Introduction

Microorganisms play key roles in biogeochemical cycling that are fundamental in maintaining climate and ecosystem stability. The structure of microbial communities is closely associated with environmental conditions and therefore is likely to evolve in the context of global change (Gutknecht et al. 2012). In the marine environment, frequent natural events and increasing human activity dramatically influence microbial community dynamics, which will change the balance of biogeochemical cycles and alter ecosystem functioning (Hutchins and Fu 2017). One of the major concerns associated with global changes is how to effectively predict variations in ecosystem functioning. Microorganisms, as major drivers of many biogeochemical processes, provide a linkage between ecosystem functioning and environments (Singh et al. 2010).

Marine microbial communities are significantly affected by environmental changes. Sanger and high-throughput sequencing in recent decades have provided an enormous amount of sequence data of molecular marker genes, including the ribosomal RNA (rRNA) gene. These data have helped to provide insights into marine microbial community dynamics (Liu et al. 2019; Needham et al. 2017; Reji et al. 2019), which are driven by environmental factors, such as salinity (Lozupone and Knight 2007) and temperature (Sunagawa et al. 2015). However, the association between environments and microbial dynamics (termed as deterministic processes) can be confounded by the effects of random events (termed as stochastic processes that include ecological drift and dispersal) (Mo et al. 2018; Wang et al. 2019; Zhou and Ning 2017). Deterministic and stochastic processes, which jointly determine microbial biogeography, vary in their relative contribution to community assembly over different temporal and spatial scales (Zhou and Ning 2017). Differences in microbial distribution impact ecosystem functioning. Their relationships can mostly be explained by the observed correlations between microbial phylogeny and functional traits and between microbial diversity and ecosystem functioning. There are ways to predict microbial functional potential based on taxonomy (Aßhauer et al. 2015; Langille et al. 2013; Louca et al. 2016). Therefore, it has become increasingly common to improve the fitness of ecosystem functioning prediction by including microbial data.

Marine microbes are highly diverse and encompass taxonomically and functionally different lineages. Complex interactions occur among microbial taxa, which underpin community stability and functioning. However, elucidation of microbial interactions is challenging and is largely dependent on correlation-based network analysis (Liu et al. 2014b; Milici et al. 2016; Zhang et al. 2014; Zhou et al. 2018). Diverse microbial communities can be divided into subcommunities, based on different criteria, such as abundance (abundant and rare taxa), functional type (e.g., autotrophic and heterotrophic taxa) and activity (active and dormant taxa). Accumulating evidence is showing that microbial subcommunities differ in their environmental sensitivity, interaction and distribution patterns (Wu et al. 2017; Zhang et al. 2014). Thus, different subcommunities may represent different consortiums and differ in their roles in ecosystem functioning prediction. Currently, the ecology of microbial subcommunities is less understood than the whole community, raising the need for a resolved community-based classification for future analysis.

By implementing rRNA gene-based amplicon sequencing, numerous previously unknown microbial lineages, even at the phylum level, have been described from the marine environment (Brown et al. 2015; DeLong 1992; Inagaki et al. 2003). These, together with those identified from terrestrial habitats, dramatically expand the tree of life (Hug et al. 2016). Refined phylogenetic analysis of molecular marker genes further demonstrates the occurrence of habitat-specific ecotypes within a lineage (Ivars-Martinez et al. 2008; Liu et al. 2014a). The diversification of microorganisms can be attributed to a joint effect of genetic and environmental variabilities, which dictate the specific evolutionary history of a taxon.

In this review, five aspects are presented (microbial assembly, interaction, functioning, activity and diversification) to show how microbial community data contribute to the understanding of marine microbial ecology. They are organized along a stepwise understanding of the role microbial communities play in marine ecosystems. A synthetic view of these aspects will provide novel insights into their interactions and complementarity, which will in turn help to stimulate new ideas on the interpretation of community compositional data and the perception of new microbial community studies.

Processes of microbial community assembly

One long debated question in community ecology is which processes determine an ecological community to assemble (Preston 1948). The current, well-established theories are primarily derived from research on animals and plants. Studies on microorganisms are scarce because of the assumption that microorganisms do not have distribution patterns due to their large numbers and small size (Baas-Becking 1934). However, using advanced sequencing technologies and statistical methods, microbial distribution patterns have been discovered in many natural environments including seawater and marine sediments (Liu et al. 2014b, 2015a; Lozupone and Knight 2007; Martiny et al. 2006). The spatial turnover of microbial communities always reflects a distance-decay relationship and/or a taxa-area relationship, which are the two most well established patterns depicting increasing community dissimilarity with spatial distance (Nekola and White 1999) and increasing taxa richness with area size (Horner-Devine et al. 2004), respectively.

A framework has been established that considers the niche and neutral theories as potential mechanisms underpinning microbial biogeography (Dini-Andreote et al. 2015; Dumbrell et al. 2010; Leibold and McPeek 2006; Table 1). The long standing niche theory shows how biodiversity is structured by physiochemical (environmental heterogeneity) and biotic (inter taxa interaction) factors (Chase and Leibold 2003). In niche theory, every taxon is assumed to have unique and non-overlapping traits, which enable it to exert different responses/effects on an environment and to occupy different ecological niches (Leibold 1995). This heterogeneity in traits also eliminates inter taxa competition by resource partition and thus allows an infinite number of taxa to coexist in an environment, leading to high microbial diversity (Leibold and McPeek 2006). In this context, niche theory cannot explain the observed differences in the abundance of different taxa, although microbial abundance under niche theory always follows a log-normal distribution pattern (Hubbell 2001). Later, Hubbell (2001) proposed a neutral ecological view on the assembly of taxa-rich communities that consisted of many rare taxa. Unlike niche theory, the neutral theory assumes that all individuals are ecologically equivalent, share the same way of life and have a similar response/effect on environments. Therefore, interactions among microbes, and between microbes and environments are ignored in the neutral theory (Hubbell 2001). Microbial diversity is controlled by randomly occurring events, such as ecological drift (change in the relative abundance of taxa in a location due to chance demographic fluctuations) and dispersal (movement of taxa across spaces) (Chave 2004; Hanson et al. 2012; Hubbell 2001). Because such ecological events happen differently to each individual taxon, the relative abundance of different taxa can be partially explained by the neutral theory (Volkov et al. 2003).

Table 1 Comparison of the niche and neutral theories
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Microbial ecology studies have consistently considered abiotic selection as a niche-based (deterministic) process (also known as habitat filters or species sorting) and ecological drift and dispersal limitation as neutral-based (stochastic) processes (Fig. 1). Selections are mostly represented by environmental correlations. Drift is difficult to measure and it often interacts with restricted dispersal (dispersal limitation) to produce a distance-decay relationship (Hanson et al. 2012). Dispersal limitation is often represented by spatial distance. However, spatial distance can be a deterministic factor when it correlates to physiochemical factors and both population size (neutral) and traits (niche) can affect the dispersal ability of different taxa (Hanson et al. 2012). These make the relationships between selection, dispersal and stochasticity complex (Evans et al. 2017). To accurately evaluate stochasticity, neutral and null models have been increasingly implemented (Chase and Myers 2011; Jeraldo et al. 2012, Table 2). Nevertheless, these models cannot distinguish different aspects of the stochastic process. To solve this problem, Stegen et al. (2013, 2015) proposed a framework to quantify the relative roles of deterministic and stochastic processes. This framework integrates both the null model and phylogenetic information and assumes that phylogenetically close taxa tend to have similar ecological traits. It contains two steps with the first to estimate the role of selection using phylogenetic dissimilarities and the second to differentiate the role of dispersal and drift using the Bray–Curtis dissimilarities among microbial communities (Jia et al. 2018; Logares et al. 2018; Stegen et al. 2013, 2015; Zhou and Ning 2017).

Fig. 1
figure 1

The assembly of microbial communities is governed by a joint effect of deterministic (niche) and stochastic (neutral) processes. Subdivision of a microbial community (according to abundance, activity, function and occupancy) into subcommunities will facilitate an accurate view of microbial assembling processes

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Table 2 Examples of studies examining the relative role of deterministic and stochastic processes in structuring microbial community
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Deterministic and stochastic processes jointly govern the assembly of microbial communities (Chave 2004). However, their relative importance varies across different spatial and temporal scales (Table 2), depending on the strength of environmental gradients and the sensitivity of the microbes to environmental changes. If the extent of environmental variation is greater than the threshold a microbe can endure, dispersal will be prevented (Wang et al. 2013), leading to the predominance of determinism. Thus, the mechanisms underlying microbial community assembly would alter over a seasonal or longer term period with changes in the magnitude of environmental heterogeneity (Dini-Andreote et al. 2015; Langenheder et al. 2012). These conclusions are mostly based on investigations of terrestrial microbial communities. By comparison, there have been very few studies on the relative roles of deterministic and stochastic processes in the marine environment. However, there is a general perception that stochastic processes have a greater effect on the assembly of planktonic bacterial and archaeal communities than deterministic processes (Table 2). This can be explained by marine prokaryotes having evolved strong adaptation capabilities to environmental changes and by spatial connectivity and seawater movement homogenizing environmental conditions. Another explanation is that the environmental factors analyzed to represent deterministic processes may be not the most relevant ones affecting community variations. Further studies are needed to confirm such a hypothesis and to compare the assembling processes between different habitats, such as coastal water vs open ocean and water vs sediment.

Different types of marine organisms differ in their responses to deterministic and stochastic processes. Wu et al. (2018) reported that determinism had a stronger effect on planktonic protist communities than on bacterial communities, which may relate to differences in their environmental sensitivity. Such different responses between bacteria and micro-eukaryotes have also been observed in soil (Powell et al. 2015a) and freshwater (Logares et al. 2018) habitats. Additionally, subcommunities that are divided by abundance, activity, functional trait or occupancy, can also undergo different ecological processes (Fig. 1). A microbial community is usually made up of a few abundant taxa and a long tail of rarer ones (Pedrós-Alió 2006, 2012). The rare taxa account for a great proportion of the microbial diversity and have been shown to assemble non-randomly and display similar distribution patterns to the abundant taxa (Galand et al. 2009; Gong et al. 2015; Liu et al. 2015b; Mo et al. 2018). Nevertheless, the abundant and rare taxa have both been observed to be differently affected by stochastic and deterministic processes (Liu et al. 2015b; Mo et al. 2018). Mo et al. (2018) reported that the rare bacterioplankton in coastal seawater had a weaker response to environmental factors than abundant taxa; this may be due to the small population size of the rare taxa, making them more susceptible to ecological drift (Nemergut et al. 2013). By contrast, a survey of bacterial communities in freshwater lakes and reservoirs revealed a greater influence of environmental changes on the rare than the abundant taxa (Liu et al. 2015b). These findings suggest complicated microbial ecological responses across distinct ecosystems. In this context, further studies are needed to gain an insight into the assembling processes of abundant and rare taxa in different environments. The most urgent need is to propose a common definition for rare taxa, facilitating a parallel comparison across studies (Jia et al. 2018). Subcommunities divided by functional traits, activity and occupancy receive less attention and have mainly been analyzed in terrestrial environments. For example, in deserts, the phototrophic community was mainly affected by stochastic processes, whereas the heterotrophic community displayed patterns mainly driven by environmental stresses (Caruso et al. 2011). The assembly of generalists and specialists in plateau lakes, however, was driven by stochastic and deterministic processes, respectively (Liao et al. 2016). While these studies provide novel and accurate information about the distribution patterns and assembling processes of microbial communities, there is an urgent need to investigate different subcommunities in the marine environment. It should be noticed that when evaluating the relative role of determinism and stochasticity, the estimated contribution from determinism is largely affected by the set of environmental factors measured, since they are not necessarily the most relevant parameters that provide the best explanatory power for the community variations.

Patterns of microbial co-occurrence

In niche theory, the microbe-microbe interactions, although being ecologically important, are less understood compared to the microbe-environment relationships (Chase and Leibold 2003). Inclusion of interactions to explain microbial distribution patterns is a great challenge, largely due to the difficulty in obtaining microbial co-cultures. An alternative way of elucidating microbial interactions is to apply correlation-based network analysis (Barberán et al. 2012; Layeghifard et al. 2017; Weiss et al. 2016), which is enhanced by the increase of community compositional data and the development of statistical tools. The most popular method used for constructing a correlation-based network is to calculate the Spearman’s rank correlation coefficients between taxa (Barberán et al. 2012; Table 2). Other methods are also available, including SPIEC-EASI, CCLasso, REBACCA, CoNet, SparCC, WGCNA, Molecular Ecological Networks Analysis, Local Similarity Analysis, Maximal Information Coefficient, etc. These methods, however, vary in their sensitivity and precision (Layeghifard et al. 2017; Weiss et al. 2016).

Nodes and edges are fundamental components of a network, representing taxa and correlations, respectively. Edge thickness often denotes the degree of a correlation, with a thicker edge representing a higher correlation coefficient. On the basis of nodes and edges, a number of parameters can be calculated to represent the topological structure of a network, including degree, density, betweenness centrality, network diameter and clustering coefficient (Newman 2003). The degree of a node describes its connectivity to other nodes, with a higher value indicating a wider correlation. The betweenness centrality of a node describes the number of shortest paths between any two nodes going through it. The nodes with high degree and low betweenness centrality potentially represent the keystone taxa of a community (Berry and Widder 2014; Liang et al. 2016). The keystone taxa are the cornerstone and initial components for a community to assemble (Berry and Widder 2014) and have recently been defined as “highly connected taxa that individually or in a guild exert a considerable influence on microbiome structure and functioning irrespective of their abundance across space and time” (Banerjee et al. 2018). A group of densely connected nodes with weak correlations to other nodes forms a module. Modular analysis can help to simplify the processes of identifying keystone taxa and/or exploring the effect of environmental factors on microbe-microbe interactions.

Despite having the potential to infer mutualistic (positive) and antagonistic (negative) effects, the co-occurrence patterns illustrated by the network analysis can have different meanings, such as similar environmental preference and lifestyle, resource partitioning and nutrient cross-feeding that do not involve direct interactions. Network analyses have consistently revealed patterns dominated by positive co-occurrences in both terrestrial and marine bacterial communities (Barberán et al. 2012; Ju et al. 2014; Liu et al. 2014b; Ma et al. 2016; Milici et al. 2016; Zhang et al. 2014; Zhou et al. 2018). This suggests a ubiquitous non hostile process in bacterial community assembling irrespective of habitat. In fact, a network analysis of prokaryotic communities in global surface seawater samples (Tara oceans) showed that positive co-occurrences accounted for up to 90% of all correlations (Lima-Mendez et al. 2015; Milici et al. 2016). This may be explained by the similar environmental preference (possibly driven by some unknown/unmeasured environmental factors) and/or high resistance of marine prokaryotes to environmental stresses, which enable them to coexist in the same ecological niche. Indeed, the finding is in line with the observation of a relatively weaker effect of determinism relative to stochasticity on the distribution of marine planktonic prokaryotes as stated above. It is also likely that auxotrophy in marine microbes contributes to the observed positive correlations, since several bacterial groups (such as the SAR11 clade of Alphaproteobacteria, Tripp et al. 2009) have been observed to gain fitness by obtaining a biomolecule from other groups. By contrast, a study of planktonic bacterial communities in the South China Sea showed that negative correlations dominated the co-occurrence patterns in active bacteria compared to positive correlations in total bacteria (Zhang et al. 2014). Resource competition may occur in different active bacterial groups. It is noticeable that microbial networks vary with space and time (Table 3). Significant changes in network topological structures have been observed in seawater at different depths (Chow et al. 2013) and between different seasons (Chafee et al. 2018). Additionally, Milici et al. (2016) reported that free-living bacterioplankton possessed highly interconnected networks compared to particle-attached communities. They postulated that the distinct nutrient-utilizing strategies of these two groups might be responsible for such a discrepancy. However, it is unexpected to find more between-taxa connections in the free-living community, since particle-attached microbes have a higher cell abundance and are physically closer to one another. This provides further evidence that the network-derived co-occurrence patterns are not always good proxies of true interactions between microbes.

Table 3 Examples of network analysis conducted in the marine ecosystem
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Several studies have attempted to use networks to infer potential functional couplings between microbes. For example, Thaumarchaeota Marine Group I (MG-I), the most abundant archaeal clade in the marine environment, capable of ammonia oxidization, has been found to co-occur with Nitrospina (Reji et al. 2019) and/or with Nitrospira when Nitrospina is absent or in low abundance (Wang et al. 2019), both of which are nitrite oxidizers. Their co-occurrence in seawater is supported by substrate feeding (nitrite produced by MG-I is the substrate of Nitrospina/Nitrospira) and facilitates the complete nitrification process. Previous efforts to explore co-occurrence patterns between functional bacteria in marine sediments have demonstrated significant correlations between sulfate-reducing bacteria and sulfur-oxidizing bacteria, and between sulfate-reducing bacteria and nitrite-oxidizing bacteria (Liu et al. 2014b). Elucidation of co-occurrence patterns with functional gene abundance derived from GeoChip and metagenome may facilitate a more direct inference. However, the obtained co-occurrence patterns should be treated with caution when used to infer functional couplings, since they are not necessarily reflecting real interactions.

Classically, a network describes co-occurrence patterns between taxa. However, environmental variables can also be included to explore microbe-environment relationships. Additionally, considering the natural complexity of inter taxa relationships in an ecosystem, pairwise microbe-microbe correlations, derived from most current network analyses, need to be expanded to a higher order, such as three- or four-way correlations. The high-order microbial co-occurrence patterns may involve possible disruption or enhancement of another taxon to a pairwise relationship (Bairey et al. 2016). Such high-order co-occurrence patterns can also be unraveled by analyzing compositional data, as long as new and proper statistical tools are developed (Bairey et al. 2016). Although co-occurrence patterns are not appropriate to imply accurate microbial interactions, their spatiotemporal dynamics hold the potential to affect the assembling processes and ecological roles of microbial communities.

Microbial biogeochemical roles and ecosystem functioning

Phylogeny and functional traits

To better understand microbial ecosystem functioning, creating links between an individual taxon and a specific function is required. However, it is infeasible to trace all the diverse biogeochemical processes and to relate them to taxa. This problem may be solved from the point of view of the microorganisms, as microbial functional capabilities have been found to connect strongly with phylogeny (Martiny et al. 2013; Zimmerman et al. 2013). For example, the presence of functional genes related to oxygenic photosynthesis, methane oxidation and sulfate reduction has been found to be highly phylogenetically conserved (Martiny et al. 2013). In coastal seawater, recent studies have shown that microorganisms that assimilated organic matter, including starch and glucose, were phylogenetically clustered (Bryson et al. 2017; Mayali and Weber 2018), reflecting phylogenetically conserved resource partitioning in the coastal microbial loop (Bryson et al. 2017). Such phylogenetic conservation in substrate utilization supports similar distribution patterns (even at a broader taxonomic level; Philippot et al. 2010; Schmidt et al. 2016) and similar lifestyles among microbial relatives. Salazar et al. (2015) found that the particle-associated and free-living populations in the deep ocean had different phylogenetic origins. These observations enhance the possibility of inferring microbial functional traits with phylogenetic information.

However, growing evidence now suggests that variations in functional traits occur within closely related microbes, even at the loosely defined species level (Larkin and Martiny 2017). Prochlorococcus, the most abundant genus of photosynthetic organisms, diverges into high- and low-light-adapted ecotypes, which display different light-harvesting strategies (Bibby et al. 2003). Likewise, Alteromonas macleodii, a typical copiotrophic r-strategist, contains both surface and deep-sea ecotypes (Ivars-Martinez et al. 2008), which have been shown to differ substantially in their capacity to degrade algal polysaccharides (Neumann et al. 2015). On the other hand, microorganisms performing similar metabolic functions can be only distantly related (Martiny et al. 2015), the basic principle of functional redundancy. Louca et al. (2016, 2017) found high functional redundancy in both marine and plant-associated microbial communities, implying that microbial functional traits are widely spread among microbial lineages.

Horizontal gene transfer and gene gain and loss likely result in the discrepancy in phylogeny-functional trait relationships (Fig. 2a). The type of functional trait is also an important contributor, with traits with slower evolutionary rates displaying clearer phylogenetically conserved patterns. Bryson et al. (2017) reported that traits of resource utilization in marine microbes, represented by patterns of substrate assimilation, were phylogenetically cohesive, whereas those of biosynthetic activity after assimilation were heterogeneous. Additionally, functional potential (gene presence) does not necessarily mean function execution (gene expression) and functional capability is not equal to rate (the same functional trait can be different in rate). Although the capability of glucose incorporation is widespread in bacteria and displays a shallow phylogenetic clustering (Martiny et al. 2013), the glucose assimilation rates in soil bacteria have been observed to vary greatly across phylogeny and display a deep phylogenetic clustering (Morrissey et al. 2016). This finding highlights the importance of incorporating functional rates into phylogeny-function relatedness and raises the need to examine how quantitative changes in microbial function affect ecosystem functioning.

Fig. 2
figure 2

Relationships among microbial phylogeny, diversity and ecosystem functioning: (a) the relationship between phylogeny and function is complex, although many functional traits are phylogenetically conserved. Possible reasons for the decoupling of phylogeny and function are presented; (b) microbial diversity (species richness, functional and phylogenetic diversity) is increasingly shown to be positively correlated to ecosystem functioning, whereas negative correlations are also observed. These correlations stimulate a great interest in predicting ecosystem functioning with microbial data. Inclusion of both environmental and microbial data (including microbial interactions and omic data) will enhance the power of ecosystem functioning prediction

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Diversity and ecosystem functioning

There is growing evidence of a positive relationship between microbial diversity and ecosystem functioning (Cardinale et al. 2012; Delgado-Baquerizo et al. 2016b; Schnyder et al. 2018), although negative or no relationships have also been reported (Becker et al. 2012). Such relationships are derived primarily from studies on the terrestrial environment and have rarely been assessed for marine microbial communities. A study of microbial diversity-ecosystem functioning (DEF) relationship in marine surface water also supported a positive correlation, showing that a more phylogenetically diverse bacterial community had a greater level of ecosystem functioning (heterotrophic productivity measured by leucine incorporation; Galand et al. 2015). The enhancement of ecosystem functioning by increased biodiversity is thought to result from complementarity (minimal overlap) in resource use by functionally distinct taxa (Petchey and Gaston 2002) and/or through inter taxa facilitation (Hooper et al. 2005). Therefore, the relationship between diversity and ecosystem functioning is controlled by the niche-based mechanisms: differentiation in resource niche and selection effect (Krause et al. 2014).

The few studies that investigated the shape of the positive relationship between microbial diversity and ecosystem functioning have frequently uncovered a more linear relationship (Delgado-Baquerizo et al. 2016a) than the approaching-flat relationship seen for plants and animals (Cardinale et al. 2011). Such a linear relationship implies an indefinite increase of ecosystem functioning with increasing microbial diversity, challenging the idea of functional redundancy as mentioned above. In fact, Galand et al. (2018) provide evidence against the hypothesis of functional redundancy by showing a strong link between marine microbial community compositions and functional attributes using all the set of metagenomic reads. The authors emphasize the need to consider all functional aspects rather than relying only on known genes in investigating microbial DEF relationships. In addition, different processing rates seen in the same functional trait (Morrissey et al. 2016) may also provide opposing evidence against functional redundancy. However, these findings do not rule out the possibility for a partial functional redundancy, implicating that different types of functional traits may have different levels of redundancy. The idea of functional redundancy on the one hand can help to explain the high level of marine microbial diversity (different taxa are supported by a limited range of resources and conduct the same set of metabolic processes; Allison and Martiny 2008), while on the other hand can limit the extent of ecosystem functioning.

Diversity is composed of different components, including richness (taxonomic diversity), phylogeny (phylogenetic diversity) and function (functional diversity) (Fig. 2b). Different types of diversity can inform distinct microbial DEF relationships. However, taxonomic diversity is the more frequently used proxy in inferring DEF relationships, compared to functional diversity and phylogenetic diversity. It has been reported that taxonomic diversity has relatively little impact on ecosystem functioning (Nielsen et al. 2011), while functional diversity was more correlated, mostly likely by determining ecological niches and inter taxa interactions (Hooper et al. 2005; Krause et al. 2014). Nevertheless, functional diversity is always difficult to measure (functional activity) and/or requires additional sequencing efforts to analyze (functional genes). Thus, phylogenetic diversity is increasingly implemented as a proxy of functional diversity, with the thought that many functional traits are phylogenetically conserved. Indeed, a positive correlation has been found between marine surface bacterial productivity and phylogenetic diversity of the active community; no similar association was found when taxonomic diversity (Shannon index) was analyzed (Galand et al. 2015). The findings of Galand et al. (2015) highlighted that ecosystem functioning is related to the active rather than the total community that contains dormant taxa. This provides an explanation for the more frequently observed negative and/or no relationships between phylogenetic diversity of the total community and ecosystem functioning (Goberna and Verdu 2018; Pérez-Valera et al. 2015; Severin et al. 2013). The relationship between phylogenetic diversity and ecosystem functioning also relates to taxon-specific functional capability and evolutionary history (Gravel et al. 2012). Under which conditions phylogenetic diversity can be used as a representative of functional diversity should be characterized further.

The microbial DEF relationship can be confounded by environmental variations, as environmental factors can exert influences on both diversity and ecosystem functioning. Orland et al. (2018) demonstrated that pH and organic matter quantity and quality explained as much variation in CO2 production as did taxonomic diversity in lake sediments; these environmental factors exerted direct influences on ecosystem functioning due to their unrelatedness to taxonomic diversity. Comparatively, Delgado-Baquerizo et al. (2016b) found in a global set of soil samples that the DEF relationship was maintained when accounting for edaphic factors, which suggests that taxonomic diversity can exert influences on ecosystem functioning independently of environmental variabilities. A global survey of microbiome in seawater showed a decoupling of taxonomy and function, with the latter being more susceptible to environmental changes (Louca et al. 2016). Environmental conditions determine the availability of electronic donors/acceptors to microbes and shape the process of biogeochemical reactions. In addition to the environment, stochastic processes may also affect diversity and influence ecosystem functioning (Orland et al. 2018; Zhou et al. 2013). Overall, the positive DEF relationship in microorganisms is supportive of phylogenetic conservation in functional traits. More effort is needed to discern the role of different diversity components and the role of deterministic and stochastic processes in determining ecosystem functioning.

Ecosystem functioning prediction

The abovementioned relationships have stimulated great interest in using microbial data to predict ecosystem functioning (statistical simulation instead of direct measurement) (Graham et al. 2016; Powell et al. 2015b) (Fig. 2b). Here, we focus on the interactions between community (diversity and abundance) and ecosystem functioning, although physiological properties can also be related (Wieder et al. 2013). Graham et al. (2016) synthesized 82 global datasets from different ecosystems to improve the predictive power of carbon and nitrogen processing rates by the inclusion of the microbial community data. They found that the addition of both compositional and diversity data could strengthen the predictive power, although this was not applicable to all datasets. Andersson et al. (2014) demonstrated, via structural equation models, that the model that included total bacterial abundance explained 54% of the variation in nitrogenase activity in coastal sediments and by replacing total bacterial abundance with cyanobacterial biomass it could increase the predictive power.

The explanatory power of microbial data in predictive models is always lower than that of abiotic factors (Graham et al. 2014, 2016; Powell et al. 2015b), consistent with the notion that environmental factors have direct impacts on ecosystem functioning. Moreover, under different environmental conditions (e.g., temperature, Dolan et al. 2017), the explanatory power of microbial data may change. However, this does not decrease the importance of microbial data in functional prediction. Recently, Zhang et al. (2018) found that in coastal sediments adding different copy number ratios of functional and rRNA genes into stepwise regression models substantially increased the predictive power of denitrification and anammox rates, although alpha diversity and gene abundance of involved bacteria were poorly correlated to the function potentials. In addition to abundance and diversity, it is also important to include microbial interactions to the predictive model in future studies (Fig. 2b). In summary, knowledge on the distribution patterns of microbial communities is indispensable for understanding their biogeochemical and ecological functions.

Inference of microbial activity

As stated above, a microbial community contains both active and dormant members. Dormant microbes constitute the seed bank of a community. Their repeated entrance to and exit from the seed bank decouple the active and total fractions (Jones and Lennon 2010; Fig. 3). Since RNA is a good indicator of whether or not a microbe is viable, rRNA based methods have been extensively used to characterize the active community, despite the difficulty in RNA extraction and the probable biases introduced by reverse transcription.

Fig. 3
figure 3

Environmental shifts will change the relative abundance of the dormant and active fractions within a microbial community. Such changes will cause shifts in microbial co-occurrence patterns and finally affect ecosystem functioning

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Mounting evidence suggests that there is a significant difference between the total (resident) and active fractions within a microbial community. Several abundant taxa are less active in the RNA pool, whereas some highly active taxa show low abundance or are almost absent in the DNA pool (Baldrian et al. 2012; Richa et al. 2017; Romanowicz et al. 2016; Sebastián et al. 2018). For example, Cyanobacteria and the SAR11 clade of Alphaproteobacteria are the most abundant microbes in the global surface ocean; whereas the former is always disproportionately active (16S rRNA:rDNA > 1), the latter tends to be less active (Campbell and Kirchman 2013; Hunt et al. 2013; Zhang et al. 2014). Further, a refined phylogenetic analysis showed that different ecotypes of the SAR11 clade varied in their 16S rRNA:rDNA ratios (Salter et al. 2015). A similar phenomenon was also shown for different ecotypes of MG-I (Hugoni et al. 2013). Within a microbial community, activity can also vary between rare and abundant taxa (Campbell et al. 2011; Richa et al. 2017). Richa et al. (2017) reported that more than 70% of the rare taxa in coastal seawater of the Mediterranean Sea had high 16S rRNA:rDNA ratios. To explain this decoupling of abundance and activity, Campbell et al. (2011) proposed that a substantial proportion of bacteria became active when their abundance decreased, indicating that high abundance may be a constraint factor for activity or that top-down processes i.e., grazing and virus lysis could stimulate activity. Seasonality (Hugoni et al. 2013), environmental factors, such as salinity (Campbell and Kirchman 2013), and lifestyle modes (free-living and particle-attached; Li et al. 2018) have also been reported as drivers for 16S rRNA:rDNA ratio variations.

The active and total microbial communities have been found to display contrasting biogeographic patterns and respond differently to environmental factors in seawater (Zhang et al. 2014). Environmental changes would generate uncomfortable conditions for active microbes, and the adaptation and successful establishment of active microbes in a new environment are difficult (Hanson et al. 2012). By contrast, the growth of several dormant microbes can be stimulated by changing environments, contributing to microbial community succession. Considering this, the active community is likely to display a stronger distance-decay relationship than the total community (Zhang et al. 2014). Nevertheless, our understanding of the assembling processes (relative role of deterministic and stochastic processes) of the total and active communities is limited. Zhang et al. (2014) also found that the active (dominated by negative correlations) and total (dominated by positive correlations) bacterial communities exhibited different co-occurrence patterns. Frequent transitions of microbes between an active or dormant status under changing environments would lead to variations in co-occurrence patterns, causing significant alterations in ecosystem functioning (Fig. 3). As mentioned above, active microbes are directly linked to ecosystem functioning by actively carrying out biogeochemical reactions (Nannipieri et al. 2003). Thus, distinguishing the active fraction from the whole community would provide novel insights into patterns of microbial assembly, co-occurrence and DEF relationship. Moreover, the finding of a higher environmental sensitivity of active heterotrophs than active autotrophs in seawater (Zhang et al. 2014) further raises the need to treat functionally different microbial groups separately.

Another possible utilization of the rRNA:rDNA ratio is to indicate potential growth rate (Campbell et al. 2011; Lankiewicz et al. 2016), as numerous microorganisms are yet to be cultivated and their growth rates are not able to be measured (Kirchman 2016). A high rRNA:rDNA ratio may imply a high growth rate. Thus, the lower proportion of SAR11 in the RNA than in the DNA pool, as mentioned above, may indicate a slow growth mode, although it may also be due to the low number of ribosomes per cell, given its small cell size. The slow growth rate of SAR11, however, is further verified by culture-based analyses (Lankiewicz et al. 2016). In comparison, copiotrophic taxa from Alteromonas and the Rosebacter clade often display higher growth rates (Hunt et al. 2013; Lankiewicz et al. 2016). Noticeably, the rRNA:rDNA ratio has been reported to be not always effective in quantifying microbial growth rates, and protein synthesis potential has been proposed to be a more suitable interpretation (Blazewicz et al. 2013).

Insight into microbial diversification

The ribosomal gene sequences obtained from the marine environments using culture-independent methods have significantly expanded the microbial phylogenetic tree and public rRNA gene databases (the most popular are SILVA, Greengenes and RDP databases) (Brown et al. 2015; Delong 1992; Hug et al. 2016; Kubo et al. 2012). The microbial groups that have no close isolates at the time of discovering their rRNA gene sequences are often indicated by informal nomenclature, such as SAR11, MG-I and Miscellaneous Crenarchaeota Group (MCG). Further, in-depth phylogenetic analyses of the rRNA gene sequences have clustered many microbial groups, such as MG-I and MCG, into subclades (Kubo et al. 2012; Liu et al. 2014a). These subclades have been shown to respond differently to environmental changes and exhibit different habitat preferences (Lazar et al. 2016; Liu et al. 2014a). Similar niche differentiations are also shown for marine bacteria sharing high 16S rRNA gene similarities (e.g., Alteromonas macleodii as mentioned above). These observations suggest that environmental selection plays a crucial role in the process of microbial evolution. In addition to rRNA genes, functional genes can be used to investigate the diversification of functional groups. For example, Alves et al. (2018) recently elucidated the global frequency, phylogenetic diversity and habitat specificity of ammonia-oxidizing archaea using amoA, the gene encoding the active-site subunit of ammonia monooxygenase. These results highlight the importance of using community data derived from molecular marker genes to investigate the phylogenetic relationships and to infer the evolutionary histories of different microbial taxa.

However, a single marker gene is not always effective in differentiating phylogenetically close relatives. In this context, the multilocus sequence typing (MLST) analysis, which takes the phylogenetic information of several (usually 5–7) housekeeping genes, has been shown to provide a better resolution (Enright and Spratt 1999). In recent years, the increasing number of public metagenomic/genomic sequences has provided an excellent resource for determining phylogenetic relatedness among microbes with a high confidence level (Brown et al. 2015; Hug et al. 2016). These phylogenomic analyses have enabled the designation of many novel microbial phyla, including the well-known Thaumarchaeota (containing MG-I) and Bathyarchaeota (MCG) (Adam et al. 2017). Spang et al. (2015) proposed a new archaeal phylum Lokiarchaeota through phylogenomic inference. This phylum contains many eukaryotic signatures, significantly contributing to our understanding of the evolution of life.

Future perspective

The prevalence of studies on microbial communities in the marine environment benefits from the development of new sequencing technologies. The resulting increase in sequencing depth facilities the obtaining of accurate insights into microbial community structure, and in particular enhances the capability of distinguishing rare subcommunities from sequencing errors. In the era of big data, the development of sequencing technologies calls for a simultaneous introduction of new statistical methods and tools, which would help to discern microbial assembling processes and unravel high-order (three- or four-way) microbe-microbe interactions that most likely occur in natural environments. Although microbe-microbe interactions, to date derived mainly from co-occurrence networks, are important in structuring microbial communities and mediating ecological functions, such linkages are still largely elusive. Suitable analytic tools are required to construct a model that bridges the gap between microbial interactions and ecosystem functioning.

Owing to the difficulty in measuring microbial functions, inclusion of community data in the predictive modeling of ecosystem functioning is receiving increasing attention. This raises associated questions such as whether different functional types exhibit similar sensitivity to community data and vice versa, and whether relationships between community data and ecosystem functioning vary spatially and temporally. In addition, the decoupling of phylogeny and function may act as an obstacle for such modeling, which calls for a resolved phylogenetic clustering and taxon-specific functional characterization. Omics, such as metagenome, single-cell genome and metatranscriptome, in parallel with advanced analytic tools, provide great opportunities to link a community, and further to link an individual to function (Fig. 2b).

In this review, we highlight the significance of separating a whole microbial community into subgroups according to different standards. The abundance-dependent grouping has led to the realization that rare subcommunities, the members of which often show high activity, have the potential to increase the power of ecosystem functioning prediction. By contrast, functional-, and other traits-dependent grouping have received less attention. After it has been established what exists, it is then necessary to determine what is alive and whether the active taxa follow patterns seen for the total community, and how active taxa contribute to ecosystem functioning. We also need to know if functional traits of the active community are phylogenetically organized, because such information is important for constructing a direct linkage between community and ecosystem functioning. In summary, subdivision of the whole community will provide accurate and novel insights into relationships between microbial diversity, interaction and ecosystem functioning.