Resistance and resilience of the forest soil microbiome to logging-associated compaction

ABSTRACT Soil compaction is a major disturbance associated with logging, but we lack a fundamental understanding of how this affects the soil microbiome. We assessed the structural

resistance and resilience of the microbiome using a high-throughput pyrosequencing approach in differently compacted soils at two forest sites and correlated these findings with changes in

soil physical properties and functions. Alterations in soil porosity after compaction strongly limited the air and water conductivity. Compaction significantly reduced abundance, increased

diversity, and persistently altered the structure of the microbiota. Fungi were less resistant and resilient than bacteria; clayey soils were less resistant and resilient than sandy soils.

The strongest effects were observed in soils with unfavorable moisture conditions, where air and water conductivities dropped well below 10% of their initial value. Maximum impact was

observed around 6–12 months after compaction, and microbial communities showed resilience in lightly but not in severely compacted soils 4 years post disturbance. Bacteria capable of

anaerobic respiration, including sulfate, sulfur, and metal reducers of the Proteobacteria and Firmicutes, were significantly associated with compacted soils. Compaction detrimentally

affected ectomycorrhizal species, whereas saprobic and parasitic fungi proportionally increased in compacted soils. Structural shifts in the microbiota were accompanied by significant

changes in soil processes, resulting in reduced carbon dioxide, and increased methane and nitrous oxide emissions from compacted soils. This study demonstrates that physical soil disturbance

during logging induces profound and long-lasting changes in the soil microbiome and associated soil functions, raising awareness regarding sustainable management of economically driven

logging operations. SIMILAR CONTENT BEING VIEWED BY OTHERS LIMITED RESILIENCE OF THE SOIL MICROBIOME TO MECHANICAL COMPACTION WITHIN FOUR GROWING SEASONS OF AGRICULTURAL MANAGEMENT Article

Open access 31 August 2021 EROSION REDUCES SOIL MICROBIAL DIVERSITY, NETWORK COMPLEXITY AND MULTIFUNCTIONALITY Article Open access 12 March 2021 MICROBIAL FUNCTIONAL CHANGES MARK

IRREVERSIBLE COURSE OF TIBETAN GRASSLAND DEGRADATION Article Open access 13 May 2022 INTRODUCTION Soil is an essential component of forest ecosystems, mediating fundamental nutrient and

energy flow patterns that ensure forest productivity, sustain biodiversity and regulate climate stability (Bonan, 2008; Reay et al., 2008; Dominati et al., 2010; Normile, 2010). Soils are

dynamic biological matrices featuring a complex microbiome that has an integral role in virtually all ecosystem processes (Barrios, 2007). At the system level, microbial metabolism regulates

ecosystem functioning and modulates resistance and resilience to perturbations (Allison and Martiny, 2008). It is likely that measuring the microbial community structure and associated

functions can improve the ability (1) to monitor alterations of the soil system after disturbances, (2) to evaluate its capacity to recover and perhaps (3) to detect adverse effects in

ecosystem functioning before they are irreversible. Soil compaction has been recognized as a major disturbance associated with forest management (Van-Camp et al., 2004). Economically

efficient harvesting requires the use of heavy machines, causing severe compaction of the soil particularly during wet conditions and along skid trails and landings (Grigal, 2000; Marshall,

2000). Alterations in soil porosity affect pore connectivity, water infiltration, air permeability, temperature, rooting space, nutrient flow and biological activity (Greacen and Sands,

1980; Kozlowski, 1999; Richard et al., 2001; Mooney and Nipattasuk, 2003), often resulting in increased surface runoff, soil erosion, nutrient leaching and greenhouse gas emission (Worrell

and Hampson, 1997; Powers et al., 2005). As a consequence, the soil system can suffer substantial, persistent and sometimes irreversible damage, which ultimately reduces forest productivity

and ecosystem functionality. Given that the affected area can range between 10 and 40% of the total logged stand, the impact on the ecosystem can be substantial (Grigal, 2000; Luckow and

Guldin, 2007; Frey et al., 2009). The negative impact of soil compaction caused by logging on physicochemical properties has been demonstrated for years (for example, McNabb et al., 2001;

Horn et al., 2007; Ampoorter et al., 2010). In contrast, only few studies have observed significant effects on microbial properties (Dick et al., 1988; Jordan et al., 2003; Schnurr-Pütz et

al., 2006), and most such investigations reported inconsistent, equivocal or non-significant effects (Jordan et al., 1999; Chow et al., 2002; Li et al., 2004; Shestak and Busse, 2005; Busse

et al., 2006; Mariani et al., 2006; Tan et al., 2008; Jennings et al., 2012). These observations led to the general notion that microbial communities exhibit high degrees of resistance and

resilience to compaction and might not adequately reflect the ecological consequences. These previous studies commonly measured bulk parameters such as microbial biomass or were limited by

the unavailability of techniques with high taxonomic resolution to resolve the complex structure of the microbiota. With the recent advent of molecular tools, there is increasing evidence

that effects of soil compaction on microbial structure and function are probably substantial and long lasting (Frey et al., 2009, 2011; Hartmann et al., 2012). Applying high-throughput

pyrosequencing (Margulies et al., 2005) of bacterial and fungal ribosomal markers, Hartmann et al. (2012) recently described the microbial community structure in differently compacted forest

soils at far greater depth than previously possible. This large-scale survey demonstrated that logging-induced soil compaction persistently alters the microbiota. However, four factors

limited the conclusive evaluation of microbial resistance and resilience in these compacted soils. First, the experimental design did not allow for completely separating effects caused by

soil compaction from those caused by biomass removal. Second, the study did not assess soil functions that are directly dependent on physical soil properties such as air permeability and

water conductivity in order to relate changes in community structure to its edaphic background. Third, the study did not monitor the microbial response over time in order to evaluate initial

resistance and long-term resilience of the system. Finally, the study did not gather process information that could serve as proxy for changes in ecosystem functioning and to evaluate

functional redundancy of the structural changes. Here, we present a study that fills the gaps indicated above and advances our understanding of the resistance and resilience of the forest

soil ecosystem to compaction. Recently, Frey et al. (2011) reported on alterations in methanogenic community structure and methane fluxes in two controlled field experiments, in which skid

trails differing in compaction intensity were generated by logging vehicles. Driven by these findings, we launched a comprehensive assessment of physicochemical and microbial characteristics

in these soils to examine resistance and resilience of microbial community structure and associated soil functions to compaction. MATERIALS AND METHODS COMPACTION EXPERIMENT AND SOIL

SAMPLING The field experiment was conducted in Spring 2007 and 2008 at two forest sites in Switzerland, Ermatingen and Heiteren, respectively. The two independent experiments represented two

different scenarios in that the sites differed in their susceptibility to compaction (that is, soil texture) as well as in the degree of compaction induced (that is, ground contact

pressure). A detailed description of the study sites and the traffic experiments has been published previously (Frey et al., 2011). The texture at both sites was loamy, but the soil at

Ermatingen (17% clay, 47% silt and 36% sand, pH 4.6) was characterized by around 50% more clay and a higher pH when compared with the sandy soil at Heiteren (8% clay, 43% silt and 49% sand,

pH 4.0). In order to generate different degrees of compaction, soil moisture contents along projected traffic lanes (independent triplicates within 20 m distance of each other) were adjusted

to 0.17 (plastic limit, C1) and 0.35 (liquid limit, C2) gram H2O per gram of soil and equilibrated for 2 days before compaction. Compaction was induced using a fully loaded forwarder with

four passes at Ermatingen (weight of 26 tons, ground contact pressure of 240–320 kPa) and an unloaded skidder with four passes at Heiteren (14 tons, 210–280 kPa). Unaffected areas in the

vicinity of the compacted soils (that is, one meter from the center of the traffic lane) served as no impact controls (C0). Thus, the study comprised three independent wheel tracks

(triplicates) per forest site with no (C0), light (C1) and severe (C2) soil compaction per lane. The experimental layout at Ermatingen is provided as Supplementary Figure 1. A detailed soil

sampling protocol has been published previously (Frey et al., 2011). Triplicate cores from the topsoil were collected in each replicated traffic lane at a depth of 3–7 cm using steel

cylinders with a volume of around 100 cm3. The tire profiles generate a mixed and sometimes puddled stratum between the tread elements, where new structure can build up quickly after natural

drying-rewetting cycles. Hence, this stratum has limited potential to depict soil compaction and it is more suitable to sample the stratum below this depth. Furthermore, by avoiding the top

3 cm, we also excluded the litter material that was constantly falling into the tire imprint along the skid trails. Samples for soil physical measurements were collected after 1–4 days, and

within-lane replicates were analyzed individually. Samples for microbial analyses were collected at the same locations as for the physical measurements after 30, 180, 365, and 1460 days,

whereas within-lane replicates were pooled for further analysis. MEASUREMENTS OF PHYSICAL SOIL PARAMETERS AND GREENHOUSE GAS FLUXES Bulk density was determined gravimetrically after oven

drying at 105 °C and defined as the mass of dry soil divided by the sample volume. Total pore volume was determined as mass difference between saturated and oven-dried samples. Pore size

distribution was determined using the standard pressure-plate procedure for soil moisture retention curve (Hartge and Horn, 1992). Proportions of pore size classes were calculated on the

basis of the measured water desorption characteristics (Tebrügge and Düring, 1999). Saturated hydraulic conductivity (kf) was measured using an ICW soil water permeameter model 09.02

(Eijkelkamp Agrisearch Equipment, Giesbeek, the Netherlands) after soil samples have been water saturated for 72 h. Air permeability (ki) was measured after each draining step (30–300 kPa)

using an air permeameter model 08.07 (Eijkelkamp) (Gysi et al., 1999). Net soil-atmosphere fluxes of methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O) were measured in the

triplicate skid trails in monthly intervals between September and December of 2008 (medium-term response) and 2012 (long-term response). Without _a priori_ knowledge of the compaction

effects, the purpose of measuring the gas fluxes was to assess broad functional end points related to carbon and nitrogen turnover in these soils, which ultimately reflect the degree of

disturbance as well as the functional resilience in this system. Fluxes were measured as described recently (Hartmann et al., 2011; Hartmann and Niklaus, 2012; Poll et al., 2013). Static

chambers were installed in close proximity to the soil sampling spots a few weeks before the measurements. Headspace gas samples of 30 ml (total headspace volume 7.9 l) were collected at

intervals of 5, 20 and 35 min after chamber closure and analyzed using an Agilent 7890 gas chromatograph (Agilent Technologies, Palo Alto, CA, USA). Concentrations were calibrated against

standard gas mixtures simultaneously analyzed with every batch of samples, and gas fluxes were calculated by linear regression of gas concentration against time, accounting for air

temperature and pressure at the time of sampling. For each period of measurement (medium- and long-term), average gas fluxes were calculated across the 4-month period, representing an

unbiased estimate of the fluxes integrated over time. Spatial and temporal treatment effects on physical soil properties and gas fluxes were examined using a repeated measures factorial

analysis of variance (ANOVA) followed by Fisher’s protected least significant difference (LSD) _post-hoc_ test, which reasonably controls the familywise error rate as long as not more than

three treatment groups are compared (Meier, 2006). Significance levels of overall pairwise tests were adjusted for multiple comparisons using the Holm method (Holm, 1979). Non-normal data

were square-root or log transformed. PYROSEQUENCING AND QUANTITATIVE PCR OF BACTERIAL AND FUNGAL RIBOSOMAL MARKERS Total nucleic acids were extracted in duplicates from 0.5 g sieved soil (2 

mm) using a bead-beating procedure (Frey et al., 2008). DNA concentrations were determined using PicoGreen (Molecular Probes, Eugene, OR, USA). PCR amplification of partial bacterial

small-subunit ribosomal RNA genes (region V1–V3 of 16S) and fungal ribosomal internal transcribed spacers (region ITS2) was performed using 50 ng of soil DNA as described previously

(Hartmann et al., 2012). Each sample was amplified in triplicates and pooled before purification with Agencourt AMPure XP beads (Beckman Colter, Berea, CA, USA) and quantification with the

Qubit 2.0 fluorometric system (Life Technologies, Paisley, UK). Amplicons were unidirectionally sequenced using 454 pyrosequencing at the Functional Genomics Center Zurich (Switzerland)

using the GS-FLX Titanium technology (Roche 454 Life Sciences, Branford, CT, USA). Relative abundances of bacterial and fungal communities were determined by quantitative PCR on an ABI7500

Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA) with the same primers and cycling conditions as used for the pyrosequencing approach. PCR was performed using 2.5 ng DNA

in a total volume of 25 μl containing 0.5 μM of each primer, 0.2 mg ml−1 bovine serum albumin and 12.5 μl of QuantiTect SYBR Green PCR master mix (Qiagen, Valencia, CA, USA). Three standard

curves per target region (correlations ⩾0.997) were obtained using 10-fold serial dilutions (10−1 to 10−9 copies) of plasmids generated from cloned targets. Data were converted to represent

average copy number of targets per gram of soil dry weight. Spatiotemporal treatment effects were examined using repeated measures factorial ANOVA of log-transformed copy numbers followed by

Fisher’s least significant difference and Holm adjustments. PYROTAG PROCESSING Flowgrams were trimmed to low quality signals (Quince et al., 2011) and demultiplexed using MOTHUR (Schloss et

al., 2009) allowing one mismatch to the sample-specific barcode and two mismatches to the target-specific primer (Schloss et al., 2011). Flowgrams were denoised using PYRONOISE (Quince et

al., 2009) in MOTHUR to eliminate sequencing errors. The bacterial 16SV1-V2 (that is, region spanning V1 and V2) and the fungal ITS2 region were verified and extracted using V-XTRACTOR

(Hartmann et al., 2010) and its ITS counterpart (Nilsson et al., 2010) in order to remove spurious reads and compare phylogenetically consistent regions (Schloss, 2012). Sequences were

further denoised using SEQNOISE (Quince et al., 2011) in MOTHUR to eliminate PCR single-base errors. Potentially chimeric sequences were removed using the _de novo_ detection mode in UCHIME

(Edgar et al., 2011). Curated sequences were clustered into operational taxonomic units (OTUs) using the unsupervised Bayesian clustering algorithm CROP (Hao et al., 2011) and an identity

threshold of 97%. All reads in a given OTU were assigned to curated taxonomic databases using the naïve Bayesian classifier (Wang et al., 2007) in MOTHUR and a minimum bootstrap support of

60%. Bacterial and fungal reads were queried against GREENGENES (DeSantis et al., 2006; McDonald et al., 2011) and UNITE (Abarenkov et al., 2010), respectively. The consensus taxonomy of

each OTU was determined using MOTHUR as the taxonomic path represented by at least 80% of the sequences. On the basis of the consensus taxonomies, abundance data for OTUs at specific

taxonomic ranks (species, genus, family, order, class and phylum) were merged and used to generate taxonomic rank-specific matrices that were the basis for the network and the taxa-treatment

association analyses. ANALYSIS OF ALPHA AND BETA DIVERSITY Estimates of alpha diversity were calculated in MOTHUR. These estimates included the observed OTU richness, the Good’s coverage

(Good, 1953), the parametric ‘best fit’ richness estimation CatchAll (Bunge et al., 2012) and the Shannon diversity index (Magurran, 2004, Haegeman et al., 2013). As alpha diversity measures

are sensitive to differences in sampling effort, estimates were calculated based on data sets that were randomly subsampled to the same number of sequences. Spatiotemporal treatment effects

on alpha diversity estimates were examined using a repeated measures factorial ANOVA followed by Fisher’s least significant difference and Holm adjustments. Multivariate analysis of beta

diversity was performed according to the recommendations by Anderson and Willis, 2003 who proposed four components in the analysis of multivariate ecological data: (1) a robust unconstrained

ordination to determine structural similarities among communities; (2) a compatible constrained analysis with reference to a specific hypothesis; (3) a rigorous statistical test of the

hypothesis; and (4) characterization of the taxa responsible for the multivariate patterns. In accordance with this strategy, we used the following techniques for the corresponding purposes:

(1) principal coordinate analysis (PCO; Gower, 1966); (2) canonical analysis of principal coordinates (CAP; Anderson and Willis, 2003); (3) analysis of similarities (ANOSIM; Clarke, 1993)

and permutational multivariate analysis of variance (PERMANOVA; Anderson, 2001); and (4) taxon-treatment association analysis (De Cáceres and Legendre, 2009). Inter-sample Bray-Curtis

similarities (Bray and Curtis, 1957) were calculated based on standardized and square root transformed OTU abundances (Hartmann et al., 2012). ANOSIM, PERMANOVA, and CAP were run with 105

permutations as routines in PRIMER6+ (Clarke and Gorley, 2006). Both PERMANOVA and ANOSIM were applied in parallel as they are complementary, the first offering analyses of complex designs

including interactions and the second providing a universal measure of group separation while being fully non-parametric and thus robust in its application to ecological data (Lek et al.,

2011). Permutational analysis of multivariate dispersion (PERMDISP; Anderson, 2006) was used to control the influence of multivariate heterogeneity among _a priori_ groups. The

discriminative power of permutation-based analyses for pairwise comparisons of triplicates is limited by only 10 possible permutations (Clarke, 1993). In order to test compaction effects

within each site and sampling date, the within-group to among-group Bray-Curtis dissimilarities were compared using the Wilcoxon rank sum test (Wilcoxon, 1945) including Holm adjustments for

multiple comparisons. The degree of preference of each taxon for the target group relative to the other groups (that is, the point biserial correlation coefficient R) was determined using

taxon-treatment association analyses with all possible group combinations (De Cáceres and Legendre, 2009; De Cáceres et al., 2010). Singletons and doubletons, that is, taxonomic units which

were represented by only one or two sequences across the whole data set were not included in the analysis. The analyis was peformed in GINKGO (Bouxin, 2005) with 106 permutations. _P_-value

adjustments for multiple comparisons were performed using the false discovery rate (FDR; Storey, 2002). _Q_-values were determined using the software QVALITY (Käll et al., 2009), and

associations were considered significant with an FDR of 5% (_q_<0.05). On the basis of the taxonomic rank-specific matrices, abundance-based taxonomic association networks were generated

using the organic layout algorithm in CYTOSCAPE 2.8 (Smoot et al., 2011) and manual adjustments. Degree of resistance (overall association strength) and resilience (decrease of association

strength between the first and the fourth year post disturbance) was mapped onto these taxonomic association networks. RESULTS SOIL PHYSICAL PROPERTIES AND SOIL FUNCTIONS Machine passes

significantly increased bulk density and reduced porosity, saturated hydraulic conductivity and air permeability in the first week post disturbance (Table 1, Figure 1). Bulk density

increased by an average of 16 and 25% after light (C1) and severe (C2) compaction, respectively. Total pore volume simultaneously decreased by 10% and 21%, respectively. Compaction

particularly affected large-sized soil pores, reducing macropore volume by 49% (C1) and 73% (C2), respectively. Structural alterations significantly changed soil functional properties,

leading to substantial reduction in hydraulic conductivity (−51% and −94%) and air permeability (−94% and −99%), almost completely restricting water infiltration and gas exchange in C2. The

response of these properties to compaction was mostly uniform across forest sites, except for the soil texture-driven differences in hydraulic conductivity and macropore volume of the

uncompacted reference soils, causing significant _site × treatment_ interactions (Table 1). Changes in physical properties altered the greenhouse gas fluxes (Table 2, Figure 1). Compaction

significantly reduced net CH4 consumption, decreased CO2 emission and increased N2O emission. Methane consumption decreased by an average of 34% (C1) and 99% (C2), respectively, even

resulting in a net CH4 emission in C2 at Ermatingen. CO2 emission increased by 16% in C1, but this increase was statistically not significant, whereas CO2 emission significantly decreased by

24% in C2. N2O emission significantly increased with increasing compaction by an average of 94% (C1) and 181% (C2). No significant _time × treatment_ interaction was observed, indicating

that differences in greenhouse gas fluxes among treatments remained largely consistent up to around 5 years post disturbance. Treatment differences were also largely consistent across forest

sites, showing a significant _site × treatment_ interaction only for CH4. The compaction effect on CH4 was significant at both sites, but the flux alteration was more pronounced at Heiteren

than at Ermatingen. TAXONOMIC COMPOSITION After quality filtering, a total of 473 429 bacterial 16SV1-V2 and 423 720 fungal ITS2 sequences remained for community analysis and are provided

as Supplementary Data 1. These data correspond to an average of 6575±2947 bacterial 16SV1-V2 and 5885±2218 fungal ITS2 pyrotags per sample, with an average read length of 256±2 bp and 276±16

 bp, respectively. Sequence clustering yielded 6933 (782±267 per sample) bacterial and 2598 (205±96) fungal OTUs, respectively, representing an average Good’s coverage of 95±3% and 99±1%. A

complete list of the detected bacterial and fungal taxa, from phylum to OTU level, including abundance information, is provided as Supplementary Data 2. In brief, a total of 5690 (81%)

bacterial OTUs accounting for 97% of the bacterial pyrotags were identified at the phylum level. Proteobacteria (1929 OTUs, 43.2%), Acidobacteria (30.6%, 325 OTUs), and to a lesser extent

Bacteroidetes (4.6%, 551 OTUs), Actinobacteria (4.6%, 400 OTUs), Gemmatimonadetes (2.8%, 114 OTUs), Chloroflexi (2.2%, 516 OTUs), Planctomycetes (1.3%, 565 OTUs), candidate phylum AD3 (1.2%,

8 OTUs) and Firmicutes (1.1%, 288 OTUs) were the abundant bacterial phyla occurring with at least one percent relative abundance. Among the Proteobacteria, class Alpha accounted for 23.4%

(527 OTUs), Beta for 5.5% (161 OTUs), Gamma for 7.0% (517 OTUs) and Delta for 5.8% (603 OTUs) of the total abundance. The success for taxonomic assignment decreased at lower taxonomic

levels, revealing 5153 OTUs (85%), 3907 OTUs (73%), 2342 OTUs (60%), 904 OTUs (27%) and 63 OTUs (0.3%) that were identified at the class, order, family, genus and species level,

respectively. The 10 most abundant bacterial genera were _Candidatus Solibacter_ (14.0%), _Rhodoplanes_ (4.4%), _Candidatus Koribacter_ (1.2%), _Nitrospira_ (0.8%), _Flavobacterium_ (0.8%),

_Geobacter_ (0.8%), _Cytophaga_ (0.5%), _Phenylobacterium_ (0.4%), _Caulobacter_ (0.4%) and _Burkholderia_ (0.4%). A total of 2239 (86%) fungal OTUs accounting for 97% of the fungal pyrotags

were identified at the phylum level. Ascomycota (55%, 843 OTUs) and Basidiomycota (29%, 1239 OTUs) were the predominant phyla, with the former Zygomycota, Chytridiomycota and allies

accounting for 13% (157 OTUs). Below the phylum level, 1686 OTUs (92%), 1365 OTUs (88%), 1150 OTUs (86%), 1007 OTUs (77%) and 382 OTUs (45%) were identified at the class, order, family,

genus and species level, respectively. Thus, the classification success at lower taxonomic levels was much better for the fungal data set than for the bacterial data set. The 10 most

abundant fungal genera were _Russula_ (17.3%), _Mortierella_ (12.3%), _Inocybe_ (4.3%), _Clavulina_ (3.9%), _Cryptococcus_ (3.0%), _Laccaria_ (2.8%), _Hydnotrya_ (2.1%), _Neobulgaria_

(2.1%), _Hygrophorus_ (1.8%) and _Trichosporon_ (1.8%). COMMUNITY-LEVEL ABUNDANCE AND ALPHA DIVERSITY Soil compaction significantly reduced bacterial and fungal abundance (Table 3). Light

compaction had no impact, whereas severe compaction reduced the number of bacterial and fungal targets at both sites. No significant _time × treatment_ interaction was observed, indicating

that differences in relative abundance among treatments remained largely consistent between 1 and 4 years post disturbance. The decline in fungal abundance was significantly greater at

Ermatingen than at Heiteren, whereas the decline in bacterial abundance was similar at both sites (Figure 2). Compaction generally increased alpha diversity, but effects were often site and

time-dependent (Table 4). Compaction increased alpha diversity at least in C2, but the bacterial response at Ermatingen was not consistent with the overall observation. In the following, we

discuss the response of Shannon diversity as representative measure (Figure 2). Fungal diversity increased with compaction. Diversity in the sandy soils at Heiteren only increased in the

severely compacted soils, whereas diversity in the clayey soils at Heiteren increased at both compaction levels. At both sites, fungal diversity changed little after 30 days, then increased

in compacted soils after 180–365 days, and was increasingly resilient after 4 years (Supplementary Figure 2). The bacterial response at Heiteren was similar to the fungal response, showing

an increase in diversity in C2 but not in C1, as well as in the medium-term but not in the short or long-term. Bacterial diversity at Ermatingen showed a unique response. In C1, diversity

was reduced after 30 days and 4 years, but did not differ from the control soils after 180 and 365 days. In C2, diversity was reduced in the medium-term but not in the short and long-term.

Given the sensitivity of alpha diversity to sampling effort, the above results are based on rarefied data sets. However, differences in alpha diversities based on the full data sets were

identical (data not shown), which was not surprising given the high Good’s coverage of 95% and 99% for the bacterial and fungal data sets and the observed robustness of the Shannon diversity

to sampling effort (Supplementary Figure 3). Thus, patterns of alpha diversity can directly be compared with the following beta diversity measures, which are based on the complete data

sets. BETA DIVERSITY Soil compaction significantly and persistently altered the bacterial and fungal community structures, and the different compaction intensities resulted in significantly

distinct communities (Table 5, Figure 3). Compaction treatments explained 11% and 21% of the variance in the bacterial and fungal data sets, respectively. The spatial and temporal components

were also important drivers explaining 33–34% and 9–16% of the variance, respectively. Equivalent to alpha diversity, compaction effects on beta diversity were strongly dependent on forest

site and time since disturbance (Table 5). Soil microbial community structures at Heiteren appeared to be more resistant and resilient than at Ermatingen. Fungi were less resistant and

resilient than bacteria. Compaction effects were most pronounced in the medium-term, whereas communities were moderately affected in the short-term and showed trends of resilience in the

long-term. The strong compaction effects were confirmed by CAP ordinations maximizing differences in community structures among the different levels of compaction (Supplementary Figure 4).

Canonical discriminant analysis associated with CAP revealed high reclassification rates between 88% and 96%. At Ermatingen, community structures were substantially altered in both C1 and C2

(Figure 3a). Light compaction altered both communities in the medium-term, with no response in the short-term and resilience after 4 years (Figure 3b). Severe compaction caused significant

structural differences of both communities at all time points with some resilience after 4 years. At Heiteren, community structures changed little in C1, but were strongly altered in C2

(Figure 3a). The only effect of light compaction was observed on the fungal community after 180 days (Figure 3b). In contrast, severe compaction significantly altered both communities at all

time points (bacteria after 30 days with _P_=0.052) with a trend to resilience over time. Notably, the 2-dimensional representation of the principal coordinate analysis was not able to

adequately resolve differences in C2 after 4 years that became obvious when comparing within-group with among-group dissimilarities. RESISTANT, SENSITIVE AND RESILIENT MICROBIAL TAXA

Taxonomic treatment association networks revealed the complex structure of the bacterial (Figure 4) and fungal (Figure 5) communities in these soils and demonstrated which taxonomic groups

were significantly (_q_<0.05) associated with the compacted or undisturbed soils. A complete discussion of compaction-sensitive taxa is beyond the scope of this study, and we show only

salient cases; however, treatment association statistics for all taxa are provided as Supplementary Data 2. At the OTU level, a total of 127 bacterial and 117 fungal OTUs representing 7.3%

and 24.6% of the pyrotags, respectively, were significantly affected by soil compaction. Bacterial taxa that were significantly associated with compacted soils were assigned to taxonomic

groups such as Delta- and Betaproteobacteria, Firmicutes, Acidobacteria, Bacteroidetes or Chloroflexi (Figure 4). Members adapted to environments with low oxygen availability—such as: the

deltaproteobacterial genera _Geobacter_, _Anaeromyxobacter_, _Desulfuromonas_, _Desulfovibrio_, _Desulfobulbus_, _Pelobacter_, _Syntrophobacter_ and _Sulfurospirillum_; the

betaproteobacterial genera _Rhodoferax_, _Rhodocyclus_ and _Dechloromonas_; or the firmicute genera _Clostridium_, _Desulfosporosinus_, _Sporotalea_, _Desulfitobacterium_, _Thermosinus_,

_Bacillus_, _Paenibacillus_, _Acetivibrio_, _Thermincola_ and _Ethanoligenens_—were all significantly increased in compacted soils (examples are listed in the order of decreasing abundance).

Members that were primarily associated with the undisturbed soils were assigned to groups such as Alpha- and Gammaproteobacteria, Actinobacteria, as well as several candidate divisions. The

proteobacterial genera _Candidatus Odysella_ and _Steroidobacter_ or the verrucomicrobial genus _Opitutus_ were prominent genera that were reduced in compacted soils. Other taxa showed

special patterns. _Nitrospira,_ for example, was slightly increased in C0, strongly increased in C2, but negatively associated with C1, whereas _Candidatus Koribacter_ revealed the optimum

association with C1. A majority of the abundant fungal taxa were significantly affected by soil compaction (Figure 5). Generally, Basidiomycota were negatively associated with compacted

soils, whereas Ascomycota proportionally increased in compacted soils, suggesting an overall negative impact of compaction on ectomycorrhizal fungi and proportionally positive effects on

saprobic fungi and the like. Indeed, among the abundant and most strongly affected taxa, many known or putative mycorrhizal genera such as _Russula_, _Inocybe_, _Clavulina_, _Hygrophorus_,

_Elaphomyces_, _Hyphodontia_, _Boletus_, _Cortinarius_ and _Tarzetta_ were negatively affected by compaction. Conversely, many fungal genera with putative saprobic or parasitic lifestyles

such as _Cryptococcus_, _Neobulgaria_, _Trichosporon_, _Lecythophora_, _Pseudeurotium_, _Chalara_, _Scutellinia_, _Penicillium_, _Leptodontidium_, _Hypocrea_, _Asterophora_ and _Cheilymenia_

proportionally increased in compacted soils. We furthermore examined the long-term resilience of compaction-sensitive taxa as the reduction in treatment association strength over time

(Supplementary Figures 5 and 6). A substantial proportion of the compaction-sensitive taxa showed no or little resilience after 4 years, but patterns specific for certain taxonomic groups

were observed. For example, members of the Deltaproteobacteria and the Firmicutes were among the strongest bacterial indicators, proportionally increasing in compacted soils; however,

whereas none of the deltaproteobacterial indicators showed substantial resilience, firmicute indicators largely recovered after 4 years (Figure 6). In conclusion, these taxonomic treatment

association networks do not only allow visualizing the complex structure of the soil microbiota, but also help to detect patterns of resistance and resilience that are consistent across the

complete taxonomic range. DISCUSSION Allison and Martiny (2008) suggested microbial community structure as an indicator of environmental change, because this parameter is sensitive and not

immediately resilient to disturbances, and structural shifts are often associated with changes in ecosystem processes. In line with this notion, we demonstrated that changes in physical soil

properties after compaction significantly and persistently altered the soil microbiota and associated ecosystem functions, such as turnover of carbon and nitrogen. As summarized in Figure

7, the combined assessment of these properties has therefore great potential to define compaction thresholds below which there is no detrimental and irreversible impact on the soil

ecosystem. This finding is in agreement with recent studies in this field (Frey et al., 2011; Hartmann et al., 2012), but providing an unprecedentedly comprehensive view on the complex

microbial response to compaction. First, the combined assessment of physicochemical and biological characteristics in a uniquely controlled experiment provides an integrative view on how

changes in physical soil properties are linked to major shifts in the microbiota and associated soil processes. Second, to the best of our knowledge, this study is the first high-throughput

sequencing assessment of compaction effects on microbial diversity, acknowledging that effects observed in the only other high-throughput sequencing study were confounded by effects from

forest biomass removal (Hartmann et al., 2012). The power of new sequencing technologies to assess structural shifts in the soil microbiota at deep coverage and high phylogenetic resolution

provided novel information regarding the resistance and resilience of the forest soil microbiome to compaction. Logging operations can increase the frequency and duration of anoxic

conditions in forest soils (Goutal et al., 2012). In this study, logging vehicle traffic induced profound changes in soil structure, which in turn drastically reduced water and air

conductivity in the compacted skid trails (Table 1, Figure 1). It has been reported that an increase in bulk density beyond 15% can become harmful (Lacey and Ryan, 2000), a threshold that

was reached after light compaction and substantially exceeded after severe compaction. Measuring bulk density alone is, however, not sufficient to predict the consequences for soil

functional properties such as water infiltration and air permeability, as these properties are less controlled by total porosity (and thus bulk density) than by macroporosity (Young and

Ritz, 2000). Macropore volume was reduced by up to 73% in the first week post disturbance, almost completely restricting water and air infiltration in the severely compacted soils. Altered

conditions in the compacted soils reduced abundance, increased alpha diversity and shifted the composition of the microbiota (Figures 2 and 3). Whereas the decrease in abundance suggests a

potentially detrimental effect on microbial activity, the increase in alpha diversity and shift in beta diversity might indicate a loss of functional organization in these communities. The

decrease in microbial abundance is in agreement with only few previous reports (Dick et al., 1988; Frey et al., 2009) and in contradiction with the majority of studies that did not observe

an impact of forest soil compaction on microbial biomass (Jordan et al., 1999; Ponder and Tadros 2002; Li et al., 2004; Shestak and Busse, 2005; Tan et al., 2005; Busse et al., 2006; Tan et

al., 2008). Contradictory results have also been reported for bacterial and fungal alpha diversity, as we have not observed any compaction effects in a previous study (Hartmann et al.,

2012). However, given that traditional methods were limited in adequately measuring alpha diversity, this property has yet rarely been assessed in such systems. In contrast to the integrated

parameters like biomass or alpha diversity, effects on community composition have been frequently reported (Ponder and Tadros, 2002; Shestak and Busse, 2005; Busse et al., 2006;

Schnurr-Pütz et al., 2006; Frey et al., 2009, 2011; Hartmann et al., 2012). However, most of these studies were based on the first generation of molecular techniques such as phospholipid

fatty acid or terminal restriction fragment length polymorphism analysis and were thus limited in the structural resolution as well as in the taxonomic identification of compaction-sensitive

groups. In the present study, using an array of cutting-edge molecular techniques, the results dismantle the notion that the forest soil microbiome is largely resistant or resilient to

logging-induced compaction (Shestak and Busse, 2005; Busse et al., 2006). Considering that the observed effects on microbial characteristics were strongly dependent on the degree of

disturbance (for example, ground contact pressure, soil type), the contradiction with many previous studies is most likely based on the different impacts examined in the different surveys.

The lack of a unifying concept highlights the need for properly controlled experiments to determine compaction thresholds below which there is no negative impact on the soil microbiota.

Although altered physical conditions established immediately after compaction, structural shifts in the microbiota peaked in the medium-term around 6–12 months after the disturbance and

showed less response in the first few weeks after compaction. Whereas it can be expected that microbial activity will be immediately affected by water and oxygen limitations, structural

shifts of mostly slow-growing soil microorganisms likely manifest at a slower rate, in particular, as the compaction experiment was conducted in spring, where soil temperatures were low.

Four years after compaction, the community structure has recovered in lightly but not in heavily compacted soils. The lack of structural resilience correlated with the lack of functional

resilience in terms of altered greenhouse gas fluxes, supporting the notion that changes in composition are often associated with changes in ecosystem processes (Allison and Martiny, 2008).

One could argue that the observed treatment differences originated from comparing different soil horizons due to the formation of ruts and associated soil displacements. However, differences

in carbon contents among samples were minimal, suggesting that equivalent horizons were compared (Frey et al., 2011). Furthermore, we examined compaction effects within the topsoil, but the

treatment will likely have an impact along the complete depth profile, knowing that alterations in bulk density after compaction are even more persistent in subsoil (Page-Dumroese et al.,

2006). Oxygen and water limitation in the compacted tracks introduced specific structural shifts to the microbiota (Figures 4 and 5). In line with previous observations but at much higher

resolution (Schnurr-Pütz et al., 2006, Frey et al., 2011), bacteria adapted to low oxygen availability and capable of anaerobic respiration strongly increased in compacted soils. These taxa

included many known sulfate, sulfur and metal reducers like _Geobacter_, _Desulfuromonas_, _Anaeromyxobacter_, _Rhodoferax_, _Desulfovibrio_, _Desulfosporosinus_, _Desulfobulbus_ and

_Geothrix_ (Dworkin et al., 2006). Among other compaction-associated taxa, we observed genera capable of anoxygenic photosynthesis (_Rhodocyclus_), anaerobic perchlorate reduction

(_Dechloromonas_) or exhibiting other strategies of anaerobic growth with metabolically versatile or largely cryptic lifestyles (for example, _Clostridium_, _Thermosinus_, _Sporotalea_,

_Acetivibrio_, _Anaerolinea_). Accordingly, the increased bacterial diversity in compacted soils might have been caused by the high number of anaerobically respiring species in combination

with a considerable tolerance of aerobic bacteria and increased protection from protozoan grazing (Wright et al., 1995), unless conditions became critically limited as potentially observed

after 6–12 months at Ermatingen (Figure 2). Whereas some groups such as the Firmicutes showed almost complete resilience within 4 years, other groups such as the Deltaproteobacteria did not

yet recover (Figure 6). According to Allison and Martiny (2008), Deltaproteobacteria such as _Geobacter_ might therefore serve as indicators of compaction in otherwise well-aerated soils, as

they were sensitive and not immediately resilient to compaction, reflect the functional status of the system (for example, in terms of oxygen availability and greenhouse gas emission), and

are abundant thereby facilitating detection. Despite the significant impact on bacteria, many highly abundant bacterial taxa such members of the orders Solibacterales, Rhizobiales and

Gemmatimonadales did not significantly respond to compaction, indicating that certain bacterial groups indeed exhibit considerable tolerance to these disturbances as reported by other

studies (Shestak and Busse, 2005). Fungi appeared to be more sensitive and less resilient to compaction when compared with bacteria (Figures 3 and 5). This difference can in part be

explained by the generally higher sensitivity of eukaryotes to low oxygen pressures (Schnurr-Pütz et al., 2006). The fact that mycorrhizal species were almost exclusively reduced in

compacted soils also suggests negative effects on plant hosts, mechanical disruption of existing mycorrhizal networks and limited network reformation owing to restricted hyphal penetration.

Abundant compaction-sensitive mycorrhizae included genera such as _Russula_, _Inocybe_, _Clavulina_ and _Elaphomyces_ (Figure 5), whereas _Inocybe_ was largely resilient, hypogeous

_Elaphomyces_ did not recover 4 years post disturbance (Supplementary Figure 6). Non-mycorrhizal taxa proportionally increased in the compacted soils. Abundant saprobic fungi like

_Neobulgaria_, _Cryptococcus_, _Trichosporon_ and _Lecythophora_ likely benefited from freshly exposed organic matter after vegetation dieback and physical breakdown of soil aggregates,

although being largely tolerant to lower oxygen concentrations. Some compaction-associated fungi such as the aeroaquatic _Cylindrocarpon_ are reportedly adapted to periodically low

availability of oxygen (Medeiros et al., 2009). Compaction temporarily increased fungal diversity, suggesting a stimulating effect of fresh organic matter on saprobic fungi in the first year

post disturbance (Figure 2). In conclusion, the profound changes in the fungal community suggest significant and persistent alterations with respect to plant–microbe interactions and

nutrient cycling, and raise concern regarding forest productivity, juvenile tree regeneration and long-term ecosystem functioning. Structural shifts in the soil microbiota were accompanied

by changes in soil processes, reducing methane consumption, decreasing carbon dioxide emission and increasing nitrous oxide emission (Table 2, Figure 1). These changes are consistent with

previous findings (for example, Teepe et al., 2004; Keller et al., 2005; Frey et al., 2011; Goutal et al., 2012). We already reported on the higher abundance of methanogenic archaea linked

to increased methanogenesis in the compacted skid trails (Frey et al., 2011). Despite perfect matches of the primers, we recovered only very few methanotroph pyrotags in these soils and

cannot conclude on potentially co-occurring negative effects on methane oxidation. However, it can be hypothesized that the anaerobic conditions largely limited the predominantly aerobic

methanotrophs and contributed to methane emission by reducing methane oxidation. The response of the CO2 flux was highly variable and appeared to be bivalent. Moderate compaction tended to

increase CO2 emission, whereas severe compaction reduced the CO2 flux. CO2 production is driven by soil organic matter decomposition and root respiration, and it has been reported that soil

CO2 production is greater under aerobic than anaerobic conditions (Ball et al., 1999). After moderate compaction, elevated CO2 emission could be linked to enhanced microbial mineralization

of freshly exposed organic matter (Novara et al., 2012). Once water infiltration and air permeability have reached critical limits, CO2 emissions decrease due to reduced microbial activity,

root respiration and gas diffusivity (Conlin and van den Driessche, 2000; Shestak and Busse, 2005; Goutal et al., 2012). Furthermore, soil organic matter in severely compacted soils might

become physically protected from decomposition (Fleming et al., 2006). Nitrous oxide emission steadily increased with increasing compaction, but the large flux fluctuations indicate the

interplay of aerobic and anaerobic processes that generate N2O. Generally, nitrous oxide is produced anaerobically during denitrification and aerobically during nitrification at suboptimal

oxygen concentrations (Bremner, 1997), although the two processes have also been observed under contrasting oxygen conditions (Hayatsu et al., 2008). The relative ecological importance of

these processes under varying oxygen availability is not completely understood, but it has been suggested that denitrification becomes the key process for N2O production in compacted soils

(Ruser et al., 2006). Considering the wide range of microbial species across all domains of life that are involved in denitrification including their varying oxygen requirements (Hayatsu et

al., 2008), it is difficult to directly link the structural shifts of the microbiota to changes in nitrous oxide emission, but we can assume that nitrous oxide production is stimulated in

compacted soils by favoring species involved in the denitrification process (Skiba and Smith, 2000). CONCLUSION Soil compaction is a major problem inherently linked to economically efficient

logging operations. Once a soil has been compacted, a return to the initial state can be very slow, and recovery from severe compaction might take centuries rather than decades (Webb, 2002;

von Wilpert and Schäffer, 2006). As the degree of disturbance depends on factors like harvesting equipment, operation condition and site characteristics, careful operational design can

substantially mitigate the environmental impact. We observed that site characteristics such as soil type were important determinants of the degree of impact, with clayey soils exhibiting

less resistance and resilience than sandy soils. However, high moisture contents as simulated in the severely compacted skid trails led to a strong and persistent impact on the soil

microbiota and functions at both forest sites. Ultimately, site conditions and characteristics (for example, soil moisture, texture) should drive the decisions about the time of logging (for

example, rainfall, storms) and type of equipment used (for example, machine load, type of tires). We demonstrated that the combined investigation of soil physical, microbial and functional

characteristics represents a powerful tool to measure resistance and resilience of the soil system to compaction (Figure 7). The deep sequencing approach identified microbial indicators that

can assist in monitoring such disturbances in forest ecosystems and determining compaction thresholds below which there is no detrimental impact on ecosystem functioning in the long term.

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_Soil Till Res_ 53: 201–213. Google Scholar  Download references ACKNOWLEDGEMENTS We thank Roger Köchli (Research Institute WSL) and Michael Miesl (Technical University of Munich) for their

help with field measurements. Stéphane Sciacca (Research Institute WSL) and Dietmar Matthies (Technical University of Munich) are acknowledged for contributions to the experimental design.

We are grateful to the forest services at Ermatingen ct. TG (Werner Kreis) and Heiteren ct. BE (Roland Rupli) for their collaboration during the field experiments in their forest districts.

We also thank Stephanie Pfister (Agroscope ART) for providing assistance with laboratory work. The Functional Genomics Center Zurich (FGCZ) is acknowledged for the 454-pyrosequencing

service. The Genetic Diversity Center (GDC) of the ETH Zurich is acknowledged for providing computational resources. This study was funded by project no. 5233.00029.001.01 of the Swiss

Federal Research Institute WSL. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Forest Soils and Biogeochemistry, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland Martin

Hartmann, Stephan Zimmermann, Stefan Schmutz, Peter Lüscher & Beat Frey * Molecular Ecology, Research Station Agroscope Reckenholz-Tänikon ART, Zurich, Switzerland Martin Hartmann & 

Franco Widmer * Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland Pascal A Niklaus * Forest Work Science and Applied Informatics,

Technical University of Munich, Freising, Germany Johann Kremer * Natural History Museum, University of Tartu, Tartu, Estonia Kessy Abarenkov Authors * Martin Hartmann View author

publications You can also search for this author inPubMed Google Scholar * Pascal A Niklaus View author publications You can also search for this author inPubMed Google Scholar * Stephan

Zimmermann View author publications You can also search for this author inPubMed Google Scholar * Stefan Schmutz View author publications You can also search for this author inPubMed Google

Scholar * Johann Kremer View author publications You can also search for this author inPubMed Google Scholar * Kessy Abarenkov View author publications You can also search for this author

inPubMed Google Scholar * Peter Lüscher View author publications You can also search for this author inPubMed Google Scholar * Franco Widmer View author publications You can also search for

this author inPubMed Google Scholar * Beat Frey View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Martin Hartmann.

ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no conflict of interest. ADDITIONAL INFORMATION We confirm that the material discussed in the manuscript is original research, has

not been previously published, and has not been submitted for publication elsewhere. Supplementary Information accompanies this paper on The ISME Journal website SUPPLEMENTARY INFORMATION

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ARTICLE Hartmann, M., Niklaus, P., Zimmermann, S. _et al._ Resistance and resilience of the forest soil microbiome to logging-associated compaction. _ISME J_ 8, 226–244 (2014).

https://doi.org/10.1038/ismej.2013.141 Download citation * Received: 06 May 2013 * Revised: 15 July 2013 * Accepted: 15 July 2013 * Published: 12 September 2013 * Issue Date: January 2014 *

DOI: https://doi.org/10.1038/ismej.2013.141 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not

currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS * forest soil compaction * soil physical characteristics

* microbial diversity * ribosomal pyrotags * greenhouse gas fluxes * soil functions