Philippa Kaur

The logic of the model
Our spatially-resolved model was simulated in discrete grid boxes of a 100 × 100 array, which included four basic assumptions: (1) Initial individuals were assumed to secrete three public goods but may randomly mutate to lose any of those functions with a certain probability; (2) Secreting a public good created a corresponding metabolic burden, therefore in losing a function the individual would gain a benefit; (3) All public goods were essential for growth. The net growth rates of individuals were dependent on the local concentrations of public goods; (4) Substrate and public goods diffused between two grid boxes at rates proportional to the concentration gradient.
For the 1st assumption, we included three functions because it is the minimal unit and tersest design to simulate complex communities, allows for the emergence of three categories of interaction patterns, and a single cooperative LOF genotype might evolve from differential evolutionary paths (Fig. 1A, B). The genotypes were described by bit strings containing 1 and 0 which indicated the genotype could produce the corresponding public good or not, respectively. Eight genotypes could emerge during the simulations, which were the initial autonomous producer [1, 1, 1], three one-function loss genotypes (OFLGs, i.e., [1, 1, 0], [1, 0, 1], and [0, 1, 1]), three two-function loss genotypes (TFLGs, i.e., [1, 0, 0], [0, 1, 0], and [0, 0, 1]), and a nonproducing cheater [0, 0, 0] (Fig. 1A).
Fig. 1: Logic of the individual-based model.

A Possible genotypes and evolutionary relationships among them emerging from reductive evolution when starting with an autonomous genotype that performs three essential public functions. Note that in this three-function model, some genotypes, i.e., Two-function loss genotypes and cheaters, might evolve from different mother genotypes. B Interaction patterns that could possibly be established in the spatially structured communities. C Schematic of the individual-based simulations. A 100 × 100 array initialization with all autonomous phenotypic individuals (left) was conducted with a long-term stepwise iteration to investigate if diverse interaction patterns could form (right). At each time step, calculations were done from the level of individual grids (top) to whole lattice (bottom). Within each grid box, Monod equation modified by basic assumptions of the Black Queen Hypothesis was used to calculate the microbial growth, while minimum and maximum thresholds of biomass were defined to decide the division and death of individuals (top middle). Microbial individuals were allowed to randomly mutate to lose functions (top middle). Classical discretization of the diffusion equation gave local rules for updating the concentrations of public goods and nutrients in each box (middle). State changes at the individual level lead to the evolutionary dynamics of the communities, which may give rise to the formation of diverse interaction patterns (bottom).

Full size image

The 2nd and 3rd assumptions were developed from the basic mathematical assumption of the BQH [19], and defined individual growth by integrating the benefit and cost of function loss (Fig. 1C). To conceptualize the cost of performing a function, we supposed a parameter (α) which is the fraction of biomass used to produce a public good per unit time of an individual. In addition, we defined a second parameter (β) as the ratio of the amount of public goods required during each step to account for the produced public goods. Therefore the redundant fraction of public goods production was 1−βj, and lower βj reflected a higher amount of redundant public goods that could be gained from the producers by the LOF genotypes, resulting in decreased risk in association with function loss (see Supporting Information S1 for more details). During the model simulation, spatiotemporal dynamic variables, i.e., positioning of genotypes and the time points at which genotypes evolved, would be collected. We initiated the simulations by randomly distributing 100 ancestor cells [1, 1, 1] into the grid boxes and iterated for at least 1,500,000 time steps. During each time step, individuals grew, decayed, reproduced, and mutated according to the previously mentioned assumptions (Fig. 1C). We paid attention to whether stable communities with various interdependent patterns could be formed after a specified number of iterations, as well as recorded the spatiotemporal dynamics of the communities.
Diverse interdependent patterns emerged with high level of function cost and varied level of functional redundancy
For model simulations, the function cost (parameter α) and functional redundancy (parameter β) were assigned to 0.0001, 0.0005, 0.001, and 0.4, 0.6, 0.8, respectively. A total of 2891 independent simulations with 9 parameter sets displayed different community structures (Fig. 2A). When the function cost was assigned to a low level, i.e., 0.0001, the autonomous ancestor dominated the community. When function costs were assigned to higher levels, 0.0005 and 0.001, new genotypes evolved and later interacted to form three distinct types of interdependent patterns even within the same α and β combination, i.e., asymmetric functional complementation (AFC), complete functional division pattern, and one-way dependency, with the relative amounts of 1677/2891, 143/2891, and 48/2891, respectively. In addition, higher functional redundancies favored the loss of more functions, increasing complexity of the community structures.
Fig. 2: Reductive evolution shapes diverse interdependent patterns in microbial communities.

A The final (steady state) community structures across gradients of function cost (α) and functional redundancy (1-β). Results were summarized from at least 300 interdependent runs for each parameter set. Community structures were assessed after simulation for 170,000 iterations, where 98.9% (2891/2923) of runs reached steady state. According to the structures, replicates were clustered into several scenarios for each parameter set, which are shown separately in the area plots. Note that the values of β is the proportion of public goods that is required for growth, and thus 1-β reflects the level of function redundancy. B, C Six representative community dynamics on the spatial lattices were selected from one interdependent simulation with the given conditions (mut = 10−5, α = 0.001, β = 0.8), showing the evolution of three types of asymmetric functional complementary pairs (AFCPs) (B), three different paths for the evolution of pairs [0, 0, 1] & [1, 1, 0] (C). Left images indicate the distribution of different genotypes at different points in evolutionary time. Curve plot in the middle describes the community dynamics of the corresponding simulation. Schematics at right briefly summarize the spatiotemporal dynamics of each simulation: the arrays in (B) indicate one type of AFCP directly dominated the communities without competition from others; the boxes in (C) indicates the composition of ancestor or AFCP in the related time points, while the windows inside indicate the spatial coexistence of multiple AFCPs and the size of the windows represents the relative fraction of different AFCPs.

Full size image

Among the three possible kinds of interactions, the AFC pattern was the most widespread, which was the combination of a two-function-loss genotype (TFLG) and its complementary one-function-loss genotype (OFLG). For example, [0, 0, 1], which produced a single essential public good, depended on its functional complement one-function-loss partner [1, 1, 0], for the other two public goods. Specifically, three types of the asymmetric functional complementary pairs (AFCPs), that is, [0, 0, 1] coupled with [1, 1, 0], [0, 1, 0] coupled with [1, 0, 1], and [1, 0, 0] coupled with [0, 1, 1], colonized most of the grid with a similar frequency of emergence. Interestingly, under the condition of high level of cost, the emergence of AFC patterns was accompanied by some nonproducing cheaters, whose relative abundance rose with the increase in functional redundancy (Fig. 2A top row). The addition of cheaters significantly reduced the total biomass of the communities, suggesting that high functional redundancy favors the evolution of cheaters which may decrease the community productivity. In addition, function loss happened more easily with high function cost. As the function cost parameter α increased from 0.0005 to 0.001, relative abundance of TFLGs increased approximately from 55 to 70% (Fig. 2A).
Besides the AFC patterns, two additional types of interdependent patterns evolved at a relatively lower frequency. The complete functional division pattern, that is, coexistence of [0, 0, 1], [0, 1, 0], and [1, 0, 0], only evolved when both factors were at high levels (α = 0.001, β = 0.4) with a frequency of approximately 45% (143 of 319 simulations, Fig. 2A, top right), which described a scenario with high benefit and low cost of function loss, favoring the loss of more functions and consequently more likely to maintain the evolution of TFLPs. Another form of interactions that emerged was one-way dependency, where one partner performs all functions and other none (i.e., coexistence of [1, 1, 1] and [0, 0, 0]). This form emerged at a low frequency (48 out of all 2891 simulations shown in Fig. 2A), but evolved with a higher probability under the condition of a mid-level function cost and low level of functional redundancy (α = 0.0005, β = 0.6, Fig. 2A, middle left), where the extinction of [1, 1, 1] was ~2.5 times slower than in other scenarios (Supplementary Fig. 1), leading to a higher potential for the spatial proximity between [1, 1, 1] and [0, 0, 0] during evolution.
Taken together, these phenomena demonstrated that the mutualistic exchange of complementary functions happened only when function cost was high. The emergence of different interdependent interaction patterns was related to the function cost and function redundancy, especially for the complete functional division and one-way dependency pattern, which only emerged within a limited parameter range. However, even for a given combination of α and β, it still remained possible for the evolution of distinct interaction patterns, suggesting that stochastic processes may play a role.
Same interdependent patterns might evolve via different modes
Because the evolution of three kinds of AFCPs were the most common scenarios in our simulations, we then focused on the role of stochastic processes, i.e., the key random events, in deciding the winning complementary pair among the three similar but different AFCPs. As a first step, we traced the variation in the spatiotemporal dynamics, trying to cluster the numerous evolutionary dynamics into limited modes and divide the complex evolutionary courses into several stages. These simplifications would facilitate the search for key random events.
Therefore, we analyzed the dynamics of 296 simulations with a typical parameter set (α = 0.001, β = 0.8), because under this condition, only the three types of AFCPs evolved, with a similar frequency of emergence (Fig. 2A, Top left), in order to avoid interference from the other interaction patterns. As described above, any of the three types of AFCPs could potentially take over the final community under this condition (Fig. 2B; Supplementary video 1–3). Using the emergence of AFCP [0, 0, 1] & [1, 1, 0] as an example, three categories of dynamic modes could give rise to its final domination. (1) After pair [0, 0, 1] & [1, 1, 0] emerged and formed a spatial aggregation, it rapidly expanded and took over the entire grid (Fig. 2C, first line; Supplementary video 3). (2) In addition to the pair [0, 0, 1] & [1, 1, 0], spatial aggregations of another AFCP also emerged (e.g., pair [0, 1, 0] & [1, 0, 1] in Fig. 2C, second line and Supplementary video 4). In this scenario, a special spatial pattern was established in a short period after the evolution of both AFCPs e, where pairs of two complementary members exhibited strong spatial mixing, while the two different AFCPs were totally segregated. Community succession was then governed by spatial competition between the two AFCPs. If pair [0, 0, 1] & [1, 1, 0] won the competition, it would dominate the final community. (3) Spatial aggregations of all three AFCPs emerged, and then pair [0, 0, 1] & [1, 1, 0] dominated the community after outcompeting the other two AFCPs (Fig. 2C, third line; Supplementary video 5). The clustering of these three possible modes of AFC patterns was also shown by the temporal dynamics of the α-diversity across different parameter sets (Supplementary Fig. 2), where the evolution modes of the AFC patterns were clearly clustered into three possible categories, suggesting that this clustering is independent of the determined factors α and β.
In sum, the succession of interdependent patterns could be divided into two stages: (1) the emergence of spatial aggregations composed of two interdependent members with strong connections; (2) spatial competition among different aggregations drive the community to evolve to the final state, composed of only one type of interdependent interactions. Of course, if only one type of AFCP emerged, the spatial competition stage would be unnecessary during succession.
Evolutionary random events play important roles in deciding the dominant AFCP in equilibrium communities
The presence of two evolutionary stages lead us to hypothesize that the random events affecting ecological outcomes should arise from two aspects. First, in the initial evolutionary stage, the emergence of interdependent spatial aggregations should be related to the order in which new genotypes emerge. Second, the outcome of the spatial competition should be also influenced by the initial positioning of the new genotypes.
The fact that each TFLP had two possible evolutionary paths (e.g., [1, 0, 0] could inherit its function from [1, 1, 0] or [1, 0, 1]), suggested that the effects of the random order of emergence for different genotypes were highly correlated with the evolutionary lineage. Therefore, to investigate the effects of this, we analyzed the evolutionary lineage of emergence, colonization, and loss of every genotype within the 296 simulations with the typical parameter set (α = 0.001, β = 0.8). In total, there were 24 evolutionary branches leading to the evolution of the three forms of AFC patterns (8 for each, Fig. 3). Among all these branches, we summarized two key random events (Fig. 3, red and blue boxes).
Fig. 3: The evolutionary trajectories of 296 independent simulations with the typical parameter set (mut = 10−5, α = 0.001, β = 0.8).

We analyzed the evolutionary trajectories of every interdependent run and clustered them into 24 types of branches (top, see Methods). The area plot shows the final community structures and the frequencies of each branch (bottom). Blue dashed box shows the evolutionary diversification into four scenarios after the first key event occurs, while the red dashed box indicates the 24 different evolutionary trajectories that diverged after the second key event occurs. Solid boxes with colored circles represent the genotypic composition of communities at different evolutionary time points. Red arrows indicate the branches where one type of asymmetric functional complementary pair (AFCP) directly dominated the communities without competition with other AFCPs, while the blue arrows indicate the branches where one type of AFCP took over the entire space after competitions with other AFCPs. Dashed boxes at the figure labels (right) indicate different AFCPs.

Full size image

The first event occurred after two types of OFLGs emerged. After this evolutionary time point, all three public functions were included in OFLGs. With the benefit of the function loss, these two OFLGs would expand and gradually outcompete the autonomous genotype [1, 1, 1]. Thus, the first key event was whether all three OFLGs could emerge before the autonomous genotype entirely disappeared (Fig. 3, blue box). If not, the third type of AFCP would never evolve; if so, all three types of AFCPs would still have a chance to dominate the final community. In the 296 simulations, the frequencies of these two scenarios were nearly same, that is, 147 simulations were clustered to the former, while 149 simulations were clustered to the latter. The 147 simulations, where the third type of AFCP never evolved, could be then divided into three categories with similar frequencies, where two of the three OFLGs occupied the whole space and excluded the ancestral population.
The second key evolutionary event was the emergence of TFLGs (Fig. 3, red box). After the two or three types of OFLGs successfully colonized, whose functional complementary TFLGs first to emerge in the next evolutionary time would lead to the prior formation of the spatial aggregation of the AFCP. It is obvious that if no other AFCP aggregations formed later, this AFCP would dominate the final community (Fig. 3, red arrow indicated branches). Alternatively, if other AFCP aggregations formed during the expansion process, the spatial competition between different AFCPs would decide the dominant AFCP in the equilibrium communities (Fig. 3, blue arrow indicated branches). In our analysis, the chance of only one AFCP evolving reached 64.7% (198 of the 296 simulations). If only two OFLGs evolved after the first event, the frequency of only one AFCP evolving reached 79.6% (121 of the 152 simulations). In contrast, if three OFLGs evolved after the first event, there could be a relative higher possibility of two or three AFCPs evolving (47.4%), meaning that spatial competition could then be an important process.
What decided the winner of the competition? We observed that after the segregated interdependent spatial pattern was newly established, the relative region sizes occupied by different AFCPs were the key to determining the winner (Fig. 2C, the second and third lines; Supplementary video 4 and 5). We analyzed the time gaps between the emergence of the two AFCPs in the second categories of succession modes and the size of the regions they occupied (Fig. 4A). The result indicated a significantly positive correlation between the length of the time gaps and the region size the prior AFCP occupied (t-test, p  1 indicates pair [0, 0, 1] & [1, 1, 0] is more spatially associated than pair [0, 1, 0] & [1, 0, 1]. Applying these definitions, the simulation results where the advantage of prior space occupancy was not significant (left side of blue line in Fig. 4C, 33 replicates) were selected for analysis, and we found a significantly positive correlation between the relative PAD at the beginning of spatial self-organization and the ‘region size advantage’ (Fig. 5A; p  1 means the prior emerged AFCPs are more spatially associated than the second AFCPs. Red dots indicate the first to emerge AFCP won the competition in the corresponding replicate, while the green dots indicate the second to emerge AFCP won the competition. B Two typical examples of simulations initialized with premixing the two types of AFCPs, [0, 0, 1] & [1, 1, 0] and [0, 1, 0] & [1, 0, 1], which represent scenarios when initial PAI001:010 1, respectively. C The significant positive correlation between the winning frequency of pair [0, 0, 1] & [1, 1, 0] and the initial value of PAI001:010. When initial PAI001:010  > 1, final communities were more likely to be dominated by pair [0, 0, 1] & [1, 1, 0], oppositely, pair [0, 1, 0] & [1, 0, 1] were more favorable when PAI001:010  More

1.
Viver T, Orellana LH, Díaz S, Urdiain M, Ramos‐Barbero MD, González‐Pastor JE, et al. Predominance of deterministic microbial community dynamics in salterns exposed to different light intensities. Environ Microbiol. 2019;21:4300–15.
CAS  PubMed  Article  Google Scholar 
2.
Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”. Proc Natl Acad Sci USA. 2005;102:13950–5.
CAS  PubMed  Article  Google Scholar 

3.
Amann R, Rosselló-Móra R. After all, only millions? MBio. 2016;7:e00999–16.
PubMed  PubMed Central  Google Scholar 

4.
Konstantinidis KT, Ramette A, Tiedje JM. The bacterial species definition in the genomic era. Philos Trnas R Soc Lond B Biol Sci. 2006;361:1929–40.
Article  Google Scholar 

5.
Shapiro BJ, Polz MF. Ordering microbial diversity into ecologicaly and genetically cohesive units. Trends Microbiol. 2014;22:235–47.
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
McInerney JO, McNally A, O’Connell MJ. Why prokaryotes have pangenomes. Nat Microbiol. 2017;2:17040.
CAS  PubMed  Article  Google Scholar 

7.
Andreani NA, Hesse E, Vos M. Prokaryote genome fluidity is dependent on effective population size. ISMEJ. 2017;11:1719–21.
CAS  Article  Google Scholar 

8.
Cohan FM. What are bacterial species? Annu Rev Microbiol. 2002;56:457–87.
CAS  PubMed  Article  Google Scholar 

9.
Lan R, Reeves PR. When does a clone deserve a name? A perspective on bacterial species based on population genetics. Trends Microbiol. 2001;9:419–24.
CAS  PubMed  Article  Google Scholar 

10.
Fraser C, Alm EJ, Polz MF, Spratt BG, Hanage WP. The bacterial species challenge: making sense of genetic and ecological diversity. Science. 2009;323:741–46.
CAS  PubMed  Article  Google Scholar 

11.
Vázquez DP, Simberloff D. Ecological specialization and susceptibility to disturbance: conjectures and refutations. Am Nat. 2002;159:606–23.
PubMed  Article  Google Scholar 

12.
Prosser JI, Bohannan BJM, Curtis TP, Ellis RJ, Firestone MK, Freckleton RP, et al. The role of ecological theory in microbial ecology. Nat Rev Microbiol. 2007;5:384–92.
CAS  PubMed  Article  Google Scholar 

13.
Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H, et al. Fundamentals of microbial community resistance and resilience. Front Microbiol. 2012;3:417.
PubMed  PubMed Central  Article  Google Scholar 

14.
Rodriguez-R LM, Overholt WA, Hagan C, Huettel M, Kostka JE, Konstantinidis KT. Microbial community successional patterns in beach sands impacted by the Deepwater Horizon oil spill. ISME J. 2015;9:1928–40.
PubMed  PubMed Central  Article  Google Scholar 

15.
Petraitis PS, Latham RE, Niesenbaum RA. The maintenance of species diversity by disturbance. Q Rev Biol. 1989;64:393–418.
Article  Google Scholar 

16.
Narasingarao P, Podell S, Ugalde JA, Brochier-Armanet C, Emerson JB, Brocks JJ, et al. De novo metagenomic assembly reveals abundant novel major lineage of Archaea in hypersaline microbial communities. ISME J. 2012;6:81–93.
CAS  PubMed  Article  Google Scholar 

17.
Antón J, Rosselló-Móra R, Rodriguez-Valera F, Amann R. Extremely halophilic bacteria in crystallizer ponds from solar salterns. Appl Environ Microbiol. 2000;66:3052–57.
PubMed  PubMed Central  Article  Google Scholar 

18.
Gomariz M, Martínez-García M, Santos F, Rodriguez F, Capella-Gutiérrez S, Gabaldón T, et al. From community approaches to single-cell genomics: the Discovery of ubiquitous hyperhalophilic Bacteroidetes generalists. ISME J. 2015;9:1–16.
Article  CAS  Google Scholar 

19.
Mora-Ruiz MR, Font-Verdera F, Díaz-Gil C, Urdiain M, Rodríguez-Valdecantos G, González G, et al. Moderate halophilic bacteria colonizing the phylloplane of halophytes of the subfamily Salicornioideae (Amaranthaceae). Syst Appl Microbiol. 2015;38:406–16.
CAS  Article  Google Scholar 

20.
Antón J, Lucio M, Peña A, Cifuentes A, Brito-Echeverría J, Moritz, F, et al. High metabolomic microdiversity within co-occurring isolates of the extremely halophilic bacterium Salinibacter ruber. PLoS ONE. 2013;8:e64701.
PubMed  PubMed Central  Article  CAS  Google Scholar 

21.
Conrad EC, Viver T, Hatt JK, Rosselló-Móra R, Konstantinidis KT. Unrestricted but ecologically-important gene-content diversity within a natural sequence-discrete population as revealed by sequencing of 112 isolates. 2020. In review.

22.
Cuadros-Orellana S, Martin-Cuadrado AB, Legault B, D’Auria G, Zhaxybayeva O, Papke RT, et al. Genomic plasticity in prokaryotes: the case of the square haloarchaeon. ISME J. 2007;1:235–45.
CAS  PubMed  Article  Google Scholar 

23.
Konopka A, Lindemann S, Fredrickson J. Dynamics in microbial communities: unraveling mechanisms to identify principles. ISME J. 2015;9:1488–95.
PubMed  Article  Google Scholar 

24.
Millán MM, Estrela MJ, Miró J. Rainfall components: variability and spatial distribution in a Mediterranean Area (Valencia Region). J Clim. 2005;18:2682–705.
Article  Google Scholar 

25.
Santos F, Moreno-Paz M, Meseguer I, López C, Rosselló-Móra R, Parro V, et al. Metatranscriptomic analysis of extremely halophilic viral communities. ISME J. 2011;5:1621–33.
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Begon M, Townsend CR, Harper JL, editors. Ecology: from individuals to ecosystems. 4th ed. Malten, MA, USA: Blackwell Publishing Ltd; 2006.

27.
Rodriguez-R LM, Konstantinidis KT. Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomics datasets. Bioinform. 2014;30:629–35.
CAS  Article  Google Scholar 

28.
Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.
PubMed  PubMed Central  Article  CAS  Google Scholar 

29.
Oksanen J, Kindt R, Legendre P.O’Hara B. Vegan: community ecology package. Com Ecol Pack. 2007;10:631–37.

30.
Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB; a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Wu YW, Tang YH, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2014;2:26.
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Rodriguez-R LM, Gunturu S, Harvey WT, Rosselló-Móra R, Tiedje JM, Cole JR, et al. The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucleic Acids Res. 2018;43:W282–8.
Article  CAS  Google Scholar 

34.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.
CAS  PubMed  Article  Google Scholar 

35.
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.
PubMed  PubMed Central  Article  Google Scholar 

36.
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Article  CAS  Google Scholar 

37.
UniProt Consortium. Uniprot: a hub for protein information. Nucleic Acids Res. 2015;43:D204–12.
Article  CAS  Google Scholar 

38.
Rodriguez-R LM, Konstantinidis KT. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes. PeerJ Prepr. 2016;4:e1900v1.
Google Scholar 

39.
Caro-Quintero A, Konstantinidis KT. Bacterial species may exist, metagenomics reveal. Environ Microbiol. 2012;14:347–55.
CAS  PubMed  Article  Google Scholar 

40.
Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, Tiedje JM. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol. 2007;57:81–91.
CAS  PubMed  Article  Google Scholar 

41.
Viver T, Orellana LH, Hatt JK, Urdiain M, Díaz S, Richter M, et al. The low diverse gastric microbiome of the jellyfish Cotylorhiza tuberculata is dominated by four novel taxa. Environ Microbiol. 2017;19:3039–58.
PubMed  Article  Google Scholar 

42.
Haynes WM, Lide DR, Bruno TJ, editors. CRC handbook of chemistry and physics, 94th ed. London, UK: CRC Press; 2013. p. 4–89.

43.
Griebler C, Lueders T. Microbial biodiversity in groundwater ecosystems. Freshw Biol. 2009;54:649–77.
Article  Google Scholar 

44.
Konstantinidis KT, Tiedje JM. Towards a genome-based taxonomy for prokaryotes. J Bacteriol. 2005;187:6258–64.
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Oren A. Microbial life at high salt concentrations: phylogenetic and metabolic diversity. Saline Syst. 2008;4:2.
PubMed  PubMed Central  Article  CAS  Google Scholar 

46.
Pedrós-Alió C. Marine microbial diversity: can it be determined? Trends Micribiol. 2006;14:257–63.
Article  CAS  Google Scholar 

47.
Azua-Bustos A, Fairén AG, González-Silva C, Ascaso C, Carrizo D, Fernández-Martínez MÁ, et al. Unprecedented rains decimate surface microbial communities in the hyperarid core of the Atacama Desert. Sci Rep. 2018;8:16706.
CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Allison SD, Martiny JBH. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci U S A. 2008;105:11512–19.
CAS  PubMed  PubMed Central  Article  Google Scholar 

49.
Uritskiy G, Getsin S, Munn A, Gomez-Silva B, Davila A, Glass B, et al. Halophilic microbial community compositional shift after a rare rainfall in the Atacama Desert. ISME J. 2019;13:2737–49.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Ghai R, Pašić L, Fernández AB, Martin-Cuadrado AB, Mizuno CM, McMahon KD, et al. New abundant microbial groups in aquatic hypersaline environments. Sci Rep Nat 2011;1:135.
Article  CAS  Google Scholar 

51.
Burns DG, Janssen PH, Itoh T, Minegishi H, Usami R, Kamekura M, et al. Natronomonas moolapensis sp. nov., non-alkaliphilic isolates recovered from a solar saltern crystallizer pond, and emended description of the genus Natronomonas. Int J Syst Evol Microbiol. 2010;60:1173–76.
CAS  PubMed  Article  Google Scholar 

52.
López-Pérez M, Ghai R, Leon MJ, Rodríguez-Olmos Á, Copa-Patiño JL, Soliveri J, et al. Genomes of “Spiribacter”, a streamlined, successful halophilic bacterium. BMC Genom. 2013;14:787.
Article  CAS  Google Scholar 

53.
Martin-Cuadrado AB, Pašić L, Rodriguez-Valera F. Diversity of the cell-wall associated genomic island of the archaeon Haloquadratum walsbyi. BMC Genom. 2015;16:603.
Article  CAS  Google Scholar 

54.
Mirete S, Mora-Ruiz MF, Lamprecht-Grandío M, de Figueras CG, Rosselló-Móra R, González-Pastor J. Salt resistance genes revealed by functional metagenomics from brines and moderate-salinity rhizosphere within a hypersaline environment. Front Microbiol. 2015;6:1121.
PubMed  PubMed Central  Article  Google Scholar 

55.
Cray JA, Bell AN, Bhaganna P, Mswaka AY, Timson DJ, Hallsworth JE. The biology of habitat dominance; can microbes behave as weeds? Microb Biotechnol. 2013;6:453–92.
PubMed  PubMed Central  Article  Google Scholar  More

1.
Neuenschwander, S. M., Pernthaler, J., Posch, T. & Salcher, M. M. Seasonal growth potential of rare lake water bacteria suggest their disproportional contribution to carbon fluxes. Environ. Microbiol. 17(3), 781–795 (2015).
CAS  PubMed  Article  Google Scholar 
2.
Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313(5790), 1068–1072 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

3.
United Nations Environment Programme. GEO Year Book 2004/5: An Overview of Our Changing Environment (2004). https://www.unep.org/resources/report/geo-year-book-20045-overview-our-changing-environment.

4.
Lindström, E. S. Bacterioplankton community composition in five lakes differing in trophic status and humic content. Microb. Ecol. 40(2), 104–113 (2000).
PubMed  Article  Google Scholar 

5.
Ávila, M. P., Staehr, P. A., Barbosa, F. A., Chartone-Souza, E. & Nascimento, A. Seasonality of freshwater bacterioplankton diversity in two tropical shallow lakes from the Brazilian Atlantic Forest. FEMS Microbiol. Ecol. 93, fw218 (2017).
Article  CAS  Google Scholar 

6.
Zhang, H. et al. Biogeographic distribution patterns of algal community in different urban lakes in China: insights into the dynamics and co-existence. J. Environ. Sci. 100, 216–227 (2021).
Article  Google Scholar 

7.
Ji, B. et al. Bacterial communities of four adjacent fresh lakes at different trophic status. Ecotoxicol. Environ. Safe 157, 388–394 (2018).
CAS  Article  Google Scholar 

8.
Iliev, I. et al. Metagenomic profiling of the microbial freshwater communities in two Bulgarian reservoirs. J. Basic Microb. 57(8), 669–679 (2017).
CAS  Article  Google Scholar 

9.
Linz, A. M. et al. Bacterial community composition and dynamics spanning five years in freshwater bog lakes. mSphere 2(3), e00169 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Hartmann, A., Goldscheider, N., Wagener, T., Lange, J. & Weiler, M. Karst water resources in a changing world: review of hydrological modeling approaches. Rev. Geophys. 52(3), 218–242 (2014).
ADS  Article  Google Scholar 

11.
Yu, S. et al. Spatial and temporal dynamics of bacterioplankton community composition in a subtropical dammed karst river of southwestern China. Microbiol. Open 8(9), e00849 (2019).
Article  CAS  Google Scholar 

12.
Li, Q., Sun, H., Han, J., Liu, Z. & Yu, L. High-resolution study on the hydrochemical variations caused by the dilution of precipitation in the epikarst spring: an example spring of Landiantang at Nongla, Mashan, China. Environ. Geol. 54(2), 347–354 (2008).
ADS  CAS  Article  Google Scholar 

13.
Song, A., Yue, M. L. & Li, Q. Influence of precipitation on bacterial structure in a typical karst spring, SW China. J. Groundw. Sci. Eng. 6(3), 193–204 (2018).
Google Scholar 

14.
Gray, C. J. & Engel, A. S. Microbial diversity and impact on carbonate geochemistry across a changing geochemical gradient in a karst aquifer. ISME J. 7(2), 325–337 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Shabarova, T. et al. Bacterial community structure and dissolved organic matter in repeatedly flooded subsurface karst water pools. FEMS Microbiol. Ecol. 89(1), 111–126 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Li, Q. et al. Contribution of aerobic anoxygenic phototrophic bacteria to total organic carbon pool in aquatic system of subtropical karst catchments, Southwest China: evidence from hydrochemical and microbiological study. FEMS Microbiol. Ecol. 93, fix065 (2017).
Google Scholar 

17.
Stevanović, Z. & Milanović, P. Engineering challenges in karst. Acta Carsol. 44(3), 381–399 (2015).
Article  Google Scholar 

18.
Lu, X. X. et al. Water chemistry and characteristics of dissolved organic carbon during the wet season in Wulixia Reservoir, SW China. Huanjing Kexue 39(5), 2075–2085 (2018) (in Chinese with English abstract).
PubMed  PubMed Central  Google Scholar 

19.
Xin, S. L. et al. Relationship between the bacterial abundance and production with environmental factors in a subtropical karst reservoir. Huanjing Kexue 39(12), 5647–5656 (2018) (in Chinese with English abstract).
PubMed  PubMed Central  Google Scholar 

20.
National Research Council. Assessing the TMDL Approach to Water Quality Management (National Academy Press, Washington, DC, 2001).
Google Scholar 

21.
Cunha, D. G. F., do Carmo Calijuri, M. & Lamparelli, M. C. A trophic state index for tropical/subtropical reservoirs (TSItsr). Ecol. Eng. 60, 126–134 (2013).
Article  Google Scholar 

22.
Lorenzen, C. J. Determination of chlirophyll and pheo-pigments: spectrophotometric equations. Limnol. Oceanogr. 12(2), 343–346 (1967).
ADS  CAS  Article  Google Scholar 

23.
Tamaki, H. et al. Analysis of 16S rRNA amplicon sequencing options on the Roche/454 next-generation titanium sequencing platform. PLoS ONE 6(9), e25263 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Kuczynski, J. et al. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr. Protoc. Microbiol. 27(1), 1–20 (2012).
Google Scholar 

25.
Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).
PubMed  PubMed Central  Article  Google Scholar 

26.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral. J. Ecol. 18(1), 117–143 (1993).
Article  Google Scholar 

27.
Palmer, M. W., McGlinn, D. J., Westerberg, L. & Milberg, P. Indices for detecting differences in species composition: some simplifications of RDA and CCA. Ecology 89(6), 1769–1771 (2008).
PubMed  Article  Google Scholar 

28.
Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6(2), 343–351 (2012).
PubMed  Article  CAS  Google Scholar 

29.
Sanchez, G. PLS Path Modeling with R (Trowchez Editions, Berkeley, 2013).
Google Scholar 

30.
Lopez-Chicano, M., Bouamama, M., Vallejos, A. & Pulido-Bosch, A. Factors which determine the hydrogeochemical behaviour of karstic springs. A case study from the Betic Cordilleras, Spain. Appl. Geochem. 16(9–10), 1179–1192 (2001).
CAS  Article  Google Scholar 

31.
Stumm, W. & Morgan, J. J. Aquatic chemistry: chemical equilibria and rates in natural waters. In Environmental Science and Technology (eds Stumm, W. & Morgan, J. J.) (Wiley, New York, 2012).
Google Scholar 

32.
Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. R. 75, 14–49 (2011).
CAS  Article  Google Scholar 

33.
Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2(1), 589 (2011).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

34.
Li, D. et al. Microbial community evolution during simulated managed aquifer recharge in response to different biodegradable dissolved organic carbon (BDOC) concentrations. Water Res. 47(7), 2421–2430 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Miranda, C. D. & Zemelman, R. Bacterial resistance to oxytetracycline in Chilean salmon farming. Aquaculture 212(1–4), 31–47 (2002).
CAS  Article  Google Scholar 

36.
Dul’tseva, N. M., Chernitsina, S. M. & Zemskaya, T. I. Isolation of bacteria of the genus Variovorax from the Thioploca mats of Lake Baikal. Microbiology 81(1), 67–78 (2012).
Article  CAS  Google Scholar 

37.
Mohiuddin, M. M., Salama, Y., Schellhorn, H. E. & Golding, G. B. Shotgun metagenomic sequencing reveals freshwater beach sands as reservoir of bacterial pathogens. Water Res. 115, 360–369 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Fuentes, S., Méndez, V., Aguila, P. & Seeger, M. Bioremediation of petroleum hydrocarbons: catabolic genes, microbial communities, and applications. Appl. Microbiol. Biotechnol. 98(11), 4781–4794 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Gomes, B. C. et al. Analysis of a microbial community associated with polychlorinated biphenyl degradation in anaerobic batch reactors. Biodegradation 25(6), 797–810 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Cai, J. et al. Characterization of bacterial and microbial eukaryotic communities associated with an ephemeral hypoxia event in Taihu Lake, a shallow eutrophic Chinese lake. Environ. Sci. Pollut. R. 25(31), 31543–31557 (2018).
CAS  Article  Google Scholar 

41.
Zhang, S. et al. Characterization of a novel bacteriophage specific to Exiguobacterium indicum isolated from a plateau eutrophic lake. J. Basic Microb. 59(2), 206–214 (2019).
CAS  Article  Google Scholar 

42.
Li, S., Luo, Z. & Ji, G. Seasonal function succession and biogeographic zonation of assimilatory and dissimilatory nitrate-reducing bacterioplankton. Sci. Total Environ. 637, 1518–1525 (2018).
ADS  PubMed  Article  CAS  Google Scholar 

43.
Savio, D. et al. Spring water of an alpine karst aquifer is dominated by a taxonomically stable but discharge-responsive bacterial community. Front. Microbiol. 10, 28 (2019).
PubMed  PubMed Central  Article  Google Scholar 

44.
Freedman, Z. & Zak, D. R. Soil bacterial communities are shaped by temporal and environmental filtering: evidence from a long-term chronosequence. Environ. Microbiol. 17(9), 3208–3218 (2015).
PubMed  Article  Google Scholar 

45.
Subramani, T., Elango, L. & Damodarasamy, S. R. Groundwater quality and its suitability for drinking and agricultural use in Chithar River Basin, Tamil Nadu, India. Environ. Geol. 47(8), 1099–1110 (2005).
CAS  Article  Google Scholar 

46.
Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88(10), 2427–2439 (2007).
PubMed  Article  Google Scholar 

47.
Niño-García, J. P., Ruiz-González, C. & del Giorgio, P. A. Interactions between hydrology and water chemistry shape bacterioplankton biogeography across borssseal freshwater networks. ISME J. 10(7), 1755 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar  More

1.
Paul, E. A. (ed.) Soil Microbiology, Ecology and Biochemistry (Academic Press, Amsterdam, 2015).
Google Scholar 
2.
Nannipieri, P. et al. Microbial diversity and soil functions. Eur. J. Soil. Sci. 54, 655–670 (2003).
Article  Google Scholar 

3.
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil. Biol. Biochem. 83, 184–199 (2015).
CAS  Article  Google Scholar 

4.
Berg, G. & Smalla, K. Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol. Ecol. 68, 1–13 (2009).
CAS  Article  Google Scholar 

5.
Bais, H. P., Park, S.-W., Weir, T. L., Callaway, R. M. & Vivanco, J. M. How plants communicate using the underground information superhighway. Trends Plant. Sci. 9, 26–32 (2004).
CAS  Article  Google Scholar 

6.
Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663. https://doi.org/10.1111/1574-6976.12028 (2013).
CAS  Article  PubMed  Google Scholar 

7.
Praeg, N., Pauli, H. & Illmer, P. Microbial diversity in bulk and rhizosphere soil of Ranunculus glacialis along a high-alpine altitudinal gradient. Front. Microbiol. 10, 1429. https://doi.org/10.3389/fmicb.2019.01429 (2019).
Article  PubMed  PubMed Central  Google Scholar 

8.
Nacke, H. et al. Pyrosequencing-based assessment of bacterial community structure along different management types in German forest and grassland soils. PLoS ONE 6, e17000. https://doi.org/10.1371/journal.pone.0017000 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Jackson, R. B., Solomon, E. I., Canadell, J. G., Cargnello, M. & Field, C. B. Methane removal and atmospheric restoration. Nat. Sustain. 2, 436–438. https://doi.org/10.1038/s41893-019-0299-x (2019).
Article  Google Scholar 

10.
Ciais, P. et al. Climate Change 2013: The Physical Science Basis. In Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge University Press, Cambridge, 2013).
Google Scholar 

11.
Adam, P. S., Borrel, G., Brochier-Armanet, C. & Gribaldo, S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 11, 2407. https://doi.org/10.1038/ismej.2017.122 (2017).
Article  PubMed  PubMed Central  Google Scholar 

12.
Knief, C. Diversity and habitat preferences of cultivated and uncultivated aerobic methanotrophic bacteria evaluated based on pmoA as molecular marker. Front. Microbiol. 6, 1346 (2015).
Article  Google Scholar 

13.
Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).
CAS  Article  Google Scholar 

14.
Op den Camp, H. J. M. et al. Environmental, genomic and taxonomic perspectives on methanotrophic Verrucomicrobia. Environ. Microbiol. Rep. 1, 293–306. https://doi.org/10.1111/j.1758-2229.2009.00022.x (2009).
CAS  Article  PubMed  Google Scholar 

15.
Knief, C., Lipski, A. & Dunfield, P. F. Diversity and activity of methanotrophic bacteria in different upland soils. Appl. Environ. Microbiol. 69, 6703–6714. https://doi.org/10.1128/AEM.69.11.6703-6714.2003 (2003).
CAS  Article  PubMed  PubMed Central  Google Scholar 

16.
Kolb, S. The quest for atmospheric methane oxidizers in forest soils. Environ. Microbiol. Rep. 1, 336–346 (2009).
CAS  Article  Google Scholar 

17.
Plesa, I. et al. Effects of drought and salinity on European Larch (Larix decidua Mill.) seedlings. Forests 9, 320. https://doi.org/10.3390/f9060320 (2018).
Article  Google Scholar 

18.
Falk, W., Bachmann-Gigl, U. & Kölling, C. Die Europäische Lärche im Klimawandel. In Beiträge zur Europäischen Lärche (ed. Schmidt, O.) 19–27 (Bayrische Landesanstalt für Wald und Forstwirtschaft, Freising, 2012).
Google Scholar 

19.
Obojes, N. et al. Water stress limits transpiration and growth of European larch up to the lower subalpine belt in an inner-alpine dry valley. New Phytol. 220, 460–475 (2018).
Article  Google Scholar 

20.
Wieser, G. (ed.) Trees at Their Upper Limit. Treelife Limitation at the Alpine Timberline (Springer, Dordrecht, 2007).
Google Scholar 

21.
Dedysh, S. N. et al. Methylocapsa palsarum sp. nov., a methanotroph isolated from a subArctic discontinuous permafrost ecosystem. Int. J. Syst. Evol. Microbiol. 65, 3618–3624. https://doi.org/10.1099/ijsem.0.000465 (2015).
CAS  Article  PubMed  Google Scholar 

22.
Praeg, N., Wagner, A. O. & Illmer, P. Plant species, temperature, and bedrock affect net methane flux out of grassland and forest soils. Plant Soil 410, 193–206 (2017).
CAS  Article  Google Scholar 

23.
Lladó, S., López-Mondéjar, R. & Baldrian, P. Forest soil bacteria: diversity, involvement in ecosystem processes, and response to global change. Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/MMBR.00063-16 (2017).
Article  PubMed  PubMed Central  Google Scholar 

24.
Urbanová, M., Šnajdr, J. & Baldrian, P. Composition of fungal and bacterial communities in forest litter and soil is largely determined by dominant trees. Soil. Biol. Biochem. 84, 53–64. https://doi.org/10.1016/j.soilbio.2015.02.011 (2015).
CAS  Article  Google Scholar 

25.
Liu, J. et al. Characteristics of bulk and rhizosphere soil microbial community in an ancient Platycladus orientalis forest. Appl. Soil Ecol. 132, 91–98. https://doi.org/10.1016/j.apsoil.2018.08.014 (2018).
ADS  Article  Google Scholar 

26.
Uroz, S. et al. Specific impacts of beech and Norway spruce on the structure and diversity of the rhizosphere and soil microbial communities. Sci. Rep. 6, 27756. https://doi.org/10.1038/srep27756 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Štursová, M., Bárta, J., Šantrůčková, H. & Baldrian, P. Small-scale spatial heterogeneity of ecosystem properties, microbial community composition and microbial activities in a temperate mountain forest soil. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiw185 (2016).
Article  PubMed  Google Scholar 

28.
Ferrari, B., Winsley, T., Ji, M. & Neilan, B. Insights into the distribution and abundance of the ubiquitous candidatus Saccharibacteria phylum following tag pyrosequencing. Sci. Rep. 4, 3957. https://doi.org/10.1038/srep03957 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

29.
Starr, E. P. et al. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6, 122. https://doi.org/10.1186/s40168-018-0499-z (2018).
Article  PubMed  PubMed Central  Google Scholar 

30.
Brewer, T. E., Handley, K. M., Carini, P., Gilbert, J. A. & Fierer, N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat. Microbiol. 2, 16198. https://doi.org/10.1038/nmicrobiol.2016.198 (2016).
CAS  Article  PubMed  Google Scholar 

31.
Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of acidobacteria: moving beyond genes and genomes. Front. Microbiol. 7, 744. https://doi.org/10.3389/fmicb.2016.00744 (2016).
Article  PubMed  PubMed Central  Google Scholar 

32.
Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
Article  Google Scholar 

33.
Johnston-Monje, D., Lundberg, D. S., Lazarovits, G., Reis, V. M. & Raizada, M. N. Bacterial populations in juvenile maize rhizospheres originate from both seed and soil. Plant Soil 405, 337–355. https://doi.org/10.1007/s11104-016-2826-0 (2016).
CAS  Article  Google Scholar 

34.
Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Nat. Acad. Sci. USA 109, 21390–21395. https://doi.org/10.1073/pnas.1215210110 (2012).
ADS  Article  PubMed  Google Scholar 

35.
Kottke, I. & Oberwinkler, F. Comparative studies on the mycorrhization of Larix decidua and Picea abies by Suillus grevillei. Trees https://doi.org/10.1007/BF00196758 (1988).
Article  Google Scholar 

36.
Uroz, S., Buée, M., Murat, C., Frey-Klett, P. & Martin, F. Pyrosequencing reveals a contrasted bacterial diversity between oak rhizosphere and surrounding soil. Environ. Microbiol. Rep. 2, 281–288. https://doi.org/10.1111/j.1758-2229.2009.00117.x (2010).
CAS  Article  PubMed  Google Scholar 

37.
Mapelli, F. et al. The stage of soil development modulates rhizosphere effect along a High Arctic desert chronosequence. ISME J. 12, 1188. https://doi.org/10.1038/s41396-017-0026-4 (2018).
Article  PubMed  PubMed Central  Google Scholar 

38.
Mello, B. L., Alessi, A. M., McQueen-Mason, S., Bruce, N. C. & Polikarpov, I. Nutrient availability shapes the microbial community structure in sugarcane bagasse compost-derived consortia. Sci. Rep. 6, 38781. https://doi.org/10.1038/srep38781 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Turnbull, G. A., Morgan, J. A. W., Whipps, J. M. & Saunders, J. R. The role of bacterial motility in the survival and spread of Pseudomonas fluorescens in soil and in the attachment and colonisation of wheat roots. FEMS Microbiol. Ecol. 36, 21–31. https://doi.org/10.1111/j.1574-6941.2001.tb00822.x (2001).
CAS  Article  PubMed  Google Scholar 

40.
Rees, D. C., Johnson, E. & Lewinson, O. ABC transporters: the power to change. Nat. Rev. Mol. Cell. Biol. 10, 218–227. https://doi.org/10.1038/nrm2646 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Aronson, E. L., Allison, S. D. & Helliker, B. R. Environmental impacts on the diversity of methane-cycling microbes and their resultant function. Front. Microbiol. 4, 225. https://doi.org/10.3389/fmicb.2013.00225 (2013).
Article  PubMed  PubMed Central  Google Scholar 

42.
Dalal, R. C., Allen, D. E., Livesley, S. J. & Richards, G. Magnitude and biophysical regulators of methane emission and consumption in the Australian agricultural, forest, and submerged landscapes. A review. Plant Soil 309, 43–76 (2008).
CAS  Article  Google Scholar 

43.
Martins, C. S. C., Nazaries, L., Macdonald, C. A., Anderson, I. C. & Singh, B. K. Water availability and abundance of microbial groups are key determinants of greenhouse gas fluxes in a dryland forest ecosystem. Soil Biol. Biochem. 86, 5–16. https://doi.org/10.1016/j.soilbio.2015.03.012 (2015).
CAS  Article  Google Scholar 

44.
Praeg, N., Schwinghammer, L. & Illmer, P. Larix decidua and additional light affect the methane balance of forest soil and the abundance of methanogenic and methanotrophic microorganisms. FEMS Microbiol Lett. https://doi.org/10.1093/femsle/fnz259 (2020).
Article  Google Scholar 

45.
Ström, L., Mastepanov, M. & Christensen, T. R. Species-specific effects of vascular plants on carbon turnover and methane emissions from wetlands. Biogeochemistry 75, 65–82 (2005).
Article  Google Scholar 

46.
Borrel, G. et al. Genome sequence of “Candidatus Methanomassiliicoccus intestinalis” Issoire-Mx1, a third thermoplasmatales-related methanogenic archaeon from human feces. Genome Announc. 1, e004523. https://doi.org/10.1128/genomeA.00453-13 (2013).
Article  Google Scholar 

47.
Deng, Y., Liu, P. & Conrad, R. Effect of temperature on the microbial community responsible for methane production in alkaline NamCo wetland soil. Soil Biol. Biochem. 132, 69–79. https://doi.org/10.1016/j.soilbio.2019.01.024 (2019).
CAS  Article  Google Scholar 

48.
Söllinger, A. et al. Phylogenetic and genomic analysis of Methanomassiliicoccales in wetlands and animal intestinal tracts reveals clade-specific habitat preferences. FEMS Microbiol. Ecol. 92, 149. https://doi.org/10.1093/femsec/fiv149 (2016).
CAS  Article  Google Scholar 

49.
Berghuis, B. A. et al. Hydrogenotrophic methanogenesis in archaeal phylum Verstraetearchaeota reveals the shared ancestry of all methanogens. Proc. Natl. Acad. Sci. U.S.A. 116, 5037. https://doi.org/10.1073/pnas.1815631116 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Cai, Y., Zheng, Y., Bodelier, P. L. E., Conrad, R. & Jia, Z. Conventional methanotrophs are responsible for atmospheric methane oxidation in paddy soils. Nat. Commun. 7, 11728 (2016).
ADS  CAS  Article  Google Scholar 

51.
Henckel, T., Jäckel, U., Schnell, S. & Conrad, R. Molecular analyses of novel methanotrophic communities in forest soil that oxidize atmospheric methane. Appl. Environ. Microbiol. 60, 1801–1808 (2000).
Article  Google Scholar 

52.
Ricke, P., Kolb, S. & Braker, G. Application of a newly developed ARB software-integrated tool for in silico terminal restriction fragment length polymorphism analysis reveals the dominance of a novel pmoA cluster in a forest soil. Appl. Environ. Microbiol. 71, 1671–1673. https://doi.org/10.1128/AEM.71.3.1671-1673.2005 (2005).
CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Pratscher, J., Dumont, M. G. & Conrad, R. Assimilation of acetate by the putative atmospheric methane oxidizers belonging to the USCα clade. Environ. Microbiol. 13, 2692–2701. https://doi.org/10.1111/j.1462-2920.2011.02537.x (2011).
CAS  Article  PubMed  Google Scholar 

54.
Cai, Y., Zhou, X., Shi, L. & Jia, Z. Atmospheric methane oxidizers are dominated by upland soil cluster alpha in 20 forest soils of China. Microb. Ecol. 80, 859–871. https://doi.org/10.1007/s00248-020-01570-1 (2020).
CAS  Article  PubMed  Google Scholar 

55.
Täumer, J. et al. Divergent drivers of the microbial methane sink in temperate forest and grassland soils. Glob. Change Biol. https://doi.org/10.1111/gcb.15430 (2020).
Article  Google Scholar 

56.
Andreote, F. D. et al. Culture-independent assessment of Rhizobiales-related alphaproteobacteria and the diversity of Methylobacterium in the rhizosphere and rhizoplane of transgenic eucalyptus. Microb. Ecol. 57, 82–93. https://doi.org/10.1007/s00248-008-9405-8 (2009).
Article  PubMed  Google Scholar 

57.
Iguchi, H., Yurimoto, H. & Sakai, Y. Interactions of methylotrophs with plants and other heterotrophic bacteria. Microorganisms 3, 137–151. https://doi.org/10.3390/microorganisms3020137 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

58.
Ho, A. et al. Biotic interactions in microbial communities as modulators of biogeochemical processes: methanotrophy as a model system. Front. Microbiol. 7, 1285. https://doi.org/10.3389/fmicb.2016.01285 (2016).
Article  PubMed  PubMed Central  Google Scholar 

59.
Iguchi, H., Yurimoto, H. & Sakai, Y. Stimulation of methanotrophic growth in cocultures by cobalamin excreted by rhizobia. Appl. Environ. Microbiol. 77, 8509–8515. https://doi.org/10.1128/AEM.05834-11 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Veraart, A. J. et al. Living apart together—bacterial volatiles influence methanotrophic growth and activity. ISME J. 12, 1163–1166 (2018).
CAS  Article  Google Scholar 

61.
Karlsson, A. E., Johansson, T. & Bengtson, P. Archaeal abundance in relation to root and fungal exudation rates. FEMS Microbiol. Ecol. 80, 305–311 (2012).
CAS  Article  Google Scholar 

62.
Haichar, F. E. Z. et al. Plant host habitat and root exudates shape soil bacterial community structure. ISME J. 2, 1221–1230. https://doi.org/10.1038/ismej.2008.80 (2008).
CAS  Article  PubMed  Google Scholar 

63.
Tkacz, A., Cheema, J., Chandra, G., Grant, A. & Poole, P. S. Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition. ISME J. 9, 2349–2359. https://doi.org/10.1038/ismej.2015.41 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

64.
Schinner, F. et al. (eds) Methods in Soil Biology (Springer, Berlin, 1996).
Google Scholar 

65.
Barillot, C. D. C., Sarde, C.-O., Bert, V., Tarnaud, E. & Cochet, N. A standardized method for the sampling of rhizosphere and rhizoplan soil bacteria associated to a herbaceous root system. Ann. Microbiol. 63, 471–476 (2013).
CAS  Article  Google Scholar 

66.
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Nat. Acad. Sci. U.S.A. 108(Suppl 1), 4516–4522. https://doi.org/10.1073/pnas.1000080107 (2011).
ADS  Article  Google Scholar 

67.
Ihrmark, K. et al. New primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677. https://doi.org/10.1111/j.1574-6941.2012.01437.x (2012).
CAS  Article  PubMed  Google Scholar 

68.
White, T. J., Bruns, T., Lee, S. & Taylor, J. W. Amplification and direct sequencing of fungal ribosomal RNA Genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, Cambridge, 1990).
Google Scholar 

69.
Schloss, P. D. et al. Introducing mothur. Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
CAS  Article  Google Scholar 

70.
Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 25, 914–919. https://doi.org/10.1111/2041-210X.12073 (2013).
Article  Google Scholar 

71.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mah, F. VSEARCH. A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Article  Google Scholar 

72.
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).
CAS  Article  PubMed  Google Scholar 

73.
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277. https://doi.org/10.1111/mec.12481 (2013).
CAS  Article  PubMed  Google Scholar 

74.
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. https://doi.org/10.1128/AEM.00062-07 (2007).
CAS  Article  PubMed  PubMed Central  Google Scholar 

75.
Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).
CAS  Google Scholar 

76.
Martin, A. P. Phylogenetic approaches for describing and comparing the diversity of microbial communities. Appl. Environ. Microbiol. 68, 3673–3682. https://doi.org/10.1128/AEM.68.8.3673-3682.2002 (2002).
CAS  Article  PubMed  PubMed Central  Google Scholar 

77.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
Article  Google Scholar 

78.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017). http://www.R-project.org. Accessed 24 Sept 2018.

79.
Oksanen, J. et al. vegan. Community Ecology Package. R package version 2.4–4 (2017). https://CRAN.R-project.org/package=vegan. Accessed 24 Sept 2018.

80.
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 44, W3–W10. https://doi.org/10.1093/nar/gkw343 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

81.
Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16SrRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
CAS  Article  Google Scholar 

82.
White, J. R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, e1000352. https://doi.org/10.1371/journal.pcbi.1000352 (2009).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

83.
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. https://doi.org/10.1093/bioinformatics/btu494 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

84.
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114. https://doi.org/10.1093/nar/gkr988 (2012).
CAS  Article  PubMed  Google Scholar 

85.
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Article  Google Scholar 

86.
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. https://doi.org/10.1101/gr.1239303 (2003).
CAS  Article  PubMed  PubMed Central  Google Scholar 

87.
Pratscher, J., Vollmers, J., Wiegand, S., Dumont, M. G. & Kaster, A.-K. Unravelling the identity, metabolic potential and global biogeography of the atmospheric methane-oxidizing upland soil cluster α. Environ. Microbiol. 20, 1016–1029. https://doi.org/10.1111/1462-2920.14036 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

88.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

89.
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).
CAS  Article  PubMed  Google Scholar  More

1.
Tassi, F. et al. Low-pH waters discharging from submarine vents at Panarea Island (Aeolian Islands, southern Italy) after the 2002 gas blast: Origin of hydrothermal fluids and implications for volcanic surveillance. Appl. Geochem. 24, 246–254 (2009).
CAS  Article  Google Scholar 
2.
Boatta, F. et al. Geochemical survey of Levante Bay, Vulcano Island (Italy), a natural laboratory for the study of ocean acidification. Mar. Pollut. Bull. 73, 485–494. https://doi.org/10.1016/j.marpolbul.2013.01.029 (2013).
CAS  Article  PubMed  Google Scholar 

3.
Hall-Spencer, J. M. et al. Volcanic carbon dioxide vents show ecosystem effects of ocean acidification. Nature 454, 96–99 (2008).
ADS  CAS  Article  Google Scholar 

4.
Ricevuto, E., Kroeker, K. J., Ferrigno, F. & Gambi, M. C. Spatio-temporal variability of polychaete colonization at volcanic CO2 vents indicates high tolerance to ocean acidification. Mar. Biol. 161, 2909–2919. https://doi.org/10.1007/s00227-014-2555-y (2014).
CAS  Article  Google Scholar 

5.
Ricevuto, E., Vizzini, S. & Gambi, M. C. Ocean acidification effects on stable isotope signatures and trophic interactions of polychaete consumers and organic matter sources at a CO2 shallow vent system. J. Exp. Mar. Biol. Ecol. 468, 105–117. https://doi.org/10.1016/j.jembe.2015.03.016 (2015).
CAS  Article  Google Scholar 

6.
Foo, S.A., Byrne, M., Ricevuto, E., Gambi, M.C. The Carbon Dioxide Vents of Ischia, Italy, A Natural System to Assess Impacts of Ocean Acidification on Marine Ecosystems: An Overview of Research and Comparisons with Other Vent Systems. In Oceanography and Marine Biology An Annual Review. S. J. Hawkins, A. J. Evans, A.C. Dale, L. B. Firth, I. P. Smith eds. Taylor & Francis Group, 56 (2018).

7.
Mutalipassi, M. et al. Ocean acidification alters the responses of invertebrates to wound-activated infochemicals produced by epiphytes of the seagrassPosidonia oceanica. J. Exp. Mar. Biol. Ecol. 530–531, 151435 (2020).
Article  Google Scholar 

8.
Apostolaki, E. T., Vizzini, S., Hendriks, I. E. & Olsen, Y. S. Seagrass ecosystem response to long-term high CO2 in a Mediterranean volcanic vent. Mar. Environ. Res. 99, 9–15 (2014).
CAS  Article  Google Scholar 

9.
Vizzini, S., Apostolaki, E. T., Ricevuto, E., Polymenakou, P. & Mazzola, A. Plant and sediment properties in seagrass meadows from two Mediterranean CO2 vents: Implications for carbon storage capacity of acidified oceans. Mar. Environ. Res. 146, 101–108 (2019).
CAS  Article  Google Scholar 

10.
Beer, S., Björk, M., Beardall, J. Acquisition of carbon in marine plants. In: John Wiley & Sons eds. Photoshynthesis in the Marine Environment. Wiley Blackwell, Iowa, USA. pp: 95–124 (2014).

11.
Beer, S., Björk, M., Hellblom, F. & Axelsson, L. Inorganic carbon utilization in marine angiosperms (seagrasses). Funct. Plant Biol. 29, 349–354 (2002).
CAS  Article  Google Scholar 

12.
Koch, M., Bowes, G., Ross, C. & Zhang, X. H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Change Biol. 19, 103–132. https://doi.org/10.1111/j.1365-2486.2012.02791.x (2013).
ADS  Article  Google Scholar 

13.
Zimmerman, R. C., Kohrs, D. G., Steller, D. L. & Alberte, R. S. Impacts of CO2 enrichment on productivity and light requirements of eelgrass. Plant Physiol. 115, 599–607. https://doi.org/10.1104/pp.115.2.599 (1997).
CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Garrard, S. L. & Beaumont, N. J. The effect of ocean acidification on carbon storage and sequestration in seagrass beds; a global and UK context. Mar. Pollut. Bull. 86, 138–146 (2014).
CAS  Article  Google Scholar 

15.
Hendriks, I. E., Duarte, C. M. & Alvarez, M. A. Vulnerability of marine biodiversity to ocean acidification: a meta-analysis. Estuar. Coast. Shelf Sci. 86, 157–164 (2010).
ADS  CAS  Article  Google Scholar 

16.
Zimmerman, R. C., Hill, V. J. & Gallegos, C. L. Predicting effects of ocean warming, acidification, and water quality on Chesapeake region eelgrass. Limnol. Oceanogr. 60(2015), 1781–1804 (2015).
ADS  CAS  Article  Google Scholar 

17.
Pacella, S. R., Cheryl, A. B., George, G. W., Rochelle, G. L. & Burke, H. Seagrass habitat metabolism increases short-term extremes and long-term offset of CO2 under future ocean acidification. PNAS 115(15), 3870–3875 (2018).
ADS  CAS  Article  Google Scholar 

18.
Russell, B. D., Connell, S. D., Uthicke, S. & Hall-Spencer, J. M. Future seagrass beds: can increased productivity lead to increased carbon storage?. Mar. Pollut. Bull. 73, 463–469 (2013).
CAS  Article  Google Scholar 

19.
de los Santos, C. B., Godbold, J. A. & Solan, M. Short-term growth and biomechanical responses of the temperate seagrassCymodocea nodosato CO2 enrichment. Mar. Ecol. Prog. Ser. 572, 91–102 (2017).
ADS  CAS  Article  Google Scholar 

20.
Schneider, G. et al. Structural and physiological responses of Halodule wrightii to ocean acidification. Protoplasma 255, 629–641 (2018).
CAS  Article  Google Scholar 

21.
Radoglou, K. M. & Jarvis, P. G. The effects of CO2 enrichment and nutrient supply on growth morphology and anatomy of Phaseolus vulgaris L seedlings. Ann. Bot. 70, 245–256 (1992).
CAS  Article  Google Scholar 

22.
Epron, D., Liozon, R. & Mousseau, M. Effects of elevated CO2 concentration on leaf characteristics and photosynthetic capacity of beech (Fagus sylvatica) during the growing season. Tree Physiol. 16, 425–432 (1995).
Article  Google Scholar 

23.
Lin, J., Jach, M. E. & Ceulemans, R. Stomatal density and needle anatomy of Scots pine (Pinus sylvestris) are affected by elevated CO2. New Phytol. 150, 665–674 (2001).
Article  Google Scholar 

24.
Ruocco, M. et al. Genome-wide transcriptional reprogramming in the seagrassCymodocea nodosa under experimental ocean acidification. MolEcol 26, 4241–4259. https://doi.org/10.1111/mec.14204 (2017).
CAS  Article  Google Scholar 

25.
Olivé, I. et al. Linking gene expression to productivity to unravel long- and short-term responses of seagrasses exposed to CO2 in volcanic vents. Sci. Rep. 7, 42278 (2017).
ADS  Article  Google Scholar 

26.
Procaccini, G. et al. Depth-specific fluctuations of gene expression and protein abundance modulate the photophysiology in the seagrassPosidonia oceanica. Sci. Rep. 7, 42890. https://doi.org/10.1038/srep42890 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Kumar, M. et al. Proteome analysis reveals extensive light stress response reprogramming in the seagrassZostera muelleri (Alismatales, Zosteraceae) metabolism. Frontiers Plant Sci. 7, 2023 (2017).
Article  Google Scholar 

28.
Piro, A. et al. The modulation of leaf metabolism plays a role in salt tolerance of Cymodocea nodosa exposed to hypersaline stress in mesocosms. Front Plant Sci. 6, 464 (2015).
Article  Google Scholar 

29.
Dattolo, E. et al. Acclimation to different depths by the marine angiosperm Posidonia oceanica: transcriptomic and proteomic profiles. Front. Plant Sci. 4, 195. https://doi.org/10.3389/fpls.2013.00195 (2013).
Article  PubMed  PubMed Central  Google Scholar 

30.
Mazzuca, S. et al. Seagrass light acclimation: 2-DE protein analysis in Posidonia leaves grown inchronic low light conditions. J. Exp. Mar. Biol. Ecol. 374, 113–122 (2009).
CAS  Article  Google Scholar 

31.
Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
ADS  Article  Google Scholar 

32.
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
CAS  Article  Google Scholar 

33.
Watanabe, C. K. et al. Effects of elevated CO2 on levels of primary metabolites and transcripts of genes encoding respiratory enzymes and their diurnal patterns in Arabidopsis thaliana: possible relationships with respiratory rates. Plant Cell Physiol. 55(2), 341–357. https://doi.org/10.1093/pcp/pct185 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Lauritano, C. et al. Response of key stress-related genes of the seagrassPosidonia oceanica in the vicinity of submarine volcanic vents. Biogeosciences 12, 4947–4971 (2015).
Article  Google Scholar 

35
Neha, S., Gokhale, S. P. & Kumar, B. A. Effect of elevated [CO2] on cell structure and function in seed plants. Clim. Change Environ. Sustain. 2, 69–104. https://doi.org/10.5958/2320-642X.2014.00001.5 (2014).
Article  Google Scholar 

36.
Iuchi, S. et al. Regulation of drought tolerance by gene manipulation of 9-cis-epoxycarotenoid dioxygenase, a key enzyme in abscisic acid biosynthesis in Arabidopsis. Plant J. 27, 325–333. https://doi.org/10.1046/j.1365-313x.2001.01096.x (2001).
CAS  Article  PubMed  Google Scholar 

37.
Endo, A. et al. Drought induction of Arabidopsis 9-cis-epoxycarotenoid dioxygenase occurs in vascular parenchyma cells. Plant Physiol. 147, 1984–1993 (2008).
CAS  Article  Google Scholar 

38
Toh, S. et al. High temperature-induced abscisic acid biosynthesis and its role in the inhibition of gibberellins action in Arabidopsis seeds. Plant Physiol. 146, 1368–1385 (2008).
CAS  Article  Google Scholar 

39.
Dong, C. H. et al. ADF proteins are involved in the control of flowering and regulate F-actin organization, cell expansion, and organ growth in Arabidopsis. Plant Cell 13, 1333–1346 (2001).
CAS  Article  Google Scholar 

40.
Vantard, M. & Blanchoin, L. Actin polymerization processes in plant cells. Curr. Opin. Plant Biol. 5(6), 502–506 (2002).
CAS  Article  Google Scholar 

41.
Smertenko, A. P. et al. Ser6 in the maize actin-depolymerizing factor, ZmADF3, is phosphorylated by a calcium-stimulated protein kinase and is essential for the control of functional activity. Plant J. 14(2), 187–193 (1988).
Article  Google Scholar 

42.
Webster, J. & Stone, B. A. Isolation, structure and monosaccharide composition of the wall of vegetative parts of Heterozostera tasmanica (Martens ex Aschers) den Hartog. Aquat. Bot. 47, 39–52 (1994).
CAS  Article  Google Scholar 

43.
Olsen J.L., Rouzé, P., Verhelst, B., Lin, Y.-C., Bayer, T., Collen, J., Dattolo, E., De Paoli, E., Dittami, S., Maumus, F., et al. The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea. Nature 530, 331–335 (2016) https://doi.org/10.1038/nature16548.
ADS  CAS  Article  PubMed  Google Scholar 

44.
Brummel, D. A. Cell wall acidification and its role in Auxin-stimulated growth. J. Exp. Bot. 37(2), 270–276 (1986).
Article  Google Scholar 

45.
Laemmli, U. K. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227, 680–685 (1970).
ADS  CAS  Article  Google Scholar 

46.
Shevchenko, A., Tomas, H., Havlis, J., Olsen, J. V. & Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1, 2856–2860 (2007).
Article  Google Scholar 

47.
Lucini, L. & Bernardo, L. Comparison of proteome response to saline and zinc stress in lettuce. Front. Plant Sci. https://doi.org/10.3389/fpls.2015.00240 (2015).
Article  PubMed  PubMed Central  Google Scholar  More