Philippa Kaur

Anna Lena Bercht interviewed fishers in Lofoten, Norway, to assess how climate change was affecting their livelihoods.Credit: Anna Lena Bercht

I remember February 2011, when, in the Chinese megacity of Guangzhou, an older man finally overcame his scepticism about being interviewed and invited me to sit down next to him on a stone bench under a shady tree. I held my notebook on my lap, and we sat on either side of a translator and talked about his life and world for more than two hours. It was one of the most informative and revealing interviews that I had done during my fieldwork in the city.
Making it in the megacity
One of the most fundamental challenges in qualitative fieldwork is gaining access to research participants. This is often time-consuming and labour-intensive, particularly when the topic requires in-depth methods and addresses a sensitive subject.Advice that goes beyond the usual recommendations of establishing relationships with gatekeepers, ensuring anonymity for interviewees and relying on the snowball sampling technique (in which one research participant suggests further ones) is rare. In this light, I’m happy to share some simple, but often neglected, examples from my qualitative fieldwork in the lively Guangzhou (where I worked for 12 months)1 and on the remote, Arctic island chain of Lofoten, Norway (done over 4 months)2, that might offer some inspiration and encouragement.I have a background in human geography, and did my PhD on experiences of stress, coping and resilience among the Chinese population of Guangzhou in the face of the city’s rapid urbanization. I travelled there five times to help to establish research cooperation with Chinese scholars, make field observations, select a case-study site and interview locals. I, together with other PhD students, stayed in a typical Chinese high-rise apartment in a neighbourhood that wasn’t a common choice for expatriates. Living side-by-side with the locals gave us a perfect opportunity to experience genuine everyday life and Chinese culture.My first postdoctoral project after my PhD brought me to Lofoten, where I looked at psychological barriers to climate adaptation in small-scale coastal fisheries. I went to Lofoten twice. On my first visit, I travelled across the whole archipelago by bus for one month to get a profound overview of the fishing villages and local living conditions, and to conduct first interviews. During my second visit, I stayed for a total of three months in rental locations near fishing harbours, and conducted more extensive interviews.In both China and Norway, I used in-depth interviews to learn about the challenges that people face. I asked people about unemployment, about the possibility of being forced to move elsewhere and about how climate change might affect their livelihoods. This required a sensitive and thoughtful approach to ‘getting invited’ into people’s lives. In Guangzhou, German- and English-speaking Chinese students assisted me as translators (and interpreters, when needed). On Lofoten, I conducted the interviews myself in English.There are two ways to access research participants: physical access, which refers to the ability of the researcher to get in direct face-to-face contact with people, and mental access. Successful mental access means that interlocutors open up about why they think, feel and behave as they do. Physical access is a necessary condition for mental access; however, in my experience, both are equally valuable.

Chinese interviewees in Guangzou shared their feelings about the rapid urbanization of their city.Credit: Anna Lena Bercht

Compared with Lofoten, it took longer to get physical access to local inhabitants in China. Presumably, this was because of the language barrier and reliance on translators, as well as cultural differences. Trust is considered a central tenet in Chinese relationships, and time and effort are needed to let it grow. During my time in Guangzhou, I occasionally benefited from being a foreigner: people were touched that someone from abroad showed genuine interest in their well-being. In Lofoten, fishers appreciated talking to a social scientist instead of a natural scientist who would have mainly asked questions about fishing quotas and catch volume.My advice for other social scientists hoping to gain access to research participants falls into those two categories.How to get good physical accessUse local public transport. Using local public transport creates many unexpected opportunities to bump into people, get into conversations and gain relevant information. For example, while waiting at a bus stop in Lofoten, I came across an art-gallery owner from a fishing village. He wondered why I was travelling out of the peak tourism season. I ended up with an invitation to his gallery, where he introduced me to two retired fishers whom he had also invited. Without the gallerist and his proactive networking, I probably would not have been given the chance to interview these two very informative and engaging fishers.In a metro station in Guangzhou, a toddler kept staring at me and tried to touch my light hair. This small interaction led me to chat to the toddler’s father, who recommended that I talk to a local teacher to learn more about the area’s history. His advice opened up important insights into urban-restructuring processes that I would have missed otherwise.
Nine ‘brain food’ tips for researchers
Use local media. In Norway, a journalist was at the harbour to get first-hand information on the year’s cod catch, when he saw me interviewing fishers. He became curious and eager to learn more about my work. In the end, he wrote an article about my research, which was published a few days later across Lofoten. His article was a door-opener for me.People recognized me from my photo in the article and contacted me to tell me about their lives and the cod fisheries. They also invited me on their vessels and put me in touch with other key informants.Change your workplace. During fieldwork, a workplace is often needed for interview transcription, literature research and interim data analysis. Moving the workplace outside wherever you are staying during a field trip allows you to immerse yourself in the daily lives of local people and interact with them more easily. For me, such agile ‘mini-office’ locations were cafes, public libraries and picnic tables. In this way, I was able to recruit interview partners on the spot.How to create deeper mental accessWear appropriate outfits. First impressions count, always. Researchers are judged not only on what they say and how they say it, but also on how they look. Certain clothes, such as those with a political slogan or religious symbol, have certain meanings and connotations. Depending on the context and whom you talk to, your appearance could promote or impede making connections and building rapport. For instance, whereas my practical ‘outdoorsy’ get-dirty outfit was appropriate for interviews on fishing vessels, a modest appearance (non-branded clothes and a simple style) was useful in rural areas of Guangzhou.Show respect. Just like in any other relationship, respect and humility play a crucial part in building a trustworthy interviewer–interviewee relationship. Showing respect can be subtly embedded in conversations in many ways, including in the content of questions and the manner in which they are asked. When interviewees started to close down when asked about painful issues, such as underemployment or loss of identity, I upheld their privacy, comfort and security by not probing when given an evasive answer. Instead, I changed the interview focus and, when appropriate, cautiously reapproached the sensitive issue by using interview techniques such as roleplaying. Interviewees were asked to put themselves in the position of someone else, such as a spatial planner or politician, and assess the issue at hand from this perspective. Taking such an imaginary role can help to make the interviewees feel more secure and face pain more openly.Be humble. Having a modest view of yourself is essential to communicate at eye level with people. As a scientist, you can easily fall into the trap of thinking that your thoughts and concepts are somehow more valuable because you are well-educated and established. However, you are the one asking questions — and the interviewees, whether they are fishers, farmers or homeless people, often know more about many things than you do. Being aware of this is an expression of humility. I let the interviewees know that they were the local experts and I was the foreign learner.Use small talk. Small talk — including non-verbal communication, such as smiling, or connective gestures, for example handing out a handkerchief or offering some tea — has an essential bonding function. Talking about ‘safe’ topics can help the interviewee to overcome the feelings of otherness, newness and discomfort that can emerge in an interview, and fosters social cohesiveness. This can help to counteract the asymmetrical power relationship between the researcher (who asks) and the researched (who answers). For example, before substantive questioning, I created shared experiences by talking about last night’s storm or the world cod-fishing championship, which takes place every year in Lofoten. This took the relationship to a greater level of intimacy and togetherness — which small talk after finishing the interview can strengthen. I remember joking about my stamina for eating properly with chopsticks to one interviewee.Use self-disclosure. Revealing selected information about yourself and sharing your own thoughts with interlocutors can help to create and reaffirm a sphere of confidentiality and trust. Fishers in Norway would, for instance, often ask “What interested you in Lofoten coastal fisheries?” or “Why do you ask me and not the scientists from Tromsø University?” I answered such questions honestly, which assisted in creating a more balanced relationship, encouraging the interviewees to address sensitive subjects more openly and readily.Change interview sites. In several interviews, I found that the answers given tended to depend on where the interview was held and which identity that site evoked for the interviewee. For example, a fisher did not talk about climate-change concerns on his fishing vessel (any concern was masked by his existential fear of losing his livelihood as a coastal fisher), but he later that day freely discussed his worries in his home. Changing the interview site can be a helpful technique to access hidden thoughts and feelings.Above all, be realistic. You will probably make mistakes; I regretted not dressing warmly enough on a fishing vessel in Arctic weather. Locals will find you amusing, weird or impolite. They will keep out of your way, and you will never know why. And they will terminate interviews prematurely with no excuse. And that’s all right. In the end, fieldwork is a combination of planning, resources, time, skills, hard work, commitment, headache, joy — and luck. Learn from your mistakes, and accept the things you cannot change. More

During the COVID-19 pandemic, behavioral restrictions were imposed, after which various health problems were reported in many countries45,46. The pandemic has also increased food insecurity worldwide; consequently, panic buying has been observed in many countries, including Japan47. However, even in such situations, we found that diversity in local food access, ranging from self-cultivation to direct-to-consumer sales, was significantly associated with health and food security variables. Specifically, our results revealed the following five key discussion points.Urban agriculture in walkable neighborhoods bore fruit for health and food system resilience. However, the magnitude of its contribution differed depending on the type of urban agricultureThe results of this study showed that those who grew food by themselves at allotment farms and home gardens had significantly better subjective well-being and physical activity levels than those who did not. This result is in line with previous studies conducted during times free from the impact of infectious disease pandemics38,39,40. The use of direct sales was not related to subjective well-being but was significantly associated with physical activity. The reason might be that farm stand users tend to live in areas with farmland and travel to purchase fruits and vegetables at farm stands on foot or by bicycle. This result is consistent with that of a previous study demonstrating that the food environment in neighborhoods is an important component in promoting physical activity17.Our results also showed that those who grew food by themselves at allotment farms and those who purchased local foods at farm stands were significantly less anxious about the availability of fresh food both during the state of emergency and in the future than their counterparts. In contrast, home garden users showed significant differences only for the state of emergency. This result might be due to the differences in the size and yield of cultivation at allotment farms and home gardens. One lot in allotment farms in Tokyo can produce as much as or more than the average annual vegetable consumption per household in Japan48. However, home gardens are generally smaller and produce limited fresh foods for consumption, which may have influenced food security concerns.As in other countries, Japan imports much food from overseas and is deeply integrated into the large-scale global food system. However, as shown in this study, urban agriculture in Japanese suburbs forms small-scale, decentralized, and community-based local food systems. This multilayered food system can complement the disruptions and shortages of the global system when various problems occur for climatic, sociopolitical, or other reasons, such as pandemics. In fact, our empirical evidence suggests that urban agriculture in walkable neighborhoods, particularly allotment farms and direct-to-consumer sales at farm stands, contributed to the mitigation of food security concerns in neighborhood communities. This means that urban agriculture could enhance the resilience of the urban food system at a time when the global food system has been disrupted due to a pandemic. This validates recent discussions about the potential of urban agriculture to facilitate food system resilience10. Furthermore, our findings imply that the types of urban agriculture employed matter in determining the degree of contribution to food system resilience.To summarize the overall results, urban agriculture in walkable neighborhoods bore fruit for health and food system resilience during the COVID-19 pandemic. However, different types of urban agriculture exhibited varying associations with health and resilience. Allotment farms were positively related to all of the following: subjective well-being, physical activity, and food security concerns, both during the state of emergency and in the future. Home gardens were positively related to subjective well-being, physical activity, and food security concerns only during the state of emergency. Farm stands were positively related to physical activity and food security concerns both during the state of emergency and in the future.These differences may be due to the characteristics of the respective spaces. It is suggested that this diversity of urban agriculture has led to different types of people benefiting from various kinds of urban agriculture. Allotment farms were found to be associated with high subjective well-being, physical activity, and food security, but they may not be feasible for those who do not have enough physical strength because users are responsible for cultivating their lots, which measure 10–30 square meters40. In contrast, home gardens can be created even by those who are not confident in their physical strength. In fact, our study showed that women and older people engaged in home gardening more than men and younger people. In addition, direct-to-consumer sales at farm stands are the easiest way to obtain local fresh foods for those who do not have the time and space for allotment farms and home gardens. The need for urban agriculture has been argued in many countries2,3. However, little attention has been paid to its scale, accessibility, and diversity. Our study suggests that it is worthwhile to create diverse food production spaces within walkable neighborhoods while considering the diversity of people who access these spaces.Compared to other urban greenery and food retailers, the benefits of urban agriculture on subjective well-being and food security could be greaterCompared to the use of other urban green spaces, including urban parks, our results indicated that self-cultivation at allotment farms and home gardens was more strongly associated with subjective well-being. Previous studies have offered limited perspectives on the differences among various types of urban green spaces33. Our study further suggests that urban parks, allotment farms, and home gardens are differently associated with human health. However, as the reason was not determined, further research is needed.Furthermore, compared to other food retailers, such as supermarkets, convenience stores, and co-op deliveries, allotment farms and farm stands were more strongly associated with less anxiety about fresh food availability in the future. The availability of local fresh foods within walkable neighborhoods might have mitigated food security concerns because residents could grow food by themselves or directly observe farmers’ production processes, which may have made the difference from purchasing at places where the food systems were not visible.Flexibility in work style might promote urban agriculture in walkable neighborhoodsThere was an association between work style—working from home—and access to local food. According to the Ministry of Health, Labor and Welfare (https://www.mhlw.go.jp/english), 52% of Tokyo office workers worked from home during the first emergency declaration. Long commute times and high train congestion rates have been a problem in Tokyo suburbs, but remote workers have gained more time at and around their homes by reducing their commute times, increasing their opportunities to access local food in their walkable neighborhoods. Those who worked from home sought outdoor activities for refreshment and exercise and used a variety of urban green spaces during the pandemic49. Allotment farms and home gardens might be used as such urban green spaces. This result is consistent with previous studies assessing the characteristics of Canadian gardeners during the COVID-19 pandemic28,30.Until now, urban planners and policymakers have rarely taken work style into account. However, the flexibility of work styles and work hours may bring new insights; for example, those who work from home may become important players in urban agriculture. It has been pointed out that cities have a large hidden potential for urban agriculture by cultivating underused lands50. Our study suggests that such underused lands could be converted into productive urban landscapes for remote workers to engage in farming or gardening in between jobs as a hobby or as a side business.Food equity might be improved by urban agriculture in walkable neighborhoodsLocal fresh food is generally considered more expensive than junk food in high-income countries, creating social issues of food inequity. Therefore, past discussions on urban agriculture and food security have focused primarily on low-income households in socioeconomically disadvantaged areas24,25,26.In contrast, our study covered people from all income groups and found no statistically significant relationship between access to local food and income. This finding might be due to two urban cultural backgrounds regarding local food in Tokyo, that is, accessibility and affordability. First, residential segregation by income levels is not noteworthy in Tokyo and people from various income brackets live mixed in the same neighborhoods51. Therefore, most urban residents living in the suburbs have geographically equitable opportunities to access local foods. Second, local foods sold at farm stands are affordable. Prices are almost the same or cheaper than buying food at food retailers. While prices increase because of middleman margins related to shipping in the wholesale market, such increases are unnecessary when selling directly to consumers at farm stands. In addition, the allotment farm lots are not expensive to rent, particularly those operated by local municipalities (Supplementary Note 1).These two backgrounds make local fresh food physically and economically accessible to consumers of all income levels, resulting in food equity. This is particularly important because the concept of food system resilience includes the equitability perspective27.The integration of urban agriculture into walkable neighborhoods is a fruitful wayWhile the current discussion on walkable neighborhoods does not emphasize urban agriculture, our evidence indicated its effectiveness. The concept of walkable neighborhoods (e.g., the 15-min city model) stresses the decarbonization benefit of limiting vehicle travel, as well as the health benefits of promoting walking and cycling13,14,15,16. In addition, our research indicated that urban agriculture in walkable neighborhoods benefited health and well-being by increasing recreational outdoor opportunities to neighborhood communities, including remote workers. It also contributed to food system resilience by providing local foods to all people, including low-income households, when the global food system was disrupted due to the pandemic. Furthermore, recent studies on urban agriculture reported the decarbonization benefit of reducing carbon footprints in food production and distribution7,8. Small-scale and community-based urban agriculture in walkable neighborhoods might especially bring this benefit because neighborhood communities travel to farms on foot or by bicycle, which means almost no emission by distribution. While urban green spaces have various health benefits32,33,34,35, urban agriculture also contributes to food system resilience as well as carbon emission reduction, which makes it unique.Urban agriculture was once considered a failure of urban planning in Japan because it symbolized uncontrolled sprawl. This is analogous to the Western view, as urban agriculture was once considered the ultimate oxymoron1. However, our empirical evidence suggests that the urban‒rural mixture at neighborhood scales is a reasonable urban form that contributes to the resilience of the urban food system and to the health and well-being of neighborhood communities. It is no longer a failure of urban planning but a legacy of urban sprawl in the current urban context.Our study showed that integrating urban agriculture into walkable neighborhoods is a fruitful way of creating healthier cities and developing more resilient urban food systems during times of uncertainty. In cities where there is no farmland in intraurban areas, it would be considered effective to utilize underused spaces such as vacant lots and rooftops as productive urban landscapes. In growing cities where urban areas are still expanding, it would be advantageous to conserve agricultural landscapes within their urban fabrics. Our study could provide referential insights and robust evidence for urban policy to integrate urban agriculture into walkable neighborhoods.This study has potential limitations, including the timing of the survey and the measurement method that was utilized. We conducted the survey between June 4 and 8, 2020, just after the end of the first declaration of a state of emergency by the Japanese government. During this period, the main cultivation activities were planting and growing, and the harvest was just beginning. This seasonal constraint may have influenced the results. Because the survey was conducted during the pandemic, we used subjective methods to measure health and well-being status. However, the results might be different using objective methods52, thus further research is necessary. In addition, a longitudinal study is needed to determine whether the trends observed in this study were specific to the emergency period or whether they will persist after the COVID-19 pandemic. More

Analytical method validationThe results of the precision study with relative standard deviation (RSD), and accuracy are shown in Table 1. Through the precision study we found the value of RSD as less than 5%. Moreover, accuracy was done with percent recovery experiments. The results showed that the percentage recoveries for spiked samples were in the range of 95.7–103.7%.Table 1 Shows percent (%) recovery and relative standard deviation.Full size tablePhysicochemical properties and water quality indexThe investigations of the water quality properties of the Narora channel are shown in Table 2. The temperature, TDS, turbidity, and alkalinity were within the standards of the country18 and WHO19 (taken from UNEPGEMS). While pH and dissolved oxygen (D.O) were above the recommended standards indicating poor water quality. Moreover, the detected heavy metals were in the following order Ni  > Fe  > Cd  > Zn  > Cr  > Cu  > Mn. Among these heavy metals Mn, Cu, and Zn were within the recommended limits whereas Cr, Fe, Ni, and Cd were crossing the limits18 contributing to the poor quality. Furthermore, the WQI calculation will give more insights into the overall quality of water as it explains the combined effect of several physicochemical properties12. Its calculation is done simply by converting numerous variables of water quality into a single number12,20. In addition to this, WQI simplifies all the data and helps in clarifying water quality issues by combining the complex data and producing a score that shows the status of water quality2,12,21. The WQI classifies water quality status into five groups such as if WQI  Cu  > Zn  > Fe  > Zn  > Ni  > Cr from root to stalk; and Mn  > Cd  > Zn  > Cu  > Fe  > Ni  > Cr from stalk to leaves.Table 5 Heavy metal concentrations in Eichhornia crassipes (mg/kg.dw).Full size tableFigure 3MPI values in E. crassipes.Full size imageTable 6 Bioaccumulation factor (BAF), transfer factor (TF), and mobility factor (MF) in plant E. crassipes.Full size tableThese factors BAF, TF, and MF are utilized to monitor the level of anthropogenic pollution in plants and their surrounding medium2,15,32,34,35. BAF shows the concentrations of heavy metals bioaccumulated by plants from the water. If the BAF  > 1 it indicates hyperaccumulation36. So, in the present study, all the concerned heavy metals were hyperaccumulated in the plant. The TF elucidates the capability of the plant to translocate the accumulated metals to its other parts. The roots of E. crassipes showed the highest translocation capacity for Ni (1.57) as well as Zn (1.30) to other parts. If the value of TF exceeds 1, then it represents the high accumulation efficiency37,38, therefore, plants will be considered as the hyperaccumulators for the Ni and Zn. Although the Cd was the highest accumulated metal in the plant, it could have been because of its may be because of its low TF. Whereas, TF values lower than 1 for Cr, Mn, Fe, Cu, and Cd pointed out that this plant’s roots act as a non-hyperaccumulator for these heavy metals. Furthermore, the highest MF values were depicted for Mn in both cases which reflects that E. crassipes can suitably be used for phytoextraction of Mn as well as for Cd, Zn, Fe, Ni, and Cu. The BAF, TF, and MF of Cr are low in the present study, which implies that roots are limiting the Cr. Moreover, if the BAF ≤ 1.00 then it shows the capability of absorption only rather than accumulation36,37. In addition, if the values of BAF, TF, and MF exceed 1, plants can also work for phytoextraction. Furthermore, if the BAF  > 1 and TF  More

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Methods for data analysis in figuresAll analyses in figures were performed using Mathematica 12.3 (Wolfram Research, Inc., Champaign, IL, USA).Analysis in Fig. 1
C&D. To calculate integrated abundances of E. huxleyi cells and EhV, we first selected days for which all the bags had a non-null value. Values were then summed up to obtain the integrated abundance.E&J. We computed a standard linear fit between the E. huxleyi total abundances and total EhV abundances for covered and uncovered bags separately. We followed the same procedure for the correlations in panel J and provide a comparison between different models in Supplementary Fig. 5.Analysis in Fig. 2
A. The ASVs that were selected appeared at a relative abundance of at least 2% in at least 4 samples for the 0.2–2 µm 16S sequences and at least in 8 samples for the 2–20 µm 18S sequences. Abundances were concatenated for each time point and normalized by row, to have maximum relative abundance of 1 across all samples. ASVs were sorted by the position of their individual center of mass ({t}_{{CM}}) defined by$${t}_{{CM}}=,frac{mathop{sum}limits_{i}{t}_{i}f({t}_{i})}{mathop{sum}limits_{i}f({t}_{i})}$$
(1)
with i representing the different time points and f(({t}_{i})) the relative abundance of the ASV. The same figure for the individual bags in shown in Supplementary Fig. 14 and Supplementary Fig. 15.B. We selected 18S ASVs with a maximum relative abundance of at least 2% and observed in at least five samples. We averaged relative abundance across bags and then smoothed the time series with a moving average filter (width 2). Then, we grouped all ASVs into clusters based on their cosine distance using Mathematica’s FindClusters function and the KMeans method. The number of possible clusters ranged from 2 to 12, and the final number of clusters was decided using the silhouette method71. Only silhouette scores for 2 and 6 clusters were positive (between-cluster distance minus within-cluster distance).D. We subset reads that map to either Flavobacteriales or Fhodobacterales, then renormalized within each class, taking the mean over bags. Results per bag are shown in Supplementary Fig. 9.F. The turnover time was defined by the exponential rate k at which the Bray-Curtis similarity ({BC}(t)) declined over time. To this end, for a given bag, we computed the Bray–Curtis similarity between the composition vector at a starting day t’ with all following days t, giving a curve that declined roughly exponentially. For earlier starting days (for which the similarity curves declined the furthest), we found that the Bray–Curtis similarity never reached 0 but instead leveled out around ({{BC}}_{infty }=0.05) (due to ASVs that are constantly present in all the samples and maintain a minimal level of similarity between bags). Thus, we imposed an offset at(,{{BC}}_{infty }) for all fits (using Mathematica’s FindFit function) with the function:$${BC}(t)=(1-{{BC}}_{{{infty }}}) times {e}^{-kleft({t}^{{prime} }-tright)}+{{BC}}_{{{infty }}}$$
(2)
The turnover is averaged over bags, showing the standard deviation as error bars in the figure.G. To find differentially abundant ASVs, we first selected a subset of ASVs that had a maximum abundance of at least 10%, and performed Mann–Whitney U-Tests between the relative abundance values of a given ASV in the focal bag and all the other bags over all timepoints of the bloom’s demise. Correcting for multiple testing, we found four 16S ASVs that were differentially abundant in any of the bags, three of which were specific to bag 7, shown in Fig. 2g; and five 18S ASVs, two specific to bags 5 and 6 (Rhizosolenia delicatula and Aplanochytrium), one specific to bag 4 (Pterosperma), and two specific to bag 7 (MAST-1C and Woloszynskia halophila, shown in Fig. 2g).H. The divergence between bags was calculated as follows: we first measured, for each bag, the Bray–Curtis distance between this given bag and all the other bags at the end of the experiment (Supplementary Fig. 13). In order to control for the existing differences between bags at the beginning of the bloom, Bray–Curtis distances were normalized according to the differences between bags at the starting day of the E. huxleyi bloom. As the exact starting days of the bloom is not clear, we normalized for starting days 11, 12, or 13. The plot shows averages with the standard deviation as error bars. For the 18S microbiome, we first removed reads that map to E. huxleyi to reduce bias toward bag 7 (which had by far the lowest E. huxleyi abundance, Fig. 1c).Analysis in Fig. 3
A. Functional annotation of dominant 18S ASVs was based on manual literature search for the 100 most abundant 18S ASVs. Automatic annotation using the functional database created by72 gave qualitatively identical results but contained fewer organisms (covering about 50% of reads). The relative abundance of each trait was obtained by summing up the relative abundance of all the species harboring a specific trait. We used the annotations from72 to further subdivide heterotrophs into osmotrophs, saprotrophs, and other types of heterotrophy (e.g., grazing), ignoring ASVs with missing annotations.D. Growth rates were computed by fitting a linear model to the log-transformed absolute abundances. For thraustochytrids, we measured growth rates until the abundances reached their maximum, i.e., for days indicated by solid lines in Fig. 3b. For bacteria in the 0.2–2 micron fraction, we measured growth rates during the bloom and demise of E. huxleyi, i.e., for the time period after day 15 until the final day, except for bag 4 (until day 22) and bag 7 (until day 18) to account for their different bloom and demise dynamics. For bacteria in the 2–20 micron fraction, we measured growth rates similarly, starting after day 10 until the final day, except bags 4 and 7 (until day 22).E. To quantify the rate of change k of the biomass ratio of thraustochytrids to bacteria we fit a linear function to the log of biomass ratio from day 10 to the time point t where the ratio was maximal; for bag 7, this was day 18, for all others, day 23. We thus have:$$,{{log }},{BR},(t)={kt},+,{{log }},{BR},(0)$$
(3)
Analysis in Fig. 4
C&D. Since TEP accumulates over time, it cannot be expressed as a weighted sum of phytoplankton abundances. Instead, we formulate the model as a recursive relation where TEP can be produced by E. huxleyi, naked nanophytoplankton, and picophytoplankton, and degraded or lost through sinking:$${TEP}left(tright)=left(1-dright){TEP}left(t-1right)+{a}_{E}Eleft(tright)+{a}_{N}Nleft(tright)+{a}_{P}Pleft(tright),$$
(4)
The amount of TEP at time t is given by the fraction (1-d) of TEP at time t-1, where d corresponds to the fraction of TEP that is degraded between time points, plus the amount of TEP produced by the phytoplankton cells present at time t (or time t-1, which gives equivalent results). E, N, and P correspond to E. huxleyi, naked nanophytoplankton, and picophytoplankton, respectively. The parameter ({a}_{E}) corresponds to the amount of TEP produced per E. huxleyi cell, reported in panel D. ({a}_{E}) is set to be fixed through time, and different for each bag. This recursion can be solved to give an explicit expression for TEP(t):$${TEP}left(tright)=mathop{sum }limits_{{t}^{{prime} }=0}^{t}{left(1-dright)}^{t-{t}^{{prime} }}[{a}_{E}Eleft({t}^{{prime} }right)+{a}_{N}Nleft({t}^{{prime} }right)+{a}_{P}Pleft({t}^{{prime} }right)].$$
(5)
This functional form was then used to perform a linear model fitting with the constraint ({a}_{i}ge 0) for various values of the parameter d. The best fit, defined by maximum ({R}^{2}) over the resulting linear model, was used to fix d = 0.12. Our model considers that the fraction of non-calcified E. huxleyi cells in the nanophytoplankton counts is small.Larger phytoplankton cells ( >40 μm) filtered out from flow-cytometry measurements can also be a major source of TEP, despite low cell density. In order to verify this, FlowCam data was analyzed. None of the identified classes of larger phytoplankton (such as Phaeocystis or Dinobryon) increased in a systematic manner toward later stages of the bloom, explaining why larger phytoplankton were not included in the TEP model (Supplementary Fig. 24 and Supplementary Fig. 25).E. Using the smFISH method that reports the proportion of infected E. huxleyi cells, we estimated the amount of TEP produced from infected cells. We first used the least infected uncovered bags (bags 1 and 3) as a baseline to fix model parameters such as how much TEP does a non-infected cell produce. We then split the E. huxleyi abundance into an uninfected subpopulation producing T TEP/cell as in the uninfected bags, and an infected subpopulation producing I×T TEP/cells. To define I, we combined the fixed model parameters (i.e., amount of TEP produced per cell from Fig. 4d for bags 1 and 3) with the measured fraction of infected cells. We adjusted the factor I = 4 to minimize deviation of the measure total TEP concentration from the model prediction including the two subpopulations. The same procedure was used for panel H, using the corresponding model for PIC.F&G. To model the amount of PIC produced per cell we assume that the measured PIC only increases via new E. huxleyi coccoliths. The equivalent model for PIC reads$${PIC}left(tright)=left(1-dright){PIC}left(t-1right)+{a}_{E}{{max }}left(Eleft(tright)-Eleft(t-1right)right).$$
(6)
Where ({a}_{E}) is the amount of PIC produced per cell, and displayed in panel G. Using the same procedure as for TEP, we obtain the best fit for d = 0.0075. Our PIC model assumes that all PIC production comes from E. huxleyi, supported by large occurrence of E. huxleyi cells observed in scanning electron microscopy (Supplementary Fig. 1).Methods for data collectionMesocosm core setupThe mesocosm experiment AQUACOSM VIMS-Ehux was carried out for 24 days between 24th May (day 0) and 16th June (day 23) 2018 in Raunefjorden at the University of Bergen’s Marine Biological Station Espegrend, Norway (60°16′11 N; 5°13′07E). The experiment consisted of seven enclosure bags made of transparent polyethylene (11 m3, 4 m deep and 2 m wide, permeable to 90% photosynthetically active radiation) mounted on floating frames and moored to a raft in the middle of the fjord. The bags were filled with surrounding fjord water (day −1; pumped from 5 m depth) and continuously mixed by aeration (from day 0 onwards). Each bag was supplemented with nutrients at a nitrogen to phosphorus ratio of 16:1 according to the optimal Redfield Ratio (1.6 µM NaNO3 and 0.1 µM KH2PO4 final concentration) on days 0–5 and 14–17, whereas on days 6, 7 and 13 only nitrogen was added to limit the growth of pico-eukaryotes and favor the growth of E. huxleyi that is more resistant to phosphate limited conditions. Silica was not added as a nutrient source in order to suppress the growth of diatoms and to enhance E. huxleyi proliferation. Bags 5, 6, 7 were covered to collect aerosols and guarantee minimal contamination while sampling for core variables. Bags 1, 2, 3, 4 were sampled for additional assays such as metabolomics, polysaccharides profiling, and vesicles, which increase sampling time and potential for contamination.Measurement of dissolved inorganic nutrientsUnfiltered seawater aliquots (10 mL) were collected from each bag and the surrounding fjord water in 12 mL polypropylene tubes and stored frozen at −20 °C. Dissolved inorganic nutrients were measured with standard segmented flow analysis with colorimetric detection73, using a Bran & Luebe autoanalyser. Data are available in ref. 74 and values for individual bags are plotted in Supplementary Fig. 26.Measurement of water temperature and salinityWater temperature and salinity were measured in each bag and the surrounding fjord water using a SD204 CTD/STD (SAIV A/S, Laksevag, Norway). Data points were averaged for 1–3 m depth (descending only). When this depth was not available, the available data points were taken. Data are missing for the fjord in days 0–1. Outliers were removed for the following samples: bag 1 at days 0, 4, 15; bag 7 at day 15. Data are available in ref. 74.Flow cytometry measurementsSamples for flow cytometric counts were collected twice a day, in the morning (7:00 a.m.) and evening (8:00–9:00 p.m.) from each bag and the surrounding fjord, which served as an environmental reference. Water samples were collected in 50 mL centrifugal tubes from 1 m depth, pre-filtered using 40 µm cell strainers, and immediately analyzed with an Eclipse iCyt (Sony Biotechology, Champaign, IL, USA) flow cytometer. A total volume of 300 µL with a flow rate of 150 µL/min was analyzed with the machine’s software ec800 v1.3.7. A threshold was applied based on the forward scatter signal to reduce the background noise.Phytoplankton populations were identified by plotting the autofluorescence of chlorophyll versus phycoerythrin and side scatter: calcified E. huxleyi (high side scatter and high chlorophyll), Synechococcus (high phycoerythrin and low chlorophyll), nano- and picophytoplankton (high and low chlorophyll, respectively). Chlorophyll fluorescence was detected by FL4 (excitation (ex): 488 nm and emission (em): 663–737 nm). Phycoerythrin was detected by FL3 (ex: 488 nm and em: 570–620 nm). Raw.fcs files were extracted and analyzed in R using ‘flowCore’ and ‘ggcyto’ packages and all data are available on Dryad74. In particular, the gating strategy was adapted to each day and each bag and individual plots for each days and each bag can be found in the Dryad link.For bacteria and viral counts, 200 µL of sample were fixed with 4 µL of 20% glutaraldehyde (final concentration of 0.5%) for 1 h at 4 °C and flash frozen. They were thawed and stained with SYBR gold (Invitrogen) that was diluted 1:10,000 in Tris-EDTA buffer, incubated for 20 min at 80 °C and cooled to room temperature. Bacteria and viruses were counted and analyzed using a Cytoflex and identified based on the Violet SSC-A versus FITC-A by comparing to reference samples containing fixed bacteria and viruses from lab cultures. A total volume of 60 µL with a flow rate of 10 µL/min was analyzed. A threshold was applied based on the forward scatter signal to reduce the background noise. For plotting bacteria (Fig. 1h), a moving average of three successive days was used.Enumeration of extracellular EhV abundance by qPCRDNA extracts from filters from the core sampling (see above) were diluted 100 times, and 1 µL was then used for qPCR analysis. EhV abundance was determined by qPCR for the major capsid protein (mcp) gene: 5′-acgcaccctcaatgtatggaagg-3′ (mcp1F) and 5′-rtscrgccaactcagcagtcgt -3′ (mcp94Rv). All reactions were carried out in technical triplicates using water as a negative control. For all reactions, Platinum SYBER Green qPCR SuperMix-UDG with ROX (Invitrogen, Carlsbad, CA, USA) was used as described by the manufacturer. Reactions were performed on a QuantStudio 5 Real-Time PCR System equipped with the QuantStudio Design and Analysis Software version 1.5.1 (Applied Biosystems, Foster City, CA, USA) as follows: 50 °C for 2 min, 95 °C for 5 min, 40 cycles of 95 °C for 15 s, and 60 °C for 30 s. Results were calibrated against serial dilutions of EhV201 DNA at known concentrations, enabling exact enumeration of viruses. Samples showing multiple peaks in melting curve analysis or peaks that were not corresponding to the standard curves were omitted. Data are available in ref. 74. A comparison of viral counts based on flow-cytometry and qPCR is shown in Supplementary Fig. 2.FlowCam analysisSamples for automated flow imaging microcopy were collected once a day in the morning (7:00 a.m.) from each bag and the surrounding fjord, which served as an environmental reference. Water samples were collected in 50 mL centrifugal tubes from 1 m depth, kept at 12 °C in darkness, and analyzed within 2 h of sampling, using a FlowCAM II (Fluid Imaging Technologies Inc., Scarborough, ME, USA) fitted with a 300 µm path length flow cell and a 4× microscope objective. Images were collected using auto-image mode at a rate of 7 frames/second. A sample volume of 10 mL was processed at a flow rate of 0.7 mL/min. Individual objects within each sample were clustered and annotated using the Ecotaxa platform75. Absolute counts for major groups, including the most abundant ciliate category Ciliophora U04, were then exported and normalized by the individual amount of water volume processed for each sample.Data are available under “Flowcam Composite Aquacosm_2018_VIMS-Ehux” project on Ecotaxa.Scanning electron microscopy50 ml of water samples from bags or fjord were collected on polycarbonate filters (0.2 µm pore size, 47 mm diameter, Millipore). The filters were air dried and stored on petri-slides (Millipore) at room temperature. Prior to observation, a small fraction of the filter was cut and coated with 2 nm of iridium using a Safematic CCU-010 coater (Safematic GMBH, Switzerland). Samples were observed on a Zeiss Ultra SEM that was set at a working distance of 6.2 ± 0.1 mm, an acceleration voltage of 3.0 kV and an aperture size of 30 mm. The secondary electron detector was used for image acquisition.Paired dilution experimentPhytoplankton growth and microzooplankton grazing rates were estimated using the dilution method76,77. A slightly modified version of the method was used with only one low dilution level (20%) and an undiluted treatment used78. Rates calculated using this method are considered conservative but accurate when compared with those using multiple dilution levels and a linear regression. Water from bags 1–4 was collected using a peristaltic pump at ~1 m depth and mixed into a 20 L clean carboy. Water was screened through a 200 µm mesh to remove larger mesozooplankton. The collected water was shaded with black plastic and returned to shore. Dilution experiments were set-up in a temperature-controlled room, set to ambient water temperature (±2 °C). Particle-free diluent (FSW) was prepared by gravity filtering whole seawater (WSW) through a 0.45 µm inline filter (PALL Acropak™ Membrane capsule) into a clean carboy. To the FSW, WSW was gently siphoned at a proportion of 20%. The 20% dilution and 100% WSW treatments were prepared in single carboys and then siphoned into triplicate 1.2 L Nalgene™ incubation bottles. To control for nutrient limitation, additional triplicate bottles of 100% WSW were incubated without added nutrients (10 µM nitrate and 1 µM phosphate). The incubation bottles were incubated for 24 h in an outdoor tank maintained at in-situ water temperatures by a flow-through system of ambient seawater. Bottles could float freely, and the seawater inflow caused gentle agitation throughout the 24 h period. A screen was used to mimic light conditions experienced within the mesocosm bags.To quantify viral mortality, we used the paired dilution method79 which involves setting up an extra low dilution level (20%) containing water filtered through a tangential flow filter (TFF) of 100 kDå to remove viral particles. During this experiment, TFF water was produced 1–2 days prior to the dilution experiment, to ensure the chemical composition of the water was as similar as possible, and experiments could be set up in a timely manner.At T0 hours and T24 hours from all dilution experiments, sub-samples were taken for the determination of chlorophyll-a and flow cytometry. For chlorophyll-a, 100–150 mL of seawater was filtered under low vacuum pressure through a 47 mm Whatman GF/F filters (effective pore size 0.7 µm), and then extracted in 7 mL of 97% methanol at 4 °C in the dark for 12 h. All chlorophyll readings were conducted on a Turner TD700 fluorometer80. Methanol blanks were included, and all samples were corrected for phaeophytin using a drop of 10% hydrochloric acid and then reading the sample again81.Water samples (2 × 1 mL) for flow cytometry were taken at T0 and T24 of dilution experiments for the determination of phytoplankton abundances. Water samples were taken in triplicate from T0, and from each bottle at T24. Samples were immediately fixed in 20 µL of glutaraldehyde (final concentration More

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