Oceans are highly variable environments. These systems are characterized by marked fluctuations in environmental conditions (e.g., salinity, pH, temperature, nutrients, irradiance), that operate at different temporal (second to decades) and spatial scales (millimeters to kilometers), creating heterogenous seascapes (Boyd et al., 2016a; Rodríguez-Romero et al., 2022). Such fluctuations modulate the environmental envelope (i.e., a set of environmental variables and conditions that favours species occurrence; Figures 1A, B ) of marine microbial communities (Boyd et al., 2010), their distribution and ecological dynamics (Brun et al., 2015), as well as their structure and function across time and space (Tinta et al., 2015; Hutchins and Fu, 2017; Di Pane et al., 2022). However, marine microbes are not static units as they can display a range of biological strategies to short-term changes in environmental conditions such as those occurring at time-scales approximating microbial division times (i.e., within generations; Figure 1C ) (Litaker et al., 1993). These strategies mainly involve non-genetic phenotypic adjustments (i.e., phenotypic plasticity) that allow microbes to actively respond to the physiological challenges imposed by environmental fluctuations at such time-scales (Chevin et al., 2013; Schaum et al., 2013; Boyd et al., 2016a; Collins et al., 2020). For instance, short-term fluctuations (e.g., diurnal cycles) in drivers such as underwater irradiance (light intensity/quality), temperature and/or nutrient availability can potentially induce stressful conditions to marine microorganisms, triggering compensatory behavioral (e.g., vertical migration; Aumack et al., 2014) and metabolic (e.g., photosynthetic activity; Gaidarenko et al., 2019) adjustments. The outcome of these phenotypic responses can be adaptive, neutral or maladaptive (i.e., not adjusting adequately to the new environment) depending on the interaction with other environmental drivers (e.g., light x salinity; Sauer et al., 2002) and the overall effect on organismal fitness (Chevin et al., 2013). Although plasticity provides a mechanism for adaptation to changing environments (e.g., Schaum et al., 2013; Kremer et al., 2018), there are limits for plastic responses (DeWitt et al., 1998; Fox et al., 2019; Gill et al., 2022), beyond which genetic adjustments are required to persist (Fox et al., 2019; Gaitán-Espitia and Hobday, 2021). These responses (i.e., adaptive evolution) are particularly relevant if unfavorable environmental changes are difficult to track (unpredictable or occur too rapidly) and persist beyond few generations (Chevin et al., 2013; Bernhardt et al., 2020; Collins et al., 2020). Nevertheless, the ability to undergo these adjustments varies within and across populations ( Figures 1B, C ) depending on the presence of genetic variation for ecologically important traits (Chevin et al., 2013; Godhe and Rynearson, 2017), and on the strength (e.g., the intensity of selection increases with the number of drivers) and form/direction of natural selection (e.g., directional selection under ocean warming; Figure 1C ) (Brennan et al., 2017; Collins et al., 2020; Walworth et al., 2020). These components of selection are ultimately determined by the magnitude and rate of environmental change (Gaitán-Espitia et al., 2017a; Collins et al., 2020). Consequently, inadequate responses (e.g., neutral or maladaptive) to unfavorable conditions (i.e., where selection is strong and induce significant loss of fitness) can drive population declines and local extinctions ( Figure 1C ). Contrarily, adaptive responses to stress due to environmental change may lead to phenotypic and genetic changes associated to the evolution of tolerance to novel conditions via increased physiological resistance levels, behavioral (e.g., vertical migration) and life-history (e.g., dormancy) avoidance of the stressful conditions. These adaptive evolutionary responses can allow the maintenance of biodiversity and the persistence of populations despite the negative effects on fitness initially induced by environmental change ( Figure 1C ) (Boyd et al., 2016a; Schaum et al., 2016; Schluter et al., 2016; Fox et al., 2019; Collins et al., 2020; Walworth et al., 2020). In this review, we discuss some of the most relevant mechanisms underpinning the adaptive strategies of microbial organisms and their evolutionary responses and outcomes in variable and dynamic oceans.

Phenotypic plasticity offers a fundamental mechanism for marine microbes to cope with short-time environmental fluctuations, allowing them to track environmental changes and escape local extinction ( Figure 1C ) (Chevin et al., 2013). This capacity for rapid phenotypic adjustments is considered to be shaped by the level of environmental variability experienced by populations (e.g., higher plasticity in organisms from more heterogeneous environments; Boyd et al., 2016a), and the predictability of environmental change (e.g., lower plasticity in less predictable environments; Schaum and Collins, 2014; Bernhardt et al., 2020; Leung et al., 2020; Gill et al., 2022) ( Figure 2 ). Here, we described three common short-term adaptive plastic responses of microbial organisms (i.e. direct environmental sensing-response, anticipatory-memory response, and diversified bet-hedging strategy) to environmental fluctuation ( Figure 2 ). While the mechanisms are described individually in the next section, they are not mutually exclusive, as microbes may combine two or three strategies depending on the magnitude and rate of environmental variability (i.e., the strength and direction of selection).

Predictable fluctuating marine environments are expected to promote adaptive phenotypic plasticity ( Figure 2 ) (Schaum and Collins, 2014; Leung et al., 2020; Gill et al., 2022) at the cost of delaying genetic adaptation (Walworth et al., 2020). However, in environments where changes are less predictable and occur extremely rapidly for organisms to track and adjust phenotypically, plasticity is not promoted (Leung et al., 2020). Under these conditions, genetic adaptation may be the only mechanism that allows species and populations to persist locally (Gaitán-Espitia and Hobday, 2021). Although the link between environmental variability and marine microbial evolution is still poorly understood, in this section we aim to provide glimpses of factors and mechanisms modulating adaptive evolutionary dynamics of microbes in dynamic oceans.

Empirical evidences based on experimental evolution have shown that microbial populations can rapidly adapt to environmental changes (single- and multi-driver environments), throughout, for instance, intraspecific strain sorting and genetic changes (Lohbeck et al., 2012; Hoppe et al., 2018; Wolf et al., 2019). Notwithstanding, the evolutionary responses of microbes to environmental fluctuations are conditioned by the number and identity of the interacting drivers involved (Brennan et al., 2017). Faster rates of adaptation have been documented with increased number of drivers as a result of the increase in the strength of selection (Brennan et al., 2017). However, the effect of drivers on selection is not additive as only few (e.g., temperature, nutrients) explain most of the phenotypic and evolutionary changes observed (Brennan and Collins, 2015; Boyd et al., 2016a; Brennan et al., 2017; Feng et al., 2020). Particularly, temperature has been identified as one of the most dominant drivers modulating phenotypic and genetic responses in marine microorganisms comprising broad evolutionary backgrounds (Boyd et al., 2016b; Brennan et al., 2017; Barton et al., 2020; Cabrerizo et al., 2022). This important and dominant influence across microbial life forms is perhaps explained by the existence of “universal” thermodynamic constraints that limit the pace of life and the thermal window for biochemical performance (Angilletta et al., 2010). Fluctuations in dominant drivers such as temperature can also modulate the rate of adaptation and the evolutionary trajectories of marine microbes. For example, a study by Schaum et al. (2018) showed that marine diatoms can exhibit rapid evolutionary divergence and adaptation to high temperatures when populations have evolved under fluctuating environments. Here, the temporary restoration of benign heat level conditions increased population size and therefore the probability of fixing beneficial mutations required for adaptation (Schaum et al., 2018). These characteristics and their influence on adaptive evolution to changes in temperature have been linked to the “complexity” of microbial organisms in which species with small genomes (e.g., prokaryotes) have higher rates of adaptation (e.g., compensatory evolutionary shifts on metabolic traits) to warming compared to “more complex” microbes with larger genomes (Barton et al., 2020).

The rate of adaptation to changing marine environments is also regulated by evolutionary trade-offs (Gaitán-Espitia J. et al., 2017). These trade-offs reflect the costs and constraints for evolutionary change (O'Donnell et al., 2018; Aranguren-Gassis et al., 2019; Lindberg and Collins, 2020; Walworth et al., 2020), and can alter the trajectories of populations across fitness landscapes, delaying or blocking them to reach a global fitness optimum (Gaitán-Espitia J. et al., 2017; Gaitán-Espitia and Hobday, 2021). Because evolutionary trade-offs are difficult to demonstrate in nature, some proxies are used to asses them. These include selection experiments (e.g., Jin et al., 2022), and the analysis of functional/phenotypic and genetic correlations (e.g., Argyle et al., 2021; Walworth et al., 2021b). In the marine diatom Chaetoceros simplex, a trade-off from a functional correlation between high‐temperature tolerance and increased nitrogen requirements is suggested to underlie the inhibited thermal adaptation under nitrogen limitation conditions (Aranguren-Gassis et al., 2019). Similarly, in the marine diatom Phaeodactylum tricornutum, adaptation to high CO₂ was mediated by a functional trade-off in which populations decreased metabolic rates while maintaining the carbon allocation and growth (i.e., fitness; Jin et al., 2022). In marine bacteria, evolutionary trade-offs have been documented when comparing strains growing in natural constant and fluctuating environments. In coastal systems, bacteria carry significantly more environmental stimuli sensing genes than strains in oligotrophic systems (e.g., open ocean; Held et al., 2019). This represents a functional trade-off due to the elevated cost of maintenance of signal transduction systems (TCS) for strains in coastal areas (i.e. dynamic and fluctuating) compared to strains in oceanic (i.e. relatively constant) environment (Mackey et al., 2015). Other evolutionary trade-offs are mostly documented as a result of challenging conditions, these trade-offs can also exist under ameliorated conditions, as evidenced in the study by Lindberg and Collins (2020). In this work, populations of Chlamydomonas reinhardtii evolved to allocate a smaller proportion of carbon to growth while increasing their ability to tolerate and metabolise reactive oxygen species (ROS; Lindberg and Collins, 2020).

Understanding how microbes cope with and adapt to variability in their environments is fundamental for understanding their ecological resilience and adaptive potential in the face of anthropogenic climate change. Unfortunately, most of the mechanistic understanding regarding the environmental-molecular-evolutionary link underpinning the short- and long-term adaptive responses discussed here, have been based on changes in one or two environmental drivers. However, environmental variation and climate change involve multivariate changes (Boyd et al., 2015) that can alter the ecological and evolutionary significance of genetic and non-genetic responses in marine microbes. From the phenotypic plasticity perspective, it is unclear to what extent the number of changing drivers and the type of interactions (i.e., additive, antagonistic or synergetic) can alter the capacity of natural populations to employ plastic strategies such as direct sense-response, anticipatory/memory and bet-hedging. Moreover, we do not know if these responses are differentially modulated across time and space, depending on the level of environmental variation (e.g., higher production of resting cells in more variable environments; lower ecological memory in less variable environments), the existence of physiological constraints (e.g., capacity and costs for activation and maintenance of sensing-regulatory mechanisms), and the signal/duration of the molecular mechanisms underpinning such responses (e.g., different epigenetic factors have different lasting effects). From an evolutionary perspective, we know that fluctuating environments are likely to generate more dynamic adaptive fitness landscapes compared to constant environments. However, in most of the experimental evolution studies, the dynamics have been assessed in stable conditions of one or two drivers, which are characterised by a static local fitness optimum (Steinberg and Ostermeier, 2016). This limits our capacity to understand and predict the tempo and mode of evolution of microbes in fluctuating, multi-driver environments.

JDGE and BA conceived the idea and designed the work. BA led the manuscript writing with feedback and input from ZL and JDGE. All authors contributed to the article and approved the submitted version.

JDGE was supported by the Research Grants Council (ECS 27124318) of Hong Kong.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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