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\begin{document}

\title{Direct and higher-order interactions in plant communities under
increasing weather persistence}



\author[1]{Simon Reynaert}%
\author[2]{Jonas Lembrechts}%
\author[3]{Hans De Boeck}%
\author[4]{Chase Donnelly}%
\author[5]{Lin Zi}%
\author[5]{Lingjuan Li}%
\author[2]{Ivan Nijs}%
\affil[1]{University of Antwerp Faculty of Sciences}%
\affil[2]{University of Antwerp}%
\affil[3]{Plant and Vegetation Ecology research group}%
\affil[4]{University of Antwerp Department of Mathematics and Computer Science}%
\affil[5]{University of Antwerp Department of Biology}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Climate change is increasing the weather persistence in the
mid-latitudes, prolonging both dry and wet spells compared to historic
averages. These newly emerging environmental conditions destabilize
plant communities, but the role of species interactions in this process
is unknown. Here, we tested how direct and higher-order interactions
(HOIs) between species may change in synthesized grassland communities
along an experimental gradient of increasing persistence in
precipitation regimes. Our results indicate that species interactions
(including HOIs) are an important determinant of plant performance under
increasing weather persistence. Out of the 12 most parsimonious models
predicting species productivity, 75 \% contained significant direct
interactions and 92 \% significant HOIs. Inclusion of direct
interactions or HOIs respectively tripled or quadrupled the explained
variance of target species biomass compared to null models only
including the precipitation treatment. Drought dominated the plant
responses, with longer droughts increasing direct competition but also
HOI-driven facilitation. Despite these counteracting changes, drought
intensified net competition. Grasses were generally more involved in
competitive interactions whereas legumes had a stronger affinity for
facilitative interactions. Under longer drought, species affinity for
nutrient rich or wet environments resulted in more negative direct
interactions or HOIs, respectively. We conclude that higher-order
interactions, crucially depending on species identity, only partially
stabilize community dynamics under increasing weather persistence.%
\end{abstract}%



\sloppy


\textbf{Direct and higher-order interactions in plant communities under
increasing weather persistence}

\textbf{Abstract}

Climate change is increasing the weather persistence in the
mid-latitudes, prolonging both dry and wet spells compared to historic
averages. These newly emerging environmental conditions destabilize
plant communities, but the role of species interactions in this process
is unknown. Here, we tested how direct and higher-order interactions
(HOIs) between species may change in synthesized grassland communities
along an experimental gradient of increasing persistence in
precipitation regimes. Our results indicate that species interactions
(including HOIs) are an important determinant of plant performance under
increasing weather persistence. Out of the 12 most parsimonious models
predicting species productivity, 75 \% contained significant direct
interactions and 92 \% significant HOIs. Inclusion of direct
interactions or HOIs respectively tripled or quadrupled the explained
variance of target species biomass compared to null models only
including the precipitation treatment. Drought dominated the plant
responses, with longer droughts increasing direct competition but also
HOI-driven facilitation. Despite these counteracting changes, drought
intensified net competition. Grasses were generally more involved in
competitive interactions whereas legumes had a stronger affinity for
facilitative interactions. Under longer drought, species affinity for
nutrient rich or wet environments resulted in more negative direct
interactions or HOIs, respectively. We conclude that higher-order
interactions, crucially depending on species identity, only partially
stabilize community dynamics under increasing weather persistence.

\textbf{Keywords:} higher-order interactions, grasslands, facilitation
and competition, species interactions, increasing weather persistence,
drought

\textbf{Introduction}

Climate warming changes the inter- and intra-annual variability of
precipitation regimes in the mid-latitudes (Pendergrass et al., 2017),
including shifts towards more persistent summer weather with longer dry
and wet spells compared to historic averages (Pfleiderer et al., 2019).
With more extreme precipitation regimes (Fig. 1), we expect that
ecosystems will be subjected more often to prolonged plant
stress-inducing extreme conditions, due to the accompanying changes in
soil water and temperature (Felton et al., 2021; Knapp et al., 2008;
Reynaert et al., 2021; Zhao \& Khalil, 1993), and potential changes in
nutrient cycling (Borken \& Matzner, 2009). For example, when dry
periods become longer, or occur at higher temperatures (Collins et al.,
2013), soil water declines more, increasing the chance of crossing
mortality thresholds (De Boeck et al., 2018; Knapp et al., 2008).
Moreover, longer droughts can increase the risk of nutrient leaching
upon rewetting, potentially leading to nutrient shortages in the longer
term (Borken \& Matzner, 2009; Klaus et al., 2020).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Theoretical framework indicating how increasing
precipitation variability can influence soil moisture regimes as an
extension of Knapp et al. (2008), who primarily discussed the effects of
increased weather intensity (i.e. larger, more irregular event sizes).
Grey bars represent daily precipitation. Day of the year and soil water
content are abbreviated by DOY and SWC, respectively. Under climate
change, we expect an increase in both intensity and persistence of
summer weather in the mid-latitudes (Pfleiderer et al., 2019; Spinoni et
al., 2018). In all panels, the precipitation totals are equal to
historical weather (a). More intense weather (b) results in less
frequent but larger rainfall events intermitted with longer dry spells
under higher temperatures, which could exacerbate drought/flooding
effects. More persistent weather (c) results in prolonged dry and wet
periods, which could also lead to more extreme variation in soil water
content. In both cases, the chance increases that critical soil moisture
thresholds are exceeded, negatively affecting ecosystem functioning
(Ciais et al., 2005).

While research has explored how climate shifts may alter selection of
species with specific traits (Siepielski et al., 2017), whether
interspecific interactions will change, or how species interactions
shape community responses to these regime changes remains unclear (Grant
et al., 2014). Direct species interactions, i.e. the direct effect of
plant A on plant B and vice versa (Fig. 2), are important determinants
of ecosystem functioning (Schmitz et al., 2008). They can be
facilitative (positive) or competitive (negative). In grasslands,
legumes often facilitate grasses through N-fixation, which increases
ecosystem productivity (Ball et al., 2020), in return for improved
microclimatic conditions such as wind protection (Walker et al., 2003).
Conversely, fast-growing dominant grasses often compete with one another
for limited resources, which can result in productivity loss (Corbin \&
D'Antonio, 2004; Fay et al., 2003). During episodes of extreme abiotic
conditions, when water (drought) or oxygen (flooding) become limited,
direct interactions between species may become more positive, negative,
or not change (Grant et al., 2014; Van den Berge et al., 2014). The net
effect of direct interaction changes on ecosystem functioning (e.g.,
productivity) under such conditions is ecosystem specific and depends on
their strength and direction, but also on the affected species. For
example, under drought, increased competition between dominant species
can exacerbate the productivity decline (Fay et al., 2003; Grant et al.,
2014; Suttle et al., 2007).

In diverse communities, indirect interactions can also emerge, through
interaction chains of direct interactions (Wootton, 1993) or through
higher-order interactions (HOIs) (Billick \& Case, 1994). With
interaction chains, apparent indirect interaction effects may appear
between two distant plants, due to a shared relationship with a third
(e.g., species A modifies performance of species B, in turn affecting
performance of species C). Unlike interaction chains, HOIs remain poorly
understood, although they play an important role in determining net
community dynamics and the stability of complex ecosystems by modifying
direct interactions (Bairey et al., 2016; Billick \& Case, 1994; Brooker
et al., 2008; Grilli et al., 2017; Li et al., 2021; Mayfield \&
Stouffer, 2017; Singh \& Baruah, 2021; Xiao et al., 2020). HOIs can
either be defined as the combined effect of two or more neighboring
plants on a third target plant (Fig. 2), which is determined by the
nature of these neighbors' own pairwise interaction(s) (Levine et al.,
2017; Mayfield \& Stouffer, 2017), or, as the effect of one species on
the interaction between two others (Billick \& Case, 1994). Such
interaction effects could arise when a Nitrogen (N)-fixing legume C is
introduced into a plant community with scarce resources, reducing N
competition between two neighboring grasses (Fig. 2). Recent studies,
both theoretical and observational, have indicated that HOIs may
mitigate intense direct competitive effects between neighbors,
facilitating long-term species coexistence in highly diverse plant
communities (Grilli et al., 2017; Li et al., 2021; Singh \& Baruah,
2021; Xiao et al., 2020). Thus, Grilli et al. (2017) suggest that HOIs
could make communities more robust against ecological perturbations.
However, their impact on ecosystem functioning and how they are
influenced by shifting climate regimes remains unclear (Bimler et al.,
2018; Brooker et al., 2008; Xiao et al., 2020). Elucidating their role
is important to better understand climate-driven changes in diverse
plant communities.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Schematic representation of differences between
direct interactions and higher-order interactions (HOIs). Direct
interactions involve two plants, a neighbor and a target, while HOIs (in
their simplest form), include a neighbor pair and a target plant. For
example, although species A (neighbor) may outcompete species B (target)
in the absence of species C (negative direct interaction), a mixture
also including species C (neighbor), reducing the intensity of
competition between A and B (less negative interaction) by supplying a
scarce resource to both, may actually benefit (facilitate) the growth of
species B (positive HOI).

Exposure to more extreme dry and wet spells may lead to fundamental
changes in species interaction networks (Grant et al., 2014; Olsen et
al., 2016). The stress-dominance hypothesis predicts that the relative
importance of environmental filtering increases while competition
decreases along a gradient of environmental stress (Coyle et al., 2014;
Weiher \& Keddy, 1995). By extension, the stress-gradient hypothesis
presumes an increase in the relative importance of facilitation compared
to competition along such gradients (Bertness \& Callaway, 1994).
Despite recent evidence questioning the generality of these hypotheses
(De Boeck et al., 2018; Grant et al., 2014; Maestre \& Cortina, 2004;
Metz \& Tielborger, 2016; Singh \& Baruah, 2021), directional shifts in
mean interactions along environmental gradients have been observed in
many ecosystems, indicating important changes in ecosystem functioning
(Coyle et al., 2014; De Boeck et al., 2018; Olsen et al., 2016).

We explored if and how increasing persistence in summer precipitation
regimes (i.e. precipitation variability) changes the number, strength
and directionality of species interactions that influence plant biomass
production, including both direct interactions and HOIs. We subjected
experimental grassland mesocosms with 12 common C3 species (six grasses,
three non-N-fixing forbs, three legumes) to an 8-level gradient of
precipitation regimes. These regimes varied the duration of consecutive
dry and wet periods from 1 to 60 days while keeping total precipitation
constant (Reynaert et al., 2021). Our research questions were whether
species interactions, either direct ones or HOIs, determine species
biomass under altered precipitation persistence, and whether these
interactions change as drought stress intensifies (i.e., the main driver
of plant responses to increasing summer precipitation persistence;
Reynaert et al. (2021); Reynaert et al. (2022)).

We hypothesized that (1) HOIs would generally counteract direct
competitive interactions, weakening net species interactions and
stabilizing community dynamics; (2) species interactions would overall
become more positive (or less negative) across the community with
increasing drought severity, indicating either a decline in competitive
interactions and potentially increased facilitation, or a more prominent
role of environmental filtering, leading to increased competitive
release; (3) species identity would determine the nature of
interactions, with more facilitation in interactions with N-fixing
legumes and more competition when considering dominant grasses.

\textbf{Methods \& materials}

\textbf{Data collection}

From July to November 2019, a 120-day open air precipitation
manipulation experiment was conducted in Flanders, Belgium (51\selectlanguage{ngerman}°09\selectlanguage{english}'41\selectlanguage{english}"N,
04deg24\selectlanguage{english}'9\selectlanguage{english}"E). In total, 256 mesocosms (29.5 cm inner diameter and 50 cm
depth), each containing the same synthesized grassland plant community
with three individuals of 12 common C3 species (36 plants per community;
Fig. S1), were evenly distributed across eight identical plots of 3 m
diameter and subjected to a gradient of increasingly longer dry-wet
period alternations (Reynaert et al., 2021). The precipitation
persistence gradient contained eight different regimes of dry-wet period
alternations, ranging from 1 to 60 consecutive days with or without
precipitation and starting with either a dry or a wet period. This
yielded 16 regimes in total, including 1, 3, 6, 10, 15, 20, 30 and 60
consecutive days dry/wet. All regimes had a total of 60 days with and 60
days without precipitation. Historically, Belgian weather patterns
following 1-day dry-wet alternations are most common (KMI, 2019;
Reynaert et al., 2021). The precipitation regimes were created by
blocking out ambient precipitation with rainout screens which were
automatically deployed only during rain, and applying dripper irrigation
(Reynaert et al., 2021). Precipitation was equal in all regimes on each
irrigation day (6.87~L/m\textsuperscript{2}, being 1.5 times the Belgian
daily average of a rainy day to compensate for excess evapotranspiration
in mesocosms) as well as in total across the experimental period.
Mesocosms had holes in the bottom, allowing free drainage and preventing
flooding (Reynaert et al., 2021). Per regime, a total of 16 replicate
mesocosms was evenly distributed across 4 plots. The position of these
replicates was different in every plot to account for potential edge
effects.

The 12 grassland species represented a broad gradient in plant
functional traits (Table S1) and included six grasses (\emph{Agrostis
capillaris} ~L. (AC),~\emph{Anthoxanthum odoratum} ~L.
(AO),~\emph{Deschampsia cespitosa} ~(L.) P. Beauv. (DC),~\emph{Phleum
pratense} ~L. (PHP),~\emph{Poa pratensis} ~L. (POP),~\emph{Holcus
lanatus} ~L. (HL)); three N\selectlanguage{english}-fixing forbs (\emph{Lotus corniculatus} ~L.
(LC),~\emph{Trifolium pratense} ~L. (TP),~\emph{Trifolium medium} ~L.
(TM)); and three non\selectlanguage{english}-N\selectlanguage{english}-fixing forbs (\emph{Centaurea jacea} ~L.
(CJ),~\selectlanguage{english}\emph{Lychnis flos-cuculi} ~L. (SF),~\emph{Plantago lanceolata}
~L. (PL)). Seeds were obtained from a seed company in the Netherlands
(Cruydt-Hoeck) and sown in separate seedling containers at the start of
April 2019. By May, viable seedlings of similar size per species were
transplanted directly into the mesocosms (Reynaert et al., 2021). We
maximized interspecific interactions by planting them in a hexagonal
grid where each individual had different neighbors at 4.5 cm interspace,
and avoiding clumping (Fig. S1). The spatial configuration in our
containers was aimed at representing each species pair at the most
comparable extent possible, with 92\% of the 66 possible pairs occurring
either one or two times (Table S2). Species positions and north-south
orientation were equal in all mesocosms. Mesocosms were weeded regularly
over the course of the experiment to prevent (re)colonization.

Average volumetric soil water content (SWC) over 0-30 cm depth was
recorded half-hourly in two to four mesocosms per precipitation regime
by a CS650-DS Reflectometer (Campbell(r) Scientific INC., Logan, Utah,
USA). Because the impact of increasing weather persistence was shown to
be mainly related to drought in the 2019 growing season (Reynaert et
al., 2021), the daily averages of mean SWC values per regime were
utilized to calculate different drought indices which represented the
persistency of the precipitation regime. These included drought stress
intensity (at relative extractable water (REW) of 0.4, 0.2 and 0.1),
total time below permanent wilting point, and the length of the longest
consecutive period below permanent wilting point (Reynaert et al., 2021;
Reynaert et al., 2022; Vicca et al., 2012). Following Vicca et al.
(2012), drought stress intensity was calculated by determining a soil
moisture threshold, and integrating the duration and extent to which
soil moisture declined below it over the experimental period.

At the end of the experiment, all standing dead and live biomass above
4.5 cm height was harvested per species per mesocosm, in 12 out of the
16 replicate mesocosms per regime (all available individuals of the same
species per mesocosm per paper bag). Dead biomass was included since
death at the time of measurement, does not indicate a lack of previous
interact with its neighbors. In half of these 12 mesocosms, one third of
the individuals per species was randomly removed for another study. We
corrected for this by multiplying the plant mass in these bags by 1.5
(per regime, every species was discarded twice in every unique position;
Fig. S1). Combining the biomass of all harvested individuals per species
in a mesocosm accounted for variation caused by differences in local
neighbor setting. Biomass bags were oven-dried at 70 degC for
\textgreater{}72 h and immediately weighed with 0.01 g accuracy. To
achieve dry plant mass per species per mesocosm, we subtracted the
average mass of 58 oven-dried empty bags (SD = 0.08 g). Plants that had
little or no biomass above 4.5 cm height because they remained too small
during the experiment (\textless{} 17 \%, n = 2281) were given a mass of
0.01 to account for being below our detection limit (Lubbe et al.,
2021).

\textbf{Statistical analysis}

All statistical analyses were performed in R version 4.0.2 (R Core Team,
2019). Significance was assumed for p-values \textless{} 0.05. Graphs
were constructed utilizing the packages dplyr (Wickham et al., 2015),
igraph (Csardi \& Nepusz, 2006) and ggplot2 (Wickham, 2016).

To model direct interactions and HOIs, we adapted the approach from
Mayfield and Stouffer (2017), which allows the use of biomass data.
Their methodology suggests to construct models with the characteristic
of interest of a target species in the community (here: biomass) as
response variable and (the biomass of) all neighboring species and their
statistical interactions as explanatory variables, after which all
significant terms from the most parsimonious equivalent models (e.g., \selectlanguage{greek}Δ
\selectlanguage{english}AIC \textless{} 2) per target species are of interest. The estimates of
significant model terms can then be utilized to obtain information about
species interactions, direct ones and HOIs, in the studied plant
community. Because biomass data was pooled per species per mesocosm in
our experiment, we did not focus on the role of individual plant
position but rather on species identity. Our central question was if and
how the growth performance of a target species, expressed as accumulated
dry biomass over the growing season, was affected by the growth
performance of neighboring species under increasingly persistent
precipitation regimes in diverse grassland communities.

As described earlier, we opted to represent our treatments in the models
by a drought index. Out of all calculated drought indices (section 3.1),
the length of the longest consecutive period below permanent wilting
point captured species biomass effects caused by increasingly persistent
summer precipitation regimes the best (lowest AIC, adj. R\selectlanguage{ngerman}² = 0.56; Table
S3), allowing us to more accurately linearize the biomass responses
(Fig. S2 \& S3) and model the wet and dry start regimes along a common
dimension (Reynaert et al., 2021).

First, we normalized all biomass data by taking the natural logarithm of
each biomass observation divided by the mean biomass per species across
all regimes. This approach is similar to the log-response ratio, a
technique often applied in meta-analysis, where values \textgreater{}0
indicate above average plant performance, and values \textless{}0 below
average (Van Sundert et al., 2021). By doing so, effect sizes become
comparable among species with distinct intrinsic characteristics, i.e.
the coefficients of models with different target species are
standardized and directly comparable. Since all effects become relative,
they can be interpreted as the extent to which a change in mass of a
neighbor species (relative to its mean size) affects the mass of a
target species (relative to its mean size). We then created multiple
linear models of all possible species combinations including every
species as response variable (target) and all others (neighbors), the
drought index and their two-way interactions as explanatory variables
(Fig. 2; Eqn. 1).

\selectlanguage{english}\textbf{?} (BM\textsubscript{A,i}) = Intercept +
\selectlanguage{greek}α\selectlanguage{english}\textsubscript{1}BM\textsubscript{B,i} +
\selectlanguage{greek}α\selectlanguage{english}\textsubscript{2}BM\textsubscript{C,i} +
\selectlanguage{greek}α\selectlanguage{english}\textsubscript{3}BM\textsubscript{B,i}BM\textsubscript{C,i} + \selectlanguage{greek}γ\selectlanguage{english}Index +
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1}BM\textsubscript{B,i}Index +
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{2}BM\textsubscript{C,i}Index +
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{3}BM\textsubscript{B,i}BM\textsubscript{C,i}Index +
\ldots{} + \selectlanguage{greek}ε \selectlanguage{english}\textsubscript{i} (Eqn 1)

With \textbf{?} (BM\textsubscript{A,i}) the expected standardized total
dry biomass (BM) of target species A in a specified mesocosm i,
BM\textsubscript{B,i} and BM\textsubscript{C,i} the standardized total
dry biomass of neighbor species B and C, respectively, in that mesocosm,
Index the length of the longest consecutive period below permanent
wilting point, \selectlanguage{greek}α \selectlanguage{english}and \selectlanguage{greek}β \selectlanguage{english}the species interaction (model) coefficients and
\selectlanguage{greek}ε \selectlanguage{english}\textsubscript{i} the errors. Model terms including only one species
(BM\textsubscript{B,i} or BM\textsubscript{C,i}) represent direct
interactions. Model terms including both species
(BM\textsubscript{B,i}BM\textsubscript{C,i}) indicate HOIs.

To avoid overfitting and other issues related to insufficient data
(Martyn et al., 2021), we only included the simplest, i.e. the
three-way, HOIs (Fig. 2). Intraspecies interactions were excluded from
the models because we pooled the species biomass for the three
individuals per mesocosm, which accounts for variation caused by
individual plant position. Following Grant et al. (2014), we interpret
the general variation in interspecies interactions knowing that distant
(i.e. non-direct neighbor; Fig. S1) intraspecies effects may also
contribute to the estimated interaction coefficients. Because plant
density and positions are the same in all mesocosms we assume that found
model coefficients primarily reflect variation in plant performance
related to changes in interspecies interactions and drought effects. Per
response variable (target species), we then compared full models with
direct-interaction only and null models (only including the drought
index as explanatory variable), retaining only the most parsimonious
model per target species (\selectlanguage{greek}Δ \selectlanguage{english}AIC \textless{} 2). In the remnant models,
the strength and direction of the interaction(s) was determined by the
model estimates of the explanatory variable(s). Significant model
coefficients excluding the drought index (\selectlanguage{greek}α \selectlanguage{english}coefficients; Eqn 1)
represent interaction strengths at a drought length of zero. All other
significant model coefficients (\selectlanguage{greek}β \selectlanguage{english}coefficients; Eqn 1) indicate a change
of interaction strength along the gradient of increasing drought length.
To calculate interaction strengths at maximum drought index, the maximum
index value (39), was used to fill in Eqn 1. If the model coefficient
was not significant, the interaction strength was assumed to be zero. By
doing so, we were able to isolate the most important direct interactions
and three-way HOIs per target species in the plant community.

Although our models suggest the capacity to detect unidirectional
effects of species on each other and thus causation (influenced species
as dependent variable or target, influencing species as independent
variables or neighbors in Eq. 1), they can actually only detect the
balance of these unidirectional effects, and thus correlation. We
therefore considered every found interaction coefficient as the
bidirectional outcome of the (potential) unidirectional effects,
regardless of whether the species of interest was included as target or
neighbor in the model.

Utilizing the interaction coefficients obtained in this way, we then
calculated the total number of interactions that involved this species
(either as target or neighbor in the models). This was done separately
for direct interactions and HOIs. Direct interaction coefficients
between the same species pair originating from different models were
only counted once per species. Other coefficients represented unique and
equally relevant 3-way HOIs and were all counted per species. Next, we
calculated the mean direct interaction and HOI strengths by averaging
all positive and negative interactions per species, again utilizing the
mean values of coefficients between the same species pairs for direct
interactions and all coefficients for HOIs. We then visualized species
interaction networks for direct interactions, HOIs, net interactions
(sum of direct interactions and HOIs per species pair) and their changes
along the drought gradient. Finally, to test if species characteristics
determined any of the observed patterns, we conducted various
non-parametric Spearman correlation tests, utilizing mean interaction
strengths and total number as response variables and average species
biomass (at zero or maximum drought index) as well as Ellenberg nutrient
and moisture indices as explanatory variables.

\textbf{Results}

Species interactions (including HOIs) played an important role in
determining species performance, expressed as aboveground biomass
production, under changing precipitation regimes. Out of the 12 most
parsimonious models (\selectlanguage{greek}Δ\selectlanguage{english}AIC \textless{} 2), 75\% contained significant
direct interactions and 92\% significant HOIs (Table S4). Null models,
which only included the drought index as explanatory variable, explained
10\% of the variance in species biomass on average (Table S4). Inclusion
of direct interactions increased the variance explained to 28\% on
average (Table S4). Full models, also including HOIs, explained 43\% of
variance in target species biomass on average (Table S4).

At zero drought index, the majority of direct interactions (75\%, n =
24) (stacked bars above zero; Fig 3a) were positive compared to only
half of the HOIs (50\%, n=108) (Fig. 3b). Although a larger proportion
of significant direct interactions became more negative (50\%, n = 12)
(stacked bars below zero; Fig. 3c) with increasing drought length (i.e.,
index change), there were still more positive mean direct interactions
at maximum drought index overall (72\%, n = 11; Fig. 4c). For HOIs, the
total number of positive and negative interactions stayed similar (52\%
positive, n = 75) (Fig. 3d), though on average, more species were
involved in facilitative HOIs at maximum drought index (58\%, n = 12;
Fig. 4d). Whereas opposite interaction effects generally neutralized
each other at zero drought index, overall, competition and facilitation
became more pronounced with intensifying drought stress (Fig. 4). As
such, average direct competition increased in strength and prevalence
along the drought gradient while more HOIs became facilitative (Fig. 4).
Despite these opposite changes, mean direct interaction and HOI strength
did not inversely correlate at either side of the drought gradient (Fig.
S4).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Direct interaction and higher-order interaction (HOI)
effects grouped per species (as target or neighbor) at zero drought
index (a, b; \selectlanguage{greek}α \selectlanguage{english}coefficients in Eqn 1) and their change (c, d; \selectlanguage{greek}β
\selectlanguage{english}coefficients in Eqn 1) with increasing length of the longest consecutive
period below permanent wilting point (PWP). Positive values indicate
facilitative effects, while negative values indicate competitive
effects. The number of interactions (number of blocks inside each bar)
indicates how often a species is involved in a direct interaction or HOI
with a different species or species pair. Direct interactions between
the same species pairs from different models were averaged. The size and
orientation of individual boxes indicates the size and direction (+/-)
of effects (standardized effect sizes). Colors refer to functional group
of the species (red: non-N-fixing Forb, green: Grass, blue: N-fixer).
Non-significant interactions were excluded. For full species names, see
methods section 3.1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Mean direct (a, c) and higher-order interaction (b,
d) effects grouped per species in the most parsimonious models at zero
and maximum drought index. To calculate mean direct interactions, the
average of bidirectional direct interactions is taken. Positive values
indicate facilitative effects, while negative values indicate
competitive effects. Colors refer to functional group of the species
(red: non-N-fixing Forb, green: Grass, blue: N-fixer). Higher-order
interaction is abbreviated by HOI. Non-significant interactions were
excluded. For full species names, see methods section 3.1.

Species identity determined the nature of interactions. Out of the four
species involved in competitive interactions on average at maximum
drought index, three were grasses in both direct interactions and HOIs
(Fig. 4c,d). Forbs (incl. N-fixers) were on average more involved in
facilitation relative to their species number, especially at maximum
drought index (Fig. 4). Furthermore, Ellenberg's moisture and nutrient
indices correlated with average interaction effects. At maximum drought
index, direct interactions became more negative with increasing affinity
to nutrient rich environments (p = 0.02; Fig. S5b), while HOIs became
more negative with increasing hydrophilicity (p = 0.04; Fig. S5a). No
correlations were found between average interaction strength or number
and average species biomass (Fig. S6, Table S5).

Interaction networks became stronger and more negative at maximum
drought index (Fig. 5). Net interactions (sum of direct interactions and
HOIs) were generally most positive on average for N-fixers, more
negative for non-N-fixing forbs and most negative for grasses (Table 1).
Moreover, the majority of net interactions became negative for grasses
and non-N-fixing forbs at maximum drought index, while the majority of
net N-fixer interactions remained facilitative. Both at zero and maximum
drought index, N-fixers were proportionally most involved in net
interactions, followed by forbs and grasses (Table 1). Average net
interaction strengths did not change noticeably with increasing drought
index within functional groups (Table 1).

\textbf{Table 1.} Number and average strength of all significant
positive and negative net interactions (sum of direct interactions and
HOIs) involving grasses, (non-N-fixing) forbs or N-fixers at zero and
maximum drought index, respectively. Non-significant interactions were
excluded. See Figure 5 for the complete network overview.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& \textbf{At zero drought index} & \textbf{At zero drought index} &
\textbf{At zero drought index} & \textbf{At maximum drought index} &
\textbf{At maximum drought index} & \textbf{At maximum drought
index}\tabularnewline
\midrule
\endhead
& \textbf{Grass} & \textbf{Forb} & \textbf{N-fixer} & \textbf{Grass} &
\textbf{Forb} & \textbf{N-fixer}\tabularnewline
\# (+) interactions & 20 & 14 & 15 & 21 & 12 & 16\tabularnewline
\# (-) interactions & 19 & 7 & 10 & 23 & 13 & 14\tabularnewline
mean interaction effect & -0.23 & -0.08 & 0.11 & -0.23 & -0.03 &
0.2\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Species networks indicating the net strength of
interactions (sum of direct interactions and higher-order interactions
per species pair) in the most parsimonious models (\selectlanguage{greek}Δ\selectlanguage{english}AIC \textless{} 2)
at zero (a) and at maximum (b) drought index. The color of the network
links indicates whether the net interaction is positive (blue) or
negative (red). The color of the species relates to its functional group
including grasses (green), non-N-fixing-forbs (brown) and N-fixers
(purple). When multiple interactions were found between the same species
pair in different models, their strengths were averaged. The thickness
of the connections indicates net interaction strength. Non-significant
interactions were excluded. For full species names, see methods section
3.1.

\textbf{Discussion}

Inclusion of species interactions (including HOIs) increased the
accuracy of plant performance models along the drought gradient compared
to models only including the drought effect. Direct interactions
improved the explanatory power of models more on average compared to
HOIs, though the latter were present in a larger number of the most
parsimonious models (Table S4). This finding affirms the necessity to
include at least some HOIs to improve net species interaction estimates
in complex and diverse ecological networks (Li et al., 2020; Li et al.,
2021; Martyn et al., 2021; Mayfield \& Stouffer, 2017). Particularly in
plant communities subjected to drought, where species interactions can
change drastically (Grant et al., 2014; Van den Berge et al., 2014),
facilitation and/or competition may be overestimated without inclusion
of HOIs, limiting our ability to predict ecosystem trajectories (Brooker
et al., 2008). Incorporating HOI approaches in more studies could thus
improve the predictability of species performance under climate change.

Contrasting with the stress gradient hypothesis (Bertness \& Callaway,
1994), more net interactions became negative on average along the
drought gradient. This occurred because direct interactions
predominantly shifted to more negative with longer drought, while HOIs
were doing the opposite, albeit without fully neutralizing intensified
competition (Fig. 4). In line with Xiao et al. (2020) and Li et al.
(2021), these findings indicate that HOIs may influence community
stability by partially counteracting increased direct competitive
effects under increasing precipitation variability. While Xiao et al.
(2020) argue that especially intraspecific HOIs play a stabilizing role,
our experimental findings suggest that interspecific HOIs may also
contribute to stabilizing community dynamics (Bairey et al., 2016;
Grilli et al., 2017; Singh \& Baruah, 2021). Hence, persistence of
highly diverse communities under increasing drought stress could be
promoted by interspecies HOIs counteracting stronger direct competitive
effects, thus stabilizing community dynamics, and preventing dominance
of one single species group (Brooker et al., 2008). Since such
stabilizing HOIs are more likely to be generated in multi-species
assemblages (Grilli et al., 2017), this observation may also point at an
additional mechanism through which more diverse communities could be
better equipped to withstand climate extremes (Brooker et al., 2008; De
Boeck et al., 2018).

In terms of directionality, the majority of HOIs showed a shift towards
stronger facilitation (or weaker competition) along the drought gradient
on average (positive values in Fig. 3d), in line with the stress
gradient and dominance hypotheses (Bertness \& Callaway, 1994; Coyle et
al., 2014). However, three additional species exerted direct competitive
pressures at maximum drought index on average (Fig. 4c), and more net
interactions became competitive (Fig. 5; Table 1). Hence, while the
majority of direct competitive interactions was weakened by positive
HOIs, the strengthening of some competitors may have led to a
destabilized system where more species were being outcompeted under
longer drought (Table 1; Fig. 5). This is in agreement with some studies
(Maestre et al., 2005; Olsen et al., 2016; Saccone et al., 2009), but
contradicted by others (Brooker et al., 2008; He et al., 2013). Having
more species in negative direct interactions (and more net competitive
interactions) under longer drought indeed suggests that some species
(e.g. \emph{Phleum pratense} ) could become more competitive under water
shortage because of, for example, favorable resource acquisition traits
(Kraft et al., 2015; Olsen et al., 2016).

In that regard, we observed that direct interactions became more
negative with increasing Ellenberg's nutrient indices under longer
drought (Fig. S5b), indicating that species with an affinity for
resource rich environments became involved in more direct competitive
interactions. Hence, increasing weather persistence seemed to not only
affect species interaction networks directly through intrinsic negative
responses to changes in soil water availability (i.e. Ellenberg's
moisture indices; Fig. S5a), but also indirectly through effects of
drought on nutrient availability. These intensified direct competitive
effects may thus also reflect differences in plant resilience during
post-drought rewetting and recovery phases, when the flush of nutrients
related to Birch effects induces transient periods of high nutrient
availability (Birch, 1958; Borken \& Matzner, 2009), allowing rapid
recovery of more resilient species.

However, positive interactions still made up half of all interactions
under extreme drought (Fig. 5b), and the average strength of net
interactions per functional group did not change noticeably with drought
(Table 1). Together with the striking productivity declines (Fig. S2 \&
S3) and diversity losses related to the intrinsic drought sensitivity of
individual species (Reynaert et al., 2021), these observations may
indicate a primary role of environmental filtering (i.e. drought), and
only to a lesser extent shifts in species interactions as the primary
cause for aboveground productivity declines under drought (Coyle et al.,
2014; Elst et al., 2017). Following Maestre et al. (2009), we pose that
under extreme environmental stress (e.g., prolonged drought), species
could potentially also stop growing due to exceedance of intrinsic
ecological thresholds, mostly independently of the community dynamics
(i.e. facilitative or competitive interactions) in their growing
environment (De Boeck et al., 2018; Reynaert et al., 2021; Soliveres et
al., 2015). The non-linearity in survival responses (Fig. S5; Reynaert
et al. (2021)), could indeed suggest such a mechanism. Similar negative
threshold responses of aboveground plant productivity have been observed
in many terrestrial ecosystems, particularly in relation to the duration
of dry spell anomalies (Felton et al., 2021). Taken together, these
results indicate that both intrinsic responses to increasing drought and
intensifying interspecific competitive effects (Olsen et al., 2016) lead
to the observed changes in community dynamics by the end of the first
120 days of the experiment.

In line with our third hypothesis, forbs (particularly N-fixers) tended
to be more involved in facilitative interactions on average, while
grasses seemed the strongest competitors (Table 1; Fig. 5). This role of
species identity (Martyn et al., 2021; Weigelt et al., 2002) was
confirmed by the relationships with Ellenberg's moisture and nutrient
indices at maximum drought index, with species with an affinity for
wetter and more resource rich environments experiencing or exerting more
negative HOIs or direct interactions on average, respectively (Fig.
S5a,b). Additionally, the disproportionate contribution of N-fixers to
positive interactions and grasses to negative interactions suggests that
some species (or species groups) are crucial in defining the dynamics of
complex species networks (Singh \& Baruah, 2021). With the available
information, we cannot fully determine whether N-fixer importance is
because of active facilitative traits (e.g., improving water or nutrient
supply), or due to passive competitive release as these smaller species
rapidly disappeared with increasing drought stress (i.e. were more
susceptible to environmental filtering), or because they prevent other
more competitive neighbors from colonizing (e.g., via vegetative
propagation) (Maestre et al., 2003).

Some aspects of this study's methodology should be considered. First,
the presented direct vs three-way higher-order counteracting interaction
system may not fully reveal the nature of net community dynamics.
Inclusion of HOIs beyond simple three-way terms may shed more light on
the overall strength of facilitation and competition, but was not
feasible in this study because of insufficient data (Martyn et al.,
2021). Nonetheless, previous studies have indicated that inclusion of
more complex (i.e., 4-way, 5-way, etc.) HOIs leads to progressively
diminished returns in explaining community dynamics (Li et al., 2021),
highlighting the importance of the modelled interactions. Second, the
exclusion of intraspecific and density effects likely perturbed the
estimation of interaction coefficients, since intraspecific interactions
(both direct and HOIs) and competitor density influence net community
dynamics in diverse ecosystems (Chesson, 2000; Li et al., 2021; Xiao et
al., 2020). However, because individuals of the same species were never
planted directly next to one-another and the initial planting density
was identical in every mesocosm, we believe that the observed changes
primarily reflect plant responses in function of neighbor performance
and drought (Grant et al., 2014). Finally, the species interactions
investigated here represent short-term responses to altered persistence
in summer precipitation (Reynaert et al., 2021). Since we already
observed strong differences in species performance related to the
extremity of the precipitation regime, later assessment of interactions
would likely disproportionally reflect the effects of only a few single
species since many had died already under extreme summer drought after
120 days (Reynaert et al., 2021). Consequently, the identified
interactions likely represent the most important ones that contributed
to the observed declines of species under the imposed climate trend.

\textbf{Conclusion}

Modelling and disentangling the role of HOIs in community dynamics
remains challenging because of their complexity, extensive dataset
requirements and a lack of widely applicable methodologies. More
targeted manipulation experiments filtering out the environmental noise
that perturbs the estimation of interaction coefficients could aid in
bridging the gap between theoretical modelling and meaningful ecological
understanding of diverse communities. We took a first step in that
direction, by experimentally demonstrating that species interactions,
including HOIs, can significantly change along a gradient of
increasingly persistent precipitation regimes. Inclusion of HOIs did not
only improve model explanatory power, but also shed more light on
potential stabilizing mechanisms for increasing competition under
drought. Hence, HOI approaches could further improve the accuracy of
species performance models under rapidly changing environmental
conditions, an imperative prerequisite for targeted and efficient
ecosystem management.

\textbf{Conflict of interest}

The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.

\textbf{Data availability}

The corresponding data of this study will be made publicly available
online in a Zenodo repository at {[}INSERT DOI{]} and in the previously
published Dryad repository at https://doi.org/10.5061/dryad.k98sf7m52.

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