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Resource availability drives bacteria community
resistance to pathogen invasion via altering bacterial
pairwise interactions

Mei Li, Thomas Pommier, Yue Yin, Wenhui Cao, Xiaohui Zhang, Jie Hu, Yann Hautier, Tianjie Yang, Yangchun Xu, Qirong Shen, et al.

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► To cite this version:

Mei Li, Thomas Pommier, Yue Yin, Wenhui Cao, Xiaohui Zhang, et al. Resource availability drives bacteria community resistance to pathogen invasion via altering bacterial pairwise interactions. Environmental Microbiology, 2022, 10.1111/1462-2920.16184. hal-03776248

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Distributed under a Creative Commons Attribution 4.0 International License
Resource availability drives bacteria community resistance to pathogen invasion via altering bacterial pairwise interactions

Running title: Resource availability drives community invasion resistance

Authors and Affiliations

Mei Li<sup>1,2,3</sup>, Thomas Pommier<sup>4</sup>, Yue Yin<sup>1</sup>, Wenhui Cao<sup>1</sup>, Xiaohui Zhang<sup>1</sup>, Jie Hu<sup>1,3,5</sup>, Yann Hautier<sup>3</sup>, Tianjie Yang<sup>1*</sup>, Yangchun Xu<sup>1</sup>, Qirong Shen<sup>1</sup>, George A. Kowalchuk<sup>3</sup>, Alexandre Jousset<sup>1</sup>, Zhong Wei<sup>1</sup>

<sup>1</sup> Joint International Research Laboratory of Soil Health, Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Key Lab of Bio-interaction and Plant Health, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-based Fertilizers, Nanjing Agricultural University, 210095, Nanjing, PR China.

<sup>2</sup> Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.

<sup>3</sup> Institute for Environmental Biology, Ecology & Biodiversity, Utrecht University, Padualaan 8, 3584CH Utrecht, The Netherlands.

<sup>4</sup> Univ Lyon, Université Claude Bernard Lyon 1, CNRS, INRAE, VetAgro Sup, UMR Ecologie Microbienne, F-69622 Villeurbanne, France.

<sup>5</sup> UMR 6553 Ecobio, CNRS-University of Rennes, Avenue du Général Leclerc, 35042, Rennes Cedex, France.

* For correspondence

Email: tjyang@njau.edu.cn; Tel: (86)025-84395212; Fax: (86)025-84395212

**Originality-Significance Statement:** Microbial interactions are acknowledged to determine community resistance to pathogen invasion. However, the role resource availabilities play on pathogen invasion and community interactions remains underestimated. In this study we addressed this issue under high and low resource availability conditions. As a result, resident communities harboring more intensive antagonistic interactions in high resource availability could inhibit pathogen density better than facilitative communities. Oppositely facilitative communities showed higher resistance to pathogen invasion when resource availability was low. This work may

This article has been accepted for publication and undergone full peer review but has not been
through the copyediting, typesetting, pagination and proofreading process which may lead to
differences between this version and the Version of Record. Please cite this article as doi:
10.1111/1462-2920.16184

Accepted Article
provide a framework to predict pathogen inhibition of resident communities under dynamical
availability of environmental resources.

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## Summary

Microbial interactions within resident communities are a major determinant of resistance to pathogen invasion. Yet, interactions vary with environmental conditions, raising the question of how community composition and environments interactively shape invasion resistance. Here we use Resource Availability (RA) as model parameter altering resistance of model bacterial communities to invasion by the plant pathogenic bacterium *Ralstonia solanacearum*. We found that at high RA, interactions between resident bacterial species were mainly driven by the direct antagonism, in terms of the means of invader inhibition. Consequently, the competitive resident communities with a higher production of antibacterial were invaded to a lesser degree than facilitative communities. At low RA, bacteria produced little direct antagonist potential, but facilitative communities reached a relatively higher community productivity, which showed higher resistance to pathogen invasion than competitive communities with lower productivities. This framework may lay the basis to understand complex microbial interactions and biological invasion as modulated by the dynamic changes of environmental resource availability.

## Introduction

Host-associated microbial communities can function as a line of defense against pathogens, thereby protecting their associated host organism (He et al., 2014; Wei et al., 2015). This process can also be viewed from the perspective of biological invasions where the members of resident communities can impact the ability or inability of invading species to establish in an ecosystem (Vivant et al., 2013; Kurkjian et al., 2020). The characteristics of resident communities are important for determining the

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outcomes of biological invasions (Li et al., 2019; Mickalide and Kuehn, 2019). For example, the interactions between microbes in resident communities can not only influence their survival, growth and contribution to community function (Raes and Bork, 2008; Strom, 2008; Moons et al., 2009; Ghoul and Mitri, 2016), but also impact the community resistance to pathogen invasion. Microbes influence their direct environment for instance by consuming resources and excreting metabolites (Ratzke and Gore, 2018). These changes to the environment influence the growth and survival of both the microbe that originally altered the environment as well as other microbial species that are present, whether that be resident organisms or potential invaders. Facilitative interactions between residents can potentially increase the availability of resource niches via the production of secondary metabolites or public goods that can also be utilized by an invader (Stachowicz, 2001; Mallon et al., 2015; Bulleri et al., 2016). On the other hand, competing species can inhibit each other, for instance via the production of toxic metabolites, which may have negative effects on both resident and invading species (Becker et al., 2012; Hu et al., 2016).

However, microbial interactions are sensitive to a range of environmental factors, such as resource availability (Kinkel et al., 2011). Theoretical studies have suggested that certain mutualisms can become competitive under high nutrient conditions (Bull and Harcombe, 2009), and it has been shown that two yeast strains can interact in at least seven qualitatively different ways depending on the nutrient concentrations encountered (Hoek et al., 2016). In the highly fluctuating rhizosphere environment, where resource availability varies in space and time (Sauer et al., 2006; Hu et al., 2018; Hu et al., 2020), the same microbial community may be suppressive or conducive to pathogens. Although multiple studies have reported a shift in bacterial interactions in response to changes in resource availability levels, it is generally unknown how shifts
in these interactions affect a community's susceptibility or resistance to pathogen invasion. We have previously demonstrated that the interactions within the resident bacterial community can reliably predict pathogen invasion both in lab microcosms and the plant rhizosphere (Li et al., 2019). Facilitative resident communities were more prone to invasions, while antagonistic resident communities were invaded to a lesser extent. In this study, we aimed to specifically explore if these relationships between bacterial interactions and community resistance to pathogen invasion were modulated by resource availability. Higher nutrient concentrations would be expected to allow bacterial populations to metabolize larger amount of growth substrates, like antimicrobial substance, hereby having a larger impact on their surrounding environment (Basan et al., 2015). Accordingly, we hypothesized that higher nutrient concentrations would lead to stronger antagonistic interactions and invader inhibition. Especially, competitive communities could also produce higher levels of antagonism than facilitative communities. Competitive communities should therefore provide greater resistance to the invader at high resource availability (Figure 1A). As opposed to conditions of high resource availability levels, resident communities at low resource availability conditions might produce lower levels of antagonism. We therefore expect that invasion success may, to some extent be driven by resident community productivity. If facilitative resident community are able to reach higher population densities than antagonistic communities (Li et al., 2019), we expect that facilitative communities might better resist to the invader under these conditions (Figure 1A), as they may be able to occupy more niche space at low resource availability.

To validate this hypothesis, we examined how resource availability impacted pairwise interactions within the resident bacterial community, and how these changes affected the resident community's resistance to the invasion of the plant pathogenic

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bacterium, *Ralstonia solanacearum*, the causal agent of bacterial wilt disease (Salanoubat et al., 2002; Jiang et al., 2017). Specifically, we first tested the pairwise interactions between resident bacteria by co-culture (cultured two bacteria together) and supernatant assays (cultured one bacterium in the presence of sterile supernatant from one of the other bacterial strains) at low and high resource availability levels (Figure 1B). Two-species communities were then confronted by a *R. solanacearum* invasion at either high or low resource availability, with invasion success being measured as the resulting density of *R. solanacearum* (Co-culture assay in Figure 1C). To examine the toxicity of tested communities towards the *R. solanacearum*, we also tested the invasion success in the presence of sterile supernatant from each two-species community at low and high resource availability levels (Supernatant assay in Figure 1C).

## Results

### Resource availability influences pairwise interactions between resident bacterial species

Each bacterial species exhibited a different pattern of positive, negative or neutral effects on the other strains, and the magnitude and directionality of these effects were differently influenced by resource availability (Figure 2A, 2B and Figure S1). For example, strain *Ralstonia pickettii* QL-A6 (Rp) showed a stronger negative effect on strain *Lysinibacillus sphaericus* HR92 (Ls) at low resource availability as compared to high resource availability, and strain *Chryseobacterium daecheongense* WR21 (Cd) showed a negative effect on strain *Flavobacterium johnsoniae* WR4 (Fj) at low resource availability, but no significant effect on this strain at high resource availability.
Production of toxic metabolites is another driver of bacterial interaction. Here we found resident bacteria produced less toxic metabolites in the low-resource availability conditions (Figure 2C), but their interactions were stronger in coculture assays (Figure 2A). For example, the metabolites contained in the supernatant of strain C. *daecheongense* WR21 (Cd) did not significantly inhibit the growth of strain *Bacillus amyloliquefaciens* T-5 (Ba), but we observed strong inhibitory effect on strain *B. amyloliquefaciens* T-5 (Ba) in coculture (Figure S1). In contrast, supernatants from high-resource media cultures showed stronger negative effects on the growth of other strains (Figure 2D). Moreover, there was a significant relationship between the mean intensity of interaction in coculture and in supernatant at the high resource availability level (R²=0.59, P<0.001, dark blue in Figure 2E), with no relationship being observed at low resource availability (R²=0.09, P=0.0519, light blue in Figure 2E). These results suggest that the pairwise interactions between resident bacterial species were mostly driven by the production of secondary metabolites at high resource availability.

## Resource availability alters bacterial pairwise interactions with effects on productivity of resident community and its toxicity towards invader

The productivity of bacterial monoculture can affect pairwise interactions in a bacterial community, and the community productivity is the outcome of bacterial interaction. We found the productivity of bacterial monocultures and their pairwise consortia were significantly affected by resource availability (Figure S2). With no surprise, higher resource availability led to increased productivity of the resident community (Figure S2). Moreover, mean intensity of interaction between resident species was positively correlated with productivity of the resident community at both high (R²=0.47, P<0.001, dark blue in Figure 3A) and low (R²=0.39, P<0.001, light blue in Figure 3A) resource availability. As a result, there was no interactive effect between resource availability

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and interactions within resident community on the productivity of the resident community (Table 1). However, different resident bacterial species affected the productivity of the resident community in resource-dependent manner (Table S3). For instance, the specie Ba had strong negative effects on productivity of the resident community at high resource availability but did not appear to have clear effects on productivity of the resident community at low resource availability, the specie Ls had positive effects on productivity of the resident community at high resource availability but had no effect on productivity of the resident community at low resource availability (Table S3).

We found that higher resource availability led to a greater level of resident community toxicity towards pathogen (Figure S3). Furthermore, mean intensity of interaction between resident species in coculture had no effect on resident community toxicity towards pathogen at low resource availability (R²=0.019, P=0.3622, light blue in Figure 3B), but was negatively correlated with resident community toxicity towards pathogen (R²=0.39, P<0.001, dark blue in Figure 3B). Therefore, there was an interactive effect between resource availability and interaction within the resident community (mean intensity of interaction in coculture) on resident community toxicity towards pathogen (Table 1). It also showed that the resident species identities had clear effects on the resident community toxicity towards pathogen (Table S3). At high resource availability, while the species Cd, Ba and Ls increased the resident community toxicity towards pathogen, the other resident species did not appear to have clear effects on resident community toxicity towards pathogen (Table S3). At low resource availability, the resident species Fj, Cd, Da and Ba had positive effects on resident community toxicity towards pathogen, but the species Ls and Rp had no effect on resident community toxicity towards pathogen (Table S3).
Mechanisms by which resource availability modulates two-species bacterial
community resistance to pathogen invasion

We examined model resident communities resistance to pathogen invasion as judged
by final invader density. We found that higher resource availability led to a reduced
invasion success of pathogen (Figure S4). In addition, invader success correlated
positively with the mean intensity of interaction between resident species at high
(R²=0.35, P<0.001, dark blue in Figure 4A) resource availability, but an opposite
correlation was observed at low resource availability (R²=0.4, P<0.001, light blue in
Figure 4A). Thus, there was an interactive effect between resource availability and
interaction within the resident community (mean intensity of interaction in coculture) on
invasion success of pathogen (Table 1). Compared to positive controls (Ralstonia
solanacearum-only: dark and light blue dashed lines in Figure 3), pathogen densities
were lower in the presence of resident species at both high and low resource
availability. Furthermore, the resident species identities also had clear effects on the
density of the invader (Table S3). At high resource availability, while the species Fj, Da
and Ba had negative effects on invader density, the other resident species did not show
significant effects on invader density (Table S3). At low resource availability, the
resident species Fj and Da had negative effects on invader density, the species Cd, Ba
and Ls had no effect on invader density, but Rp had a slightly positive effect on invader
density (Table S3).

At low resource availability, invader density decreased with increasing productivity
of resident community (R²=0.33, P<0.001, light blue in Figure 4B) and resident
community toxicity towards pathogen (R²=0.13, P=0.017, light blue in Figure 4C).
However, we found that productivity of resident community provided better prediction
of invader density at LRA, as compared to resident community toxicity towards

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pathogen (Table 2). In high resource availability conditions, invader density was positively linked with productivity of resident community ($R^2=0.4$, P<0.001, dark blue in Figure 4B), but negatively linked with resident community toxicity towards pathogen ($R^2=0.33$, P<0.001, dark blue in Figure 4C). Mechanistically, this could be explained by a trade-off relationship between productivity and secondary metabolites producing of bacterial community at high resource availability as suggested by a negative correlation between the productivity of the resident community and its toxicity towards pathogen at high resource availability ($R^2=0.27$, P<0.001, dark blue in Figure S5), but not at low resource availability (light blue in Figure S5). Moreover, we found invader density was influenced more by resident community toxicity towards pathogen than productivity at HRA (Table 2).

Together, these results suggest that facilitative communities that reached higher total densities suppressed the invader more efficiently in low resource availability conditions. High resource availability conditions yielded a contrasting pattern in which competitive communities that achieve lower total community densities, but produced more direct antagonist compounds, provided a stronger inhibition of the pathogen growth.

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Microbial communities are structured by interactions between their constituent species in the context of their abiotic environment (Lax et al., 2020). Interactions within a resident community can have important consequences for ecosystem functions such as the ability of the community to resist biological invasions (Li et al., 2019; Mickalide and Kuehn, 2019). For example, it has been observed that communities with a greater proportion of competitive interactions are better able to constrain invader growth than more facilitative communities (Li et al., 2019). However, the nature of microbial interactions is impacted by environmental conditions such as the level of nutrient availability, and how such impacts are related to community resistance to biological invasion is generally unknown. In this study, we explored how changes in resource availability affect relationships between interactions within model resident communities and their ability to resist pathogen invasion. At high resource availability, we found that more competitive resident communities produced more direct antagonism and subsequently better resisted pathogen invasion than more facilitative communities. At low resource availability, facilitative communities that reached higher total productivity were more resistant to pathogen invasion than more competitive communities. Understanding how resource availability influences the interactions between resident bacteria is thus important for predicting the dynamics and outcomes of biological invasions.

In line with a recent study, we found that interactions between resident

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bacterial species were mostly driven by the production of secondary
metabolites in high resource availability conditions (Ratzke et al., 2020). Given
the results of our assays (supernatant assays) involving exposure to spent
media, it appeared that the observed microbial interactions involved
modification of the environment, for instance via the production of inhibitory
compounds. In high resource availability conditions, microbes have sufficient
substrates to produce relatively large amounts of inhibitory compounds, thereby
leading to a greater level of influence on other microbial strains (Goldford et al.,
2018; Estrela et al., 2019; Niehaus et al., 2019). Interestingly, we found resident
bacteria produced little toxic metabolites in low resource availability conditions,
but there were strong competitive interactions between them in coculture
assays. This suggests that interactions at low resource availability were not
driven by the production of toxic metabolites but by competition for resources,
presumably because there were not sufficient resources to allow for large
investment into the production of such secondary metabolites (Westhoff et al.,
2020).

Resource availability showed a strong negative effect on the density of the
model pathogen invader (Table 1), which contrasts with other studies (Davis et
al., 2000; Kuebbing et al., 2013; Mallon et al., 2015). For example, Mallon *et al.*
found that increasing resource availability may promote *E. coli* invasions due to
relaxed resource competition (Mallon et al., 2015). This discrepancy may be
explained by the fact that bacteria were likely able to consume all the available
resources during our experiments without addition of new external resources (Yang et al., 2017). Furthermore, resident communities produced inhibitory compounds that reduced the growth of invader *Ralstonia solanacearum* under conditions of high resource availability as compared to low resource availability conditions (Figure S3).

We also observed an interactive effect between the pairwise interactions within the resident community and resource availability conditions on invader density. Mechanistically, the greater resistance to pathogen invasion of competitive communities at high resource availability level was linked to direct inhibition of the invader by antagonistic communities (Figure 2B and 4C). However, facilitative communities were more resistance to pathogen invasion at the low resource availability level, which can be explained by the fact that facilitative communities reached higher total community productivity (Figure 2A and 4B). Our results (Table 2) suggest that under high resource availability conditions, antagonism is an important determinant of community invasion resistance (Li et al., 2019; Ratzke et al., 2020). This result is also in line with a previous finding, where the increase in the antagonistic activity was found to increase the invasion resistance of *Pseudomonas* resident communities (Hu et al., 2016). As our results suggested that interactions in low resource availability conditions were driven by competition for resources, communities that showed higher productivity may be able to better occupy niche spaces, thereby outcompeting invaders (Romanuk et al., 2009; van Elsas et al., 2012; Elton,
2020).

We conclude that resource availability can modulate bacterial community
resistance to pathogen invasion by changing pairwise interactions within the
resident community. In many cases, microbial interactions may not be driven
by a single parameter — resource availability in our case — but by a set of
multiple parameters such as pH, temperature or other environmental factors.
However, also in these cases, interactions are mediated by modifying and
reacting to the environment (Ratzke and Gore, 2018; Pacheco et al., 2019),
which can influence the community functions.

It should be noted that natural plant rhizosphere is far more complex than
the simple artificial systems we have used in this study. Referring to natural
conditions, more species in the synthetic community would represent more the
real circumstance. However, it showed that more species diversity would
increase the antagonistic interactions in the synthetic community and the
community would collapse (Becker et al., 2012). Moreover, experiments
conducted within one trophic level suggest that pairwise bacterial interactions
can predict three-species bacterial interactions with as high as 90% accuracy
(Friedman et al., 2017). While predicting interactions in species-rich
communities might require additional information about potentially emerging
higher-order interactions (Friman et al., 2016; Grilli et al., 2017; Levine et al.,
2017), these findings suggest that qualitative information regarding species
growth in pairwise co-cultures can be used to predict the interactional outcomes
of up to 8-species communities (Friedman et al., 2017). Although our framework need to be expandable to more multivariate and more complex natural systems, it laid the basis for understanding how more complex microbial interactions, and the functions they yield at the community level, are impacted by changing environmental conditions.

## Experimental Procedures

### Bacterial strains and the assembly of pairwise resident communities

We used *R. solanacearum* strain QL-Rs1115 (Wei et al., 2015) tagged with the pYC12-mCherry plasmid (Tan et al., 2016) as the model invading pathogen in our experiments. Model resident communities were created using six bacterial strains isolated from the tomato rhizosphere from location from which the pathogen was also isolated (Qilin [118°57' E, 32°03' N], Nanjing, China). The strain collection used as model communities contained the isolates listed in Table S1 (*F. johnsoniae* WR4, *C. daecheongense* WR21, *D. acidovorans* WR42, *B. amyloliquefaciens* T-5, *L. sphaericus* HR92 and *R. pickettii* QL-A6), which have previously been shown to provide protection for associated host plants by inhibiting *R. solanacearum* pathogen growth via resource competition or direct toxin production (Huang et al., 2013; Wei et al., 2015; Tan et al., 2016; Huang et al., 2017; Li et al., 2019). These species can also be distinguished easily by colony morphology based on a previous study (Li et al., 2019), which makes it possible to test the pairwise interactions between each other. Moreover, these six taxa have high relative abundance in the rhizosphere microbiome (Lagos et al., 2015; Gu et al., 2020; Li et al., 2022). We have also previously

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demonstrated that the interactions within these resident bacterial community
can reliably predict pathogen invasion both *in vivo* and *in vitro* (Li et al., 2019).
In this study, we aimed to specifically further explore if these relationships
between bacterial interactions and community resistance to pathogen invasion
are modulated by resource availability. Therefore, model resident communities
were constructed by using all six resident bacterial strains in all possible one-
or two-species combinations (21 communities in total, Supporting Information
Table S2). We used a substitutive design so that all communities were set up
with the same initial total bacterial density (10⁵ cells ml⁻¹) and evenness (i.e.
multispecies communities had equal ratios of each species).

Medium and bacterial culture

We set up two different resource availability treatments in this study: 100% NB medium (nutrient broth: glucose 10.0 g l⁻¹, tryptone 5.0 g l⁻¹, yeast extract 0.5 g l⁻¹, beef extract 3.0 g l⁻¹, pH 7.0) providing high resource availability (HRA), and 10% NB medium (by diluting 100% NB medium with sterile water, pH 7.0) providing low resource availability (LRA).

Prior to each experiment, one colony of each strain, recovered from -80°C
20% glycerol stocks, was selected and grown in liquid NB with 170 r.p.m.
agitation at 30°C for 12 h. Bacteria were then washed three times by
centrifugation (5000 r.p.m for 5 min), resuspended in 0.85% NaCl and adjusted
to a density of 10⁷ cells ml⁻¹ for each resident bacterium and 10⁶ cells ml⁻¹ for
the invading pathogen.

*Determining pairwise interactions between resident community species at both*
*high and low resource availability by coculture assay*

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In order to investigate if and how the resource availability influenced the interaction between bacterial strains, we quantified the strength and direction of each pairwise interaction between resident species at high or low resource availability. To this end, we compared the growth of each species alone and in the presence of each of the other species in two-species co-cultures (Foster and Bell, 2012). All monocultures were inoculated with a starting density of 10⁵ cells ml⁻¹, and co-cultures were inoculated with half of this starting cell density for each species. Resident species were grown for 36 h in liquid 100% NB or 10% NB medium in 48-well microtiter plates (ending volume of 700 µl per well) at 30°C with shaking (170 r.p.m). Bacterial densities of each community were measured as optical density at 600 nm (OD₆₀₀) using a SpectraMax M5 plate reader (Molecular Devices, Sunnyvale, CA, USA). Bacterial growth was measured as colony number units (CFU) per ml by serial dilution and plating on NB agar plates after 48 h growth.

To test the significance of the effects of resident bacteria on each other, the productivity of each species (CFU) was log₁₀-transformed prior to a t-test to compare mean differences between each bacterium in co-culture as compared to monoculture, with P-values below 0.05 being considered statistically significant. The strength of pairwise interactions between two species (here *i* and *j*) was determined by comparing the final productivity of each species (CPᵢ and CPⱼ) in two-species co-cultures with their productivities (MPᵢ and MPⱼ) in monocultures (log₁₀(CPᵢ / MPᵢ)). We then determined the directionality of

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interaction. Interactions were considered facilitative if species *j* had a significant positive effect on species *i*, competitive if the effect was significantly negative and neutral if there was not significant effect. We also calculated the mean intensity of interaction (MIF) of co-cultures as an average of log₁₀-transformed pairwise interactions using the following formula:

$$MIF_{ij} = \frac{1}{2}[\log(CP_i / MP_i) + \log(CP_j / MP_j)]$$
The two-species community was defined as facilitative when MIF > 0, competitive when MIF < 0.

*Determining pairwise interactions between resident community species at both high and low resource availability by supernatant assay*

To assess the impact of resource availability on direct interference competition between resident community species (i.e. toxicity within two-species resident community), we compared the growths of each bacterial strain in the presence of supernatant from each of the other bacteria, which was collected from high resource availability (100% NB) and low resource availability (10% NB) medium, respectively. Briefly, after 36 h of growth in NB or 10% NB medium on a shaker at 170 r.p.m, 30°C, the 6 resident bacterial monocultures were filter sterilized to remove living cells (0.22 µm filter). Subsequently, 20 µl of sterile supernatant from each strain's culture and 2 µl overnight culture of each resident bacterial strain (density of 10⁷ cells ml⁻¹) were inoculated into 180 µl of fresh NB medium (10X diluted, in order to better reflect the effect of the supernatant), respectively. Control treatments were inoculated with 20 µl of 10X diluted NB media instead of the bacterial supernatant. Each treatment was conducted in triplicate. All bacterial cultures were grown for 24 h at 30°C with shaking (170 r.p.m) before
measuring OD₆₀₀ as resident bacterial density using a SpectraMax M5 plate
reader.

To test the significance of direct toxicity of resident bacteria on each other,
we conducted a t-test to compare mean differences between bacterial density
(OD600) from the treatment with exposure to bacterial supernatants and the
control treatment, with P-values below 0.05 being considered statistically
significant. We used the same method as described for the co-culture assay to
determine the strength and directionality of direct toxicity of resident bacteria
on each other. We also calculated mean intensity of interaction between
resident species in supernatant assay (MIF_S) via the method used to calculate
the mean intensity of interaction in the co-culture assay. Two-species
communities were defined as facilitative when MIF_S > 0, antagonistic when
MIF_S < 0.

Measuring resident community toxicity towards Ralstonia solanacearum at high and low resource availability

In order to link pairwise interactions between resident species to resident
community toxicity towards the pathogen at both high and low resource
availability, we quantified pathogen growth in the presence of two-species
resident community supernatants, which were collected after growth under the
conditions of high (100% NB) or low (10% NB) resource availability, respectively.
Briefly, after 36 h of growth in 100% or 10% NB medium on a shaker at 170
r.p.m, 30°C, two-species resident community cultures were filter-sterilized to
remove living cells (0.22 µm filter). Subsequently, 20 µl of sterile supernatant
from each community's culture and 2 µl overnight culture of R. solanacearum

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(density of $10^6$ cells ml$^{-1}$) were added to 180 µl of fresh 10X diluted NB medium. Control treatments were inoculated with 20 µl of 10X diluted NB media instead of the community supernatant. Each treatment was conducted in triplicate. Bacteria were grown for 24 h (30°C, 170 r.p.m) before bacterial densities were measured as optical density at 600 nm with a SpectraMax M5 plate reader. Pathogen inhibition was defined as the percentage of reduction in pathogen growth compared to pathogen growth in the control treatment.

*Measuring invasion success in microcosms at high and low resource availability*

Bacterial communities were constructed according to the scheme provided in Supporting Information Table S2. Each resident community was first inoculated into media of the two resource availability treatments (with a starting density of $10^5$ cells ml$^{-1}$). The invader *R. solanacearum* QL-Rs1115 (tagged with the pYC12-mCherry plasmid) was subsequently introduced into all communities (with a starting density of $10^4$ cells ml$^{-1}$). Communities were incubated at 30°C with 170 r.p.m orbital agitation for 36 h (200 µl together with bacteria and medium per well of 96-well plate), a time chosen to allow all communities to reach stationary phase. Invader density was measured as the red mCherry protein fluorescence intensity (excitation: 587 nm, emission: 610 nm) using a SpectraMax M5 plate reader. Wells contained a total of 200 µl of liquid, including 196 µl of media, 2 µl inoculum of constructed resident community and 2 µl inoculum of the invader. Control treatments were inoculated with 2 µl of sterile water instead of the constructed resident community. Each treatment was replicated four times. To control for the auto-fluorescence of resident community, we also grew each community at the two levels of resource availability and subtracted these values from those obtained from co-cultures

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with the invader.

## Statistical analyses

To test how resource availability influences the pairwise interactions between resident species, we used t-tests to compare mean differences between resident bacterial interactions at high and low resource availability levels, with P-values below 0.05 being considered statistically significant. We used linear regression to test whether the secondary metabolites from each resident strain (Mean intensity of interaction between resident species in supernatant) affected the pairwise interactions between resident species (Mean intensity of interaction between resident species in coculture) at high and low resource availability levels.

In order to determine how resource availability-modulated changes in pairwise interactions between resident species influence the community resistance to pathogen invasion (invader density), we used linear regression to analyze relationships between pairwise resident bacterial interactions (Mean intensity of interaction between resident species in coculture) and invader density at low and high resource availability levels. Furthermore, we also used linear regression to disentangle the mechanisms behind the impact of resident bacterial interactions on pathogen invasion at low and high resource availability levels. Specifically, we focused on exploring how changes in the pairwise interactions between resident species, as influenced by resource availability, affected pathogen density via effects on resident community productivity and toxicity towards pathogen.

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To explore the identity effects (Yang et al., 2017) of the resident species on
properties of resident communities, we conducted a model which expressed
the productivity of resident communities, resident community toxicity towards
invader and density of the invader as a function of the presence of each species
(binary predictors), respectively. Before all analyses, pathogen density data
were log₁₀-transformed to fulfill the parametric model assumptions (i.e. linear
regression). All data were analyzed using the R 4.0.2 program (www.r-project.org).

## Acknowledgements

This research was financially supported by the National Natural Science
Foundation of China (31972504). M.L. was supported by Chinese Scholarship
Council (CSC). There is no conflict of interest to declare.

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Table and Figure legends

**Table 1.** ANOVA table summarizing the interactive effects of pairwise interactions within the resident community and resource availability on the productivity of resident communities, resident community toxicity towards invader and density of the invader. Significant effects (P < 0.05) are highlighted in bold and the ‘up’ and ‘down’ arrows denote positive and negative effects respectively.

Accepted Article
<table><caption>**Table 2.** ANOVA table summarizing the effect of resident community toxicity towards invader and productivity of resident community on invader density. The ‘up’ and ‘down’ arrows denote positive and negative effects respectively.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Productivity of resident community</th><th colspan="3">Resident community toxicity towards invader</th><th colspan="3">Invader density</th></tr><tr><th>df</th><th>F</th><th>P</th><th>df</th><th>F</th><th>P</th><th>df</th><th>F</th><th>P</th></tr></thead><tbody><tr><td>Mean intensity of interaction in coculture (MIF)</td><td>1</td><td>108.32</td><td><strong>&lt;0.001</strong></td><td>1</td><td>7.30</td><td><strong>0.008</strong> ↓</td><td>1</td><td>1.21</td><td>0.2741</td></tr><tr><td>Resource availability (RA)</td><td>1</td><td>230.08</td><td><strong>&lt;0.001</strong></td><td>1</td><td>65.96</td><td><strong>&lt;0.001</strong></td><td>1</td><td>145.06</td><td><strong>&lt;0.001</strong></td></tr><tr><td>MIF * RA</td><td>1</td><td>1.72</td><td>0.1938</td><td>1</td><td>22.55</td><td><strong>&lt;0.001</strong></td><td>1</td><td>39.32</td><td><strong>&lt;0.001</strong></td></tr><tr><td>No. of Residuals</td><td colspan="3">86</td><td colspan="3">86</td><td colspan="3">86</td></tr></tbody></table>

<table><thead><tr><th rowspan="2"></th><th colspan="3">Invader density at high resource availability</th><th colspan="3">Invader density at low resource availability</th></tr><tr><th>df</th><th>F</th><th>P</th><th>df</th><th>F</th><th>P</th></tr></thead><tbody><tr><td>Resident community toxicity towards invader</td><td>1</td><td>27.03</td><td><strong>5.567e-06</strong> *** ↓</td><td>1</td><td>8.99</td><td><strong>0.004551</strong> ** ↑</td></tr><tr><td>Productivity of resident community</td><td>1</td><td>12.14</td><td><strong>0.001169</strong> ** ↑</td><td>1</td><td>20.86</td><td><strong>4.278e-05</strong> *** ↑</td></tr><tr><td>No. of Residuals</td><td colspan="3">42</td><td colspan="3">42</td></tr></tbody></table>

**Fig. 1** Schematic figure depicting hypothesis and experimental design. (A) invasion success as a function of interactions between resident species driven by resource availability, (B) pairwise interactions between resident bacteria by co-culture and supernatant assays, (C) two-species communities were confronted by a pathogen invasion, at low and high resource availability levels. LRA and HRA denote low resource availability and high resource availability, respectively.
**Fig. 2 Resource availability influences pairwise interactions between resident bacterial species.** Pairwise interactions at low (A) and high (B) resource availability levels, and toxicity within resident communities at low (C) and high (D) resource availability levels. (E) Relationships between resident pairwise interactions (mean intensity of interaction) in supernatant and in coculture at low and high resource availability levels. Grey shaded areas depict 95% confidence intervals of the logistic regression. Horizontal and vertical lines for each point indicate error bars, which denote mean ± 2 SEM. LRA and HRA denote low resource availability and high resource availability, respectively. Coculture refers to the test of pairwise interactions between bacteria in coculture. Supernatant denotes the effects of sterile supernatant from each strain on the other strains.

**Fig. 3 Resource availability alters bacterial pairwise interactions with effects on productivity of resident community and its toxicity towards invader and productivity of resident community.** The relationships between mean intensity of interaction in coculture and (A) productivity of resident communities, (B) resident community toxicity towards pathogen at low and high resource availability. In x-axis of both panels A and B, values below and above zero denote for competitive and facilitative resident communities, respectively. LRA and HRA denote low resource availability and high resource availability, respectively. Grey shaded areas depict the 95% confidence interval of the logistic regression, horizontal and vertical lines in each dot in the figures are error bars, which denote for ± 2 SEM.

Accepted Article
**Fig. 4 Resource availability alters bacterial pairwise interactions with effects on community resistance to pathogen invasion.** The relationship between (A) mean intensity of interaction between resident species in coculture, (B) productivity of resident community, (C) resident community toxicity towards invaders and invader density at low and high resource availability, respectively. Dashed lines show the baseline invader densities in control treatments (invader-only). For the x-axis in panel A, values below and above zero denote for competitive and facilitative resident communities, respectively. LRA and HRA denote low resource availability and high resource availability, respectively. Grey shaded areas depict 95% confidence intervals of the logistic regression, and horizontal and vertical lines for each point indicate error bars, which denote mean ± 2 SEM.
<figure><img src="image_6.png" /></figure>

(B) Bacterial interactions

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

(C) Pathogen invasion

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>figure 1.eps</figcaption></figure>
<figure><img src="image_11.png" /></figure>

LRA-Coculture

<figure><img src="image_12.png" /></figure>

HRA-Coculture

E

<figure><img src="image_13.png" /></figure>

Mean intensity of interaction
between resident species in supernatant

<figure><img src="image_14.png" /></figure>

LRA-Supernatant

<figure><img src="image_15.png" /></figure>

HRA-Supernatant

Competitive

Facilitative

Neutral

<caption>figure 2.eps</caption>
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

Mean intensity of interaction between resident species in coculture

figure 3.eps
<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /><figcaption>figure 4.eps</figcaption></figure>