Pseudomonas fluorescens and Pseudomonas putida) into laboratory microcosms inoculated with 680 different mixtures of bacteria derived from naturally occurring microbial communities collected in the field. Using 16S rRNA gene amplicon sequencing to characterise microcosm starting composition, and high-throughput assays of community phenotypes including productivity and invader survival, we determined that productivity is a key predictor of invasion resistance in natural microbial communities, substantially mediating the effect of composition on invasion resistance. The results suggest that similar general principles govern invasion in artificial and natural communities, and that factors affecting resident community productivity should be a focal point for future microbial invasion experiments.Common garden experiments that inoculate a standardised growth medium with synthetic microbial communities  suggest that the ability of the community to resist invasions by additional microbial taxa can be predicted by the overall community productivity (broadly defined as cumulative cell density and/or growth rate). However, to the best of our knowledge, no common garden study has yet investigated the relationship between microbial community composition and invasion resistance in microcosms whose compositional differences reflect natural, rather than laboratory-designed, variation. We conducted experimental invasions of two bacterial strains  grow and consume resources faster than other combinations – this is known as achieving high productivity.Jones et al. conducted invasion experiments across hundreds of naturally-occurring microbial communities collected from woodland puddles that form in the exposed roots of beech trees. Each community contained different combinations of bacteria, but they all largely survived by breaking down leaf litter, so Jones et al. created a tea from beech leaves in which to grow these natural communities in the lab. The relationships between community composition, productivity and invasion resistance were then assessed using a combination of DNA sequencing, measurements of community growth and measurements of invader survival. Jones et al. found that natural combinations of bacteria that grew well together drove invasion resistance in these communities, mirroring results seen in much more artificial communities grown in the lab.These results suggest that productivity is a key factor underpinning invasion resistance in naturally-occurring microbial communities. This is a useful insight that could shape thinking about how the long-term stability of beneficial microbial communities – such as healthy gut microbiomes – might be improved, and how harmful communities – such as dental plaques – could be destabilised. The next step will be to conduct similar experiments in other natural microbe communities to see how generally applicable these results are. Microbial communities of all types are challenged by the arrival of dispersing microbes, which may displace resident taxa and alter ecosystem functioning . A primaStudies with simplified, synthetic microbial communities have suggested that community productivity is of such importance to a microbial community’s invasion resistance that it is often the main explanation for the effect that the composition of the community inoculated into the microcosm has on invasion resistance . In thesHowever, the relationship between a microbial community’s composition, productivity and its invasion resistance is far from clear-cut. For example, Here, we use natural microbial communities to test the hypotheses that (A) resident community productivity is a primary factor underpinning invasion resistance and (B) the effect of resident community composition on invasion resistance is mostly mediated by productivity. We conducted experimental invasions into 680 bacterial communities collected from rainwater pools (water-filled beech tree holes/phytotelmata). These types of model communities have been extensively used in previous microcosm experiments  and are Pseudomonas fluorescens and Pseudomonas putida) at 24, 96, and 168 hr  post-invasion , assessing the survival of two invaders  to estimate the sequence abundances of OTUs inoculated into the microcosms (Day 0). As well as estimating the starting community composition in this way (i.e. 16S genotypes present), we also took various phenotypic measurements of the communities at 7 and 14 days of growth in the laboratory microcosms (the latter being the day of invasion). Community productivity was the primary community-level phenotype of interest to us, quantified by measuring cell density and respiration before invasion. Additionally, we took phenotypic measurements related to metabolic activity as an alternative hypothesis to productivity, and to further contextualise any results related to composition and productivity. These measurements were the potential metabolic activity (ATP levels) and capacity of the communities to degrade a set of four specific substrates that we expected to be important components of these environments . The experiment allowed us to identify the main components of invasion resistance in natural communities, and assess whether the effects of resident community composition on invasion resistance primarily manifest as effects on productivity.To select the components of resident community starting composition and realised community phenotype that best predicted the two invaders’ survival in the microcosms across the three sampling points, we compared explanatory variable importance using random forest regressions . Random forest regressions included all explanatory variables relating to starting composition and realised phenotype. The best representation of composition was selected by testing different dimensionality reduction techniques and selecting the best compromise between variance explained and number of variables. We selected a network approach reducing composition to key species' communities, hereafter termed functional groups .2), with substantially better performance predicting invasions at 24 compared to 96 and 168 hr, and for P. fluorescens compared to P. putida invasions. This correlated with the number of invasions falling below the detection limit of the invasion assays; invader survival declined with time since invasion, and was also lower for P. putida than for P. fluorescens  was sometimes of comparable variable importance to the productivity variables. All these variables were approximately linearly and negatively correlated with invader survival; increasing cell yield, respiration and abundance of Functional Group 18 was correlated with lower invader success  and 20  also appeared to be somewhat important for invader survival. There was some evidence of a weak negative relationship between Simpson’s diversity and invader survival  on the above described relationships  suggesteThere was little evidence for strong effects of more specific measures of community phenotype, with ATP activity and the potential to degrade specific substrates having a weak effect overall  and 3.Having identified community productivity as the most important predictor of invasion resistance and identified the abundance of functional groups inoculated into the microcosms as the putative cause of this, we aimed to estimate the extent to which the effect of starting composition on invasion resistance was mediated by productivity. We used structural equation models (SEMs) to identify whether starting community composition was associated with lower invader survival solely because it determined the productivity of communities. Having specified the latent variables using the most important explanatory variables , we sought to understand the mediation of composition by productivity across our experiments by using three different SEMs in which these latent (dependent) variables Composition and Productivity influence the latent (independent) variable Invasion following one of these hypothesis:No mediation – only resident community starting composition (directly) affects invaders’ survivalPartial Mediation – resident community starting composition (directly) affects invaders’ survival, but also has an indirect effect through its influence on community productivity, which in turn affects invader survival.Complete Mediation – resident community starting composition only influences invaders’ survival through its indirect effect on community productivity.2 = 7.81, pval = 0.0052) - though this should be interpreted with caution as the quality of the models is not optimal (CFI ~ 0.63). Based on the estimated coefficients of the best, Partial Mediation model . The Partial Mediation model was the best fitting of the three models and was significantly better than the Complete model  difference between the Partial and Complete mediation model fits, the strong effects of productivity in the Partial model, and the sub-optimal quality of the models, we conducted an additional sensitivity analysis . This sensitivity analysis tested how sensitive model selection was to the invader survival values fell below the strict detection limit of the invasion assay . The sensitivity analysis supported the Complete (the best model in 97.4% of permutations) over the Partial mediation model (the best model in 2.6% of permutations), highlighting the marginality of the model selection result.Taking everything into account, we believe the most conservative interpretation of these results, therefore, is that most of the effects of composition occurred through productivity.We found that even in complex, species-rich bacterial communities, community productivity is a key determinant of invasion resistance, with much of the effect of community composition manifesting as effects on productivity. This is consistent with the results of earlier experiments with simpler, more artificial communities . ProductThere is ample evidence that resident community productivity reduces the potential for the growth of invading species in microbes and non-microbes alike . Cell deP. fluorescens SBW25 invasion success and resident P. fluorescens SBW25 community diversity (R2 = 0.7) in 2–5 strain communities grown in a defined laboratory medium, driven by the presence of the most productive P. fluorescens SBW25 strain type. Another more recent study with five-strain communities of Ralstonia grown in defined laboratory media, showed that resistance to invasion by another Ralstonia strain was determined by the presence of one or two strains with the fastest growth rate  – determined the level of invasion resistance realised by the community (i.e. a dominance/selection effect).Structural equation modelling revealed that productivity strongly mediates the effect of starting composition on invasion success - further emphasising the extent to which invasion resistance is mainly a side-effect of productivity. This result is similar to that of previous studies with much more artificial microbial microcosms. wth rate . What isThe most parsimonious explanation for this effect in our system was simply that this was the most productive functional group, and a higher starting abundance gave this group of bacteria a better chance of outcompeting other bacteria and raising the level of growth in the community (i.e. a priority effect). Alternatively, the starting abundance of particular functional groups in a microcosm may determine community productivity in more complex ways – for example, because the most abundant early colonisers cause local environmental changes that affect the growth potential of other colonising bacteria (e.g. through cross-feeding and competition). In dental biofilms it has been demonstrated that a full biofilm can only be achieved with a certain order of colonisation, with metabolically-similar groups of early colonisers causing local environmental changes that allow for subsequent bacteria to colonise, allowing the full development of the biofilm . SimilarThere was also some evidence that community composition impacted invasion resistance independently of the impacts of community productivity, although in a more minor way – as has been seen in a more limited sense in previous studies with artificial communities .One explanation for this is that groups of bacteria which were abundant in some communities but not others were functionally distinct; specialising on resource-poor or resource-rich environments  and degrPseudomonas may be prevented from invading by the presence of species that alter the growth medium towards more acidic pH values . Although it is hard to make a direct comparison, superficially at least, this weak relationship contrasted with previous experiments using artificial communities constructed from isolates or created by dilution-to-extinction of natural communities, where the relationship was clearer (We found some evidence for the expected negative relationship between the diversity (Simpson’s diversity of OTUs) of the inoculated community and the invaders’ survival  in this  clearer . Nonethe clearer , the weaP. putida; see Materials and methods: Laboratory techniques). Qualitatively, these results corroborate the idea that invaders with conspecific residents (P. putida) are less successful – although the general mechanisms of invasion resistance appear similar in both P. fluorescens and P. putida experiment , it is possible that some non-bacterial microorganisms such as fungi or phage survived and could have affected invader survival. This is difficult to avoid and so future studies might improve explanatory power by keeping the inoculated communities even more intact, characterising compositional effects on invasion at multiple taxonomic and trophic levels simultaneously (again using metagenomic sequencing). Thirdly, additionally characterising community composition nearer to the point of invasion may have improved explanatory power and better contextualised the results. Our unpublished work with a subset of these communities suggests that despite our lab acclimation period , some more minor compositional changes still occur in these communities after inoculation (as should be expected), with communities converging towards a similar composition but maintaining a high level of diversity. Characterising these changes and accounting for them in models of future experiments might therefore help confirm, for example, whether differences in communities’ invasion resistance are the result of the rate at which certain functional groups of bacteria take over the community during growth.Regarding productivity, the strong predictive power of productivity at 7 days (i.e. 7 days prior to invasion) implies that the temporal dynamics of the community growth is an important component of invasion resistance. Increasing the temporal resolution of growth measurements may therefore have improved the predictive power, as would methods to better distinguish active vs dormant proportions of the population.Aside from productivity and composition, measuring additional variables in the growth period including pH  and bettExperiments with artificial communities have suggested that the composition of microbial communities mostly affects invasion resistance by affecting their productivity . Our expA new generation of microbial invasion ecology experiments with more natural microbial communities  might thFagus sylvatica beech trees (−1 cyclohexamide (Sigma-Aldrich) to remove fungi, and the communities allowed to grow for 7 days at 22°C to reach stationary phase and acclimatise to laboratory conditions. Communities were then frozen in 60% glycerol with NaCl solution  to produce a frozen community archive, and revived from these frozen stocks for further experiments.We used naturally occurring bacterial communities collected from 680 tree hole communities across Southern England between August 2013 and April 2014. The term ‘tree hole’ refers to the naturally occurring, semi-permanent pools of rainwater that collect in the pans formed by the buttress roots of ch trees . BacteriThus, these communities were ‘natural communities’ to the extent that they had only been subject to manipulations for practical purposes – dilution for filtering, the removal of fungi, acclimation to the laboratory growth medium, and freezing/thawing. We consider that the 1:4 dilution for filtering was minor compared to that typically used in dilution-to-extinction experiments . The removal of fungi as a practical simplification of the communities is perhaps most contentious manipulation, as fungi are likely to be important decomposers of leaf litter in this system. However, previous experiments also typically worked with bacteria-only resident communities and so our results are comparable. Finally, freezing the communities and re-growing them from frozen stocks for each experiment was a practical step to allow repeatable experiments using the same, fully sequenced starting community. To ameliorate this, the whole procedure of re-growing, functionally characterising and invading communities was repeated in four separate assays and the means of these four pseudo-replicated measurements used for the final analysis.https://www.mrdnalab.com; Shallowater, TX, USA), with primers and barcodes removed according to their standard procedure.Communities were sequenced at the point of freezing/archiving and thus, when we talk about ‘composition’ in this paper, we are referring to the composition of the inocula added to the microcosms at the start of the experiment – as is conventional for microbial diversity/composition-invasion resistance experiments. Briefly, immediately prior to archiving, DNA was extracted from all communities using ZR-96 DNA Soil extraction kits . Extractions were amplified on the V4 region of the 16S rRNA gene, using the barcoded 515F and 806R PCR primers using the HotStarTaq Plus Master Mix Kit . The PCR cycle used was: 94°C for 3 min, followed by 28 cycles of 94°C for 30 s, 53°C for 40 s and 72°C for 1 min, followed by a final elongation step at 72°C for 5 min. Sequencing was performed by MR DNA using the MiSeq platform (The common garden culture medium selected for this experiment was a ‘beech leaf tea’ (BLT), designed to approximate the predominant nutrient conditions found in tree holes. To produce this medium, 50 g of autumn-fall beech leaves were autoclaved with 500 ml of distilled water to create a concentrated solution that was diluted 32-fold to produce the final BLT culture medium. To begin the common garden experiment, communities were revived from the acclimatised stocks by adding 50 μl of each stock to 1.8 ml of BLT in 1.8 ml deep-well 96 well plates and growing for 14 days at 22°C before invasion treatments were applied.During the 14 day growth period prior to invasion, cell density and respiration were characterised at 7 (7 days before invasion) and 14 days (the day of invasion) as estimates of productivity.Cumulative density of the communities was measured by sub-sampling the communities and staining with Thiazole Orange  at concentration of 100 nM for 15 min and analysing cultures using flow cytometry . Thiazole Orange is a membrane permeant nucleic acid stain, staining both live and dead bacteria and thus this assay represents the total cumulative yield in each community. Fluorescence gating against negative (beech tea) controls was used to count the number of fluorescent cells in each community (cumulative cell density).2 released was absorbed by the indicator gels above the cultures, and the gel colour changed from pink to purple. Colour change is read via a spectrophotometer prior to suspension above cultures (OD600 at 0 hr), and after 24 hr of respiration/after suspension above cultures. The change in colour is used to calculate mgCO2 released in the 24 hr period using a standard curve.Respiration of each community was measured by sub-sampling and using the MicroResp system . Briefly, agar-set indicator gels were suspended above growing sub-cultures at 7 and 14 days in a deep-well 96-well plate in an airtight system. As growing cells respired, COWe also took phenotypic measurements related to the metabolic activity of communities at 7 and 14 days by performing assays of ATP and enzyme activity. ATP activity assays provide a general estimate of how metabolically active communities are, and we expected that communities that were more metabolically active (i.e. producing more ATP) would be more able to actively defend against invaders (e.g. through resource competition or direct competition).ATP activity assays were performed at 7 and 14 days of community growth prior to invasion using the BacTiter-Glo Cell Viability Assay . This assay is a two-step process that releases ATP stored inside cells in order that it can bind to bind to ATP-activated luciferase present in the formula. The maximum luminescence generated in the immediate 5 min reading period is thus proportional to the amount of ATP in the sample, which is calculated exactly by converting into concentration in nM using a standard curve.Enzyme activity assays measured the degree to which communities were degrading particular substrates present in the BLT. We measured the metabolism of the following substrates by the following enzymes; hemi-cellulose by β-xylosidase, chitin by β-N-acetylhexosaminidase, cellulose by β-glucosidase and phosphate groups by phosphatase. These substrates are common in beech leaf litter , the comPseudomonas fluorescens SBW25 and Pseudomonas putida KT2440 – were separately introduced into each of the 680 communities. To perform these two invasion assays, communities were homogenised and aliquoted out twice into 237.5 μl volumes in sterile white microtitre plates. Each invader was grown in 10 ml of BLT medium for 96 hr prior to invasion, and 10 μl added to the community aliquots with 2.5 μl of 100 mM IPTG solution . Invaders reached mean densities of 2.5 X 107 ± cells/mL in 48 hr, and so this represented a high invasion pressure of approximately 105 invader cells entering each community containing an average of 105 cells according to cytometry readings.After the 14 day growth period during which productivity and metabolic activity were characterised, two lux-tagged, IPTG-inducable bacteria - Pseudomonas were chosen as invaders both because of the aforementioned evidence of their abundance on beech leaves, and because they are fast-growing, metabolically versatile bacteria that are common colonisers, contaminants and pathogens in microbial communities . As detailed in our Results, anecdotally our results support the idea that invasion resistance is greater where there is a high abundance of conspecifics, but that the mechanisms of invasion resistance are similar regardless. However, there could have been many more reasons for this and species-level resolution for OTUs is unreliable  during log phase, so we used the calibration curve to convert luminescence values into invader cell densities. Invader survival was defined as the density of metabolically active invader cells/ml−1 in the resident communities after some defined period since invasion . The upper detection limit of the luminescence assay was 12 lumens (the maximum background luminescence of 1000 sterile BLT microcosms), which equates to estimated invader survival detection limits of 6.26 x 103 cells/mL for P. fluorescens and 1.06 x 104 cells/mL for P. putida. These detection limits were accounted for in our sensitivity analysis  which suggested that values below the detection limit contained some real invader signal as well as noise.Luminescence of lux-tagged bacterial strains is directly related to the density of metabolically active cells . TherefoIn total, therefore, we assayed 680 communities (n = 680) for invasion resistance to two invaders with invader survival measured at three time points . 680 was the number of communities remaining from the original set of 753 communities after the removal of communities with less than 10,000 sequences and without all growth assay and/or invasion assay data. This entire 3 week long assay of 4063 measurements  was repeated four times  from frozen stocks to the last measurement . Values used in the experimental analysis are the means calculated from these technical replicates. No explicit power analysis was used as our study was not straightforwardly hypothesis-driven (or at least there were many possible hypotheses) and we had a very large number of replicates (n = 680).https://www.mrdnalab.com) as described previously . These sequences were then trimmed to 250 bp and aligned using MUSCLE multiple sequence alignment in Geneious 2.0. A phylogenetic tree was created with a Maximum Likelihood method of phylogenetic inference using RaxML with 100 bootstraps using the GTR+G sequence evolution model. After construction, this tree was rooted by specifying Halobacterium salinarum as the outgroup. It is important to briefly note the limitations of using such a 16S gene tree when interpreting our results about phylogenetic effects. Whilst 16S is arguably the optimum single-locus choice for microbial phylogeny analysis, 16S tree trees’ efficacy is limited by problems such as horizontal gene transfer, different versions of 16S genes within a single organism and the large differences in the similarity of 16S genes among species depending on the genus considered  was computed from the species abundance table using the diversity function from the function rao.diversity from the R package SYNCSA . Rao’s qGiven the difficulty of analysing the effects of 581 OTUs with only 680 samples, we opted to reduce the dimensionality of the starting composition data before proceeding with our main analyses, testing seven approaches.For the first five approaches, we simply aggregated the abundances of the OTUs at each of five taxonomic ranks , according to the species-level taxonomic assignment of the OTU. This approach was intended to account for the fact that higher level taxonomic groupings may drive compositional effects  and relatedly, take account for the fact that species-level resolution for OTUs is unreliable .For the sixth approach, we performed a principal coordinates analysis of the distance matrix of the Jensen-Shannon divergence  of OTU aPaenibacillus species that had less than three members. This was done on the basis of the high abundance of Paenibacillus borealis across the communities, and the high connectivity of Paenibacillus OTUs. This whole process resulted in the selection of 19 functional groups  as the representation of composition for this approach.For the seventh and final approach, we calculated a network representing significant correlations between OTU abundances inferred with SparCC , consideP. fluorescens and P. putida invader survival at 24, 96, and 168 hr  as the response variable. In total, there were therefore 21 random forests compared . We compared the dimensionality-reduction techniques by plotting the total variance explained against the number of variables permuted in each of the 21 random forests . To do this, before our main analyses, we computed 7 sets of random forest regressions using only these dimensionality reductions of starting composition as the explanatory variables, and  forests . This re forests  to be th10 transformed after adding a pseudocount (+1) to each of the density/ml estimates, in order to deal with zero values. All the explanatory variables except for the three diversity indices (Simpson’s diversity and phylogenetic distance from the invader) were log10 transformed.All statistical analyses were performed in the R programming language . Before We used random forest regressions implemented using the ‘randomForest’ package  to find P. fluorescens and P. putida invader survival.For the main analysis, random forests were computed for each of the six sub-experiments (2 invaders x three time points). For each community, starting composition was computed using the selected dimensionality reduction approach i.e. the mean sequence abundance (across species in the group) in each of the 19 (+1) functional groups). Additionally, the 14 phenotypic measurements of the communities were included as explanatory variables, totalling 37 explanatory variables. We used the variable importance values from the random forests to narrow down the 37 explanatory variables to the best predictors of The random forest approach was useful for identifying the key explanatory variables for invader survival in our system, but as a black box approach, it is difficult to use to understand the shape of the relationships between explanatory and response variables, and assess fit. In our second part of our main analysis therefore, we sought to estimate the putative chain of causation between composition, productivity and invasion resistance using structural equation modelling (SEMs). This approach allowed us to test three scenarios to estimate the extent by which the productivity mediated the effect of composition on invasion (see main text). SEMs were implemented using the lavaan package .For these models, we created a latent variable called ‘Productivity’ including cell yield at 7 and 14 days. We chose these variables because in two out of the six of experiments  cell yield and 7 days was most important, whilst in the other 4  cell yield at 14 days (the day of invasion) was most important . Althoug10-transformations applied previously . The three structural equation models described in Results  were entered as latent variables in these models to explain the six invader survival end-points (response variables), after the same logWe conducted sensitivity analyses as part of the SEM analysis, in order to validate that the results of the main analysis on the raw data were not an artefact of random noise below the detection threshold of the luminescence assay (which was converted into CFU/ml units). For this analysis, we randomly shuffled the under detection limit invader survival values 999 times by sampling the CFU-converted under detection limit data without replacement using the ‘sample’ function (base package). To assess sensitivity, we examined the average model fit parameters for the Complete, Partial and No Mediation models fit to the 999 generated datasets. When values below the detection limit were randomly shuffled, the model comparison very marginally supported the Complete (mean ΔAIC = 0.97) over the Partial mediation model (mean ΔAIC = 0.02). However, the AIC values of all three models also increased by 1383.71–1400 (i.e. fit decreased) - indicating that at least some the values below the conservative detection limit were informative in the original models. Despite the sensitivity of the result therefore, the nature of the change was consistent with the idea that productivity-independent effects of composition played a role in invasion resistance alongside effects of productivity.Other R packages used include ‘rstudioapi’ for setting working directory , ‘tibbleOSF and Github, copy archived at https://doi.org/10.5281/zenodo.7640512 .Thank you for submitting your work entitled "Productive bacterial communities exclude invaders" for consideration by eLife.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in As mentioned in the review of the Reviewing Editor, we believe that your study would have great potential for an entirely new submission, which we would allow if you can resolve the issues mentioned by the reviewers. The novelty of your study is that you took natural communities into the lab and tested under uniform conditions how their diversity affected the time to exclusion of an internal vs. an external invader, and how this effect of community composition on ecosystem functioning was mediated by community productivity. However, your analysis and writing do not reflect this narrative but rather compare the effects of some minimal proxies of community composition/diversity with those of the covariate community productivity, excluding the link diversity-productivity. Furthermore, it is not clear if you bacterial communities are only containing bacteria or also fungi or other organisms. For further details please carefully study the three attached reviews.Reviewer #1:I have some mixed feelings about this manuscript. On the one hand, I like the fact that natural microbial communities, rather than experimentally assembled communities or communities established through serial dilution, were used to study microbial invasions. Experimentally assembled microbial communities are much simpler than natural communities, and the use of serial dilution to generate diversity gradient runs the risk of confounding species diversity effects with species composition effects. The use of natural communities that differ in diversity and composition eliminates these problems. On the other hand, however, there are several issues in the manuscript that complicate the interpretation of the reported data.First, if I am not mistaken, it seems that no treatment was done to eliminate fungi in the water samples collected from tree holes, thus fungal communities were likely present in the experimental microcosms. If this is the case, fungi may have competed with bacteria for resources, and played a role in regulating bacterial invasion in the experiment. Thus although this study did not mention fungi at all, the reported findings may be vulnerable to alternative explanations related to the role of resident fungal communities for bacterial invasion.Pseudomonas species as invaders invites question about the generality of the reported findings. This small number of invading species contrasts sharply with the large number of different resident communities used in the experiment. A greater number of bacterial species, with different degrees of evolutionary relatedness, should have been used to ensure the robustness of the findings.Second, the use of only two closely related Third, the choice of the large inoculum size  is also puzzling. The initial population size of the invaders actually was so high that they were probably greater than population sizes of most, if not all, resident species. Such large propagule pressure almost never exists in nature, making this study an unrealistic exploration of biological invasions. Apparently, the two invader species were competitively inferior to some of the resident species, and declined over time in abundance after their introduction, and some invader populations were probably on their way to extinction. In this context, the term "invasion success", quantified as invader abundance, is also misleading.eLife.Overall, I feel these issues are significant and cannot be addressed with revision, and recommend the rejection of this manuscript for publication in Reviewer #2:Pseudomonas). The study is innovative, because the authors used natural bacterial communities isolated from tree holes .In their paper, Jones et al. conducted an experiment testing whether residential community composition, species richness or productivity of several hundred bacterial communities determines the invasion success of invading bacterial species  and suffer from a fundamental misconception (2).Regarding (1): experiments manipulating species richness have shown that reducing diversity in a working ecosystem generally reduces its functioning. In contrast, it is less clear if natural (i.e. working) ecosystems of higher species richness function better than natural ecosystems with lower richness. One of the main problems are confounding environmental variables such as soil fertility. Thus, the novel experiment that nobody had done so far would be to take the species of these natural ecosystems and recreate them under common environmental conditions, e.g. by planting plant communities in the same composition in an experimental garden as they occur in their natural environments. The study done by the authors is the first ever doing almost exactly this and thus super novel . I say "almost exactly", because it is not clear to me how well the authors could know that (a) their OTUs are species and (b) their communities contained only those species, i.e. only bacteria and no other organisms .Regarding (2): there have been long, often misguided discussions aboutthe importance of "species richness per se" in affecting ecosystem functioning vs. other factors, in particular species identities and species composition. The authors take this even further and compare the importance of diversity  with the importance of an "ecosystem function" as covariate on invasion success, i.e. ecosystem functioning (say EF). This is like earlier arguments saying that when assessing effects of plant species richness (D) on community biomass (EF) one should consider only plots with high plant cover (P). However, the hypothesis is D → EF, and for that to be true it does not matter which mechanism (and how), in this case D → P → EF, mediates the causal relationship D → EF. When "importance" of explanatory variables is assessed, one cannot compare mediator variables with explanatory variables, but rather different explanatory variables, for example an environmental factor such as temperature (T) and D, where T and D ideally should be fully orthogonal to each other. In other words, you can compare T → EF and D → EF, but to look into the system that you have above you should use for example path analysis (SEMs) with arrows D → P, P → EF and D|P → EF, the latter indicating the effect of D on EF that is not mediated by P. Understanding (2), the authors do not find that P is more important than D in explaining EF, but that P provides a mechanistic explanation on the effect of D on EF. It is always like this in biodiversity research!I have further major points that currently lead me to suggest rejection of this manuscript, but may hopefully allow the authors to write a new, more impactful manuscript.(3) The EF measure in this study is not really invasion success but rather exclusion success or exclusion time, which could be calculated for each community as time until reaching the detection limit, using the three time points available. Especially for invaders, it seems more logical to start with low numbers, where they can be in full growth phase, than with carrying capacity. Still, I think the authors did a legitimate experiment by starting with carrying capacity, but then it is a test of exclusion success (better exclusion time) of the native community or persistence success (persistence time) of the invader.(4) I would strongly suggest to not only use random forests for analysis. To make this study comparable to the earlier experimental study of the same system , it would be far better to use general linear (mixed) models. Regarding proxies for D, I would suggest to use more PCo axes instead of simply 6 derived groups of communities. For example, taking the first 5-10 PCo axes or so would seem to much better incorporate the community-composition aspect of D than do the 6 groups. Also, I'm surprised that the authors did not calculate some phylogenetic diversity measures, which should be possible because they did calculate phylogenetic distances to the invaders. Overall, I think a much richer analysis than presently done would be possible with the excellent data.(5) It was difficult to find out the relevance of using two invaders and why it was an advantage that they were genetically closely related. The authors should mention more prominently that one represented an "external invader" and the other an "internal invader". Similar distinctions have been made for invasion in the Jena Experiment, for example. They should also more deeply discuss the effect of this "treatment factor". [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Reviewer #1:I have some mixed feelings about this manuscript. On the one hand, I like the fact that natural microbial communities, rather than experimentally assembled communities or communities established through serial dilution, were used to study microbial invasions. Experimentally assembled microbial communities are much simpler than natural communities, and the use of serial dilution to generate diversity gradient runs the risk of confounding species diversity effects with species composition effects. The use of natural communities that differ in diversity and composition eliminates these problems.On the other hand, however, there are several issues in the manuscript that complicate the interpretation of the reported data.First, if I am not mistaken, it seems that no treatment was done to eliminate fungi in the water samples collected from tree holes, thus fungal communities were likely present in the experimental microcosms. If this is the case, fungi may have competed with bacteria for resources, and played a role in regulating bacterial invasion in the experiment. Thus although this study did not mention fungi at all, the reported findings may be vulnerable to alternative explanations related to the role of resident fungal communities for bacterial invasion.Our apologies – there was indeed a treatment of cyclohexamide to remove fungi prior to producing the frozen archive of communities, but we neglected to mention it in the Methods and Materials before. They’re now in Field techniques: Field sampling of communities (line 452) and it’s also detailed in another publication of ours .Second, the use of only two closely related Pseudomonas species as invaders invites question about the generality of the reported findings. This small number of invading species contrasts sharply with the large number of different resident communities used in the experiment. A greater number of bacterial species, with different degrees of evolutionary relatedness, should have been used to ensure the robustness of the findings.This is indeed a limitation of the study – in order to give the results wider generality, one would have to test this with a much more diverse set of invading species. However, this was not our primary research question and it was impractical to do this whilst also having good replication of the resident communities. For this reason we, like most similar experiments before us, chose to focus our experimental design on having a large number of resident communities, representing a gradient of diversity and composition. This was especially important in this study, because the diversity and composition differences amongst natural communities are far less extreme than those typically seen across communities artificially designed to differ in their diversity and composition (requiring more extensive sampling to capture differences). This was the first study of its type and so we feel this was appropriate.We have now been more explicit about the reasoning for choosing two closely-related invaders in Methods: Laboratory techniques: Choice of invaders (lines 580-597).Third, the choice of the large inoculum size  is also puzzling. The initial population size of the invaders actually was so high that they were probably greater than population sizes of most, if not all, resident species. Such large propagule pressure almost never exists in nature, making this study an unrealistic exploration of biological invasions.5 invader cells is large and may seem unusual from the standpoint of conventional invasion ecology. Reviewer 3 also notes that “Especially for invaders, it seems more logical to start with low numbers, where they can be in full growth phase, than with carrying capacity”. However, as noted by Mallon et al., (2018), “microbial invasions are usually an anomaly within the conventional invasion framework…natural or anthropogenic driven microbial invasions may start with an initially large population size” comparable to, or larger than that of the resident community. Mallon et al., (2018) gives the example of high levels of E. coli introduction via faecal deposition on soil, and in this study and previous studies similar to ours  they also introduced invaders at approximately the same density as the resident community. This idea is also supported by the ‘community coalescence’ paradigm in microbial ecology, which posits that microbes from distinct microbial communities frequently invade one another as entire communities . We now give these examples in our Methods: Invasion assays (line 551) section, as well as providing specific estimates (based on previous studies) of invading and resident bacteria in natural tree hole communities. This system-specific evidence lends greater confidence to the idea that our invasion set-up – under which the number of invading bacteria were approximately equal to the number already established in the resident community – is not unrealistic. Furthermore, in this ecosystem where the main input is likely to be the annual autumn litterfall, natural invasions are likely to occur at low frequency but high magnitude (in terms of propagule/inoculum size).We agree that the inoculum of 10This said, we agree that our use of the term ‘invasion success’ implied that we were talking about invasion-from-rare in the more conventional ecological sense, and have therefore changed the terminology to ‘invader survival’ for clarity, following van Elsas et al., (2012) and Mallon et al., (2018). This fits with the comment of Reviewer 3 on the use of a large inoculum that “Still, I think the authors did a legitimate experiment by starting with carrying capacity, but then it is a test of exclusion success (better exclusion time) of the native community or persistence success (persistence time) of the invader”.In case of remaining doubt, other similar papers  have introduced invaders at lower densities than the resident community . Even under these conditions, the number of invading species is likely to be greater than the population size of many resident species – though the overall invading population size was less. However, perhaps more importantly, introducing a lower number of invaders did not appear to affect the way invasion resistance operates; invasion success is lower when they are introduced at a lower density (i.e. the intercept changes), but the diversity-invasion success relationship is broadly similar (i.e. the slope is the same) .In summary, there is evidence that our invasion conditions are comparable to previous studies, are a realistic approximation of high microbial invasion pressures in nature, and that the fundamental relationships of invasion resistance to composition do not appear to change with the number of individuals introduced. We therefore believe our choice of inoculum size was reasonable, balancing realism with practicality (e.g. ensuring the majority of invasions were detectable) and allowing us to study the community-side of invasion under conditions in which invader-side conditions were unlikely to be limiting.Apparently, the two invader species were competitively inferior to some of the resident species, and declined over time in abundance after their introduction, and some invader populations were probably on their way to extinction. In this context, the term "invasion success", quantified as invader abundance, is also misleading.P. fluorescens/P. putida invader survival’.We agree that the term “invasion success” – especially the “success” part, could be misleading. The invader did indeed decline from its starting density over time from its initial density, and so ‘success’ is probably the wrong term to use and we have now changed this to ‘E. coli at 108 cells/g soil and observed much more dramatic decline to a maximum of ~3000 (8 log CFU) invader cells/g after 24 hours, which declined further over time. Similarly, Eisenhauer et al., (2013) introduced Pseudomonasputida into established microcosms at 5% relative abundance, which declined to below 5% relative abundance in almost all communities. However, van Elsas et al., (2012) used the term ‘invader survival’ and Eisenhauer et al., (2013) used the term ‘invasion’, rather than ‘invasion success’ and so again, we agree that the term ‘success’ is misleading and have used ‘invader survival’ accordingly.It is also worth emphasising here that the decline in abundance was almost inevitable; unless the invader completely took over the community, it could not expect to maintain the same density as the resident species. This is a typical result in microbial invasion experiments in closed (where no new resources are added) microcosms where the invader is introduced at a high density after an initial period of community establishment . For example, van Elsas et al., (2012) introduced Overall, I feel these issues are significant and cannot be addressed with revision, and recommend the rejection of this manuscript for publication in eLife.Reviewer #2:In their paper, Jones et al. conducted an experiment testing whether residential community composition, species richness or productivity of several hundred bacterial communities determines the invasion success of invading bacterial species (two strains of Pseudomonas). The study is innovative, because the authors used natural bacterial communities isolated from tree holes .The found several community types that cluster according to species composition and two of types were particularly vulnerable to invasion, even though the species diversity was comparable to the other community types. This difference in community composition lead to differences in community productivity, which then in turn determined invasion success of the two invaders. The authors found that community diversity explained only negligible part of the invasion success. These findings have large implications for the field of microbial invasion ecology. The authors argue convincingly that future studies should go beyond the idea of community diversity and community composition by measuring as many community functions as possible and by conducting such experiment in a more dynamic system.The figures are great. They are carefully prepared, easy to understand and they can be interpreted without reading the full paper.Thank you for these kind words recognising the innovation of our study and the communication of it – they were very motivating and helpful in rewriting the manuscript. In particular, regarding your kind comments on our figures – the figures have now changed as is necessitated by the rewrite, but we kept this comment in mind throughout and tried to use similar elements in our new figures to communicate the re-analysis as effectively as before (and hope we have done so!).1. Impact statement: When reading through the abstract and the introduction, it seems to be that the statement should rather read "A microbial community's productivity is a better predictor of its invasion resistance than its diversity".Abstract and Introduction have now been changed and should reflect this.To me it seems that productivity is better than composition, but composition is still much better than diversity. For example, see statement on lines 106/107. This result is a bit lost in the title and impact statement. Or maybe the introduction should be targeted a bit more towards composition.Yes this is the correct interpretation of our results. We agree that the diversity discussion is distracting, and so have now rewritten the Introduction to focus more on composition and its effects on productivity and thus invasion resistance.2. L. 202: 48% is very high! Many researchers can only dream of such explanatory power.Thank you for the reassurance – this gives us greater confidence that the results are important and sound.3. L. 228: This is the only paragraph directly following up the Introduction, whereas the rest of the Discussion wasn't introduced as thoroughly. I definitely suggest rewriting the Introduction to move away from the pure diversity focus, bringing in composition, how composition and growth may be related, and maybe even the naturalisation hypothesis.Thank you for your advice – we have rewritten the Introduction to lean away from diversity, and talk more explicitly about the relationship between composition and productivity.4. L. 327-33: This is written in an unnecessarily convoluted and complex fashion. I suggest rephrasing to get the main message better across.We agree and this has been removed/rewritten in the new manuscript.Reviewer #3:As a plant ecologist and biodiversity scientist myself, I cannot judge some of the technical issues and therefore assess this manuscript from my own point of view. In fact, I think the authors did a very novel and excellent study in the still and ever-increasing, hot biodiversity-ecosystem functioning topic. But they do not take sufficient advantage of it (1) and suffer from a fundamental misconception (2).Regarding (1): experiments manipulating species richness have shown that reducing diversity in a working ecosystem generally reduces its functioning. In contrast, it is less clear if natural (i.e. working) ecosystems of higher species richness function better than natural ecosystems with lower richness. One of the main problems are confounding environmental variables such as soil fertility. Thus, the novel experiment that nobody had done so far would be to take the species of these natural ecosystems and recreate them under common environmental conditions, e.g. by planting plant communities in the same composition in an experimental garden as they occur in their natural environments. The study done by the authors is the first ever doing almost exactly this and thus super novel . I say "almost exactly", because it is not clear to me how well the authors could know that (a) their OTUs are species?Thank you for your kind comments regarding the novelty of the study, and apologies that we did not communicate well the points about which you are unclear.Regarding (a) OTUs do not equal species, though are the common species-like unit used in culture-independent microbial ecology using the sequencing method used here (16S amplicon sequencing) – sorry if you know this already I am just not presuming knowledge. In our bioinformatic pipeline, the 581 OTUs were assigned specieslevel taxonomy using BLASTn against a curated GreenGenes database . However, the species-level assignments are intended to be more descriptive than quantitative, given that species-level resolution for OTUs is unreliable (as we have now pointed out on lines 594-597 and 678-683). Nonetheless, despite these limitations we think that the use of OTUs is a reasonable decision for three primary reasons.First, using OTUs makes our results comparable with previous microbial ecology studies looking at diversity-invasion relationships – which have tended to use at ‘strain diversity’ (sometimes attributing a species-level ID to these strains based on 16S amplicon sequencing of a monoculture). For example, De Roy et al., (2013) used 17 strains to make artificial resident communities, several of which were of the same genus. Similarly, van Elsas et al., (2012) randomly isolated strains from soil and used these to assemble communities (without sequencing the strains). Thus whilst some of the OTUs in our dataset that share the same genus may be the same species, previous studies with artificial communities also have this limitation and our results are at least comparable with them. We think that the use of OTUs provides an improvement upon experiments that use artificial communities constructed only from species that are able to be isolated, whilst maintaining a good level of comparability with them.Second, the diversity-invasion relationship was intended to only be a sub-hypothesis in our study – though admittedly, our previous Introduction gave the impression that it was the main focus (and has now been rewritten in response to this issue being raised by Reviewer 2). In our new manuscript, we focus more on composition more widely and our use of functional groups rather than OTUs not only gives our models greater explanatory power but also means that we rely less on individual species-level assignments in interpreting our results.Thirdly, our previous computational analyses of these communities shows that despite this lack of resolution, one can observe that there are distinct community classes that correspond to distinct functional profiles (inferred from predicted metagenomes), which we now mention in Discussion lines 314-317 and Methods: Dimensionality reduction approaches tested 700-702 .And (b) their communities contained only those species, i.e. only bacteria and no other organisms ?Our apologies – as well as a filtering step to remove large organisms, there was indeed a treatment of cyclohexamide to remove fungi prior to producing the frozen archive of communities, but we neglected to mention it in the Methods and Materials before. They are now in Field techniques: Field sampling of communities (line 454-468) and it’s also detailed in another publication of ours using the same communities .There is still a possibility, however, that some organisms survived filtering and anti-fungal treatment . Additionally, there may be bacteriophages present that are having an impact on invader survival, either directly or indirectly. Some of these limitations also apply to previous experiments – especially those that artificial communities created from diluted natural communities  – and so our results are at least comparable when talking about the effects of bacterial community composition. Nonetheless, it is certainly still possible that nonbacterial effects could explain some of the unexplained variation and we now discuss this in Discussion: Unexplained variation (line 387). We would very much like to see a future study that uses shotgun sequencing to unpick the relative contributions of different Kingdoms on invasion resistance in even less-altered natural communities.Regarding (2): there have been long, often misguided discussions aboutthe importance of "species richness per se" in affecting ecosystem functioning vs. other factors, in particular species identities and species composition. The authors take this even further and compare the importance of diversity  with the importance of an "ecosystem function" as covariate on invasion success, i.e. ecosystem functioning (say EF). This is like earlier arguments saying that when assessing effects of plant species richness (D) on community biomass (EF) one should consider only plots with high plant cover (P). However, the hypothesis is D → EF, and for that to be true it does not matter which mechanism (and how), in this case D → P → EF, mediates the causal relationship D → EF. When "importance" of explanatory variables is assessed, one cannot compare mediator variables with explanatory variables, but rather different explanatory variables, for example an environmental factor such as temperature (T) and D, where T and D ideally should be fully orthogonal to each other. In other words, you can compare T → EF and D →> EF, but to look into the system that you have above you should use for example path analysis (SEMs) with arrows D → P, P → EF and D|P → EF, the latter indicating the effect of D on EF that is not mediated by P. Understanding (2), the authors do not find that P is more important than D in explaining EF, but that P provides a mechanistic explanation on the effect of D on EF. It is always like this in biodiversity research!This detailed critique was very welcome and fundamentally reshaped our re-analysis and discussion. We now split to analysis into 3 components:– First, responding to Comment 4 of yours (below), we make sure that we are statistically capturing the effect of 16S composition in an appropriate way. To do this, we compared 7 types of random forests computing composition with 7 different dimensionality reduction approaches (see lines 709-722). Note that we substituted the single 6-level variable ‘community type’  used in the previous manuscript with 19+1 variables representing the abundances of the main functional groups . Both of these groupings  were generated using correlations amongst OTU abundances and there is congruity between the 19+1 functional groups and the previous 6 community types . Moreover, the 19+1 functional groups mean species’ abundances was found to be the most appropriate to explain invader survival, with a predictive power comparable to when all OTUs were included in the model . Thus we opted to use the functional groups in this manuscript, which also represents a more conservative approach than our choice in the previous version, i.e. using 1 categorical variable with six levels/community types.– Second, also in response to Comment 4 of yours, we now use random forest regression as only a part of our analysis – to identify the compositional and functional explanatory variables with the greatest explanatory power (which may overlap). We agree that one cannot really compare the importance of composition with the importance of productivity, since these co-vary and are not orthogonal. ‘Variable importance’ in random forests does not really mean ‘importance’ as it is in common usage, and in our previous manuscript we probably did not communicate this well and tended to conflate the two. Variable importance values in the random forest are estimates of each variables’ explanatory power independent of the other explanatory variables or samples in the model. Nonetheless, even if two explanatory variables have different importance values, the variance explained by them is likely to at least partly overlap with the variance explained by one or more other variables. Thus, we use the random forest to select compositional/genotypic and phenotypic variables whose overlap/mediation we then explore using SEMs (see below).2 obtained for the Invasion latent variable) is similar whether you assume this is partial or complete. The fit of the partial model is the best of the three models , though this is sensitive to values below the strict detection limit of the assay and so we work with the more conservative scenario of strong mediation but with some independent effects in discussion. Note that the ‘partial mediation’ model includes the paths Composition → Productivity, Productivity → Invader survival and Composition|Productivity → Invader survival. Following your wording, this analysis concludes that productivity provides the partial, predominant mechanistic explanation for the effect of composition, but that there are likely also independent effects of composition.– Thirdly and finally, as recommended by you, we use structural equation models to estimate the extent to which productivity mediates the effect of starting composition on invasion resistance, using the subset of explanatory variables selected using random forest. We do this by building and comparing 3 SEMs assuming no, partial mediation and complete mediation of the effect of composition  by productivity (second latent variable including resident community cell density) on invasion . Comparing these models, we see that there is definitely mediation of the effect of composition by productivity, and the overall explanatory power  The EF measure in this study is not really invasion success but rather exclusion success or exclusion time, which could be calculated for each community as time until reaching the detection limit, using the three time points available. Especially for invaders, it seems more logical to start with low numbers, where they can be in full growth phase, than with carrying capacity. Still, I think the authors did a legitimate experiment by starting with carrying capacity, but then it is a test of exclusion success (better exclusion time) of the native community or persistence success (persistence time) of the invader.Our response to Reviewer 1’s Third/Fourth point is relevant here, but in response to your specific comments – yes, we agree upon second consideration that our previous use of the term ‘invasion success’ was misleading. We now adopt the term ‘invader survival’ , which was used in a previous key manuscript exploring similar questions in artificial communities . We liked the suggestion of calculating the time until reaching the detection limit, though unfortunately our data was not suited to this. We only took 3 discrete measurements at the 3 timepoints; these timepoints were not extracted from a larger dataset with the sufficient longitudinal sampling (e.g. hourly reads), which would have made a calculation of exclusion time more feasible. Whilst preferable, therefore, a calculation of the time until reaching detection limit/exclusion time would have unfortunately been very imprecise and statistically underpowered.(4) I would strongly suggest to not only use random forests for analysis. To make this study comparable to the earlier experimental study of the same system , it would be far better to use general linear (mixed) models.2 included .As described above, we have taken this advice and random forest regression is now only one, ‘black box’ step in our analysis, which is complemented by the ‘clear box’, explicit SEM approach, as recommended by you. For further ease of comparison, we also now include a plot of diversity-invader survival with the linear model regression line and RRegarding proxies for D, I would suggest to use more PCo axes instead of simply 6 derived groups of communities. For example, taking the first 5-10 PCo axes or so would seem to much better incorporate the community-composition aspect of D than do the 6 groups.We agree that using the 6 derived groups is an imprecise summary of the effect of composition, and have taken your advice by moving away from these. In the first step of our analysis, we now compare seven different dimensionality reduction approaches for permuting the compositional explanatory variables, selecting the functional groups approach as the best in terms of explanatory power vs number of variables.Also, I'm surprised that the authors did not calculate some phylogenetic diversity measures, which should be possible because they did calculate phylogenetic distances to the invaders. Overall, I think a much richer analysis than presently done would be possible with the excellent data.We have now calculated Rao’s D  and included this in the random forests, which indicates no strong effect of phylogenetic diversity, as for phylogenetic distance. This is now shown in Figure 2 and 3 and we discuss it in our Results (lines 168-170), Discussion (lines 361-381) and Methods 659-670.(5) It was difficult to find out the relevance of using two invaders and why it was an advantage that they were genetically closely related. The authors should mention more prominently that one represented an "external invader" and the other an "internal invader". Similar distinctions have been made for invasion in the Jena Experiment, for example. They should also more deeply discuss the effect of this "treatment factor".See our response to Reviewer 2’s second comment. In short, we have followed your advice and now been more explicit about the reasoning for choosing two closely-related invaders in Discussion (lines 373-376) and Laboratory techniques: Choice of invaders (line 578-597).References:De Roy, K., Marzorati, M., Negroni, A., Thas, O., Balloi, A., Fava, F., Verstraete, W., Daffonchio, D., and Boon, N. (2013). Environmental conditions and community evenness determine the outcome of biological invasion. Nature Communications, 4(1), 1383. https://doi.org/10.1038/ncomms2392Eisenhauer, N., Schulz, W., Scheu, S., and Jousset, A. (2013). Niche dimensionality links biodiversity and invasibility of microbial communities. Functional Ecology, 27(1), 282–288. https://doi.org/10.1111/j.13652435.2012.02060.xHe, X., McLean, J. S., Guo, L., Lux, R., and Shi, W. (2014). The social structure of microbial community involved in colonization resistance. The ISME Journal, 8(3), 564–574. https://doi.org/10.1038/ismej.2013.172Mallon, C. A., Le Roux, X., van Doorn, G. S., Dini-Andreote, F., Poly, F., and Salles, J. F. (2018). The impact of failure: Unsuccessful bacterial invasions steer the soil microbial community away from the invader’s niche. The ISME Journal, 12(3), 728–741. https://doi.org/10.1038/s41396-017-0003-yPascual-García, A., and Bell, T. (2020a). Community-level signatures of ecological succession in natural bacterial communities. Nature Communications, 11(1), 2386. https://doi.org/10.1038/s41467-020-16011-3Pascual-García, A., and Bell, T. (2020b). functionInk: An efficient method to detect functional groups in multidimensional networks reveals the hidden structure of ecological communities. Methods in Ecology and Evolution, 11(7), 804–817. https://doi.org/10.1111/2041-210X.13377Rillig, M. C., Antonovics, J., Caruso, T., Lehmann, A., Powell, J. R., Veresoglou, S. D., and Verbruggen, E. (2015). Interchange of entire communities: Microbial community coalescence. Trends in Ecology and Evolution, 30(8), 470–476. https://doi.org/10.1016/j.tree.2015.06.004Rivett, D. W., and Bell, T. (2018). Abundance determines the functional role of bacterial phylotypes in complex communities. Nature Microbiology, 3(7), 767–772. https://doi.org/10.1038/s41564-018-0180-0van Elsas, J. D., Chiurazzi, M., Mallon, C. A., Elhottova, D., Kristufek, V., and Salles, J. F. (2012). Microbial diversity determines the invasion of soil by a bacterial pathogen. Proceedings of the National Academy of Sciences, 109(4), 1159–1164. https://doi.org/10.1073/pnas.1109326109


We turn graphene from an inert electronic material to the most nonlinear material for the terahertz range using a small voltage. Graphene is conceivably the most nonlinear optoelectronic material we know. Its nonlinear optical coefficients in the terahertz frequency range surpass those of other materials by many orders of magnitude. Here, we show that the terahertz nonlinearity of graphene, both for ultrashort single-cycle and quasi-monochromatic multicycle input terahertz signals, can be efficiently controlled using electrical gating, with gating voltages as low as a few volts. For example, optimal electrical gating enhances the power conversion efficiency in terahertz third-harmonic generation in graphene by about two orders of magnitude. Our experimental results are in quantitative agreement with a physical model of the graphene nonlinearity, describing the time-dependent thermodynamic balance maintained within the electronic population of graphene during interaction with ultrafast electric fields. Our results can serve as a basis for straightforward and accurate design of devices and applications for efficient electronic signal processing in graphene at ultrahigh frequencies. The development of efficient broadband electronic frequency multipliers and modulators operating at the technologically important terahertz frequency range and under normal ambient conditions is of great relevance, but also challenging  enhances the power absorption of the terahertz driving field and consequently provides a control knob that allows for tuning the nonlinear response. On the other hand, too high carrier density and hence the strongly metallic phase of graphene with increased electronic heat capacity will diminish the material’s thermodynamic nonlinearity. This suggests the existence of optimal doping in graphene, favoring its terahertz nonlinearity, which can be controlled externally. Here, we demonstrate such an efficient control of graphene nonlinearity by varying its free carrier density by electrical gating.Recent experiments on terahertz high-harmonic generation (HHG) in graphene directly provided the measurement of its nonlinear coefficients up to the seventh order (EF) on terahertz saturable absorption (SA) and HHG, as well as the associated nonlinear carrier dynamics in gated graphene. We achieve this using nonlinear terahertz spectroscopy using single- and multicycle terahertz driving fields with a peak electric field strength reaching 80 kV/cm. Within the experimentally tested range of EF, increasing from ~50 to 200 meV relative to the Dirac point, transient changes in the terahertz field transmission of graphene in response to a terahertz driving field of 80 kV/cm are enhanced by a factor of ~70. For the same range of increasing EF, an enhancement in the power conversion efficiency in third terahertz harmonic generation by almost two orders of magnitude has been achieved.Being only one atomic layer thick and having a gapless bandstructure, graphene is a material for which wide tuning of the Fermi energy is easily achievable using a small gating voltage of only a few volts, not only in one band but also between the valence and conduction bands across the Dirac point. This offers a simple route to control the terahertz nonlinearity of graphene. Here, we demonstrate the effects of the carrier density , as shown in EF = 200 meV), the rise in the carrier temperature becomes modest, as the absorbed terahertz energy is to be shared among a very large number of carriers, leading to a weaker nonlinear response. Hence, there is an optimal carrier density that provides the largest nonlinearity .Because the electronic terahertz nonlinearity of graphene is facilitated by the absorption of the driving terahertz field by the free carriers and the subsequent thermodynamic process involving the electronic and phononic subsystems, it is the density of these free carriers that provides the key to controlling the terahertz nonlinearity in this material. That is, the terahertz nonlinearity relies on the presence of free carriers, and increasing the free carrier density increases the power absorption and leads to a stronger nonlinear response . At the same time, the two-band model would require the introduction of additional adjustable parameters .Our calculations based on the thermodynamic model presented here use a single-band picture, in which only intraband free carrier dynamics, subjected to the conservation of carrier density within the terahertz-excited band, is considered . Whereas this approach is strictly valid for graphene with a large 4:polyethylene oxide (PEO)] deposited on the graphene layer and the gate electrodes, as shown in On the basis of our simulations that predict a large gate tunability of the nonlinear response in both single-cycle and multicycle cases, we have conducted two types of nonlinear terahertz spectroscopy experiments on our electrically gated graphene sample: (i) table top laser–based intense terahertz pump–terahertz probe (TPTP) spectroscopy using single-cycle broadband terahertz pulses, as shown in V0 has compensated for this hole doping and shifted the Fermi energy to a minimum near the Dirac point, which corresponds to the peak resistance of the sample in EF is considered relative to the Dirac point and is never uniformly zero at ∣V − V0∣ = 0 due to inhomogeneity in the doping and gating field across the graphene layer, as well as a finite thermal broadening of the electron distribution, given kBT = 26 meV at room temperature T = 300 K, resulting in the finite peak resistance at ∣V − V0∣ = 0. Increasing the absolute gating voltage away from V0 leads to an increase in the absolute value of EF, whether for electrons or holes, as shown in EF is related to the free carrier density, Nc, through vF = 1 × 106 m/s is the Fermi velocity in graphene.V − V0∣ simply as the gating voltage. The graphene sample was excited by terahertz pump pulses with peak electric field strength ranging from 1 to 80 kV/cm. The effects induced by the terahertz pump were probed by a weak terahertz probe pulse, at various values of EF controlled by the gating voltage. E = Ered − Eblack, multiplied by four for clarity) that show a larger pump-induced change in transmission for the case of the larger gating voltage.T0 before the pump decreases with the gating voltage due to increasing absorption when the carrier density increases by gating. With the intense terahertz pump, a transient increase in the transmission occurs. This nonlinearity becomes much more pronounced at a larger gating voltage. The secondary smaller features observed at ~16.5 ps in T/T0 of the terahertz probe obtained from the data of T/T0 in T/T0 on both the terahertz pump electric field and the gating voltage is shown in T/T0, i.e., the nonlinear response of graphene, increases with both the terahertz pump field and the gating voltage. We further see the saturation of ΔT/T0 setting in for gating voltages exceeding 0.7 V, which mimics the behavior of the graphene sheet resistance shown in We further observe from f1 = 0.3 THz up to the seventh order, namely centered at the higher-harmonic frequencies f3 = 0.9 THz, f5 = 1.5 THz, and f7 = 2.1 THz. We note here that a conservative estimate of the corresponding nonlinear electronic susceptibilities for HHG from graphene in the perturbative regime (namely at lower driving terahertz fields below 30 kV/cm) are as high as χ(3)~10−9 m2/V2, χ(5)~10−22 m4/V4, and χ(7)~10−38 m6/V6, for the third-, fifth-, and seventh-order harmonics, respectively  is enhanced substantially with increasing EF. Above this doping level and up to EF~260 meV, we see from EF at high doping levels  to be equivalent to the values corresponding to the experimentally applied gating voltage ∣V − V0∣ in the range from 0 to 2 V, as shown in EF is never uniformly zero at ∣V − V0∣ = 0. Therefore, we assigned a value of EF= 50 meV to the gating voltage ∣V − V0∣ = 0, which is consistent with the puddle energy estimates reported in previous works on similar graphene samples . Another free parameter used in our calculations is the momentum scattering time τ0 associated with the linear response of graphene to low terahertz fields and is considered to be a function of EF (T0 indicated in 0 and EF as carefully chosen pairs of values that reproduce the nonlinear response to the intense driving signals as well. We note that choosing other pairs of τ0 and EF in our calculations resulted in a significant deviation between the model and the experimental data. With the obtained range of EF and τ0, we extract a carrier mobility of ~3000 cm2/Vs, which is consistent with values reported for typical CVD graphene (0 with EF indicates that long-range Coulomb scattering dominates the carrier scattering process (In our model calculations, we considered a range of The gigantic terahertz nonlinearity of graphene can be efficiently tuned by modest gating voltages. Our findings thus pave the way for exploiting graphene in applications that require a straightforward control of its nonlinear behavior. Therefore, applications such as graphene-CMOS hybrid devices for tunable frequency conversion, efficient nonlinear terahertz modulators, shutters, switches, and conceivably other applications, are all expected to benefit from both the gigantic nonlinearity of graphene and the possibility to efficiently tune it by a small gating voltage. Graphene also allows for electrical modulation at exceptionally high speeds, limited ultimately by the carrier cooling time of a few picoseconds, thus corresponding to a bandwidth of a few hundreds of gigahertz. Graphene-based amplitude (3 crystal, following the pulse-front tilting technique (In the TPTP experiment, intense single-cycle terahertz pulses with a peak electric field strength up to 80 kV/cm are generated by optical rectification of 800 nm, 40 fs Ti:sapphire laser pulses in a LiNbOIn the multicycle experiments, the graphene sample was excited by terahertz pulses generated by the accelerator-based TELBE superradiant source (4 with 8:1 weight ratio in a solution of methanol (The gate-tunable graphene sample, as shown in


Immune checkpoint blockade (ICB) persistently provides a prognosis improvement but only in a small fraction of patients with cancer due to immunotherapy resistance induced by the consecutive activated oncogenic pathways, including MAPK, Akt, and WNT pathway partially driven by Metadherin (MTDH). However, there is no study to investigate the potential role and mechanisms of MTDH in ICB-treated cancers. Here, we systematically explored the cohorts from The Cancer Genome Atlas (TCGA) and independent cancer cohorts. Elevated MTDH expression was founded to associate with a worse overall survival and poorer immune response in patients with cancer. Dysregulated tumor-infiltrating immune cells and inhibitory immune checkpoint expression were correlated with MTDH expression. Furthermore, the mutual interactions between epithelial-to-mesenchymal-transition, m6A-RNA-methylation, and MTDH may illustrate the potential mechanisms of MTDH resistant to ICB treatment. Although more designed experiments and trials are needed in the future, targeting MTDH may help to overcome immunotherapy resistance in a wide range of cancers.  •MTDH associates with prognosis and immunotherapy response for patients with cancer•MTDH associates with dysregulated tumor immune environment and checkpoint expression•The MTDH/m6A RNA methylation/EMT pathway may contribute to immunotherapy resistance Biological sciences; Molecular biology; Expression study Cancer ranks as a leading cause of death and a massive barrier to increasing life expectancy globally in decades . AccordiMetadherin (MTDH), first identified in primary human fetal astrocytes by rapid subtraction hybridization and named AEG-1, is a well-known oncogene in various cancers, including head and neck cancer, lung cancer, liver cancer, breast cancer, and melanoma . MTDH prTo systematically investigate the mRNA expression of MTDH in pan-cancer levels, we performed the pairwise comparisons of MTDH mRNA in tumor samples and paired-adjacent normal tissues in the available 22 kinds of cancers from the TCGA dataset. As shown in Since higher expression of MTDH was found in cancer, we explored the prognostic value of MTDH expression in the above 12 kinds of cancer. As shown in To validate the above findings, independent GEO cohorts were applied to analyze the associations of MTDH expression with overall survival in cancer. High expression of MTDH remained an unfavorable overall survival indicator in these independent cohorts , fPD1, and tumor immunogenicity score can correlate with the objective response for ICB treatment in TCGA pan-cancer . Here, wAs shown in We further analyzed four independent cancer cohorts to confirm the negative correlation of MTDH expression with immunotherapy response. As shown in + T cells, CD4+ central memory T cells (CD4+ Tcm), and Th1 cells were increased significantly in MTDH-low expression patients across diverse cancer types  positively correlated with the MTDH/m6A/EMT pathway score across diverse cancers. In contrast, CD4Our study is the first investigation focusing on the associations between MTDH expression and immunotherapy response in multiple cancer cohorts, which consistently indicated that high expression of MTDH might resist cancer immunotherapy. The immune response is determined by the interactions between tumor cells and the immune environment. The mechanism of resistance to immunotherapy is obscure, but both tumor-cell-intrinsic and tumor-cell-extrinsic factors may be involved .+ DCs, and ineffective response to immune checkpoint therapy .Further information and requests for resources and reagents should be directed to the lead contact, GC Zhu (This study did not generate new unique reagents.https://xenabrowser.net/). Only Primary Tumor and matched normal tissues were saved for further analysis. Clinical data for TCGA were download from a TCGA-Clinical Data Resource (CDR)  . The tumce (CDR) . The avaGSE65858 , lung adGSE31210 ; invasivGSE20685 ) and an GSE20685  based onThe pooled objective response rate for immune checkpoint blockade in different types of cancer in the TCGA dataset has been described previously  and updaThe proportions of tumor-infiltrating immune and stromal cell populations in TCGA cancer were deconvoluted from corresponding bulk RNA sequencing data by xCell , MCP-CouThe differential gene expression (DGE) analysis was applied to compare the variable genes between MTDH-high and MTDH-low groups by the limma package. Genes with p-value < 0.01, FDR < 0.05 and absolute value of log2FC >1 would be considered as DGE genes and inputted in GO and KEGG pathway enrichment analysis by using the clusterProfiler package . GO termThe protein-protein interaction (PPI) network between MTDH, EMT, and m6A RNA methylation was constructed by String online web  and visuA pairwise student t-test was used to compare the MTDH expression in tumors and matched adjacent normal tissues. Kaplan-Meier analysis or multivariate cox analysis was performed for survival analysis. The Pearson coefficient was applied to analyze correlations. Wilcox test was used to compare the difference between the two variables. Meta-analysis with the random effect model was applied to test the pooled effect of MTDH expression on immunotherapy response in four cohorts with immune checkpoint blockade treatment. All reported p-values are two-tailed, and for all analyses, p < 0.05 is considered statistically significant unless otherwise specified. Statistical analyses were performed using R (version 4.0.1).No additional resource was used in this study.


N, whose dielectric function is described by the Lorentz–Drude model. The axial optical force N, wavelength l) are mainly discussed. Numerical results show that the optical force and torque peaks can be adjusted by increasing the thickness of the graphene coating, and can not be adjusted by changing l. However, l can change the magnitude of the optical force and torque. The numerical results have potential applications involving the trapped graphene-coated gold nanosphere.In the framework of the generalized Lorenz–Mie theory (GLMT), the optical force and torque on a graphene-coated gold nanosphere by a vector Bessel beam are investigated. The core of the particle is gold, whose dielectric function is given by the Drude–Sommerfeld model, and the coating is multilayer graphene with layer number  Light carries both linear and orbit angular momentum. During the interaction between light and small particles, the angular momentum will be transferred from light to particle, and the particles will experience optical force and torque. Optical tweezers, which are based on the optical force and torque, have been used for the manipulation and rotation of microscopic objects, and have found many particularly appealing applications in the field of biomedical engineering. Traditional optical tweezers use Gaussian beams, which suffer from diffraction. To overcome the diffraction, some novel manipulation techniques based on nondiffracting beams ,2,3,4 haIn recently years, gold nanoparticles have proven to be an excellent tool for optical-tweezer-based micromanipulation due to their properties of large polarizability, relatively low cytotoxicity, and localized surface plasmon resonance (LSPR) ,43,44, aIn recent years, graphene has attracted much attention because of its extraordinary electro-optical properties. Adding a graphene coating on gold nanoparticle shows many advantages and has many novel applications. The graphene can protect the nanoparticles since it can avoid the oxidation of nanoparticles . A graphMany studies have been devoted to the optical force on a graphene-coated particle, with emphasis on the effect of the graphene coating. Caused by the local field enhancement due to surface plasmon resonance and the nonlinear response of graphene, a tunable optical force on graphene-coated nanoparticles can be observed . With a N, whose dielectric function is described by the Lorentz–Drude model. The generalized Lorenz–Mie theory for optical force and torque are given. The scattering coefficients are the traditional Mie scattering coefficients for a coated sphere, and the beam-shaped coefficients are given based on the angular spectrum decomposition method and multipole expansion. The optical force and torque are expressed in terms of the surface integration of a Maxwell stress tensor. l, and polarization. A conclusion of the present work is outlined in The rest of the paper is organized as follows. A general theory of the optical force and torque on a graphene-coated gold nanosphere by a vector Bessel beam is given in O, which is the origin of the coordinate system Consider a graphene-coated gold nanosphere illuminated by a vector Bessel beam, as shown in A is a constant parameter for matching theoretical and experimental results, The core of the particle is a gold nanosphere, whose radius is ld model ,62:(1)ε1N, and its thickness is The coating is multilayer graphene with layer number ude mode ,63:(3)ε:u denoting the polarization state is [The incident beam is an state is ,65(5)Qud (ASDM):E=According to the generalized Lorenz–Mie theory (GLMT), the incident and scattered electric fields can be expanded using VSWFs and BSCs  ,67:(6)EiAccording to the GLMT, we can obtain the relation between the expansion coefficients of scattered fields S a surface enclosing the particle. The Maxwell stress tensor The optical force exerted on a particle by a beam is proportional to the net momentum removed from the incident beam, and can be expressed in terms of the surface integration of a Maxwell stress tensor<F>=<∮Sn^given byA↔=14πεEENote that Equations –23) hol hol23) hThe optical torque exerted can also be expressed in terms of the Maxwell stress tensor according to the GLMT as:Substituting the electromagnetic fields into Equation  and aftel, polarization, half-cone angle N. In our calculation, the thickness of monolayer graphene was N, so the thickness of the whole coating was The theory developed in the previous section was used to calculate the optical force and torque exerted on a graphene-coated gold nanosphere placed in a vector Bessel beam. The axial components of the optical force The axial optical force exerted on a graphene-coated gold nanosphere was first investigated. N, the LSPR peaks shift to the longer wavelength. This is mainly caused by the phase retardation effect with the increase of the graphene coating thickness, which is the same as the redshift of extinction spectra [N) is further increased, the peaks shift to the longer wavelength, and the islands become wider.The effects of increasing the layer number of graphene coating to  spectra . TherefoNext, the axial optical force exerted on a graphene-coated gold nanosphere by a first-order (N) on the axial optical force by a first-order vector Bessel beam were also investigated. N), the LSPR peaks shift toward a longer wavelength and also become wider. Furthermore, with the increase of the N, the LSPR peaks cover a larger range of the half-cone angle, and the magnitude of the axial optical forces decrease. These characteristics can be explained by the same method used in the case of The effects of increasing the graphene coating thickness , the peaks shift to a longer wavelength. For The axial optical torque by a zeroth-order Bessel beam was first calculated. ℏ and a spin angular momentum The effect of increasing the beam order to N-layered graphene. The axial optical force and torque were numerically discussed, with particular emphasis on the effects of the graphene coating thickness (layer number N) and beam parameters, including the half-cone angle l, and polarizations. Numerical results showed that LSPR peaks can be seen when a graphene-coated gold nanosphere is placed in a vector Bessel beam. With the increase of the graphene coating thickness, the LSPR peaks shifted toward a longer wavelength, and they became wider. Furthermore, the increase of the graphene coating thickness made the optical force and torque become smaller. Furthermore, the LSPR peaks were very sensitive to the beam parameters l, and polarizations. Thus, by choosing suitable beam parameters and graphene coating thickness, we can provide a better manipulation and rotation of the particle at desirable wavelengths.The optical force and torque on a graphene-coated gold nanosphere by a vector Bessel beam were investigated in the framework of the generalized Lorenz–Mie theory. The dielectric function of the gold core was described by the Drude–Sommerfeld mode, and that of the graphene coating was given by the Lorentz–Drude model. The coating was a