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Short-term machinery impact on microbial activity and diversity in a
compacted forest soil
Agnese Bellabarba
a,b
, Laura Giagnoni
c
, Alessandra Adessi
a,*
, Elena Marra
d
, Andrea Laschi
e
,
Francesco Neri
a
, Giovanni Mastrolonardo
a
a
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, I-50144, Florence, Italy
b
Genexpress Laboratory, Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, I-50019 Sesto Fiorentino, Italy
c
Department Civil, Environmental, Architectural Engineering and Mathematics (DICATAM), University of Brescia, I-25123 Brescia, Italy
d
Institute of Research on Terrestrial Ecosystems (IRET), National Research Council of Italy (CNR), 50019 Sesto Fiorentino, (FI), Italy
e
Department of Agricultural, Food and Forest Sciences (SAAF), University of Palermo, I-90128, Palermo, Italy
ARTICLE INFO
Keywords:
Sustainable forest operations
Soil disturbance
Skidding
Microbiome
NGS sequencing
ABSTRACT
Forest soils are complex ecosystems including a huge biodiversity including different microbial communities that
are responsible for soil nutrient cycling and organic matter decomposition. One of the main threats to forest soil
health is soil compaction caused by forest exploitation activities and, in particular, by wood extraction opera-
tions. These latter may change the physical, chemical and, in turn, biological properties of soil, eventually
impacting tree growth and regeneration. However, there is a signicant lack of knowledge in understanding the
response of soil microbial communities to soil compaction. Furthermore, most of previous studies did not
properly frame the short-term response of soil microbiota to compaction, which could serve as an early indicator
of soil health. This study aims to investigate and monitor the short-term response of forest soil to severe
compaction stress integrating soil physico-chemical analysis with biological and biochemical analysis. To
investigate the early response of soil microbial communities to compaction over time, forest soil was analyzed 8
and 12 months after the repeated passage of a tractor pulling some logs simulating skidding operations in a
heavily trafcked area for wood extraction. Despite the initial strong increase in bulk density (up to 42 %), the
soil almost recovered from compaction after just one year. Most of the soil chemical parameters (i.e. pH, C, and N
content) were not affected by soil compaction, while the analysis of enzymatic activity showed a change of some
functions upon soil compaction in the rst 8 months, followed by a substantial recovery after one year. The
microbial communities showed different responses to compaction, highlighting a greater bacterial community
resilience in the short term compared to the fungal community, which showed persistent and signicant shifts
between compacted and not-compacted soil. We also identied some indicator species that may be useful for
monitoring early changes in microbial communities and activities following soil compaction.
1. Introduction
Soil is a fundamental component of forest ecosystems, playing a
pivotal role in supporting forest health and providing essential
ecosystem services (Vanermen et al., 2021). This complex ecosystem
harbors an incredible diversity of microbial communities playing a
crucial role in soil nutrient cycling and organic matter decomposition
(Dominati et al., 2010; Hartmann et al., 2014; Nielsen and Ball, 2015),
directly impacting on forest productivity (Lewandowski et al., 2019).
One of the main threats to forest soils and their huge biodiversity is soil
compaction (Vanermen et al., 2021). Indeed, forest operations imply an
unavoidable impact on forest soils, particularly during timber extraction
(Picchio et al., 2020). Rutting, alteration to soil plasticity, and soil
compaction are all possible negative effects of intensive logging activ-
ities having an adverse impact on soil (Cambi et al., 2015; Marra et al.,
2021). The impact degree of logging operations on soil compaction
depends on soil properties such as texture, water content, and initial
bulk density (Cambi et al., 2015) as well as on the wood extraction
system (Ampoorter et al., 2012), and work organization, e.g. machine
load and weight and number of machine passes (Marra et al., 2018;
* Corresponding author.
E-mail address: alessandra.adessi@uni.it (A. Adessi).
Contents lists available at ScienceDirect
Applied Soil Ecology
journal homepage: www.elsevier.com/locate/apsoil
https://doi.org/10.1016/j.apsoil.2024.105646
Received 15 December 2023; Received in revised form 6 September 2024; Accepted 12 September 2024
Applied Soil Ecology 203 (2024) 105646
Available online 26 September 2024
0929-1393/© 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-
nc-nd/4.0/ ).
Picchio et al., 2020).
Ground-based timber extraction systems can impact soil physical
properties by decreasing porosity, increasing bulk density and soil
penetration resistance (D'Acqui et al., 2020; Lee et al., 2020). The
decrease in soil porosity, hydraulic conductivity, and water inltration
can signicantly affect soil microbial communities (Hartmann et al.,
2014; Jensen et al., 1996). Indeed, these changes of soil structure alter
the habitat of microorganisms, for instance favouring a microbial com-
munity centered on species capable of withstanding anoxic conditions
(Frey et al., 2009; Hartmann et al., 2014). Consequently, changes in the
physical, chemical, and biological properties of soil can lead to changes
in tree growth and regeneration, further inuencing the structure and
function of microbial community (Mariotti et al., 2020). However, there
is still a signicant lack of knowledge in understanding the response of
soil microbial communities to soil compaction (Hartmann et al., 2014).
Studies investigating how soil microorganisms respond to ground-based
forest logging, through measurements of microbial biomass and activity
or by using newer techniques like next-generation sequencing, report a
range of effects. These effects vary from signicant impacts to no change
in the soil microbial communities (Busse et al., 2006; Frey et al., 2009;
Hartmann and Niklaus, 2012; Hartmann et al., 2014; Jennings et al.,
2012; Jordan et al., 2003; Mariani et al., 2006; Schnurr-Pütz et al., 2006;
Tan et al., 2008; Wilhelm et al., 2017). However, most of these studies
were conducted in harvested forest areas, where the impacts of logging
operations cannot be fully separated from those caused by the reduction
of canopy cover and biomass removal (Hartmann et al., 2014; Latterini
et al., 2023).
Understanding the response of microbial communities to soil
compaction is crucial for predicting the sustainability of forest man-
agement. This issue is particularly relevant in Europe, where there has
been a gradual intensication in the use of vehicles that are more
powerful and efcient, but also heavier, hence leading to greater im-
pacts on soil (Horn et al., 2007; Vossbrink and Horn, 2004). Recognizing
the importance of soil health, European Commission launched the EU
Soil Strategy for 2030. Aligned with the Biodiversity Strategy for 2030
(COM/2020/380), this initiative, titled “A Soil Deal for Europe,” aims to
promote sustainable soil management in agriculture and forestry sec-
tors, facilitating the transition towards healthy soils (K¨
oninger et al.,
2022; Panagos et al., 2022). These policies, as well as a sustainable forest
and soil management, should also rely on the assessment and monitoring
of the inuence of soil disturbance on the composition, functions and
activity of soil microbial communities (Wilhelm et al., 2017), as the
latter play a crucial role in key soil processes. Such an assessment can
even provide an early warning signal of soil and forest health decline
because of forest management practices, estimating these impacts before
they become irreversible (Hartmann et al., 2014). Indeed, some studies
highlighted the early impacts of several types of disturbance (such as
logging or burning) on forest soil bacterial and fungal communities
(Ammitzboll et al., 2021, 2022). However, several of the most recent
studies, investigating the effect of different disturbances on forest mi-
crobial communities with high-throughput DNA sequencing, were car-
ried out through long-term surveys that failed to capture the dynamics of
these communities in the short-term.
This study aims to investigate the short-term response of forest soil to
severe compaction stress by integrating soil physico-chemical with
biological and biochemical analysis (microbial community composition,
diversity, and enzyme activities), hypothesising the latter allowing to
get an insights into the short-term response of soil to compaction,
potentially serving as early indicators of soil health decline resulting
from forest management practices. With this aim, a soil in Vallombrosa
forest (Tuscany, Italy) was analyzed 8 and 12 months after a compaction
event caused by repeated skidding, for monitoring the evolution of the
early response of soil microbial communities to compaction.
2. Materials and methods
2.1. Study area
The study was conducted in central Italy, in the Vallombrosa
Biogenetic Reserve (44◦44
′
12.87” N, 11◦32
′
45.73
″
E), which is in the
municipality of Reggello (Florence Province). The study area was
included in a forest parcel of 5.78 ha between 920 and 980 m a.s.l. and
was characterized by moderate steep terrain (mean slope =20 %). The
climate is characterized by a mean annual air temperature of 11.7 ◦C and
a mean annual precipitation of 1037 mm. The soil developed on sand-
stone material and was classied as Dystric Cambisol based on the World
Reference Base for Soil Resources (Schad, 2016). The forest vegetation is
an articial and pure plantation of silver r (Abies alba Mill.) 63 years
old. The forest management in this area was applied since the XIV
century by Vallombrosa's monks. The aim of their silviculture was to
produce high quality timber to be used also for buildings, through pure
silver r stands with a rotation period of almost 100 years, applying
thinning and a nal clear cut, followed by the next articial renovation
(plantation) (Bottalico et al., 2014). No silvicultural or logging opera-
tion has been conducted in the study area, where no vehicle has had
access for at least the last 40 years. The experimental design employed
the use of a tractor New Holland T6050 equipped with a winch Pro Forst
SWE 8500. Its unloaded mass was 6190 kg and was equipped with two
480 mm wide tires (480/65 R28 136D TL MULTIBIB) and two 600 mm
wide tires (600/65 R38 153D TL MULTIBIB). The study simulated a real
trees extraction operation by skidding, except that trees were not actu-
ally cut in the designated area. In fact, four logs of silver r extracted
from a different area in the same forest were used during skidding op-
erations in the study area. On September 2020, the skidding extraction
was carried out by the described tractor skidding the four logs. It began
the skidding operations with a rst passage, creating the temporary trail
used for all subsequent passes, for a total of 30. The tractor moved uphill
in the direction of the slope (20 %) pulling the logs creating the same
disturbance caused by real skidding operations in a heavily trafcked
area.
2.2. Soil sampling and analysis
The sampling scheme is reported in Fig. S1. Along the skid trail, one
rectangular plot (length 8 m, width 2 m) was marked out on the ground.
To determine soil compaction three intact cores of soil were collected
after the compaction event within the rectangular plot, one sample each
meter, inside both left and right wheel ruts (six samples in total), and six
samples were collected between the wheel ruts. At the same time, to
determine the bulk density (BD) of the undisturbed soil, i.e. not com-
pacted, six intact cores of soil were collected 8 m away, outside the
tractor trail in a contiguous undisturbed surface, hereafter termed as
“off-trail”. All soil samples were collected from the top 10 cm of the
mineral soil layer using a steel cylinder (7.5 cm inner diameter and 10
cm height). The same sampling procedure was repeated eight months
and one year after to determine the trend of physical soil characteristics
in the compacted soil. The soil samples were oven-dried until constant
weight at 105 ◦C and then weighted for determining the BD knowing the
volume of the steel cylinder used for sampling.
For evaluating the dynamics of biochemical properties and the mi-
crobial communities of the compacted soil, the same sampling design for
BD determination was used, but changing sampling method and number
of replicates. A total of nine soil samples were taken by a shovel from the
top 10 cm of mineral soil after removing any litter material: six along the
trail, three inside the ruts (combining together sub-samples from left and
right rut) and three between the ruts, taking one sample each meter both
inside and between the ruts, and three samples 8 m away outside the
trail, one sample each meter, for comparison, as off-trail control. The
sampling was repeated twice, during late spring (T1, beginning of May
2021), at the beginning of vegetative growth period, and one year after
A. Bellabarba et al.
Applied Soil Ecology 203 (2024) 105646
2
disturbance, at the end of vegetative growth period (T2, September
2021). Hence, this study did not consider the soon-after compaction
period (T0) for the biological and biochemical analysis, as the primary
focus was not on evaluating the direct disturbance effect of compaction
on soil microbial communities. Additionally, although microbial activity
might be promptly affected by disturbed soil conditions, the slow-
growing fraction of the microbial community could take more time to
experience structural changes (Hartmann et al., 2014). Lastly, cold
mountain temperatures slow down microbial processes and capturing
the specicity of microbial winter activity was beyond the scope of this
study; therefore, the winter period was not considered.
At the same time of soil sampling (T1 and T2), soil respiration was
measured by an EGM-1 PP Systems portable gas analyzer (Hitchin, UK)
equipped with an SRC-1 closed air-circulation chamber (1.17 dm
3
in
volume). CO
2
efux was measured between 9:00 and 11:00 a.m. by
applying 12 PVC collars along the trail, each collar at a one-meter dis-
tance from each other, four inside and between the ruts and four 8 m
outside the trail.
The soil samples were handled in the laboratory within 24 h after
sampling and sieved at 2 mm at eld moisture. Then, they were divided
into three homogenous aliquots: one aliquot was stored at 4 ◦C for
enzyme activities assay, one aliquot was air-dried for physico-chemical
analysis, and the third was stored at −80 ◦C for microbial community
analysis. The pH was measured in a suspension in distilled water (1:2.5)
with an XS pH-metre model PC8. Total organic C and N contents were
measured on 5–15 mg of nely ground and oven-dried (60 ◦C overnight)
samples by dry ash combustion using a Carlo Erba NA 1500 CNS
analyzer (Carlo Erba Instruments, Milan, Italy). The measured C was
assumed to be all in organic form since the moderately acidic soil pH is
incompatible with the presence of carbonates. Soil texture was deter-
mined by the hydrometer method (Gee and Bauder, 1986), on two
composite soil samples obtained by bulking together the samples used to
determine BD. Available P was determined by Olsen method (Olsen and
Dean, 1965), and the P concentrations were determined by spectro-
photometry at 880 nm with the sulfo-molybdic acid reagent (Murphy
and Riley, 1962). The soil samples, stored at 4 ◦C, were used to measure
soil enzyme activities related to the main biogeochemical (C, P, N, and
S) cycles: the acid and alkaline phosphomonoesterase activities
(Tabatabai and Bremner, 1969) for P cycle, the protease activity (Ladd
and Butler, 1972) for N and C cycle, and the β-glucosidase activity
(Tabatabai, 1982) as the major glycosyl-hydrolases member and the
arylsulfatase activity for S cycle (Tabatabai and Bremner, 1970).
2.3. Soil DNA extraction, sequencing, and bioinformatics processing
Total genomic DNA was extracted from 0.5 g of sieved soil samples,
stored at −80 ◦C, using the GenElute™ Soil DNA Isolation Kit (Sigma-
Aldrich, St. Louis, MO, USA) according to the manufacturer's in-
structions. DNA purity and quantity were assessed by electrophoresis on
0.8 % agarose gel and using an ND-1000 Spectrophotometer (NanoDrop
Technologies, Labtech, Ringmer, UK). DNA quantication was per-
formed using QubitTM 4 Fluorometer and QubitTM ssDNA Assay Kit
(Thermo Fisher Scientic), then standardized to a concentration of 10
ng/
μ
L. Amplicons preparation and sequencing were carried out at
IGATech (Udine, Italy). In particular, the bacterial V3-V4 hypervariable
regions of 16S rRNA and the Internal Transcribed spacer (ITS) regions
were PCR-amplied with primers 341F (5
′
- CCTACGGGNBGCASCAG -3
′
)
- 805R (5
′
- GACTACNVGGGTATCTAATCC -3
′
) (Takahashi et al., 2014)
and with ITS1F (5’-TCCGTAGGTGAACCTGCGG -3
′
) - ITS4R (5’-
TCCTCCGCTTATTGATATGC-3
′
) (White et al., 1990), respectively. Li-
braries were prepared using a custom Illumina 16S Metagenomic
Sequencing Library Preparation protocol and were sequenced on a
MiSeq instrument (Illumina, San Diego, CA) using 300-bp paired-end
mode. To process the Illumina reads of bacterial and fungal soil com-
munities, the DADA2 pipeline (Callahan et al., 2016) (version 1.22.0)
was used in RStudio software 4.1.2 (R Core Team, 2022). For 16S rRNA
reads, ltering and trimming parameters were maxEE =c(1,1), trun-
cLen =c(280,250), and trimLeft =c(17,21). Sample inference, the
merging of paired reads, and the removal of the chimera were performed
with default parameters to obtain the full denoised sequences. The
taxonomic assignment was carried out by comparing our 16S rRNA se-
quences against the SILVA database v.138.1 (Pruesse et al., 2007)
(condence 80 %). For ITS sequences, the packages Biostrings (v.
2.62.0) (Pag`
es et al., 2022) and ShortRead (v. 1.52.0) (Morgan et al.,
2009) were used to identify and count the primers present on raw fastq
les. Instead, Cutadapt (v. 2.8) (Martin, 2011) was used to remove ITS
primers. Then, the ltering and trimming step was performed with the
following settings: maxEE =c(2,2), maxN =0. Dereplication, sample
inference, merging of paired reads, and the removal of the chimera were
performed with default parameters according to the ITS workow of
DADA2 (Callahan et al., 2016). The UNITE ITS database (v. 8.3) (Nilsson
et al., 2018) was used to align and classify ITS sequences.
2.4. Bioinformatic and statistical analysis
Before each statistical analysis, the Shapiro–Wilk test was applied to
evaluate data distribution and the Levene's test was applied to evaluate
homogeneity of variance. Enzymatic activities were analyzed by one
way ANOVA and the Tukey's post hoc test (p value <0.05) was used
after. BD and soil chemical variables were analyzed by two way ANOVA
using compaction conditions (off-trail, inside, and between the ruts) and
time after compaction (0, 8 and 12 months, T0, T1 and T2, respectively;
T0 was included only in the analysis of BD data) as xed factors. The
above cited statistical analyses were performed using SPSS, version 29
(IBM Corp., Armonk, NY, USA).
Annotated Amplicon sequence variants (ASVs) bacterial and fungal
datasets were processed in RStudio environment (version 4.1.2) (R Core
Team, 2022) mostly with functions of the vegan package (v. 2.5–6)
(Oksanen, 2010). ASVs with a relative abundance lower than 0.01 % in
all the samples were removed from both datasets. Rareed bacterial and
fungal datasets were generated, with subsample sizes of 10,000 and
8000 sequences per sample respectively, using the function rrarefy in the
vegan package (v. 2.5–6). Rarefaction curves were generated with the
function rareed in the vegan package (v. 2.5–6). For alpha diversity, the
richness (Sobs), Pielou's index (J), and the Shannon diversity index were
estimated using the estimateR and diversity functions in the vegan
package. Index plots were carried out with the ggplot2 package (v.
3.3.6) (Wickham, 2016). Differences in
α
-diversity indices were detected
performing the Kruskal-Wallis test with in stats (v. 4.1.2) package, fol-
lowed by the post-hoc Dunn Test, with the Benjamini–Hochberg
correction for multiple comparisons, using the FSA (v. 0.9.3) (Ogle et al.,
2021) and rcompanion (v. 2.4.18) packages (Mangiaco, 2020). The
tests were performed i) among different levels of soil compaction (inside
the ruts, between the ruts and off-trail) at each sampling time (T1 and
T2) and ii) between different sampling times (T1 and T2) for each level
of soil compaction. Hellinger transformation of rareed ASV abun-
dances was performed before ordinations and statistical analysis of Beta
diversity. Non-metric multidimensional scaling (NMDS) ordination of
Bray–Curtis distances was carried out using the function ordinate of the
phyloseq R packages (v. 1.38.0) (McMurdie and Holmes, 2013). Dif-
ferences in community structure were investigated between off- and in-
trail samples, and among different levels of soil compaction (inside the
ruts, between the ruts and off-trail) at T1 and T2 sampling times
(respectively 8 and 12 months after soil compaction). Permutational
multivariate analysis of variance (PERMANOVA) of Bray–Curtis
dissimilarity metrics was executed on Hellinger transformed rareed
ASVs abundances with the adonis2 function in vegan package (v. 2.5–6).
Since PERMANOVA could be affected by non-homogeneous within-
group dispersion of data (Anderson, 2001), the Analysis of multivariate
homogeneity (PERMDISP) was also fullled to test if the average within-
groups dispersion was the same in all groups (Anderson, 2006) inves-
tigated through the functions betadisper and permutest implemented in
A. Bellabarba et al.
Applied Soil Ecology 203 (2024) 105646
3
the vegan package (v. 2.5–6). Moreover, the analysis of similarity test
(ANOSIM) was carried out as conrmation of PERMANOVA outputs by
using anosim function. The number of permutations was set to 99,999 for
all analyses, and a P <0.05 was considered as signicant. In relative
abundance analysis, bar plots of average relative abundances (%) at the
order level were created with the ggplot2 package (v. 3.3.6). Only orders
with an average relative abundance of at least 3 % were reported for
both datasets. Only genera with a relative abundance of at least 1 % in at
least one sample were considered for each condition. Statistical differ-
ences in relative abundances of bacterial and fungal taxa, at order and
genus level, were determined by performing Kruskal–Wallis among
different conditions (off-trail, inside, and between the ruts) at T1 and T2
sampling times, respectively 8 and 12 months after the compaction.
Then post hoc Dunn Test, with the Benjamini–Hochberg correction for
multiple comparisons, was performed i) between different degrees of
soil compaction at the two sampling times and ii) between different
sampling times for each condition. Distance-based redundancy analysis
was performed using the dbrda function (McArdle and Anderson, 2001)
in the vegan package (v. 2.5–6). To t environmental vectors signi-
cantly correlated with microbial community structure into db-RDA
ordination, the function envt implemented in the vegan package was
used with 9999 permutations. The enzymatic activities were considered
signicant for p <0.05 after Benjamini–Hochberg correction for mul-
tiple comparisons. Db-RDA biplots were generated with the scores
function of vegan package and the ggplot function in the ggplot2 package
(v. 3.3.6). Indicator species analysis was conducted for each degree of
soil compaction (inside the ruts, between the ruts, and off- trail) at
different sampling times with the function multipatt implemented in
indicspecies package (De Caceres et al., 2016). Rareed ASVs abun-
dances were used as the community data matrix. The Indicator Value
indices (Dufrˆ
ene and Legendre, 1997) were measured for ASVs-soil
compaction association analyses and the number of permutations was
set to 99,999.
3. Results
3.1. Soil physicochemical properties and enzymatic activities
The studied soil had a silty loam texture (34 % of sand and 10.5 % of
clay) and an initial BD of 0.76 g cm
−3
. The results of the two-way
ANOVA analysis on BD data showed a signicant difference among
different level of soil compaction (P < 0.01) and time after compaction
(P =0.02), but a non-signicant interaction effect between the two
factors (P =0.08). Repeated skidding caused an immediate signicant
soil compaction as the increase of soil BD was 42 % inside the ruts (1.07
g cm
−3
) and 26 % between the ruts (0.96 g cm
−3
) in comparison to off-
trail samples (undisturbed soil) (Fig. 1). At T2 the soil compaction level
was reduced by 50 % inside the ruts (0.92 g cm
−3
, 21 % higher than the
pre-compaction conditions), and by 35 % between the ruts (0.89 g cm
−3
,
17 % higher than the pre-compaction conditions).
The two-way ANOVA revealed a non-signicant interaction effect
between different level of soil compaction (off-trail, inside, and between
the ruts) and time after compaction (T1 and T2) also on soil chemical
properties. Soil compaction did not signicantly affect pH, C and N
content and soil respiration at both the sampling times, while a signif-
icant main effect of both time and compaction condition was found on
available P (Table 1). Available P was signicantly lower at T2
compared to T1 and in the off-trail compared to the compacted soil
conditions.
At T1, the arylsulfatase activity was signicantly lower between the
ruts than in the off-trail soil, while acid and alkaline phosphomonoes-
terase activities were signicantly lower inside the ruts compared to
Fig. 1. Soil BD at T0 (soon after compaction), T1 (8 months after compaction) and T2 (12 months after compaction) at different level of soil compaction (in the off-
trail samples, in inside the ruts and between the ruts samples). The results of the two-way ANOVA analysis show a signicant difference among different level of soil
compaction (P <0.01) and time after compaction (P =0.02), and a non-signicant interaction effect between the two factors (P =0.08).
A. Bellabarba et al.
Applied Soil Ecology 203 (2024) 105646
4
between the ruts (Fig. 2). Also, the protease activity was signicantly
lower inside the ruts compared to both off-trail and between the ruts
soils (Fig. 2). At T2 no signicant differences were observed in the
alkaline phosphomonoesterase, arylsulfatase and protease activities,
while the acid phosphomonoesterase activity was signicantly higher
inside the ruts than off-trail and between the ruts, showing contrasting
results compared with the results at T1 (Fig. 2). The β-glucosidase ac-
tivity inside and between the ruts was signicantly lower than the off-
trail at both sampling times (Fig. 2).
3.2. Taxonomy of bacterial and fungal communities
A total of 2,737,524 16S rRNA and 2,582,064 fungal ITS high-quality
sequences were obtained, with an average of 152,085 ±35,847 of 16S
rRNA sequences and an average of 143,448 ±33,811 ITS sequences per
sample. After quality ltering, denoising, and removal of chimeras,
approximately 297,113 high-quality 16S rRNA sequences, and 70,185
ITS sequences remained for the following analysis. The sequencing
depths were enough to accurately describe the biodiversity within the
bacterial and fungal communities (Fig. S2). As a result, the 16S rRNA
sequences obtained were classied into 4675 ASVs assigned to both
Archaea and Bacteria Kingdoms, 37 phyla, 81 classes, 163 orders, 209
families, and 287 genera. Instead, ITS sequences resulted in a total of
1422 ASVs, 14 phyla, 32 classes, 63 orders, 88 families, and 135 genera.
A complete list of the detected bacterial and fungal ASVs, including the
related taxonomic assignment, is provided (File S1).
In all soil samples analyzed, the predominant bacterial phyla were
Acidobacteriota, with an average relative abundance between 20.7 %
Table 1
Soil pH, total C and N, available P, and respiration at T1 (8 months after compaction) and T2 (12 months after compaction) in the off-trail samples, in inside the ruts and
between the ruts samples. Reported values are means ±standard deviations of 3 replicates, apart from soil respiration for which the number of replicates is 4.
Time Levels of soil compaction pH TOC
(g kg
.1)
N
(g kg
.1
)
C/N P Olsen
(mg 100 g
−1
)
Soil respiration
(g CO
2
m
−2
h
−1
)
Between the ruts 5.44 ±0.11 4.37 ±0.85 0.26 ±0.03 16.7 6.11 ±1.85 0.53 ±0.17
T1 Inside the ruts 5.39 ±0.15 5.35 ±1.11 0.31 ±0.10 17.9 3.68 ±0.54 0.54 ±0.22
Off-trail 5.35 ±0.10 4.84 ±0.42 0.30 ±0.02 16.1 7.20 ±2.40 0.51 ±0.20
Between the ruts 5.48 ±0.06 3.73 ±2.39 0.19 ±0.09 18.5 1.78 ±0.92 0.55 ±0.28
T2 Inside the ruts 5.49 ±0.06 3.80 ±2.29 0.21 ±0.12 17.6 1.53 ±0.67 0.30 ±0.22
Off-trail 5.56 ±0.17 5.05 ±0.56 0.31 ±0.04 16.2 6.16 ±1.60 0.54 ±0.44
Levels of soil compaction P =0.950 P =0.528 P =0.193 –P =0.049 P =0.129
Statistics
a
Time P =0.060 P =0.324 P =0.157 –P =0.002 P =0.930
Levels of soil compaction
*Time P =0.444 P =0.679 P =0.559 –P =0.569 P =0.213
a
The results of the two-way analysis of variance using compaction conditions (inside the ruts; between the turs; and off-trail) and time after compaction (T1 and T2)
as xed factors are shown. Values in bold highlight level of signicance of P <0.05.
Fig. 2. Soil enzyme activities (arylsulfatase, beta-glucosidase, alkaline and acid phosphomonoesterase, and protease activities) in off-trail samples (blank bar), inside
the ruts samples (black bar), and between the ruts samples (grey bar), at T1 (a) and at T2 (b). The error bars indicate the standard deviation. For each enzymatic
activity, different letters indicate statistical differences among different levels of soil compaction at each sampling time based on the Tukey test (P <0.05).
A. Bellabarba et al.
Applied Soil Ecology 203 (2024) 105646
5
and 35.3 %, Proteobacteria (25 % to 35 %), Actinobacteriota (5 % – 12
%), Verrucomicrobiota (6 % – 10 %), Bacteroidota (6 % – 10 %),
Planctomycetota (4 % – 6 %), Chloroexi (1 % – 3 %) and Firmicutes (2 %
– 3 %) (Fig. S2 A). Alphaproteobacteria was the most abundant class of
Proteobacteria with an average relative abundance between ~16 % and
~21 %, followed by Gammaproteobacteria, which accounted for a range
of mean relative abundance from 9 % to 14 %.
At order level, a core microbiota (i.e. taxonomic groups shared by all
conditions) was composed of seven orders with an average relative
abundance higher than 3 % (Fig. 3A). These “core” taxa were composed
by Acidobacteriales (~16 % – ~11 %), Burkholderiales (~3 % – ~6 %),
Chitinophagales (~4 % – ~5 %), Chthoniobacterales (~5 % – ~8 %),
Rhizobiales (~8 % – ~15 %), Solibacterales (~3 % – ~5 %) and Sub-
group 2 (~4 % – ~11 %) (Fig. 3A). Signicant differences in bacterial
relative abundances across different conditions were investigated both
at order and genus level at each sampling time and entirely reported in
File S2. In particular, at sampling time T1 shifts of the order relative
abundances among inside and between the ruts samples were observed.
The orders Acidobacteriales, Subgroup 2, and WD260 were signicantly
more abundant inside the ruts compared to between the ruts samples
(Fig. 3A, File S2). Differently, Sphingomonadales and Vicinamibacter-
ales were signicantly less abundant inside the ruts than between the
ruts samples (Fig. 3A, File S2). Additionally, the order Saccha-
rimonadales were signicantly more present in compacted soils (be-
tween and inside the ruts samples) than in the undisturbed soils (off-trail
samples) (File S2). Moreover, the post hoc comparisons performed at
sampling time T1 showed that genera Gemmatimonas and Sphingomonas
were signicantly less abundant in inside the ruts than in between the
ruts samples (Fig. 3C and File S2).
Regarding the bacterial genera, 15 genera showed an average rela-
tive abundance of 1 %, or higher, in at least one group of soil conditions
tested (Fig. 3C, File S2): the most abundant genera were Candidatus
Solibacter (average relative abundance ranged from 3 to 6 % approxi-
mately), Candidatus Udaeobacter (~3 % – ~6 %), Bryobacter (~3 % – ~6
%), Puia (~1 % – ~3 %), Acidothermus (~1 % – ~2 %) and Streptococcus
(~1 % – ~2 %) (Fig. 3C, File S2). The bacterial genera prole in inside
the ruts was deeply different than in between the ruts and in off-trail
samples (Fig. 3C). In Inside the ruts samples, the genera Candidatus
Udaeobacter, Candidatus Solibacter, Granulicella, and Bryobacter were
more abundant than off-trail and between the ruts samples (Fig. 3C).
Overall, the fungal community was dominated by the phylum
Ascomycota with a range of mean relative abundance from 67 % (inside
the ruts samples at T2)” to 82 % (inside the ruts at T1 and off-trail
samples at T1), together with Basidiomycota (6 % – 20 %), Rozello-
mycota (<1 % – 5 %), Zoopagomycota (<1 % – 2 %) and Olpidiomycota
only detected in between the ruts samples at T1 with a maximum rela-
tive abundance of 2.24 % (Fig. S3 B). At the order level, the orders
Helotiales and Chaetothyriales showed a relative abundance higher than
3 % (range of ~18 % – ~30 %) in all soil conditions (Fig. 3B, File S2). At
T1 and T2 the relative abundances of some taxa signicantly varied
between levels of soil compaction reecting the diversity in structure of
fungal communities. Indeed, at T1 the order Tremellales was signi-
cantly less abundant in not-compacted than compacted soil samples
(Fig. 3B, File S2), while Capnodiales and the order GS37 showed the
opposite trend, being signicantly enriched in not-compacted soil
samples (Fig. 3B and File S2). At T2, the order Trichosporonales were
signicantly less abundant in the compacted soil conditions (File S2).
Concerning the fungal communities, thirty-one genera displayed an
average relative abundance of 1 %, or higher, in at least one group
(Fig. 3D, File S2). Among these, the genera characterized by a high
relative abundance were Cenococcum, with a range of mean relative
abundance equal to ~3 % – ~12 %, Ciliophora (~2 % – 6 %), Crypto-
sporiopsis (~0.8 % – ~7 %), Hygrophorus (<1 % – ~10 %), Inocybe (<1
% – ~14 %), Lambertella (<1 % – ~7 %), Oidiodendron (~1.1 % – ~3 %)
and Saitozyma (~2 % – ~5 %) (Fig. 3D). Following the Dunn Test post
hoc, at T1 the relative abundance of genus Saitozyma resulted
signicantly higher in the compacted soil conditions (Fig. 3D and File
S2).
3.3. Overall diversity of bacterial and fungal communities
Alpha diversity values showed that all samples were characterized by
high species richness (S
obs
) in both the fungal and bacterial communities
(Fig. 4A & B). At each sampling time, the S
obs
did not differ signicantly
in any soil condition (between and inside the ruts, and off-trail)
(Fig. 4A), indicating that soil compaction did not reduce species rich-
ness. Moreover, in the bacterial community, S
obs
remained largely un-
varied among the two sampling times. In general, for bacteria the
Evenness Pielou's index (J') was consistently high in all the soil condi-
tions examined and considerably uniform among sampling times T1 and
T2 (Fig. 4A). However, at T1 the Evenness Pielou's index (J') was
signicantly lower inside the ruts than between ruts, hinting that higher
soil compaction slightly amplies the dominance of some bacterial
species (Fig. 4A). Similar to bacteria, at T1 the fungal dataset displayed
constant values of S
obs
(i.e fungal richness) among different soil
compaction levels (Fig. 4B). Instead, the fungal communities in all the
soil conditions had a lower even distribution of ASVs compared to the
bacteria dataset (Fig. 4B). Moreover, the Evenness Pielou's index (J')
signicantly increased in between the ruts samples with sampling times,
suggesting a time-effect on ASVs distribution at this degree of soil
compaction (Fig. 4B). Bacterial and fungal communities did not differ in
relation to Shannon index, showing H values relatively high and stable
across all the soil conditions tested (Fig. 4A, Fig. 4B).
For bacterial communities, the NMDS ordination plots illustrated a
progressive split between the off-trail and in-trail samples (both inside
and between the ruts samples) over time (Fig. 5A–B). The most
remarkable clustering was visible at sampling time T1, in which the
inside the ruts samples clustered separately from the off-trail and be-
tween the ruts samples (Fig. 5A). Indeed, at T1, signicant differences in
community structure at different degrees of soil compaction were
observed, as strongly conrmed by PERMANOVA results (Fig. 5A), as
well as by ANOSIM statistics (Fig. 5A) and corroborated by PERMDISP
test (Fig. 5A). In agreement with the NMDS plot, the range of rank
dissimilarity was signicantly lower in inside the ruts than between the
ruts and the off-trail samples (Fig. S4), hinting that the bacterial com-
munity inside the ruts samples truly differs from the other two groups.
Although the NMDS plot showed an evident separation between not-
compacted and compacted soil samples, no signicant differences in
community structure were observed between these groups at T2
(Fig. 5B). Concerning the fungal dataset, the clustering analysis showed
a consistent grouping of samples between compacted and not-
compacted soil (Fig. 5C–D). At sampling time T1, according to the sta-
tistical analysis soil compaction signicantly altered the fungal com-
munity structure, leading to a signicant differentiation of communities
among different levels of compaction (inside the ruts, off-trail, and be-
tween the ruts) and between compacted and not-compacted soil condi-
tions (Fig. 5C). Furthermore, soil compaction effects were most
pronounced at T2 as persistent and signicant shifts were recorded here
between compacted and not-compacted soil samples as hinted by the
ordination analysis (Fig. 5D).
3.4. Relationship between enzymatic activities and community structure
Distance-based redundancy analysis (db-RDA) coupled with multiple
regression analysis was used to evaluate the relationship between both
microbial community structure and soil enzyme activities. The rst two
axes together explained 36.07 % and 48.65 % of the variance in the
bacterial communities, respectively at T1 and T2 (Fig. 6A–B). Con-
cerning the fungal communities, the total variance explained was 30.28
% and 37.44 % at T1 and T2, respectively (Fig. 6C). Db-RDA plots
showed the relationships among enzyme activities and the structure of
both the microbial communities, and their activities growth trends along
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Applied Soil Ecology 203 (2024) 105646
6
with the changes of soil microbial communities in different soil condi-
tions. (Fig. 6 and File S3).
At T1, the enzyme activities associated with the bacterial structure
were arylsulfatase, protease, and alkaline phosphatase activity (Fig. 6A,
File S3). Consistently with enzyme activity results (Fig. 2), the regres-
sion analysis showed that the arylsulfatase activity was positively
associated with changes in bacterial structure in the undisturbed soil
(off-trail samples), while the protease and alkaline phosphatase activ-
ities progressively increased along with changes in bacterial structure in
medium compacted soil (between ruts samples) (Fig. 6A). Moreover,
major increases in these enzymatic activities did not appear to be con-
nected with the structure of the bacterial community in heavily com-
pacted soil (inside ruts samples) (Fig. 6A).
At T2, the enzyme activities signicantly associated to bacterial
community structure changed compared to T1 as acid phosphomono-
esterase and β-glucosidase emerged as newly related enzymes, while
protease was no longer signicant (Fig. 6B; File S3). In particular, the
increment of acid phosphatase activity was connected to changes in
bacterial community composition of compacted soils (inside ruts sam-
ples), while other activities (β-glucosidase, alkaline phosphatase, and
arylsulfatase activity) were more associated with undisturbed soils (off-
trail samples) (Fig. 6B), although they showed lower activity (Fig. 2).
For fungi, a lower number of relationships between soil functionality
and community structure were observed at T1 (Fig. 6C; File S3). Simi-
larly to bacteria, the arylsulfatase activity increased along with changes
in fungal community composition in undisturbed soils (off-trail sam-
ples), while the protease activity increased along with changes in fungal
community composition in medium disturbed soils (between ruts sam-
ples) (Fig. 6C; File S3). Also, the structure of the fungal community in
compacted soils (inside ruts samples) was not associated with these two
activities. At T2, no enzymatic activity was signicantly related to
changes in fungal community structure in both compacted and not-
compacted soils (File S3).
3.5. Bacterial and fungal indicator species of soil compaction
We additionally conducted an indicator species analysis to uncover
taxonomic groups that were signicantly (Indicator Value index P <
0.05) associated with compacted (both inside and between the ruts
samples) or off-trail soils (undisturbed soils). For the bacterial dataset, at
T1 22 ASVs, 7 ASVs, and 5 AVSs, respectively for inside the ruts, be-
tween the ruts and off-trail samples, were identied as indicator species,
as signicant differences in bacterial community structure were
observed (Fig. S5A, Table S4). More precisely, the association analysis
revealed ASVs-soil compaction association patterns that were specic
and exclusive for each condition tested (Fig. S5A, Table S4). Bacterial
Fig. 3. Taxonomic composition of bacterial microbial communities (orders in A, genera in C) and fungal communities (orders in B, genera in D) enriched in different
conditions (off-trail, between and inside the ruts) at T1 and T2 sampling times. In panels A) and B) average abundances (%) at order level are reported for each tested
condition. Only orders with an average relative abundance of at least 3 % (or higher) are reported. In panels C) and D), heatmaps with mean relative abundances (%)
at genus level of bacterial and fungal communities, respectively, in different conditions. Only the genera with a mean relative abundance of at least 1 % in at least one
sample are shown.
A. Bellabarba et al.
Applied Soil Ecology 203 (2024) 105646
7
Fig. 4. Effect of soil compaction and time on
α
-diversity of bacterial (A) and fungal (B) communities in differently compacted soils (off-trail, between and inside ruts)
at T1 and T2 sampling times. Values of richness (S
.obs
), the Evenness Pielou's index (J), and Shannon (H) index are reported on the y axis. Horizontal lines of boxes
represent the median, whereas the whiskers represent the maximal and minimal values. In Tables, mean values of observed richness (S.
obs
), the Evenness Pielou's
index (J') and Shannon index (H) are reported for each community. Different superscript capital letters indicate results of Dunn test's post-hoc among different levels
of soil compaction (between the ruts, inside the ruts and off-trail) at each sampling time. Different superscript small letters indicate results of Wilcoxon test for each
level of soil compaction among different sampling times (P <0.05).
A. Bellabarba et al.
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8
taxa that were signicantly associated with inside the ruts samples were
assigned to taxonomic groups at the class level as Alphaproteobacteria,
Acidobacteriae, Gammaproteobacteria, Verrucomicrobiae, Kapabac-
teria, Bacteroidia, Vampirivibrionia and Chthonomonadetes (Fig. S5A,
File S1, Table S4). Consistent with the obtained results, most of the in-
dicator ASVs found, associated with inside the ruts samples, were afl-
iated with orders belonging to Subgroup 2, Acidobacteriales (genera
Occallatibacter), WD260, and Chitinophagales (genera Puia ASV_379,
and ASV_680) (Fig. S5A, File S1, Table S4). Other signicant indicators
ASVs, for inside the ruts samples, were attributed to Rhizobiales A0839,
Pedosphaerales, Kapabacteriales, Solibacterales (specically Candidatus
Solibacter), Obscuribacterales, Chthonomonadales (genera Chthonomo-
nas), Caulobacterales (Fig. S5A, File S1, Table S4).
As soil compaction signicantly altered the fungal community
structure at T1 and T2, fungal indicator species were also investigated
for fungal datasets (Fig. S5B–E). At T1, no ASVs were detected as
signicantly associated with inside the ruts samples (Fig. S5B, File S1,
Table S4), while for off-trail and between the ruts groups some indicator
species were identied (Fig. S5B–E, File S1, Table S4). The genera
signicantly associated with between the ruts samples were Ciliophora,
Phacidium, Sanchytrium, Xenopolyscytalum, whereas Sclerococcum ahtii,
Capnobotryella and ASVs attributed to the class of Lecanoromycetes were
pinpointed as good indicators for off-trail soils, at T1 (Fig. S5B and C,
File S1, Table S4). At T2 the indicator species signicantly associated
with between the ruts and off-trail samples changed compared to T1.
Indeed, the only ASVs detected was ASV_274 (phylum Ascomycota) for
the between the ruts group, and ASV_40 for both between and inside the
ruts groups (i.e in-trail) (phylum Ascomycota) (Fig. S5D–E, File S1,
Table S4). For the not-compacted soil samples, the ASVs detected were
related to Vanrija albida, Ciliophora, Collophora paarla and an ASV
attributed to the Sordariomycetes (ASV_71) (Fig. S5D–E, File S1,
Table S4). Moreover, single indicator ASVs were associated with specic
conditions, such as ASV_181 (class Eurotiomycetes) for inside the ruts
samples and ASV_40 (class Leotiomycetes) for compacted soil samples
(both inside and between the ruts) (Fig. S5D–E, File S1, Table S4).
4. Discussion
4.1. Soil physicochemical properties and enzymatic activities
In our study area, after 30 repeated passes by a tractor simulating
skidding operations in a heavily trafcked area for wood extraction, the
soil was severely compacted. Indeed, the BD increased by 42 % inside
the ruts, a result higher than the average values reported in two pub-
lished meta-analyses about the impacts on soil after forest logging
(Ampoorter et al., 2012; Latterini et al., 2023). This condition was due to
the high wheel trafc intensity (i.e. number of passes), although it is
commonly reported that the rst passes have the highest relative impact,
thus already resulting in a high compaction degree (Ampoorter et al.,
2012; Han et al., 2006). Furthermore, Vallombrosa forest soil showed a
very low initial (undisturbed) soil BD (0.76 g cm
−3
) and therefore the
soil was characterized by high porosity and was prone to compaction
(Ampoorter et al., 2012). Arguably, compaction should have had a
certain impact on soil structure and, consequently, on air uxes and
hydraulic conductivity (Hartmann et al., 2014). This compaction con-
dition, however, was transient as the BD rapidly decreased after 1 year
from disturbance (T2) in the compacted soil, in the same manner both
between and inside the ruts, as showed by the lack of signicant inter-
action effect by the two-way ANOVA analysis between the different
levels of compaction and time after compaction. Many studies report soil
recovery periods longer than 5 years (Croke et al., 2001; Hartmann
et al., 2014; Marchi et al., 2016). However, our data suggests a still
incomplete soil recovery at T2. Bulk density (BD) values remained 15 %
above those of the undisturbed soil, a threshold reported to be still
Fig. 5. Effect of soil compaction on β-diversity of bacterial (panels A and B) and fungal (panels C and D) communities at T1 (A and C) and T2 (B and D) the sampling
time. Non-metric multidimensional scaling plot is based on Bray–Curtis distance on Hellinger transformed ASVs abundance of bacterial and fungal community
structures. Different colours indicate the split in off-trail samples and in-trail samples (between the ruts and inside the ruts samples); shapes refer to different degrees
of soil compaction (off-trail, between the ruts and inside the ruts samples). Stress values are reported for each NMDS. Statistical differences with permutational
multivariate analysis of variance (PERMANOVA), analysis of similarities (ANOSIM), and Analysis of multivariate homogeneity (PERMDISP) between off- and in-trail
samples, and among different levels of soil compaction (between the ruts, inside the ruts and off-trail) at different sampling time (T1 and T2) are reported in tables. F-
ratio (F) for PERMDISP, the estimation of the variance component (R
2
) for PERMANOVA and the Global R for ANOSIM are reported together with the level of
signicance (ns, not signicant; * P <0.05; ** P <0.01).
A. Bellabarba et al.
Applied Soil Ecology 203 (2024) 105646
9
detrimental for soil functions (Lacey and Ryan, 2000).
Compaction did not signicantly affect C and N content and just a
slight decreasing trend of these two elements may be noticed in our
compacted soil after 1 year from disturbance. Conversely, a general
decrease of SOM (soil organic matter) in compacted conditions, even in
the short term (1–3 years), has been observed by a meta-analysis of
studies carried out in several Italian broadleaf's forests (Latterini et al.,
2023). However, the analyzed enzyme activities related to soil biogeo-
chemical cycles (beta-glucosidase and protease activities for SOM dy-
namics or acid and alkaline phosphomonoesterase activities for P cycle),
showed a signicant decrease inside the ruts in comparison to undis-
turbed soil (off-trail) at T1, indicating a potential slowing down of biotic
SOM decomposition due to soil compaction. In this regard, soil enzyme
activities can be considered as a soil quality indicator, allowing to
highlight early soil stress or soil functionality recovery (Dick et al.,
1996).
The analysis of soil enzymatic activities may provide a valid link
between the metabolic response of soils and changes in microbial
communities to soil disturbance (Frąc et al., 2011; Nannipieri et al.,
2012; Tan et al., 2008). Soil functionality and biodiversity are strictly
related to soil physicochemical conditions, such as nutrient availability,
water and air uxes, porosity, and soil structure. Soil compaction re-
duces the microenvironments with optimal conditions for soil microbial
growth and could lead to lower activity of specic groups (Kihara et al.,
2012; Lu et al., 2019). Indeed, after 8 months from tractor passage,
enzyme activities related to biogeochemical cycles were reduced by soil
disturbance showing a short-time soil functionality change as previously
reported by Tan et al. (2008). The db-RDA analysis showing a negative
association between enzyme activities and soil microbial communities
(bacteria and fungi) conrmed the negative impact in soil functionality
and communities structure induced by soil disturbance.
After 1 year since compaction (T2), the negative effect of soil
disturbance was no longer observable. In fact, most of the enzyme ac-
tivities, except betaglucosidase activity, did not show signicant lower
values of compacted soils compared to the undisturbed soil, indicating a
general fast recovery of soil functionality, as observed in previous ex-
periments in agricultural soils (Kwiatkowska and Joniec, 2022). In
particular, the increase of acid phosphomonoesterase activity inside of
the ruts, observed at T2, can be related to a microbial restoration,
conrming that the phosphomonoesterase activities can be considered a
suitable indicator of soil health (Acosta-Martinez et al., 2018). In addi-
tion, the db-RDA analysis conrmed the positive association between
bacterial community composition and acid phosphomonoesterase. The
metabolic re-activation of soil organisms may result by the increase of
soil porosity due to the decrease of soil compaction, as previously re-
ported (Xue et al., 2018).
Fig. 6. Distance-based redundancy analysis (RDA) plots between enzymatic activities and bacterial (A and B) and fungal community structures (C). Different colours
indicate the split in off-trail and in-trail samples (between the ruts and inside the ruts samples); shapes refer to different degrees of soil compaction (off-trail, between
the ruts and inside the ruts samples). Vectors indicate enzymatic activities signicantly correlated with microbial community structures (ENVFIT analysis).
A. Bellabarba et al.
Applied Soil Ecology 203 (2024) 105646
10
4.2. Diversity of bacterial and fungal communities
The microbial community structures can be used as an indicator for
soil health due to their sensitivity to disturbances, and their structural
variation can be strictly associated with changes in soil ecosystem pro-
cesses (Allison and Martiny, 2008). Our investigation on microbial
communities was conducted in a relatively short period compared to
previous works (García-Carmona et al., 2021; Hartmann et al., 2014;
Lewandowski et al., 2016), challenging the idea that short-term studies
are insufcient to assess the signicant effects of forest logging opera-
tions on soil microbial communities. This remark is particularly true for
the bacterial community, which in our work was provisionally altered in
its structure and composition by soil compaction. Indeed, soil compac-
tion considerably affected the bacterial beta diversity only after eight
months from soil disturbance, thus exerting a temporary effect on the
bacterial community composition, to promptly return to the initial state
showing good resilience to the disturbance introduced in the longer
term. Although the common idea is that a complete recovery of bacterial
communities from soil compaction may take place in years, this is not
always the case. Ammitzboll et al. (2022), for instance, found that the
bacterial community structures in a post-logging compacted soil showed
a similar temporal trend to our ndings, conrming the great abilities of
bacteria to restore the initial community composition on a monthly
scale. In general, increasing trends of bacterial alpha diversity were
recorded along with soil disturbances such as compaction (García-Car-
mona et al., 2021; Hartmann et al., 2014; Longepierre et al., 2022) or
salvage-logging operations and wildre (Ammitzboll et al., 2022; Bowd
et al., 2022). Conversely, in our soils, bacterial alpha diversity was not
affected by compaction. In particular, species richness remained
consistently high over time for all compaction levels. Similarly, previous
studies related to other disturbances, such as wildre-affected soils
(Weber et al., 2014) and severe organic matter removal treatments
(Wilhelm et al., 2017), did not reveal any signicant bacterial alpha-
diversity trends, as well as no signicant impact of soil compaction on
richness expressed as microbial biomass carbon MBC at surface-medium
soil depth (Busse et al., 2006; Nazari et al., 2021; Shestak and Busse,
2005). However, the decrease of the Pielou index in compacted soil
suggested changes in bacterial communities possibly linked to the
temporary leading of dominant species more tolerant and resistant to
disturbance (Sun et al., 2017; Tanentzap et al., 2013) who may benet
from post-compaction soil changes. Overall, considering that microbial
diversity guarantees the “multifunctionality” of soil ecosystems
(Delgado-Baquerizo et al., 2016; Tardy et al., 2014), a high starting
richness and diversity of the bacterial community may ensure greater
plasticity and inherent capacity to adapt to soil compaction (Shade et al.,
2012).
Modied soil conditions due to soil compaction signicantly altered
fungal community structures, leading to a clear differentiation between
in- and off-trail samples at the two times observed. Indeed, the variation
of beta-diversity over time between compacted and non-compacted soil
samples revealed the scarce resilience ability of fungal communities to
restore the initial community structure. The higher sensitivity to
compaction of fungal communities compared to bacteria is consistent
with previous ndings pointing out this lack of resilience of total fungal
community to disturbance (Hartmann and Niklaus, 2012; Hartmann
et al., 2014).
Fungal community species richness was not affected by soil
compaction, similarly to what was observed for bacterial species rich-
ness. However, the slightly compacted soil (between the ruts samples)
underwent a more even distribution of detected ASVs, indicating that
mild soil compaction did not translate into a greater dominance of
fungal species resistant to perturbation, but rather into an increase of
biodiversity in the short-term. Highly diverse and even microbial com-
munities, in which several species are functionally equivalent, may show
higher functional stability under environmental disturbance ensuring
good functionality in stress conditions (Wittebolle et al., 2009). In this
perspective, the signicant increase in fungal evenness can be inter-
preted as an improved ability of the soil ecosystem to carry out over-
lapping functions in the longer term. The greater resilience ability of
bacteria compared to fungi can be due to the higher bacterial growth
rates and turnover in soil habitats (Reischke et al., 2014; Rousk and
Bååth, 2011) and the major use of readily available C sources, preferred
to the degradation of more complex carbon substrates, which is a
prerogative of the fungal community (Hicks et al., 2022; Reischke et al.,
2014).
The shifts recorded in the bacterial community structures are in
accordance with relative abundance data. Indeed, eight months
following the compaction event, different degrees of soil compaction led
to distinct enrichment of bacterial relative abundances. Oligotrophic
highly metabolically versatile microorganisms of Acidobacteriales (Ho
et al., 2017) and Subgroup 2 (class Acidobacteriae) were signicantly
more abundant in the most compacted soil, probably due to their
capability to tolerate uctuations in water availability (Ward et al.,
2009) coupled with their ability to grow across different oxygen gradi-
ents (Eichorst et al., 2018), especially in low oxygen tension environ-
ments as compacted forest soils (Bruce et al., 2010). Indeed, peat
microbiota in anoxic microcosms was persistently and highly composed
of Subgroups 1, 2, and 3 (Hausmann et al., 2018). For fungi, signicant
variations in the relative abundances of two orders reected changes in
community structures. The orders Tremellales with species known for
their saprotrophic lifestyle was signicantly more abundant in com-
pacted soils, as Saitozyma podzolica (formerly Cryptococcus podzolicus) a
typical soil-borne yeast that frequently occurred in different forest soils
(França et al., 2016; Maˇ
sínov´
a et al., 2016; Middelhoven, 2006; Yurkov
et al., 2016). S. podzolica is considered a good indicator in acids and
well-drained soils (Yurkov, 2018) but is frequently present in disturbed
post-mining (Detheridge et al., 2018; Monteiro Moreira and Martins do
Vale, 2020) and re-affected soils (Orumaa et al., 2022), hinting its key
role in the recovery of degraded lands. Conversely, not-compacted soils
were signicantly characterized by species of order Capnodiales, whose
greater abundance was previously correlated with phosphorus avail-
ability in forest soil (Mason et al., 2021), in accordance with our data
showing a higher P availability in the undisturbed soils.
After one year, the fungal community compositions of high- and
medium-compacted soils (ruts and between the ruts, respectively)
showed similar abundance proles and different from the abundance
prole of undisturbed soil. Multiple studies have reported that post-
disturbance fungal communities mainly undergo changes in different
ratios between saprotrophic and symbiotrophic organisms in disturbed
soils (Ammitzboll et al., 2022; García-Carmona et al., 2021; Hartmann
and Niklaus, 2012; Hartmann et al., 2014; Sun et al., 2017). Specically,
logging management frequently shifted fungal community composition
promoting the increase of saprophytes to the detriment of mycorrhizal
(Ammitzboll et al., 2022; Hartmann and Niklaus, 2012; Hartmann et al.,
2014). Saprophytes are usually fungi highly tolerant to environmental
stresses (Bastida et al., 2017) involved in soil organic matter decom-
position, contributing to the soil nutrient enrichment process and thus to
the recovery of post-disturbance soils (Bowd et al., 2022; García-Car-
mona et al., 2021). The analyzed soil samples were rich in mycorrhizal
genera (such as Cenoccum, Hygrophorus, Inocybe, Lambertella, Oidioden-
dron), therefore clear differences between disturbed and undisturbed
soils in terms of mycorrhizal abundance were not recorded. Nonetheless,
in our study the abundance data and related statistics hinted a slightly
increasing trend of saprophytic fungi in disturbed soils, conrming
previous ndings.
4.3. Bacterial and fungal indicator species
Indicator species analysis provided key information on ASVs asso-
ciated with different levels of compaction, and that can be useful in soil
monitoring and in predicting the possible long-term evolution of soil
conditions. Microorganisms belonging to bacterial taxa
A. Bellabarba et al.
Applied Soil Ecology 203 (2024) 105646
11
Chitinophagaceae, as the genus Puia, play an important role in organic
matter decomposition and C cycling. In this case, the association of
genus Puia identied as indicator species in inside the ruts soil samples,
may be due to a decrease of C mineralization in more compacted soil (De
Neve and Hofman, 2000). The nding of denitrifying bacteria Candi-
datus Solibacter, often found in compacted forest soil (Hartmann and
Niklaus, 2012; Hartmann et al., 2014), as a genus associated with soil
compaction is consistent with the reduction trend of N content recorded
in our soil in the short-term and is coherent with the increase of deni-
trication processes in wheel-trafc-induced soil compaction
(Longepierre et al., 2022; Wolkowski, 1990). The indicator species
analysis also conrmed that the class Acidobacteriae was signicantly
associated with soil compaction, as for members of genus Occallatibacter,
which were previously found in restored peatlands, in which compac-
tion increased during mining process (Kaupper et al., 2021). The un-
cultured bacteria of orders WD260 (Gammaproteobacteria) are common
inhabitants of forest soils and peatlands (Sabrekov et al., 2021; Seitz
et al., 2022), but no information is available about their ecological and
functional role. Similarly, the genus Chthonomonas (phyla Armatimo-
nadota) has few cultured and poorly characterized representatives.
Fewer indicator species of fungal communities were associated with
compaction compared to bacteria, and only to mildly compacted soil
samples (between the ruts). The orders Leotiomycetes (i.e. Phacidium)
were related to weakly disturbed soils, as previously observed in soils
slightly burnt (Ammitzboll et al., 2021), or compacted by grazing (Wu
et al., 2022). Also, the hemicellulolytic fungi Xenopolyscytalum (Leo-
tiomycetes) were associated with light compaction changes, and previ-
ously shown as taxa affected by timber harvesting (Leung et al., 2016).
5. Conclusion
The present study described the short-term response of a forest soil
after a severe compaction stress induced by repeated skidding
mimicking logging activities for wood extraction operations. Despite the
initial strong increase in bulk density, the soil compaction degree was
reduced by 50 % in the most compacted part of the trail after just one
year, highlighting a fast soil recovery. The chemical parameters of soil
analyzed were not affected by soil compaction, with the only exception
of available phosphorus, suggesting that a meaningful evaluation of a
complex system like soil cannot be limited to those parameters, partic-
ularly in the short-term. Analysis of enzymatic activity reveals a sensi-
tivity (decrease) of some functions to compaction, which are in turn
related to the structure of the microbial community analyzed. In our
microbiota analysis, the microbial communities showed different re-
sponses to compaction, highlighting a greater bacterial community
resilience in the short term compared to the fungal community. None-
theless, in soil particularly rich in species, such as our forest soil,
considerable differences in terms of relative abundances were not
observed. In this regard, the high degree of functional redundancy and
complementarity that characterized bacterial communities, together
with high observed biodiversity, may function as an “ecological pre-
ventive measure” for the soil ecosystem lessening the functional shifts
induced by environmental disturbances. In this context, the indicator
species identied in our study, such as the genera Candidatus Solibacter
and Puia, may be useful for monitoring early changes in microbial
communities and activities following soil compaction. Our study's
ndings contribute to the knowledge of the impact of soil compaction on
microbial communities in forest soils and provide insights into the short-
term microbial response to soil compaction, which could prove to be
early indicators useful for predicting the long-term sustainability of
forest management.
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.apsoil.2024.105646.
Funding
This research was supported by SKIDDFORW project, funded by the
University of Florence.
CRediT authorship contribution statement
Agnese Bellabarba: Writing – review & editing, Writing – original
draft, Methodology, Formal analysis, Data curation. Laura Giagnoni:
Writing – review & editing, Writing – original draft, Methodology,
Formal analysis, Data curation, Conceptualization. Alessandra Adessi:
Writing – review & editing, Supervision, Methodology, Conceptualiza-
tion. Elena Marra: Writing – review & editing, Writing – original draft,
Formal analysis, Data curation. Andrea Laschi: Writing – review &
editing, Conceptualization. Francesco Neri: Writing – review & editing,
Funding acquisition, Conceptualization. Giovanni Mastrolonardo:
Writing – review & editing, Writing – original draft, Supervision,
Methodology, Funding acquisition, Formal analysis, Data curation,
Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgments
The authors would like to express their gratitude to the Reparto
Carabinieri Biodiversit`
a di Vallombrosa for providing the authorised
worksite. The authors also thank Riccardo Caselli for his help in the eld
and lab work, and Cristiano Foderi for his help in the eld work and data
analysis.
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