Results
\label{results}
Soil physico-chemical
properties
\label{soil-physico-chemical-properties}
Site effect on soil physico-chemical-biological
properties
\label{site-effect-on-soil-physico-chemical-biological-properties}
The site appeared as the main factor affecting the soil physico-chemical
parameters (Table 1, a). The PCO allows to visualize the direction of
change in soil parameters between the both sites (Figure 1). The site
effect on each parameter is detailed in the main test of Table 2. The
texture was as expected, i.e. the sandy site was characterized by a
higher sand content, while the silty site was characterized by a higher
silt and clay content. In terms of nutrient status, the silty site
showed a higher fertility level with a higher P and Ca content as well
as a higher CEC, rate of base saturation and pH, whereas the sandy site
showed a higher C:N ratio. In terms of physical properties, the quantity
of macroaggregate was higher in silty site, whereas the stability of
aggregates and the silt+clay fraction was higher in sandy site. Finally,
the fungal richness was strongly impacted by the site, showing a higher
value in sandy site.
Farming effect on soil physico-chemical
properties
\label{farming-effect-on-soil-physico-chemical-properties}
The farming effect on soil physico-chemical-biological parameters was
lower when compared to the site effect (Table 1, a). In addition, a
marginally significant interaction effect between site and farming was
noticed (Table 1, a), suggesting that the farming effect slightly relies
on the type of soil. Based on that results, we analyze the farming
effect on each site independently. The site-based PCO allows to
visualize the direction of change in soil parameters between the both
farming systems (Figure 2). The overall farming effect as well as the
site-related farming effect on each parameters is detailed in the main
test and pairwise test of Table 2, respectively. We also noticed that
farming-sensitive soil parameters were not the same according to the
site.
The sandy soil was characterized by a higher humidity, HWC and total
biomass content under organic farming and a higher phosphorus, and
silt+clay content under conventional farming. A slight increase of
microaggregates quantity and a better aggregate stability was also
noticed under organic farming (Figure 2, Sandy site). The silty soil was
characterized by a higher potassium, magnesium, silt and clay content
under conventional farming, and a higher aggregate stability under
organic farming (Figure 2, Silty site). Although the discrimination
between conventional and organic farming seems to be stronger in silty
site, the pairwise test of Table 1 revealed a stronger farming effect in
sandy soil. These results may be explained by the samples corresponding
to “CONV-3” in sandy site , where their distance from the rest of
samples was high (Figure XX, Sandy site), and mainly due to the
particularly dried soil conditions.
Spatial component effect on soil physico-chemical
properties
\label{spatial-component-effect-on-soil-physico-chemical-properties}
The spatial component (plot) ranked as the second main factor affecting
the soil physico-chemical properties (Table 1, a). A strong interaction
effect between the farming system and the spatial component was noticed,
suggesting a strong influence of the spatial component on the farming
effect. On PCO, the distance between each farming system of each pair
(represented by the form of the symbol) varies.
Soil microbial community
diversity
\label{soil-microbial-community-diversity}
We obtained 1 070 378 bacterial 16S (29 733±3780 per sample) and 2 404
498 fungal ITS2 (66 792±13 815 per sample) high-quality sequences from
the 36 samples. Sequence clustering yielded 7 643 (3227±268 per sample)
bacterial and 2 681 (751±86 per sample) fungal OTUs.
Overall site effect on microbial
diversity
\label{overall-site-effect-on-microbial-diversity}
The site appeared as the main driver of beta diversity for both bacteria
and fungi (Table XX, a). The site was able to explain 34% and 20% of
bacterial and fungal community variability, respectively (Table XX, a).
PCOs allow to visualize the major site effect on community composition,
represented by the discrimination of samples on the first axis (Figure
XX). At high taxonomic level, the relative abundance of some dominant
taxa such as Alphaproteobacteria, Actinobacteria, Verrucomicrobia and
Zygomycota was higher at the sandy site, while some other dominant taxa
such as Chloroflexi, Deltaproteobacteria, Gammaproteobacteria,
Acidobacteria, Betaproteobacteria, Planctomycetes and Glomeromycota
showed a higher abundance at the silty soil (Figure XX).
In terms of alpha diversity, fungi were more rich and less even at the
sandy site as compared to the silty site (Figure XX and Table XX, a).
The site effect on bacterial alpha diversity, however, was not
significant (Table XX, a).
Overall farming effect on microbial
community
diversity
\label{overall-farming-effect-on-microbial-community-diversity}
The farming effect ranked as second driver of beta-diversity for both
bacteria and fungi (Table XX, a). The farming system was able to explain
11% and 18% of bacterial and fungal community variability,
respectively (Table XX, a). PCOs allow to visualize the farming effect
on community composition, represented by the discrimination of samples
on the second axis (Figure XX). For bacteria, the difference in
community composition was only due to the dissimilarity instead of
heterogeneity of variance. For fungi, however, the difference observed
in community composition may also be attributed to variance
heterogeneity (supplementary data, Permdisp). Consequently, the PCO
helps to visualize the partitioning of these two sources of difference.
Here, the discrimination between the farming groups (organic and
conventional) can be easily visualized on PCO (Figure 2) leading to
conclude that the difference observed in fungal community composition
was likely due to the studied factors. As the farming effect on
community composition was detected at high taxonomic, we identified the
farming-sensitive taxa. Some dominant taxa, such as
Bacteroidetes , Chloroflexi, Actinobacteria,
Alphaproteobacteria , Gammaproteobacteria and
Ascomycota were showed to be more abundant under conventional
farming. Some others, however, such as Nitrospirae,
Acidobacteria , Planctomycetes, Deltaproteobacteria,
Basidiomycota and Glomeromycota, were more abundant under
organic farming (Figure XX). While they are less abundant, some
bacterial taxa recently recognized as Candidate phylum such as
TM7, WS2, OP11, OP3 and WS3 showed a significant change in their
relative abundance between organic and conventional farming. The
putative ecological roles provided by these farming-sensitive taxa to
the agroecosystem will be discussed below with respect to the current
literature.
In terms of alpha-diversity, the farming system had less impact than for
the beta-diversity. The only significant farming effect was detected on
fungal richness: overall, organic farming was more rich and less even
than the conventional farming (Figure XX).
Farming effect interacts with site
and spatial
component
\label{farming-effect-interacts-with-site-and-spatial-component}
The magnitude of farming effect was influenced by the site (Table XX,
interaction terms between farming and site). The pairwise test (Table 1,
b) showed a stronger farming effect (lower avg sim) on beta-diversity at
the silty site when compared to the sandy site, for both bacteria and
fungi. PCO allows to visualize the stronger farming effect at the silty
site, as the distance between SiltCONV and SiltORGA was higher than the
distance between SandCONV and the SandORGA (Figure XX).
In the same way, the magnitude of farming effect was influenced by the
spatial location (Table XX, interaction terms between farming and plot).
The pairwise test for each pair of plot is provided in Table XX, b.
Consequently, the farming effect on beta-diversity should be analysis
with respect to the site and spatial location.
In the same way for alpha-diversity, the farming effect on bacteria
tended to be stronger in silty site than in sandy site (Figure XX). For
fungi, however, the magnitude of farming effect tended to be similar in
the both sites, as no significant interaction between farming and site
was noticed (Table XX, Figure XX). As soon as the farming effect was
significant, the organic farming showed a higher value of richness, but
a lower value of evenness. An interactive effect with the spatial
location on bacteria and fungi was also detected. The respective
pairwise test for each pair of plot is provided in Table XX, b.
Relationship between community composition and soil
physico-chemical-biological
properties
\label{relationship-between-community-composition-and-soil-physico-chemical-biological-properties}
Some physico-chemical-biological properties were closely related to the
microbial beta-diversity variability. The best descriptors of bacterial
community composition included the silt content, the pH, the silt-clay
fraction, the HWC, the macroaggregates, the aggregate stability, the
microaggregate, the sand and clay content, the bacterial richness and
evenness, the N and K content, and the fungal richness. In the same way,
the best descriptors of fungal community composition included the silt
content, the aggregate stability, the microaggregates, pH, the silt-clay
fraction, the macroaggregates, the HWC, the sand and clay content, the
bacterial richness and evenness, the fungal richness, the total biomass
and the nitrogen content.
Among the chemical soil parameters, pH, HWC, N and K were identified to
be good descriptors of the bacterial beta diversity variability. For
fungi, the analysis identified
The distLM analysis identified the particle size (clay, silt and sand),
pH, soil humidity, and some nutrients (nitrogen, phosphorus, calcium and
magnesium) to be the best predictors of the bacterial and fungal
community composition (Figure XX). The model successfully explains the
main trends of the bacterial and fungal variance displayed in the
constrained ordination (Figure XX).
As depicted by the fungal dbRDA, particles size (clay, silt and sand),
humidity, pH, calcium and phosphorus clearly discriminate the fungal
communities between the silt and the sand site. The
discrimination between the organic and conventional
farming, however, is not obvious: magnesium seems to be the best
variable to separate conventional from organic farming.
Regarding the bacterial dbRDA, there was also a clear discrimination
between the silt and sand site mainly drove by the
particle size (clay, silt and sand) and humidity. Here, pH, calcium and
phosphorus were good predictors of organic farming at the
silt site, while magnesium seems here again to be the best to
separate conventional from organic farming.
Beta-diversity
\label{beta-diversity}
The site appeared as the main driver of beta diversity, explaining 34%
and 20% of bacterial and fungal community variability, the
farming system ranked second, explaining 11% and 18% of
bacterial and fungal variability, and the spatial component
(plot) ranked third, explaining 11% and 13% of bacterial and
fungal variability, respectively (Figure 2 and Table 1, a). The PCO
allows to visualise the difference in community composition between the
sites and the farming systems (Figure 2). The first axis separates the
silty and sandy site, while the second axis separates the conventional
and organic farming system. For bacteria, the difference in community
composition was only due to the dissimilarity instead of heterogeneity
of variance. For fungi, however, the difference observed in community
composition may also be attributed to variance heterogeneity
(supplementary data, Permdisp). Consequently, the PCO helps to visualize
the partitioning of these two sources of difference. Here, the
discrimination between the site groups (silty and sandy), the farming
groups (organic and conventional) and spatial component (plots) can be
easily visualized on PCO (Figure 2) leading to conclude that the
difference observed in fungal community composition was likely due to
the studied factors.
A strong influence of site on the farming effect was evidenced, as
reported by the interaction effect between site and farming (Table 1,
a). The pairwise test (Table 1, b) showed a lower average of similitude
(avg sim), i.e. a stronger farming effect on beta-diversity at the sandy
site when compared to the silty site. Moreover, the contrasted farming
effect on community composition between the both site was stronger for
fungi. PCO allows to visualize the stronger farming effect at the silty
site, as the distance between SiltCONV and SiltORGA was higher than the
distance between SandCONV and the SandORGA (Figure XX).
The spatial location had also a strong influence on the farming effect,
as reported by the interaction effect between plot and farming (Table 1,
a). The detail of the farming effect for each pair of plot is provided
in the pairwise test (Table 1, b).
Alpha-diversity
\label{alpha-diversity}
The sandy site tend to be more rich and less even as compared to the
silty site for both bacteria and fungi (Figure XX, Table XX, a). Fungi
were more sensitive to the site and farming system effects, whereas
bacteria was more sensitive to the spatial location (plot) (Table XX,
a). As reported by the significant interaction term (Table XX, a), the
farming effect on bacterial alpha-diversity tended to be stronger in
silty site than in sandy site (Figure XX). For fungi, however, the
farming effect tended to be similar in the both sites, as no significant
interaction between farming and site was noticed (Table XX, Figure XX).
As soon as the farming effect was significant, the organic farming
showed a higher value of richness, but a lower value of evenness. ⇒
Permdisp analysis