Ecology

1.
Tajika, E. Climate change during the last 150 million years: Reconstructing from a carbon cycle model. Earth Planet Sci. Lett. 160, 659–707 (1998).
Article  Google Scholar 
2.
Li, H.-L. et al. Large-scale phylogenetic analyses reveal multiple gains of actinorhizal nitrogen-fixing symbioses in angiosperms associated with climate change. Sci. Rep. 5, 14023 (2015).
ADS  PubMed  PubMed Central  Article  Google Scholar 

3.
van Velzen, R., Doyle, J. J. & Geurts, R. A resurrected scenario: Single gain and massive loss of nitrogen-fixing nodulation. Trends Plant Sci. 24, 49–57 (2019).
PubMed  Article  CAS  PubMed Central  Google Scholar 

4.
Griesmann, M. et al. Phylogenomics reveals multiple losses of nitrogen-fixing root nodule symbiosis. Science 361, eaat1743 (2018).
PubMed  Article  CAS  PubMed Central  Google Scholar 

5.
Menge, D. N. L. & Crews, T. E. Can evolutionary constraints explain the rarity of nitrogen-fixing trees in high-latitude forests?. New Phytol. 211, 1195–1201 (2016).
PubMed  Article  PubMed Central  Google Scholar 

6.
Menge, D. N. L. et al. Nitrogen-fixing tree abundance in higher-latitude North America is not constrained by diversity. Ecol. Lett. 20, 842–851 (2017).
ADS  PubMed  Article  PubMed Central  Google Scholar 

7.
Tedersoo, L. et al. Global database of plants with root-symbiotic nitrogen fixation: NodDB. J. Veg. Sci. 29, 560–568 (2018).
Article  Google Scholar 

8.
Swensen, S. M. & Benson, D. R. Evolution of actinorhizal host plants and Frankia endosymbionts. In Nitrogen-fixing Actinorhizal Symbioses, 73–104 (Springer Netherlands, 2008).

9.
Houlton, B. Z., Wang, Y.-P., Vitousek, P. M. & Field, C. B. A unifying framework for dinitrogen fixation in the terrestrial biosphere. Nature 454, 327–330 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Luo, Y. et al. Progressive nitrogen limitation of ecosystem responses to rising atmospheric carbon dioxide. Bioscience 54, 731 (2004).
Article  Google Scholar 

11.
Cheng, W., Inubushi, K., Yagi, K., Sakai, H. & Kobayashi, K. Effects of elevated carbon dioxide concentration on biological nitrogen fixation, nitrogen mineralization and carbon decomposition in submerged rice soil. Biol. Fertil. Soils. 34, 7–13 (2001).
CAS  Article  Google Scholar 

12.
Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2004).
Article  Google Scholar 

13.
Pawlowski, K. & Newton, W. E. Nitrogen-fixing Actinorhizal Symbioses. (Springer Netherlands, 2008). https://doi.org/10.1007/978-1-4020-3547-0.

14.
Dentener, F. et al. Nitrogen and sulfur deposition on regional and global scales: A multimodel evaluation. Glob. Biogeochem. Cycles. 20, GB4003 (2006).
ADS  Article  CAS  Google Scholar 

15.
Vitousek, P. M. et al. Human alteration of the global nitrogen cycle: Sources and consequences. Ecol. Appl. 7, 737–750 (1997).
Google Scholar 

16.
Benjamin, W. S. et al. Spatially robust estimates of biological nitrogen (N) fixation imply substantial human alteration of the tropical N cycle. PNAS 111, 8101–8106 (2014).
Article  CAS  Google Scholar 

17.
Li, X. et al. Seasonal and spatial variations of bulk nitrogen deposition and the impacts on the carbon cycle in the arid/semiarid grassland of inner Mongolia, China. PLoS ONE 10, e0144689 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
Lamarque, J. F. et al. Assessing future nitrogen deposition and carbon cycle feedback using a multimodel approach: Analysis of nitrogen deposition. J. Geophys. Res. Atmos. 110, D19303 (2005).
ADS  Article  Google Scholar 

19.
Tian, D. & Niu, S. A global analysis of soil acidification caused by nitrogen addition. Environ. Res. Lett. 10, 24019 (2015).
Article  CAS  Google Scholar 

20.
Binkley, D. & Högberg, P. Tamm review: Revisiting the influence of nitrogen deposition on Swedish forests. For. Ecol. Manag. 368, 222–239 (2016).
Article  Google Scholar 

21.
Lee, J. A. Unintentional experiments with terrestrial ecosystems: Ecological effects of sulphur and nitrogen pollutants. J. Ecol. 86, 1–12 (1998).
CAS  Article  Google Scholar 

22.
Southon, G. E., Field, C., Caporn, S. J. M., Britton, A. J. & Power, S. A. Nitrogen deposition reduces plant diversity and alters ecosystem functioning: Field-scale evidence from a nationwide survey of UK heathlands. PLoS ONE 8, e59031 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Vitousek, P. M., Menge, D. N. L., Reed, S. C. & Cleveland, C. C. Biological nitrogen fixation: Rates, patterns and ecological controls in terrestrial ecosystems. Philos. Trans. R. Soc. B. 368, 20130119 (2013).
Article  CAS  Google Scholar 

24.
Skogen, K. A., Holsinger, K. E. & Cardon, Z. G. Nitrogen deposition, competition and the decline of a regionally threatened legume, Desmodium cuspidatum. Oecologia 165, 261–269 (2011).
ADS  PubMed  Article  PubMed Central  Google Scholar 

25.
Salvagiotti, F. et al. Growth and nitrogen fixation in high-yielding soybean: Impact of nitrogen fertilization. Agron J. 101, 958–970 (2009).
CAS  Article  Google Scholar 

26.
Markham, J. H. & Zekveld, C. Nitrogen fixation makes biomass allocation to roots independent of soil nitrogen supply. Can. J. Bot. 85, 787–793 (2007).
CAS  Article  Google Scholar 

27.
Coley, P. D. Possible effects of climate change on plant/herbivore interactions in moist tropical forests. Clim. Change. 39, 455–472 (1998).
Article  Google Scholar 

28.
Coley, P. D., Massa, M., Lovelock, C. E. & Winter, K. Effects of elevated CO2 on foliar chemistry of saplings of nine species of tropical tree. Oecologia 133, 62–69 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Roger, M. G., Damian, J. B. & Jason, L. L. The effects of elevated [CO2] on the C:N and C:P mass ratios of plant tissues. Plant Soil 224, 1–14 (2000).
Article  Google Scholar 

30.
McLauchlan, K. K., Williams, J. J., Craine, J. M. & Jeffers, E. S. Changes in global nitrogen cycling during the Holocene epoch. Nature 495, 352–355 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

31.
Hossain, M. A., Ishimine, Y., Akamine, H. & Kuramochi, H. Effect of nitrogen fertilizer application on growth, biomass production and N-uptake of torpedograss (Panicum repens L.). Weed Biol. Manag. 4, 86–94 (2004).
CAS  Article  Google Scholar 

32.
Thomas, R. B., Bashkin, M. A. & Richter, D. D. Nitrogen inhibition of nodulation and N2 fixation of a tropical N2-fixing tree (Gliricidia sepium) grown in elevated atmospheric CO2. New Phytol. 145, 233–243 (2000).
CAS  Article  Google Scholar 

33.
Dordas, C. A. & Sioulas, C. Safflower yield, chlorophyll content, photosynthesis, and water use efficiency response to nitrogen fertilization under rainfed conditions. Ind. Crops Prod. 27, 75–85 (2008).
CAS  Article  Google Scholar 

34.
Xu, D. et al. Interactive effects of nitrogen and silicon addition on growth of five common plant species and structure of plant community in alpine meadow. CATENA 169, 80–89 (2018).
CAS  Article  Google Scholar 

35.
Roy, A. & Bousquet, J. The evolution of the actinorhizal symbiosis through phylogenetic analysis of host plants. Acta Bot. Gall 143, 635–650 (1996).
Article  Google Scholar 

36.
Swensen, S. M. The evolution of actinorhizal symbioses: Evidence for multiple origins of the symbiotic association. Am. J. Bot. 83, 1503–1512 (1996).
Article  Google Scholar 

37.
van Velzen, R. et al. Comparative genomics of the nonlegume Parasponia reveals insights into evolution of nitrogen-fixing rhizobium symbioses. Proc. Natl. Acad. Sci. 115, E4700–E4709 (2018).
PubMed  Article  CAS  PubMed Central  Google Scholar 

38.
Rogers, A., Ainsworth, E. A. & Leakey, A. D. B. Will elevated carbon dioxide concentration amplify the benefits of nitrogen fixation in legumes?. Plant Physiol. 151, 1009–1016 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
DeLuca, T. H., Zackrisson, O., Gundale, M. J. & Nilsson, M. C. Ecosystem feedbacks and nitrogen fixation in boreal forests. Science 320, 1181 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Zheng, M., Zhou, Z., Luo, Y., Zhao, P. & Mo, J. Global pattern and controls of biological nitrogen fixation under nutrient enrichment: A meta-analysis. Glob. Change Biol. 25, 3018–3030 (2019).
ADS  Article  Google Scholar 

41.
Fisher, J. B. et al. Carbon cost of plant nitrogen acquisition: A mechanistic, globally applicable model of plant nitrogen uptake, retranslocation, and fixation. Glob. Biogeochem. Cycles. 24, GB1014 (2010).
ADS  Article  CAS  Google Scholar 

42.
Gentili, F., Wall, L. G. & Huss-Danell, K. Effects of phosphorus and nitrogen on nodulation are seen already at the stage of early cortical cell divisions in Alnus incana. Ann. Bot. 98, 309–315 (2006).
PubMed  PubMed Central  Article  Google Scholar 

43.
Chen, H. & Markham, J. Using microcontrollers and sensors to build an inexpensive CO2 control system for growth chambers. Appl. Plant Sci. 8, e11393 (2020).
PubMed  PubMed Central  Article  Google Scholar 

44.
Werner, G. D. A., Cornwell, W. K., Sprent, J. I., Kattge, J. & Kiers, E. T. A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nat. Commun. 5, 4087 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Chen, H., Renault, S. & Markham, J. The effect of Frankia and multiple ectomycorrhizal fungil species on Alnus growing in low fertility soil. Symbiosis. 80, 207–215 (2020).
CAS  Article  Google Scholar 

46.
Noridge, N. A. & Benson, D. R. Isolation and nitrogen-fixing activity of Frankia sp. strain CpI1 vesicles. J. Bacteriol. 166, 301–305 (1986).
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Markham, J. H. Does Dryas integrifolia fix nitrogen?. Botany. 87, 1106–1109 (2009).
CAS  Article  Google Scholar  More

Sprouting behaviour of potato tubers
In this study, the stored tubers exhibited clear variety-specific differences in sprouting behaviour, with the Agata variety being the earliest and the Hermes variety the latest to sprout. However, within each variety, there were differences in sprouting time depending on the soil in which the tubers had grown (Suppl. Table 1). These soil-dependent differences in sprouting were greatest in the Fabiola variety (sprouting time ranged from 135 to 169 days after harvest) and least significant in the Hermes variety (158 to 168 days after harvest). By examining the storage stability of tubers broken down by soil type, we found that tubers grown in soil from the field site in Karnabrunn sprouted on average up to 9 days later than the tubers from other soils. The chemical profile of the soil collected in Karnabrunn was similar to that of the other farmland soils, especially those from Kettlasbrunn. The soil collected in Tulln showed reduced K and P contents compared to those of the other farmland soils. As expected, the potting soil differed most significantly from the other soils, i.e., there was no clay, and the exchange capacity was substantially higher than that of the farmland soils (Suppl. Table S2). Statistical analysis revealed that the chemical parameters of the soils did not correlate with the sprouting behaviour of tubers (data not shown).
Sequencing results
The sequencing of 16S rRNA gene amplicons of tubers, sprouts and soil samples yielded 9,158,550 high-quality merged reads, corresponding to an average of 33,920 reads per sample. All sequences were cut to a read length of 360 bp. To ensure sufficient diversity by maintaining an adequate sequencing depth, samples with a low read number were excluded from the analysis. Therefore, the data of four samples were excluded from further analysis (LC_T3_PS; LC_T7_K; H_T6_K: LC_T7_T; H_T6_K; F_T7_K). A detailed explanation for the sample naming can be found in Table 1. Sequencing reads from all 270 samples were clustered into operational taxonomic units (OTUs), which resulted in 11,485 OTUs with an average of 42,537 OTUs per sample.
Table 1 Abbreviation of sample names.
Full size table

The bacterial community of potato tubers during storage
To test whether the bacterial community in potato tubers changes during storage, the 16S rRNA gene amplicon data of the potato tubers of the Agata, Lady Claire and Hermes varieties, cultivated in five different soil types (T2), stored until dormancy break (T6) and until sprouting (T7) were analysed as well as samples of the sprouts (T7_Sprouts) (Fig. 1). Cultivation of the cultivar Fabiola in the soil Kettlasbrunn B did not yield sufficient tubers for analysis throughout the whole storage period, i.e., no data were available for T7 and the sprouts. Therefore, community data for the Fabiola variety were not included in the following statistical analysis.
Figure 1

Overview of potato cultivars, sampling time points and corresponding BBCH stages investigated in this study. After harvesting four tuber varieties (Agata, Fabiola, Lady Claire and Hermes) from five different soil types (T2), tubers were stored at 8–10 °C in darkness. After 2 (T3), 5 (T4) and 10 weeks (T5), tubers were sampled. To consider the individual dormancy break (T6) of each variety, tubers were sampled according to the BBCH scale at stage 03. The same procedure was performed for samples that were taken at sprouting at stage 05 (T7). Additionally, at T7, sprout samples were taken. At each sampling time point, tuber/sprout samples were used for 16S rRNA gene amplicon sequencing. The red circles mark sites on the tubers that show visible signs of sprouting.

Full size image

For the statistical analysis, we selected only OTUs that showed at least 0.01% relative abundance (1425 OTUs) and that were present in at least two of three replicates (1395 rOTUs). We considered them “reproducibly occurring OTUs” (rOTUs)2.
For calculation of alpha diversity values, read numbers were rarefied to 6785 reads in each sample. The permutation ANOVA of rOTU richness (9999 permutations) revealed significant differences in the bacterial community of tubers and sprouts between cultivars, time points and soil types (observed for cultivar (F value = 3.963, P value = 0.0211*), for time point (F value = 4.762, P value = 0.0021*) and for soil type (F value = 3.779, P value = 0.0057**). The same results were obtained with Simpson’s index for the factors cultivar (F value = 4.594, P value = 0.01*), time point (F value = 6.741, P value = 0.0004***) and soil type (F value = 5.123, P value = 0.0008***) (Suppl. Table S3). Overall, the statistical analysis showed that the soil type had the strongest impact on the microbial community in potato tubers, followed by time point and cultivar.
Similarly, the bacterial community composition (beta diversity) in potato tubers was significantly affected by the storage time, soil type and plant variety. The CAP ordination plot indicated a shift in the bacterial community of tubers from harvest to dormancy break and sprouting, which could be proven by PERMANOVA on the Bray–Curtis dissimilarity distance matrix (R2 = 0.113, P value = 0.0004***) (Fig. 2A). Additionally, the bacterial communities in tubers grown in different soil types (R2 = 0.255, P value = 0.0004***) and of different varieties were significantly different from one other (R2 = 0.027, P value = 0.0021**) (Suppl. Table S4). The results for the factor time point (P value = 0.01**), genotype (P value = 0.01**) and soil type (P value = 0.01**) could be confirmed by the multivariate generalized linear model for multivariate abundance data on the Bray–Curtis dissimilarity distance matrix (999 permutations) (Suppl. Table S4).
Figure 2

Constrained analysis of principal coordinates (CAP) of Bray–Curtis dissimilarities. CAP based on the V5–V7 regions of the 16S rRNA gene investigated for (A) tubers of the varieties (Agata, Lady Claire and Hermes) sampled at T2, T6 and T7 and (B) tubers of the varieties (Agata, Lady Claire and Hermes) sampled at T2-7 as well as sprout samples at T7. An overview of potato cultivars, soil types and sampling time points is shown in Fig. 1.

Full size image

The dynamics of bacterial communities in potato tubers during storage
To obtain more in-depth insight into the changes in community composition during storage, we compared the bacterial communities in tubers of the Agata, Lady Claire and Hermes varieties grown in potting soil at six different timepoints: T2 (harvest), T3 (2 weeks after harvesting), T4 (5 weeks after harvesting), T5 (10 weeks after harvesting), T6 (dormancy break), T7 (sprouting) and in the corresponding sprouts (T7_Sprouts) (Fig. 1). We focused here on tubers grown in potting soil, since the cultivation of potatoes in farmland soil did not yield sufficient tubers to be analysed throughout the entire storage period.
Again, we filtered data for OTUs with at least 0.01% relative abundance and “reproducibly occurring OTUs”. After both filtering steps, 1108 rOTUs remained. Before calculating alpha values, read numbers were rarefied to 6761 reads in each sample. The permutation ANOVA of richness and evenness (9999 permutations) did not reveal differences in the alpha diversity of the tuber microbiome between cultivars but between time points (observed species F value = 6.159, P value = 0.0004*** and Simpson’s index F value = 2.216, P value = 0.0263*) (Suppl. Table S3). The bacterial richness of each cultivar declined significantly during the period between harvest (T2) and the dormancy break (T6) (Suppl. Figure S1B). The CAP scaling plot of six different time points during storage and the corresponding sprouts indicated a shift from harvest to sprouting (Fig. 2B). The PERMANOVA on the Bray–Curtis dissimilarity distance (9999 permutations) revealed that the microbiomes of tuber samples differed significantly between the cultivars (R2 = 0.069, P value = 0.0004***) and time points (R2 = 0.221, P value = 0.0001***) (Suppl. Table S4). The test results for cultivar (P value = 0.01**) and time point (P value = 0.01**) were confirmed by the multivariate generalized linear model for multivariate abundance data on the Bray–Curtis dissimilarity distance matrix (999 permutations) (Suppl. Table S4). To identify the bacterial taxa that changed over time during storage, differentially abundant rOTUs were calculated with the random forest function and visualized in Fig. 3 at the genus level. The relative abundance of Staphylococcus sp. increased during the period from harvesting (T2) to dormancy breaking (T6) from approximately 0% to 11% relative abundance. A similar result was visible for Propionibacterium sp. and Acinetobacter sp. The relative abundance of both was approximately 2% in tubers after harvesting (T2) and increased during storage to 8% and 9% in dormancy broken tubers (T6), respectively, whereas the relative abundance of the taxa Iamia sp. and Nocardioides sp. decreased from approximately 10% after harvesting to 4% and 6% in already sprouted tubers (T7), respectively (Suppl. Tables S5 and S6).
Figure 3

Differentially abundant taxa at the genus level. Visualization of differentially abundant taxonomic groups at the genus level of the bacterial community in tubers of three different potato cultivars (Agata, Hermes and Lady Claire) at all sampling time points T2-7 as well as in sprout samples at T7. Differentially abundant taxa were calculated with the function varSelRF48 and visualized in barplot with the function group.abundant.taxa of the R package RAM47.

Full size image

Taxa associated with short, medium or long storage stability
To identify the bacterial taxa in the potato tuber microbiome associated with early or late potato tuber sprouting, sample data were grouped into short, medium and long storage abilities based on the individual storage time (in days from harvest until sprouting) of the samples. The data of all samples, including those of cultivar Fabiola, were considered for the following analysis (Fig. 1). After filtering for the OTUs with at least 0.01% relative abundance and presence in at least two of three replicates, 813 rOTUs were obtained. To identify rOTUs associated with short, medium or long storage stability at the beginning and end of the storage period of the potato tubers, two different analysis steps were performed. In the first step, differentially abundant rOTUs depending on storage stability (short, medium or long) and storage time points (T2, T6 and T7) were calculated with the random forest function (Suppl. Table S7). In a second step, a correlation matrix based on Spearman’s rank correlation was calculated based on the previously described factor storage stability and storage time points to identify positive or negative interactions between rOTUs (Suppl. Table S8). The same rOTUs obtained with random forest analysis, as well as with the correlation matrix, based on Spearman analysis, were filtered and named key OTUs (Suppl. Table 9). Even if both analysis steps do not provide the same evidence, they provide a meaningful indication of which OTUs are related to the factors, storage stability and storage time point. In total, we identified 24 key OTUs. The relative abundance of nine key OTUs was associated with long storage stability, meaning that the OTUs were significantly increased in samples where dormancy break (T6) and sprouting (T7) started late. These key OTUs are members of the orders Flavobacteriales, Cytophagales, Sphingobacteriales, Gaiellales, Corynebacteriales, Caulobacterales, Methylophilales and Solirubrobacterales. Additionally, the relative abundance of eight rOTUs was associated with short storage stability. These rOTUs are members of the orders Enterobacteriales, Pseudomonadales, Myxococcales, Rhizobiales, Bacillales and Burkholderiales. The relative abundance of seven key OTUs was associated with medium storage stability.
Testing the effect of selected bacterial taxa on potato tuber sprouting
In the next step, we tested whether the bacterial taxa that correlated with longer storability can directly affect the sprouting of potato tubers. Therefore, we screened a collection of bacteria isolated from seed potatoes18,20 for isolates that are homologous in the 16S rRNA gene to the key OTUs identified in the statistical analysis. We identified two isolates that were homologous to OTU_14 (Flavobacterium sp.), which correlated with late sprouting. The selected isolates were tested in an in vitro sprouting assay adapted from Hartmann et al.21. Tuber discs treated with cultures of Flavobacterium sp. isolates (AIT1165 and AIT1181) resulted in sprout growth inhibition compared to control discs treated with sterile tryptic soy broth (Fig. 4). Furthermore, we observed significant individual differences in the sprouting behaviour of the tested buds. Differences in sprouting behaviour are only partly genotype-dependent, but these differences might also be due to the natural developmental variability between buds. The apical eye on a tuber usually begins to sprout first, marking the start of the apical dominance stage22. For the sprouting assay performed in this study, we took several buds from one tuber independently of their position.
Figure 4

In vitro potato tuber sprouting assay results. Tuber buds of three different varieties (Lady Claire, Ditta and Agata) were treated with two different isolates of Flavobacterium sp. (AIT1165 and AIT1181). For evaluation, bud growth was assessed according to the first principal growth stages of the BBCH scale. Hereby, the first stage 00 is considered innate or enforced dormancy with no sprouting at all, followed by stages 01 and 02, which represent the beginning of sprouting when sprouts are visible with sizes up to  More

Phylogenetic analysis
A 1444 bp fragment of the 16S rRNA gene was amplified from the P. eucalypticola strain NP-1 T, sequenced and the sequence deposited in GenBank under accession number MN 238,862. A similarity search with this sequence was performed using EzBioCloud. Thirty valid species belonging to P. fluorescens intrageneric group (IG) proposed by Mulet et al.15 exhibited at least 97% similarity with NP-1 T, and these include P. vancouverensis ATCC 700688 T (98.8% similarity), P. moorei DSM12647T (98.8% similarity), P. koreensis Ps9-14 T (98.8% similarity), P. parafulva NBRC16636T (98.5% similarity) and P. reinekei Mt-1 T (98.5% similarity). The similarities with the other 25 species are provided in Supplementary Table S1. A phylogenetic tree based on the 16S rRNA sequence was constructed and is shown in Fig. 1. Strain NP-1 T forms a weakly supported cluster with P. kuykendallii NRRL B-59562 T, but both strains are situated on separate branches. Strain NP-1 T grouped in none known group or subgroup within P. fluorescens lineage, and it clusters of the outer edge of a much larger group containing several Pseudomonas groups/subgroups. However, Pseudomonas species cannot be identified based only on 16S rRNA analysis.
Figure 1

Neighbor-joining phylogenetic tree based on the 16S rRNA gene of Pseudomonas eucalypticola NP-1T and phylogenetically close members of Pseudomonas. The evolutionary distances were computed using the Jukes-Cantor method. The optimal tree with a sum of branch length = 0.23535266 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. Cellvibrio japonicus Ueda107T was used as outgroup.

Full size image

The MLSA approach based on the concatenated sequences of the partial 16S rRNA, gyrB, rpoB and rpoD genes, has been demonstrated to greatly facilitate the identification of new Pseudomonas strains16. According to the 16S rRNA alignment, 33 species from P. fluorescens IG and one species from P. pertucinogena IG were selected for MLSA. The concatenated sequences of the type strains of each selected species comprised a total of 3813 bp (Supplementary Table S2) and were used for phylogenetic tree construction. The analysis of concatenated gene sequences indicated that strain NP-1 T belongs to the P. fluorescens lineage, and this finding was supported by a bootstrap value of 91% (Fig. 2).However, NP-1 T still cannot be determined which group belongs to17.
Figure 2

Neighbor-joining phylogenetic tree based on concatenated 16S rRNA, gyrB, rpoB and rpoD gene partial of Pseudomonas eucalypticola NP-1T and the type strains of other Pseudomonas species. The evolutionary distances were computed using the Jukes-Cantor method. The evolutionary distances were computed using the Jukes-Cantor method e optimal tree with the sum of branch length = 1.37677586 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches.

Full size image

For further identification of NP-1 T, a phylogenomic tree inferred with GBDP was constructed by using Type (Strain) Genome Server (TYGS)18, and all reference type strains and their genome sources are listed in Supplementary Table S3. The result showed the presence of an independent branch supported by a bootstrap value of 88% that can be differentiated from the other Pseudomonas species type strains (Fig. 3) and revealed that NP-1 T clustered with P. coleopterorum LMG 28558 T and P. rhizosphaerae LMG 21640 T which affiliated with P. fluorescens IG, but does not belong to any group. Strain NP-1 T was not be affiliated with any previously described Pseudomonas species and can thus be considered to represent a novel species. Based on above-described the results, P. coleopterorum, P. rhizosphaerae, P. graminis and P. lutea were selected for further analysis with NP-1 T.
Figure 3

Phylogenomic tree of strain NP-1T and related type strains of the genus Pseudomonas available on the TYGS database. The tree inferred with FastME 2.1.6.1 based on GBDP distances calculated from the genome sequences. The branch lengths are scaled in terms of the GBDP distance formula d5. The numbers above the branches show the GBDP pseudo-bootstrap support values  > 60% from 100 replications, and the average branch support is 94.6%.

Full size image

General taxonomic genome feature
The draft genome assembly of strain NP-1 T contains 6,401,699 bp. The genome of NP-1 T, which consists of one chromosome and one plasmid, has been deposited in GenBank under the accession numbers CP056030 and CP056031, respectively. The genome has a G + C content of 63.96 mol%, as determined from the complete genome sequence, and 83.45% of the genome is coding and consists of 5,788 genes. The similarity of the genome of P. eucalypticola NP-1 T to other publicly available genomes of closely related Pseudomonas species was determined using ANI, digital DDH and G + C mol %5,6,7,8,9. Each of these comparisons yielded different ANIm and ANIb values, but the highest ANIb and ANIm values of 78.7 and 86.5 were obtained for NP-1 T and P. rhizosphaerae LMG 21640 T. The similarity between P. coleopterorum LMG 28558 T and NP-1 T was higher than that between P. graminis DSM 11363 T and P. lutea LMG 21974 T (Table 1). All ANIb and ANIm values obtained from the comparisons of NP-1 T with the other tested species were below 95%, which confirmed that strain NP-1 T belongs to an independent species. The TETRA frequencies between NP-1 T and the other tested type strains were lower than 0.99, which is the recommended cutoff value for species (Table 2). The digital DNA-DNA hybridization (dDDH) comparison with the draft genome of the type strain NP-1 T yielded low percentages ( More

1.
Seilacher, A. Begriff und Bedeutung der Fossil-Lagerstätten. N. Jahrb. Geol. Paläont. Monatsh. 1970, 34–39 (1970).
Google Scholar 
2.
Zhuravlev, A. & Riding, R. The Ecology of the Cambrian Radiation, ix + 525 pp. (Columbia University Press, New York, 2000).

3.
Erwin, D. H. & Valentine, J. W. The Cambrian Explosion: The Construction of Animal Biodiversity. X + 406 pp. (Roberts and Company Publishers Inc., Greenwood Village, CO, 2013).

4.
Landing, E. et al. Early evolution of colonial animals (Ediacaran Evolutionary Revolution–Cambrian Evolutionary Radiation–Great Ordovician Diversification Interval). Earth-Sci. Rev. 178, 105–135 (2018).
ADS  Article  Google Scholar 

5.
Briggs, D. E. G., Erwin, D. H. & Collier, F. J. The Fossils of the Burgess Shale. xvii + 238 pp. (Smithsonian Institution Press, Washington DC, 1994).

6.
Hou X. G., Siveter, D. J., Siveter, D. J., Aldridge, R. J., Cong, P. Y., Gabbott, S. E., Ma, X. Y., Purnell. M. A. & Williams, M. The Cambrian Fossils of Chengjiang, China: The Flowering of Early Animal Life. 2nd ed. 328 pp. (Wiley-Blackwell, Oxford, 2017).

7.
Peel, J. S. & Ineson, J. R. The Sirius Passet Lagerstätte (early Cambrian) of  North Greenland. Palaeontographica Canadiana 31, 109–118 (2011).
Google Scholar 

8.
Paterson, J. R. et al. The Emu Bay Shale Konservat-Lagerstätte: a view of Cambrian life from East Gondwana. J. Geol. Soc. Lond. 173, 1–11 (2016).
Article  Google Scholar 

9.
Eriksson, M. E. & Waloszek, D. Half-a-billion-year-old microscopic treasures—the Cambrian ‘Orsten’ fossils of Sweden. Geol. Today 32, 115–120 (2016).
Article  Google Scholar 

10.
Fu, D. J., Tong, G. H., Dai, T., Liu, W., Yang, Y. N., Zhang, Y., Cui, L. H., Li, L. Y., Yun, H., Wu, Y., Sun, A., Liu, C., Pei, W. R., Gaines, R. R. & Zhang X. L. The Qingjiang biota—A Burgess Shale–type fossil Lagerstätte from the early Cambrian of South China. Science 363, 1338–1342.

11.
Hu, S. X. et al. Biodiversity and taphonomy of the Early Cambrian Guanshan Biota, eastern Yunnan. Sci. China Earth Sci. 53, 1765–1773 (2010).
ADS  Article  Google Scholar 

12
Zhao, Y. L. et al. Kaili biota: a taphonomic window on diversification of metazoans from the basal Middle Cambrian, Guizhou China. Acta Geol. Sin. Engl. Ed. 79, 751–765 (2005).
Google Scholar 

13.
Robison, R. A., Babcock, L. E. & Gunther, V. G. Exceptional Cambrian fossils from Utah: a window into the age of trilobites. Utah Geol. Surv. Misc. Publ. 15(1), 97 (2015).
Google Scholar 

14.
Lerosey-Aubril, R. et al. The Weeks Formation Konservat-Lagerstätte and the evolutionary transition of Cambrian marine life. J. Geol. Soc. Lond. 175, 705–715 (2018).
Article  Google Scholar 

15.
Kimmig, J., Strotz, L. C., Kimmig, S. R., Egenhoff, S. O. & Lieberman, B. S. The Spence Shale Lagerstätte: an important window into Cambrian biodiversity. J. Geol. Soc. Lond. 176, 609–619 (2019).
Article  Google Scholar 

16.
Conway Morris, S. Cambrian Lagerstätten: their distribution and significance. Philos. Trans. R. Soc. Lond. Ser. B. Biol. Sci. 311, 49–65 (1985).
ADS  Google Scholar 

17.
García-Bellido, D. & Conway Morris, S. New fossil worms from the Lower Cambrian of the Kinzers Formation, Pennsylvania, with some comments on Burgess Shale−type preservation. J. Paleont. 73, 394–402 (1999).

18.
Müller, K. J., Walossek, D. & Zakharov, A. ‘Orsten’ type phosphatized soft-integument preservation and a new record from the Middle Cambrian Kuonamka Formation in Siberia. N. Jahrb. Geol. Paläont. Abh. 191, 101–118 (1995).
Article  Google Scholar 

19.
Martin, E. et al. The Lower Ordovician Fezouata Konservat-Lagerstätte from Morocco: age, environment and evolutionary perspectives. Gondwana Res. 34, 274–283 (2016).
ADS  Article  Google Scholar 

20.
Van Roy, P. et al. Ordovician faunas of Burgess Shale type. Nature 465, 215–218 (2010).
ADS  Article  Google Scholar 

21.
Van Roy, P., Briggs, D. E. G. & Gaines, R. R. The Fezouata fossils of Morocco; an extraordinary record of marine life in the Early Ordovician. J. Geol. Soc. Lond. 172, 541–549 (2015).
Article  Google Scholar 

22.
Gutierrez-Marco, J. C., Pereira, S., García-Bellido, D. C. & Rábano, I. Ordovician trilobites from the Tafilalt Lagerstätte: new data and reappraisal of the Bou Nemrou assemblage. Geol. Soc. Lond., Spec. Publ. https://doi.org/10.1144/SP485-2018-126 (2019).

23.
Ortega-Hernández, J. et al. A xandarellid artiopodan from Morocco – a middle Cambrian link between soft-bodied euarthropod communities in North Africa and South China. Nat. Sci. Rep. 7, 42616. https://doi.org/10.1038/srep42616 (2017).
ADS  CAS  Article  Google Scholar 

24.
Geyer, G. Late Precambrian to early Middle Cambrian lithostratigraphy of southern Morocco. Beringeria 1, 115–143 (1989).
ADS  Google Scholar 

25.
Geyer, G. Revised Lower to lower Middle Cambrian biostratigraphy of Morocco. Newsl. Stratigraphy 22, 53–70 (1990).
Article  Google Scholar 

26.
Geyer, G. & Landing, E. Latest Ediacaran and Cambrian of the Moroccan Atlas regions. Beringeria Spec. Issue 6, 7–46 (2006).
Google Scholar 

27.
Geyer, G. The earliest known West Gondwanan trilobites from the Anti-Atlas of Morocco, with a revision of the Family Bigotinidae Hupé, 1953. Fossils Strata 64, 55–153 (2019).
Article  Google Scholar 

28.
Geyer, G. A critical evaluation of the Resserops clade (Trilobita: Despujolsiidae, early Cambrian) with remarks on related redlichiacean families. Freib. Forsch. H. C 558, 1–107 (2020).
Google Scholar 

29.
Geyer, G. A new obolellid brachiopod from the Lower Cambrian of Morocco. J. Paleont. 68, 995–1002 (1994).
Article  Google Scholar 

30.
Geyer, G. An enigmatic bilateral fossil from the Lower Cambrian of Morocco. J. Paleont. 68, 710–716 (1994).
Article  Google Scholar 

31.
Geyer, G. A comprehensive Cambrian correlation chart. Episodes 42, 321–332. https://doi.org/10.18814/epiiugs/2019/019026 (2019).
Article  Google Scholar 

32.
Chen, L. et al. Paleoceanographic indicators for early Cambrian black shales from the Yangtze Platform, South China: Evidence from biomarkers and carbon isotopes. Acta Geol. Sinica 86, 1143–1153 (2012).
CAS  Article  Google Scholar 

33.
Steiner, M., Zhu, M. Y., Weber, B. & Geyer, G. The Lower Cambrian of eastern Yunnan: trilobite-based biostratigraphy and related faunas. Acta Palaeont. Sinica 40, Suppl., 63–79 (2001).

34.
Landing, E. Time-specific black mudstones and global hyperwarming on the Cambrian–Ordovician slope and shelf of the Laurentia palaeocontinent. Palaeogeogr. Palaeoclimatol. Palaeoecol. 367–368, 256–272 (2012).
Article  Google Scholar 

35
Maloof, A. C., Schrag, D. P., Crowley, J. L. & Bowring, S. A. An expanded record of Early Cambrian carbon cycling from the Anti-Atlas Margin Morocco. Can. J. Earth Sci. 42, 2195–2216 (2005).
Google Scholar 

36.
Brasier, M. D., Rozanov, A, Yu., Zhuravlev, A. Yu., Corfield, R. M. & Derry, L. A. A carbon isotope reference scale for the Lower Cambrian succession in Siberia: report of IGCP Project 303. Geol. Mag. 131, 767–783 (1994).
ADS  CAS  Article  Google Scholar 

37.
Zhu, M. Y., Babcock, L. E. & Peng, S. C. Advances in Cambrian stratigraphy and paleontology: integrating correlation techniques, paleobiology, taphonomy and paleoenvironmental reconstruction. Palaeoworld 15, 217–222 (2006).
Article  Google Scholar  More

Study area
The study was conducted across the Loess Plateau (33°43′–41°16′N, 100°54′–114°33′E) (Fig. 1a), which represents approximately 6.5% of the total area of China6. The study area is dominated by temperate, arid, and semiarid continental monsoon climates. The annual evaporation is 1400–2000 mm, and the annual temperature ranges from 3.6 °C in the northwest to 14.3 °C in the southeast on the Loess Plateau7, while the annual precipitation ranges from 150 to 800 mm, where 55–78% of the precipitation falls from June to September7. The annual solar radiation ranges from 5.0 × 109 to 6.7 × 109 J m−2. The vegetation zones are forest, forest-steppe, typical-steppe, desert-steppe, and steppe-desert zones8 from southeast to northwest.
Figure 1

Locations of the Loess Plateau region in China (a) and the sampling sites (b); image data processed by ArcGIS 10.5 http://developers.arcgis.com.

Full size image

Field sampling
According to the different climate zones and vegetation types, five classic sampling sites were selected (Fig. 1b) on the Loess Plateau, which were Yangling, Changwu, Fuxian, Ansai, and Shenmu from south to north. Drilling equipment (assembled by Xi’an Qinyan Drilling Co. Ltd, China) was used to collect soil samples from soil surface down to bedrock. At each sampling site, disturbed soil samples were collected to determine the SAP and SAK concentrations, pH, soil particle composition, and soil organic matter contents. In addition, disturbed soil samples were collected from the middle of the soil column at 1-m intervals (i.e., 0.5 m, 1.5 m, 2.5 m, 3.5 m, etc.). The drilling and sampling work was carried out from April 28 to June 28, 2016. The total numbers of disturbed soil samples collected from Yangling, Changwu, Fuxian, Ansai, and Shenmu were 103, 205, 181, 161, and 58, respectively, and the corresponding soil drilling depths were 103.5 m, 204.5 m, 187.5 m, 161.6 m, and 56.6 m, respectively.
Laboratory analyses
Undisturbed soil samples were air-dried, separated, and passed through 0.25-mm or 2-mm sieves. SAP and SAK were extracted with ammonium lactate solution and detected by spectrophotometry and flame photometry. Soil total nitrogen (STN) concentrations were determined by the Kjeldahl digestion procedure9. Soil total phosphorus (STP) concentrations were determined by molybdenum antimony blue colorimetry10. The soil organic carbon (SOC) contents were analyzed by dichromate oxidation method11. The soil particle composition was determined by laser diffraction (Mastersizer 2000, Malvern Instruments, Malvern, UK)12. According to the mixture of soil and water mass ratio of 1:1, the pH value was determined with a pH meter equipped with a calibrated combined glass electrode. The soil water content (SWC) was determined by the mass loss after drying to constant mass in an oven at 105 °C13. The calcium carbonate content was determined by the acid-neutralization method14.
Geostatistical analysis
The geostatistical analysis was chosen to determine the spatial structure of the spatially dependent soil properties15, where a semivariogram was employed to quantify the spatial patterns of the variables. The equation for the semivariogram is16:

$$ {text{R}}left( {text{h}} right) , = frac{1}{{2{text{N}}left( {text{h}} right)}}mathop sum limits_{{{text{i}} = 1}}^{{{text{N}}left( {text{h}} right)}} left[ {{text{Z}}left( {{text{x}}_{{text{i}}} } right){-}{text{Z}}left( {{text{x}}_{{{text{i}} + {text{h}}}} } right)} right]^{{2}} , $$
(1)

where for each site i, N(h) is the number of pairs separated by h, and Z(xi) is the value at location xi and Z(xi+h) for xi+h. There are four semivariogram models (spherical, exponential, linear, and Gaussian), which can be employed to describe the semivariogram, and the best fitting model is selected according to the smallest residual sum of squares (RSS) and the largest coefficient of determination (R2). The equation of each semivariogram model is16:
Exponential model:

$$ {text{R}}left( {text{h}} right) = {text{C}}_{0} + {text{C}}left[ {({1}{-}{text{exp}}( – {text{h}}/{text{A}}_{0} )} right] $$
(2)

Linear Model:

$$ {text{R}}left( {text{h}} right) = {text{C}}_{0} + left[ {{text{h}}left( {{text{C}}/{text{A}}_{0} } right)} right] $$
(3)

Spherical Model:

$$ {text{R}}left( {text{h}} right) = {text{C}}0 + {text{C}}left[ {{1}.{5}left( {{text{h}}/{text{A}}_{0} } right) – 0.{5}left( {{text{h}}/{text{A}}_{0} } right)^{{3}} } right] ;;;;;;;;; {text{h}} le {text{A}}0 $$
(4)

$$ {text{R}}left( {text{h}} right) = {text{C}}_{0} + {text{C}};;;;;;;;;;{text{h}} ge {text{A}}0 $$
(5)

Gaussian Model:

$$ {text{R}}left( {text{h}} right) = {text{C}}_{0} + {text{C}}left[ {{1} – {text{exp}}left( { – {text{h}}^{{2}} /{text{A}}_{0}^{{2}} } right)} right] $$
(6)

where C0 indicates the nugget value, which is the short-range structure that occurs at distances smaller than the sampling interval, microheterogeneity, and experimental error; C0 + C is the sill indicating the random and structural variation, and; A0 is the range indicating the spatial correlation at different distances.
Statistical analysis
Descriptive statistical analyses (maximum, minimum, average, and coefficient of variation), Pearson’s correlation analysis, and linear regression analysis was performed with SPSS 16.0 (IBM SPSS, Chicago, IL, USA). Geostatistical analysis was performed with GS + software (version 7.05). More

1.
Cody, E. M. & Dixon, B. P. Hemolytic uremic syndrome. Pediatr. Clin. North Am. 66, 235–246. https://doi.org/10.1016/j.pcl.2018.09.011 (2019).
Article  PubMed  Google Scholar 
2.
Chiang, Y. N., Penades, J. R. & Chen, J. Genetic transduction by phages and chromosomal islands: The new and noncanonical. PLoS Pathog 15, e1007878. https://doi.org/10.1371/journal.ppat.1007878 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

3.
Penades, J. R. & Christie, G. E. The phage-inducible chromosomal islands: A family of highly evolved molecular parasites. Annu. Rev. Virol. 2, 181–201. https://doi.org/10.1146/annurev-virology-031413-085446 (2015).
CAS  Article  PubMed  Google Scholar 

4.
Valilis, E., Ramsey, A., Sidiq, S. & DuPont, H. L. Non-O157 Shiga toxin-producing Escherichia coli-A poorly appreciated enteric pathogen: Systematic review. Int. J. Infect Dis. 76, 82–87. https://doi.org/10.1016/j.ijid.2018.09.002 (2018).
CAS  Article  PubMed  Google Scholar 

5.
Murinda, S. E. et al. Shiga toxin-producing Escherichia coli in mastitis: An international perspective. Foodborne Pathog. Dis. 16, 229–243. https://doi.org/10.1089/fpd.2018.2491 (2019).
Article  PubMed  Google Scholar 

6.
Feiner, R. et al. A new perspective on lysogeny: prophages as active regulatory switches of bacteria. Nat. Rev. Microbiol. 13, 641–650. https://doi.org/10.1038/nrmicro3527 (2015).
CAS  Article  PubMed  Google Scholar 

7.
Bikard, D. & Marraffini, L. A. Innate and adaptive immunity in bacteria: mechanisms of programmed genetic variation to fight bacteriophages. Curr. Opin. Immunol. 24, 15–20. https://doi.org/10.1016/j.coi.2011.10.005 (2012).
CAS  Article  PubMed  Google Scholar 

8.
Hoskisson, P. A. & Smith, M. C. Hypervariation and phase variation in the bacteriophage “resistome”. Curr. Opin. Microbiol. 10, 396–400. https://doi.org/10.1016/j.mib.2007.04.003 (2007).
CAS  Article  PubMed  Google Scholar 

9.
De Ste Croix, M. et al. Phase-variable methylation and epigenetic regulation by type I restriction-modification systems. FEMS Microbiol. Rev. 41, S3–S15. https://doi.org/10.1093/femsre/fux025 (2017).
Article  PubMed  Google Scholar 

10.
Heredia, N. & Garcia, S. Animals as sources of food-borne pathogens: A review. Anim. Nutr. 4, 250–255. https://doi.org/10.1016/j.aninu.2018.04.006 (2018).
Article  PubMed  PubMed Central  Google Scholar 

11.
Fatima, R. & Aziz, M. in StatPearls (2019).

12.
Mellor, G. E. et al. National Survey of Shiga Toxin-Producing Escherichia coli Serotypes O26, O45, O103, O111, O121, O145, and O157 in Australian Beef Cattle Feces. J. Food Prot. 79, 1868–1874. https://doi.org/10.4315/0362-028X.JFP-15-507 (2016).
Article  PubMed  Google Scholar 

13.
Kampmeier, S., Berger, M., Mellmann, A., Karch, H. & Berger, P. The 2011 German enterohemorrhagic Escherichia coli O104:H4 outbreak-the danger is still out there. Curr. Top. Microbiol. Immunol. 416, 117–148. https://doi.org/10.1007/82_2018_107 (2018).
CAS  Article  PubMed  Google Scholar 

14.
Lee, M. S. & Tesh, V. L. Roles of shiga toxins in immunopathology. Toxins (Basel) https://doi.org/10.3390/toxins11040212 (2019).
Article  PubMed Central  Google Scholar 

15.
Schmidt, H. Shiga-toxin-converting bacteriophages. Res. Microbiol. 152, 687–695. https://doi.org/10.1016/s0923-2508(01)01249-9 (2001).
CAS  Article  PubMed  Google Scholar 

16.
Herold, S., Karch, H. & Schmidt, H. Shiga toxin-encoding bacteriophages–genomes in motion. Int. J. Med. Microbiol. 294, 115–121. https://doi.org/10.1016/j.ijmm.2004.06.023 (2004).
CAS  Article  PubMed  Google Scholar 

17.
Chakraborty, D., Clark, E., Mauro, S. A. & Koudelka, G. B. Molecular mechanisms governing “hair-trigger” induction of shiga toxin-encoding prophages. Viruses https://doi.org/10.3390/v10050228 (2018).
Article  PubMed  PubMed Central  Google Scholar 

18.
Bloch, S. et al. Inhibition of Shiga toxin-converting bacteriophage development by novel antioxidant compounds. J. Enzyme Inhib. Med. Chem. 33, 639–650. https://doi.org/10.1080/14756366.2018.1444610 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

19.
Fang, Y., Mercer, R. G., McMullen, L. M. & Ganzle, M. G. Induction of Shiga Toxin-Encoding Prophage by Abiotic Environmental Stress in Food. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01378-17 (2017).
Article  PubMed  PubMed Central  Google Scholar 

20.
Smith, D. L. et al. Comparative genomics of Shiga toxin encoding bacteriophages. BMC Genomics 13, 311. https://doi.org/10.1186/1471-2164-13-311 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

21.
Kakoullis, L., Papachristodoulou, E., Chra, P. & Panos, G. Shiga toxin-induced haemolytic uraemic syndrome and the role of antibiotics: A global overview. J. Infect. 79, 75–94. https://doi.org/10.1016/j.jinf.2019.05.018 (2019).
Article  PubMed  Google Scholar 

22.
Kavanagh, D., Raman, S. & Sheerin, N. S. Management of hemolytic uremic syndrome. F1000Prime Rep 6, 119. https://doi.org/10.12703/P6-119 (2014).
Article  PubMed  PubMed Central  Google Scholar 

23.
James, C. E. et al. Lytic and lysogenic infection of diverse Escherichia coli and Shigella strains with a verocytotoxigenic bacteriophage. Appl. Environ. Microbiol. 67, 4335–4337. https://doi.org/10.1128/aem.67.9.4335-4337.2001 (2001).
CAS  Article  PubMed  PubMed Central  Google Scholar 

24.
Smith, D. L. et al. Multilocus characterization scheme for shiga toxin-encoding bacteriophages. Appl. Environ. Microbiol. 73, 8032–8040. https://doi.org/10.1128/AEM.01278-07 (2007).
CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Eichhorn, I. et al. Lysogenic conversion of atypical enteropathogenic Escherichia coli (aEPEC) from human, murine, and bovine origin with bacteriophage Phi3538 Deltastx2::cat proves their enterohemorrhagic E. coli (EHEC) progeny. Int. J. Med. Microbiol. 308, 890–898. https://doi.org/10.1016/j.ijmm.2018.06.005 (2018).
Article  PubMed  Google Scholar 

26.
Khalil, R. K., Skinner, C., Patfield, S. & He, X. Phage-mediated Shiga toxin (Stx) horizontal gene transfer and expression in non-Shiga toxigenic Enterobacter and Escherichia coli strains. Pathog. Dis. https://doi.org/10.1093/femspd/ftw037 (2016).
Article  PubMed  Google Scholar 

27.
Smith, D. L. et al. Short-tailed Stx phages exploit the conserved YaeT protein to disseminate Shiga toxin genes among enterobacteria. J. Bacteriol. 189, 7223–7233. https://doi.org/10.1128/JB.00824-07 (2007).
CAS  Article  PubMed  PubMed Central  Google Scholar 

28.
Botos, I., Noinaj, N. & Buchanan, S. K. Insertion of proteins and lipopolysaccharide into the bacterial outer membrane. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. https://doi.org/10.1098/rstb.2016.0224 (2017).
Article  Google Scholar 

29.
Knirel, Y. A. et al. Variations in O-antigen biosynthesis and O-acetylation associated with altered phage sensitivity in Escherichia coli 4s. J. Bacteriol. 197, 905–912. https://doi.org/10.1128/JB.02398-14 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

30.
Golomidova, A. K. et al. Branched lateral tail fiber organization in T5-like bacteriophages DT57C and DT571/2 is revealed by genetic and functional analysis. Viruses https://doi.org/10.3390/v8010026 (2016).
Article  PubMed  PubMed Central  Google Scholar 

31.
Kulikov, E. E., Golomidova, A. K., Prokhorov, N. S., Ivanov, P. A. & Letarov, A. V. High-throughput LPS profiling as a tool for revealing of bacteriophage infection strategies. Sci. Rep. 9, 2958. https://doi.org/10.1038/s41598-019-39590-8 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
van der Ley, P., de Graaff, P. & Tommassen, J. Shielding of Escherichia coli outer membrane proteins as receptors for bacteriophages and colicins by O-antigenic chains of lipopolysaccharide. J. Bacteriol. 168, 449–451. https://doi.org/10.1128/jb.168.1.449-451.1986 (1986).
Article  PubMed  PubMed Central  Google Scholar 

33.
Kunisaki, H. & Tanji, Y. Intercrossing of phage genomes in a phage cocktail and stable coexistence with Escherichia coli O157:H7 in anaerobic continuous culture. Appl. Microbiol. Biotechnol. 85, 1533–1540. https://doi.org/10.1007/s00253-009-2230-2 (2010).
CAS  Article  PubMed  Google Scholar 

34.
Golomidova, A. K., Kulikov, E. E., Babenko, V. V., Kostryukova, E. S. & Letarov, A. V. Complete genome sequence of bacteriophage St11Ph5, which Infects uropathogenic Escherichia coli strain up11. Genome Announcements https://doi.org/10.1128/genomeA.01371-17 (2018).
Article  PubMed  PubMed Central  Google Scholar 

35.
Golomidova, A. K., Naumenko, O. I., Senchenkova, S. N., Knirel, Y. A. & Letarov, A. V. The O-polysaccharide of Escherichia coli F5, which is structurally related to that of E. coli O28ab, provides cells only weak protection against bacteriophage attack. Arch. Virol. 164, 2783–2787. https://doi.org/10.1007/s00705-019-04371-1 (2019).
CAS  Article  PubMed  Google Scholar 

36.
Knirel, Y. A. et al. Structure and gene cluster of the O antigen of Escherichia coli F17, a candidate for a new O-serogroup. Int. J. Biol. Macromol. 124, 389–395. https://doi.org/10.1016/j.ijbiomac.2018.11.149 (2019).
CAS  Article  PubMed  Google Scholar 

37.
Zdorovenko, E. L. et al. Structure of the O-polysaccharide of Escherichia coli O87. Carbohyd. Res. 412, 15–18. https://doi.org/10.1016/j.carres.2015.04.014 (2015).
CAS  Article  Google Scholar 

38.
Zdorovenko, E. L. et al. Corrigendum to “Structure of the O-polysaccharide of Escherichia coli O87” [Carbohydr. Res. 412 (2015) 15–18]. Carbohydrate Res. 464, 1. https://doi.org/10.1016/j.carres.2018.04.013 (2018).

39.
Zdorovenko, E. L. et al. O-Antigens of Escherichia coli strains O81 and HS3–104 are structurally and genetically related, except O-Antigen glucosylation in E. coli HS3–104. Biochemistry 83, 534–541. https://doi.org/10.1134/S0006297918050061 (2018).
CAS  Article  PubMed  Google Scholar 

40.
Kulikov, E. E. et al. Genomic sequencing and biological characteristics of a novel Escherichia coli bacteriophage 9g, a putative representative of a new Siphoviridae genus. Viruses 6, 5077–5092. https://doi.org/10.3390/v6125077 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Golomidova, A. K. et al. Escherichia coli bacteriophage Gostya9, representing a new species within the genus T5virus. Adv. Virol. 164, 879–884. https://doi.org/10.1007/s00705-018-4113-2 (2019).
CAS  Article  Google Scholar 

42.
Kulikov, E. et al. Isolation and characterization of a novel indigenous intestinal N4-related coliphage vB_EcoP_G7C. Virology 426, 93–99. https://doi.org/10.1016/j.virol.2012.01.027 (2012).
CAS  Article  PubMed  Google Scholar 

43.
Prokhorov, N. S. et al. Function of bacteriophage G7C esterase tailspike in host cell adsorption. Mol. Microbiol. 105, 385–398. https://doi.org/10.1111/mmi.13710 (2017).
CAS  Article  PubMed  Google Scholar 

44.
Zdorovenko, E. L. et al. Corrigendum to “Structure of the O-polysaccharide of Escherichia coli O87”. Carbohydrate Res. 412, 15–18. https://doi.org/10.1016/j.carres.2018.04.013 (2015).
CAS  Article  Google Scholar 

45.
Sambrook, J., Fritsch, E. F. & Maniatis, T. Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, New York, 1989).
Google Scholar 

46.
Samson, J. E., Magadan, A. H., Sabri, M. & Moineau, S. Revenge of the phages: Defeating bacterial defences. Nat. Rev. Microbiol. 11, 675–687. https://doi.org/10.1038/nrmicro3096 (2013).
CAS  Article  PubMed  Google Scholar 

47.
Pawlak, A. et al. Salmonella O48 serum resistance is connected with the elongation of the lipopolysaccharide O-Antigen containing sialic acid. Int. J. Mol. Sci. https://doi.org/10.3390/ijms18102022 (2017).
Article  PubMed  PubMed Central  Google Scholar 

48.
Coggon, C. F. et al. A novel method of serum resistance by Escherichia coli that causes urosepsis. mBio https://doi.org/10.1128/mBio.00920-18 (2018).
Article  PubMed  PubMed Central  Google Scholar 

49.
Kintz, E. et al. Salmonella enterica Serovar Typhi lipopolysaccharide O-antigen modification impact on serum resistance and antibody recognition. Infect. Immunity https://doi.org/10.1128/IAI.01021-16 (2017).
Article  Google Scholar 

50.
Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl. Acad. Sci. U.S.A. 97, 6640–6645. https://doi.org/10.1073/pnas.120163297 (2000).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar  More

1.
Ineson, P., Leonard, M. A. & Anderson, J. M. Effect of collembolan grazing upon nitrogen and cation leaching from decomposing leaf litter. Soil Biol. Biochem. 14, 601–605. https://doi.org/10.1016/0038-0717(82)90094-3 (1982).
Article  Google Scholar 
2.
Petersen, H. & Luxton, M. A. comparative analysis of soil fauna populations and their role in decomposition processes. Oikos 39, 288–388. https://doi.org/10.1016/j.pedobi.2006.08.006 (1982).
Article  Google Scholar 

3.
Drake, H. L. & Horn, M. A. As the worm turns: The earthworm gut as a transient habitat for soil microbial biomes. Annu. Rev. Microbiol. 61, 169–189. https://doi.org/10.1146/annurev.micro.61.080706.093139 (2007).
CAS  Article  PubMed  Google Scholar 

4.
Liu, Y. et al. Higher soil fauna abundance accelerates litter carbon release across an alpine forest-tundra ecotone. Sci. Rep. 9, 10562. https://doi.org/10.1038/s41598-019-47072-0 (2019).
CAS  Article  Google Scholar 

5.
Hopkin, S. P. Biology of the Springtails (Insecta: Collembola) (Oxford University Press, Oxford, 1997).
Google Scholar 

6.
Maaß, S., Caruso, T. & Rillig, M. C. Functional role of microarthropods in soil aggregation. Pedobiologia 58, 59–63. https://doi.org/10.1016/j.pedobi.2015.03.001 (2015).
Article  Google Scholar 

7.
Bergstrom, D. M., Convey, P. & Huiskes, A. H. L. Trends in Antarctic Terrestrial and Limnetic Ecosystems: Antarctica as a Global Indicator (Springer, Berlin, 2006). .
Google Scholar 

8.
Convey, P. Antarctic terrestrial biodiversity in a changing world. Polar. Biol. 34(11), 1629–1641. https://doi.org/10.1007/s00300-011-1068-0 (2011).
Article  Google Scholar 

9.
Convey, P. et al. The spatial structure of Antarctic biodiversity. Ecol. Monogr. 84(2), 203–244. https://doi.org/10.1890/12-2216.1 (2014).
Article  Google Scholar 

10.
Wauchope, H. S., Shaw, J. D. & Terauds, A. A snapshot of biodiversity protection in Antarctica. Nat. Commun. 10(1), 946. https://doi.org/10.1038/s41467-019-08915-6 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
Chown, S. L. et al. The changing form of Antarctic biodiversity. Nature 522(7557), 431–438. https://doi.org/10.1038/nature14505 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

12.
Agamennone, V. et al. The microbiome of Folsomia candida: An assessment of bacterial diversity in a Wolbachia-containing animal. FEMS Microbiol. Ecol. 91(11), 1–10. https://doi.org/10.1093/femsec/fiv128 (2015).
CAS  Article  Google Scholar 

13.
Zhu, D. et al. Exposure of soil collembolans to microplastics perturbs their gut microbiota and alters their isotopic composition. Soil Biol. Biochem. 115, 302–310. https://doi.org/10.1016/j.soilbio.2017.10.027 (2018).
CAS  Article  Google Scholar 

14.
Bahrndorff, S. et al. Diversity and metabolic potential of the microbiota associated with a soil arthropod. Sci. Rep. 8(1), 1–8. https://doi.org/10.1038/s41598-018-20967-0 (2018).
CAS  Article  Google Scholar 

15.
Ding, J. et al. Effects of long-term fertilization on the associated microbiota of soil collembolan. Soil Biol. Biochem. 130, 141–149. https://doi.org/10.1016/j.soilbio.2018.12.015 (2019).
CAS  Article  Google Scholar 

16.
Anslan, S., Bahram, M. & Tedersoo, L. Temporal changes in fungal communities associated with guts and appendages of Collembola as based on culturing and high-throughput sequencing. Soil Biol. Biochem. 96, 152–159. https://doi.org/10.1016/j.soilbio.2016.02.006 (2016).
CAS  Article  Google Scholar 

17.
Terauds, A. et al. Conservation biogeography of the Antarctic. Divers Distrib. 18(7), 726–741. https://doi.org/10.1111/j.1472-4642.2012.00925.x (2012).
Article  Google Scholar 

18.
Terauds, A. & Lee, J. R. Antarctic biogeography revisited: Updating the Antarctic Conservation Biogeographic Regions. Divers Distrib. 22(8), 836–840. https://doi.org/10.1111/ddi.12453 (2016).
Article  Google Scholar 

19.
Greenslade, P. An Antarctic biogeographical anomaly resolved: The true identity of a widespread species of Collembola. Polar Biol. 41(5), 969–981. https://doi.org/10.1007/s00300-018-2261-1 (2018).
Article  Google Scholar 

20.
Carapelli, A. et al. Evidence for cryptic diversity in the “pan-Antarctic” springtail Friesea antarctica and the description of two new species. Insects 11, 141. https://doi.org/10.3390/insects11030141 (2020).
Article  PubMed Central  Google Scholar 

21.
Carapelli, A., Convey, P., Frati, F., Spinsanti, G. & Fanciulli, P. P. Population genetics of three sympatric springtail species (Hexapoda: Collembola) from the South Shetland Islands: Evidence for a common biogeographic pattern. Biol. J. Linn. Soc. 120, 788–803. https://doi.org/10.1093/biolinnean/blw004 (2017).
Article  Google Scholar 

22.
Collins, G. E., Hogg, I. D., Convey, P., Barnes, A. D. & McDonald, I. R. Spatial and temporal scales matter when assessing the species and genetic diversity of springtails (Collembola) in Antarctica. Front. Ecol. Evol. 7, 76. https://doi.org/10.3389/fevo.2019.00076 (2019).
Article  Google Scholar 

23.
Collins, G. E. et al. Genetic diversity of soil invertebrates corroborates timing estimates for past collapses of the West Antarctic Ice Sheet. PNAS 117, 22293–22302. https://doi.org/10.1073/pnas.2007925117 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

24.
Holmes, C. J. et al. The Antarctic mite, Alaskozetes antarcticus, shares bacterial microbiome community membership but not abundance between adults and tritonymphs. Polar Biol. 42, 2075–2085. https://doi.org/10.1007/s00300-019-02582-5 (2019).
Article  Google Scholar 

25.
Vecchi, M., Newton, I. L. G., Cesari, M., Rebecchi, L. & Guidetti, R. The microbial community of tardigrades: Environmental influence and species specificity of microbiome structure and composition. Microb. Ecol. 76(2), 467–481. https://doi.org/10.1007/s00248-017-1134-4 (2018).
CAS  Article  PubMed  Google Scholar 

26.
Delgado-Baquerizo, M. et al. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere. Ecology 99(3), 583–596. https://doi.org/10.1002/ecy.2137 (2018).
Article  PubMed  Google Scholar 

27.
Chu, H. et al. Soil bacterial diversity in the Arctic is not fundamentally different from that found in other biomes. Environ. Microbiol. 12(11), 2998–3006. https://doi.org/10.1111/j.1462-2920.2010.02277.x (2010).
CAS  Article  PubMed  Google Scholar 

28.
Siciliano, S. D. et al. Soil fertility is associated with fungal and bacterial richness, whereas pH is associated with community composition in polar soil microbial communities. Soil Biol. Biochem. 78, 10–20. https://doi.org/10.1016/j.soilbio.2014.07.005 (2014).
CAS  Article  Google Scholar 

29.
Zouache, K. et al. Composition of bacterial communities associated with natural and laboratory populations of Asobara tabida infected with Wolbachia. Appl. Environ. Microb. 75, 3755–3764. https://doi.org/10.1128/aem.02964-08 (2009).
CAS  Article  Google Scholar 

30.
Potapov, A. A., Semenina, E. E., Korotkevich, A. Y., Kuznetsova, N. A. & Tiunov, A. V. Connecting taxonomy and ecology: Trophic niches of collembolans as related to taxonomic identity and life forms. Soil Biol. Biochem. 101, 20–31. https://doi.org/10.1016/j.soilbio.2016.07.002 (2016).
CAS  Article  Google Scholar 

31.
De Wever, A. et al. Hidden levels of phylodiversity in Antarctic green algae: Further evidence for the existence of glacial refugia. Proc. R. Soc. B 276, 3591–3599. https://doi.org/10.1098/rspb.2009.0994 (2009).
Article  PubMed  Google Scholar 

32.
Vyverman, W. et al. Evidence for widespread endemism among Antarctic micro-organisms. Polar Sci. 4(2), 103–113. https://doi.org/10.1016/j.polar.2010.03.006 (2010).
ADS  Article  Google Scholar 

33.
Finlay, B. J. & Clarke, K. J. Ubiquitous dispersal of microbial species. Nature 400, 828–828. https://doi.org/10.1038/23616 (1999).
ADS  CAS  Article  Google Scholar 

34.
Chown, S. L. & Convey, P. Structure and temporal variability across life’s hierarchies in the terrestrial Antarctic. Philos. Trans. R. Soc. B 362, 2307–23331. https://doi.org/10.1098/rstb.2006.1949 (2007).
Article  Google Scholar 

35.
Convey, P., Biersma, E. M., Casanova-Katny, A. & Maturana, C. S. Refuges of Antarctic diversity. Chapter 10. In Past Antarctica (eds Oliva, M. & Ruiz-Fernández, J.) 181–200 (Academic Press, Burlington, 2020). https://doi.org/10.1016/B978-0-12-817925-3.00010-0.
Google Scholar 

36.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583. https://doi.org/10.1038/nmeth.3869 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584. https://doi.org/10.7717/peerj.2584 (2016).
Article  PubMed  PubMed Central  Google Scholar 

40.
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with qiime 2’s q2-feature-classifier plugin. Microbiome 6(1), 90. https://doi.org/10.1186/s40168-018-0470-z (2018).
MathSciNet  Article  PubMed  PubMed Central  Google Scholar 

41.
Price, M. N., Dehal, P. S. & Arkin, A. P. Fasttree 2-approximately maximum-likelihood trees for large alignments. PLoS One 5(3), e9490. https://doi.org/10.1371/journal.pone.0009490 (2010).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

42.
Lahti, L. & Shetty, S. Microbiome R package. http://microbiome.github.io (2012–2019).

43.
Ssekagiri, A., Sloan, W. T. & Ijaz, U. Z. microbiomeSeq: An R package for analysis of microbial communities in an environmental context. ISCB Africa ASBCB Conference. http://www.github.com/umerijaz/microbiomeSeq (2017).

44.
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 (2014).
ADS  CAS  Article  Google Scholar 

45.
Oksanen, J., et al. Vegan: Community ecology package. R package version 2.5-6. https://github.com/vegandevs/vegan (2019).

46.
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43(7), e47. https://doi.org/10.1093/nar/gkv007 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

47.
Chen, H. & Boutros, P. C. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinf. 12, 35. https://doi.org/10.1186/1471-2105-12-35 (2011).
Article  Google Scholar 

48.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer, New York. https://ggplot2.tidyverse.org (2016).

49.
Warnes, G. R., et al. gplots: Various R Programming Tools for Plotting Data. R package version 3.0.1.1. https://CRAN.R-project.org/package=gplots (2019). More

1.
Arrigo, K. R. & Dijken, G. L. Secular trends in Arctic Ocean net primary production. J. Geophys. Res. Oceans. 116, C09011 (2011).
ADS  Google Scholar 
2.
Thomas, D. N. Sea Ice Ch 4 (Wiley Blackwell, Oxford, 2017).
Google Scholar 

3.
Arrigo, K. R. et al. Massive phytoplankton blooms under Arctic sea ice. Science 336, 1408–1408 (2012).
ADS  CAS  Article  Google Scholar 

4.
Assmy, P. et al. Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice. Sci. Rep. 7, 40850 (2016).
ADS  Article  Google Scholar 

5.
Horvat, C. et al. The frequency and extent of sub-ice phytoplankton bloom in the Arctic Ocean. Sci. Adv. 3, e1601191 (2017).
ADS  Article  Google Scholar 

6.
Ardyna, M. et al. Environmental drivers of under-ice phytoplankton bloom dynamics in the Arctic Ocean. Elem. Sci. Anth. 8, 30 (2020).
Article  Google Scholar 

7.
Ardyna, M. et al. Under-ice phytoplankton blooms: Shedding light on the “invisible” part of Arctic primary production. Front. Mar. Sci. 7, 608032 (2020).
Article  Google Scholar 

8.
Rysgaard, S. & Glud, R. N. Carbon cycling in Arctic marine ecosystems: Case study Young Sound (ed. Rysgaard, S. & Glud, R. N.) 62–94 (Meddelelser om Grønland, Bioscience Vol 58, Copenhagen, Denmark, the Commission for Scientific Research in Greenland, 2007).

9.
Meire, L. et al. Marine-terminating glaciers sustain high productivity in Greenland fjords. Glob. Chang. Biol. 23, 5344–5357 (2017).
ADS  Article  Google Scholar 

10.
Randelhoff, A. et al. Pan-Arctic Ocean primary production constrained by turbulent nitrate fluxes. Front. Mar. Sci. 7, 150 (2020).
Article  Google Scholar 

11.
Holding, J. M. et al. Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off. Biogeosciences 16, 3777–3792 (2019).
ADS  CAS  Article  Google Scholar 

12.
Juul-Pedersen, T. et al. Seasonal and interannual phytoplankton production in a sub-Arctic tidewater outlet glacier fjord, SW Greenland. Mar. Ecol. Prog. Ser. 524, 27–38 (2015).
ADS  Article  Google Scholar 

13.
Sejr, M. K. et al. Evidence of local and regional freshening of Northeast Greenland coastal waters. Sci. Rep. 7, 13183 (2017).
ADS  Article  Google Scholar 

14.
Boone, W. et al. Circulation and fjord-shelf exchange during the ice-covered period in Young Sound-Tyrolerfjord, Northeast Greenland (74°N). Estuar. Coast. Shelf Sci. 194, 205–216 (2017).
ADS  Article  Google Scholar 

15.
Haine, T. W. N. et al. Arctic freshwater export: Status, mechanisms, and prospects. Glob. Planet Change 125, 13–35 (2015).
ADS  Article  Google Scholar 

16.
Carmack, E. C. et al. Freshwater and its role in the Arctic Marine System: Sources, disposition, storage export, and physical and biogeochemical consequences in the Arctic and global ocean. J. Geophys. Res. Biogeosci. 121, 675–717 (2015).
Article  Google Scholar 

17.
Lund-Hansen, L. C. et al. Will low primary production rates in the Amundsen Basin (Arctic Ocean) remain low in a future ice-free setting, and what governs this production?. J. Mar. Syst. 205, 103287 (2020).
Article  Google Scholar 

18.
Dahl, E., Bagøien, E., Edvardsen, B. & Stenseth, N. C. The dynamics of Chrysochromulina species in the Skagerrak in relation to environmental conditions. J. Sea. Res. 54, 15–24 (2005).
ADS  Article  Google Scholar 

19.
Hansen, P. J., Nielsen, T. G. & Kaas, H. Distribution and growth of protists and mesozooplankton during a bloom of Chrysochromulina spp. (Prymnesiophyceae, Prymnesiales). Phycologia 34, 409–416 (1995).
Article  Google Scholar 

20.
Nielsen, T. G., Kiørboe, T. & Bjørnsen, P. K. Effects of a Chrysochromulina polylepis subsurface bloom on the planktonic community. Mar. Ecol. Prog. Ser. 62, 21–35 (1990).
ADS  Article  Google Scholar 

21.
Hällfors, G. & Niemi, Å. A Chrysochromulina (Haptophyceae) bloom under the ice in the Tvärminne Archipelago, southern coast of Finland. Acta Soc. Fauna Flora Fenn. 50, 89–104 (1974).
Google Scholar 

22.
Manton, I. Chrysochromulina tenuispine sp. nov. from arctic Canada. Br. Phycol. J. 13, 227–234 (1978).
Article  Google Scholar 

23.
Green, J. C. & Leadbeater, B. S. C. The Haptophyte Algae ch. 13 (Systematics Association, London, 1994).
Google Scholar 

24.
Hansen, P. J. & Hjorth, M. Growth and grazing responses of Chrysochromulina ericina (Prymnesiophyceae): The role of irradiance, prey concentration and pH. Mar. Biol. 141, 975–983 (2002).
CAS  Article  Google Scholar 

25.
Anderson, R., Charvet, S. & Hansen, P. J. Mixotrophy in chlorophytes and haptophytes—Effect of irradiance, macronutrient micronutrient and vitamin limitation. Front. Microbiol. 9, 1704 (2018).
Article  Google Scholar 

26.
Anderson, R. & Hansen, P. J. Meteorological conditions induce strong shifts in mixotrophic and heterotrophic flagellate bacterivory over small spatio-temporal scales. Limnol. Oceanogr. 9999, 1–11 (2019).
Google Scholar 

27.
McKie-Krisberg, Z. M., Gast, R. J. & Sanders, R. W. Physiological responses of three species of Antarctic mixotrophic phytoflagellates to changes in light and dissolved nutrients. Microbiol. Ecol 70, 21–29 (2015).
CAS  Article  Google Scholar 

28.
McKie-Krisberg, Z. M., Sanders, R. W. & Gast, R. J. Evaluation of mixotrophy-associated gene expression in two species of polar marine algae. Front. Mar. Sci. 5, 273 (2018).
Article  Google Scholar 

29.
Rysgaard, S., Nielsen, T. G. & Hansen, B. W. Seasonal variation in nutrients, pelagic primary production and grazing in a high-Arctic coastal marine ecosystem, Young Sound, Northeast Greenland. Mar. Ecol. Prog. Ser. 179, 13–25 (1999).
ADS  CAS  Article  Google Scholar 

30.
Bendtsen, J., Mortensen, J. & Rysgaard, S. Seasonal surface layer dynamics and sensitivity to runoff in a high Arctic fjord (Young Sound/Tyrolerfjord, 74°N). J. Geophys. Res. Oceans. 119, 1–18 (2014).
Article  Google Scholar 

31.
Krawczyk, D. W. et al. Spatial and temporal distribution of planktonic protists in the East Greenland fjord and offshore waters. Mar. Ecol. Prog. Ser. 538, 99–116 (2015).
ADS  CAS  Article  Google Scholar 

32.
Søgaard, D. H., Deming, J. W., Meire, L. & Rysgaard, S. Effects of microbial processes and CaCO3 dynamics on inorganic carbon cycling in snow-covered Arctic winter sea ice. Mar. Ecol. Prog. Ser. 611, 31–44 (2019).
ADS  Article  Google Scholar 

33.
Rysgaard, S. et al. Ikaite crystal distribution in winter sea ice and implications for CO2 system dynamics. Cryosphere 7, 707–718 (2013).
ADS  Article  Google Scholar 

34.
Søgaard, D. H. et al. Autotrophic and heterotrophic activity in Arctic first-year sea ice: Seasonal study from Malene Bight, SW Greenland. Mar. Ecol. Prog. Ser. 419, 31–45 (2010).
ADS  Article  Google Scholar 

35.
Grasshoff, K., Kremling, K. & Ehrhardt, M. Methods of Seawater Analysis (WILEY-VCH Verlag GmbH, Weinheim, 1999).
Google Scholar 

36.
Steemann-Nielsen, E. The use of radio-active carbon (C14) for measuring organic production in the sea. ICES J. Mar. Sci. 18, 117–140 (1952).
Article  Google Scholar 

37.
Søgaard, D. H. et al. The relative contributions of biological and abiotic processes to carbon dynamics in subarctic sea ice. Polar Biol. 36, 1761–1777 (2013).
Article  Google Scholar 

38.
Platt, T., Gallegos, C. L. & Harrison, W. G. Photoinhibition of photosynthesis in natural assemblages of marine phytoplankton. J. Mar. Res. 38, 687–701 (1980).
Google Scholar 

39.
Jespersen, A. M. & Christoffersen, K. Measurements of chlorophyll-a from phytoplankton using ethanol as extraction solvent. Arch. Hydrobiol. 109, 445–454 (1987).
CAS  Google Scholar 

40.
Ralph, P. J. & Gademann, R. Rapid light curves: A powerful tool to assess photosynthetic activity. Aquat. Bot. 82, 222–237 (2005).
CAS  Article  Google Scholar 

41.
Jassby, A. D. & Platt, T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547 (1976).
ADS  CAS  Article  Google Scholar  More