Soil food web stability in response to grazing in a semi-arid prairie: The importance of soil textural heterogeneity

Soil Biology & Biochemistry 97 (2016) 131e143

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Soil food web stability in response to grazing in a semi-arid prairie: The importance of soil textural heterogeneity
s a, d, f, *, John C. Moore a, e, Rodney T. Simpson a, Greg Selby a, Pilar Andre Francesca Cotrufo a, b, Karolien Denef c, Michelle L. Haddix a, e, E. Ashley Shaw a, d, Cecilia Milano de Tomasel a, d, Roberto Molowny-Horas f, Diana H. Wall a, d a

Natural Resource Ecology Laboratory, Colorado State University, Ft. Collins, CO 80523, USA Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA Central Instrument Facility, Chemistry Department, Colorado State University, Fort Collins, CO 80523, USA d Department of Biology, Colorado State University, Fort Collins, CO 80523, USA e Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523, USA f CREAF, Cerdanyola del Vall es, 08193, Spain b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 8 October 2015 Received in revised form 12 February 2016 Accepted 19 February 2016 Available online 10 March 2016

Grazing of grasslands by large herbivores is a form of land use intensification that affects not only plant communities but also soil biota and the ecosystem services that it provides. While grassland ecosystem responses to grazing have been extensively studied, few studies have focused on the effects of aboveground herbivores on belowground diversity and functions. In this work, we quantified effects of grazing on the structure, function and dynamic stability of soil food webs. We sampled a long-term grazing manipulation in a semi-arid shortgrass steppe (USA Great Plains) at sites showing contrasting soil textures. Treatments included native steppe plots that have been moderately grazed since 1939 paired with plots totally protected from grazing since 1996. We sampled our plots for soil C and N, and for soil biota, separated microbes and micro- and mesofauna in trophic functional groups and defined trophic relationships. We used models to estimate carbon and nitrogen mineralization, energy flow throughout the food web, interaction strengths between trophic groups at steady-state and, eventually, asymptotic (near-equilibrium or local) stability (Moore and de Ruiter, 2012). Soil food web response to grazing depended on soil texture and organic matter content. In our food webs, most energy flowed through the fungal and bacterial detritus-based channels (sensu Moore and Hunt, 1988). There was a clear asymmetry between the amount of energy flowing through each of the two channels and, the higher this asymmetry, the higher was food web stability. Stability was affected by both grazing and soil properties (increased under grazing in high clay soils with high organic matter content but decreased in less organic loam sandy soils), and positively associated with soil organic matter content. Overall, we found that the carbon flow through the soil food web of the shortgrass steppe is responsive to grazing in ways that altered stability and that structural, functional, and dynamic attributes are sensitive parameters for evaluating soil response to land use under changing scenarios. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Grazing Semi-arid grasslands Soil biodiversity Soil fauna Food web stability

1. Introduction In recent decades, land use change and intensification have caused significant losses of belowground biodiversity across agricultural landscapes (Tsiafouli et al., 2015) as well as detrimental

* Corresponding author. Present address: Department of Biology, Colorado State University, Fort Collins, CO 80523, USA.
s). E-mail address: [email protected] (P. Andre http://dx.doi.org/10.1016/j.soilbio.2016.02.014 0038-0717/© 2016 Elsevier Ltd. All rights reserved.

effects on the environmental services it provides (Bardgett and van der Putten, 2014). Pasturelands occupy almost half of the usable land surface making managed grazing the most widespread land use globally (Suttie et al., 2005). Global ecosystem response to grazing is highly variable and depends on bioclimatic and edaphic conditions, with very arid and very humid biomes being more sensitive than mesic temperate biomes (Asner et al., 2004). Grassland soils have been intensively studied for physical and chemical responses to grazing while few studies have focused on

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the impacts of grazing on abundance and biodiversity of soil microbial and invertebrate communities. Positive and negative responses of microbial biomass to grazing have been reported and it has been suggested that moderate grazing is beneficial to fertile soils while detrimental to unproductive soils (Bardgett and Wardle, 2003). The structure of the soil microbial community is typically modified by livestock activity (Ingram et al., 2008) in the sense that fungal to bacterial biomass ratio increases under grazing (Bardgett et al., 1997). Most researchers agree on negative effects of high stocking rates on soil invertebrate abundance but, at moderate rates, increasing grazing intensity may result in both increasing or decreasing invertebrate community size (King and Hutchinson, 2007; Bardgett et al., 1993). Changes in functional and taxonomic diversity have been reported for almost every type of soil invertebrate (see Ingham and Detling, 1984 and Wang et al., 2006 for nematodes; Dombos, 2001 for collembolans; Kay et al., 1999 and Kinnear and Tongway, 2004 for mites; Qi et al., 2011 for protists; Mulder et al., 2008 for a variety of groups). Inconsistent responses of invertebrates to grazing are the norm and may be explained by a number of factors including different spatial patterns in functional group's abundance as a consequence of specific niche requirements and performance (Brown et al., 1995). The soil community is a highly structured complex network of trophic interactions among plants, microbes and invertebrates shaped by the availability and quality of basal plant-based and detritus-based resources (Moore et al., 2004), in turn determined by plant type and amount of primary productivity (Wardle et al., 1998). Competition for resources is determinant at this basal level of the detritus-based food web while, at higher trophic levels, predation becomes more influential for the regulation of the soil biota (Wardle, 2006). Together with these bottom-up and topdown trophic forces, abiotic factors interact to shape the soil community depending on specific tolerances to environmental conditions. Soil texture is a major determinant of soil habitability due to its direct relationship with organic matter protection and soil C content (Six et al., 2002) and also with a soil's ability to retain water (Rawls et al., 2003). Large herbivores impact soil biota through several mechanisms that can be grouped into soil physical disturbance, changes in plant composition and changes in the quantity and quality of resources returned to soil. Trampling by herbivores causes soil compaction and restricts air and water movement through the soil. Susceptibility to trampling greatly depends on soil texture, with finetextured soils being more fragile than coarse-textured soils (Schrama et al., 2013). Predaceous and omnivorous nematodes and oribatid mites appear to be negatively affected by soil compaction, while other mite orders such as Prostigmata are favored (Schon et al., 2012; Clapperton et al., 2002; Bowman and Arts, 2000). But soil biotic response to grazing is primarily mediated by plants. Mammal herbivores consume variable proportions of the aboveground net primary productivity (ANPP) and return a part of it to the soil in the form of labile excreta, speeding up nutrient
n et al., 2012). In response to defoliation, turnover (Medina-Rolda plants increase the proportion of C they allocate belowground and change root exudation patterns towards increasing inputs of basal resources that fuel soil food webs (Mikola et al., 2009). However, these peaks of labile nutrients attributable to excreta or root exudation as well as their positive effects for microbial abundance and growth are very short-lasting (Hamilton et al., 2008). In the long term, pastoralism induces changes in vegetation by favoring species more or less palatable that produce litter of different quality depending on climate and ecosystem productivity (Díaz et al., 2007). Resource quality and individual plant species characteristics will then play a significant role in structuring soil biota and its

functioning (Porazinska et al., 2003). Typically, these combined effects fuel soil food webs by increasing available C and N and as a result, enhancing microbial biomass and available resources to the upper levels of the web (Bardgett and Wardle, 2003). The bacterial energy channel benefits more than the fungal channel from increased nutrient availability (Bardgett et al., 2001), and is linked to decreasing soil stability and nutrient retention (Six et al., 2006). There is growing awareness that the delivery of soil services may be endangered by the loss of diversity from soil use intensification. The insurance hypothesis (Loreau et al., 2001) proposes that, even if a reduced number of species can ensure soil essential processes at a given moment, impoverished soil communities would be less stable and less resilient to disturbance and environmental changes over time. However, stability and resilience do not emerge directly from taxonomic or functional diversity but rather from the pattern of trophic interactions among species (Dunne et al., 2002; McCann, 2000). In this work, our aim was to quantify effects of grazing on soil food web structure and to model subsequent consequences to nutrient cycling and food web stability as influenced by soil physicochemical characteristics. We hypothesized that (a) grazing will affect soil food web structure, with the bacterial channel being favored by grazing to the detriment of the fungal channel, (b) food web stability will be reinforced by grazing and (c) the stability of the soil food web will depend on soil type. We tested these hypotheses by quantifying the biota and C and N pools in soils sampled from grazed and ungrazed plots in a semi-arid steppe across a gradient of soil texture. We then built up soil food webs, modeled matter and energy fluxes through channels and calculated food web stability based on interaction strength between trophic groups. 2. Materials and methods 2.1. Description of the study area Our study area is located at the Shortgrass Steppe (SGS) Long Term Ecological Research Station (Northeast Colorado, USA). Average annual rainfall is 327 mm, 70% of which falls during the growing season (Sala and Lauenroth, 1982). January minimum temperature ranges from
9 to 4  C and July maximum temperature from 32 to 38  C. The growing season is short, with a frost-free period lasting 121e180 days per year (Pielke and Doesken, 2008). Vegetation is dominated by the C4 grasses blue grama -Bouteloua gracilis (Willd. ex Kunth) Lag. ex Griffiths- and buffalograss -Bouteloua dactyloides (Nutt.) J.T. Columbus-. Subdominant vegetation includes the plains prickly pear cactus -Opuntia polyacantha Haw.-, € vethe C3 western wheatgrass -Pascopyrum smithii (Rybd.) A. Lo and a variety of shrubs and forbs (Rebollo et al., 2013). We worked at three sites (Site A: 40º520 4.5500 N, 104º41026.9200 W, 1657 m.a.s.l.; Site B: 40º520 5.7600 N, 104º400 44.2600 W, 1660 m.a.s.l.; Site C: 40º500 53.600 N, 104º420 26.0700 W, 1657 m.a.s.l.). The surrounding topography is very gentle and the three sites are flat. The SGS has been naturally grazed by bison and other native herbivores for more than 10,000 years (Milchunas et al., 2008). Since 1939, the pasture has also been grazed by cattle with a moderate intensity (27 heiferdays ha
1, ~40% forage utilization). In 1996, a total herbivore exclusion experiment was set up at these grazed pastures. Total absence of large and small (lagomorphs and rodents) vertebrate herbivores has been guaranteed since then by a fence designed to deter digging and climbing mammals (Rebollo et al., 2013). 2.2. Sampling In May 2014, immediately after snowmelt, we sampled one

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decomposition rates (d
1) of the active and slow fractions respectively. Since Total C ¼ Ca þ Cs, we calculated Ca, and obtained Cs as the difference between total C (estimated as mentioned above) and Ca. The determined pool sizes (Table 4) were used to initialize the model described below.

grazed (G treatment) and one ungrazed (UG treatment) plot at each of the above mentioned three sites. The grazed plots (GA, GB, GC) were the same size (30  30 m2) than the ungrazed plots (UGA, UGB, UGC) and were provisionally set up 10 m north of each of them. We divided each of the resulting six plots in 225 4 m2 square subplots, discarded the 56 edge subplots, and randomly chose 10 of the remaining 169 squares for sampling. Two adjacent soil samples (15 cm deep and 5 cm in diameter) were extracted from the center of each subplot (ten replicates per plot for each group of the soil biota). The first sample was used to heat extract soil arthropods. The second sample was halved lengthwise to obtain two subsamples. One subsample was designated for nematode extraction, the other one for bacterial and fungal direct counts, protist extraction and soil physical and chemical analyses. This design was dictated by the existence of only one ungrazed plot per site. Based on the reduced dispersal capacity of belowground biota, we assumed independence between samples within each plot.

2.4. Microbial and faunal functional diversity and community structure We assessed microbial functional diversity by phospholipid fatty acid (PLFAs) analyses of the 10 samples per plot after Denef et al. (2007). Briefly, lipids were extracted from 6 g of freezedried soil with a phosphate buffer:choloroform:methanol solution at a 0.8:1:2 ratio (in vol.) and partitioned by solid phase extraction into neutral lipids, glycolipids and phospholipids. Phospholipids were then methylated to obtain their fatty acid methyl esters (FAMEs). FAMEs were analyzed by gas chromatography-mass spectrometry with a Trace GC Ultra coupled to a Thermo ISQ (Thermo Scientific). Both inlet and transfer lines were set at 280  C. The oven temperature was programmed at 80  C for 30 s, a ramp of 15  C per min to 330  C, and held at 330  C for 8 min. FAMEs were identified based on mass spectral and retention times matches to a 27 FAME mixture (Supelco), a bacterial acid methyl ester CP mixture (Matreya) and a GLC-110 Mixture (Matreya). Additional polyunsaturated fatty acids (PUFA) were detected based on mass spectral matching to the NIST Mass Spectral Library (v14, www.nist.gov). Quantified FAMEs included markers indicative of Gramþ bacteria (iC15:0, aC15:0, aC16:0 and aC17:0), actinomycetes (10MeC16:0, 10Me-C17:0, 10Me-C18:0), saprophytic fungi (C18:2u6,9c and C18:1u9c) and also non-specific bacterial markers (C13:0, C14:0, C17:0 and C18:0) (Frostegård et al., 1993; Denef et al., 2009). To assess functional invertebrate diversity, we extracted microand mesofauna from the 10 soil samples per plot. Densities were estimated for amoeba, flagellates and ciliates using the Most Probable Number Method (Darbyshire et al., 1974) on 10 g soil samples. Nematodes were isolated from 20 g soil samples using Baermann funnels (Baermann, 1917). Microarthropods were heatextracted from soil cores using Tullgren funnels (Moore et al., 2000). Protists, nematodes and microarthropds were pooled in functional groups. To define functional groups, we aggregated species based on similar predators, preys and life traits (Moore et al., 1988). For each group, we calculated C-biomass abundance (in mass unit C per unit area) based on mean individual weights (Table 2), assuming that 50% of the dry weight corresponds to C whatever the group.

2.3. Soil physical and chemical analyses Soil physical and chemical properties (Table 1) were measured from composite samples resulting from pooling the 10 subsamples extracted from each plot after sieving at 2 mm and homogenization. Total C and total N were measured after dry combustion with a Leco TruSpec (LECO, St. Joseph, Michigan) and the amount of inorganic C was determined by pressure-calcimeter method after Sherrod et al. (2002). Nitrate-N and nitrite-N were measured by flow injection analysis (USEPA method 353.2) on soil 2 M KCl extracts (Keeney and Nelson, 1987). We determined the size of the soil organic matter (SOM) pools by laboratory incubation in the dark at 25  C. For each plot, three sub-samples were taken from a composite soil sample made of 10 soil columns. Each sub-sample consisted of 50 g of soil adjusted to 60% water holding capacity and sealed in a Mason jar. Headspace CO2 was measured with a infra-red gas analyzer (LI-COR, Lincoln, NE, USA) at increasing time intervals until respiration rate stabilized. We then determined the size and turnover of the active C and slow C pools by fitting the curve of carbon respired per unit of time
n and Paustian to the two-pool first-order equation from Andre (1987):



Ccum ðtÞ ¼ 1
e
ka *t þ Cs 1
e
ks *t

133

(1)

where Ccum (t) is the cumulative soil respiration at time t, Cs (mg C g
1 soil) is the size of the slow fraction and ka and ks are the

Table 1 Soil properties at sites A, B and C in grazed (G) and non grazed (UG) plots. Analyses were performed on one composite sample (made of 10 combined sub-samples) per plot. Site A

Bulk density (g cm
3) Sand (%) Clay (%) Silt (%) Textural class Water content (%) pH (0.01 M CaCl2) EC (mmho cm
1) C/N Total C (%) % Of C as inorganic C Total N (%) NO3eN (mg N g soil
1) NH4eN (mg N g soil
1)

Site B

Site C

UG

G

UG

G

UG

G

1.01 42.81 27.38 29.81 Clay loam 6.24 7.06 186.4 9.1 1.98 2.18 0.22 0.0022 0.0022

1.08 41.97 30.13 27.9 Clay loam 6.23 7.28 178.7 9.1 1.85 3.77 0.20 0.0022 0.0019

1.3 78.9 13.23 7.86 Sandy loam 2.76 6.13 68.3 8.8 0.95 2.29 0.11 0.0015 0.0025

1.56 84.36 10.32 5.31 Loamy sand 1.7 6.31 86.7 8.5 0.65 4.54 0.08 0.0009 0.0013

1.48 82.8 10.32 6.88 Loamy sand 1.08 6.26 52.3 8.6 0.74 2.28 0.09 0.0003 0.0011

1.45 82.94 11.73 5.33 Loamy sand 1.91 6.1 54.9 8.4 0.75 2.24 0.09 0.0007 0.0018

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 75 0

G18 G16

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0

G15 G14

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0

G13 G12

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0

G11 G10

0 0 0 0 0 0 0 0 0 0 50 0 0 0 0 0 50 0 0 0 0 0 0 0 0 0 0 0 0 0 25 25 0 0 0 0 0 50 0 0 0 0

G9 G8

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0

G7 G6

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 25 25 25 0 0 0 0 0 25 0 0 0 0

G5 G4 G1

0 0 0 0 25 25 25 25 0 0 0 0 0 0 0 0 0 0 0 0 0 3.40E
05 7.70E
06 1.00E
06 1.04E
06 9.00E
07 6.00E
08 7.40E
08 1.10E
07 1.00E
09 1.20E
09 1.90E
11 2.70E
06 1.00E
06 3.20E
06 6.60E
06 2.70E
06 6.65E
13 # 2.30E
06 0.9 0.6 0.9 0.5 0.6 0.25 0.6 0.38 0.95 0.95 0.95 0.5 0.5 0.5 0.35 0.5 1 1 1 1 1 G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18

Predaceous dilurans Predaceous mites Nematophagous mites Predaceous nematodes Omnivorous nematodes Phytophagous nematodes Bacteriophagous nematodes Fungivorous nematodes Ciliates Amoeba Flagellates Fungivorous Cryptostigmata Fungivorous Prostigmata Proturans Symphyla Collembolans Bacteria Fungi Active SOM Slow SOM Roots

8 8 8 10 10 10 10 10 7 7 10 8 8 8 8 8 4 10 8 11 10

1.33 1.84 1.84 3 8 1.08 2.68 1.92 6 6 6 1.2 1.84 1.84 0.25 1.84 1.2 1.2 1 1 1

0.34 0.35 0.35 0.37 0.37 0.37 0.37 0.37 0.4 0.4 0.4 0.35 0.35 0.35 0.36 0.35 0.3 0.3 1 1 1

Feeding preferences (%) Individual biomass (g, dw)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 75 25 0 0 0 0 20 20 20 20 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 7 7 7 7 7 0 0 0 13 13 7 7 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 20 60 0 0 0 0 0

2.6. Energy fluxes and C and N mineralization

G17 G3 G2

We designed our soil food webs based on trophic relationships between available food resources and soil biota functional groups after Hunt et al. (1987) (Fig. 1). Table 2 shows resource and group attributes used to initialize the models described below. We divided soil organic matter in two (active and slow) pools based on recalcitrance and quantified them based on the laboratory incubation described above. We did not estimate root biomass but assumed that this was not a limiting resource and assumed a theoretical value of 300 g C m
2 for the shortgrass steppe. To make our results comparable to those of Hunt et al. (1987) for the SGS, we adopted their methods and estimated bacterial and fungal biomasses from direct count of slides under epifluoresence microscope following Bloem (1995). This method measures total (dead and alive) fungal biomass and to get an estimate of the alive fraction, we divided the calculated fungal biomass by 10 (Hunt et al., 1987). Faunal trophic groups were attributed the C biomass (in g C m
2) we had previously calculated for community structure. Food webs were organized in three energetic channels (Fig. 1): the root channel included four functional groups, the bacterial channel nine and the fungal channel eleven. Nematophagous mites and predaceous nematodes and mites fed on members of more than one channel. We characterized each channel by its total biomass calculated as the sum of the biomass of all its members. For groups feeding on diverse channels, relative contribution to each channel was calculated based on feeding preferences (Table 1). Since fungi are more effective than bacteria in decomposing recalcitrant organic matter (de Boer et al., 2005) and since bacteria mainly process high quality organic matter, we assigned to fungi twice the preference for the slow SOM pool than for the fast SOM pool and the opposite to bacteria.

AE (%)

PE (%)

2.5. Food web structure

DR (yr
1) C:N

Table 2 Parameters used to initialize the food web model. C to N ratio, death rate (DR), assimilation efficiency (AE), production efficiency (PE) and individual biomass for the functional groups of the SGS food web. Feeding preferences of consumers (functional groups G1 to G18) on resources, including roots and SOM. For most groups, physiological parameters were taken from Hunt et al. (1987); for proturans, symphylans and diplurans, see Curry (1986), Dindal €m and Persson (2011) and Persson et al. (1980); for SOM pools see Parton et al., 1989. #Unitary fungal biomass is expressed in g m
1. (1990), Humphreys (1979), Reichle (1977), Engelmann (1968), Simon (1964), Malmstro

134

We simulated C and N mineralization by soil food webs after Hunt et al. (1987). The procedure assumes that the system is at a steady state and that for a given consumer, biomass production per unit of time equals losses by predation and natural death over the same period, and accounts for the energetic efficiency. Then, for any predator j feeding on a single type of prey, the feeding rate Fj (g C m
2 yr
1) was calculated as:

Fj ¼

dj Bj þ Mj aj pj

(2)

with dj ¼ specific death rate of j (yr
1), Bj ¼ j mean annual population size (g C m
2), Mj ¼ death rate of j due to predation, aj ¼ assimilation rate, and pj ¼ production rate. For predators that feed on several resources with selective intensities, preferences are incorporated to the model as:

Fij ¼ Pn

wij Bi

k¼1

wkj Bk

Fj

(3)

with Fij being the feeding rate of predator j on prey i, wij the preference of predator j for prey i, and n the number of functional groups in the web. k represents the summation of all n trophic groups. A mineralization rate (Cij), in terms of respired CO2eC (g C m
2 yr
1) was calculated for each predator after:


 Cij ¼ aj 1
pj Fij

(4)

N mineralization rate (g N m
2 yr
1) was derived from Fij based on C/N ratio of prey (CNi) and predator (CNj) as

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135

Table 3 PLFA content (nmol PLFA g soil
1) in grazed (G) and non grazed (UG) plots at sites A, B and C (mean ± SE, with n ¼ 10). Effects of grazing and texture on different PLFAs: significance (p) from two-way ANOVA. Site A

Site B

UG

G

Site C

UG

G

Total microbial PLFAs 25.51 ± 1.57 23.68 ± 1.75 16.28 Unspecific microbial PLFAs 12.33 ± 0.91 11.46 ± 0.94 8.68 Unspecific bacterial PLFAs 2.25 ± 0.13 2.12 ± 0.14 1.38 Gram þ PLFAs 6.84 ± 0.45 6.31 ± 0.53 4.18 Actinomycete PLFAs 3.75 ± 0.19 3.47 ± 0.23 1.84 Fungal PLFAs 0.34 ± 0.03 0.31 ± 0.04 0.19 Fungal:bacterial ratio 0.037 ± 0.001 0.037 ± 0.03 0.029

± ± ± ± ± ± ±

UG

Effect of grazing (p value) Effect of texture (p value) G

2.03 13.190 ± 1.08 15.09 ± 1.33 15.13 ± 0.10 1.04 7.66 ± 0.70 7.99 ± 0.77 7.82 ± 0.58 0.19 1.08 ± 0.1 1.34 ± 0.11 1.37 ± 0.07 0.59 2.98 ± 0.27 3.85 ± 0.35 3.90 ± 0.26 0.25 1.35 ± 0.15 1.76 ± 0.16 1.87 ± 0.11 0.03 0.12 ± 0.01 0.15 ± 0.01 0.16 ± 0.02 0.004 0.028 ± 0.03 0.029 ± 0.002 0.031 ± 0.002

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.002

0.29 0.32 0.16 0.18 0.42 0.32 0.97

Table 4 Density of the functional groups at each site (A, B, C) for grazed (G) and non grazed (UG) plots, size of the active and slow SOM pools and percentage of C allocated to each of the three energy channels that integrate the food web. Density of functional groups and SOM pools size are expressed in kg C ha
1 for the 15 upper soil cm (Mean ± SE; n ¼ 10 for biota and n ¼ 3 for SOM pools). Carbon density has been estimated from direct counting of each functional group, including bacteria and fungi. SOM pools were estimated from laboratory incubation of soil samples. **: trophic groups (also in bold) whose abundances significantly depended on site (ANOVA, site p < 0.0001) but not on land use. Site A

Basal resources Active SOM Slow SOM Functional groups Bacteria** Fungi Ameba Flagellates Ciliates Phytophagous nematodes Bacteriophagous nematodes Fungivorous nematodes** Predaceous nematodes Omnivorous nematodes** Collembolans Fungivorous Prostigmata Nematophagous Prostigmata Fungivorous Cryptostigmata Predaceous Mesostigmata Proturans Symphylans Diplurans (Japygidae) Total invertebrate biomass Contribution of channels Detrital bacterial channel (%) Detrital fungal channel (%) Herbivorous channel (%)

Nij ¼ aj

Site B

Site C

UG

G

UG

G

UG

G

841.5 ± 154.1 41443.9 ± 222.1

792.9 ± 181.8 37731.9 ± 181.8

576.2 ± 315.6 17392.7 ± 958.3

1039.1 ± 194.3 11571.4 ± 847.9

1324.0 ± 603.8 14357.6 ± 603.8

525.9 ± 70.1 15514.6 ± 70.1

218.5 ± 46.1 33.3 ± 8.6 0.033 ± 0.008 0.001 ± 0.001 0.008 ± 0.003 0.09 ± 0.02 0.81 ± 0.05 0.3 ± 0.04 0.01 ± 0.01 0.95 ± 0.11 0.04 ± 0.01 0.46 ± 0.08 0.01 ± 0.005 0.21 ± 0.05 0.07 ± 0.01 0.006 ± 0.003 0.002 ± 0.002 0.016 ± 0.002 254.8 ± 10.2

180.4 ± 28.3 42.0 ± 10.2 0.036 ± 0.012 0.001 ± 0.001 0.013 ± 0.005 0.041 ± 0.007 0.53 ± 0.09 0.19 ± 0.03 0.01 ± 0.01 0.85 ± 0.13 0.04 ± 0.007 0.33 ± 0.07 0.001 ± 0.0004 0.13 ± 0.02 0.03 ± 0.01 0.009 ± 0.004 0.003 ± 0.002 0.016 ± 0.002 224.7 ± 11.7

48.5 ± 5.2 38.5 ± 10.9 0.172 ± 0.053 0.001 ± 0.0001 0.014 ± 0.004 0.122 ± 0.023 0.78 ± 0.12 0.15 ± 0.02 0.02 ± 0.01 0.37 ± 0.08 0.01 ± 0.005 0.27 ± 0.03 0.04 ± 0.03 0.17 ± 0.03 0.09 ± 0.03 0.003 ± 0.002 0.002 ± 0.002 0.016 ± 0.002 89.27 ± 10.9

88.6 ± 20.0 60.4 ± 17.0 0.091 ± 0.021 0.008 ± 0.004 0.018 ± 0.005 0.121 ± 0.022 0.91 ± 0.15 0.16 ± 0.03 0.02 ± 0.01 0.63 ± 0.07 0.02 ± 0.06 0.43 ± 0.10 0.05 ± 0.01 0.19 ± 0.05 0.05 ± 0.01 0.003 ± 0.002 0.002 ± 0.002 0.016 ± 0.002 151.8 ± 16.4

266.1 ± 24.4 79.2 ± 15.8 0.055 ± 0.013 0.005 ± 0.003 0.013 ± 0.002 0.052 ± 0.014 0.55 ± 0.05 0.10 ± 0.01 0.01 ± 0.01 0.33 ± 0.04 0.01 ± 0.01 0.44 ± 0.10 0.03 ± 0.02 0.10 ± 0.03 0.08 ± 0.04 0.009 ± 0.004 0.005 ± 0.003 0.035 ± 0.003 347.2 ± 15.3

202.6 ± 46.3 42.5 ± 7.5 0.077 ± 0.015 0.001 ± 0.001 0.018 ± 0.009 0.043 ± 0.006 0.82 ± 0.11 0.11 ± 0.03 0.002 ± 0.001 0.34 ± 0.05 0.07 ± 0.05 0.40 ± 0.11 0.06 ± 0.03 0.25 ± 0.08 0.18 ± 0.04 0.011 ± 0.004 0.005 ± 0.003 0.035 ± 0.003 247.5 ± 7.5

86.50 13.46 0.04

80.94 19.04 0.02

55.92 43.90 0.18

59.50 40.40 0.10

76.78 23.20 0.02

82.49 17.47 0.04

! Pj 1
F CNi CNj ij

(5)

For each food web, mineralization was simulated based on the mean biomass (n ¼ 10) of each trophic group obtained from field samples. To get an estimate of the variability, we ran the model ten times per web. Mineralization rates were calculated for energy channels as the sum of mineralization rates contributed by every group belonging to each channel, weighted by food preferences. The model also provided energy flux rates (g C m
2 yr
1) between components of the food web for each plot. 2.7. Food web resilience and stability We constructed the interaction matrices of our six plots following de Ruiter et al. (1995). The elements of the matrix are the partial derivatives of growth equations for the functional groups at equilibrium, and represent the interaction strengths between

functional groups, i.e. the size of the effects of each functional group on each other's dynamics near equilibrium (in yr
1). The negative per biomass effect of a predator j on prey i, aij, was calculated as:

Fij aij ¼
cij Xi* ¼
Bj

(6)

and aji, the positive per biomass effect of prey i on predator j, as

aji ¼ aj pj cij Xj* ¼

aj pj Fij Bi

(7)

with Xj* and Xi* representing the theoretical biomasses of j and i at equilibrium, aj and pj the assimilation and production rates of the predator respectively, and cij the consumption coefficient of j on i. Following May (1973), we assumed that a system near equilibrium is stable if all the eigenvalues of the interaction strength matrix (the Jacobian matrix) that defines the food web have negative real parts (lmax < 0). The return time, an estimate of the resilience of the food web, will be then roughly proportional to the

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136

Phytophagous nematodes Flagellates

Roots

Ciliates

Omnivorous nematodes

Amoeba Bacteria Bacteriophagous nematodes

Active SOM pool

Predaceous nematodes Nematophagous mites

Symphylans Proturans

Fungi

Predaceous mites

Fungivorous nematodes Collembolans

Slow SOM pool

Fungivorous Cryptostimata

Japygidae

Fungivorous Prostigmata

Fig. 1. Connectedness food web of the SGS. Affiliation of functional groups to energetic channels is indicated by text colors: red for members of the fungal channel, blue for members of the bacterial channel and green for members of the root channel. Groups marked in black belong to more than one channel. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).

reciprocal of the largest eigenvalue (RT ¼
1/lmax, for all l < 0) (Pimm and Lawton, 1977). The diagonal terms of the matrix represent intraspecific interaction strengths (namely the self-regulation of each group) and were calculated as:

aii ¼
cii Xi* ¼
si di

(8)

where di represents the specific death rate (yr
1) of species and s (dimensionless) is the value leading to the minimum intraspecific interaction needed for matrix stability (Neutel et al., 2002). In this sense, the value of s is then a measure of the stability of the food web and, the smaller its value, the more stable will the matrix be.

Scaling (MDS). We compared plots for differences in microbial community structure by one-way non parametric Analysis of Similarities (ANOSIM) applied to treatment and site. Contribution of microbial markers to differences was assessed by Similarity Percentages (SIMPER) analyses. We performed MDS, ANOSIM and SIMPER with the non-parametric multivariate analysis software PRIMER v6 (Clarke and Gorley, 2006). The same methods were used to compare plots for differences in soil invertebrate communities. In this case, the BrayeCurtis similarity matrix was built up using the biomass-C abundance (g C m
2) of each group previously log transformed and standardized by total C abundance. 3. Results

2.8. Statistics

3.1. Soil physical and chemical characteristics

We tested effects of grazing on the biomass abundance of microbial and invertebrate functional groups by two-way ANOVA (with SPSS v.19) with site and treatment as factors and with n ¼ 10 for each plot. When the normality requirements were not met (flagellates, predaceous nematodes, collembolans, proturans, diplurans and symphylans), we used the non-parametric KruskaleWallis test for comparisons between sites and the ManneWhitney test for comparisons between treatments. To assess effects of grazing on mineralization and food web stability, we applied the same ANOVA to the C and N mineralization rates and to the values of s values obtained from the model described above. We applied the model to average biomasses (n ¼ 10) and SOM pools (n ¼ 3) per plot (see Table 4) and ran the model ten times per plot so, in this last case, n ¼ 10 represent 10 simulations (and not 10 samples). We compared our plots for differences in microbial community structure based on the relative abundance (% nmol PLFA g soil
1) of all microbial PLFAs found in each plot with n ¼ 10 per plot. These relative abundances were log transformed and used to build up a BrayeCurtis similarity matrix as a base for a Multidimensional

Despite being close to each other and in comparable topographic positions, sites A, B and C were very different in soil texture (Table 1) with Site A having the most fine-textured soil and Site C the most coarse-textured. Accordingly, Site A showed the lowest bulk density and the greatest water holding capacity and was the richest in total soil C and N content. Soils at Site C were the poorest in available inorganic N. Soil physical characteristics, SOM content and size of the SOM pools were independent on grazing. 3.2. Microbial and faunal functional diversity Microbial PLFA biomass was significantly higher in Site A than in sites B and C (Table 3). The biomass attributable to diverse invertebrate groups was similar in all plots with the exception of fungivorous and omnivorous nematodes whose biomass was significantly higher (p < 0.001) in Site A than in sites B and C (Table 4). Microbial community structure depended on sites (R ¼ 0.29 p ¼ 0.001) with Site A being different from sites B and C (Fig. 2). These differences are attributable to different proportions of some

P. Andr
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non-biomarker PLFAs. Among biomarker PLFAs, slightly greater proportion of 10Me-C16:0 (actinomycetes) and C18:1u9c (fungi) contributed to segregate Site A from sites B and C. However, differences were very low since similarity between all sites was 90%. As for microbes, the invertebrate community structure was affected by site (R ¼ 0.833 p ¼ 0.06) with Site A differing from sites B and C (Fig. 2) in greater abundance of omnivorous nematodes and lower abundance of bacteriophagous nematodes. Differences between B and C were due to crytostigmatic mites that were more abundant in Site C. Differences between sites were greater for invertebrate communities than for microbial with 60% similarity between all sites (Fig. 2). Grazing had no effect on the structure of microbial or faunal communities (R ¼ 0.011, p ¼ 0.361 for microbes; R ¼ 0.028, p ¼ 0.18 for invertebrates). 3.3. Food web structure and energetics We identified 18 functional groups that were present in all plots and, as a consequence of this, web connectance (C ¼ L/S2), defined as the number of realized links (L) among those possible (S2), had the same value (0.14) for all webs. Most biomass (97e99%) was allocated to primary consumers (bacteria and fungi), and decreasing amounts corresponded to secondary consumers (0.5e2.4%) and top predators (0.03e0.2%) (Table 4). In sites A and C, total soil biomass was dominated by bacteria that accounted for 77e86% of total carbon while fungi only provided 13e23%. Site B showed significantly less microbial biomass than sites A and B and also a different structure, with lower contribution of bacteria (54e58%) and higher contribution of fungi (up to 43%). In all cases, only a very small proportion of total C biomass (<2.5%) was attributable to micro- and mesofauna. Mirroring this pattern and, in terms of contribution to biomass, the bacterial channel was clearly dominant in sites A and C but not in Site B in which biomass was much more equitably distributed between the fungal and bacterial channels. The herbivore channel was very poorly represented (less than 0.2%) in all plots (Table 4). At the primary consumer level, much more C entered the food web via bacteria than via fungi (Fig. 3); the imbalance between the amount of energy canalized through bacterial and fungal channels was the highest in Site A (6.1 times greater in the bacterial than in the fungal channel in plot UGA and 4.2 times greater in plot GA), and the lowest in Site B (1.6 in UGB, 1.8 in GB) whit Site C showing intermediate values (3.4 in UGC, 4.2 in GC). We also calculated the ratio between the amount of C delivered by consumers to predators at the top of the food web and the amount of C transferred from basal resources to primary consumers

(a) Microbial PLFAs

137

at the bottom (Fig. 3), and interpreted this ratio as a measure of the energetic efficiency of each channel. By far, the highest efficiency was found in the root-based channel (values ranging from 0.9 to 0.02), followed by the fungal channel (0.029e0.001) and with the bacterial channel being the less efficient (0.001e0.0005). We derived C and N mineralization rates from the same model. The resulting C and N mineralization rate differed significantly across sites (p < 0.0001 for C, p ¼ 0.005 for N, Table 5). C mineralization was always led by bacteria (Fig. 4a), whose contribution varied with site (p < 0.0001). However, when it came to N mineralization the relative importance of bacteria declined in favor of other groups, particularly fungi and omnivorous and bacteriophagous nematodes (Fig. 4c). Contribution to N mineralization by these three groups depended on site. Fungi participation was significantly higher (p < 0.0001) in sites B and C than in Site A, while the contribution of omnivorous and bacteriophagous nematodes was more important (p < 0.005) at sites A and B than at Site C (Fig. 4c). The bacterial channel contributed a large fraction of total C mineralization than the fungal channel (Fig. 4b), with its relative contribution being significantly lower (p < 0.001) in Site B (about 60%) than in sites A and C (about 82 and 80% respectively). The fungal channel played a greater role for N than for C mineralization (Fig. 4d) and its contribution also depended on site (p < 0.001): about 60% contribution in Site B compared to 36% and 41% in sites A and C. Grazing did not affect soil biota biomass or its contribution to SOM mineralization. 3.4. Food web stability and resilience The eigenvalues of every interaction matrix always had negative real parts indicating that all the six food webs were locally stable. Relative stability as measured by s, depended on site and on treatment and was the highest in Site A (i.e., lowest s) and the lowest in Site B (i.e., highest s) (Fig. 5a). Treatment had conflicting effects on stability depending on site, with grazing increasing stability in Site A and decreasing stability in Site C. The s values estimated from the interaction matrices were low in all webs (ranging from 0.0024 in GA to 0.018 in UGB) suggesting high stability for these soils. Return time was higher in sites A and B than in Site C and increased with grazing in sites B and C while decreasing in Site A (Fig. 5b). Relative stability and resilience were related to soil texture. Grazing significantly increased the relative stability (s) of the webs and lengthened their return time in the fine-textured and SOM rich soils of Site A but had the opposite effect in the coarser and poorer

(a) Invertebrate groups UG-C G-B

UG-A

Similarity levels G-C

G-A

G-B

UG-B G-C

UG-C

G-A UG-A

UG-B

60 % 80 % 90 % 95 %

Fig. 2. Non-metric multidimensional scaling (MDS) applied to soil microbial (a) and invertebrate communities (b) in sites A, B and C and in grazed (G) and non-grazed (UG) plots. The analysis was performed on the relative biomass of the PLFA markers (nmol %) for microbes and on the relative biomass (% weight) on each invertebrate functional group for fauna. The six plots were assembled based on similarity levels obtained from hierarchical cluster analyses.

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138

(a)

UGA

Predators

0.5 Consumer s

0.4

47

Consumer s

Fung i

1063

175

Herbivore 1.3

1.0 Consumer s

0.5

873

205

Bacteria

Fung i

299

185

Fung

Bacteria 1149

341 SOM

Fung i

Roots

Roots

GC

Predators

1.0

0.04

Consumers

14

29

0.9

2.1

SOM

Consumer s

Herbivore

Herbivore

270

1.2

10

22

0.06

10

480

0.03

Consumers

GB

Consumers

UGC

Predators

0.6

0.3

Bacteria

Roots

SOM

0.6

Roots

39

2.5

1.0

Predators

Consumer s

Herbivore

Herbivore

SOM

0.8

10

(c)

Fung i

0.1

Consumers

31

10

UGB

Predators

0.9

Consumers

Bacteria

Roots

SOM

(b)

5.7

41

13

Bacteria

Consumer s

4.2

0.02

Consumers

GA

Predators

Fung

Bacteria 917

214 SOM

Herbivore 0.9 Roots

Fig. 3. Simplified energy flow through each energetic channel (in kg C ha
1 yr
1) of the food web for grazed (G) and ungrazed (UG) plots at site A (a), site B (b) and site C (c).

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139

Table 5 Mineralization rates (n ¼ 10) in the three sites (A, B, C) for grazed (G) and non grazed (UG) plots. Shown values are the mean of 10 simulations of the model by Hunt et al. (1987). ** Significant (ANOVA, p < 0.001) effect of site on N mineralization. N mineralization (kgN.ha
1 yr
1) Site A

Microbes Bacteria Fungi** Protists Amoeba Flagellates Ciliates Nematodes Herbivorous Bacteriophagous Fungivorous** Predaceous Omnivorous** Collembolans Mites Fungivorous Prostigmata Nematophagous Prostigmata Fungivorous Cryptostigmata Predaceous Mesostigmata Proturans Symphylans Diplurans (Japygidae) TOTAL**

Site B

Site C

Site C

UG

G

UG

G

UG

G

UG

G

12.15 14.07

20.12 8.80

5.16 12.72

9.18 19.27

24.00 19.27

17.52 13.63

490.2 160.9

792.1 100.5

203.9 145.3

300.5 215.2

UG

G

782.4 213.8

667.6 155.2

0.095 0.003 0.014

0.209 0.017 0.048

0.449 0.004 0.024

0.249 0.011 0.064

0.150 0.037 0.034

0.217 0.001 0.029

0.297 0.010 0.043

0.650 0.048 0.149

1.397 0.012 0.075

0.776 0.031 0.199

0.466 0.106 0.105

0.674 0.003 0.090

0.020 1.534 0.131 0.017 4.312 0.024

0.010 0.716 0.060 0.013 4.463 0.020

0.050 1.511 0.075 0.014 1.733 0.010

0.051 1.520 0.069 0.016 3.249 0.005

0.017 0.890 0.050 0.006 1.643 0.002

0.016 1.735 0.042 0.001 1.593 0.019

0.204 4.537 1.312 0.157 12.755 0.273

0.096 2.118 0.601 0.095 13.205 0.230

0.502 4.469 0.752 0.129 5.137 0.116

0.511 4.495 0.689 0.143 9.619 0.063

0.170 2.633 0.495 0.046 4.859 0.027

0.164 5.131 0.424 0.008 4.713 0.215

0.149 0.007 0.054 0.019 0.0002 0.00001 0.005 32.60

0.119 0.0004 0.038 0.008 0.0024 0.00005 0.006 34.66

0.120 0.011 0.047 0.035 0.0006 0.0004 0.005 21.98

0.184 0.011 0.055 0.020 0.0010 0.0001 0.005 33.97

0.205 0.004 0.022 0.029 0.0011 0.0012 0.012 46.38

0.289 0.022 0.078 0.066 0.0024 0.0006 0.011 35.27

1.719 0.081 0.625 0.186 0.003 0.0002 0.039 673.3

1.372 0.005 0.434 0.078 0.028 0.0005 0.049 911.8

Functional groups

100

80

80

60

60

40

40

20

20 0 UGA

GA

UGB

GB

UGC

GC

(c)

1.387 0.124 0.545 0.348 0.007 0.0047 0.043 364.3

2.129 0.128 0.640 0.197 0.012 0.0014 0.041 535.4

2.363 0.046 0.255 0.274 0.013 0.0142 0.098 1008.2

3.339 0.258 0.896 0.605 0.028 0.0075 0.087 839.5

Energy channels

(b)

100

UGA

GA

UGB

GB

UGC

GC

UGA

GA

UGB

GB

UGC

GC

(d)

100

100

80

80

60

60

40

40

20

20

0

Site B

G

0

Mineralized N (%)

Site A

UG

(a) Respired C (%)

CO2-C emission (kgC ha
1 yr
1)

0 UGA

GA

UGB

GB

UGC

GC

Bacteria / Bacterial channel

Omnivorous nematodes

Amoeb

Fungi / Fungal channel

Bacteriophagous nematodes

Othe

Root channel Fig. 4. Contribution (%) of functional groups e(a) and (c)- and energy channels- (b) and (d)- to C and N mineralization by soil biota. Data for grazed (G) and non-grazed (UG) plots at sites A, B and C.

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140

0.022

(a)

4.02

0.018

(b)

RT (-1 / λmax)

3.98

s

0.014 0.010 0.006 0.002

3.94 3.90 3.86 3.82

UGA GA

UGB GB

UGC GC Mean ± SE

UG

GA

UGB GB

UGC GC

Mean ± 2 SE

Fig. 5. Food web stability (a) and return time (b) at sites A, B, C for grazed (G) and non-grazed (UG) plots. For stability, treatment p < 0.0001, site p < 0.0001, treatment  site p < 0.004. For return time, treatment p ¼ 0.05, site p < 0.0001, treatment  site p < 0.0001 (ANOVA).

soils of Site C (Fig. 5).

4. Discussion In this work, we found that in the shortgrass steppe, soil organic matter content as well as soil microbial and invertebrate abundance were insensitive to long-term grazing at moderate intensity. This finding refutes our first hypothesis about positive effects of large herbivores on soil bacterial abundance and functions. Insensitivity of the SGS to grazing had previously been pointed out for SOM (LeCain et al., 2002; Burke et al., 1999) as well as for some groups of the soil biota, mainly nematodes (Wall-Freckman and Huang, 1998), and microarthropods (Leetham and Milchunas, 1985). The SGS is thought to be resilient to herbivory due to its long evolutionary grazing history (Milchunas and Lauenroth, 1993). As in other arid and semi-arid prairies, effects of moderate grazing on plant productivity and standing biomass are small due to the low proportion of the C pool exposed to cattle, as most net primary production is allocated belowground (Lauenroth et al., 2008). Soil spatial heterogeneity is a prominent characteristic of the SGS that shows great differences in organic matter content across soil textural gradients (Burke et al., 1989, 1999). Clay and silt content is a robust predictor of soil ability to stabilize organic matter (Parton et al., 1989), with fine-textured soils generally containing more organic C and N than coarse or medium textured soils. Soil ability to retain water also increases with clay and organic matter content, which is of paramount importance for soil biota to overcome the extreme summer drought. There is general agreement € per, 2004) on positive spatial corre(Wardle, 1992; Müller and Ho lation between microbial biomass and clay and soil organic matter. However, we found that fungal biomass is constant across different soils and bacterial biomass variable but independent of soil texture and soil carbon content. Unlike bacteria, fungi are relatively independent of nutrient spatial distribution in soils because hyphal growth allows them to transfer nutrients from rich to poor soil microsites (Frey et al., 2003). The low bacterial biomass found in soils with current favorable physical conditions and SOM levels might be the legacy of past disturbances. Unlike the bacteria, soil invertebrate functional groups are evenly distributed across the prairie independently of soil type or SOM content. The widespread lack of spatial correlation between soil invertebrate and resources was foreseeable since, together with resource availability, a number of concurrent factors (e.g. no specialized food regime, low dispersal abilities relative to microbes, or correlation with water spatial distribution) operate together to shape invertebrate biodiversity distribution in soils (Minor, 2011).

Exceptions to the rule are omnivorous and bacteriophagous nematodes whose abundance is the greatest in fine-textured soils that offer higher prey availability and more favorable water status than coarse-textured soils (Brussaard, 1998; Griffiths, 1994). Since most alive soil biomass is attributable to microbes, not surprisingly they are responsible for most C mineralized by the soil food web (Reichle, 1977). Nevertheless, we found that soil invertebrates are important contributors to N mineralization, and this is particularly true for omnivorous and bacteriophagous nematodes and amoeba. The importance of these three functional groups is € ter et al., 2003; Villenave et al., 2004) and documented (Schro contribution of mites and collembolans to N mineralization has also been described for litter (Neher et al., 2012) but requires further study in soil (Osler et al., 2004; Filser, 2002). We had hypothesized that soil food web stability will depend on soil type. We have found significant variation in soil food web stability, with soils showing the highest carbon content also sheltering the most stable food webs, which is in agreement with predictions derived from model ecosystems. Productivity, understood as the availability of basal resources, shapes both the trophic architecture of the community and its resilience. At the lowest end of the productivity gradient, a minimum level of resources is required to sustain short food chains and, as productivity increases, the web can include additional trophic levels and become more complex while still stable. However, high levels of productivity may bring instability in the absence of compensatory mechanisms (Rosenzweig, 1971; Moore et al., 1996). Among the mechanisms underpinning food web stability is compartmentalization of trophic interactions within the food web, i.e. the non-random pattern of interspecific links within the webs in which species are organized making part of separate energy channels eventually coupled by predators (Moore and Hunt, 1988; Moore et al., 1988). The food webs studied in this work are comprised of three energetic channels of which the herbivorous root-based channel is of very low importance, with the majority of energy (read C) being channeled through two detrital channels based on bacteria and fungi as primary consumers. There are clear energetic differences between channels, with the bacterial channel being less efficient than the fungal channel in transferring energy bottom-up. Our six food webs differ by the proportion of energy channelized through one channel or the other and we have found a consistent positive relationship between the stability of the food web and the asymmetry between both channels. Our results are in line with findings of Rooney et al. (2006) who have suggested that food web stability depends on the maintenance of the heterogeneity of distinct energy channels.

P. Andr
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We had also posited that grazing will positively affect soil food web stability, and formulated this hypothesis based on expected higher SOM content in grazed than in ungrazed prairies. The expected increase in soil C did not occur, but grazing increased relative soil food web stability in carbon-rich soils and decreased it in carbon-poor soils. This response is difficult to explain with our data, since together with productivity and compartmentalization, other characteristics of the food webs that have not been considered in this work have proved to contribute to model ecosystem stability, such as the asymmetric distribution of positive and negative intraspecific interaction strengths along trophic levels (de Ruiter et al., 1995) and the presence of weak interactions embedded in trophic loops containing strong consumereresource interactions (McCann et al., 1998; Neutel et al., 2002). Interestingly, food web stability has proved to be responsive to grazing even when the abundance of none of the functional groups that integrate the web is significantly modified. Soil food web stability and resilience seem to be more sensitive to land-use than other food web metrics (de Vries et al., 2012; Digel et al., 2014). This behavior is in accordance with the response of model ecosystems subjected to press-perturbations (i.e. experimental alterations of species densities) that indicate that changes in stability do not correlate forcefully with changes in the biomass or feeding rate of any particular member of the food web (Moore and de Ruiter, 2012). Our results constitute a snapshot of the situation of the soil SGS ecosystem at the very beginning of the growing season, which explains some particularities such as the small size of the soil herbivore channel. Recent samplings conducted in the shortgrass steppe (Frouz et al., 2013) prove that root associated invertebrates (particularly plant-parasitic nematodes) may be particularly abundant in these soils and their low representation in our works is undoubtedly due to the lack of active roots at the time of sampling. The almost total absence of gram negative bacteria in our soils may be due to the same reason, since this bacterial group typically resides in the rhizosphere (Hawkes et al., 2007). There is growing evidence that most of the carbon that fuels the detrital soil food web channels is provided by plants in the form of root exudates (Pollierer et al., 2007). However, these exudates are labile and short-lasting (Ruf et al., 2006) and it is unlikely that they are able to provide energy to the soil biota during the whole year, as is evidenced by the negligible amount of labile carbon present in our soils before the start of the growing season. During the long frost winter period, recalcitrant materials (litter and dead roots) might sustain the soil metabolism giving rise to seasonal alternation of the basal resources. Exploratory samplings performed some

1.2

200 150 100 50 0

5. Conclusions There is growing evidence that land use intensification is altering belowground biodiversity. However, the direction and implications of the disturbance are not clear since specific or sectoral indicators show controversial responses depending on soil properties and on the nature of the land use (Thomson et al., 2015). Addressing the relationship between soil biodiversity and soil service resilience is essential to preview ecosystem response to environmental changes, and metrics depicting the structure of the soil food web are promising for this purpose. In this work, we demonstrate that soil food web stability is sensitive to grassland management even in the absence of measurable changes in the biomass of the various functional groups that integrate the soil biota or in the current carbon and nitrogen mineralization rates. Stability is highly dependent on soil characteristics, with sandy soils being much more sensitive to intensification than more finetextured soils. Our data suggest that the stability of the soil food web is positively related to compartmentation, i.e. to asymmetry in the amount of energy channeled through the bacterial and fungal soil detrital channels. We have found evidence of seasonal variations in the quality of the resources available to soil biota which in turn forecasts fluctuations in the stability of the web over time. As other authors have suggested (McMeans et al., 2015) further efforts are required to include temporal and spatial variability in the prediction of soil food web stability.

(b)

-1

250

months later showed significant shifts in soil biota over the year, with lower abundances in summer than in early spring (Fig. 6) which also suggests seasonal changes in mineralization and in the structure and stability of the soil food web. In this work, we have studied effects of grazing on soil biodiversity at the level of functional trophic groups and interactions. Recent advances in soil ecosystem knowledge have led to the consensus that functional diversity more that species diversity drives key soil ecosystem processes such as carbon and nitrogen cycling (Heemsbergen et al., 2004). We found that effects of grazing on belowground biodiversity manifest though shifts in energetic flows between functional groups more than though changes in their individual abundance or biomass. Specifically, grazing affects the relative amount of basal resources exploited by fungi and bacteria that behave differently in nutrient turnover (Six et al., 2006) and may then have important implications for ecosystem processes such as plant production and species composition (Pastor and Naiman, 1992) or carbon sequestration (Bailey et al., 2002).

1.4

(a) mg C. kg dry soil

mg C. kg dry soil

-1

300

141

1.0 0.8 0.6 0.4 0.2

Fungi

Bacteria

0.0

Early spring

* Protists

Arthropods Nematodes

Total fauna

Full summer

Fig. 6. Carbon biomass (kg C ha
1 yr
1) of soil microbes (a) and invertebrates (b) in the middle of the growing season (July 2013) and immediately after soil thaw (early spring, April 2014). Spring data from the grazed plot at site B in this work; summer data from a grazed plot with similar soil physical and chemical characteristics. (*) Only amoeba were more abundant in summer than in spring; ciliates and flagellates almost disappeared during summer drought.

142

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Acknowledgments We want to acknowledge Dr. Daniel Milchunas (Long-Term Ecological Research Project, Colorado State University) for his kindness in allowing access to very rare and fragile experimental plots and in providing invaluable advice on the ecology of the shortgrass steppe. We also thank Dr. Greg Butters at the Soil Physics Laboratory of the Colorado State University for kindly analyzing our soils for hydrological characteristics. This work has been supported by the European Commission under the contract PIOF-GA-2012326361 of the FP7 People program (Marie Curie IOF Fellowship)
s. with P. Andre References
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