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\begin{document}

\title{Saprotrophs and nitrifiers, not pathogens, drive plant-soil feedbacks in
a semi-arid grassland}



\author[1]{Pierre-Luc Chagnon}%
\author[2]{Charlotte Brown}%
\author[2]{James Cahill}%
\affil[1]{Université de Montréal}%
\affil[2]{University of Alberta}%


\vspace{-1em}



  
  \date{May 19, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
By influencing soil biota and nutrient cycling, plants can alter their
fitness and that of their neighbours through plant-soil feedbacks.
However, we need more detailed understanding of the biological causes of
such feedbacks. This includes identifying the soil microbial guilds
causing them. As most plant-soil feedbacks have been reported to be
negative, the dominant paradigm is focused on the accumulation of soil
pathogens. This neglects the full range of soil microorganisms likely to
influence plant performance. Here, we studied plant growth responses to
field-collected soil inocula in a large-scale greenhouse study coupled
with next-generation sequencing. Although we found a high prevalence of
negative plant-soil feedbacks, the microorganisms driving the responses
were largely saprotrophs and N-cycling prokaryotes, not pathogens. These
results show that negative plant-soil feedbacks need not be caused by
trophic interactions, highlighting the need to bring back nutrient
cycling as a central component of plant-soil feedbacks.%
\end{abstract}%



\sloppy


\emph{Introduction}

Plants modify the soil they grow in, be it through taking up nutrients
(Fireman \& Hayward, 1952; Zinke, 1962), returning litter of varying
chemical quality thereby impacting nutrient cycling (e.g., Wedin \&
Tilman, 1990; Hobbie, 1992), or accumulating specific soil organisms in
their rhizosphere (Bever \emph{et al.} , 2010). This, in turn,
influences their success through plant-soil feedbacks (PSF) (van der
Putten\emph{et al.} , 2013). In the 1980's and the 1990's, most
attention has been paid to PSF driven by abiotic nutrient dynamics
(e.g., Chapin, 1980, 1991; Tilman, 1980; Chapin \emph{et al.} , 1986;
Horner \emph{et al.} , 1988; Kuiters, 1990). Correlations have been
observed between nutrient use efficiency and litter chemical quality,
such that plants from nutrient-poor environments tend to favor their own
success (i.e., experience positive PSF) by producing a recalcitrant
litter (Berendse, 1994; Bryant \emph{et al.} , 1991), which slows down
nutrient cycling and increase their competitive ability (Wedin \&
Tilman, 1990). However, seminal work showing implications for soil-borne
disease in sand dune succession (van der Putten \emph{et al.} , 1993)
has prompted a shift of focus towards a role for pathogens in driving
plant community structure (e.g., Dobson \& Crawley, 1994; Thrall
\emph{et al.} , 1997). This still transpires in more recent views of PSF
not being driven by litter chemistry, but by the accumulation of
specific soil biota, emphasizing pathogens (e.g., Klironomos, 2002;
Bauer \emph{et al.} , 2015; Bever\emph{et al.} , 2015; Albornoz \emph{et
al.} , 2017).

Plants accumulate a variety of organisms in their rhizosphere (e.g.,
bacteria, fungi, nematodes) ranging from harmful to beneficial. Because
the bulk of measured PSF tend to be negative (Kulmatiski \emph{et al.} ,
2008), many have proposed PSF as a mechanism promoting plant coexistence
through enemy accumulation by dominants (Olff \emph{et al.} , 2000;
Mangan \emph{et al.} , 2010; Reinhart, 2012). As a result, a
disproportionate amount of research has focused around pathogen
accumulation in the rhizosphere (e.g., Mills \& Bever, 1998;
Mangan\emph{et al.} , 2010; Bagchi \emph{et al.} , 2010,2014; Sarmiento
\emph{et al.} , 2017). Pathogens have been suggested to explain the
underyielding of low-diversity plots in plant diversity experiments
(Schnitzer\emph{et al.} , 2011; de Kroon \emph{et al.} , 2012; van
Ruijven \emph{et al.} , 2020), and the negative density dependence of
plant growth in a variety of environments (e.g., old fields: Klironomos,
2002; shrublands: Lalibert\selectlanguage{ngerman}é \emph{et al.} , 2015; temperate forests:
Packer \& Clay, 2000; tropical forests: Mangan \emph{et al.} , 2010).
However, negative feedbacks need not be caused by enemies (Bever
\emph{et al.} , 1997; Bever, 1999). Indeed, the accumulation of less
efficient microbial mutualists (Bever, 2002) or of microbial competitors
(Hodge \emph{et al.} , 2000) can also make a plant grow better in
heterospecific vs conspecific soil, leading to negative PSF even in the
absence of pathogens. Likewise, positive PSF have often been ascribed to
mutualistic symbionts (e.g., Klironomos, 2002; Revillini \emph{et al.} ,
2016), ignoring the variety of soil biota guilds that can generate
positive PSFs. For example, although plants can grow better in a soil
with specific symbiotic partners (e.g., mycorrhizal fungi), they can
also positively respond to the presence of specific microorganisms that
(1) better cycle/solubilize nutrients (Marschner \emph{et al.} , 2011),
(2) are less efficient competitors for mineral nutrients (Hodge \emph{et
al.} , 2000), or (3) are less virulent pathogens (Agrios, 2005;
Dominguez-Begines \emph{et al.} , 2020). Yet, very few studies have
specifically addressed the role for non-symbiotic and non-mutualistic
organisms in driving positive PSF (Yang \emph{et al.} , 2013).

While there is a need to identify the full spectrum of microbial
taxa/guilds that drive PSF, this could be hampered by the tremendous
diversity of the soil microbiome. The rhizosphere of a single plant
individual can easily be colonized by thousands of microbial taxa
(Wang\emph{et al.} , 2018), which could make it hard to identify
individual microbial taxa driving PSF. One way forward could be to
directly correlate PSF to soil microbial \selectlanguage{greek}α-\selectlanguage{english}diversity or community
structure, regardless of individual microbial taxa and their guild
affiliation. However, while many have argued that plant performance
could be tied to the diversity of their microbiome (van der Heijden
\emph{et al.} , 1998; Laforest-Lapointe \emph{et al.} , 2017), a higher
diversity also opens the way to more complex indirect interactions among
microorganisms (Hector \emph{et al.} , 2010), with PSF potentially being
simply more complex and unpredictable, not more positive (e.g., Tardif
\& Shipley, 2015). It thus remains unsure whether community-level
properties of the microbiome can be of any help to simplify PSF
predictions.

Another key knowledge gap in PSF theory is to determine how PSF-driving
microorganisms respond to environmental filters. A pivotal assumption of
PSF as key drivers of plant and microbial community assembly is that the
microorganisms influencing plant performance must be responsive to plant
community structure (Bever \emph{et al.} , 1997). Yet, many studies have
identified soil abiotic properties as the core drivers of soil
microbiome (e.g., Dumbrell \emph{et al.} , 2010). If this is true for
microorganisms influencing plant growth, this means that pot-based PSF,
where soil chemistry is kept homogeneous while plant identity is
manipulated, may have limited ecological relevance. Also, if the wide
array of microorganisms causing PSF all respond differently to
environmental filters, predicting PSF along ecological gradients (e.g.,
Smith-Ramesh \& Reynolds, 2017; Bennett \& Klironomos, 2018) will be
challenging. For example, fertile soils may promote higher plant density
and dominance by a few competitive species (Fraser \emph{et al.} ,
2015), conditions that would promote pathogen accumulation and negative
PSF (Thrall \emph{et al.} , 2007). On the other hand, high soil
fertility could also relieve plants from microbial competitors for
nutrients (e.g., Liu \emph{et al.} , 2016), which may rather lead to
positive PSF. The high soil microbial diversity appears again as an
obstacle towards a mechanistic understanding of PSF.

In this study, we aimed at providing a more comprehensive explanation
for PSF observed in a grassland ecosystem, focusing on the growth
responses of two abundant C3 grasses: \emph{Koeleria macrantha} (Ledeb.)
Schult and \emph{Bromus inermis} Leyss. Building on a previous
greenhouse experiment where we measured \emph{Bromus} and\emph{Koeleria}
growth responses to 490 soil inocula collected in the field (Chagnon
\emph{et al.} , 2018), here we characterized the soil microbiome from a
large subset of these inocula. This allowed us to identify the microbial
taxa that best correlate with altered plant performance (i.e.,
non-neutral PSF), and answer the following questions:

\begin{enumerate}
\tightlist
\item
  Are negative PSF driven largely by microbial pathogens or due to other
  microbial guilds?
\item
  Can plant growth response to soil inocula be predicted based on
  microbial \selectlanguage{greek}α-\selectlanguage{english}diversity or community structure?
\item
  Are microorganisms causing PSF more correlated with gradients in plant
  community structure than with proximate soil properties?
\end{enumerate}

\emph{Material and methods}

\emph{Study site description, sampling design and plant growth response
measurement}

During summer 2015 we conducted a field study at the University of
Alberta's Mattheis Ranch, located in southern Alberta and at the
northern edge of the Great Plains (50\selectlanguage{ngerman}°53\selectlanguage{english}'29.9\selectlanguage{english}"N 111deg55\selectlanguage{english}'07.4\selectlanguage{english}"W). The
vegetation was a semi\selectlanguage{english}-arid mixed grassland dominated by \emph{Koeleria
macrantha} (Ledeb.) Schult, \emph{Hesperostipa comata} (Trin. \& Rupr.)
Barkworth, \emph{Pascopyron smithii} (Rydb.) A Love and \emph{Bromus
inermis} Leyss. (Shorthouse, 2010).

To evaluate plant-soil feedbacks in this system, we sampled 49
10-cm-diameter soil cores in 10 different plots, for a total of 490 soil
samples. Details on the soil sampling methods can be found in
Chagnon\emph{et al.} (2018). These soils were used as living inocula in
a greenhouse experiment, where \emph{Bromus} or \emph{Koeleria} were
grown in a 9:1 mixture of sterile soil and living inoculum. The sterile
soil consisted of a 1:1 mixture of a sandy soil collected at our study
site, and fine sand. Using such a design, any growth response by
\emph{Bromus}or \emph{Koeleria} was expected to be caused largely by
soil biota (Brinkman \emph{et al.} , 2010). After 15 weeks of growth,
plants were harvested and total (shoot + root) dry mass was used as a
measure of plant growth response to soil inocula (i.e., PSF). The
remaining methods described below are new to this paper, and were not
part of the original publication (Chagnon \emph{et al.} , 2018).

To identify soil microorganisms potentially generating plant growth
responses in our experiment, we combined the data generated from the
previously described greenhouse experiment with targeted soil DNA
sequencing. Of our 490 initial soil living inocula, we selected 230
inocula that would also be analyzed for microbial community structure,
representing 23 samples in each of our 10 plots. The 23 inocula were
chosen in a way that maximized their spatial dispersion in all four
quadrants of the plot (Fig. S1). The field soil subsamples had been kept
at -20C until DNA extraction, which was within 2 months after soil
collection.

\emph{Molecular analyses}

Soil DNA was extracted using MoBio PowerSoil kits (r) following
manufacturer's instructions. Prokaryotic and fungal DNA were amplified
targeting the 16S and ITS rDNA regions, respectively. The primers used
for the amplification were the 515f-806R couple (targeting the
hypervariable V4 region) for 16S and the ITS1f-ITS2 couple for ITS
(targeting the ITS1 subregion) (White \emph{et al.} , 1990; Gardes \&
Bruns, 1993; Gilbert \emph{et al.} , 2014). PCR amplifications were
conducted with 0.2 \selectlanguage{greek}μ\selectlanguage{english}M primers, 0.2mM dNTPs, 0.5 units of hi-fidelity Taq
polymerase (New England Biolabs), and 1 \selectlanguage{greek}μ\selectlanguage{english}L of DNA extract in 25 \selectlanguage{greek}μ\selectlanguage{english}L
reactions. Thermocycling was done as follows: initial denaturation at
95\selectlanguage{ngerman}°C for 1 minute, 30 cycles of denaturation (95°C 30s) - annealing
(16S: 50°C / ITS: 52°C for 30s) -- elongation (72°C 1 min), and a final,
5 min extension at 72°C. Blanks were included to ensure absence of DNA
amplification when no soil was added in DNA extraction kits. Positive
amplicons were purified and sequenced using the Illumina MiSeq
technology (Genome Québec Innovation Center).

Sequences were demultiplexed and paired-ends were merged in QIIME
(Caporaso \emph{et al.} , 2010), after which we proceeded to a quality
filtering (quality score \textgreater{} 25) and a removal of sequences
with homopolymers longer than 10 bases. Chimera removal and operational
taxonomical unit (OTU) clustering was performed in the open source
software VSEARCH (Rognes \emph{et al.} , 2016), at a 97\% similarity
threshold. Taxonomy was assigned in QIIME using the curated databases
Silva and UNITE (Pruesse \emph{et al.} , 2007; Abarenkov \emph{et al.} ,
2010). We removed OTUs occurring in only one sample and occurrences with
5 reads or less, considering these as potential artefacts. We rarefied
our OTU tables (16S and ITS) to 8000 reads per sample for fungi and 1100
reads for prokaryotes to correct for imbalanced sequencing depth per
sample, due to the stochastic nature of sequencing. Sequence data were
deposited in SRA under project \# 7716646.

\emph{Statistical analyses}

\emph{What microbial guilds cause PSF?}

To identify microbial OTUs specifically associated with positive or
negative PSF for \emph{Bromus} and/or \emph{Koeleria} , we conducted an
indicator species analysis (Dufrêne \& Legendre, 1997). To this end, we
first created bins of soil inocula that were associated with altered
biomass relative to uninoculated plants for either of our plant species,
or both. We had 9 bins, with a factorial combination of plants to be
influenced (2 species: \emph{Bromus} or \emph{Koeleria} ) and 3 types of
impact on plant growth (positive, negative or neutral)
(3\textsuperscript{2} = 9 combinations). To get a plant growth threshold
from which plant performance would be considered increased or decreased,
we computed plant growth responses to all inocula as \emph{t} -scores:

\begin{equation}
t=\frac{G_{\text{in}}-\ G_{\text{st}}}{\left(\frac{\text{SD}_{\text{in}}}{\sqrt{n}}\right)}\nonumber \\
 \end{equation}

, where \emph{G\textsubscript{in}} represents mean growth with the
inoculum, \emph{G\textsubscript{st}} is the mean growth in sterile soil
only, and \emph{SD\textsubscript{in}} and \emph{n} are, respectively,
the standard deviation in growth of inoculated pots, and the number of
inoculated pots. We thus considered the soil inoculum to have a positive
impact on plant growth (i.e. a positive plant-soil feedback) if growth
was higher in inoculated soil relative to uninoculated soil, and vice
versa for negative PSF. We identified a threshold \emph{t} -score
corresponding to an 80\% confidence interval with our corresponding
number of degrees of freedom (\emph{d.f.} = 13). In other words, we
considered the PSF significantly different from 0 if there was less than
a 20\% probability that such a growth difference inoculated and control
plants had arisen by chance. As this 20\% threshold was selected
arbitrarily, we conducted a sensitivity analysis and show that the
number of indicator OTUs retrieved is fairly robust to the threshold
chosen (Fig. S2). The indicator species analyses were conducted using
the R package \emph{indicspecies} (De Caceres \& Legendre, 2009).
Despite the high number of individual tests in these analyses, we kept
the \selectlanguage{greek}\emph{α} \selectlanguage{english}threshold at 0.05. \emph{P} -value correction methods would
have yielded way too stringent thresholds to consider associations as
significant, given the very high number of rare microbial OTUs. We thus
validated the robustness of our indicator species analysis to type I
errors by running 1000 indicator species analyses on randomized datasets
(microbial metacommunities shuffled by keeping number of sequence reads
constant by soil sample and by microbial OTU), and found that on
average, by chance, we retrieve \textasciitilde{} 40 and 80 indicator
prokaryotes and fungi respectively (Fig. S3). However, we also compared
the identity of the indicator OTUs retrieved by multiple indicator
species analyses on our real dataset vs. on our randomized datasets, and
found that indicator OTUs were very constant from run to run in the
former, but much more variable in the latter (Fig. S4). This shows that
the indicator OTUs reported here are not likely to be random subsets of
our metacommunity that have arisen as type I errors.

In order to determine the contribution of different microbial guilds to
PSF, we assigned indicator microbial OTUs to guilds based on the
literature. For prokaryotes, we relied on published papers, manually
querying scientific search engines for papers mentioning these taxa.
Prokaryotic OTUs that could not be assigned to a tentative guild (21 out
of 66 indicator OTUs) were left out of downstream analyses. To assign
fungal OTUs to guilds, we used the open-source database FunGuild
(Nguyen\emph{et al.} , 2016). After guild assignment, we conducted
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}-tests to determine if certain microbial guilds were
disproportionately associated with certain PSF types (e.g., pathogens
overrepresented as indicator OTUs of negative PSF). We note here that
with indicator species analysis, we were unlikely to assign
generalist/ubiquitous taxa to any of our specific PSF ``bins'', because
the analysis is by essence based on variations in relative abundances.
Thus, if some pathogens, for example, are very widespread in the
microbial metacommunity, they are unlikely to be retrieved as indicator
of negative PSF. To address this issue, we systematically assigned all
fungal OTUs to a guild using FunGuild, and we assigned the most
widespread prokaryotes OTUs to guilds manually. This way, we could
validate that the absence of a given guild to particular PSF bins was
not an artefact, due to the ubiquity of taxa from that guild.

\selectlanguage{greek}\emph{Can we predict PSF based on microbial α or β-diversity?}\selectlanguage{english}

We used linear mixed models (LMMs) to regress measured PSF against
prokaryotic and fungal (1) richness and (2) Shannon's diversity
(exponential form; Hill, 1973), as estimators of \selectlanguage{greek}\emph{α} -\selectlanguage{english}diversity. We
also included prokaryotic and fungal community structure as predictors
using site loadings from a non-metric multidimensional scaling (using
Hellinger-transformed microbial community data) constrained over two
axes. We constructed separate models to predict \emph{Koeleria}
vs\emph{Bromus} growth responses, including the identity of the block
from which the inoculum came from as a random factor in the models, and
the state variable indicating whether the sample came, within the block,
from the \emph{Bromus} -invaded plot vs the native vegetation plot. We
used the R package nlme (Pinheiro \emph{et al.} , 2020) for LMMs
construction.

\emph{Do microorganisms causing PSF respond more strongly to plants or
soil?}

We used redundancy analyses to regress microbial community structure
against environmental data matrices. Environmental matrices combined (1)
soil properties (Mehlich-III extractible P,
NH\textsubscript{4}\textsuperscript{+},
NO\textsubscript{3}\textsuperscript{-}, pH and gravimetric moisture
measured in fresh soils, as described in Chagnon \emph{et al.} , 2018)
and (2) plant community structure. To include the latter as a predictor
of microbial community structure, we used the first 5 axes of a
principal component analysis (using Hellinger-transformed plant
abundance data), which collectively explained 75\% of the variance in
plant community structure (Table S1). This yielded RDAs with 10
explanatory variables (5 soil and 5 plant predictors, respectively). We
ran RDAs separately for prokaryotes and fungi. For each, we conducted
one RDA with the indicator OTUs, and one with the rest of the microbiome
(i.e., the non-indicator OTUs). Then, to verify whether the indicator
OTUs responded idiosyncratically to the explanatory variables
(hypothesizing that they would be tied more strongly to plant community
structure), we evaluated the co-inertia between the axes loadings of the
explanatory variables using Procrustes rotations using the R
package\emph{vegan} (Oksanen \emph{et al.} , 2019). A poor correlation
between these axes loadings would indicate those explanatory variables
that best describe the indicator OTUs are not the same as for the rest
of the microbiome.

\emph{Results}

Each soil inoculum contained \textasciitilde{} 125 prokaryotic and
\textasciitilde{}150 fungal OTUs (Fig. S5), for a regional total of 887
and 1534 OTUs, respectively. These inocula tended to generate neutral to
negative growth responses for \emph{Koeleria} , but positive responses
for \emph{Bromus} , with a lack of correlation between the PSF
experienced by the two species (Fig. 1, Spearman's \selectlanguage{greek}\emph{ρ} \selectlanguage{english}=
0.042,\emph{P} = 0.53). \emph{Bromus} and \emph{Koeleria} were also
influenced by different OTUs as evidenced by our indicator species
analysis (Fig. 2). However, no microbial OTU was found to influence
\emph{Koeleria}only. This could indicate a lower sensitivity of
\emph{Koeleria} to the soil microbiome, which is in line with its plant
growth responses (\emph{t} -score values) spanning less units than for
\emph{Bromus}(\emph{Koeleria} {[}-3,1{]} except for an outlier at +3 ;
\emph{Bromus}{[}-4,4{]}) (Fig. 1). We note a complete absence of OTUs
influencing positively one species but negatively the other. Such
combinations of ecological outcomes are the ones expected to drive the
strongest PSF, yet are absent from a metacommunity of over 2400
microbial OTUs.

\emph{What microbial guilds are causing PSF?}

We found 66 indicator prokaryotes out of 887 OTUs and 110 indicator
fungi out of 1534 OTUs. Guild affiliation was possible for 45
prokaryotes and 33 fungi among our indicator OTUs. We found no
significant overrepresentation of fungal pathogens as indicator OTUs of
negative plant growth response (Fig. 2, \selectlanguage{greek}\emph{χ\textsuperscript{2}}\selectlanguage{english} =
9.79, \emph{d.f.} = 10, \emph{P} = 0.46, Fig. 2). Rather, only one of
the 33 indicator fungi was a pathogen (negatively affecting\emph{Bromus}
). All the remaining 32 indicator fungi were saprotrophs, and either
positively (9) or negatively (3) affected both plants, or negatively
affected \emph{Bromus} only (20). Twelve of the 33 indicator fungi
(36\%) similarly affected \emph{Bromus} and \emph{Koeleria} , while the
remainder (64\%) had species-specific effects (a necessary condition for
microorganisms to drive species-specific PSF). Regarding the
prokaryotes, of the 66 indicator OTUs, the majority (37) were associated
with improved performance for both \emph{Bromus} and \emph{Koeleria} .
Conversely, 6 negatively affected \emph{Bromus} growth and 23 were
detrimental to both species. Thus, as opposed to fungi, few indicator
prokaryotes (9\%, 6 out of 66) had species-specific effects on plants.
Indicator prokaryotes were mostly related to N cycling (bacterial and
archaeal nitrifiers) or were primary producers (\emph{Chloroflexi spp.}
). Interestingly, while the vast majority of prokaryotic saprotrophs
were associated with positive plant growth responses (18 out of 26
saprotrophic OTUs), fungal saprotrophs tended to be associated with a
negative rather than positive plant growth response (23 vs. 9 OTUs,
respectively). This contrast between prokaryotes and fungi was
statistically significant (\selectlanguage{greek}\emph{χ\textsuperscript{2}}\selectlanguage{english} =
8.16,\emph{d.f.} = 1, \emph{P} = 0.004).

There was a surprising scarcity of symbiotic indicator OTUs. Out of the
18 arbuscular mycorrhizal fungi and the 16 fungal endophytes (mostly
dark septate endophytes) found in our dataset, none were identified as
indicator OTU. Similarly, among prokaryotes, we did not find any of the
typical plant-growth promoting lineages (e.g., \emph{Pseudomonas spp}
.,\emph{Burkholderia spp} ., \emph{Bacillus spp} .) to be associated
with consistently improved plant growth in our greenhouse assay, even
though they were present in the total microbiome.

\selectlanguage{greek}\emph{Can we predict PSF based on microbial α-diversity and community
structure?}\selectlanguage{english}

PSFs were poorly predicted by microbial \selectlanguage{greek}α-\selectlanguage{english}diversity, considering both
Shannon's diversity or OTU richness (Table 1). \emph{Koeleria} was not
responsive either to microbial community structure (i.e., ordination
axes scores), while \emph{Bromus} growth was significantly influenced by
fungal community structure (\emph{P} = 0.03, Table 1). \emph{Bromus}grew
better with inocula from \emph{Bromus} -invaded plots (i.e., positive
PSF), while the reverse was true for \emph{Koeleria} (Table 1).
Collectively, these contrasts between our two plant species provide
further evidence for the species-specific nature of plant response to
microbial community structure.

\emph{Do PSF-causing microorganisms respond more to vegetation than to
soil?}

Indicator OTUs causing PSF were not more strongly associated with plant
community structure than with soil properties, as compared to the rest
of the soil microbiome (Fig. 3). In fact, procrustean analyses revealed
a very strong correlation between the predictors of microbial community
structure when comparing indicator vs non-indicator prokaryotes
(\emph{r} = 0.86, \emph{P} = 0.002). A similar result was found for
fungi (\emph{r} = 0.88, \emph{P} \textless{} 0.001). For both
prokaryotic and fungal communities, the strongest predictors were soil
pH, moisture and N levels, as well as two plant community structure
ordination axes associated mainly with the abundances of
\emph{Bromusinermis} , \emph{Nassella viridula} and \emph{Carex
stenophylla}(plant ordination axes 2 and 3, jointly explaining
\textasciitilde{} 34\% of plant community structure, Fig. 3, Table S1).
Although we had found that high-pH, high-moisture and low-fertility
soils were conducive to negative PSF in these soils (Chagnon \emph{et
al.} , 2018), we did not find strong associations between these soil
properties and pathogenic indicator OTUs in our canonical ordinations
(Fig. S6). Likewise, we did not find that the proportion of fungal
sequence reads belonging to pathogenic OTUs was significantly influenced
by soil properties (assessed through generalized additive mixed models),
except for a very weak unimodal trend with soil moisture (Fig. S7).
However, this trend explained less than 5\% of the variance.
Collectively, these results further confirm that pathogens were not
strongly associated with the decrease in plant growth observed with
inocula collected from low-nutrients, high-pH and high-moisture soils
(Chagnon \emph{et al.} , 2018).

\emph{Discussion}

Overall, our results support the idea that soil microorganisms are
drivers of PSF: they influence plant growth in a species-specific way
(Fig. 1, 2) and they respond, to some extent, to plant community
structure (Fig. 3). However, we show that contrary to expectations, the
main microbial drivers of PSF were not pathogens. We also show that
contrary to other studies arguing that microbial \selectlanguage{greek}α-\selectlanguage{english}diversity is a major
driver of various ecosystem functions and plant performance (e.g.,
Delgado-Baquerizo \emph{et al.} , 2016; Wagg \emph{et al.} , 2014), here
plant growth response was much more tied to the presence of specific
microorganisms (Fig. 2), rather than to community-level properties such
as richness or diversity (Table 1).

\emph{The role for non-pathogenic microorganisms in PSF}

Many microbial OTUs were indicators of negative PSF in our system (Fig.
2), which is consistent with many other studies showing accumulation of
soilborne detrimental organisms by plants (e.g., Mangan \emph{et al.} ,
2010; Bagchi \emph{et al.} , 2014; Lalibert\selectlanguage{ngerman}é \emph{et al.} , 2015).
However, our results also suggest that non-pathogenic soil
microorganisms are an overlooked group of soil biota that may drive
negative PSF and plant coexistence. Soil microorganisms are known to be
efficient competitors for mineral nutrients, especially N (Liu \emph{et
al.} , 2016). They have high nutrient uptake capacity and high demand
for it (Kuzyakov \& Xu, 2013). Denitrifying prokaryotes efficiently
compete against roots for NO\textsubscript{3}\textsuperscript{-} when
roots deplete soil O\textsubscript{2} through respiration (Philippot
\emph{et al.} , 2002, 2009). Nitrifiers, on the other hand, compete with
roots for NH\textsubscript{4}\textsuperscript{+}, which they require as
an energy source (Prosser, 1990). Knowing that N mineralization rates in
unfertilized grasslands are expected to be insufficient to meet both
microbial and plant demand (Woodmansee \emph{et al.} , 1981),
exploitation competition between plant and soil microorganisms is
expected to be intense in our N-poor natural grassland. As a result, a
plant that accumulates microbial competitors for N in its rhizosphere
may suffer from negative PSF.

Our evidence that non-pathogenic microbes can cause negative PSF should
also lead to reconsideration of the intended target of a variety of root
exudates. For example, antimicrobial compounds secreted by plants (e.g.,
quinones, terpenoids, flavonoids) (Brigham \emph{et al.} , 1999; Bais,
2006) may target soil microorganisms broadly. Not strictly soilborne
pathogens. \emph{Bromus,} for example, has specifically been found to
produce surprisingly high amounts of polyphenol oxidase in its
rhizosphere (Holzapfel \emph{et al.} , 2010), a class of enzymes known
to degrade mycotoxins (e.g., Alberts \emph{et al.} , 2009). Plants are
also known to interfere broadly with soil microbial growth by exuding
protons, phenolics, glucanases or chitinase (Weisskoppf \emph{et al.} ,
2006). Conversely, many non-pathogenic microorganisms have been
demonstrated to inhibit plant nutrient uptake through inhibition of
mycorrhiza formation (Duponnois \& Garbaye, 1991), or degradation of
nutrient-mobilizing compounds secreted by plants (e.g., organic acids)
(Marschner \emph{et al.} , 2011). Taken collectively, all these
mechanisms bring compelling evidence to the idea that plants are
involved in antagonistic interactions with soil microorganisms broadly,
not only the ones that are trying to colonize their tissues (i.e.,
pathogens).

Positive PSF were also common in our system, especially for\emph{Bromus}
(Fig. 1). These were not linked to mutualistic/symbiotic OTUs (Fig. 2),
even though these guilds were well represented in our microbial
metacommunity (which included \emph{Bacillus} spp.,\emph{Pseudomonas}
spp., arbuscular mycorrhizal fungi, dark septate endophytes, etc.). This
could be explained by the fact that the impact of belowground symbionts
on plants tend oscillate along a mutualism-parasitism continuum, which
is contingent upon the abiotic environment (Johnson \emph{et al.} ,
1997; Hirsch, 2004). More generally, this shows how positive PSF must be
interpreted beyond the idea of mutualistic symbiont accumulation. Our
indicator OTUs for positive PSF were non-symbiotic nutrient cyclers
(fungal saprotrophs, prokaryotic nitrifiers, etc.), showing that plants
can benefit from microorganisms contributing to nutrient cycling more
broadly, and not necessarily in a symbiotic manner. We should also keep
in mind that mutualistic guilds (e.g., arbuscular mycorrhizal fungi) can
drive negative PSF (Bever, 1999, 2002; Chagnon \emph{et al.} ,
\emph{unpublished data} ).

\selectlanguage{greek}\emph{Microbial α-diversity poorly predicts plant growth}\selectlanguage{english}

Even if microbial \selectlanguage{greek}α-\selectlanguage{english}diversity has been suggested to drive crucial
ecosystem functions including plant productivity and diversity (van der
Heijden \emph{et al.} , 1998; Delgado-Baquerizo \emph{et al.} , 2016;
Laforest-Lapointe \emph{et al.} , 2017), here we found that microbial
richness and Shannon's diversity are poor predictors of PSF. Plants
responded to specific OTUs (Fig. 2), or groups of microorganisms (Table
1), but not to community diversity per se. This, however, does not
completely rule out microbial \selectlanguage{greek}α-\selectlanguage{english}diversity as a potentially important
component of PSF. If there exists concurrent variation in (1) the impact
of different plants on microbial diversity and (2) the response of these
different plants to such diversity, then non-neutral PSF are to be
expected. We found poor evidence for the latter in our study (Table 1).

\emph{Are PSF-driving microorganisms strongly tied to vegetation
structure?}

A major, and often taken for granted, assumption of PSF theory is that
soil microorganisms will respond to shifts in plant community structure.
This is a necessary prerequisite for plant and soil microbial
communities to oscillate and eventually converge to a stable equilibrium
(Bever, 1999). While many studies have found soil organisms to be
responsive to plant community structure (e.g., Eom \emph{et al.} , 2000;
Bezemer \emph{et al.} , 2010; Chagnon \emph{et al.} , 2020), many others
have found that soil is rather the dominant filter (e.g.,
Dumbrell\emph{et al.} , 2010). Here, we found that plant community
structure and soil properties jointly determined microbial community
structure (Fig. 3). Interestingly, we found that PSF-causing OTUs were
not more strongly tied to the plant community than to the soil. This
suggests that if soil properties are sufficiently heterogeneous at local
scales, it could reduce the importance of PSF as drivers of plant and
microbial community assembly. In this context, the soil microbiome could
be more tied to soil properties and less responsive to plant community
structure, thereby disrupting the feedback dynamics between plants and
soil microorganisms.

\emph{PSF and grassland invasion by Bromus}

\emph{Bromus} tended to experience positive growth responses to soil
inocula, especially those from \emph{Bromus} -invaded plots (Table 1).
This could appear as evidence for enemy release, whereby the
invasive\emph{Bromus} is escaping pathogens that drive negative PSF on
native species (e.g., Klironomos, 2002). However, the lack of pathogenic
OTUs specifically detrimental to \emph{Koeleria} (Fig. 2) fails to
support this hypothesis. Positive PSF developed by \emph{Bromus} in
these grasslands may rather arise from its capacity to establish
beneficial interactions with non-symbiotic, nutrient-cycling
microorganisms. This deserves further consideration in invasion ecology,
with many study providing evidence for exotic species experiencing less
negative PSF (Klironomos \emph{et al.} , 2002; Reinhart \emph{et al.} ,
2003; Aldorfova\emph{et al.} , 2020), but few actually demonstrating the
implications for soil-borne enemies in this pattern (e.g., Beckstead \&
Parker, 2003). Exotic species could be more versatile in developing
positive interactions with foreign soil microbiota. Our results show
(Fig. 2) that non-pathogenic and non-symbiotic microorganisms deserve
more attention that they have received in prior research on PSF in the
context of plant invasion (e.g., Vogelsang \& Bever, 2009; Zhang et al.,
2010).

\emph{Conclusion}

Literature on PSFs is in need for more mechanistic approaches to unravel
the biological drivers of the observed plant growth responses to soil
biota. In this study, we provide compelling evidence contradicting the
common assumption that negative plant growth response to soil biota is
driven by pathogenic microorganisms. We also show that positive plant
growth responses are very rarely driven by well-known nutritional or
biocontrol microorganisms such as plant growth promoting bacteria or
mycorrhizal fungi. Rather, our results point to chemoautotrophs
(N-cyclers) and saprotrophs as the main drivers of plant growth response
in our system. This, as a whole, constitutes a plea to bring back
nutrient cycling as a central component for the development of PSF
(e.g., in't Zandt \emph{et al.} , \emph{in press} ), in a literature
that has become dominated by trophic, top-down interactions (i.e.,
pathogens and nutritional symbionts).\emph{}

\emph{Acknowledgements}

The authors acknowledge the technical support of Gisela Stotz, Alec
Carrigy, Logan Fairgrieve-Park, Megan Ljubotina and Valerie Marshall in
the field. P.-L.C. was funded by the Killam Trust and the Fonds de
Recherche Qu\selectlanguage{ngerman}ébécois Nature et Technologies (FRQNT). J.F.C. received
support from the Natural Sciences and Engineering Research Council
(NSERC) and the Rangeland Research Institute (research grant).

\emph{Figure Legends}

\textbf{Figure 1.} Spearman's rank correlation between the growth
response of \emph{Bromus} and \emph{Koeleria} to our soil inocula.
Responses are presented as \emph{t} -scores (unitless) based on the
comparison of plant growth for inoculated replicates vs. for sterile
controls.

\textbf{Figure 2.} Microbial guilds associated with plant growth
response to our soil inocula. In a) for prokaryotes, and in b) for
fungi, we show the number of OTUs that are beneficial (green ``+''
symbols) or detrimental (red ``-`` symbols) to \emph{Bromus}
and/or\emph{Koeleria} . ``0'' in the left panels indicate no plant
growth response. Only bins (e.g. +,+) that had associated OTUs were
included though all were tested.

\textbf{Figure 3.} Redundancy analyses (RDAs) depicting the association
between OTUs and their predictors (i.e., soil properties and plant
community structure). a) and b) show RDAs for the non-indicator
prokaryotes and the indicator prokaryotes, respectively. Likewise, c)
and d) show RDAs for the non-indicator fungi the indicator fungi,
respectively. Each symbol represents a single OTU, and is colour coded
in b) and d) as a function of its impact on \emph{Bromus}
and/or\emph{Koeleria} . Percentages on the canonical axes indicate the
amount of inertia constrained. Abbreviations:
NH\textsubscript{4}\textsuperscript{+} and
NO\textsubscript{3}\textsuperscript{-} are respectively, soil
KCl-extractible ammonium and nitrates;
PO\textsubscript{4}\textsuperscript{3-} = Mehlich-III-extractible
phosphorus ; \% Moist = gravimetric soil moisture ; Veg 1 to 5 = site
scores from the 5 principal components of the plant community ordination
(PCA on Hellinger-transformed community data).

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