While community interaction co-limitation can extend to other BPNs (Table 1), we use the micronutrient cobalamin (vitamin B₁₂) to provide evidence for CIC because of its importance in microbial community interactions and potential to co-limit primary production in open ocean and coastal waters (Croft et al., 2005; Bertrand et al., 2007; Ramanan et al., 2016; Sokolovskaya et al., 2020). Cobalamin is a cobalt-containing organometallic micronutrient that’s structure was first elucidated in 1956 by Nobel laureate Dr. Dorothy Crowfoot Hodgkin (Hodgkin et al., 1956). It is a powerful cofactor that performs rearrangements and methylation during biochemical reactions (Banerjee and Ragsdale, 2003; Dowling et al., 2012). Cobalamin can only be synthesized de novo by select bacteria and archaea, and it is required by auxotrophic bacteria and an estimated 50% of eukaryotic phytoplankton (Martens et al., 2002; Croft et al., 2005). Auxotrophy in eukaryotic phytoplankton is believed to arise from cobalamin’s role as a cofactor for the enzyme methionine synthase (MetH), which is the final step in methionine synthesis (Croft et al., 2005; Helliwell et al., 2011). All eukaryotic phytoplankton appear to have a cobalamin dependent MetH, while a subset have an additional cobalamin independent one (MetE) that is less efficient and more costly to use (Bertrand et al., 2013). Cyanobacteria produce and use their own cobalamin-like molecule, pseudo-cobalamin, that is largely unavailable to eukaryotic algae but can be re-modeled into cobalamin by a subset of heterotrophic bacteria and a small group of algae, if the alpha ligand (DMB) is available (Helliwell et al., 2016; Ma et al., 2020). This discovery improved, and complicated, our understanding of the marine cobalamin cycle, which now must consider pseudo-cobalamin and remodelers as a potential source of cobalamin. Cobalamin has a significant history at the intersection between biological oceanography and microbial ecology, which has been reviewed (Sañudo-Wilhelmy et al., 2014; Helliwell, 2017) but we will briefly outline its role in algal–bacterial interactions and as a limiting micronutrient here because of its relevance to community interaction co-limitation.

Cobalamin has an important role in microbial interactions and is regularly considered as a model metabolite exchanged in beneficial microbial interactions (Ramanan et al., 2016). Auxotrophic phytoplankton can acquire cobalamin through sustaining obligate or facultative interactions with B₁₂-producing bacteria and/or archaea or by uptake of the vitamin from its surrounding environment, referred to as scavenging (Amin et al., 2012; Kazamia et al., 2012; Bertrand et al., 2015). However, the strategy an organism uses to obtain sufficient cobalamin is likely affected by the availability of the nutrient or associated producer. The factors that influence methods of cobalamin acquisition and the regulation of cobalamin transfer or exchange between organisms are still poorly understood although relevant for understanding CIC. Interactions between cobalamin producers and consumers can be selected for and strengthened, in part, by the reciprocal transfer of nutrients and/or the active selection of beneficial bacteria through secondary metabolites (Kazamia et al., 2012; Grant et al., 2014; Shibl et al., 2020). In particular, the exchange of organic, labile carbon for cobalamin is relatively well documented, and it is expected that other nutrients, such as nitrogen species, amino acids, sulfonates, sugar derivatives or specific growth factors like indol-3-acetic acid could be exchanged as well (Amin et al., 2012; Ramanan et al., 2016; Durham et al., 2019).

Cobalamin is found at picomolar to sub-picomolar concentrations in the open ocean (Sañudo-Wilhelmy et al., 2014) and has already been shown to limit or co-limit phytoplankton communities in multiple regions of the ocean (e.g., coastal McMurdo Sound and the Ross Sea, Gulf of Alaska, coastal NE Atlantic) (Bertrand et al., 2007, 2015; Koch et al., 2011; Barber-Lluch et al., 2019). These studies investigated cobalamin limitation using bottle incubation nutrient addition experiments, occasionally layering omics approaches to investigate mechanisms of limitation (Bertrand et al., 2015). A few studies have directly measured the concentration of cobalamin(s) in the water column (Gobler et al., 2007; Heal et al., 2014). Determining potential for cobalamin limitation through examining stoichiometry is not yet feasible since cobalamin production rates, requirements, and quotas are still poorly defined. Cobalamin availability is thought to depend on the rate of photodegradation, remodeling, and balance of supply and demand (driven by B₁₂ producers and B₁₂ consumers). But, cobalamin production can be limited by other nutrients required for bacterial growth (iron or nitrogen) or cobalamin production (e.g., cobalt).

It is also useful to consider the more extensively studied example of nitrogen fixation in the context of CIC. Since a number of reviews summarizing the current advances in understanding marine nitrogen fixation are available (e.g., Sohm et al., 2011; Zehr and Capone, 2020), we will only briefly highlight some key findings on community interactions as it pertains to CIC. Nitrogen fixation is performed by diazotrophs and comprises an important source of fixed nitrogen for non-diazotrophs in the ocean, especially during periods of low fixed nitrogen availability (Karl et al., 2002). However, diazotrophic activity, and therefore nitrogen supply, can be limited by iron and phosphorous availability, generating a community that is co-limited by iron and/or phosphorous and nitrogen (Held et al., 2020). In many cases, diazotrophs, such as Trichodesmium spp., independently fix atmospheric carbon but it remains unclear if the diazotroph benefits in any way from the release/exchange of fixed nitrogen. Some diazotrophic cyanobacteria observed to be associated with haptophytes appear to lack the ability to fix atmospheric carbon, thereby relying on the supply of organic carbon from the haptophytes (Thompson et al., 2012). Carbon and nitrogen exchange between the two symbionts therefore relies on adequate growth conditions for both organisms. Several recent studies also suggest a direct influence of DOM availability on nitrogen fixation rates when (non-cyanobacterial) heterotrophs dominate the diazotrophic community (Bonnet et al., 2013; Rahav et al., 2015, 2016). The nutrients produced within these symbiosis could be available to the surrounding community through active or passive transfer and, as such, suggest that nitrogen fixation is well suited to be investigated through the lens of CIC.

A major goal in modern marine biogeochemistry is to develop a predictive understanding of the relationships between microbial communities and biogeochemistry (Boyd et al., 2015; Moore et al., 2018; Dutkiewicz et al., 2020; Van de Waal and Litchman, 2020). Predicting future changes in such processes as primary production and carbon export to the deep ocean is a challenging task due to the large number of controlling factors (e.g., nutrient supply, temperature) and large uncertainties in the future trajectories of these variables. However, it is widely acknowledged that community composition and diversity, particularly size class distributions and the abundance of different functional groups, play a large role in determining primary production, nutrient cycling, and aspects of the biological carbon pump (Finkel et al., 2010; Tréguer et al., 2018). The studies listed in section “Evidence of Community Interaction Co-limitation in the Literature” have documented instances where community productivity and composition are constrained by the biological production of resources like cobalamin. While it can and has been argued that whatever limits the production of that biologically produced resource is the true limiting nutrient (Karl, 2000), without understanding the production, exchange and susceptibility to change of that biological produced nutrient, we risk remaining in the dark about important controls on microbial community productivity and limiting our capacity to predict responses to change. Using cobalamin as an example, we highlight the potential role CIC may play in global biogeochemical processes.

Cobalamin’s impact on phytoplankton size distributions is one example of the biogeochemical consequences of BPNs. Phytoplankton cell size is often controlled by nutrient availability, with large phytoplankton such as diatoms typically dominating in nutrient-rich regions, and smaller phytoplankton species dominating in oligotrophic waters (Finkel et al., 2010). Effects of cell size on biogeochemical cycles are partly a result of differences in sinking velocity and impacts on grazer community (Finkel et al., 2010; Ward et al., 2012). For example, large cells sink faster than small cells and therefore may contribute more to the biological carbon pump (Ward et al., 2012). Several studies have suggested that high cobalamin availability favors the growth of larger phytoplankton (>5 μm) perhaps because diatoms and dinoflagellates are more likely than other groups to be cobalamin auxotrophs and have many larger representatives (Croft et al., 2005; Sañudo-Wilhelmy et al., 2006; Gobler et al., 2007; Bertrand et al., 2015). In contrast, cyanobacteria may gain competitive advantage in low cobalamin environments because they produce and use their own cobalamin-like molecule, pseudo-cobalamin (Bertrand and Allen, 2012; Helliwell et al., 2016; Heal et al., 2017). This suggests that cobalamin availability may drive changes in size classes and functional groups that could influence contribution to the biological carbon pump. Identifying the mechanisms and interactions that underpin how BPNs impact community composition could help further determine CIC’s role in biogeochemical cycles.

Cobalamin CIC also influences global biogeochemical processes by controlling primary production, and, potentially, algal bloom development. A study at the Spanish Coast, which provides high temporal resolution data across all four seasons, cobalamin was shown to co-limit phytoplankton growth in combination with inorganic nutrients in March. We hypothesized earlier that this scenario likely represents Case B, where cobalamin production is limited by DOM. This period of co-limitation was followed by the transition to cobalamin being the primary limiting nutrient for phytoplankton growth in April at the onset of the spring bloom (most productive period of the year is observed in May), when inorganic nutrients are high and bacterial counts low (Barber-Lluch et al., 2019). This could suggest that although limitation by inorganic nutrients in April was partly relieved, phytoplankton remained cobalamin limited. In turn, the limited phytoplankton growth and DOM production continued to restrict cobalamin production, thereby hindering the community’s ability to quickly recover from nutrient limitation. Although an argument could be made that non-auxotrophic phytoplankton growth may eventually relieve DOM limitation of cobalamin producers, it is unclear how the enhanced non-auxotrophic phytoplankton growth, and subsequent DOM production, affect the timing and strength of cobalamin production. This suggests that the strength and mechanisms of the cobalamin feedback loop affected community productivity and succession over the span of weeks. Furthermore, cobalamin limitation in spring may result in a shift in phytoplankton community to the dominance of species with cobalamin-independent growth at the onset of the bloom. Perhaps due to higher resource use costs for cobalamin-independent growth (Bertrand et al., 2013), this may consequently result in a time-lag in maximal productivity and an increased nutrient consumption, hence decreasing the overall productivity of a bloom. Investigating the spatio-temporal patterns of CIC, especially during periods of high productivity, could uncover additional mechanisms of CIC and its influence on global primary production in the ocean, now and in the future.

Considering the controls of CIC and biological produced nutrients could prove important when predicting the impact of on-going climatic change. Assessing the direct impact on CIC is challenging, due to large uncertainties in predicting the spatial extent and intensity of such environmental changes, and the limited understanding of potential biological responses to them. However, an improved understanding of the relationships between key environmental variables and BPNs might be a first step to anticipate the fate of CIC in the future ocean. Here we explore the possible impact predicted environmental conditions might have on cobalamin availability and requirements, and their potential consequences for CIC.

Cobalamin availability, and instances of cobalamin CIC, might be influenced by changes in light, nutrient supply and sea surface temperature (SST) (Bopp et al., 2001, 2013; Sarmiento et al., 2004a,b; Doney et al., 2012). An increase in irradiance is predicted to occur in many ocean regions due to enhanced stratification and sea-ice retreat (Grebmeier et al., 2010; Doney et al., 2012). This could have a negative impact on the availability of cobalamin by increasing its photodegradation (Juzeniene and Nizauskaite, 2013), thereby enhancing the potential for cobalamin limitation and CIC. One factor that could influence the patterns of CIC in the future ocean is the predicted changes in the supply of other essential nutrients (Tagliabue et al., 2017; Moore et al., 2018). For example, an increased supply of a limiting nutrient in a given region may result in an increase in productivity and a shift to another nutrient becoming limiting or co-limiting. Changes in SST are likely to have an impact on metabolic rates (Talling, 1955; Eppley, 1972) which could influence cobalamin production rates and competition for cobalamin, or other BPNs, within communities. Labile DOM availability has been suggested to be a more important factor for bacterial production than temperature in polar regions (Kirchman et al., 2009) indicating tightly coupled changes in metabolic rates of phytoplankton and bacteria. Other studies have suggested a larger increase in bacterial activity and remineralization rates compared to photosynthesis rates with increasing temperature (Lopez-Urrutia et al., 2006) suggesting a larger increase in cobalamin production compared to consumption by phytoplankton, thereby reducing the potential for cobalamin limitation. However, enhanced bacterial growth could also result in increased competition for cobalamin, as auxotrophic bacterial growth will likely also positively correlate with temperature. Overall, the response of cobalamin CIC will be a result of the balance between the relative response of cobalamin producers, phytoplankton growth and (competing) bacterial cobalamin consumers, as well as how their nutrient production rates and cellular quotas/nutrient demand changes. Few relationships and feedback mechanisms of CIC are identified to date and a more detailed understanding of the effects on community interactions is required. Therefore, predicting the response of CIC to environmental changes might benefit from incorporating ecological and evolutionary theories that aim to understand microbial interactions and dependencies. Furthermore, considering evolutionary forces underpinning microbial interactions may streamline evaluation of possible adaptations of microbial communities to environmental change in the future.

The idea that microbial communities are more than the sum of the species present, due to the intricate web of interactions between community members, is foundational to the concept of CIC. Microbial interactions underpin productivity, composition, and resilience in microbial communities, factors that determine a community’s contribution to larger ecological processes. To date, the field studies of nutrient co-limitation that lead us to hypothesize that CIC may play an important role in larger biogeochemical cycles are largely focused on bulk biogeochemical parameters and processes: by and large, the measurements and principles applied in such studies fail to examine the elaborate interactions in marine microbial communities. To attempt to elucidate the experience, and response, of microbial communities under nutrient co-limitation, additional steps must be taken that understand the potential ecological and evolutionary processes at play. Ultimately, this could help to streamline identification of key relationships and underpinning interactions and begin to systematically investigate the factors that influence them.

Microbial interactions are ubiquitous in the marine environment and are usually classified into categories such as mutualism, commensalism, and competition (Ramanan et al., 2016). However, these associations are extremely dynamic and can quickly switch from cooperation to competition, and vice versa, depending on an organism’s metabolic status and nutrient availability (Seyedsayamdost et al., 2011; Hoek et al., 2016). These associations are not (entirely) random, and it has been shown that specific groups of bacteria and algae tend to co-occur in the marine environment (Zelezniak et al., 2015; Milici et al., 2016). Studies have discovered that phytoplankton, or “hosts,” modulate their associated bacterial communities through excreted metabolites (Fu et al., 2020; Shibl et al., 2020). This is beneficial since the close interactions a phytoplankton supports in its phycosphere could determine its access to resources and impact its physiology, productivity, and survival (Amin et al., 2009; Bolch et al., 2011, 2017). Therefore, clarifying the direction, plasticity, and intensity of microbial interactions during periods of BPN limitation are relevant factors that can be used to identify cases and predict consequences of CIC. A more detailed understanding of when and why close mutualistic interaction are successful strategies would be particularly beneficial in the context of resource limitation: it affects the energy put into nutrient acquisition, ultimately impacting an organism’s productivity and fitness. Exchanging nutrients in a direct interaction could allow more consistent access to resources and therefore stability during periods of nutrient limitation. However, there are risks associated with this strategy as it relies on the fitness and presence of a partner and could be more energetically costly. In contrast, an organism that is scavenging nutrients from the surrounding environment may have more restricted access when a resource becomes scarce. The success of this organism relies on efficient acquisition strategies, as there would most likely be competition for the resource from all the consumers within the community. To draw an example from CIC, organisms that are in direct, mutualistic interaction exchanging cobalamin during times of limitation may out-compete scavengers, which would influence composition and species succession in the community. The prevalence of interaction-based nutrient acquisition strategies within a microbial community should be acknowledged when interpreting instances of CIC as it could identify organisms more vulnerable to CIC (e.g., scavengers) and factors that influence formation of cooperative interactions. Incorporating microbial interactions into resource limitation studies could uncover larger ecological or evolutionary patterns and perspectives regarding when and where these strategies are successful.

Community interaction co-limitation can be viewed as the imbalance of resource supply and demand between groups within a community, which is likely to be impacted by community composition. Therefore, enhanced insight into the ecological principles that control microbial community organization could enable better predictions of the patterns and mechanisms controlling CIC, and their susceptibility to change. Recently, computational modeling at a scale unattainable by culture studies alone, has identified important controls on community assembly (Pacheco et al., 2019). Microbial community assembly appears replicable and can be explained by a mix of environmental factors, nutrient availability, microbial interactions, and metabolite profiles of key species (Friedman et al., 2017; Louca et al., 2017; Goldford et al., 2018; Fu et al., 2020). Cross-feeding is a nutrient-centric type of microbial interaction and is becoming recognized as central control of microbial community assembly (Goldford et al., 2018; Pacheco et al., 2019). Cross-feeding is the transfer of metabolites between organisms that improve the fitness of recipient and is classified into specific types based on reciprocity of exchange (D’Souza et al., 2018; Smith et al., 2019). Until recently, it was assumed the cross-feeding had to have a metabolic cost for the producing organisms, however, it has been demonstrated that metabolites which do not impact an organism’s fitness when excreted (e.g., costless) enrich minimal nutrient environments and support interactions between species (Pacheco et al., 2019).

Analogies can be made between specific types of cross-feeding and cases of CIC, as the BPN is produced by one group of organisms and used by another. For example, Case A of CIC can be compared to a simple case of metabolite cross-feeding where one organism releases a metabolite that is used by another organism (Smith et al., 2019). However, unlike CIC, the original classification of metabolite cross-feeding defined the exchanged metabolite as a waste product that cannot be further metabolized by the producing bacteria. Case B is comparable to mutual, or two way, cross-feeding because of the exchange of both labile carbon and cobalamin between organisms. These analogies are most easily transferrable to a bipartite interaction scale, however, CIC adds an important dimension to cross feeding concepts, as it emphasizes that possibility that “public good” metabolites can limit production at a community level. Interdisciplinary research into cross-feeding and resource limitation could identify other metabolites, or their precursors, that control community production and assembly further identifying instances of CIC. Exploring the labile carbon requirements of cobalamin producers or nitrogen fixers is an exciting avenue for further research that could provide additional insight into Case B of CIC. Systematically integrating community assembly models and theories into research surrounding CIC (and vice versa) would be a powerful step forward in enabling predictions of the prevalence of and mechanisms behind CIC.

Ecological interactions between micro-organisms are the result of millions of years of co-evolution and thus ecological theories cannot be discussed without the appreciation of evolution. Evolutionary theories are being developed to describe how the ecological interactions between micro-organisms emerged and control microbial community productivity and composition. Recent studies have sought to better understand the evolution of microbial interactions using evolutionary game theory, examining the fitness response of individuals that are interacting within a community (Frey, 2010; Hummert et al., 2014). These are particularly powerful when coupled with genome-scale metabolic networks which allow growth predictions about interactions and metabolic exchange based on organism’s genomes (Zomorrodi and Segrè, 2017). Such research is developing theoretical principles, based on evolutionary mechanisms, which could more accurately describe resulting future shifts in the form and function of microbial communities. Incorporating such theories into resource limitation research could provide a framework to anticipate the response of CIC and key microbial interactions in the modern and future ocean.

One evolutionary theory that could be particularly relevant in resource-limited environments is The Black Queen Hypothesis, which describes the evolution of obligate relationships in microbial communities (Morris et al., 2012; Mas et al., 2016). It hypothesizes that “mutualistic” obligate interactions can arise because of leaky production of a “common good,” which could be a nutrient, by the “helper,” which prompts loss of function in another organism, the “beneficiary” (Mas et al., 2016). This produces an obligate interaction between the beneficiary and helper, as the beneficiary is no longer able to produce the common good nutrient for the fitness benefit of a reduced genome (Giovannoni et al., 2014; Mas et al., 2016). This theory is also particularly relevant as it considers the cost of production of a particular “public good” which could be relevant for understanding why such cases of CIC can occur. Investigating the cost of the BPN (production vs. acquisition) could help researchers understand which BPNs are likely to be a limiting nutrient in the environment and factors that influence them. The theory may shed some light on the occurrence and evolutionary benefit of microbial dependencies and nutrient exchange during times of resource limitation. Once the types (e.g., obligate or facultative) of key interactions underpinning instances of CIC are identified, the details of nutrient exchange and the associated feedback loop might become clearer, furthering investigation into factors that influence an interaction’s stability and potentially helping to predict responses in an eco-evolutionary framework. Therefore, systematically incorporating developments in microbial evolution and ecology will support better synthesis, interpretation and prediction of key microbial interactions that underpin cases of CIC.

While the presence of co-limitation in the ocean is known to be widespread (e.g., around 8% of the surface ocean is estimated to experience co-limitation by nitrogen and iron; Browning et al., 2017), we largely lack a detailed understanding of mechanisms behind co-limitation and its broader effect on global biogeochemical cycles. Studies investigating CIC are scarce to date, resulting in limited knowledge about the variety of biologically produced resources that can lead to CIC. Low spatial and temporal resolution in documented cases of CIC restricts our understanding of the spatial extent and seasonal distribution of CIC and its driving factors. However, understanding the mechanisms and susceptibility of CIC to change in future ocean directly relies on researcher’s ability to detect and identify it. This is not straightforward, as it requires the combination of knowledge on different scales including individual organism’s nutritional requirements, community dynamics, and global patterns of nutrient availability and species distribution.

Bottle incubation bioassay experiments offer a valuable insight into the oceanic distribution of nutrient limitations, despite their limitations (e.g., bottle-induced changes in community composition; failure to capture grazing pressure). These studies often do not fully resolve the type of co-limitation which exists and on their own don’t allow researchers to draw conclusions about the community interactions at play. Identifying the biological nutrient producers and consumers in any given environment is a first step in assessing the supply and demand that constrains nutrient availability. However, these identifications will not always reveal the key interactions or community dynamics underpinning the CIC. As highlighted previously, it is important to understand the role bacteria play in nutrient availability and exchange in microbial communities. As such, an important avenue for further research into CIC is the recognition and incorporation of bacterial community response into resource limitation and nutrient addition bioassays.

Auxotrophy is widespread in the marine environment and could have major implications for microbial community interdependencies and nutrient requirements, yet it is still poorly understood (Johnson et al., 2020). To identify additional types of CIC, and monitor the cases of CIC presented, it is critical to understand the occurrence of auxotrophy in species as well as auxophore (metabolites required by auxotrophs) requirements and the factors that influence them. This includes the occurrence of precursor auxotrophy, a budding field of interest (Paerl et al., 2017, 2018a). Additionally, knowing when a community is dominated by auxotrophs might suggest that a community is more vulnerable to cases of CIC. Finally, having an ongoing atlas of common auxophores and instances of auxotrophies in specific primary producers could be of use for studies of CIC.

Sensitive molecular analysis techniques will be required to investigate the interactions and uncover the feedback loops that are critical for defining and monitoring cases of CIC. A successful analysis would uncover (1) which organisms are stressed for or limited by a BPN, (2) which organisms are producing that nutrient, (3) what is limiting the production/transformation of that nutrient and (4) what the nature of interactions between the relevant microbial groups. Although this task seems daunting, it is becoming more feasible with current advances in multi-layered omics analysis.

One approach to uncover the mechanisms of CIC present is by assessing gene or protein expression profiles through meta-transcriptomic or meta-proteomic analyses during nutrient incubation experiments in field studies. Gene expression analyses could allow the identification of organisms that are stressed for or produce a BPN. For example, through meta-transcriptomics, organisms involved in cobalamin cycle and the feedback loops that controlled cobalamin availability were identified in the Southern Ocean (Case B, cobalamin CIC) (Bertrand et al., 2015). Lab cultures can also be a powerful tool for assessing changes in gene and protein expression under varying growth conditions, including pure cultures and co-cultures of interacting microbial species, and can facilitate the identification of relevant target genes and proteins (Helliwell et al., 2018; Wu et al., 2019). Recently, by incorporating various meta-omics techniques, researchers have already made significant advances in the identification of key mechanisms and metabolites involved with microbial interactions that could lead to additional types of CIC (Amin et al., 2015; Bertrand et al., 2015; Durham et al., 2017; Helliwell et al., 2018; Shibl et al., 2020).

Meta-omics studies can also serve as the basis for developing targeted approaches for detecting and monitoring CIC, for instance through identifying critical metabolites and biomarker genes or protein abundances that indicate different nutrient limitation scenarios (Bertrand et al., 2015; Chappell et al., 2015; Heal et al., 2019; Ustick et al., 2021). Such targeted assays require previous knowledge about producers and consumers present and the specific genes of proteins expression patterns sensitive to certain nutrient limitations. However, they can be used more broadly, as the analysis can be faster, and more sensitive and specific than meta-omics approaches. As such, they are a useful tool for assessing global nutrient limitation and CIC patterns when used alone or in combination with bioassay experiments. For example, the use of biomarker peptides for MetE and CBA1 expression (cobalamin stress markers) can be used to indicate regions of cobalamin stress or limitation in the field (Bertrand et al., 2012, 2013). Other biomarker peptide mapping studies have collected valuable data about the nitrogen, iron, and phosphorus status of marine cyanobacterial communities (Saito et al., 2015). These methods could also be used for identification of organisms responsible for biological nutrient production using proteins involved in biological nutrient synthesis (e.g., CobO protein for cobalamin production). However, there is still a big gap in our knowledge about suitable targets to detect nutrient stress for most marine phytoplankton for known limiting or co-limiting nutrients, particularly BPNs involved in CIC.

Furthermore, putting CIC in a global context requires incorporating such evidence found at the organismal and community level with larger oceanographic trends to identify potential spatio-temporal patterns and driving factors that control the underpinning mechanisms of limitation. To map and predict the consequences of CIC in the modern and future ocean, we must apply interdisciplinary methods and approaches capable of elucidating important feedback loops that control nutrient exchange and availability in microbial communities and key factors that influence them. Recent advances in experimental design and molecular approaches provide a hopeful outlook.