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Subjects and facilityWe observed two groups of Atlantic bottlenose dolphins (six different individuals in total) housed at the marine zoo “Marineland Mallorca”. One of the groups was composed of four individuals (G1) and the other was constituted by five individuals (G2). The two adult males and one of the females were the same in both groups (Table 1). Group composition changed due to the transfer of individuals to another pool of the zoo and due to the arrival of new individuals from another aquatic park.Table 1 Age, sex, group, and identification number in the network of the subject dolphins. M male, F female.Full size tableThe dolphins were kept in three outdoor interconnecting pools: the main performance pool (1.6 million liters of water), a medical pool (37.8 thousand liters of water) and a small pool (636.8 thousand liters of water). During the observational periods, the dolphins had free access to all the pools. Underwater viewing at the main and the small pool was available through the transparent walls around the rim of the pools.Ethics statementThis study was approved by the UIB Committee of Research Ethics and Marineland Mallorca. This research was conducted in compliance with the standards of the European Association of Zoos and Aquaria (EAZA). All subjects tested in this study were housed in Marineland Mallorca following the Directive 1999/22/EC on the keeping of animals in zoos. This study was strictly non-invasive and did not affect the welfare of dolphins.Behavioral observations and data collectionBehavioral data were collected in situ by APM from May to November 2016 for G1 and from November 2017 to February 2018 for G2. All observational periods were also recorded using two waterproof cameras SJCAM SJ4000. Observations were conducted at the main pool between 8:00 a.m. and 11:00 a.m. Due to the schedules and dynamics of the zoo, we were unable to collect data outside this period. Dolphin social behavior was registered and videotaped for 30 min–2 h each day. Only data from sessions that lasted at least 30 min were included in the analysis. We did not collect any data during training or medical procedures and resumed the observational session a few minutes after the end of these events.We recorded all occurrences of affiliative and aggressive interactions, the identities of the involved individuals and the identity of the dolphin initiating the contact. Aggressive contacts were defined by the occurrence of chasing, biting, and hitting, as established in previous studies37,38,39,40,41. Affiliative contacts were defined as contact swimming, synchronous breathing and swimming (at least 30″ of continuous swimming) or flipper-rubbing, as established in previous studies37,39,40,41,43.To assess the strength of the affiliative bonds in both groups, we calculated the index of affiliative relationships (IA) between dolphins following the procedure described in Yamamoto et al. For calculating the IA we recorded the relative frequencies of synchronous swimming since it is a well-defined affiliative behavior in dolphins. Data of synchronous swimming were recorded using group 0–1 sampling44 at 3-min intervals. This method consists of the observation of individuals during short periods and the recording of the occurrence (assigning to that period a 1) or non-occurrence (assigning to that period a 0) of a well-defined behavior44. For calculating the IA for each couple, the number of sampling periods in which synchronous swimming between individuals A and B occurred (XAB) was divided by the number of sampling periods in which individuals A and B were observed (YAB): (IA=frac{{X}_{AB}}{{Y}_{AB}})39,45. Therefore, the IA reflects the level of affiliation for each dolphin dyad based on the pattern of synchronous swimming. This index served to construct the general affiliative social networks of both groups of dolphins.Temporal network constructionTemporal networks can provide insight into social events such as conflicts and post-conflict interactions in which the order of interactions and the timing is crucial. Furthermore, they allow us to calculate the probabilities of the different affiliative and aggressive interactions occurring in the group.We used behavioral observations to construct temporal networks for each group. Each dolphin was treated as a node (N) with their aggressive and affiliative interactions supplying the network links. We divided the daily observations into periods of 3 min. In each period, we assigned a positive (+ 1), negative (− 1) or neutral (0) interaction to each pair of dolphins. That is, if during the period a pair of dolphins displayed affiliative interactions, we assigned a + 1 to the link between that pair of nodes, if they were involved in a conflict, we assigned a − 1, and if the pair did not engage in any interaction, we assigned to that link a 0. If during the same period, the pair displayed both aggressive and affiliative interactions we considered the last observed interaction. Therefore, we obtained an adjacency matrix (an N × N matrix describing the links in the network) for each group of dolphins. Thus, for each day we had a series of different signed networks of the group, each network representing a 3-min period.Social network analysis: time-aggregated networks and network motifsWe collapsed the temporal networks of each day in time-aggregated networks. This procedure consists in aggregating the data collected over time within specific intervals to create weighted networks. The sign and the weight of the links characterize these networks, indicating the valence and duration of the interaction respectively. Thus, they are static representations of the social structure of the group of dolphins. To obtain these time-aggregated networks we proceeded as follows:First, for each day we aggregated the values of each interaction of the temporal networks until one link qualitatively changed. We considered a qualitative change if one interaction passed from being negative (− 1) to positive (+ 1) meaning that the pair of dolphins reconciled after the conflict or vice versa, or if a new affiliation (+ 1) or aggression (− 1) took place, that is the link changed from being neutral (0) to positive or negative. If a link changed from being negative or positive to being neutral, we did not consider that this interaction has changed qualitatively. For example, if dolphins interacted positively during two periods of time, then they ceased to interact (neutral) and finally they engaged in an aggressive interaction, the total weight of the interaction in the resulting time-aggregated network would be of + 2. Therefore, a conflict or an affiliation may extend over multiple periods containing several contacts, and is considered finished when the interaction changes its valence. In this way, we obtained a series of time-aggregated networks for each day, which retain the information on the duration, timing, and ordering of the affiliative and aggressive events in the group.We examined the local-scale structure of the affiliative-aggressive social networks using motif analysis. Thus, for each group, we analyzed the network motif representation of the temporal and time-aggregated networks, identifying and recording the number of occurrences of each motif.Model of affiliative and aggressive interactionsWe built two models (a simple and a complex one) that aim to simulate the dynamics of aggressive and affiliative interactions of a group of four dolphins. These models were created using the observed probabilities of each affiliative or aggressive interaction between individuals in group G1. We only used the data of G1 since we had more hours of video recordings and, thus, more statistics of the pattern of dolphins’ interactions. Both models return affiliative/aggressive temporal networks constituted by four nodes and different aggressive, affiliative, or neutral interactions between the six possible pairs of individuals in the network. We simulated data for 20 periods of 3 min per day for a total of 80 days to mimic the empirical data time structure. We obtained one temporal network for each period (1600 temporal networks in total) and ran 100 realizations of each model.Our models work as follows: At the beginning of the simulations, all the interactions between the four nodes are neutral (0). In each period, we select a pair of nodes randomly and assign to that link a positive (+ 1) or a negative (− 1) interaction with probability p (calculated previously for each type of interaction). These interactions correspond to spontaneous aggressions and affiliations. In the complex model, if in the previous period a conflict took place, before assessing spontaneous interactions we first evaluated the different possible post-conflict contacts that could occur (reconciliation, new aggressions, and affiliations). Therefore, for reconciliations, we change the valence of the interaction from negative to positive with a certain probability. Then, we also randomly choose a pair of nodes including one of the former opponents and assign to that link a positive or negative interaction with the observed probabilities to simulate the occurrence of new affiliations (third party-affiliation) or redirected aggressions arising from the previous conflict. We keep on doing this procedure period by period. Lastly, we obtained the time-aggregated networks for the two models.The simpler model only includes the probability of aggression and affiliation between group members, whereas the complex one also includes the patterns of conflict resolution previously observed. In this way, the complex model serves to assess the influence of post-conflict management mechanisms on the observed pattern of aggressive/affiliative networks. That is, the complex model also keeps track of past actions. Thus, depending on the interaction of the previous step, the probability of the following interaction changes based on the observed pattern of conflict resolution strategies.Calculation of the observed probabilities of affiliative and aggressive interactionsFor the simple model, we calculated the probability of general aggression and affiliation per day without distinguishing between types of positive and negative interactions. Thus, we obtained the number of periods in which an aggressive or affiliative contact took place per day and divided it by the total number of periods of that day (probability of general aggression or affiliation per 3-min period). With these probabilities, we calculated the mean probability of general aggression and affiliation per period.For the complex model, we calculated the probabilities of reconciliation, new affiliations/aggressions, and spontaneous affiliations/aggressions per day. That is, the probability that former opponents exchange affiliative contacts after an aggressive encounter (reconciliation), the probabilities that a conflict may promote new affiliations (third-party affiliation) or new conflicts (redirected aggression) between one of the opponents and a bystander in the same day, and the probability of affiliative or aggressive interactions not derived from a previous conflict (spontaneous interactions). To classify affiliations and aggressions in these categories we used the temporal networks, examining the interactions that took place after a conflict between opponents and between them and bystanders. If the opponents reconciled or affiliated with a bystander after a fight, we assumed that the following affiliative or aggressive interactions were spontaneous and were not a consequence of that conflict. Thus, to calculate the number of spontaneous affiliations, we subtracted the number of reconciliations and new affiliations from the total number of affiliations per day. For spontaneous aggressions, we subtracted the number of new aggressions to the total number of aggressions per day. Then, we obtained the probability of spontaneous affiliation and aggression per period.Using the previous probabilities, we obtained the rate (r) of reconciliation, new aggression and new affiliation per minute with the following formula:({p=1-e}^{-rDelta t}). Using the same formula, we finally calculated the probability of reconciliation, new aggression and affiliation per 3-min period used in the complex model (Supplementary Table 1 for details of probabilities calculation).Network-motif analysisWe also carried out a network-motif analysis. As we did not consider the identities or sex of the nodes in these models, we grouped the obtained motifs into equivalent categories considering the pattern of interactions between nodes. We also classified the motifs obtained from the real data of G1 into those equivalent categories. Finally, we compared the pattern of equivalent network motifs of the observed social network of dolphins and the ones of the two models. To do so we calculated the Spearman’s rank correlation coefficient (rs), defined as a nonparametric measure of the statistical dependence between the rankings of two variables: ({r}_{s}=frac{covleft({rg}_{X}{rg}_{Y}right)}{{sigma }_{{rg}_{X}}}{sigma }_{{rg}_{Y}}); rgX and rgY are the rank variables; cov (rgX rgY) is the covariance of the rank variables, and σrgX and σrgY are the standard deviations of the rank variables. Therefore, this coefficient allows us to assess the statistical dependence between the motif ranking of the real data and the one of each model.Computational implementationsAll the models, network construction, visualization and motif analysis were generated and implemented using MATLAB R2018b. More

During recent decades, pathogens that originated in bats have become an increasing public health concern. A major challenge is to identify how those pathogens spill over into human populations to generate a pandemic threat1. Many correlational studies associate spillover with changes in land use or other anthropogenic stressors2,3, although the mechanisms underlying the observed correlations have not been identified4. One limitation is the lack of spatially and temporally explicit data on multiple spillovers, and on the connections among spillovers, reservoir host ecology and behavior, and viral dynamics. We present 25 years of data on land-use change, bat behavior, and spillover of Hendra virus from Pteropodid bats to horses in subtropical Australia. These data show that bats are responding to environmental change by persistently adopting behaviors that were previously transient responses to nutritional stress. Interactions between land-use change and climate now lead to persistent bat residency in agricultural areas, where periodic food shortages drive clusters of spillovers. Pulses of winter flowering of trees in remnant forests appeared to prevent spillover. We developed integrative Bayesian network models based on these phenomena that accurately predicted the presence or absence of clusters of spillovers in each of 25 years. Our long-term study identifies the mechanistic connections among habitat loss, climate, and increased spillover risk. It provides a framework for examining causes of bat virus spillover and for developing ecological countermeasures to prevent pandemics. More

We aimed to understand the impact of changing pCO2 (400 vs. 800 ppm, representing current and projected year 2100 concentrations) on Prochlorococcus and Synechococcus and its effects on their interactions with the co-cultured heterotrophic “helper” bacterium Alteromonas sp. EZ55. Consistent with our previous research [7], EZ55 was more strongly affected by year 2100 pCO2 than any of the photoautotrophs in our study despite the primary dependence of the latter organisms’ metabolism on CO2. Strikingly, elevated pCO2 tended to reduce or eliminate the effect of co-culture on EZ55, with far fewer genes being significantly differentially transcribed relative to axenic EZ55 at the same pCO2. Thus, pCO2 strongly impacted the metabolic conversation between cyanobacteria and EZ55. Our detailed analysis of differentially regulated metabolic pathways suggested three mutually reinforcing mechanisms underlying this dynamic interaction: (i) pCO2 impacts on the release of ‘leaky’ cyanobacteria-derived metabolites, (ii) alteration of the dynamics of competition over inorganic nutrients between the co-cultured organisms, and (iii) modulation of bacterial and phytoplankton stress states. We explore each of these mechanisms in further detail below.Carbon cycling of “leaky” metabolites in co-cultureThe media we used for coculturing phytoplankton and bacteria contained no exogenous carbon sources; therefore, EZ55 was dependent on cyanobacterial exudates to grow, and it is likely that much of its changed transcription reflected changing availability of extracellular metabolites in the medium. The significant upregulation of carbon catabolism and transport genes as well as chemotaxis genes in co-cultures relative to axenic EZ55 supports the view that bacterial remineralization of cyanobacteria-secreted organic compounds is a driving force in these simple ecosystems. Additionally, changes in transcription of carbohydrate catabolism and transport genes provide clues as to which metabolites were being secreted under different experimental conditions (Fig. 5).Fig. 5: Proposed reconstruction of Alteromonas EZ55 ecophysiology.Reconstructions are shown for four different community contexts (axenic culture, or co-culture with Prochlorococcus MIT9312, Synechococcus WH8102, or Synechococcus CC9311) at 400 or 800 ppm pCO2, reflecting possible changes in the availability of C compounds, growth limiting factors, and stress conditions consistent with differential gene transcription observations. EZ55 image was obtained by cryoelectron microscopy from the sessions reported in Hennon et al. [7]. Background colors for each partner correspond to the bar colors in Fig. 3.Full size imageLike all oxygenic phototrophs, the cyanobacteria studied here fix carbon using the enzyme rubisco, which also catalyzes the undesirable photorespiration reaction leading to the production of 2PG instead of photosynthate. Phytoplankton in the field and in culture have been observed to excrete low molecular weight carboxylic acids including glycolate [39,40,41]. Photorespiratory glycolate is one of the most abundant sources of carbon in the oceans [38] and a preferred growth substrate for some marine heterotrophic bacteria [42]. Moreover the bacterial glcD gene for converting glycolate to glyoxylate is ubiquitously transcribed in the ocean [41, 43]. Although EZ55 lacks a specific transporter for glycolate, it can be taken up by the cell using the same transporters used for acetate and lactate uptake [44, 45], both of which were upregulated in co-culture conditions at 400 ppm (Fig. 3). Our data also showed differential regulation of enzymes involved in glycolate catabolism pathways, with at least one pathway upregulated in co-culture with each cyanobacterial strain (Fig. 3). We further demonstrated that EZ55 cultures were capable of growth on glycolate as a sole source of carbon, possibly using a novel GlcDF fusion protein (Fig. S11) and/or a plant-like LOX/GOX enzyme (Fig. 4). Thus, photorespiratory byproducts are likely a source of carbon for EZ55 in these cultures, particularly in the presence of MIT9312, which has no detectable enzymes for reclaiming 2PG on its own.There was also evidence that EZ55 utilized amino acids, organic acids, and fatty acids produced by phytoplankton under certain conditions in these cultures (Fig. S9). Lactate, acetate, and propanoate transporters and catabolism pathways were upregulated in co-culture with all cyanobacteria, as was pyruvate dehydrogenase with MIT9312, but only at 400 ppm. Both valine and glycine catabolism were also upregulated at 400 ppm in co-culture with the two Synechococcus strains, and fatty acid catabolism was upregulated in co-culture with MIT9312 and CC9311 at 400 ppm pCO2. Most of these substances have been directly or indirectly observed in cyanobacterial cultures in previous studies. For example, glycolate, lactate, acetate, and pyruvate have been directly measured in Prochlorococcus spent media [39], and co-culture with Prochlorococcus can fulfill the SAR11 growth requirement for glycine and pyruvate [46]. Fatty acid catabolism genes may have targeted membrane vesicles which are abundantly released by Prochlorococcus and other marine bacteria and may be a significant source of carbon for heterotrophs in the ocean [47, 48]; if so, future studies should investigate if WH8102 produces fewer vesicles than the other two cyanobacteria, explaining the differential transcription of beta-oxidation genes observed here.Valine, fatty acid, and propanoate catabolic pathways intersect with the formation of propanoyl-coA which in bacteria is generally fed into the TCA cycle through the methylcitrate pathway [49], which was significantly downregulated at 400 ppm in co-culture with all cyanobacteria even though other genes in these pathways were upregulated. Therefore, it is not clear what the ultimate fate of carbon from these sources is, although it is possible that EZ55 may be able to convert propanoyl-coA into a TCA cycle intermediate through another alternative pathway (e.g. as has been described in Mycobacterium tuberculosis via the methylmalonyl pathway [50]).Notably, gene transcription related to the utilization of all these products declined at 800 ppm pCO2 (Figs. 3, S8, S9). This was not unexpected for enzymes in the glycolate utilization pathways, as the increased CO2/O2 ratio at 800 ppm should decrease the rate of photorespiration relative to carbon fixation and therefore the availability of photorespiratory metabolites like glycolate [51, 52]. It is not clear, however, why organic and fatty acids would be less abundant in cyanobacterial exudates at 800 ppm. One possibility is that cyanobacteria release fewer of these compounds into the medium at high pCO2 because of a change in their internal redox state under these conditions favoring full oxidation of photosynthate. If future pCO2 conditions fundamentally alter the character of phytoplankton exudates, this could have profound implications for evolution and ecosystem functioning in future oceans.Evidence for inorganic nutrient limitation and competitionAutotrophic cyanobacteria and heterotrophic EZ55 were unlikely to compete over carbon under our experimental conditions, but we observed evidence of competition over inorganic nutrients such as N, P, and Fe. EZ55 phosphate, ammonium, and iron transporters, nitrogen regulatory protein P-II, and glutamine synthetase (the primary gateway for N assimilation in bacteria) were all more highly transcribed for all co-cultures compared to axenic cultures at 400 ppm pCO2 (Fig. S6), suggesting a switch from axenic carbon limitation to nutrient limitation in the presence of a continual supply of photosynthetically derived carbon (Fig. 5). On the other hand, few nutrient transporters were upregulated compared to axenic under 800 ppm pCO2. Although gene transcription data alone is not sufficient to conclude whether Alteromonas is limited by inorganic or organic nutrients, the reduced importance of nutrient acquisition suggests that EZ55 is carbon limited under these conditions just as it is in the absence of cyanobacteria.There were comparatively few species-specific changes in EZ55 nutrient transporter gene transcription. One example was an ammonium transporter, which was strongly upregulated in co-culture with both open ocean cyanobacteria (MIT9312 and WH8102) at 400 ppm pCO2. This may reflect a response to a comparatively high affinity for N in cyanobacteria adapted to the permanently oligotrophic open ocean, making them much stronger competitors for limiting N than coastal CC9311. N competition with EZ55 has been observed to increase the relative competitive fitness of Prochlorococcus vs. Synechococcus (coastal strain WH7803) in 3-way co-cultures [53]. In contrast, WH8102 appears to have higher N demand under 800 ppm pCO2, significantly upregulating a nitrate transporter and several genes related to urea utilization (Fig. S2). This may be explained by the enhanced transcription of carbon fixation genes and faster exponential growth rates observed in WH8102 at elevated pCO2, increasing N demand, and may indicate that WH8102 was C limited at 400 ppm.It is important to note that different N sources were provided in PEv medium (in which axenic EZ55 and MIT9312 co-cultures were grown) and SEv medium (in which CC9311 and WH8102 co-cultures were grown), with NH4+ in the former and NO3- in the latter. However, we do not think this difference can explain the observed changes in gene regulation, since EZ55 is capable of growth using either N source. It is interesting to note, however, that EZ55’s ammonium transporter was upregulated in both media types (Fig. S6), suggesting it may be benefitting from ammonium excreted by Synechococcus in SEv co-cultures.Impacts of co-culture and pCO2 on stress conditionsEZ55 showed less transcription of stress-related genes at 400 than 800 ppm pCO2, and also less evidence of stress in co-culture with any cyanobacterium than in axenic culture by itself. Nearly every gene in the EZ55 genome related to protection from H2O2 was downregulated in co-culture at 400 ppm, as were a suite of other stress-related genes (Fig. 2); on the other hand, many of these genes were significantly upregulated relative to axenic conditions at 800 ppm. Additionally, at 800 ppm there was a pronounced difference in EZ55 H2O2 defense gene transcription between cyanobacterial partners. As we described previously [7], both monofunctional catalases were downregulated at 800 ppm in co-culture with MIT9312, as were 2 of 3 alkylhydroperoxide reductase genes (although the third was significantly upregulated). In contrast, the monofunctional catalase genes were significantly upregulated in co-culture with WH8102 at 800 ppm. Elevated transcription of genes involved in the biosynthesis of glycine betaine, an osmoprotectant which has also been shown to function as an antioxidant [54, 55], provides further evidence for increased oxidative stress in co-culture with Synechococcus at 800 ppm in EZ55.Some indication of the mechanism behind EZ55’s changing stress level under co-culture and elevated pCO2 can be seen in the dynamics of three stress-related RNA polymerase sigma factors. Both rpoE and rpoH, responsible for controlling envelope and heat stress regulons, respectively, were downregulated at 400 ppm in co-culture relative to axenic and 800 ppm conditions; rpoE was significantly upregulated at 800 ppm pCO2. These trends are consistent with starvation-induced oxidative stress under both axenic and 800 ppm conditions, as discussed above. In contrast, rpoS was upregulated at 400 ppm pCO2, strongly so in co-culture with MIT9312. RpoS is a specialized sigma factor that accumulates under conditions of nutrient deprivation or as cells enter the stationary phase and serves to increase general stress resistance [56, 57]. For example, in Escherichia coli RpoS was shown to play a crucial role for survival during nitrogen deprivation [58]. While the decoupling of the transcription of oxidative stress genes like catalase from rpoS transcription was unexpected, rpoS trends are consistent with EZ55 being nutrient limited at 400 ppm pCO2 (Fig. S6) and with the upregulation of catalase in co-culture with MIT9312, but not WH8102 or CC9311, at 400 ppm (Fig. 2).In contrast to EZ55, differentially transcribed genes related to stress responses were rare in cyanobacteria at 800 ppm. While both MIT9312 and WH8102 had significant growth impairments at 800 ppm (Fig. S1), there was little evidence of a stress-specific gene transcription response in either strain. DNA mismatch repair genes were enriched as a group at 800 ppm in Prochlorococcus, although the only individual stress-related protein that was differentially transcribed was a HLI protein that was strongly downregulated at 800 ppm. No stress-related genes or gene sets were enriched in WH8102, and the small number of differentially transcribed stress genes in CC9311 (e.g., heat-shock and HLI proteins) were all downregulated at 800 ppm. This could indicate a dependence of both MIT9312 and WH8102 on their co-cultured EZ55 partner for protection, as neither of these cyanobacterial genomes contains catalase or several other stress-response genes common in heterotrophic bacteria. It could also indicate that they have different stress response mechanisms than those that have been characterized in heterotrophic bacteria; for instance, several hypothetical proteins of unknown function were differentially regulated in each cyanobacterium between the pCO2 conditions. Finally, it is possible that the stresses experienced by MIT9312 and WH8102 occurred in the initial days after transfer into fresh media (i.e., the significantly extended lag period observed for both), and were alleviated by the late log phase when the cultures were sampled for RNA sequencing.Summary overview of metabolic responsesWe have shown that the response to elevated pCO2 in our algal:bacterial co-cultures was driven more by interspecies interactions than by CO2-specific responses themselves. While it is important to note that we do not have direct culture-based evidence for some of these claims, we feel that gene transcription evidence is strong for several conclusions regarding the interactions in our cultures (Fig. 5).First, increased pCO2 appears to have fundamentally altered the amount and/or types of carbon compounds secreted by all three cyanobacterial strains examined, placing EZ55 into a stationary-phase metabolic state nearly indistinguishable to being in culture media with no added carbon source at all. We suggest that this is driven directly by the higher CO2:O2 ratio, which lowered the rate of photorespiration and subsequent release of 2PG and/or glycolate and indirectly may have reduced the amount of incompletely oxidized carbon released by cyanobacteria by changing the intracellular redox state [59]. Possibly because of the changing supply of carbon, EZ55 also appeared to transition away from a state of nutrient competition with its cyanobacterial partners, exemplified by decreased transcription of nutrient transporters at elevated pCO2 (Fig. S6).Second, co-culture at 400 ppm clearly reduced stress on EZ55 relative to either axenic growth or co-culture growth at 800 ppm, possibly due to the provision of a more reliable source of C as described above by the cyanobacterial partner under these conditions. In contrast, both MIT9312 and WH8102 clearly experienced elevated stress, potentially related to the changes in EZ55’s metabolism under these conditions. One of the major conclusions from our previous work [7] was the finding that EZ55 reduced catalase transcription at 800 ppm pCO2, eliminating the “helper” effect that Prochlorococcus depends on to grow in culture [13, 14]. In this work we see that the catalase response in co-culture with MIT9312 was opposite that in co-culture with the two Synechococcus strains. One possible explanation for this lies in the fact that MIT9312, unlike the other three strains in this study, did not possess a complete 2PG catabolism pathway and therefore likely excreted this product where it was subsequently catabolized by EZ55. We confirmed by genomic analysis (Figs. S10–S13) and culture experiments (Fig. 4) that EZ55 was able to grow on glycolate as a sole carbon source, and that its intracellular H2O2 concentration was elevated compared to growth on glucose. We suggest that more 2PG was secreted by MIT9312 at 400 ppm pCO2 due to the lower CO2:O2 ratio, and that growth on this carbon source increased EZ55’s internal oxidative stress load, resulting in higher transcription of H2O2 defenses such as catalase (Fig. 2). If true, this provides one possible explanation of why the “helper” relationship broke down at elevated pCO2 – by leaking 2PG as a readily available growth substrate for EZ55 at 400 ppm, MIT9312 forced EZ55 to maintain a high degree of intracellular ROS defense, leading to the well-characterized ability of EZ55 to cross-protect Prochlorococcus strains from the relatively lower H2O2 concentrations in the bulk environment, and allowing MIT9312 to eliminate two energetically costly enzymatic pathways. When higher pCO2 reduced the rate of photorespiration, EZ55’s need to produce excess catalase decreased, resulting in lower levels of protection, and concomitant growth impairments, for MIT9312.This is an example of how leaky Black Queen functions allow organisms like Prochlorococcus to streamline their metabolism while simultaneously creating stable interdependencies within their communities. However, it also shows how Black Queen-stabilized exchanges can break down. If our hypothesized relationship between pCO2 and catalase production is correct, then this system depends on the passive release of a metabolic by-product that evolved under a set of atmospheric pCO2 conditions that have been largely stable for thousands of years – but this leaves the system particularly vulnerable to the rapid changes in pCO2 currently taking place and may leave Prochlorococcus with no protection at all in the future ocean. If Prochlorococcus is outcompeted by less-streamlined competitors, this could reduce the overall efficiency of primary production in the open ocean gyres with possible positive feedbacks on CO2 accumulation in the atmosphere. Subsequent experiments should examine whether Prochlorococcus can overcome this imbalance through adaptive evolution quickly enough to avoid serious disruptions of its current niche.In conclusion, these results provide further support for the observation that axenic cultures do not provide a good window into the behavior of natural communities. The metabolism of Alteromonas sp. EZ55, a ubiquitous consumer in the ocean, was strongly dependent on its community context, and relatively subtle shifts in the chemical environment induced by elevated pCO2 were sufficient to significantly remodel its physiology. Moreover, the transcriptional response of EZ55 to changing pCO2 was much greater than that of any of the photoautotrophs examined, suggesting that more work is needed to understand the often-ignored heterotrophic bacteria associated with marine primary producers and how they will respond to global ocean change. Thus, further research is indicated on some of our core findings and hypotheses (e.g., the role of 2PG, and the nature of the carbon exchanged between the cyanobacteria and Alteromonas) via metabolomics or direct substrate measurements. These results further highlight the importance of laboratory experiments using co-cultures as an experimentally tractable intermediate between oversimplified axenic cultures and overly complicated natural communities. They also highlight the dominant role that primary producers play in determining the metabolism and interactions of the organisms that depend on them for sustenance. More

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These authors contributed equally: Yannick Garcin, Enno Schefuß, Greta C. Dargie, Simon L. LewisAix Marseille University, CNRS, IRD, INRAE, CEREGE, Aix-en-Provence, FranceYannick Garcin & Ghislain GassierInstitute of Geosciences, University of Potsdam, Potsdam, GermanyYannick GarcinMARUM—Center for Marine Environmental Sciences, University of Bremen, Bremen, GermanyEnno SchefußSchool of Geography, University of Leeds, Leeds, UKGreta C. Dargie, Bart Crezee, Dylan M. Young, Andy J. Baird, Paul J. Morris & Simon L. LewisSchool of Geography and Sustainable Development, University of St Andrews, St Andrews, UKDonna Hawthorne, Ian T. Lawson & George E. BiddulphIFP Energies Nouvelles, Earth Sciences and Environmental Technologies Division, Rueil-Malmaison, FranceDavid SebagInstitute of Earth Surface Dynamics, Geopolis, University of Lausanne, Lausanne, SwitzerlandDavid SebagFaculté des Sciences et Techniques, Université Marien Ngouabi, Brazzaville, Republic of the CongoYannick E. Bocko & Y. Emmanuel Mampouya WeninaÉcole Normale Supérieure, Université Marien Ngouabi, Brazzaville, Republic of the CongoSuspense A. IfoÉcole Normale Supérieure d’Agronomie et de Foresterie, Université Marien Ngouabi, Brazzaville, Republic of the CongoMackline MbembaFaculté de Gestion des Ressources Naturelles Renouvelables, Université de Kisangani, Kisangani, Democratic Republic of the CongoCorneille E. N. Ewango & Joseph Kanyama TabuFaculté des Sciences, Université de Kisangani, Kisangani, Democratic Republic of the CongoCorneille E. N. EwangoInstitut Supérieur Pédagogique de Mbandaka, Mbandaka, Democratic Republic of the CongoOvide Emba & Pierre BolaSchool of Geography, Geology and the Environment, University of Leicester, Leicester, UKGenevieve Tyrrell, Arnoud Boom & Susan E. PageSchool of Water, Energy and Environment, Cranfield University, Bedford, UKNicholas T. GirkinBritish Geological Survey, Centre for Environmental Geochemistry, Keyworth, UKChristopher H. VaneInstitute of Earth Sciences, University of Lausanne, Lausanne, SwitzerlandThierry AdatteNEIF Radiocarbon Laboratory, Scottish Universities Environmental Research Centre (SUERC), Glasgow, UKPauline GulliverSchool of Biosciences, University of Nottingham, Nottingham, UKSofie SjögerstenDepartment of Geography, University College London, London, UKSimon L. Lewis More