Most ecosystems on Earth rely on dynamic communities of microorganisms which help to cycle nutrients in the environment. There is increasing concern that climate change may have a profound impact on these complex networks formed of large numbers of microbial species linked by intricate biochemical relationships.

Any species within a microbial community can acclimate to new temperatures by quickly tweaking their biological processes, for example by activating genes that are more suited to warmer conditions. Over time, a species may acclimate or adapt to new conditions. However, the community as a whole can also respond to these changes, and often much faster, by simply altering the abundance or presence of its members through a process known as species sorting. It remains unclear exactly how acclimation, adaptation and species sorting each contribute to the community’s response to a temperature shift – an increasingly common scenario under global climate change.

To address this question, Smith et al. investigated how species sorting and acclimation may help whole soil bacterial communities to cope with lasting changes in temperature. To do so, soil samples from a single field site (and therefore featuring the same microbial community) were incubated for four weeks under six different temperatures. Genetic analyses revealed that, at the end of the experiments, distinct communities specific to a given temperature had emerged. They all differed in species composition and the types of biological functions they could perform.

Further experiments showed that each community had been taken over by strains of bacteria which grew best at the new temperature that they had been exposed to, including extreme warming scenarios never seen in their native environment. This suggests that these organisms were already present in the original community. They had persisted even under temperatures which were not optimal for them, acting as a slumbering (‘latent’) ‘reservoir’ of traits and functional abilities that allowed species sorting to produce distinct and functionally capable communities in each novel thermal environment. This suggests that species sorting could help bacterial communities to cope with dramatic changes in their thermal environment.

Smith et al.’s findings suggest that bacterial communities can cope with warming environments much better than has been previously thought. In the future, this work may help researchers to better predict how climate change could impact microbial community structure and functioning, and most crucially their contributions to the global carbon cycle.

Microbes are drivers of key ecosystem processes. They are tightly linked to the wider ecosystem as pathogens, mutualists, and food sources for higher trophic levels, and also play a central role in ecosystem-level nutrient cycling, and therefore, ultimately in global biogeochemical cycles. Temperature has a pervasive influence on microbial communities because of its direct impact on microbial physiology and fitness (Oliverio et al., 2017; García et al., 2018; Smith et al., 2019). There is therefore great interest in understanding how temperature fluctuations impact microbial community dynamics and how those impacts affect the wider ecosystem (Bardgett et al., 2008).

Temperature varies at practically all biologically relevant timescales, from seconds (e.g., sun/shade), through daily and seasonal fluctuations, to longer-term changes, including anthropogenic climate warming and fluctuations over geological timescales. Whole microbial communities can respond to temperature changes over time and space through phenotypic (especially, physiological) plasticity (henceforth, ‘acclimation’), as well as genetic adaptation in their component populations (Bennett et al., 1990; Kishimoto et al., 2010; Blaby et al., 2012; Kontopoulos et al., 2020a). Microbial thermal acclimation can occur relatively rapidly (timescales of minutes to days) through processes such as activation and up- or downregulation of particular genes and alteration of fatty acids used in building cell walls (Suutari and Laakso, 1994). Adaptation is a necessarily slower process (timescales of weeks or longer) occurring either through selection on standing genetic variation in the population or that arising through recombination and mutation (Bennett et al., 1990; Padfield et al., 2016; Barton et al., 2020).

In addition, a third key mechanism through which microbial communities can respond to changing temperatures is species sorting (Leibold et al., 2004; Wu et al., 2018): changes in community composition through species-level selection where taxa maladapted to a new temperature are replaced by those that are pre-adapted to it. This can happen either relatively rapidly through the resuscitation or suppression of taxa that are already present (Lennon and Jones, 2011; Wisnoski and Lennon, 2021), or more slowly through immigration-extinction dynamics driven by dispersal from the regional species pool (Langenheder and Székely, 2011; Wu et al., 2018). Resuscitation may be an important mechanism driving species sorting in microbial communities in particular because many microbial taxa have the capacity to form environment-resistant spores when conditions are unfavorable, and then rapidly activate metabolic pathways and resuscitate in favorable conditions. This effectively widens their thermal niche to allow persistence in the face of temperature change (Lennon and Jones, 2011; Wisnoski and Lennon, 2021). In order for rapid resuscitation of dormant taxa to allow species sorting to drive community-level adaptation, there must be a wide source pool of species to select from. Indeed, sequencing studies have revealed the presence of thousands of distinct microbial taxa in small environmental samples, most occurring at low abundance (Lynch and Neufeld, 2015; Sogin et al., 2006; Thompson et al., 2017). There is also strong evidence that bacteria are often found well outside of their thermal niche. For example, thermophilic taxa are often found in cold ocean beds and cool soils (Marchant et al., 2008; Hubert et al., 2009; Zeigler, 2014). Thus, a significant reservoir of latent microbial functional diversity may be commonly present for species sorting to act upon (Lennon and Jones, 2011; Wisnoski and Lennon, 2021).

Understanding the relative importance of acclimation, adaptation, and species sorting in the assembly and turnover (succession) of microbial communities is key to determining the rate at which they can respond to different regimes of temperature fluctuations. For example, a combination of acclimation and species sorting through resuscitation would enable rapid responses to sudden temperature changes, relative to adaptation. A number of past studies have investigated responses of microbial community composition and functioning to temperature changes, showing that composition can respond rapidly to warming (Allison and Martiny, 2008; Aydogan et al., 2018), often correlated with responses of ecosystem functioning (Karhu et al., 2014; Melillo et al., 2017; Yu et al., 2018). However, a mechanistic basis of these community-level responses remains elusive, both in terms of how individual taxa respond to changing temperatures in a community context and the relative importance of acclimation, adaptation, and species sorting. The community context of the responses of individual microbial populations is important because interactions between strains can constrain or accelerate acclimation as well as adaptive evolution (Scheuerl et al., 2020). Also, while the importance of species sorting in microbial communities per se has been studied (Van der Gucht et al., 2007; Langenheder and Székely, 2011; Székely and Langenheder, 2014), work on this issue in the context of environmental temperature is practically nonexistent.

A further consideration is whether differing temperature conditions, such as the frequency and magnitude of temperature fluctuations, may influence the life history strategies of the taxa in the community (Gilchrist, 1995; Basan et al., 2020), which will in turn alter the relative importance of sorting, acclimation, and adaption. In order to identify the life history strategies of bacteria, we must quantify their phenotypic traits, such as growth rates and yield (Malik et al., 2020). Quantifying these traits can allow us to identify growth specialists ($r$-strategists) and carrying-capacity specialists ($K$-strategists) (Marshall, 1986), and thus test whether these strategies are differentially favored in different thermal environments. By identifying life history strategies, we can consider the ecosystem implications of any adaptation-, acclimation-, or sorting-driven changes in microbial communities (Malik et al., 2020).

Here, we investigate whether species sorting and latent functional diversity alone can influence the response of soil bacterial communities to changes in environmental temperature. To this end, we subject replicate communities, shielded from immigration, to a wide range of temperatures in the laboratory. In order to understand the mechanistic basis of observed community-level changes, we analyze the phylogenetic structure and functional traits of the resulting component taxa.

We performed a species-sorting experiment to investigate how microbial communities respond to shifts in temperature (Figure 1). After each community incubation at a given temperature, we estimated the thermal optimum ($T_{\text{opt}}$) for every isolated strain by measuring the thermal performance curve (TPC) of its maximal growth rate across several temperatures (Figure 1D). This allowed us to determine how strain-level thermal preferences and niche widths vary with community growth (isolation) temperature, and the presence of taxa pre-adapted to the new temperature. We also performed a phylogenetic analysis of the overall assemblage to identify whether deep evolutionary differences predict which taxa (and their associated traits) are favored by sorting at different temperatures. To quantify strain-level functional traits, we measured their available cellular metabolic energy (ATP), respiration rates, and biomass yield at population steady state (carrying capacity), which allowed us to identify $r$- vs. $K$-strategists as well as trade-offs between different strategies.

Figure 1

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Here, using a novel species-sorting experiment, we have studied the extent to which species sorting and acclimation can influence the responses of soil bacterial communities to temperature change. We find that when replicate soil bacterial communities sampled from a temperate environment are subjected to a wide range of temperatures for 4 weeks, in microcosms where immigration is not possible, strains with thermal preferences matching the local conditions emerge consistently. The strong correspondence between strain-level optimal growth temperatures and isolation temperatures (Figure 2A) indicates that a pool of taxa with disparate thermal physiologies, including those maladapted to the ambient thermal conditions, persisted in the parent community. This result is reinforced by fact that the $T_{\text{opt}}$s of standard temperature isolates were largely independent of the temperatures that their community had been previously grown at (Figure 2B), indicating that strains maladapted to that temperature had not been eliminated and were able to be resuscitated. Therefore, we conclude that most ‘sorting’ occurred during the isolation step of our experiment rather than during the 4-week incubation period – the thermal optima of taxa isolated reflects the isolation conditions.

While a 4-week period is arguably too short for mutation- or recombination-driven thermal adaptation in environmental samples (as a significant degree of generational turnover is required [Bennett et al., 1990; Lenski, 2017; Chase et al., 2021]), it is worth considering the possibility that some of the community-level emergence of thermally adapted strains could have been driven by rapid evolution through selection on standing trait variation. Indeed, stochastic mapping of thermal physiological traits on the prokaryotic tree of life has shown that $T_{\text{opt}}$ evolves relatively rapidly compared to other traits such as niche width or activation energy (thermal sensitivity) (Kontopoulos et al., 2020a). This is consistent with adaptive evolution experiments, which have shown that bacteria as well as archaea can rapidly adapt to new temperatures by shifting their $T_{\text{opt}}$ (Bennett et al., 1990; Kishimoto et al., 2010; Blaby et al., 2012; Smith et al., 2019). The molecular mechanisms underlying such rapid evolution are still being investigated, but structural changes to enzymes that alter their melting temperatures appear to be a key mechanism when adaptation to relatively high temperatures is called for (Pucci and Rooman, 2017). While determining whether such mechanisms can be operationalized over the duration of our sorting experiment was beyond the scope of our study, this is still arguably a very short time frame for significant shifts in thermal optima due to selection on standing variation alone. Furthermore, the communities that remained after 4 weeks of growth at the six temperatures consisted of taxonomically distinct sets of strains, and the $T_{\text{opt}}$s of the overall set of taxa exhibited a significant phylogenetic signature (Figure 3). This indicates that the observed systematic differences in $T_{\text{opt}}$ across the temperature-specific communities were driven by species sorting on preexisting physiological variation across strains rather than thermal adaptation of single strains. Overall, we therefore conclude that species sorting played a dominant role in determining the response of the parent community to abrupt changes in temperature, in the absence of immigration, and with negligible adaptation.

We also detected systematic turnover in functional traits that likely underpin the change in thermal optima with species sorting. There were differences in the taxa isolated at different temperatures, with more Proteobacteria at lower temperatures and more Firmicutes at higher temperatures (all ${\displaystyle T_{\text{opt}}}$ > 35°C were Firmicutes). Furthermore, these phyla were partitioned in the $r$-$K$ and thermal generalism-specialism trait spaces (Figure 4). Proteobacteria were found to be relatively $K$-selected thermal specialists and Firmicutes relatively $r$-selected thermal generalists. These findings are inconsistent with a generalist-specialist trade-off in which increasing thermal niche width is proposed to inevitably incur a metabolic cost, reducing maximal growth rates (Huey and Hertz, 1984; Angilletta, 2009). As with our findings, recent work on phytoplankton thermal performance traits also failed to detect a generalist-specialist trade-off (Kontopoulos et al., 2020a), questioning its universality in microbes. Since the existence of such a trade-off plays a key role in life history theory, there would be value in further experiments to confirm the generality of this finding.

The increased growth rates and lower respiration rates of Firmicutes relative to Proteobacteria found here are also largely consistent with datasets from meta-analyses of bacterial rates (Figure 5). Additionally, previously reported values for cellular ATP content have generally been found to be higher for Firmicutes than Proteobacteria with more than tenfold greater intracellular ATP content reported for Bacillus versus Pseudomonas strains (Hattori et al., 2003), some of the major representatives of Firmicutes and Proteobacteria in this experiment, respectively. This suggests that these phyla tend to allocate resources to growth and respiration in fundamentally different ways. One explanation for these seemingly divergent strategies may be found in Firmicutes deriving extra energy though fermentation pathways. There is a mechanistic trade-off between growth rate and yield whereby bacteria may increase their rate of ATP production by supplementing aerobic respiration with fermentation (Pfeiffer et al., 2001). Fermentation pathways increase the rate of ATP production but result in lower total yield, allowing populations to reach higher growth rates but lower carrying capacity from the same resource input. This is consistent with the apparent $r$ vs. $K$ selection trade-off observed in our results. The differences in scaling relationship between ATP content and respiration rate may provide further evidence of differences in metabolic pathways utilized. Across Proteobacteria, ATP content has a scaling exponent of approximately 1 with respiration rate, indicating that these strains are deriving ATP solely from aerobic respiration (Figure 4B). The fact that Firmicutes have a lower scaling exponent (0.60 ± 0.07), that is, that they are generating higher levels of ATP than expected at lower rates of respiration, may indicate that they derive ATP from alternative pathways alongside aerobic respiration. These differences in metabolic strategies reflect underlying differences in the efficiency of growth, that is, carbon use efficiency (CUE), between these taxa (Smith et al., 2021). Moreover, CUE varies systematically with temperature in a phylogenetically structured manner (Pold et al., 2020; Smith et al., 2021). Thus, community turnover due to temperature change is likely to have a profound impact on community-level functional traits, such as CUE (Domeignoz-Horta et al., 2020).

In contrast to the strong association between $T_{\text{opt}}$ and incubation temperature in the sorting temperature isolates, we did not observe any similar relationships in the standard temperature isolates, where mesophiles were consistently recovered regardless of prior incubation conditions. This indicates that species sorting was incomplete (in that maladapted taxa were not driven extinct), implying that bacterial communities can be resilient to temperature change at the community level. Taxa suited to different temperatures are able to ‘switch on’ as conditions become suitable, allowing community-level functional plasticity due to the latent functional diversity present within communities. Although mesophiles were recovered from all incubation temperatures in our standard temperature experiment, there was the same taxonomic bias as seen in the sorting temperature isolates – more Firmicutes were recovered from higher temperatures. This is probably a reflection of the propensity of Firmicutes to form endospores and remain dormant until conditions are favorable, upon which they invest resources into rapid growth to gain a competitive advantage over other taxa, consistent with our life history trait findings of $r$-specialism in Firmicutes (Lennon and Jones, 2011). In comparison, the Proteobacteria in this experiment were generally more suited to oligotrophic environments (e.g., Collimonas; Leveau et al., 2010), where constituent species are expected to present low growth rate and high carrying capacity ($K$ specialists; Fierer et al., 2007), as well as increased respiration (Keiblinger et al., 2010). This idea is supported by the observation that we isolated the strains from sandy, acidic soil (i.e., oligotrophic) (Fornara et al., 2013), and sequencing studies revealed that Proteobacteria are the most abundant phylum (Macdonald et al., 2015). We do not suggest that this adoption of $r$ vs. $K$ strategy is general to all Firmicutes and Proteobacteria. Indeed, meta-analysis reveals little consistency in the phyla associated with copiotrophy or oligotrophy (Ho et al., 2017). Nor do we suggest that warming is likely to result in selection for Firmicutes over Proteobacteria – community temperature responses are not likely to be consistent at coarse phylogenetic levels (Oliverio et al., 2017). However, the results presented here are consistent with phylum-specific traits for the majority of our isolates when compared to each other.

Patterns of microbial community succession in nature are driven by the differences in growth strategy between taxa that we report here. Studies have revealed taxonomic groups associated with different stages of microbial succession, with patterns broadly consistent across timescales of days (Noll et al., 2005; Shrestha et al., 2007; Rui et al., 2009) and years (Nemergut et al., 2007; Banning et al., 2011) and even over thousands and tens of thousands of years, as revealed through sequencing of soil sediments (Jangid et al., 2013). Generally, across these studies, the phyla Firmicutes and Bacteroidetes are associated with early succession, while other phyla such as Actinobacteria and Acidobacteria are more abundant at later stages of succession. Proteobacteria are less consistent at the phylum level, with Alphaproteobacteria associated with late succession, Betaproteobacteria associated with early succession and Gammaproteobacteria variously associated with different stages of succession in different studies. Isolated taxa reveal a strong association between early succession and high growth rates (Shrestha et al., 2007) as well as rRNA operon copy numbers, a key determinant of bacterial growth rate (Klappenbach et al., 2000). The $K$-selected taxa may therefore be thought of as general constituents of soil, associated with standard low turnover of carbon, while the $r$-selected taxa may be seen as more opportunistic from their involvement in early succession. Indeed, signatures of community-level differences in $r$- vs. $K$-selection have been observed in microbial communities at different successional stages (Pascual-García and Bell, 2020). Fluctuating temperatures may therefore drive repeated assembly dynamics via sorting on latent microbial diversity, leading to functional community changes through time. However, the frequency and magnitude of temperature fluctuations may also influence the life history strategies of the taxa in the community (Gilchrist, 1995; Basan et al., 2020).

Although we report patterns broadly consistent with previous findings at the phylum level, bacteria isolated from the environment will always represent only a small, incomplete subset of the overall diversity of the natural community. Previous 16S sequencing of the field site sampled here has revealed Proteobacteria to be the most abundant phylum, followed by Verrucomicrobia, Acidobacteria, Actinobacteria, and Firmicutes, respectively (Macdonald et al., 2015). That the majority of our isolates are from the Firmicutes and that we isolated no Acidobacteria or Verrucomicrobia, despite their expected relative abundances in these soils, is not surprising. Firmicutes are consistently overrepresented in culture libraries (Schloss et al., 2016; Floyd et al., 2005), while most members of the Acidobacteria and Verrucomicrobia are notoriously difficult to reliably culture (Kielak et al., 2010; Kalam et al., 2020). Therefore, caution should be taken when interpreting community responses from culture-based studies like ours.

In summary, we have found that resuscitation of latent functional diversity driven by phenotypically plastic responses of single taxa to temperature change can allow whole bacterial communities to track dramatic changes in temperature. Community function is expected to be driven by interactions between the most abundant taxa (Rivett and Bell, 2018) and therefore changes in the abundance of taxa with temperature variation are likely to drive profound changes in overall community functioning (mediated by community-level variation in traits such as CUE). In particular, $r$- vs. $K$-selection is likely to vary with temperature change at the community level, from daily to seasonal successional trajectories, driven by species sorting. Furthermore, climate change is expected to lead to increased temperature fluctuations (Vasseur et al., 2014), both in magnitude and frequency. This may potentially lead to more frequent species sorting effects over short timescales, further driving changes in community composition through time. Overall, these results show that latent diversity in thermal physiology combined with temperature induced species sorting is likely to facilitate the responses of microbial community structure and functioning to climatic fluctuations.

16S sequences have been deposited in NCBI GenBank under accession codes ON804144:ON804203. Data and code to reproduce all analyses in this manuscript are provided on GitHub: https://github.com/smithtp/latent-diversity, (copy archived at swh:1:rev:d58dfefa598c6f701c0e7eb1da289aa9fc06c27b).

1. 1. Smith TP
   2. Mombrikotb S
   3. Ransome E
   4. Kontopoulos D
   5. Pawar S
   6. Bell T

   (2022) Dryad Digital Repository

   Latent functional diversity may accelerate microbial community responses to temperature fluctuations.

   https://doi.org/10.5061/dryad.f1vhhmh0g
2. 1. Smith TP
   2. Mombrikotb S
   3. Ransome E
   4. Kontopoulos D
   5. Pawar S
   6. Bell T

   (2022) NCBI

   ID ON804144. Pseudomonas sp. strain 04_04_02 16S ribosomal RNA gene, partial sequence.

   https://www.ncbi.nlm.nih.gov/search/all/?term=ON804144
3. 1. Smith TP
   2. Mombrikotb S
   3. Ransome E
   4. Kontopoulos D
   5. Pawar S
   6. Bell T

   (2022) NCBI

   ID ON804203. Bacillus sp. (in: Bacteria) strain 50_RT_06 16S ribosomal RNA gene, partial sequence.

   https://www.ncbi.nlm.nih.gov/search/all/?term=ON804203

1. 1. Allison SD
   2. Martiny JBH

   (2008) Colloquium paper: resistance, resilience, and redundancy in microbial communities

   PNAS 105 Suppl 1:11512–11519.

   https://doi.org/10.1073/pnas.0801925105

   • PubMed
   • Google Scholar
2. Conference

   1. Angilletta MJ

   (2009)

   Thermal acclimation

   In Thermal Adaption: A Theoretical and Empirical Synthesis. pp. 126–156.

   • Google Scholar
3. 1. Aydogan EL
   2. Moser G
   3. Müller C
   4. Kämpfer P
   5. Glaeser SP

   (2018) Long-term warming shifts the composition of bacterial communities in the phyllosphere of galium album in a permanent grassland field-experiment

   Frontiers in Microbiology 9:144.

   https://doi.org/10.3389/fmicb.2018.00144

   • PubMed
   • Google Scholar
4. 1. Banning NC
   2. Gleeson DB
   3. Grigg AH
   4. Grant CD
   5. Andersen GL
   6. Brodie EL
   7. Murphy DV

   (2011) Soil microbial community successional patterns during forest ecosystem restoration

   Applied and Environmental Microbiology 77:6158–6164.

   https://doi.org/10.1128/AEM.00764-11

   • PubMed
   • Google Scholar
5. 1. Bardgett RD
   2. Freeman C
   3. Ostle NJ

   (2008) Microbial contributions to climate change through carbon cycle feedbacks

   The ISME Journal 2:805–814.

   https://doi.org/10.1038/ismej.2008.58

   • PubMed
   • Google Scholar
6. 1. Barton S
   2. Jenkins J
   3. Buckling A
   4. Schaum CE
   5. Smirnoff N
   6. Raven JA
   7. Yvon-Durocher G

   (2020) Evolutionary temperature compensation of carbon fixation in marine phytoplankton

   Ecology Letters 23:722–733.

   https://doi.org/10.1111/ele.13469

   • PubMed
   • Google Scholar
7. 1. Basan M
   2. Honda T
   3. Christodoulou D
   4. Hörl M
   5. Chang YF
   6. Leoncini E
   7. Mukherjee A
   8. Okano H
   9. Taylor BR
   10. Silverman JM
   11. Sanchez C
   12. Williamson JR
   13. Paulsson J
   14. Hwa T
   15. Sauer U

   (2020) A universal trade-off between growth and lag in fluctuating environments

   Nature 584:470–474.

   https://doi.org/10.1038/s41586-020-2505-4

   • PubMed
   • Google Scholar
8. 1. Bennett AF
   2. Dao KM
   3. Lenski RE

   (1990) Rapid evolution in response to high-temperature selection

   Nature 346:79–81.

   https://doi.org/10.1038/346079a0

   • PubMed
   • Google Scholar
9. 1. Blaby IK
   2. Lyons BJ
   3. Wroclawska-Hughes E
   4. Phillips GCF
   5. Pyle TP
   6. Chamberlin SG
   7. Benner SA
   8. Lyons TJ
   9. Crécy-Lagard V de
   10. Crécy E de

   (2012) Experimental evolution of a facultative thermophile from a mesophilic ancestor

   Applied and Environmental Microbiology 78:144–155.

   https://doi.org/10.1128/AEM.05773-11

   • PubMed
   • Google Scholar
10. 1. Blomberg SP
    2. Garland T
    3. Ives AR

    (2003) Testing for phylogenetic signal in comparative data: behavioral traits are more labile

    Evolution; International Journal of Organic Evolution 57:717–745.

    https://doi.org/10.1111/j.0014-3820.2003.tb00285.x

    • PubMed
    • Google Scholar
11. 1. Bouckaert R
    2. Vaughan TG
    3. Barido-Sottani J
    4. Duchêne S
    5. Fourment M
    6. Gavryushkina A
    7. Heled J
    8. Jones G
    9. Kühnert D
    10. De Maio N
    11. Matschiner M
    12. Mendes FK
    13. Müller NF
    14. Ogilvie HA
    15. du Plessis L
    16. Popinga A
    17. Rambaut A
    18. Rasmussen D
    19. Siveroni I
    20. Suchard MA
    21. Wu CH
    22. Xie D
    23. Zhang C
    24. Stadler T
    25. Drummond AJ

    (2019) BEAST 2.5: an advanced software platform for bayesian evolutionary analysis

    PLOS Computational Biology 15:e1006650.

    https://doi.org/10.1371/journal.pcbi.1006650

    • PubMed
    • Google Scholar
12. 1. Campbell CD
    2. Chapman SJ
    3. Cameron CM
    4. Davidson MS
    5. Potts JM

    (2003) A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil

    Applied and Environmental Microbiology 69:3593–3599.

    https://doi.org/10.1128/AEM.69.6.3593-3599.2003

    • PubMed
    • Google Scholar
13. 1. Chase AB
    2. Weihe C
    3. Martiny JBH

    (2021) Adaptive differentiation and rapid evolution of a soil bacterium along a climate gradient

    PNAS 118:e2101254118.

    https://doi.org/10.1073/pnas.2101254118

    • PubMed
    • Google Scholar
14. 1. DeLong JP
    2. Okie JG
    3. Moses ME
    4. Sibly RM
    5. Brown JH

    (2010) Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life

    PNAS 107:12941–12945.

    https://doi.org/10.1073/pnas.1007783107

    • PubMed
    • Google Scholar
15. 1. Domeignoz-Horta LA
    2. Pold G
    3. Liu XJA
    4. Frey SD
    5. Melillo JM
    6. DeAngelis KM

    (2020) Microbial diversity drives carbon use efficiency in a model soil

    Nature Communications 11:3684.

    https://doi.org/10.1038/s41467-020-17502-z

    • PubMed
    • Google Scholar
16. 1. Fierer N
    2. Bradford MA
    3. Jackson RB

    (2007) Toward an ecological classification of soil bacteria

    Ecology 88:1354–1364.

    https://doi.org/10.1890/05-1839

    • PubMed
    • Google Scholar
17. 1. Floyd MM
    2. Tang J
    3. Kane M
    4. Emerson D

    (2005) Captured diversity in a culture collection: case study of the geographic and habitat distributions of environmental isolates held at the American type culture collection

    Applied and Environmental Microbiology 71:2813–2823.

    https://doi.org/10.1128/AEM.71.6.2813-2823.2005

    • PubMed
    • Google Scholar
18. 1. Fornara DA
    2. Banin L
    3. Crawley MJ

    (2013) Multi-nutrient vs. nitrogen-only effects on carbon sequestration in grassland soils

    Global Change Biology 19:3848–3857.

    https://doi.org/10.1111/gcb.12323

    • PubMed
    • Google Scholar
19. 1. Foster KR
    2. Bell T

    (2012) Competition, not cooperation, dominates interactions among culturable microbial species

    Current Biology 22:1845–1850.

    https://doi.org/10.1016/j.cub.2012.08.005

    • PubMed
    • Google Scholar
20. 1. García FC
    2. Bestion E
    3. Warfield R
    4. Yvon-Durocher G

    (2018) Changes in temperature alter the relationship between biodiversity and ecosystem functioning

    PNAS 115:10989–10994.

    https://doi.org/10.1073/pnas.1805518115

    • PubMed
    • Google Scholar
21. 1. Gilchrist GW

    (1995) Specialists and generalists in changing environments. I. fitness landscapes of thermal sensitivity

    The American Naturalist 146:252–270.

    https://doi.org/10.1086/285797

    • Google Scholar
22. 1. Hattori N
    2. Sakakibara T
    3. Kajiyama N
    4. Igarashi T
    5. Maeda M
    6. Murakami S

    (2003) Enhanced microbial biomass assay using mutant luciferase resistant to benzalkonium chloride

    Analytical Biochemistry 319:287–295.

    https://doi.org/10.1016/s0003-2697(03)00322-1

    • PubMed
    • Google Scholar
23. 1. Heath TA
    2. Holder MT
    3. Huelsenbeck JP

    (2012) A Dirichlet process prior for estimating lineage-specific substitution rates

    Molecular Biology and Evolution 29:939–955.

    https://doi.org/10.1093/molbev/msr255

    • PubMed
    • Google Scholar
24. 1. Ho A
    2. Di Lonardo DP
    3. Bodelier PLE

    (2017) Revisiting life strategy concepts in environmental microbial ecology

    FEMS Microbiology Ecology 93:1–14.

    https://doi.org/10.1093/femsec/fix006

    • PubMed
    • Google Scholar
25. 1. Hubert C
    2. Loy A
    3. Nickel M
    4. Arnosti C
    5. Baranyi C
    6. Brüchert V
    7. Ferdelman T
    8. Finster K
    9. Christensen FM
    10. Rosa de Rezende J
    11. Vandieken V
    12. Jørgensen BB

    (2009) A constant flux of diverse thermophilic bacteria into the cold arctic seabed

    Science 325:1541–1544.

    https://doi.org/10.1126/science.1174012

    • PubMed
    • Google Scholar
26. 1. Huey RB
    2. Hertz PE

    (1984) IS a jack-of-all-temperatures a master of none?

    Evolution; International Journal of Organic Evolution 38:441–444.

    https://doi.org/10.1111/j.1558-5646.1984.tb00302.x

    • PubMed
    • Google Scholar
27. 1. Jangid K
    2. Whitman WB
    3. Condron LM
    4. Turner BL
    5. Williams MA

    (2013) Soil bacterial community succession during long-term ecosystem development

    Molecular Ecology 22:3415–3424.

    https://doi.org/10.1111/mec.12325

    • PubMed
    • Google Scholar
28. 1. Kalam S
    2. Basu A
    3. Ahmad I
    4. Sayyed RZ
    5. El-Enshasy HA
    6. Dailin DJ
    7. Suriani NL

    (2020) Recent understanding of soil Acidobacteria and their ecological significance: a critical review

    Frontiers in Microbiology 11:580024.

    https://doi.org/10.3389/fmicb.2020.580024

    • PubMed
    • Google Scholar
29. 1. Karhu K
    2. Auffret MD
    3. Dungait JAJ
    4. Hopkins DW
    5. Prosser JI
    6. Singh BK
    7. Subke J-A
    8. Wookey PA
    9. Agren GI
    10. Sebastià M-T
    11. Gouriveau F
    12. Bergkvist G
    13. Meir P
    14. Nottingham AT
    15. Salinas N
    16. Hartley IP

    (2014) Temperature sensitivity of soil respiration rates enhanced by microbial community response

    Nature 513:81–84.

    https://doi.org/10.1038/nature13604

    • PubMed
    • Google Scholar
30. 1. Keiblinger KM
    2. Hall EK
    3. Wanek W
    4. Szukics U
    5. Hämmerle I
    6. Ellersdorfer G
    7. Böck S
    8. Strauss J
    9. Sterflinger K
    10. Richter A
    11. Zechmeister-Boltenstern S

    (2010) The effect of resource quantity and resource stoichiometry on microbial carbon-use-efficiency

    FEMS Microbiology Ecology 73:430–440.

    https://doi.org/10.1111/j.1574-6941.2010.00912.x

    • PubMed
    • Google Scholar
31. 1. Kielak A
    2. Rodrigues JLM
    3. Kuramae EE
    4. Chain PSG
    5. van Veen JA
    6. Kowalchuk GA

    (2010) Phylogenetic and metagenomic analysis of Verrucomicrobia in former agricultural grassland soil

    FEMS Microbiology Ecology 71:23–33.

    https://doi.org/10.1111/j.1574-6941.2009.00785.x

    • PubMed
    • Google Scholar
32. 1. Kishimoto T
    2. Iijima L
    3. Tatsumi M
    4. Ono N
    5. Oyake A
    6. Hashimoto T
    7. Matsuo M
    8. Okubo M
    9. Suzuki S
    10. Mori K
    11. Kashiwagi A
    12. Furusawa C
    13. Ying BW
    14. Yomo T

    (2010) Transition from positive to neutral in mutation fixation along with continuing rising fitness in thermal adaptive evolution

    PLOS Genetics 6:e1001164.

    https://doi.org/10.1371/journal.pgen.1001164

    • PubMed
    • Google Scholar
33. 1. Klappenbach JA
    2. Dunbar JM
    3. Schmidt TM

    (2000) Rrna operon copy number reflects ecological strategies of bacteria

    Applied and Environmental Microbiology 66:1328–1333.

    https://doi.org/10.1128/AEM.66.4.1328-1333.2000

    • PubMed
    • Google Scholar
34. 1. Kontopoulos DG
    2. Sebille E
    3. Lange M
    4. Yvon‐Durocher G
    5. Barraclough TG
    6. Pawar S

    (2020a) Phytoplankton thermal responses adapt in the absence of hard thermodynamic constraints

    Evolution 74:775.

    https://doi.org/10.1111/evo.13946

    • Google Scholar
35. 1. Kontopoulos DG
    2. Smith TP
    3. Barraclough TG
    4. Pawar S

    (2020b) Adaptive evolution shapes the present-day distribution of the thermal sensitivity of population growth rate

    PLOS Biology 18:e3000894.

    https://doi.org/10.1371/journal.pbio.3000894

    • PubMed
    • Google Scholar
36. 1. Kumar S
    2. Stecher G
    3. Suleski M
    4. Hedges SB

    (2017) TimeTree: a resource for timelines, timetrees, and divergence times

    Molecular Biology and Evolution 34:1812–1819.

    https://doi.org/10.1093/molbev/msx116

    • PubMed
    • Google Scholar
37. 1. Langenheder S
    2. Székely AJ

    (2011) Species sorting and neutral processes are both important during the initial assembly of bacterial communities

    The ISME Journal 5:1086–1094.

    https://doi.org/10.1038/ismej.2010.207

    • PubMed
    • Google Scholar
38. 1. Lawrence D
    2. Fiegna F
    3. Behrends V
    4. Bundy JG
    5. Phillimore AB
    6. Bell T
    7. Barraclough TG

    (2012) Species interactions alter evolutionary responses to a novel environment

    PLOS Biology 10:e1001330.

    https://doi.org/10.1371/journal.pbio.1001330

    • PubMed
    • Google Scholar
39. 1. Leibold MA
    2. Holyoak M
    3. Mouquet N
    4. Amarasekare P
    5. Chase JM
    6. Hoopes MF
    7. Holt RD
    8. Shurin JB
    9. Law R
    10. Tilman D
    11. Loreau M
    12. Gonzalez A

    (2004) The metacommunity concept: a framework for multi-scale community ecology

    Ecology Letters 7:601–613.

    https://doi.org/10.1111/j.1461-0248.2004.00608.x

    • Google Scholar
40. 1. Lennon JT
    2. Jones SE

    (2011) Microbial seed banks: the ecological and evolutionary implications of dormancy

    Nature Reviews. Microbiology 9:119–130.

    https://doi.org/10.1038/nrmicro2504

    • PubMed
    • Google Scholar
41. 1. Lenski RE

    (2017) Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations

    The ISME Journal 11:2181–2194.

    https://doi.org/10.1038/ismej.2017.69

    • PubMed
    • Google Scholar
42. 1. Leveau JHJ
    2. Uroz S
    3. de Boer W

    (2010) The bacterial genus Collimonas: mycophagy, weathering and other adaptive solutions to life in oligotrophic soil environments

    Environmental Microbiology 12:281–292.

    https://doi.org/10.1111/j.1462-2920.2009.02010.x

    • PubMed
    • Google Scholar
43. 1. Lynch MDJ
    2. Neufeld JD

    (2015) Ecology and exploration of the rare biosphere

    Nature Reviews. Microbiology 13:217–229.

    https://doi.org/10.1038/nrmicro3400

    • PubMed
    • Google Scholar
44. 1. Macdonald CA
    2. Crawley MJ
    3. Wright DJ
    4. Kuczynski J
    5. Robinson L
    6. Knight R
    7. Al-Soud WA
    8. Sørensen SJ
    9. Deng Y
    10. Zhou J
    11. Singh BK

    (2015) Identifying qualitative effects of different grazing types on below-ground communities and function in a long-term field experiment

    Environmental Microbiology 17:841–854.

    https://doi.org/10.1111/1462-2920.12539

    • PubMed
    • Google Scholar
45. 1. Makarieva AM
    2. Gorshkov VG
    3. Li BL

    (2005) Energetics of the smallest: do bacteria breathe at the same rate as whales?

    Proceedings. Biological Sciences 272:2219–2224.

    https://doi.org/10.1098/rspb.2005.3225

    • PubMed
    • Google Scholar
46. 1. Malik AA
    2. Martiny JBH
    3. Brodie EL
    4. Martiny AC
    5. Treseder KK
    6. Allison SD

    (2020) Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change

    The ISME Journal 14:1–9.

    https://doi.org/10.1038/s41396-019-0510-0

    • PubMed
    • Google Scholar
47. 1. Marchant R
    2. Franzetti A
    3. Pavlostathis SG
    4. Tas DO
    5. Erdbrugger I
    6. Unyayar A
    7. Mazmanci MA
    8. Banat IM

    (2008) Thermophilic bacteria in cool temperate soils: are they metabolically active or continually added by global atmospheric transport?

    Applied Microbiology and Biotechnology 78:841–852.

    https://doi.org/10.1007/s00253-008-1372-y

    • PubMed
    • Google Scholar
48. Book

    1. Marshall KC

    (1986) R- and K-Selection and Microbial Ecology

    Springer.

    https://doi.org/10.1007/978-1-4757-0611-6

    • Google Scholar
49. 1. Melillo JM
    2. Frey SD
    3. DeAngelis KM
    4. Werner WJ
    5. Bernard MJ
    6. Bowles FP
    7. Pold G
    8. Knorr MA
    9. Grandy AS

    (2017) Long-Term pattern and magnitude of soil carbon feedback to the climate system in a warming world

    Science 358:101–105.

    https://doi.org/10.1126/science.aan2874

    • PubMed
    • Google Scholar
50. 1. Molina-Venegas R
    2. Rodríguez MÁ

    (2017) Revisiting phylogenetic signal; strong or negligible impacts of polytomies and branch length information?

    BMC Evolutionary Biology 17:1–10.

    https://doi.org/10.1186/s12862-017-0898-y

    • PubMed
    • Google Scholar
51. 1. Münkemüller T
    2. Lavergne S
    3. Bzeznik B
    4. Dray S
    5. Jombart T
    6. Schiffers K
    7. Thuiller W

    (2012) How to measure and test phylogenetic signal

    Methods in Ecology and Evolution 3:743–756.

    https://doi.org/10.1111/j.2041-210X.2012.00196.x

    • Google Scholar
52. 1. Nemergut DR
    2. Anderson SP
    3. Cleveland CC
    4. Martin AP
    5. Miller AE
    6. Seimon A
    7. Schmidt SK

    (2007) Microbial community succession in an unvegetated, recently deglaciated soil

    Microbial Ecology 53:110–122.

    https://doi.org/10.1007/s00248-006-9144-7

    • PubMed
    • Google Scholar
53. 1. Noll M
    2. Matthies D
    3. Frenzel P
    4. Derakshani M
    5. Liesack W

    (2005) Succession of bacterial community structure and diversity in a paddy soil oxygen gradient

    Environmental Microbiology 7:382–395.

    https://doi.org/10.1111/j.1462-2920.2005.00700.x

    • PubMed
    • Google Scholar
54. 1. Oliverio AM
    2. Bradford MA
    3. Fierer N

    (2017) Identifying the microbial taxa that consistently respond to soil warming across time and space

    Global Change Biology 23:2117–2129.

    https://doi.org/10.1111/gcb.13557

    • PubMed
    • Google Scholar
55. 1. Padfield D
    2. Yvon-Durocher G
    3. Buckling A
    4. Jennings S
    5. Yvon-Durocher G

    (2016) Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton

    Ecology Letters 19:133–142.

    https://doi.org/10.1111/ele.12545

    • PubMed
    • Google Scholar
56. 1. Pagel M

    (1999) Inferring the historical patterns of biological evolution

    Nature 401:877–884.

    https://doi.org/10.1038/44766

    • PubMed
    • Google Scholar
57. 1. Pascual-García A
    2. Bell T

    (2020) Community-Level signatures of ecological succession in natural bacterial communities

    Nature Communications 11:1–11.

    https://doi.org/10.1038/s41467-020-16011-3

    • Google Scholar
58. 1. Pawar S
    2. Dell AI
    3. Savage VM
    4. Knies JL

    (2016) Real versus artificial variation in the thermal sensitivity of biological traits

    The American Naturalist 187:E41–E52.

    https://doi.org/10.1086/684590

    • PubMed
    • Google Scholar
59. 1. Pfeiffer T
    2. Schuster S
    3. Bonhoeffer S

    (2001) Cooperation and competition in the evolution of ATP-producing pathways

    Science 292:504–507.

    https://doi.org/10.1126/science.1058079

    • PubMed
    • Google Scholar
60. 1. Pold G
    2. Domeignoz-Horta LA
    3. Morrison EW
    4. Frey SD
    5. Sistla SA
    6. DeAngelis KM

    (2020) Carbon use efficiency and its temperature sensitivity covary in soil bacteria

    MBio 11:e02293-19.

    https://doi.org/10.1128/mBio.02293-19

    • PubMed
    • Google Scholar
61. 1. Pruesse E
    2. Peplies J
    3. Glöckner FO

    (2012) Sina: accurate high-throughput multiple sequence alignment of ribosomal RNA genes

    Bioinformatics 28:1823–1829.

    https://doi.org/10.1093/bioinformatics/bts252

    • PubMed
    • Google Scholar
62. 1. Pucci F
    2. Rooman M

    (2017) Physical and molecular bases of protein thermal stability and cold adaptation

    Current Opinion in Structural Biology 42:117–128.

    https://doi.org/10.1016/j.sbi.2016.12.007

    • PubMed
    • Google Scholar
63. 1. Revell L. J

    (2012) Phytools: an R package for phylogenetic comparative biology (and other things)

    Methods in Ecology and Evolution 3:217–223.

    https://doi.org/10.1111/j.2041-210X.2011.00169.x

    • Google Scholar
64. 1. Revell LJ
    2. Freckleton R

    (2013) Two new graphical methods for mapping trait evolution on phylogenies

    Methods in Ecology and Evolution 4:754–759.

    https://doi.org/10.1111/2041-210X.12066

    • Google Scholar
65. 1. Rivett DW
    2. Lilley AK
    3. Connett GJ
    4. Carroll MP
    5. Legg JP
    6. Bruce KD

    (2017) Contributions of composition and interactions to bacterial respiration are reliant on the phylogenetic similarity of the measured community

    Microbial Ecology 74:757–760.

    https://doi.org/10.1007/s00248-017-0982-2

    • PubMed
    • Google Scholar
66. 1. Rivett DW
    2. Bell T

    (2018) Abundance determines the functional role of bacterial phylotypes in complex communities

    Nature Microbiology 3:767–772.

    https://doi.org/10.1038/s41564-018-0180-0

    • PubMed
    • Google Scholar
67. 1. Romanova ND
    2. Sazhin AF

    (2010) Relationships between the cell volume and the carbon content of bacteria

    Oceanology 50:522–530.

    https://doi.org/10.1134/S0001437010040089

    • Google Scholar
68. 1. Rui J
    2. Peng J
    3. Lu Y

    (2009) Succession of bacterial populations during plant residue decomposition in rice field soil

    Applied and Environmental Microbiology 75:4879–4886.

    https://doi.org/10.1128/AEM.00702-09

    • PubMed
    • Google Scholar
69. 1. Scheuerl T
    2. Hopkins M
    3. Nowell RW
    4. Rivett DW
    5. Barraclough TG
    6. Bell T

    (2020) Bacterial adaptation is constrained in complex communities

    Nature Communications 11:754.

    https://doi.org/10.1038/s41467-020-14570-z

    • PubMed
    • Google Scholar
70. 1. Schloss PD
    2. Girard RA
    3. Martin T
    4. Edwards J
    5. Thrash JC

    (2016) Status of the archaeal and bacterial census: an update

    MBio 7:e00201-16.

    https://doi.org/10.1128/mBio.00201-16

    • PubMed
    • Google Scholar
71. 1. Schoolfield RM
    2. Sharpe PJ
    3. Magnuson CE

    (1981) Non-Linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory

    Journal of Theoretical Biology 88:719–731.

    https://doi.org/10.1016/0022-5193(81)90246-0

    • PubMed
    • Google Scholar
72. 1. Shrestha PM
    2. Noll M
    3. Liesack W

    (2007) Phylogenetic identity, growth-response time and rRNA operon copy number of soil bacteria indicate different stages of community succession

    Environmental Microbiology 9:2464–2474.

    https://doi.org/10.1111/j.1462-2920.2007.01364.x

    • PubMed
    • Google Scholar
73. 1. Smith TP
    2. Thomas TJH
    3. García-Carreras B
    4. Sal S
    5. Yvon-Durocher G
    6. Bell T
    7. Pawar S

    (2019) Community-level respiration of prokaryotic microbes may rise with global warming

    Nature Communications 10:5124.

    https://doi.org/10.1038/s41467-019-13109-1

    • PubMed
    • Google Scholar
74. 1. Smith TP
    2. Clegg T
    3. Bell T
    4. Pawar S

    (2021) Systematic variation in the temperature dependence of bacterial carbon use efficiency

    Ecology Letters 24:2123–2133.

    https://doi.org/10.1111/ele.13840

    • PubMed
    • Google Scholar
75. 1. Sogin ML
    2. Morrison HG
    3. Huber JA
    4. Mark Welch D
    5. Huse SM
    6. Neal PR
    7. Arrieta JM
    8. Herndl GJ

    (2006) Microbial diversity in the deep sea and the underexplored “ rare biosphere.”

    PNAS 103:12115–12120.

    https://doi.org/10.1073/pnas.0605127103

    • PubMed
    • Google Scholar
76. 1. Suutari M
    2. Laakso S

    (1994) Microbial fatty acids and thermal adaptation

    Critical Reviews in Microbiology 20:285–328.

    https://doi.org/10.3109/10408419409113560

    • PubMed
    • Google Scholar
77. 1. Székely AJ
    2. Langenheder S

    (2014) The importance of species sorting differs between habitat generalists and specialists in bacterial communities

    FEMS Microbiology Ecology 87:102–112.

    https://doi.org/10.1111/1574-6941.12195

    • PubMed
    • Google Scholar
78. 1. Thompson LR
    2. Sanders JG
    3. McDonald D
    4. Amir A
    5. Ladau J
    6. Locey KJ
    7. Prill RJ
    8. Tripathi A
    9. Gibbons SM
    10. Ackermann G
    11. Navas-Molina JA
    12. Janssen S
    13. Kopylova E
    14. Vázquez-Baeza Y
    15. González A
    16. Morton JT
    17. Mirarab S
    18. Zech Xu Z
    19. Jiang L
    20. Haroon MF
    21. Kanbar J
    22. Zhu Q
    23. Jin Song S
    24. Kosciolek T
    25. Bokulich NA
    26. Lefler J
    27. Brislawn CJ
    28. Humphrey G
    29. Owens SM
    30. Hampton-Marcell J
    31. Berg-Lyons D
    32. McKenzie V
    33. Fierer N
    34. Fuhrman JA
    35. Clauset A
    36. Stevens RL
    37. Shade A
    38. Pollard KS
    39. Goodwin KD
    40. Jansson JK
    41. Gilbert JA
    42. Knight R
    43. Earth Microbiome Project Consortium

    (2017) A communal catalogue reveals earth’s multiscale microbial diversity

    Nature 551:457–463.

    https://doi.org/10.1038/nature24621

    • PubMed
    • Google Scholar
79. 1. Van der Gucht K
    2. Cottenie K
    3. Muylaert K
    4. Vloemans N
    5. Cousin S
    6. Declerck S
    7. Jeppesen E
    8. Conde-Porcuna JM
    9. Schwenk K
    10. Zwart G
    11. Degans H
    12. Vyverman W
    13. De Meester L

    (2007) The power of species sorting: local factors drive bacterial community composition over a wide range of spatial scales

    PNAS 104:20404–20409.

    https://doi.org/10.1073/pnas.0707200104

    • PubMed
    • Google Scholar
80. 1. van Veluw GJ
    2. Petrus MLC
    3. Gubbens J
    4. de Graaf R
    5. de Jong IP
    6. van Wezel GP
    7. Wösten HAB
    8. Claessen D

    (2012) Analysis of two distinct mycelial populations in liquid-grown Streptomyces cultures using a flow cytometry-based proteomics approach

    Applied Microbiology and Biotechnology 96:1301–1312.

    https://doi.org/10.1007/s00253-012-4490-5

    • PubMed
    • Google Scholar
81. 1. Vasseur DA
    2. DeLong JP
    3. Gilbert B
    4. Greig HS
    5. Harley CDG
    6. McCann KS
    7. Savage V
    8. Tunney TD
    9. O’Connor MI

    (2014) Increased temperature variation poses a greater risk to species than climate warming

    Proceedings. Biological Sciences 281:20132612.

    https://doi.org/10.1098/rspb.2013.2612

    • PubMed
    • Google Scholar
82. 1. Wisnoski NI
    2. Lennon JT

    (2021) Stabilising role of seed banks and the maintenance of bacterial diversity

    Ecology Letters 24:2328–2338.

    https://doi.org/10.1111/ele.13853

    • PubMed
    • Google Scholar
83. 1. Wu W
    2. Lu HP
    3. Sastri A
    4. Yeh YC
    5. Gong GC
    6. Chou WC
    7. Hsieh CH

    (2018) Contrasting the relative importance of species sorting and dispersal limitation in shaping marine bacterial versus protist communities

    The ISME Journal 12:485–494.

    https://doi.org/10.1038/ismej.2017.183

    • PubMed
    • Google Scholar
84. 1. Yu H
    2. Deng Y
    3. He Z
    4. Van Nostrand JD
    5. Wang S
    6. Jin D
    7. Wang A
    8. Wu L
    9. Wang D
    10. Tai X
    11. Zhou J

    (2018) Elevated CO2 and warming altered grassland microbial communities in soil top-layers

    Frontiers in Microbiology 9:1–10.

    https://doi.org/10.3389/fmicb.2018.01790

    • PubMed
    • Google Scholar
85. 1. Zeigler DR

    (2014) The geobacillus paradox: why is a thermophilic bacterial genus so prevalent on a mesophilic planet?

    Microbiology 160:1–11.

    https://doi.org/10.1099/mic.0.071696-0

    • PubMed
    • Google Scholar

Author details

1. Thomas P Smith

   The Georgina Mace Centre for the Living Planet, Imperial College London, Ascot, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   thomas.smith1@imperial.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4038-9722

2. Shorok Mombrikotb

   The Georgina Mace Centre for the Living Planet, Imperial College London, Ascot, United Kingdom

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Emma Ransome

   The Georgina Mace Centre for the Living Planet, Imperial College London, Ascot, United Kingdom

   Contribution

   Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Dimitrios - Georgios Kontopoulos

   1. LOEWE Centre for Translational Biodiversity Genomics, Frankfurt, Germany
   2. Senckenberg Research Institute, Frankfurt, Germany

   Contribution

   Software, Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5082-1929

5. Samraat Pawar

   The Georgina Mace Centre for the Living Planet, Imperial College London, Ascot, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8375-5684

6. Thomas Bell

   The Georgina Mace Centre for the Living Planet, Imperial College London, Ascot, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2615-3932

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