b) Predictor variablesPlot size (ha)1 6� 1 16� 3 31� 3Fertilization

(kg N ha�1 yr�1)129� 6 37� 8 32� 8

Livestock density(LSU ha�1 d�1)1

159� 48 132� 26 68� 15

Cuts (yr�1)1 1.4� 0.2 1.1� 0.2 1.2� 0.1Species richness herbs2 20� 1.5 14� 1.1 9� 0.5Species richness grasses2 10� 0.5 8� 0.4 8� 0.4Species richness legumes2 3� 0.4 2� 0.2 1� 0.2Plant species evenness2 0.8� 0.01 0.8� 0.01 0.9� 0.02Plant height (cm)3 17.9� 1.9 15.8� 1.1 25.2� 2.6Plant cover (%)3 93� 1 86� 2 91� 1Soil pH4 6.4� 0.1 6.9� 0.1 6.8� 0.2Soil water

content (% dw)462� 2 35� 1 78� 11

Collembola abundance(indm�2)5

30833� 4279 19170� 3029 16842� 1952

Enchytraeidaeabundance (indm�2)5

1571� 257 1751� 226 2147� 333

Lumbricidaebiomass (gm�2)5

38.9� 19.0 21.7� 17.7 29.2� 20.9

K. Birkhofer et al. / Soil Biology & Biochemistry 43 (2011) 2200e22072202

Berlin). Holes were filled with a bait substrate made of cellulose,agar-agar, bentonite, and bran flakes (Eisenbeis, 1998). The stickswere placed vertically into the soil using a stainless steel knife toslit the soil. Altogether, 1024 bait lamina sticks (N¼ 4 in 2008 andN¼ 8 in 2009 per plot and date) were placed in the soil (cf. Försteret al., 2004; Hamel et al., 2007). The upmost hole was at surfacelevel and holes reached 7 cm deep into the soil. Bait lamina sampleswere taken from a 5� 5 m subplot in each grassland plot, witha minimum distance of 20 m to the plot’s edges. After 14 days ofexposure (see Table 1a for exact dates of placement), all baitlaminas were collected and bait consumption recorded; holes werecategorized as either empty or filled to provide an estimate ofoverall feeding activity (Graenitz and Bauer, 2000).

In addition to bait consumption, several potential predictorvariables, such as soil fauna abundance, vegetation characteristicsand soil properties were surveyed in 2008 and 2009 (see Table 1bfor details). Soil mesofauna abundance (Acari, Collembola andEnchytraeidae) was sampled by collecting four soil cores (diameter8 cm, depth 5 cm) from each of the subplots (see above) at eachsample date (Table 1a). Soil mesofauna was extracted usinga modified heat extraction system (Kempson et al., 1963). Earth-worms were hand sorted from two large soil cores (diameter20 cm; depth 10 cm) per date and subplot. Soil water content(Forster, 1995) and pH values (Hoffmann, 1991) were measuredfrom an independent soil sample at each plot and date.

Vegetation height and coverage were estimated from threereplicated 50� 50 cm areas per subplot. Percentage cover of plantswas estimated and maximum height was measured in each of thefour corners of a 50� 50 cm wooden frame in all three replicatesper subplot. In spring 2008 and 2009 we sampled vegetationrecords with a size of 4� 4 m and identified all occurring vascularplant taxa. From this data we calculated functional group speciesrichness for grasses, herbs and legumes and Pielou’s evenness forfunctional group richness. Management data resulted from a 2007and 2008 questionnaire of farmers (Fischer et al., 2010) and weused the data from the years prior to our sampling dates aspredictor for management intensity.

2.3. Statistical analysis

To analyze the relationship between soil fauna feeding data andabiotic and biotic predictor variables we followed a three-stepapproach: 1. We tested for the relationship between bait-laminaconsumption and indicator groups that included predictor vari-ables based on management intensity, plant functional groupcomposition, vegetation characteristics, soil properties and soil

Table 2Pearson correlation coefficients between all continuous variables from distance based lin

# Variable name 1 2 3 4 5 6

1 Lumbricidae biomass (m2)2 Enchytraeidae abundance (m2) 0.213 Collembola abundance (m2) 0.33 0.104 Plot size (ha) �0.01 �0.01 �0.155 Fertilization (kg N ha�1) 0.10 0.01 �0.08 �0.136 Cuts per year 0.21 0.19 0.03 �0.18 0.607 Livestock_density (LSU ha�1 d�1) �0.09 �0.07 �0.04 �0.08 �0.07 �8 Soil water content 0.35 0.22 0.05 0.24 �0.189 Soil pH �0.06 �0.17 �0.11 0.37 0.0810 Plant cover (%) 0.26 �0.15 0.17 0.18 �0.0111 Plant height (cm) 0.13 �0.03 0.09 0.44 0.0112 Species richness herbs 0.00 �0.24 0.17 �0.35 �0.39 �13 Species richness grasses 0.10 �0.08 0.13 �0.17 �0.22 �14 Species richness legumes 0.00 �0.19 0.11 �0.42 �0.15 �15 Plant species evenness �0.03 0.18 �0.03 0.31 0.1016 Bait consumption 0.16 �0.07 0.36 �0.21 �0.08 �

fauna (for classification see Table 1). This distance-based linearmodel first accounted for variation explained by differencesbetween sample years and among exploratories and then parti-tioned variation in bait consumption based on the pre-definedindicator groups (partial regression, see also Borcard et al., 1992;Legendre and Legendre, 1998). 2. Significant indicator groupsfrom this model were further tested with a second distance basedlinear model for the relationships between individual predictorvariables within the indicator group and bait consumption. 3. Togain a better understanding of our results we constructed a struc-tural equation model to test for multiple causal pathways amongthese significant predictor variables on soil fauna and baitconsumption.

Distance-based linear models (DistLM, Legendre and Anderson,1999; McArdle and Anderson, 2001) were used to identify theexplanatory power of different predictor variables and to build themost parsimonious model from these variables. Distance basedredundancy analysis was then used to perform an ordination of thefitted values from this models to show the relationship betweenindividual predictor variables from significant indicator groups andbait consumption. Prior to analysis individual predictor variableswere grouped into subsets, so called indicators (see Table 1), thatwere then analyzed for their combined relationship with baitconsumption (Anderson et al., 2008). Variation in bait consumptionwas partitioned among these subsets of variables (see Table 1;Anderson and Gribble, 1998) and the amount of variation thatvariables within each subgroup accounted for is given for eachindicator subgroup in the results section. Soil typewas not includedas predictor variable as it was not independent of exploratoryidentity and continuous management variables were selectedinstead of management classes, as they provide a more detaileddescription of management intensity. Distance-based linearmodelsare conceptually related tomultiple regression, but allow for the useof any distance measure, obtain P values by permutation and do notassume that errors are independent or normally distributed(Anderson et al., 2008). Resemblancematrices based on square-roottransformed bait consumption data were generated fromBrayeCurtis distances. All continuous variables (with the exceptionof soil pH)were log(xþ 1) transformed prior to analysis. The highestcorrelation between individual predictor variables was 0.7 (seeTable 2), a value at which multi-colinearity may not be consideredproblematic (Anderson et al., 2008). The most parsimoniousdistance-based linear model was selected applying the Akaikeinformation criterion (AIC).We used a stepwise selection procedureand P values were obtained by 10,000 permutations using thesoftware PRIMER 6.1.11 with the PERMANOVA 1.0.1 add-on.

ear models and structural equation models.

7 8 9 10 11 12 13 14 15

0.420.14 �0.180.05 �0.20 0.110.10 �0.12 0.30 0.040.15 �0.14 0.14 0.17 0.420.34 0.11 �0.08 �0.20 0.06 �0.230.06 0.00 0.22 �0.18 0.26 �0.15 0.490.22 0.07 �0.22 �0.22 0.09 �0.24 0.67 0.440.13 �0.03 �0.10 0.13 0.25 0.28 �0.36 0.01 �0.210.08 0.04 0.03 �0.02 0.15 �0.10 0.30 0.34 0.39 �0.04

Species richnesslegumes

0 2 4 6

Bait

cons

umpt

ion

(em

pty

hole

s)

0

2

4

6

8

10a

2008 2009

Species richnessgrasses

4 9 14

b c

Sample year

Fig. 2. a) Median, quartiles (boxes) and 90th and 10th percentile (whiskers) of feedingactivity of soil fauna in grassland plots in 2008 and 2009, b) relationship betweenlegume species richness and bait consumption and c) relationship between grassspecies richness and bait consumption.

Table 3Results of a distance based linear model partitioning the variation in bait-laminaconsumption between five indicator groups after accounting for variationexplained by differences between sample years and among exploratories. The mostparsimonious model was selected based on the AIC criterion and included thepredictor subgroups plant functional group composition and soil fauna.

Indicator group res.df regr.df R2 SS(trace)

Pseudo-F P CumulativeR2

Plant functionalgroupcomposition

84 8 0.105 8564.9 2.92 0.014 0.242

Soil fauna 81 11 0.052 4229.4 1.99 0.095 0.294Management 77 15 0.026 2108.0 0.73 0.624 0.320Soil properties 75 17 0.025 2007.8 1.41 0.226 0.344Vegetation

characteristics73 19 0.014 1104.2 0.77 0.505 0.358

K. Birkhofer et al. / Soil Biology & Biochemistry 43 (2011) 2200e2207 2203

We used structural equation modeling (SEM, Kline, 1998) toevaluate “complex hypotheses involving multiple causal pathways”(McCune and Grace, 2002, page 234) among legume and grassspecies richness, soil fauna abundance and bait consumption. Theselection of these predictors was based on the result of our distancebased linearmodels, suggesting that the richness of particular plantfunctional groups (legumes and grasses) was the best predictor forbait consumption in the most parsimonious model. Structuralequation models provide a statistical framework to study multipleprocesses that may control systemproperties and arewell-suited toevaluate complex hypotheses that are based on interactionnetworks (McCune and Grace, 2002). Plant functional groups mayeither affect the activity of soil fauna or may have an effect viaalteration of soil fauna abundances and biomasses. In our SEM,pathways (arrows) between boxes represent processes that areassumed to operate in the study system, whereas boxes representmeasured variables from the study. Double headed arrows indicatethat two variables may affect each other vice versa. An a priorimodel was developed that included possible causal relationshipsbetween species richness of plant functional groups that hada significant relationship with bait consumption (legumes andgrasses, but not herbs) in our distance based linear model. Inaddition, our distance-based linear model also suggested a rela-tionship between soil fauna abundance and soil fauna feedingactivity and we therefore allowed for effects of legume and grassspecies richness on bait consumption via Enchytraeidae and Col-lembola abundance or Lumbricidae biomass. We further includedthe possibility of an impact of earthworms as important ecosystemengineers on Collembola and Enchytraeidae. All soil fauna abun-dance biomass data was log(xþ 1) transformed to approximatea normal distribution. Finally, our model allowed for intercorrela-tion between species richness of herbs and grasses. Analysis of thestructural equation model was carried out using AMOS 5.0 soft-ware. As test for the fit of the model we report c2 values for modelfit, the goodness-of-fit index (GFI, Tanaka and Huba, 1985) and thecomparative fit index (CFI, Bentler, 1990) to provide a range ofmodel fit indices as suggested by Byrne (2010). The c2 valueprovides a measure of the magnitude of differences betweenobserved and expected covariance in the model. A P-value above0.05 suggests a high probability that the difference betweenmodel-implied and observed covariance may be due to chance (Grace,2008) and therefore suggests a good model fit (Browne andCudeck, 1993). However, model fit in SEM’s should always besupported by more than one fit index and we therefore provide theCFI and GFI in addition. For these indices values above 0.9 suggesta good model fit (Byrne, 2010).

3. Results

Overall, 28,337 Collembola, 1924 Enchytraeidae and 1600Lumbricidae were counted from soil samples. To account for vari-ation in bait consumption explained by differences betweensampling years or among exploratories we first fitted the indicatorgroups sampling year (df¼ 2,90; R2¼ 0.084; P¼ 0.002) andexploratory identity (df¼ 3,89; R2¼ 0.046; P¼ 0.098) to the baitconsumption data. Bait consumption was almost twice as high in2009 compared to 2008 (Fig. 2a). According to the 1024 bait laminastrips analyzed, variation in bait consumption was best explainedby plant functional group composition (Table 3; df¼ 8,84;R2¼ 0.105; P¼ 0.014) in the most parsimonious model afteraccounting for sampling year and exploratory identity. In this mostparsimonious model, soil fauna was included as second indicatorgroup (df¼ 11,81; R2¼ 0.052; P¼ 0.095) and together with plantfunctional group composition explained 29.4% of the variation inbait consumption (Fig. 3). If indicator groups were analyzed

individually and independent of each other (marginal tests),management variables (df¼ 5,87; R2¼ 0.031; P¼ 0.642), soilchemistry (df¼ 3,89; R2¼ 0.013; P¼ 0.623) or vegetation charac-teristics (df¼ 3,89; R2¼ 0.028; P¼ 0.283) were not significantlyrelated to soil fauna feeding activity.

After identifying a significant relationship between plant func-tional group composition and bait consumption, we used a seconddistance based linearmodel to test for the relationship of individualpredictor variables within this indicator group and bait consump-tion. Bait consumption was positively related to the species rich-ness of legumes (df¼ 90; R2¼ 0.130; P< 0.001) and grasses(df¼ 89; R2¼ 0.048; P¼ 0.013; Fig. 2b and c) in the most parsi-monious model, with both predictors together explaining almost18% of the variation in bait consumption. The vectors of the threeplant functional groups in Fig. 3 (# 4, 5 and 6) suggest that speciesrichness of different functional groups account for identical parts ofvariation, only with differences in the proportion of explainedvariation (Fig. 3).

To understand if the observed difference in bait consumptionbetween years was a consequence of plot selection and plantspecies richness, we compared the number of herb, grass andlegume species in our 2008 and 2009 grassland plots. Functionalgroup species richness was significantly higher in grassland plots in2009 compared to 2008 (F1,90¼ 8.89, P< 0.001). Yet, as herb and

dbRDA 1 (89.9% of fitted & 26.4% of total variation)

-40 -30 -20 -10 0 10 20 30 40

dbR

DA

2 (9

.7%

of f

itted

& 2

.9%

of t

otal

var

iatio

n)

-15

-10

-5

0

5

10

15

AEG 2008 AEG 2009 HEG 2008 HEG 2009 SEG 2008 SEG 2009

465*

21 3

Fig. 3. Distance based redundancy analysis providing an ordination plot of the fitted values from our distance-based linear model for bait consumption. The strength and directionof the relationship between predictor variables from the selected indicator groups and dependent variables is visualized by vectors that are based on correlations between eachvariable and the dbRDA axis scores. 1, Abundance Enchytraeidae; 2, Plant functional group evenness; 3, Abundance Collembola; 4, Species richness legumes; 5, Species richnessherbs & biomass Lumbricidae; 6, Species richness grasses.

K. Birkhofer et al. / Soil Biology & Biochemistry 43 (2011) 2200e22072204

legume species richness did not differ between our 2008 plots andthe identical plots from 2009 (not our study plots in 2009) and onlygrass species richness differed significantly (F1,110¼ 4.59, P¼ 0.026),this difference did not seem to be primarily related to shifts in plantcommunity composition between years but to our plot selectionthat included more diverse grassland plots in 2009. Consideringthat herb species richness had the same positive effect on baitconsumption when analyzed separately for each year (2008:R2¼ 0.10; P¼ 0.006; 2009: R2¼ 0.11; P¼ 0.031), the observed yeareffect on bait consumptionmay be rather due to differences in plantspecies richness among plots in 2008 and 2009 than to interannualdifferences in climate.

The structural equation model was designed to evaluatecompeting hypotheses involving multiple pathways betweenlegume and grass species richness and bait consumption in bothyears. We further included three soil fauna taxa, as these predictorsalso were significantly related to bait consumption rates in ourdistance-based linear models. The SEM provided an excellent fitaccording to three indices of model fit (c2¼ 2.197, df¼ 1; P¼ 0.138;GFI¼ 0.992; CFI¼ 0.978). The model suggested a highly significant,positive effect of legume species richness on bait consumption(Fig. 4; regression weight¼ 0.567, P< 0.001). This result impliesthat if legume species richness goes up by 1, the mean soil faunafeeding activity goes up by 0.567. In addition to legume species

Lumbricidaebiomass

Species richnesslegumes

Species richnessgrasses

Enchytraeidaeabundance

Collembolaabundance

Soil fauna feedingactivity

*

***

***0.1-0.20.2-0.3> 0.3

Fig. 4. Structural equation model showing the relationships between legume andgrass species richness and soil fauna as predictors of soil fauna feeding activity. Solidlines represent positive and dashed lines negative path coefficients (standardizedregression weights). Line thickness represents the magnitude of the path coefficient;significant relationships are shown in black. Error terms for the unexplained variancein all endogenous variables are not included in the figure. *** P< 0.001; ** P< 0.01;*P< 0.05; ns P> 0.05.

richness, bait consumption was also positively affected by Col-lembola abundance (regressionweight¼ 2.214, P< 0.001), so that ifthe logarithm of Collembola abundance goes up by 1, the mean soilfauna feeding activity goes up by 2.214. Collembola abundance wasfurther positively affected by earthworm biomass (regressionweight¼ 0.185, P¼ 0.015), as the logarithm of Collembola abun-dance goes up by 0.185, if the logarithm of the biomass in earth-worms goes up by 1. Legume species richness had no significantimpact on soil fauna, but the intercorrelation between speciesrichness of legumes and grasses was highly significant (regressionweight¼ 1.907, P< 0.001).

4. Discussion

Two significant patterns arose from our large-scale study on thefeeding activity of invertebrates in temperate grassland soils. First,species richness of two plant functional groups (legumes andgrasses) was positively related to feeding activity of soil fauna,whereas land-use intensity, vegetation characteristics, soil prop-erties and herb species richness showed no significant relationship.This result highlights the important role of above-belowground-relationships in terrestrial ecosystems (Van der Putten et al.,2009) and demonstrates the importance of specific traits of plantfunctional groups for belowground processes (see also Wardleet al., 1997; Hooper et al., 2005). Second, the positive effect ofearthworm biomass via Collembola abundance on bait consump-tion suggests that soil engineers may stimulate the feeding activityof the edaphic fauna via changes in the structure of the below-ground community. Together, these findings have considerableimplications for the prediction of patterns and processes in soilunder future environmental change (Fierer et al., 2009).

In a comprehensive meta-analysis, increasing plant speciesrichness was shown to generally affect decomposer activity posi-tively (Balvanera et al., 2006). At least two non-exclusive mecha-nisms may explain this relationship, as plant species richness caninfluence soil processes by impacting microclimate or resourcequality (Knops et al., 2001). Concerning microclimate, increasingplant diversity in grasslands may decrease soil temperature by upto 3 �C (Spehn et al., 2000). Temperature reduction bymore diversevegetation most probably stimulates soil fauna activity, since anincrease of 2.5 �C in annual mean temperature has been shown tosignificantly stress edaphic communities (Briones et al., 1997;Carrera et al., 2009). A positive effect of vegetation cover on thefeeding activity of soil fauna was shown for vineyards wherehighest levels of feeding were found in green-covered soils ascompared to open soils (Sturm et al., 2002). In tropical rain forests,

K. Birkhofer et al. / Soil Biology & Biochemistry 43 (2011) 2200e2207 2205

litter coverage markedly increased the feeding activity of soil fauna(Römbke et al., 2006).

For Mediterranean grasslands, Dimitrakopoulos (2010) reportsa positive correlation between decomposition rate and litter-species richness, but results additionally depended on thecomposition and on the evenness of the litter-species mixture. Thiscomplex relationship may partly explain why meta-analyticalapproaches to the few and spatially scattered small scale experi-ments on the decomposition dynamics in mixed-species leaf litterdid not deliver very consistent results (Gartner and Cardon, 2004).According to these authors, for example, non-additive patterns inthe abundance and activity of decomposers were observed in only55% and 65% of leaf mixes, respectively. Significant results suggestthat changes in litter diversity may either stimulate consumption insoil by increasing both availability of suitable food sources anddiversity of microhabitats, or may decrease it, e.g. by reducing thehabitable space provided by and the buffering capacity of the litterlayer under conditions of accelerated decomposition (cf.Hättenschwiler et al., 2005). Scherber et al. (2010) suggest thatherbivorous soil organisms are directly affected by plant speciesrichness in grassland soils, e.g. by root exudates or root production.

In our study, soil fauna feeding activity was positively related tolegume and grass species richness. Wardle (2005, 2006) empha-sizes that specific functional traits of plant species are the majordeterminants of interactions between soil fauna and plants.Viketoft et al. (2009) demonstrated such a close link betweenindividual plant functional groups and nematode feeding guilds.Bacterial-feeding nematodes, for example, were significantly moreabundant in the presence of legume and grass species, compared toherb dominated experimental patches (see also Viketoft et al.,2005; Wardle, 2005). Individual plant species or functionalgroups affect microbial communities and these interactions areknown to cascade up to affect soil fauna (Viketoft et al., 2009) anddecomposition processes (Loreau, 1998; Ehrenfeld et al., 2005).

Decomposition processes in grasslands are affected by thecomposition of plant communities, primarily because a largeproportion of plant material is available for decomposition from theroot system (Seastedt, 1988). McLaren and Turkington (2010)showed that removal of legume species lead to a short-termdecrease in root decomposition and the authors concluded thataltered rhizodeposits and different rates of soil dryingmay have ledto the observed lower root decomposition in absence of legumes.The presence of legumes promoted microbial biomass and earth-worm populations in experimental grasslands and Milcu et al.(2008) suggested that soil fauna directly benefited from nitrogen-rich litter resources or dead roots with associated nitrogen richrhizobia (see also Milcu et al., 2006). Such resource-mediatedeffects on soil fauna potentially led to the observed higherdecomposition rates in treatments with legumes (Milcu et al.,2008).

Here, we did not observe a positive effect of legume speciesrichness on earthworm biomass or a relationship between earth-worm biomass and feeding activity. However, earthworm biomasspromoted soil fauna feeding activity via a positive effect on Col-lembola abundance. Our current knowledge on the relationshipbetween plant diversity, microclimate and belowground processesis too poor for sufficiently explaining the positive impact of legumeand grass species richness on the feeding activity of soil faunafound in our study. However, our finding of predictors of feedingactivity in the soil at larger spatial and temporal scales maysignificantly increase the predictability of biological processes insoil at appropriate levels of resolution.

Previous studies have reported results on the effects of Col-lembola abundance on feeding activity of soil fauna that resembleour effect of Collembola abundance on bait consumption. Whereas,

the positive relationship between Collembola abundance andfeeding activity of soil fauna observed in our study is in line withearlier results that related bait consumption to Collembola activityunder laboratory conditions (for review see Larink, 1997; Hellinget al., 1998), in grassland soils in laboratory mesocosms that werestocked with different densities of Collembola, mites and earth-worms, microarthropods had no effect on bait consumption (VanGestel et al., 2003). Based on the results of a similar microcosmstudy, Taylor et al. (2010) concluded that differences in thecommunity structure of soil invertebrates are buffered at lowertrophic levels and thus do not affect ecosystem processes. However,in the study of Van Gestel et al. (2003), feeding activity of soil faunawas significantly higher in presence of earthworms and increasedwith density. Like in our study, this positive effect of earthworms onthe abundance of Collembola may be best explained by theecosystem engineering function of earthworms (Lavelle, 2002).Collembola can not actively alter the structure of their soil habitatand therefore depend on the availability of existing pores andburrows (Wolters, 2001). The observed effect of earthworms onCollembola may have cascaded down to affect feeding activity ofsoil fauna via the earthworm-Collembola pathway. Our finding thatearthworm biomass may facilitate feeding activity of soil fauna viaa positive effect on Collembola highlights the complexity of inter-actions in soil food webs and should caution us toward the use ofoversimplified laboratory designs to study interactions inecosystem functioning of the soil fauna.

Though in our field study we accounted for the multitude ofbiotic and abiotic factors potentially driving structure and func-tioning of the soil fauna in our field study, no additional effects ofsoil characteristics or agricultural management on feeding activityof soil fauna were observed. This contrasts with earlier studies atsmaller spatial scales that documented a negative effect ofmanagement practices on feeding activity of soil fauna. Fertilizerapplication and conventional management affected feeding activityof soil fauna in cereal systems (Graenitz and Bauer, 2000) andvineyards (Reinecke et al., 2008). This may be due to a smallermanagement gradient in our grassland systems than in the above-mentioned studies. As we did not focus on the impact of land-useintensity on soil fauna abundance, but instead analyzed the rela-tionship between biotic and abiotic factors and a functionalparameter of soil communities, our results do not contradict earlierstudies showing pronounced effects of grassland management onsoil fauna communities (Bardgett and Cook, 1998; Cole et al., 2005,2006). They rather document that potential management effects onthe soil fauna do not always correspond to functional characteris-tics of these communities.

4.1. Conclusion

To conclude, our investigations in temperate grasslands suggestthat the positive relationship between legume and grass speciesrichness and feeding activity in soil is a general pattern that hasmeasurable effects even when regional management or environ-mental conditions are markedly different. This aspect deservesmuch more attention in studies aiming at predicting the conse-quences of large scale changes in ecosystem structure and func-tioning in response to climate change. In addition, the observedrelationship between earthworm biomass and Collembola abun-dance indicates that changes in the activity of ecosystem engineersmay considerably affect other components of the belowgroundcommunity. This in turn may feedback to decomposition processes,as suggested by the positive relationship between feeding activityin soil and the abundance of endogeic soil fauna. We thus suggestthat agri-environment schemes aiming at the protection ofbelowground activity and associated ecosystem functions in

K. Birkhofer et al. / Soil Biology & Biochemistry 43 (2011) 2200e22072206

temperate grasslands may generally focus on maintaining plantdiversity and earthworm conservation rather than on individualmanagement strategies.

Acknowledgements

We thank Christiane Meub, Christine Kröber, Christine Tandler,Janine Groh, Kathrin Stötzel, Nico Radermacher, Sabine Rauch andSusanne Vesper for assistance during field and labwork. Commentsby three anonymous referees and the subject editor helpedimproving an earlier version of the manuscript. The work has beenfunded by the DFG Priority Program 1374 “Infrastructure-Biodi-versity-Exploratories” (WO 670/7-1). Field work permits weregiven by the responsible state environmental offices of Baden-Württemberg, Thüringen, and Brandenburg (according to x 72BbgNatSchG).

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