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\begin{document}

\title{Soil legacies mediated by plant community traits steer grassland vegetation}



\author[1]{Robin Heinen}%
\author[1]{Emilia Hannula}%
\author[1]{Jonathan De Long}%
\author[1]{Martine Huberty}%
\author[1]{Renske Jongen}%
\author[1]{Anna Kielak}%
\author[1]{Katja Steinauer}%
\author[1]{Feng Zhu}%
\author[1]{T. Bezemer}%
\affil[1]{Netherlands Institute of Ecology}%


\vspace{-1em}



  
  \date{May 05, 2024}


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\selectlanguage{english}
\begin{abstract}
Plant-soil feedback is commonly pointed out as driver of plant community dynamics and species co-existence. However, experimental evidence for soil legacy effects of conditioning plant communities on responding plant communities under natural conditions is lacking. We conditioned 192 grassland plots with plant communities with different ratios of grasses and forbs and fast and slow-growing plants. Soil microbial legacies were most evident for soil fungi. Soil abiotic parameters did not change in response to conditioning. The soil legacies affected the composition of the succeeding vegetation. Plant communities of a specific functional type caused negative feedbacks on succeeding plants when they belonged to the same functional type. Richness, relative species cover and belowground biomass of the responding vegetation were all influenced by the growth rate of the conditioning community. We conclude that plant-soil feedbacks play an important role in vegetation assembly of  natural communities.%
\end{abstract}%



\sloppy




\textbf{Introduction}

Plants and soil organisms are interdependent and the microbiome in the
soil is shaped by the plant that grows in the soil (Phillipot \emph{et
al} . 2013; Bardgett \& Van der Putten 2014). This microbial signature
can remain as a legacy in the soil after the plant is gone, and the soil
legacy can affect another plant growing later in the same soil
(Kulmatiski \emph{et al.} 2008; Van der Putten \emph{et al.} 2013;
Teste\emph{et al.} 2017; Eppinga \emph{et al.} 2018). It is often
speculated that soil legacy effects created by plants play an important
role in regulating plant community dynamics. Recent studies suggest that
inoculation of soils with biotic legacies can change plant community
development under natural conditions (Wubs et al., 2017; Wubs et al.,
2019). However, experimental evidence for soil legacy effects of plant
communities with different characteristics on responding plant
communities in natural systems is lacking (Reynolds \emph{et al.} 2003;
Ehrenfeld \emph{et al.} 2005; Van der Putten \emph{et al.} 2013).

Herbaceous grassland plant species such as grasses (monocots) and forbs
(dicots) differ fundamentally in functional traits such as root
architecture (Craine \emph{et al.} 2001; 2002; Ravenek \emph{et
al.}2016), water and nutrient acquisition (Tjoelker \emph{et al.} 2005;
Ravenek \emph{et al.} 2016), and in chemistry and defense (Latz \emph{et
al.} 2015; 2016; Zhang, Van der Putten \& Veen 2016). These differences
between plant functional types can modulate soil communities
(Kos\emph{et al.} 2015; Heinen \emph{et al.} 2018) and, consequently,
create soil legacy effects that affect following plant growth (Latz
\emph{et al.} 2015; Zhang, Van der Putten \& Veen 2016; Wubs \& Bezemer
2018; Heinen \emph{et al.} 2018; Heinen, Biere \& Bezemer, 2019).
Generally, grass and forb species exhibit negative conspecific soil
legacy effects (Kulmatiski \emph{et al} . 2008), which is often
explained by the accumulation of specialized pathogens (Van der Putten
\emph{et al} . 2013; 2016). However, growing in conspecific soil can
also lead to positive effects through the accumulation of suitable
mutualists in the soil (Morrien et al., 2017; Hannula \emph{et al.}
2017; Teste \emph{et al} . 2017). Experiments with individually potted
plants show that grasses often have increased performance on soils
conditioned by forb species and \emph{vice versa} , where specialized
pathogens may not have accumulated (Petermann \emph{et al.} 2008; De
Kroon \emph{et al.} 2012; Wubs \& Bezemer 2018). As species-specific
communities of soil organisms develop around the roots of plants, soil
legacies may become stronger over time (Diez \emph{et al.} 2010). While
it has been shown that individual plants in the field influence their
local soil community (De Rooij-Van der Goes, Peters \& Van der Putten
1998; Bezemer \emph{et al.}2006; Casper \& Castelli 2007; Van de Voorde
\emph{et al.} 2011; Hannula\emph{et al.} 2019), how plant communities
with different characteristics drive soil legacies in the field and what
the consequences of these legacies are on the establishment of
responding mixed plant communities in these soils is not known (Van der
Putten\emph{et al.} 2013; Ehrenfeld \emph{et al.} 2005; Kardol \emph{et
al.}2007).

We grew six different plant communities in a temperate grassland. Each
plant community consisted of a combination of grass and/or
non-leguminous forb species (hereafter: forbs) which were grown in
different ratios (0:100; 25:75; 75:25 or 100:0\% forb:grass,
respectively). The plots were exposed to different durations of
conditioning by starting the treatments in different years (two-year
legacy was started in 2015; one-year legacy was started in 2016). In
total, there were 192 plots. After the conditioning phase (in 2017), all
plant communities were removed from the soil, and an identical seed
mixture of 33 grassland species was sown in each treatment plot as a
responding plant community. In both phases we recorded the abundance of
all plant species, soil abiotic characteristics, and soil fungal and
bacterial community composition.

We hypothesized that manipulation of the composition of the conditioning
plant communities will result in different microbial soil legacies, and
specifically in the accumulation of specialized soil pathogens and
mutualists. Second, we hypothesized that in the response phase, grasses
and forbs would be less abundant in soils that had been dominated by
their own functional type in the conditioning phase, due to the
accumulation of soil pathogens. Third, we hypothesized that these
effects would be stronger in soils with a two-year legacy than in
one-year legacy soils, due to the gradual development of specific soil
microbiomes over time. Lastly, we hypothesized that soil legacy effects
would be mediated by microbial changes in the soil, rather than by soil
abiotic characteristics.

\textbf{Materials and Methods}

Experimental setup

\emph{Study site.}

In 2015, the field experiment was set up in a restored grassland site
(abandoned from agricultural use in 1996), ``De Mossel''
(Natuurmonumenten, Ede, The Netherlands, 52\selectlanguage{ngerman}° 04´ N, 5° 45´ E). Soils are
holtpodzol, sandy loam (94\% sand, 4\% silt, 2\% clay,
\textasciitilde{}4\% organic matter, 5.2 pH, 2.5 mg
kg\textsuperscript{-1} N, 4.0 mg kg\textsuperscript{-1} P, 16.5 mg
kg\textsuperscript{-1} K) (Jeffery \emph{et al.} 2017). Average daily
temperatures in the area are 16.7 °C in summer months and 1.7 °C in
winter months. Average monthly precipitation ranges from 48 to 76 mm
(\emph{www.climate-data.org}).

\emph{Phase 1: Conditioning phase.}

For the conditioning phase, 96 plots of 166 \selectlanguage{english}[?] 250 cm were each divided
into two 83 \selectlanguage{english}[?] 250 cm experimental subplots. Plots were allocated into
four blocks and within each block, each plot was randomly allocated to
specific treatment combinations; see below.

To test soil legacy effects in the field, six different conditioning
plant communities were designed, each consisting of six species of
grasses and forbs. The experimental design of the conditioning phase has
been described in full detail in De Long \emph{et al.} (2019). The plant
species were randomly selected from two separate pools of species, so
that three communities consisted of random combinations from the first
pool and the three remaining communities consisted of random
combinations from the second pool.

To test the effects of plant functional types in the conditioning plant
community, the ratios between grasses and forbs in each seed mixture
were manipulated, and each mixture was prepared in four different
forb:grass ratios (i.e., 0:100\%, 25:75\%, 75:25\% 100:0\% forb:grass).
To test whether soil legacy effects would become stronger when the
period of conditioning was longer, the two subplots within each plot
were conditioned for different durations (i.e., a one-year or two-year
legacy). Each treatment combination was replicated across four blocks,
totalling 6 plant communities \selectlanguage{english}[?] 4 forb:grass ratios x 2 conditioning
durations x 4 replicate blocks (=192) subplots.

In May 2015 (i.e., two-year legacy treatments), for each plot, one of
the two subplots weas randomly assigned to one of the above treatments.
All vegetation of the subplot was removed, while the other subplot was
left untouched in that year. Removal included the top soil layer of
approximately 5 cm, which generally contains the highest density of
roots in this grassland system. This was done to remove the most
dominant roots and prevent re-growth of non-target plant species, as
well as minimize dominant pre-existing soil legacy effects of the
previous plant communities that had grown in the soils. Each stripped
subplot was then sown with one of the prepared target communities
described above.

In May 2016 (i.e., one-year legacy treatments), all vegetation was
removed (as described above) from the remaining untouched subplot in
each of the 96 plots. The subplots were sown with the same target
community as the other subplot in that plot the year before.

\emph{Phase 2: Responding phase}

On 12-16 June 2017, the vegetation was removed from all subplots within
each plot using a sod-cutting machine (IB300, IBEA, Tradate, Italy) (De
Long \emph{et al.} 2019). All sods were cut to a standard depth of 3 cm.
This was done to remove most of the thicker roots (and prevent re-growth
in the responding plant community), while also allowing us to retrieve
most of the remaining sandy soil from the sods. On 20 June 2017, each
subplot was sown with the 24 species that were used in the conditioning
phase and that occur at the site (Bezemer, personal observation). In
addition, 10 response species that occur in the area were included to be
used for other work beyond the scope of this study (Table S1). Plots
were watered regularly in the first month to assist establishment of the
germinating seedlings. Plots were left to develop naturally and
disturbance was minimalized during sampling events.

\emph{Vegetation assessments (conditioning and responding phase).}

During the second half of May 2017 (conditioning phase), and again in
August 2018 (responding phase), vegetation assessments were performed in
each subplot. In each plot, the percentage of vegetation cover was
estimated visually for each plant species within a 50 x 100 cm frame. In
addition, the percentage moss cover and bare ground were recorded. The
frame was placed approximately in the middle of the plot in order to
exclude potential edge effects. The cover of the different functional
types (i.e., grasses, non-leguminous forbs) was calculated as the
cumulative cover of each species belonging to the respective functional
type.

\emph{Soil sampling for abiotic parameters and soil microbial analysis}

Nine soil cores were taken from each experimental subplot (1.3 cm
diameter, 10 cm depth). these cores were then bulked and homogenized. A
2 mL tube was filled with a subset of homogenized soil for molecular
analysis at the day of sampling and stored at -80 degC. Soils were sampled
twice, both at roughly the same time that the plant community
composition was recorded; once at the end of the conditioning phase,
just before the vegetation was removed (May 2017) in order to establish
the conditioning effects on the soil microbial community. The second
sampling was used to assess whether the legacy effects persisted over
time and took place roughly two months after the establishment of the
responding plant community (September 2018).

\emph{Soil abiotic parameters}

Soil samples used to measure soil abiotic characteristics were dried at
40 degC and sieved through a 2 mm sieve. Approximately three grams of
dried soil was mixed with 30 ml of 0.01 M CaCl\textsubscript{2}. The
mixture was shaken for 2 h on a mechanical shaker with linear movement
at 250 rpm. The samples were centrifuged for 5 m at 3000 rpm. Then, 15
mL of the supernatant was filtered through a Whatman Puradisc Aqua 30
syringe filter with cellulose acetate membrane. Subsequently, 12.87 mL
of the filtrate and 130 \selectlanguage{greek}μ\selectlanguage{english}L HNO\textsubscript{3} were mixed in a clean 15
mL tube. The sample was mixed using a vortex and analyzed for soil
extractable micronutrients (Fe, K, Mg, P, S, Zn) using an inductively
coupled plasma - optical emission spectrometer (ICP-OES, Thermo
Scientific iCAP 6500 Duo Instrument with axial and radial view and CID
detector microwave digestion system). The remaining part of the filtrate
was transferred to a 15 mL tube to measure soil pH,
NO\textsubscript{2}+NO\textsubscript{3} and NH\textsubscript{4}. After
recording the pH (inoLab pH 7310), the soil extracts were analyzed on a
QuAAtro Autoanalyzer (Seal Analytical, Mequon, Wisconsin, USA).

\emph{Microbial sequencing}

DNA was extracted from 0.75 g of soil using the Power Soil DNA
extraction kit (Qiagen) according to the manufacturer's instructions.
The primers ITS4ngs and ITS3mix targeting the ITS2 region of fungal
genes (Tedersoo \emph{et al.} 2015) and the primers 515FB and 806RB
(Caporaso \emph{et al.} 2012; Apprill \emph{et al.} 2015; Parada\emph{et
al.} 2016) targeting the V4 region of the 16S rRNA gene in bacteria were
used. Presence of PCR product was verified using agarose gel
electrophoresis. The PCR products were purified using Agencourt AMPure
XP magnetic beads (Beckman Coulter). Adapters and barcodes were added to
samples using the Nextera XT DNA library preparation kit set A, B, and C
(Illumina, San Diego, CA, USA). The final PCR product was purified again
with AMPure beads, checked using agarose gel electrophoresis and
quantified using a Nanodrop spectrophotometer before equimolar pooling.
We pooled all fungal samples (192) from each time point in one Illumina
Miseq PE250 run and divided the bacterial samples over two separate runs
(96 samples each). With two time points analysed, this resulted in two
MiSeq runs for fungi and four runs for bacteria. Libraries were
sequenced at McGill University and Genome Quebec Innovation Center.
Extraction negatives and a mock community were used and further
sequenced in each sequencing run. This mock community consisted of 10
fungal species and associated bacteria, and was included to control for
the potential variation between sequencing runs and to increase the
accuracy of the bioinformatics analysis.

Bacterial sequences and fungal sequences were analysed using the PIPITS
pipeline and the Hydra pipeline, respectively (Gweon \emph{et al.} 2015;
De Hollander 2017). In short, fungal sequences were paired using VSEARCH
and quality was filtered using standard parameters of the pipeline. The
ITS2 region was extracted using ITSx (Bengtsson-Palme \emph{et
al.}2013). Short reads were removed, and sequences were clustered based
on a 97\% similarity threshold using VSEARCH and fungal chimeric
sequences were removed by comparing with the UNITE uchime database. The
representative sequences were identified using the RDP classifier
against the UNITE database (Abarenkov \emph{et al.} 2010) and clustered
further into phylotypes. For bacterial sequences, VSEARCH was used to
pair and cluster sequences. For classification, the SINA classification
was used with the SILVA database. Fungi were assigned to potential
functions using FunGuild (Nguyen \emph{et al.} 2016) and curated using
in-house databases (Hannula \emph{et al.} 2017; Tedersoo \emph{et
al.}2015; Mommer \emph{et al.} 2018). For potential-plant pathogenic
fungi, the target plant species was checked based on available
literature (Watanabe 2018). The sequences created in this study are
deposited to ENA with accession number PRJEB31856.

The data from the conditioning phase (May 2017) and two months after
establishment of the responding phase (September 2017) were analyzed
together. We first filtered out microbial taxa that were present in less
than ten samples and had an abundance of less than 0.01\%. For ITS data,
the sequences derived from other organisms than fungi were removed and
for 16S data, sequences originating from mitochondria and chloroplasts
were removed. For both bacteria and fungi, samples with less than 1000
reads remaining were removed from the dataset and read numbers were
normalized using total sum scaling (TSS).

Mock communities were used to inspect the filtering done for fungi and
to compare the runs with each other. After filtering, we detected the
same 13 fungal OTUs in all mock communities sequenced and the abundances
of these OTUs in the mock communities in different sequencing runs were
highly correlated (Pearson correlation, R\textsuperscript{2}=0.97,
p\textless{}0.001).

\emph{Multivariate analyses of soil abiotic parameters, soil fungal,
bacterial and plant communities}

We tested the effects of conditioning time, conditioning plant
community, and forb:grass ratio including all interactions on soil
abiotic composition (soil nutrients and including soil pH) with a
permutational analysis of variance (permanova; 999 permutations) using
Euclidian distances. Furthermore, the effects on soil fungal (ITS2),
soil bacterial (16S), and plant community composition were assessed with
permanova (999 permutations) using Bray-Curtis dissimilarity. Fungal and
bacterial data were square root-transformed and standardized using
Wisconsin double standardization prior to calculating Bray-Curtis
dissimilarity. Plot number was included in the models as a random effect
to indicate that the one- and two-year conditioned subplots belong to
the same plot. All multivariate analyses were performed in R, using the
`\emph{vegan} ' package (R Core Team 2018; Oksanen \emph{et al.} 2018)
and community composition was visualized using ordination based on
non-metric multidimensional scaling, using the `\emph{ggplot2} ' package
(Wickham 2016).

To assess whether responding plant communities responded to conditioning
time and forb:grass ratio, and whether particular responding plant
species drove these responses, we performed (restricted) redundancy
analyses with either forb:grass ratio (categorical), or conditioning
time (categorical) as explanatory variables. These analyses were
performed and visualized for each of the six conditioning communities
separately. Redundancy analyses and visualizations were performed in
Canoco 5.03 (Microcomputer Power, Ithaca NY, USA).

\emph{Conditioning effects on soil fungal guilds.}

We tested the effects of conditioning time, conditioning plant
community, and forb:grass ratio including all interactions on i)
specific groups of soil fungi (AMF, saprotrophs, total pathogens, forb
pathogens, grass pathogens, and the dominating grass
pathogen\emph{Slopeiomyces cylindrosporus} ); ii) plant cover (i.e.,
total plant cover, forb and grass cover) with general linear mixed
models. Plot number was included in the model as random effect, to
indicate that one- and two-year conditioned subplots belonged to the
same plot.

Normality and homogeneity of the residuals were checked and data were
transformed when necessary (indicated in the respective summary tables).
All mixed models were performed in R using the `\emph{nlme} ' package
(Pinheiro \emph{et al.} 2018).

\emph{Path analysis of relationships between conditioning and responding
plant communities mediated via soil abiotic and biotic parameters}

Dissimilarity matrices were calculated between all samples. We used
Bray-Curtis dissimilarity for plants, fungi and bacteria with above
described transformations, and Euclidean distance for the abiotic
parameters using the `\emph{vegan} ' package (R Core Team 2018;
Oksanen\emph{et al.} 2018). For plant communities, all plant species
present in less than three samples were removed prior to analysis in
order to diminish the effect of rare plant species. Dissimilarity
matrices during conditioning and responding phases were calculated
separately. Mantel tests were carried out to explore the correlations
between the distance matrices using Pearson's correlation coefficients
with 999 permutations using a Monte-Carlo permutation test to test the
\emph{a priori}assumptions that conditioning plants will change soil
communities (e.g., Heinen \emph{et al.} 2018; Morrien \emph{et al.}
2017) and abiotic conditions (e.g., Bezemer \emph{et al.} 2006; Zhang,
Van der Putten \& Veen, 2016), and that this in turn alters the
performance of responding plant communities (plant-soil feedback,
Ehrenfeld \emph{et al.} 2005; Van der Putten \emph{et al.} 2013). We
created a model that reflects these pathways separately (Figure 3), to
visualize how the conditioning plant community affects the responding
plant community.

\emph{Relationships between conditioning and responding plant species.}

To explore relationships between the abundance of plant species that
were sown in the conditioning plant communities (May 2017) and present
in the responding plant communities (August 2018), we constructed
correlation plots for each of the six conditioning plant communities. In
these correlation plots, relationships between conditioning and
responding plant species are indicative of soil-mediated effects between
the species (i.e., plant-soil feedbacks). We included only those species
that comprised greater than 0.25\% average cover and that were present
in at least 3 plots. Furthermore, we included grass and forb cover and
total cover, to reveal whether observed vegetation patterns are driven
by individual conditioning or responding species. All correlation plots
were corrected for multiple comparisons using a Bonferroni correction.
For visualization, all pairwise correlations were shown at a
significance of p\textless{}0.01. All correlation matrices were
constructed using the package `\emph{corrplot} ' in R (Wei \& Simko
2017).

\emph{Conditioning treatment effects, via soil, on responding plant
communities}

Soil legacies that were created by \emph{conditioning} treatments
influenced \emph{responding} plant communities. The forb:grass ratio in
sowing treatments in the \emph{conditioning} phase resulted in grass and
forb covers that differed significantly from each other (Figure 1a, b).
This, in turn influenced the relative abundance of grasses and forbs in
the \emph{responding} plant communities. Specifically, grass abundance
in the \emph{responding} communities was lower in plots with a legacy of
higher grass abundance (Figure 1c), while forb abundance in
the\emph{responding} communities was significantly lower in plots with a
legacy of higher forb abundance (Figure 1d). The pattern did not depend
on plant community identity and was observed in each of the six
experimental plant communities (Supplementary Figures 1,2). Furthermore,
the observed grass and forb cover in the \emph{conditioning} phase and
the \emph{responding} phase at the subplot level, showed the same
significant relationships (Supplementary Figure 3).

Conditioning time (i.e. 1 or 2 years) affected the total cover of
the\emph{responding} plant communities, with higher total cover in the
plots during the responding phase after a two-year conditioning legacy
(mean cover \textasciitilde{}80\% vs. \textasciitilde{}90\%,
Supplementary Table 2).

There were significant main effects of \emph{conditioning} plant
community, forb:grass ratio and conditioning time on the
\emph{responding} plant community structure (Supplementary Table 3). The
effects of forb:grass ratio strongly differed between the six different
conditioning plant communities, indicated by a significant interaction
between the two (Supplementary Figure 4). The forb:grass ratio
significantly affected responding plant community structure in three out
of six conditioning communities. In the affected communities
(Supplementary Figure 5), \emph{responding} species of a specific
functional type were often negatively associated with their own
respective abundance in the \emph{conditioning} phase.

Conspecific and heterospecific soil legacy effects were assessed and
visualized using correlation plots including \emph{conditioning}
and\emph{responding} plant species (Supplementary Figure 6). There were
only a limited number of (predominantly positive) conspecific effects,
and these effects )were not consistent between the six plant
communities. For instance, we observed positive conspecific
relationships for \emph{Rumex acetosella} (in community
4),\emph{Clinopodium vulgare} (in community 5), \emph{Taraxacum
officinale}and \emph{Holcus lanatus} (both in community 6). Only one
negative conspecific relationship was observed, for \emph{Anthoxanthum
odoratum}(in community 5). Furthermore, there were heterospecific
relationships between \emph{conditioning} plant species and other
\emph{responding}plant species in each of the experimental communities
(Supplementary Figure 6). Finally, there were \emph{conditioning} plant
species that had a strong effect on cover of specific functional types
in the\emph{responding} phase. For instance, in community 2, cover in
the\emph{conditioning} plant community of \emph{Achillea
millefolium}positively - and \emph{H. lanatus} negatively - affected
grass cover in the \emph{responding} plant communities. In community 3,
\emph{A. millefolium} and \emph{Briza media} cover in the
\emph{conditioning}plant community negatively affected forb cover. In
community 5,\emph{Festuca ovina} cover in the \emph{conditioning} plant
community, negatively affected \emph{responding} grass cover
(Supplementary Figure 6).

\emph{Conditioning treatment effects on soil communities and abiotic
parameters}

When the cover of forbs was experimentally reduced, the relative
abundance of soil pathogenic fungi decreased concomitantly (Figure 2a,
Supplementary Table 4). Especially grass pathogens, dominated
by\emph{Slopeiomyces cylindrosporus} showed similar patterns as observed
for total relative abundance of pathogens (Figures 2b,c, Supplementary
Table 4). Forb-specific pathogens had a significantly higher relative
abundance in one than in two-year legacies (Figure 2d, Supplementary
Table 4) while the relative abundance of saprotrophs was higher in plots
with two-year than one-year legacies (Figure 2e, Supplementary Table 4).
Arbuscular mycorrhizal fungi were not affected by conditioning
treatments (data not shown, Supplementary Table 4).

After the conditioning phase, the soil bacterial community structure was
significantly affected by conditioning plant community and conditioning
time (Supplementary Table 5, Supplementary Figure 7). The soil fungal
community structure was significantly affected by conditioning plant
community, conditioning time and by forb-grass ratio, but the effect of
forb-grass ratio differed between conditioning plant communities
(Supplementary Table 5, Supplementary Figure 8). The structure of soil
abiotic parameters was affected by conditioning community and
conditioning time (Supplementary Table 5).

\par\null

\emph{Soil-mediated pathways between conditioning and responding plant
communities}

The composition of the \emph{conditioning} plant communities
significantly explained the composition of the \emph{responding} plant
communities (Figure 3). The fact that these two plant communities were
separated in time (visualized by a dashed arrow) indicates that the
effects of \emph{conditioning} plant community on the responding plant
communities must be mediated via the soil. The \emph{conditioning} plant
community significantly explained community composition of soil fungi
and bacteria in the \emph{conditioning} phase, but did not affect the
composition of soil abiotic parameters. Importantly, microbial and soil
abiotic parameters measured at the end of the \emph{conditioning} phase,
significantly explained these parameters measured again three months
after the transition to \emph{responding} phase. The soil fungal
composition (but not bacteria or abiotic parameters) measured in
the\emph{responding} phase correlated with the composition of
the\emph{responding} plant communities.

\textbf{Discussion}

Here, we show in a field experiment that compositionally different plant
communities create legacies in the soil that, in turn, alter the
composition of subsequent plant communities that establish in these
soils. Plant communities with different ratios of grasses and forbs
created unique soil microbiomes, and these effects were most notable in
the soil fungal community. These soil legacies, in turn, affected the
responding plant communities. Specifically, both grass and forb
abundances in the responding phase were negatively affected by their
respective abundance in the previous plant community and this effect was
mediated by soil processes. We show that manipulating the composition of
the vegetation in grasslands alters the microbiome in the soil, and that
this alters the succeeding vegetation.

Plant communities dominated by plants of a certain functional type
create legacies that negatively impact plants from the same functional
type. This result is very robust, as the same pattern was observed in
all six plant communities that were used to condition the soil in this
field experiment. This finding is also in strong agreement with previous
work from pot studies (Kulmatiski \emph{et al.} 2008; Petermann \emph{et
al.} 2008; De Kroon \emph{et al.} 2012; Wubs \& Bezemer, 2018). The
functional type a plant belongs to also alters community structure in
soil fungi (Kos \emph{et al} . 2015; Heinen \emph{et al.} 2018).

In the soil of plant communities that had more grasses, we found an
accumulation of fungal pathogens (dominated by grass-associated fungal
pathogens). Interestingly, the abundance of forb-associated pathogens
was very low and there was no relationship with the abundance of forbs
in the vegetation. Grasses are a phylogenetically close group of plants.
Forbs, on the other hand, are a broad phylogenetic group. The
specificity of grass pathogens may be one reason why grass-associated
pathogens may drive grass abundance in the field, but no such phenomenon
was observed for forbs. Specialized forb pathogens are unlikely to
accept hosts from all forb families, given the phylogenetic breadth of
the group and as a result their individual abundances may not drive
abundance of forbs as a group. Specialized pathogens may often attack a
range of hosts if they are closely related but some pathogens have
rather broad host range (Barrett \& Heil 2012) . Importantly, our
results indicate that negative soil legacy effects on grasses observed
in mid-successional grasslands, can be, at least partially, explained by
accumulation of pathogens (Kulmatiski \emph{et al.} 2008; Van der
Putten\emph{et al.} 2013).

Our results further reveal that both bacteria and fungi in the soil both
respond to the conditioning plant communities that grow in the soil.
However, only the conditioning effects on fungal communities are
longer-lasting, and have knock-on effects on the subsequent responding
plant communities. We may conclude that soil bacterial communities,
although responsive to conditioning treatments, play a lesser role in
the community dynamics in responding plant communities. This finding is
in strong agreement with recent findings that soil fungal communities
are strongly shaped over time by plants, whereas bacteria are shaped
less by plants, but more by varying environmental conditions over time
(Hannula \emph{et al.} accepted manuscript). Soil legacy effects in
natural communities are likely not driven by one taxon specifically, but
rather by the composition of the soil fungal community as a whole
(Semchenko \emph{et al.} 2018; Bennett \& Klironomos 2018;
Mommer\emph{et al.} 2018, but see Harrison \& Bardgett, 2010).
Importantly, we show that conditioning effects of plant communities on
soil biota, outweigh the effects on soil abiotic parameters, and are
drivers of soil legacy effects on plant growth in the field.

One potential confounding factor in the results is that plant roots and
seeds originating from the conditioning plant community could have been
left behind in the soil after the conditioning community was removed and
that these may influence responding communities via this pathway. There
were some positive conspecific relationships between
\emph{conditioning}and \emph{responding} plant species, but the observed
effects were community-specific. For instance, a positive conspecific
relationship was observed for \emph{Rumex acetosella} . This species
flowers very quickly and produces many seeds. It is possible that seeds
produced during the conditioning phase, and that entered the seedbank,
caused an increased local abundance of this species in the responding
communities. Furthermore, we observed a positive conspecific
relationship for\emph{Clinopodium vulgare} and \emph{Holcus lanatus} .
Both species regrow from root systems in pot experiments (Heinen, pers.
obs.) and hence regrowth may drive the observed relationships. However,
it is unlikely that these effects have had a strong effect on
the\emph{responding} plant community as a whole, as the strongest
relationships - observed between functional types in
the\emph{conditioning} versus the \emph{responding} plant communities -
were negative and thus cannot be explained by regrowth or seed
production. We therefore conclude that soil legacy effects must be the
dominant driver of these effects.

In this study we find that at the plant species level, we find very few
indicators for conspecific plant-soil feedbacks and the effects are
strongly conditioning plant community specific. This is an interesting
finding as the field site that was used in this study has been the soil
donor site for countless plant-soil feedback studies over the past
decades. In the majority of these studies, plant species grown in soils
from this site have negative conspecific feedback effects (e.g. REF).
This indicates that individual plant-soil feedbacks as observed in
individual pot studies, may be counter-balanced by other plant species
that simultaneously grow in (and thus condition) the soil in natural and
diverse plant communities. We may speculate that conspecific plant-soil
feedbacks could play a larger role in less diverse or more disturbed
systems such as dune vegetations or in very early secondary succession.
However, future work is needed to investigate the role of plant
diversity or ecosystem complexity in plant-soil feedbacks in the field.

In conclusion, we show here that the ratios between plants of different
functional types within a plant communities mediate plant-induced
microbial soil legacies and that these legacies determine the
composition of plant communities in the field. Importantly, this means
that by managing current plant communities in the field, we can
influence the composition of future plant communities and the ecological
functions they provide. This opens new avenues for optimizing nature
management practices, which is vitally important in the face of global
change, for instance in making nature more robust to climate change or
invasions.

\textbf{End notes}

\emph{Supplementary Information}

Supplementary Information is linked to the online version of the paper.

\emph{Acknowledgements}

The authors thank Wim van der Putten and Paul Kardol for comments on an
earlier version of the manuscript. We thank Martijn van der Sluijs,
Simon Vandenbrande, Roeland Cortois, Tess van de Voorde, Eke Hengeveld,
Thibault Costaz, Minggang Wang and various undergraduate students for
assistance in the field. We thank Erik Slootweg, Koen Verhoogt and
Gabrielle de Jager for assistance with vegetation recording. Sequencing
was performed in collaboration with McGill University and Genome Quebec
Innovation Center. The work was supported by the Dutch organization for
Scientific Research (NWO Vici grant no 865.14.006). This is a NIOO-KNAW
publication.

\emph{Author Contributions}

TMB designed the field experiment. RH, FZ, MH, TMB, executed the first
phase of the field experiment. All authors contributed to maintenance of
the second phase of the experiment. RH, SEH, JDL, FZ, KS, MH, RJ, TMB
collected field data. SEH and RH analyzed data. RH led the writing of
the manuscript, in close collaboration with SEH, JDL and TMB. All
co-authors contributed critically to the manuscript and approved the
final version for publication.

\emph{Author Information}

Reprints and permissions information is available in the online version
of the paper. Authors declare no competing interests. Correspondence and
requests for materials should be addressed
to~\emph{\href{mailto:R.Heinen@nioo.knaw.nl}{\nolinkurl{R.Heinen@nioo.knaw.nl}}}
or~\emph{\href{mailto:M.Bezemer@nioo.knaw.nl}{\nolinkurl{M.Bezemer@nioo.knaw.nl}}}

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\textbf{Figures}

Figure 1. \textbf{Conditioning plant communities with different
forb:grass ratios create different soil legacies.} Experimental
manipulation of the forb:grass ratio in the conditioning phase resulted
in different levels of \textbf{a)} grass and \textbf{b)} forb cover in
the conditioning plant communities. This, in turn, created soil legacy
effects that negatively affected the cover by \textbf{c)} grasses
and\textbf{d)} forbs, respectively. Dots represent actual data points,
and a linear trendline was fitted (with a 95\% confidence interval).
Significant effects are presented in the figures. Asterisks represent
significance levels (* p\textless{}0.05; ** p\textless{}0.01; ***
p\textless{}0.001). Summary statistics are presented in Supplementary
Table 2.

Figure 2. \textbf{Conditioning plant communities accumulate functionally
different fungal communities.} Different fungal guilds were affected by
different experimental treatments. The forb:grass ratio in the
conditioning plant communities altered the accumulation of
\textbf{a)}fungal pathogens, which were predominantly \textbf{b)}
grass-associated fungal pathogens, and which were rich in \textbf{c)}
the grass-associated fungal pathogen \emph{Slopeiomyces cylindrosporus}
. Conditioning time affected the levels of \textbf{d)} forb-associated
fungal pathogens, and \textbf{e)} saprotrophic fungi. Dots represent
actual data points, and a linear trendline was fitted (with a 95\%
confidence interval). Significant effects are presented in the figures.
Asterisks represent significance levels (* p\textless{}0.05; **
p\textless{}0.01; *** p\textless{}0.001). Summary statistics are
presented in Supplementary Table 4.

Figure 3. \textbf{Soil legacy effects of conditioning plant communities
on responding plant communities are mediated by soil fungi.} A path
analysis shows the relationships between conditioning plant communities
and responding plant communities in the plant-soil feedback field
experiment, via soil fungal and bacterial communities and soil abiotic
parameters. All subplots are included in the analysis. Arrows represent
significant correlations (Mantel r and p-values) between Bray-Curtis
dissimilarity matrices of plant and soil microbial communities and
Euclidean distances for soil abiotic parameters. Direct relationships
are represented by solid black arrows and the indirect (soil-mediated)
relationship between conditioning and responding plant communities is
represented by a dashed black arrow.

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