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Leaf profitability
We calculated the economic profitability of leaves and analysed the patterns in relation to leaf size, environment and durability (longevity). There was wide variation in leaf profitability with a mean value (± SD) of 3.4 ± 3.5% day−1 and 5th/95th percentile range of 0.29–10.3% day−1. There are no previous studies reporting leaf profitability values for direct comparison, but they can be re-calculated based on published values for leaf payback time. For example, Williams et al.21 reported payback time values in several Piper species from a Mexican rainforest, corresponding to leaf profitability values between 0.01 and 33% day−1. Poorter et al.23 calculated payback times corresponding to leaf profitability values of 1.25–50% day−1 , depending on species type, light environment, and growth conditions (very high values were observed for seedlings grown under non-limiting, hydroponic conditions). The mean values of our study are lower, but in line with values calculated from payback time of Kikuzawa and Lechowicz25 (2.2 ± 2% day−1), because they consider the mean labour time and the favourable period length, as we also applied in our calculations (see “Methods” and Supplementary File S2 online).
One factor that could influence profitability is the size of a production unit. For example, a large leaf may imply a higher cost required for structural support; therefore, the changes in profitability will depend on how gains and expenses vary with size. As it turned out, we found that leaf profitability was positively related to leaf size (Fig. 2A), but the percentage of variance explained was not especially high (R2 = 0.07, P  More

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Impact of glacial retreat on the benthic ecosystem
Most enriched POM δ13C concentration in the inner cove location (B2) indicates a potential melt-water input near the glacier (Table S2). The δ13C signature of diatoms showed a similar spatial concentration gradient along the cove, but was slightly more enriched than POM δ13C. This signature of freshwater influence has also been detected in other Antarctic regions. For example, the enriched δ13C of POM and diatoms in Potter Cove was recently reported16. In the enclosed environment beneath glaciers, δ13C might be enriched due to increased HCO3− utilization and production of organic materials17. The POM and diatom δ15N concentrations showed the lack of parallel gradients over the study area. The POM δ15N, especially phytoplankton values, is affected by their nutrient sources. Snow melt-water input occasionally appears from the local creeks throughout the Marian Cove, and the melt-water is associated with the nutrient input as well. Thus, the POM and diatom δ15N concentrations seemed to reflect the melt-water input throughout the cove.
The coastline of the inner locations (B1–B2;  More

Nutrient, FOM, and physiochemical contents of the fecal manures
For the three main fertility nutrients (N, P and K) in the fecal manures (Table 1a) the E manure contained the highest N level at 80 µg/g, 16-fold more than the other samples. In addition, it had a high P level of 100 µg/g (along with PC1 and PC3) compared with an average of 34.66 µg/g for the others, and a moderate K level of 150 µg/g (others ranged from 125.00 to 350.00 µg/g. The FOM content ranged from 1.00 to 1.50% for all the animal manures (Table 1a). For the general physiochemical properties, chicken manures showed a relatively high salinity (Table 1b: 3.52 ± 0.28 to 4.01 ± 0.64 ppt), and these ionic salts caused a high conductivity (11.27 ± 0.69 to 12.79 ± 0.08 mS/cm) and high water saturation (73.29 ± 0.40 to 80.56 ± 3.17 mS/cm), giving less available water for plant roots. A moderate level of salinity, conductivity and water content, such as those in E, are generally preferred for farming soil. The water content of the manure was lowest in G2, goats fed with P. purpureum (23.75 ± 3.07%), but increased two-fold in G1 goats fed with D. eriantha (49.35 ± 6.08%), suggesting a different type of grass feed might confer different fecal liquid and dry contents, or the chemical composition influenced the water activity in the goat’s gut. The R manure also had a low water content (41.53 ± 5.57%).
Table 1 Characteristics of (a) NPK and FOM, and (b) physiochemical levels of the different animal fecal manures.
Full size table

The visual observation of each manure was in accord with the reported water content, with a dry and hard texture (insufficient moisture) for G1, G2, and R manures, and a wet texture for the chicken, cattle, pig, and buffalo manures. Too much water in the soil can adversely affect aeration, where the plant’s roots receive insufficient oxygen and rot. Moreover, most plants prefer a slightly acidic soil pH of 6.2–6.8, which was found in the R, MB and E composts (Table 1b). The manure pH can change through reactions such as organic matter decomposition that affects the availabilities of NPK39.
From the overall comparison of the NPK nutrients and physiochemical properties, the fecal manures from the Phuparn chicken species might be less suitable as a fertilizer because of the poor N level, and high level of salts and conductivity. Indeed, the manures from most species contained a low N level, and those from goat and rabbit also had a relatively low water content (insufficient water for a plant to absorb nutrients from the soil through the roots, to the trunk, leaves and fruits). A moderate water content in the manure was appropriate. But of course, a somewhat low water level could be adjusted by adding water. The biophysiochemical quality analyses of the manures suggested that the E manure would be appropriate as a fertilizer because of the enhanced NPK levels, low salinity, low conductivity, slightly acidic-neutral pH, and a moderate water content (Table 1b).
Quantification of total bacteria in the fecal manures
The total number of bacteria in copies/g DW fecal manure was derived from the 16S rRNA gene qPCR. Figure 1a showed the data were consistent between independent triplicate samples, and that the E and R manures had the lowest bacterial counts (at 2.1 × 109 and 2.5 × 109 copies/g DW, respectively). The other animal fecal manures contained more than 5 × 109 copies/g DW, and the greatest bacterial load was found in PC2 and MB at 2.6 × 1010 and 1.9 × 1010 copies/g DW, respectively, while PC1 and PC3 had a significantly lower level (p  98% of the sequencing coverage of taxonomic compositions at the genus and species levels, respectively (Table 2). The average Good’s coverage indices were 99.54% and 99.43% for the genus and species levels, respectively (Supplemental Table 1). This was consistent with the plateau rarefaction curves, showing the frequencies of OTUs become constant despite increasing sequencing reads, meaning a sufficient sequencing coverage was obtained (Supplemental Fig. 1).
Table 2 Good’s coverage indices (estimated sequencing coverage) and alpha diversity indices of bacterial taxonomic profiles by 16S rRNA gene sequences at the (a) genus and (b) species levels.
Full size table

Combining the count of total 16S rRNA gene copies with the OTU percent compositions gave the quantitative number of copies of each OTU in a community. These quantitative microbiota data were then used to compute the alpha and beta diversity measurements. The alpha diversity revealed that the microbiota of E had the relatively most diverse OTUs at both the genus and species levels (Chao richness, Fig. 2a,c), regardless of having the lowest total bacterial count (Fig. 1). The low total bacterial abundance but high diversity in E underlined that the various OTUs of bacteria in E might be present in small numbers when compared to the copy numbers of OTUs in the other animal manures. Note that the alpha diversity obtained by considering the distribution of general OTUs among the different animal manures were similar (Shannon diversity, Fig. 2b,d).
Figure 2

Alpha diversity measurements of OTU compositions at (a,b) genus and (c,d) species levels, by richness (Chao) and evenness (Shannon). Box plot with bar representing the mean from three sequencing replicates, and asterisk (*) indicates a statistically significant difference by one-way ANOVA at p  1% abundance). In (b), the OTUs where Mothur could not identify the genus name were denoted by small letters (p_ abbreviates phylum; o_, order; and f_, family) to the deepest taxonomic names that could be identified.

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Relative frequencies of plant symbiotic and pathogenic bacterial genera
Plant symbiosis and pathogenic bacteria were analysed across the different animal manures. Manures PC1-3, G1, G2 and CC demonstrated generally abundant symbiotic bacteria comprised of the genera Pseudomonas, Bacillus, Arthrobacter, Flavobacterium, Alcaligenes and Streptomyces (Fig. 4a). For examples, PC2 contained abundant Alcaligenes (1.16 × 1010 cells/g DW), Pseudomonas (3.35 × 109), Flavobacterium (2.82 × 108) and Arthrobacter (3.8 × 108). Streptomyces was only found in G1, G2 and E in moderate numbers (5.9–29.0 × 106 cells/g DW).
Figure 4

Comparative number of bacterial genera categorized as plant (a) symbiotic or (b) pathogenic genera across the different animal manures. Data represented mean ± S.D. List of bacteria categorized as plant symbionts and pathogens were downloaded from the Virulence Factor Database (VFDB).

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For the plant pathogenic bacteria, high levels were found in the manures that also contained high levels of plant symbiosis bacteria (PC1-3, G1, G2 and CC), except for E, plus the other manures, such as S, D and MB. The E had none of VFDB-listed plant pathogens. Thus, all manures except for E contained plant pathogenic bacteria, and these were from Escherichia, Shigella, Enterococcus, Clostridium, Acinetobacter, Treponema, Staphylococcus and Bacteroides (Fig. 4b). This finding correlated with the percent abundance of genera (Fig. 3b) where, examples, Treponema were relatively high in PCO and PC3 at 2.03 × 108 and 1.31 × 108 cells/g DW, respectively. Escherichia were relatively low in R and DC at 1.15 × 105 and 2.06 × 105 cells/g DW, respectively. In contrast, only E did not contain any VFDB-listed plant pathogenic bacteria suggesting that E offers a plant pathogen-free organic fertilizer.
Relationship among bacterial communities, and statistical correlation with the biophysiochemical properties
The NMDS demonstrated both the reproducibility of the data between the independent triplicate samples, except for R and PCO. The larger variation in bacterial communities were found generally between the manures from different animal species, while the minor variation in bacterial communities were found between the breeds, or the feeding diets. For examples, the variation in quantitative microbiota profiles between G1, G2 and E, compared with PC1-3 (p = 0.085) and MB (p = 0.59) (Supplemental Fig. 2). Indeed, the quantitative microbiota structures belonging to the six animal manures that had all the VFDB’s categorized plant pathogens (PC, S, R, D and MB, except PCO) were rather distant from E (p = 0.101, 0.052, 0.089, 0.104 and 0.099, respectively).
Seven parameters (NPK and four physiochemical properties) were analyzed for possible Pearson’s correlation with any of the quantitative microbiota structures. The N level was strongly correlated (p = 0.01) to the structures and in the same direction of the E (Fig. 5). The percent water, salinity and conductivity characteristics were significant and associated in the direction opposite to E, and also to the G1, G2 and PCO microbiota structures. On the other hand, the PC1, PC2 and MB microbiota structures were strongly associated with the water content, salinity and conductivity. These parameters are suggested to be important in controlling the diversity of microbiota structures.
Figure 5

Relationship among bacterial communities via NMDS constructed from thetayc distance coefficients among quantitative microbiota (stress value = 0.15, R2 = 0.86), and Pearson’s correlation with nutrients and physiochemical properties (AMOVA, p  More

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Percoll density gradient centrifugation
The removal of the associated bacteria from KMHK cells against four Percoll density gradients, 90%, 90–50%, 90–50–30% and 90–50–30–10% were shown in Fig. 2. There was no significant difference of the remaining total bacterial counts (with an initial total bacterial count of 6.36 ± 0.04 log10 CFU/mL) between algal samples centrifuged with 90% and 90%–50% Percoll gradients, but decreased significantly from 5.02 ± 0.2 log10 CFU/mL to 4.38 ± 0.05 log10 CFU/mL when dinoflagellate samples were centrifuged with 90–50–30% Percoll gradient with no further increase on adding another layer of 10% density to the gradient (i.e., 90–50–30–10%). These suggested that the highest bacterial removal capacity was achieved by centrifugation of the KMHK cells with the three-layer discontinuous (90–50–30%) gradient. This gradient was adopted in subsequent experiments in the present study, but it was different from Cho et al. who centrifuged the small Haptphyta, Isochrysis galbana (6–12 μm) with the five-layer discontinuous gradient (50%–40%–30%–20%–10%) and harvested the algal cell in between 40 and 30% Percoll5,27. As far as we know, this is the only previous study employing discontinuous gradient for algal culture, and it is obvious the optimized gradient composition varies among algal species, probably because of the diverse algal size and morphology. Vu et al. (2018) suggested that cells with a swimming ability may swim away from the concentrated zone after centrifugation, resulting in low cell recovery efficiency11. In this study, however, the swimming ability of KMHK cells was lost only temporarily for several minutes after centrifugation. This indicated that KMHK cell recovery would not be affected if the supernatant were removed immediately after centrifugation.
Figure 2

Total bacterial count in the algal sample after centrifugation with different Percoll density gradients. All data are presented as means ± standard deviations of three independent experiments (n = 3). Different letters on the top of the bar indicate that the means were significantly different among gradients at p ≤ 0.05 according to one-way analysis of variance followed by Tukey multiple comparison tests.

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Density gradient centrifugation provides an excellent alternative to filtration and micro-pipetting to physically separate bacterial cells from the algal cells, especially for fragile cell separation11. The density medium provides padding and thus protects the algal cells from the shearing force and enhance the separation efficiency17. Although filtration is one of the commonly adopted separating techniques because of its convenience, inexpensive and easy to use11, it is infeasible for use with dinoflagellate samples. However, membrane filters can be easily clogged by the algal cells and thus prolong the processing time of the filtration. Our previous experience found that more than 1 h was required to filter 10 mL of a dinoflagellate sample with 1 L of sterile medium for washing. More, algal cell recovery becomes extremely difficult when the cells are stuck firmly to the filter, and the algal cells may be easily damaged during cell harvest. This is a very important consideration for fragile cells of the unarmored dinoflagellates as they are vulnerable to the shear force generated during filtration. Micropipetting method applied in Coscinodiscus wailesii was laborious, and a skilled operator was required to pipette the algal cells from a culture droplet and sequential wash in droplets19.
In the gradient centrifugation, the commonly used density media include Ficoll, Ludox and Percoll. Of these, Ficoll is not suitable for marine samples because it is a polysaccharide-based medium that is non-isotonic at high medium concentrations and becomes viscous liquid when dissolved in seawater28,29. Ludox and Percoll are both silicon-based density media compatible with seawater. Percoll is preferred for cell separation because it is less cytotoxic to the algal cells than is Ludox30. Few studies have demonstrated the feasibility of using Percoll density gradient centrifugation for microalgal samples17,31. Two fragile dinoflagellate species, Heterosigma carterae and Cochlodinium polykrikoides, have been efficiently harvested and recovered through centrifugation using 90% Percoll17,31.
Bacterial removal by basic and extended protocols
In the present study, Percoll density gradient centrifugation was coupled with antibiotic treatment. It is because the use of antibiotics is one of the most common bacterial killing methods but antibiotics alone rarely achieve complete bacterial elimination from the algal culture11. Antibiotic susceptibility testing results in this study reveal that the bacteria associated with KMHK culture were sensitive to the antibiotic cocktail used, that is, a combination of 100 U of penicillin, 100 µg/mL streptomycin, 100 µg/mL gentamicin and 1 µg/mL tetracycline. Similarly, Ki and Han also reported that the combination of 100 mg/L streptomycin, 150 mg/L ampicillin, 150 mg/L penicillin G and 200 mg/L gentamicin effectively killed bacteria without having detrimental effects on the dinoflagellates Peridinium bipes and A. tamarense12. Guillard demonstrated that most algal species tolerated 100 mg/L penicillin, 25 mg/L streptomycin and 25 mg/L gentamicin reasonably well32. However, many red colonies, identified as those of Rhodopirellula baltica through 16 s rDNA sequencing analysis, were observed on the antibiotic susceptibility testing plate of KMHK cells containing only penicillin, streptomycin and gentamicin in our study. Tetracycline was therefore included in the present antibiotic treatment based on previous report that Rhodopirellula sp. was highly susceptible to 0.5 ppm of tetracycline33. It is common to modify the antibiotic cocktail for different algal cultures since the antibiotics depends on the microbiome. For instance, Su et al. treated Alexandrium cultures with a combination of gentamycin, streptomycin, cephalothin and rifampicin for 7 days to obtain axenic cultures13.
The effectiveness of the basic and extended protocols to remove bacteria in the algal samples is summarized in Fig. 3. The total bacterial count significantly reduced from initial 5.79 ± 0.22 log10 CFU/mL to 4.88 ± 0.05 log10 CFU/mL (p ≤ 0.05) after centrifuged with 90% Percoll (Step 1), even though the primary aim of this step was to condense the algal cells. This is probably due to the removal of numerous free-living bacteria in the supernatant, which was discarded. In the subsequent two gradient centrifugation using 90–50–30% density layers (Steps 2 and 3), approximately 12% of bacteria were further removed at p ≤ 0.05. The bacterial count did not show any additional reduction even when a step of a 90–50–30% density gradient centrifugation was added between Steps 3 and 4 (data not shown). After Step 4 with 48-h antibiotic treatment, the bacterial count was significantly reduced by 31% (Fig. 3a). These results indicated that numerous algae-associated bacteria could be effectively inhibited using the antibiotics but these steps could not completely eradicate the bacteria. No further decline in the bacterial count was found after Step 5 but decreased significant after Step 6, although both steps employed the same gradient centrifugation (90–50–30%). This could probably be the killing effect of the extended incubation of the bacteria with the remaining antibiotics, and/or the bacterial count significantly reduced by the gradient centrifugation in Step 5 was offset by the intracellular bacteria released from algal cell lysis during the antibiotic treatment. The effect of antibiotic exposure time on bacterial removal efficiency was shown in Table 1 below, while the existence of intracellular bacteria inside the dinoflagellate cells remains controversial6 and deserves more in-depth studies. The residue antibiotics, bacteria and algal cell debris were reported to suppress the algal cell growth17,31, these suppressions could be effectively removed in the present study as shown by algal regrowth (Fig. 3).
Figure 3

Bacterial removal in the KMHK sample using the basic and extended protocols. (a) Total bacterial count after different steps in the basic protocol. Initial: the initial bacterial count in KMHK samples at the beginning; Supplementary Fig. 1 illustrate the Step 1 to 6 of basic protocol. (b) Total bacterial count after the basic and extended protocols. Extended protocol refers to the descriptions in material and method. (c) Total bacterial count in the treated KMHK cultures at different days of cultivation. (d) Algal cell concentration during the regrowth of the treated KMHK cultures. All data are presented as mean ± standard deviations of three independent experiments (n = 3). Different letters on the top of the bar indicate that means were significantly different among samples at p ≤ 0.05 according to one-way analysis of variance followed by Tukey multiple comparison tests.

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After all six steps of the present protocol, the total bacterial count remaining in the KMHK culture was 1.13 ± 0.07 log10 CFU/mL (equivalent to approximately 13 CFU/mL), indicating that  > 99.9% of the bacteria were removed. Nevertheless, the bacterial count increased significantly to 5.75 ± 0.14 log10 CFU/mL (equivalent to approximately 5.86 × 105 CFU/mL) in day 7 KMHK regrow culture after the protocol (Fig. 3c). We used the extended protocol to remove the remaining bacteria by repeating the 48-h antibiotic incubation and Steps 4–6 after the basic protocol (Fig. 3b). However, the total bacterial count did not have any significant change between the basic and extended protocols (Fig. 3b), demonstrating that the additional steps, that is, repeating the 48-h antibiotic incubation and Steps 4–6, in the extended protocol failed to eliminate the few remaining bacteria in the KMHK culture. Similar to the basic protocol, the remaining bacteria regrew significantly in day 3 and day 7 KMHK cultures after the extended one (Fig. 3c). More, the additional steps of antibiotic treatment and centrifugation even damaged the algal cells, as reflected by the regrowth of KMHK cells was strongly inhibited by the extended protocol (Fig. 3d). After the extended protocol, the KMHK cell density at day 7 was only half of that after the basic protocol that regrew normally and reached the cell density of approximately 10,000 cells/mL after 7 days, comparable to that of a routine culture. The algal growth rates (µ) after the basic and extended protocols were 1.29 and 1.14, respectively. This implied that the additional steps of antibiotic treatment in the extended protocol damaged the algal cells. It has been reported that antibiotics treatment could interfere the peptidoglycan biosynthesis and eventually inhibited chloroplast division34. The extended protocol was also ineffective in removing bacteria, probably due to limited bacterial removal capacity and/or insufficient dose and exposure time of antibiotics.
Effect of initial algal cell density and antibiotic exposure times on bacterial removal
Bacterial cell concentration, antibiotic dose and antibiotic exposure time are critical factors affecting the efficiency of bacterial removal in algal samples. We hypothesized that the incomplete bacterial removal after the basic protocol was attributable to (1) the bacterial concentration in the algal culture exceeded the treatment capacity and (2) the dose and exposure time used in the antibiotic treatment were insufficient. To test insufficient dosing, a double antibiotic dose was used but there was no significant difference in the amount of bacterial removal between the normal and double doses of antibiotics used in the treatments (data not shown). Our observations also showed that the cell density of the 7-day KMHK culture treated with a double dose of antibiotics decreased from 10,000 to 489 cells/mL, clearly indicating a high antibiotic dose severely damaged the algal cells.
Both the amount and percentage of bacterial removal were independent of the initial algal cell density (Table 1). At least 94.49% of bacteria were removed in all treatments. With a 48-h antibiotic exposure time, the percentage of bacterial removal did not have any significant changes with increases of initial algal cell density, and were 94.49%, 99.84% and 99.93% at high, moderate and low densities, respectively. Similarly, no significant difference in the amount of bacterial removal (in terms of Log10 CFU/mL) was observed between low and moderate initial algal cell densities but were significantly higher than that at high initial algal cell density. With 96-h of antibiotic exposure, bacterial removal percentage was 100% at both high and low algal densities and 98.53% at moderate algal density. Similarly, bacterial removal of low and moderate initial algal cell densities were significantly higher than that at high initial algal cell density. These results indicated that the bacterial removal ability was independent of the initial algal cell density.
Table 1 Bacterial removal in the algal cultures with different initial algal cell densities and antibiotic exposure times in the basic protocol.
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When compare the antibiotic exposure time, bacterial removal with 48-h antibiotic treatment was significantly higher than that with other exposure times, regardless of the initial algal density (Table 1). A complete bacterial removal (100%) was detected under three conditions, that is, high and low initial densities with 96-h exposure time and moderate initial algal density with 72-h exposure time. The antibiotic exposure times ranging from several hours to 1 week have been reported in pervious researches, depending on the method used, and the bacterial cell count and composition in the algal sample13,32. The exposure time should be considered and controlled carefully because prolonged exposure to antibiotics may damage the algal cells and suppress their growth. However, the present study reveals antibiotic exposure time was not a critical factor for achieving 100% bacterial removal. On the other hand, the bacterial removal might be related to the bacterial cell count and composition present in the algal culture at the beginning. The microbiome can also change frequently during the routine cultivation of the algal cultures35.
Algal cells obtained from different protocols were then cultured for 7 days and then subjected to bacterial counting. A substantial number of bacteria was found in all treatments at the end of the algal regrow cultures, including the 72-h exposure achieving 100% bacterial removal, except two 96-h antibiotic exposure treatments (Table 1). Even in these two 96-h exposure treatments with 100% bacteria-free algal cultures, bacteria were detected after several generations of the algal culture (data not shown). These indicated that the axenicity of algal culture may not be guaranteed even when no bacteria are detected after treating the algal samples with our developed protocol.
The reappearance of bacterial growth may be due to the existence of a very small proportion of bacteria attached onto the algal cells after the protocol which was too little to be detected. Dinoflagellates are covered with complex and irregular cell surface structures36. Steric hindrance from parts of these algal surface areas may protect the firmly attached bacteria, making their detachment from the algal cells during gradient centrifugation difficult and decreasing antibiotic accessibility. Another possible explanation is that a few bacteria may have developed antibiotic resistance11, but this is unlikely in the present study as antibiotic resistance usually develops when the bacteria are continuously exposed to a nonlethal dose of an antibiotic37. Although why a few bacteria remained on the algal cells and regrew rapidly along with algal cell growth are poorly understood, it is necessary to have additional treatments such as serial dilution selection to ensure a true axenic algal culture is obtained.
Selection of axenic algal cells through serial dilution
After 7 days of cultivation, KMHK cells from all dilutions described in Fig. 1 survived. No bacterial growth was observed in 42 KMHK cultures (out of total 45 cultures), although bacterial colonies were observed in one of the day 7 KMHK cultures (one culture in the 100 dilution) and in another two of the day 21 KMHK cultures (one in 100 dilution and one in 10–1 dilution) in the first trial (Supplementary Table 1). The results reiterate that a high proportion of the KMHK cells in the population is in fact axenic and the ratio of axenic to non-axenic clones in the algal cell population is high after the basic protocol, it is therefore highly feasible to obtain the axenic clones from the population through such serial dilution approach. Similar approaches have been reported in previous studies5,12,38. For instance, Ki and Han dispersed the algal cells in a 96-well plate after filtration and antibiotic treatment12. Sena et al. also serially diluted the cyanobacterial sample, Arthrospira spp., after antibiotics treatment38. Although the axenic clone could be obtained by plating the algal cells on agar5, this approach was not feasible for marine dinoflagellates because the cells were unable to grow on a solid medium.
Verification of axenicity of algal cultures
It has been reported that some marine bacteria grow very slowly on agar, something like 50 days, and some of them are unculturable39,40. Therefore, confirming the axenic state of the algal cultures is paramount. The axenic state of the two selected KMHK cultures was tested and results of DAPI epifluorescence microscopy show that no bacteria were observed in the treated KMHK cells (Fig. 4b) while bacteria were found in the untreated cells (Fig. 4a). For the rDNA sequencing analysis, a 1500-bp PCR product was obtained after bacterial 16S rDNA amplification (Fig. 5a). The BLAST of the sequence reveal that it shared 99.59% similarity with 16 s rDNA sequence in the plastid gene of K. mikimotoi (accession no. AB027236). Similarly, the 600-bp amplicon observed in the amplification of fungal ITS shared 99.67% similarity with the ITS sequence of K. mikimotoi (accession no. KT733616; Fig. 5c). These results confirm the absence of both culturable and unculturable bacteria and fungi in the treated KMHK cultures. It has been reported that the algal cultures must continually be treated with antibiotics in order to maintain their axenic status15, but algal cells may die after several sub-cultures because of prolonged antibiotic exposure. When this happens, it is usually too late to recover the cultures. In the present study, regular monitoring of the axenicity of the cultures was performed through bacterial colony counting, DAPI epifluorescence microscopy and rDNA sequence analysis. The established axenic cultures were maintained generations after generations without adding any antibiotics, and no bacteria were found in any of the sub-cultures being tested even after 30 generations (data not shown). The axenic cultures of KMHK were established successfully and maintained sustainably, indicating this methodology was a promising approach applicable to other unarmored dinoflagellates. To the best of our knowledge, this is the first successful establishment of an axenic culture for the unarmored dinoflagellate K. mikimotoi.
Figure 4

DAPI epifluorescence microscopic images of KMHK and AT6 samples under ×1000 magnification: (a) untreated (control) and (b) treated KMHK samples; (c) untreated (control) and (d) treated AT6 samples.

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Figure 5

PCR amplification of bacterial 16 s rDNA for (a) KMHK and (b) AT6 samples and that of fungal ITS region for (c) KMHK and (d) AT6 samples obtained from basic protocol and serial dilution. +ve: positive control, -ve: negative control.

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Development of axenic culture of A. tamarense using our established methodology
The established method was applied to obtain the axenic cultures of another dinoflagellate species, A. tamarense (AT6), a well-known paralytic shellfish toxin–producing agent which has been extensively studied in the past decades41,42,43,44. The AT6 culture with an initial bacterial count of 7.9 ± 0.08 log10 CFU/mL was subjected to the basic protocol and the total bacterial counts recorded after Steps 3, 4 and 6 were shown in Fig. 6a. The result was generally similar to that of KMHK culture using the basic protocol (Fig. 3a), except no bacteria were detected in the AT6 culture after Step 6. Even though 100% bacterial removal was achieved, bacterial regrowth was observed on days 3 and 7 of the treated AT6 culture (Fig. 6b). The bacterial count regrew significantly to 6.71 ± 0.08 log10 CFU/mL after 7 days of cultivation (Fig. 6c). The regrowth of bacteria from the treated AT6 culture achieving 100% bacterial removal confirmed that few bacteria attached at some points onto the algal surface were shielded. Biegala et al. reported that associated bacteria were attached onto the cell surface within the sulci and cingula of A. tamarense6.
Figure 6

Bacterial removal in the Alexandrium tamarense (AT6) samples using the basic protocol. (a) Total bacterial count against different steps. Initial: the initial bacterial count present in AT6 at the beginning. (b) Total bacterial count in the AT6 culture obtained after the protocol at different days of cultivation. (c) Algal cell concentration during the regrowth of the AT6 culture obtained after the protocol. All data are presented as means ± standard deviations of three independent experiments (n = 3). N.D.: not detected. Different letters on the top of the bar indicate that means were significantly different among different samples at p ≤ 0.05 according to one-way analysis of variance followed by Tukey multiple comparison tests.

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For the serial dilution selection of AT6 cells after the basic protocol, culturable bacteria were observed in 11 of the 15 cultures in 100 dilution (5/5 in trial 1; 4/5 in trial 2 and 2/5 in trial 3) after 7 days of algal cultivation. All these 15 cultures showed bacterial regrowth after 21 days of algal cultivation, but the number of cultures with bacterial regrowth decreased to 1 and 4 of the 15 cultures in 10–1 dilutions after 7 and 21 days of algal cultivation, respectively. In 10−2 dilutions, no culturable bacteria were observed in all the cultures after both 7 and 21 days of algal cultivation (Supplementary Table 1). These results further demonstrate the feasibility of using the stepwise serial dilution method to select axenic algae, and 10–2 dilutions offer the highest probability in acquiring the axenic clones. The bacterial status of two of these potential axenic AT6 cultures was further assessed through DAPI epifluorescence microscopy and bacterial rDNA and fungal ITS sequencing analysis. No bacteria were observed in the DAPI epifluorescence image of the treated AT6 cultures compared to the untreated control cultures (Figs. 4c,d). Neither bacterial 16 s rDNA band (Fig. 5b) nor fungal ITS region (Fig. 5d) was amplified in the treated AT6 samples. These results confirmed the axenic status of the AT6 cultures.
Our established methodology
The present results demonstrate the potential of our methodology to be used in the establishment of axenic cultures for both armored and unarmored dinoflagellates. Figure 7 summarizes the workflow and procedures of our methodology. This promising approach combines three techniques, Percoll density gradient centrifugation, antibiotic treatment and serial dilution. Density gradient centrifugation considerably reduces the bacterial population by the physical separation between the associated bacteria, mainly the free-living and loosely attached bacteria, and the dinoflagellate cells on the basis of cell size. Percoll density layers not only provide a matrix for separating the two types of cells effectively but also cushion the dinoflagellate cells against the impact of the mechanical force. The Percoll density layers together with the bactericidal action of the antibiotic treatment typically eradicate  > 99% of the associated bacteria from the dinoflagellate culture. Our strategies not target at removing the remaining  More