Environmental stability promotes generalist taxa which increase resistance to environmental shocks in methanogenic microbial communities

doi:10.21203/rs.3.rs-3738978/v1

Download PDF

Research Article

Environmental stability promotes generalist taxa which increase resistance to environmental shocks in methanogenic microbial communities

https://doi.org/10.21203/rs.3.rs-3738978/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Different microbial communities are impacted to disproportionately by environmental disturbances. For example, methanogenic microbial communities, which underpin biotechnologies such as anaerobic digestion are particularly susceptible to disturbances, as they are highly syntrophic and dysbiosis in one of the necessary trophic groups can cause system failure. The degree to which such a community can remain stable when faced with a disturbance is referred to as resistance. However, the factors which infer a community’s resistance are unknown. It has previously been hypothesised that resistance may be increased by prior exposure to disturbances or general environmental instability. In this study, we investigate the impact of historical environmental stability on ecological phenomena and in turn, the resistance of a methanogenic microbial community to shocks.

Results

Three separate methanogenic consortia, which were subjected to varying degrees of historical environmental stability, and displayed different levels of resistance to an organic overload were sampled. Their community composition was assessed using high throughput sequencing of 16S rRNA genes. A suite of ecological analysis were applied to determine the effect of environmental stability on ecological phenomena such as microbial community assembly, microbial niche breadth and the rare biosphere and in turn, the effect of these phenomena on community resistance. The community which experienced the most historical stability displayed the most functional resistance to a major disturbance (shock organic load) had a higher number of persistently rare taxa. Environmental instability led to more temporal variation in community beta diversity and phylogenetic dispersion and resulted in a community dominated by taxa with a narrow niche breadth (i.e. specialists), which was more susceptible to deterministic influences caused by a shock organic overload.

Conclusions

These results indicate that frequent, varied environmental disturbances reduce microbial community resistance. This reduced resistance is characterised by a microbial community with lower numbers of rare taxa and a dominance of specialist taxa with narrow niche breadths. Therefore, environmental stability promotes the development of a microbial community which is more resistant to shocks.

16S rRNA

Community Assembly

Generalist

Specialist

Anaerobic Digestion

The impact of environmental disturbances on microbial communities has been a central theme of ecological study for decades ^1–3, with implications for global biogeochemical cycling ⁴, ecosystem functioning ³, environmental biotechnology ^5,6 and human health ⁷. For example, methanogenic microbial communities, play a large role in carbon cycling ⁸ and underpin biotechnologies such as anaerobic digestion ⁹. These communities are particularly susceptible to disturbances, as they are highly syntrophic and dysbiosis in one of the necessary trophic groups can cause system failure ^10,11.

The ability of a microbial community to maintain stability despite disturbances is called resistance, whereas its capacity to recover to its original state is known as resilience ¹. These properties vary among microbial communities, however it is still unclear what causes these variations. One theory is that prior exposure to environmental disturbances increases resistance and resilience ⁶. However, the influence of ecological processes and properties, which are altered by historical disturbances on resistance and resilience requires further investigation. For example, environmental disturbances can affect niche breadth, community assembly, community stability and the extent of the rare biosphere and result in varying degrees of resistance and resilience.

Niche breadth refers to the extent of a given taxa’s niche, relative to prevailing environmental conditions and is directly influenced by environmental (in)stability ¹². Taxa with wider niches are considered generalists, whereas taxa with narrow niches are considered specialists ¹³. It is often stated that generalist taxa will thrive in different environments as they have a wider niche and are not inhibited by variable conditions ¹³. In contrast, specialists tend to dominate in stable environments where they can utilise resources more efficiently within a narrow niche ¹⁴. Therefore, historical disturbances should lead to the proliferation of generalist taxa, thus increasing resistance and resilience to further disturbances.

The rare biosphere plays a role in fulfilling niches which are altered due to environmental disturbances by acting as a “seed bank”, which supplies new taxa to occupy new or varied niches ¹⁵. In the absence of a disturbance, there are some natural fluctuations in community composition about a stable state of equilibrium, with little opportunities for drastic change in microbial community composition ¹⁶. However, during a disturbance, if total community function is to remain the same, members of the original community must persist (resistance) or be replaced by organisms which can fulfil the same niche, potentially seeded by the rare biosphere ¹⁷. Frequent biotic disturbances can increase the proportion of rare taxa in a community ¹⁸ and may therefore strengthen the likelihood of the rare biosphere fulfilling a new or altered niche space.

The ecological properties of a microbial community which impart resistance may be developed during microbial community assembly or re-assembly after a disturbance. Therefore, frequent disturbances may compound these effects. Microbial community assembly is broadly categorised as stochastic or deterministic ¹⁹. The natural variations in microbiome stability are often stochastic in nature. Deterministic community assembly often refers to external forces which drive community development such as environmental gradients or disturbances, which may modify niches and force the community to change its composition ^20,21.

This study tested the effect of environmental (in)stability on community niche breadth, microbial community assembly, the composition of the rare biosphere and microbial community stability in order to determine what community properties improve resistance to a large disturbance. Three laboratory microcosms of methanogenic microbial consortia were subjected to varying degrees of environmental stability before receiving a massive influx of excess carbon, which would typically result in acidosis and community dysbiosis. In depth ecological analysis of the microbial communities prevailing in each of these microcosms revealed that historical environmental instability led to a community with inflated numbers of specialist taxa and reduced resistance to a shock organic load.

2.1 Experimental Design

Granular sludge was sampled from three methanogenic bioreactors which were subjected to varying degrees of environmental stability ²². Associated physiochemical parameters were reported previously ²² and are summarised in Fig. 1. The bioreactors were supplied with a synthetic dairy wastewater consisting of skimmed milk powder, trace elements and macronutrients. After a period of approximately 40 R2 and R3 were subjected to various small disturbances (D1-D4) through variations in influent composition (Fig. 1). R1 was maintained stable for the entire period of operation. R2 was subjected to repeated disturbances in the form of excess carbon influxes by increasing the concentration of the influent. R3 was subjected to a range of disturbances which are common in methanogenic bioreactors including ammonium, a temperature drop, reduced pH and sulfide toxicity (Fig. 1). Subsequently, all three bioreactors were subjected to a massive carbon influx by increasing the chemical oxygen demand (COD) concentration of the feed 8-fold.

2.2 16S rRNA Gene Amplicon Sequencing

Samples for 16S rRNA gene amplicon sequencing were taken from each microbial community at 9 timepoints (T1-T9) throughout the experiment, before and after a disturbance was applied (Fig. 1). DNA extraction, sequencing of the 16S rRNA gene and bioinformatic sequence processing were performed as described previously ²². In addition to previously reported bioinformatic processing, PICRUSt2 ⁴⁷ within the QIIME2 environment was used to recover KEGG enzymes and MetaCyc pathway predictions (not done in the original publication) for all the samples. For this purpose, we used the parameters --p-hsp-method pic --p-max-nsti 2 in qiime picrust2 full-pipeline [https://github.com/gavinmdouglas/q2-picrust2]. Statistical analysis was performed using R software and the details are provided in the Supplementary Material. Raw amplicon sequencing data used in this study has been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the BioProject accession number PRJNA1030189.

3.1 Physiochemical data

Physiochemical, bioreactor performance data and basic measures of alpha and beta diversity were reported in detail previously ²² and are summarised in Fig. 1. R1 demonstrated the most resistance to the shock in terms of performance (soluble COD removal), R2 performed similarly, although slightly worse than R1, whereas R3 performed the worst overall and did not recover to previous levels of performance by the end of the experiment (Fig. 1) ²². In addition, each reactor was sampled at 9 different timepoints throughout the trial (T1-T9) and alpha diversity analysis indicated that the variable environmental conditions of R3 led to significantly higher Shannon diversity at T8, right before the applied shock ²² (Fig. 1).

3.2 Local Contribution to Beta Diversity

The local contribution of each sample to the mean beta diversity (LCBD), across all samples was calculated to identify which reactor had more variation in microbial community composition over time (Fig. 2). Beta diversity varied more in R3, when measured in terms of ASV abundance (Bray-Curtis) and phylogeny (unifrac) (Fig. 2), particularly at T9. Beta dispersion analysis was applied to identify and compare the degree of inter-sample variability within groups (reactors). Significant differences in phylogenetic (unifrac) beta dispersion were identified between R3 and R1, indicating that the phylogenetic composition of R3 was more variable over time than R1. Potential community functions were also estimated using Picrust2 and a distance was calculated between samples using Hierarchical Meta Storm (HMS) ²³. This distance was also subjected to LCBD and beta dispersion analysis (Fig. 2), revealing that functional variations were more limited than taxonomic or phylogenetic variations, indicating that some functional redundancy was present in the community of all reactors. However, a huge change in the functional composition of R3 was observed at T9, after application of the massive shock load.

3.3 Microbial Community Stability

A group of core taxa (limited to 30) with stable relative abundance and minimum coefficient of variation in each reactor were identified. These taxa did not vary significantly in composition in response to the applied disturbance regimes (Fig. 3). The coefficient of variation for the stable community of R3 was higher (CV: 0.0606) than R1 (CV: 0480) and R2 (CV: 0419), indicating that although this community was considered stable in its own right, it was more variable than R1 or R2. The core communities of each reactor also differed in composition (Fig. 3). The stable community of R1 was dominated by the Streptococcus sp., Methanolinea sp., Paludibacter sp., and Desulfovibrio sp., as well as members of the Anaerolinaceae family (Midas_g_667) and the order Cloacimonadales (Midas_g_71685). The stable community of R2 was dominated by Smithella sp., Methanolinea sp., Methanomassiliicoccus sp., as well as members of the family Cloacimonadaeae (Midas_g_63301). The stable community of R3 was heavily dominated by a member of the Spirochaetaceae family (Midas_g_14041), particularly at T9 after the shock load, with lower abundances of Leptolinea sp, genera from the Bacteroidetes_vadinHA17 family (Midas_g_19) and the order Syntrophales (Midas_g_134).

In contrast, the taxa that varied significantly with time and had a high correlation with respect to sampling day in each reactor were also identified (Fig. S1). The most prominent changes over time in reactor 1 included increases in candidate order Moranbacteria (midas_g_11415) and Methanobacterium sp. In R2 Methanospirillum sp. and a genus in the Syntrophomonadaceae family (midas_g_53834) increased in relative abundance over time, whereas Syntrophobacter sp. decreased. The most obvious change in the microbial community composition of R3 was an increase in relative abundance of genera from the Eubacteriaceae family (midas_g_2229), the class Thermodesulfovibrionia (midas_g_581) and Anaeromusa − Anaeroarcus sp.. There was also a higher abundance of a genus in the Spirochaetaceae family (midas_g_14041) (Fig. S1).

3.4 Community Assembly

The relative contributions of stochastic and deterministic influences on community assembly were assessed using two methods; the nearest taxon index (NTI) and normalized stochasticity ratio (NST) ²⁴, both of which revealed similar patterns. Microbial community assembly is considered entirely deterministic when NST is 0% and entirely stochastic when NST is 100%. A 50% stochasticity ratio separates deterministic (< 50%) and stochastic (> 50%) assembly processes ²⁴. A positive NTI value indicates deterministic influence, whereas a negative value indicates stochastic processes dominate.

Community assembly in all 3 reactors was largely stochastic in nature (i.e. NST > 0.5) and remained fairly stable in R1 and R2. However, there was a slightly different temporal pattern in R3, which showed a slight curve over the course of the trial, indicating that stochasticity remained high, but decreased slightly until T5, before increasing again to T8. The final timepoint in R3 however, experienced a drastic increase in deterministic influences, as evidenced by both NST (< 0.5) and NTI (> 2) values.

3.5 Rare taxa

The relative contribution of abundant taxa, conditionally rare taxa, persistently rare taxa and other rare taxa was calculated according to the methods outlined by Yang et al.²⁵. Conditionally rare taxa are defined as taxa which exhibit a maximum relative abundance at least 100 times higher than its minimum value. Persistently rare taxa are defined as taxa which never exhibited a maximum relative abundance 5 times greater than its minimum. Other rare taxa are defined as taxa which exhibited a maximum relative abundance between 5 and 100 times greater than it’s minimum ²⁵.

No conditionally rare taxa were identified in any of the reactors investigated. However, some trends were apparent within the persistently rare taxa and other rare taxa. There was a decreasing trend in the contribution of persistently rare taxa from R1 to R3, which corresponded to a similar trend in the degree of functional resistance that each reactor demonstrated when faced with the applied organic loading rate (OLR) shock load (Fig. 1), i.e. R3 had the least persistently rare taxa and responded worst to the large disturbance. However, the inverse was observed for other rare taxa (Fig. 5). This indicates that there were no large fluctuations in taxa abundance, resulting in conditionally rare taxa (i.e. maximum relative abundance at least 100 times higher than its minimum value). However, R3 did have more taxonomic fluctuations resulting in the presence of other rare taxa (i.e. maximum relative abundance between 5 and 100 times greater than its minimum).

3.6 Specificity

Taxa which were identified as specific to a given timepoint were identified using specificity analysis (Fig. S2-S4). 23 taxa were identified in both R1 and R2 and 22 taxa were identified in R3. The taxa were plotted over time and the incidences of environmental disturbances were indicated to identify the influence of individual disturbances on taxonomic composition (Fig. S2-S4). This approach identifies taxa, which hare highly specific to a given sample or samples. In R3, several acidogenic genera such as Clostridium spp. and Acidaminococcaceae sp. were identified after application of the OLR shock (Fig. S4).

3.7 Microbial Generalists and Specialists

In order to ascertain how different reactor histories impacted microbial lifestyles (i.e. generalists or specialists), the MicroNiche ²⁶ package was used. To determine how the predominant microbial lifestyles in each reactor impacted the their resistance to large environmental shocks, each reactor was considered to be a separate environment and only timepoints 1–8 (prior to the OLR shock) were considered for this analysis. In total, 124 genera were identified which fit the criteria for having a generalist or a specialist lifestyle across all three reactors. R1 contained 82 generalists and 7 specialists, R2 contained 71 generalists and 9 specialists (Fig. 6). R3 however, contained only 39 generalists but had 18 specialists (Fig. 6). The overlap between these 124 taxa was also considered to identify taxa which coincided with each other. Figs. S5-S7 show that there was much less overlap in R3 due to the presence of more specialists. A full list of specialist and generalist taxa and their classification in relation to each reactor is provided as supplementary Table S1.

This study found that unstable environmental conditions (in R3), resulted in the development of a microbial community rich in specialist taxa. This led to reduced resistance to a shock load, which manifested as a large shift from stochastic community assembly to deterministic community assembly (Fig. 4) and ultimately functional failure of the microbial community. In contrast, a stable environment (in R1) led to the proliferation of generalist taxa, which increased shock resistance and reduced the effect of deterministic community assembly. The failure of R3 was surprising as the microbial community at T8 prior to the application of the final shock load had a significantly higher alpha diversity (Fig. 1) than R1 or R2. This is contrary to a lot of research which demonstrates that increased diversity leads to increased stability, resistance and functioning in other anaerobic bioreactors ^27,28, but also other environments including the human gut ²⁹ and soils ^30–32, thus indicating that it is not diversity itself which imparts resistance, but some underlying ecological property of the community. Therefore, it is important to investigate the properties of the R3 community, which were altered by its unstable operational-history and led to reduced resistance.

4.1 Unstable environment leads to variable community composition

The core communities of R1 and R2 remained more stable than R3 throughout the course of the trial (Fig. 3) and community beta diversity fluctuated less in R1 and R2 than R3 (Fig. 2), indicating that the unstable environment of R3 led to more changes in microbial community composition. Interestingly, beta dispersion analysis indicated that there was no significant variation between the reactors in inter-sample variability when only abundance was considered, i.e. bray-curtis (Fig. 2). However, when phylogeny (unifrac) was used to assess beta dispersion (Fig. 2), significant differences were found between R1 and R3, indicating that the unstable conditions in R3 led to more phylogenetic dispersion in temporal samples in R3 over the course of the trial. Community function remained much more stable than community composition in all three reactors, particularly prior to T9 (Fig. 2) and no significant differences in beta dispersion were observed, indicating that the fluctuations in microbiome composition did not have an effect on community function and a degree of functional redundancy in all three reactors. However, a large shift in function was observed in R3 at T9, indicating that the OLR shock changed the microbial communities functional profile. A large proportion of the temporally stable community of R3 was made up of fermentative microorganisms including the Spirochaetaceae family (Midas_g_14041) (Fig. 3). Since this analysis included all timepoints (including after the shock load), only taxa which were unaffected by the shock would be identified. Additionally, taxa which were enriched over time (Fig. 3) included Streptococcus sp. and a Eubacteriaceae family (midas_g_2229) (Fig. S1). Specifcity analysis also identified several fermentative taxa such as Clostridium sp. which proliferated at this timepoint (Fig. S3). Therefore, the fermentative function of the microbial community was maintained or even enhanced throughout the shock.

The indication that phylogenetic composition (unifrac distance metric) was more variable between sampling timepoints in R3 is interesting. As the Unifrac distance metric does not consider abundance, it is a particularly useful metric for excluding the effect of the rare biosphere in diversity analysis ³⁵. The greater phylogenetic dispersion between sampling timepoints in R3 therefore were not a result of fluctuations in rare taxa. However, rare taxa have been repeatedly suggested to play a role in resistance to environmental disturbances ^36,37 and functional redundancy. Therefore, the composition of the rare biosphere was analysed in further detail, see section 2.5.

4.2 Distinct environmental disturbances promoted the establishment of specialist taxa

The absence of any conditionally rare taxa in R3 is surprising as environmental disturbances have previously been shown to promote temporal fluctuations in the rare biosphere ^17,36 and lead to the development of conditionally rare taxa ³⁸. In addition, our analysis indicated that persistently rare taxa made up a lower proportion of the total community in R3. Therefore, it may be the case that although, these rare taxa did not increase enough in abundance to meet the threshold of conditionally rare taxa, they did increase to some extent, thus resulting in a lower proportion of persistently rare taxa. This also explains the observation that the number of other rare taxa were the highest in R3 (Fig. 5).

The taxa which shifted from persistently rare to other rare taxa may have been specialised to deal with a given disturbance (e.g. ammonia), but due to the short nature of the applied disturbances, did not establish enough to become conditionally rare. Indeed, the unstable operation of R3 did lead to the proliferation of a microbial community with a higher proportion of specialist taxa than the control reactor (R1) (Fig. 6). The prevalence of specialist microbes in R3 was likely caused by the variety of applied disturbances, which led to the development of a larger number of narrow niches and less overlap (Fig. S4) throughout the reactor operation. R2 had a similar number of specialist and generalist taxa to R1, indicating that the organic load increases in R2 did not lead to a proliferation of specialist microbes.

Generalist taxa have a wider niche breadth, and are able to survive under a wider set of conditions than specialists ³⁹. In contrast, specialists are more discriminant in the environments in which they thrive ²⁶. The relative proportion of generalists and specialists in a microbial community is thought to influence its stability in the face of a disturbance ⁴⁰. Muller (2019) hypothesised that communities with a high proportion of specialist taxa would be more susceptible to functional breakdown in the face of a disturbance as the population performing the dominant function has a narrower niche breadth and will not be able to resist the change in conditions brought on by a disturbance. The present study provides evidence for this hypothesis in that the more specialist community of R3 exhibited a large degree of functional change in response to a disturbance, whereas the generalist communities of R1 and R2 were able to resist the applied shock.

The observation that different microbial life strategies evolved in the respective reactors and subsequent divergent responses of each reactor to a large disturbance indicates that a community made up of generalist microbes is more resistant to large disturbances. The proliferation of generalists was enhanced in R1 due to the stable environment. This is unexpected as generalist species are often thought to gain an advantage from environmental heterogeneity ¹³. However, the distinct nature of each disturbance applied to R3 (i.e. pH, NH₃, temperature and sulfate) may have led to the establishment of microbes which were specialised to deal with them.

Much more taxa overlap was observed in R1 and R2 (Fig. S4) indicating that more interspecies interactions may have developed, potentially as a result of more consistent environmental conditions. Indeed a recent meta-analysis including pan-genomic data indicated that gnomes of generalists are enriched in functions involved with species-species interactions ⁴¹. The extent of interspecies interactions in a microbial community can impact its resistance to a disturbance as key interdependencies, e.g. sharing of public goods ⁴² are buffered from sudden taxa die-off and ecosystem function is less reliant on a single keystone species, which may be inhibited by a disturbance ^43,44. It has also been suggested that lower numbers of interspecies interactions reduce ecosystem stability ⁴⁵ and increased interspecies interactions have even been shown to increase robustness in methanogenic communities ⁴⁶. Therefore, despite lower diversity, the generalist lifestyle which prevailed in R1 and R2 was crucial in improving their resistance to a major organic carbon influx.

The environmental disturbances applied to R3 led to a more variable microbial community over time and significantly more phylogenetic dispersion within R3 samples than R1. The core community of R3 was also more variable. This made R3 less resistant to the shock load and more susceptible to deterministic influences on community assembly, ultimately leading to a functional shift in the community towards a taxonomic composition with more fermentative microorganisms. The reduced resistance of R3 to was concluded to be due to the development of different niche spaces which were occupied by specialist taxa, whereas the stable environment in R1 led to proliferation of generalist taxa. Because specialist taxa were more abundant in R3, less overlap between respective taxa was detected, indicating that there were fewer interspecies interactions, which made the system less robust to changing conditions. These results have implications for microbial communities and indicate that environmental stability promotes the development of a microbial community which is more resistant to shocks.

COD – Chemical Oxygen Demand

rRNA – Ribosomal RNA

NCBI - National Center for Biotechnology Information

LCBD – Local contribution to beta diversity

HMS – Hierarchical Meta Storm

NTI – Nearest Taxon Index

NST – Normalised Stochasticity Ratio

OLR – Organic Loading Rate

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and material

Raw sequencing data can be found in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the BioProject accession number PRJNA1030189.

Competing interests

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

Funding

This publication has emanated from research supported by Science Foundation Ireland (SFI) through the SFI Research Professorship Programme entitled Innovative Energy Technologies for Biofuels, Bioenergy and a Sustainable Irish Bioeconomy (IETSBIO₃; grant number 15/RP/2763) and the Research Infrastructure grant Platform for Biofuel Analysis (Grant Number 16/RI/3401). UZI is supported by EPSRC (EP/P029329/1 and EP/V030515/1).

Authors' contributions

Simon Mills: Conceptualization, Data curation, Investigation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Umer Z. Ijaz: Data formal analysis, Software, Visualization, Supervision, Writing – review & editing. Piet N.L. Lens: Supervision, Project administration, Writing – review & editing, Funding acquisition.

Acknowledgements

The authors thank Borja Khatabi Soliman Tamayo, Dr Diana Quispe and Marlee Wasserman (NUIG, Ireland) for their help and support during the laboratory work.

Shade, A. et al. Fundamentals of Microbial Community Resistance and Resilience. Front. Microbiol. 3, (2012).
Allison, S. D. & Martiny, J. B. H. Resistance, resilience, and redundancy in microbial communities. Proc. Natl. Acad. Sci. 105, 11512–11519 (2008).
Philippot, L., Griffiths, B. S. & Langenheder, S. Microbial Community Resilience across Ecosystems and Multiple Disturbances. Microbiol. Mol. Biol. Rev. 85, e00026-20 (2021).
Osburn, E. D., Badgley, B. D., Strahm, B. D., Aylward, F. O. & Barrett, J. E. Emergent properties of microbial communities drive accelerated biogeochemical cycling in disturbed temperate forests. Ecology 102, e03553 (2021).
Luo, G. et al. New steady-state microbial community compositions and process performances in biogas reactors induced by temperature disturbances. Biotechnol. Biofuels 8, 3 (2015).
Berninghaus, A. E. & Radniecki, T. S. Shock loads change the resistance, resiliency, and productivity of anaerobic co-digestion of municipal sludge and fats, oils, and greases. J. Clean. Prod. 362, 132447 (2022).
Moya, A. & Ferrer, M. Functional Redundancy-Induced Stability of Gut Microbiota Subjected to Disturbance. Trends Microbiol. 24, 402–413 (2016).
Thauer, R. K., Kaster, A. K., Seedorf, H., Buckel, W. & Hedderich, R. Methanogenic archaea: Ecologically relevant differences in energy conservation. Nat. Rev. Microbiol. 6, 579–591 (2008).
Trego, A. C., Mills, S. & Collins, G. Granular biofilms: Function, application, and new trends as model microbial communities. Crit. Rev. Environ. Sci. Technol. 1–24 (2020). doi:10.1080/10643389.2020.1769433
Zhu, X. et al. Metabolic dependencies govern microbial syntrophies during methanogenesis in an anaerobic digestion ecosystem. Microbiome 8, 22 (2020).
Hao, L. et al. Novel syntrophic bacteria in full-scale anaerobic digesters revealed by genome-centric metatranscriptomics. ISME J. 14, 906–918 (2020).
Kassen, R. The experimental evolution of specialists, generalists, and the maintenance of diversity. J. Evol. Biol. 15, 173–190 (2002).
Chen, Y. J. et al. Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem. ISME J. 15, 2986–3004 (2021).
Xu, Q. et al. Microbial generalists and specialists differently contribute to the community diversity in farmland soils. J. Adv. Res. 40, 17–27 (2022).
Wisnoski, N. I. & Lennon, J. T. Stabilising role of seed banks and the maintenance of bacterial diversity. Ecol. Lett. 24, 2328–2338 (2021).
Fassarella, M. et al. Gut microbiome stability and resilience: elucidating the response to perturbations in order to modulate gut health. Gut 70, 595–605 (2021).
Coveley, S., Elshahed, M. S. & Youssef, N. H. Response of the rare biosphere to environmental stressors in a highly diverse ecosystem (Zodletone spring, OK, USA). PeerJ 3, e1182 (2015).
Wang, Z., Leite, M. F. A., Jiang, M., Kuramae, E. E. & Fu, X. Responses of soil rare and abundant microorganisms to recurring biotic disturbances. Soil Biol. Biochem. 177, 108913 (2023).
Zhou, J. & Ning, D. Stochastic Community Assembly: Does It Matter in Microbial Ecology? Microbiol. Mol. Biol. Rev. 81, e00002-17 (2017).
Chase, J. M. Stochastic Community Assembly Causes Higher Biodiversity in More Productive Environments. Science (80-. ). 328, 1388–1391 (2010).
Ning, D. et al. A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nat. Commun. 11, 4717 (2020).
Mills, S., Yen Nguyen, T. P., Ijaz, U. Z. & Lens, P. N. L. Process stability in expanded granular sludge bed bioreactors enhances resistance to organic load shocks. J. Environ. Manage. 342, 118271 (2023).
Zhang, Y., Jing, G., Chen, Y., Li, J. & Su, X. Hierarchical Meta-Storms enables comprehensive and rapid comparison of microbiome functional profiles on a large scale using hierarchical dissimilarity metrics and parallel computing. Bioinforma. Adv. 1, vbab003 (2021).
Ning, D., Deng, Y., Tiedje, J. M. & Zhou, J. A general framework for quantitatively assessing ecological stochasticity. Proc. Natl. Acad. Sci. 116, 16892–16898 (2019).
Yang, S., Winkel, M., Wagner, D. & Liebner, S. Community structure of rare methanogenic archaea: insight from a single functional group. FEMS Microbiol. Ecol. 93, fix126 (2017).
Finn, D. R. et al. MicroNiche: an R package for assessing microbial niche breadth and overlap from amplicon sequencing data. FEMS Microbiol. Ecol. 96, (2020).
Carballa, M., Regueiro, L. & Lema, J. M. Microbial management of anaerobic digestion: Exploiting the microbiome-functionality nexus. Curr. Opin. Biotechnol. 33, 103–111 (2015).
Briones, A. & Raskin, L. Diversity and dynamics of microbial communities in engineered environments and their implications for process stability. Curr. Opin. Biotechnol. 14, 270–276 (2003).
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
Wagg, C. et al. Diversity and asynchrony in soil microbial communities stabilizes ecosystem functioning. Elife 10, e62813 (2021).
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).
Liu, S. et al. Phylotype diversity within soil fungal functional groups drives ecosystem stability. Nat. Ecol. Evol. 6, 900–909 (2022).
Konopka, A., Lindemann, S. & Fredrickson, J. Dynamics in microbial communities: unraveling mechanisms to identify principles. ISME J. 9, 1488–1495 (2015).
Yang, G., Ryo, M., Roy, J., Hempel, S. & Rillig, M. C. Plant and soil biodiversity have non-substitutable stabilising effects on biomass production. Ecol. Lett. 24, 1582–1593 (2021).
Lynch, M. D. J. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).
Jia, X., Dini-Andreote, F. & Salles, J. F. Unravelling the interplay of ecological processes structuring the bacterial rare biosphere. ISME Commun. 2, 96 (2022).
Liang, Y. et al. Differentiation strategies of soil rare and abundant microbial taxa in response to changing climatic regimes. Environ. Microbiol. 22, 1327–1340 (2020).
Shade, A. et al. Conditionally Rare Taxa Disproportionately Contribute to Temporal Changes in Microbial Diversity. MBio 5, 10.1128/mbio.01371-14 (2014).
Granot, I., Shenkar, N. & Belmaker, J. Habitat niche breadth predicts invasiveness in solitary ascidians. Ecol. Evol. 7, 7838–7847 (2017).
Muller, E. E. L. Determining Microbial Niche Breadth in the Environment for Better Ecosystem Fate Predictions. mSystems 4, e00080-19 (2019).
von Meijenfeldt, F. A. B., Hogeweg, P. & Dutilh, B. E. A social niche breadth score reveals niche range strategies of generalists and specialists. Nat. Ecol. Evol. 7, 768–781 (2023).
Smith, P. & Schuster, M. Public goods and cheating in microbes. Curr. Biol. 29, R442–R447 (2019).
Cordero, O. X. & Datta, M. S. Microbial interactions and community assembly at microscales. Curr. Opin. Microbiol. 31, 227–234 (2016).
Stachowicz, J. J. Mutualism, Facilitation, and the Structure of Ecological Communities: Positive interactions play a critical, but underappreciated, role in ecological communities by reducing physical or biotic stresses in existing habitats and by creating new habitats on . Bioscience 51, 235–246 (2001).
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).
Embree, M., Liu, J. K., Al-Bassam, M. M. & Zengler, K. Networks of energetic and metabolic interactions define dynamics in microbial communities. Proc. Natl. Acad. Sci. 112, 15450 LP – 15455 (2015).
Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).

No competing interests reported.

MillsetalSupplementaryInformation.pdf

Download PDF

Version 1

posted

You are reading this latest preprint version

Environmental stability promotes generalist taxa which increase resistance to environmental shocks in methanogenic microbial communities

Status:

Version 1

Abstract

Figures

1 Background

2 Methods

2.1 Experimental Design

2.2 16S rRNA Gene Amplicon Sequencing

3 Results

3.1 Physiochemical data

3.2 Local Contribution to Beta Diversity

3.3 Microbial Community Stability

3.4 Community Assembly

3.5 Rare taxa

3.6 Specificity

3.7 Microbial Generalists and Specialists

4 Discussion

4.1 Unstable environment leads to variable community composition

4.2 Distinct environmental disturbances promoted the establishment of specialist taxa

5 Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1