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The data for this study were collected in the austral summer of 2016, on board the BAS research ship RRS James Clark Ross38. Biological abundance data was taken from photographic images from Brasier et al.10 (see methodology within). The images were taken with a Shallow Underwater Camera System (SUCS), in transects of 10 photographs, 10 m apart. Replicant transects were separated by 100 m, at water depths of 500 m, 750 m and 1000 m10. Each photograph in the analysis was 0.51 m2 in area. In the analyses, ‘VME unknown’ was biological material unidentifiable to phylum, but distinguishable as VME taxa, e.g., branched or budding fragments which could be bryozoan or cnidarian species. The percentage cover of encrusting species was recorded by Brasier et al.10, as the number of individual colonies was not always possible to distinguish between colonising taxa10. Physical variables, such as substrate texture, were also observed from the SUCS images10.
Taxonomic resolution of the data originally collected was dependent upon the ability to distinguish the organism in the images10. For our analysis, the taxonomic hierarchy analysed related to the abundance level of that group, as lower abundance, finer scale taxonomic groupings would be zero-inflated so not able to be used within our methodology (cf. Milns et al.39). Taxonomic groupings included were Annelida, Arthropoda, Cnidaria, Echinodermata, Mollusca, Bryozoa, Actinopteri, Porifera and Echinodermata. The echinoderm taxa Ophiuroides and Euryalids also occurred in large enough numbers to be analysed as separate groups. The taxa in each of these groupings can be seen in the Brasier et al.10
The raw data (taxon ID from photographs) was highly zero-inflated (84.9% of entries), so data grouping was performed to capture the fine-scale (individual photographs at ∼cm scale) and large-scale (replicate transects at ∼km scale) ecology of the ecosystems. For the fine-scale network there were 527 samples (abundances from individual photographs) and twelve nodes. Four were physical nodes (Depth, Region, Substrate and Substrate Texture, Table 1), two were functions of the specimens present (percent encrusting), five were taxa classified to Phylum level (Arthopoda, Bryozoa, Porifera, Cnidaria and Echinodermata) and there was one bin-group (VME Unknown) Supplementary Table 2.
Table 1 Table of network properties for the two networks found. Chains are defined as series of dependencies containing more than two nodes. Link density is mean number of connections per node, connectance is the number of connections/number of nodes*(number of nodes-1).
Full size table

For the large-scale network, the data from all the photographs in each event (up to three replicate transects) was combined to form 21 samples with 16 nodes. For factor nodes (Region, Texture and Substrate) the modal variable was taken, taxa nodes were summed, and the mean was used for Percent Encrusting and Depth. Texture was excluded from analyses due to the high zero-count. Two nodes were functions of the specimens present (percent encrusting), ten taxa were analysed (Annelid, Arthropod, Cnidarian, Echinoderm, Mollusc, Bryozoan, Actinopteri, Ophiuroidea, Euryalida, Porifera) and one bin-group (VME Unknown) was used. The combination of the two networks enabled the investigation of both physical and biotic interactions across multiple spatial scales.
We chose these relatively coarse taxonomic groupings in order to maximise the statistical power of our analyses. This statistical power enabled us to reconstruct a complex network of dependencies, thus revealing the subtle relationships between different taxa. The coarse grouping is a limitation of this method, because the homogenisation of some diverse groups, such as echinoderms, groups together organisms with variable life modes and traits (and therefore differing relationships with other taxa). However, the coarse level of identification was necessary with this volume of data (over 500 photographs), and the patchiness and rarity of many taxa. Future studies involving a higher density of photographs will allow for genus or species level separation, and/or an analysis based upon functional traits classification.
Analysis
One approach to understanding how ecosystems function is to consider the ecosystem as a network (cf. Miln et al.38). Different taxa or groups of taxa are considered ‘nodes’ and their interactions are described as ‘connections’ (more usually known as ‘edges’ in Baysian networks), which link interacting taxa together. A node that the connections feed into are called ‘parents’. Work has centred on gene regulatory networks17, neural information flow networks, and with more recent applications to ecological and palaeoecological networks18,39,40,41. It is important to note that the structure produced by the BNIA reflects the associations caused by co-localisations (two taxa which both have a high abundance), not by a specific interaction, for example predation. By using BNIA, direct dependencies between taxa can be detected, minimising auto-correlation between two nodes. For example, if A depends on B which depends on C, there could be a correlation between A and C. However, this correlation would not represent an interaction or association between A and C, merely the two correlations between A and B and B and C. BNIA enables only the realised dependencies to be found, ensuring only actual interactions and associations are found.
Bayesian network inference was performed in Banjo16, The BNIA software used was Banjo v2.0.0, a publicly available Java based algorithm39,41. Banjo uses uniform priors, so boundaries for the different discretized groups were chosen to ensure even splitting between the groups. Discretized data were input into Banjo, which then generated a random network based on the input variables. A greedy search was then performed to find a more likely network than the random one generated. This search was repeated 10 million times for each set of input data and the most probable network was then output. The maximum number of parents was set to 3 to limit artefacts19.
The BNIA used require discrete data, which ensures data noise is masked, and only the relative densities of each taxon are important39. We split the data into three intervals; zero counts, low counts and high counts. Low counts consisted of counts below the median for the taxa group and high counts were counts over the median. Medians were used over means because for some groups the high counts were very high, and would result in a very small number of samples grouped in the highest interval (cf. Milns et al.39). A large amount of bins maintains the amount of information present in the dataset, while fewer bins provide more statistical power, and greater noise masking. Yu19 has shown that for ecological data sets three different bins is a good balance. Zero was treated as a separate entity because the presence of one individual is very different to a zero presence, in contrast to zero gene expression, for example. Data preparation for Banjo (grouping and discretization) was carried out in R42, as was the statistical analysis of the data. Further analysis of banjo outputs, when required, used the functional language Haskell43. The scripts are available on Github (github.com/egmitchell/bootstrap).
To minimise outliers bias, 100 samples were bootstrapped at 95% level by randomly selecting 95% of the total number of samples for each analysis20,43, and then finding the subsample network using Banjo. For each connection calculated, the probability of occurrence was calculated, and the resultant distributions analysed to find the number of Gaussian sub-distributions using normal mixture models44. This probability distribution was bimodal for each data set, which suggests that there were two distributions of connections, those with low probability of occurrence, and those with highly probable connections. The final network for each area was taken to be those connections which were highly probable. The threshold for being labelled ‘highly probable’ depended on the network (as determined by the normal mixture modelling analyses): 53% for fine-scale network and 51% for the large-scale network. The magnitude of the occurrence rate is indicated in the network by the width of the line depicting the connection.
The direction of the connection between nodes indicates which node (taxon) has a dependency on the other node (taxon). For each connection, the directionality was taken to be the direction that  occurred in the majority of bootstrapped networks. Where there was no majority (directional connections have a probability between 0.4 and 0.6), the connection was said to have bi-directionality, or indicated a mutual dependency.
The IS can be used to gauge the type and strength of the interaction between two nodes. If the IS = 1, this corresponds with a positive correlation. When node 1 is high, node 2 will be high. An IS of −1 corresponds to a negative correlation: High node 1 corresponds to a low node 2. An IS = 0 does not mean there is no correlation between the two nodes. IS = 0 means that the interaction is non-monotonic. Sometimes node 1 will be positively correlated with node 2, sometimes negatively. The mean IS for each connection was calculated for each sample area.
Contingency test filtering
In order to avoid Type I errors introduced by high zero counts, which is common in ecological data sets, we excluded rare taxa, which were found in under 33% of the grid cells. Note, that this method of exclusion could potentially mean that a taxon with high abundance in a very limited area is excluded from analyses. To further guard against Type I errors, we also used a method of contingency test filtering that removed from consideration a connection between two variables whose joint distribution showed no evidence of deviation from the distribution expected from their combined marginal distributions (chi-squared tests, p  > 0.25)39. This threshold was used to ensure no chance of removing truly dependent dependencies, so that only artefacts such as those found between high zero counts were removed from consideration. These links were provided to the BNIA to exclude from consideration.
Inference
Inferring how one node (taxa or physical variable) is likely to change given another node being in a given state is done by calculating the probability of node A being in a given state given node B is in a given state. All nodes that have dependencies between A and B and so are included in the calculation:

$${mathrm{P}}left( {{mathrm{A|B}}} right) = mathop {sum}limits_{n = 1}^{n = N – 1} {frac{{mathop {prod }nolimits_{s = 1}^{s = S} {mathrm{P}}({mathrm{B}}_n|A_{n + 1})}}{{mathop {sum}nolimits_{m = 1}^N {P(B|A_m)P(A_m)} }}}$$

The N are the total number of nodes in the chain and n and m are the indices for the chain of nodes of length N connecting the first and end nodes. S are the number of discrete states for each node, which are indexed s. In order to infer the likely change of one taxon’s (A) abundance on another’s abundance (B), the probabilities of all taxa that occur in the network between A and B have to be taken into account. For example, for the probability that B is in a Zero abundance state given A is in a high abundance state, and given that B are connected through a dependency of B on C and C on A, the probability is calculated as follows: the probabilities of C existing in all states is calculated given a High abundance state for A, and then the probabilities for B existing in a Zero state is calculated for each state of C and then summed together to give the probability of B in Zero given A in high. To work out the inferred change in B given a change in A from High to Zero would involve taking the difference between the probabilities for each state of B given A is high with each state of B given A is low. For the code used to generate these inferred probabilities, please see ref. 45.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Our results show that a species’ distribution—measured as change in mean center of biomass and change in northern and southern range extent—depends on the species relationship with oceanographic versus static environmental variables. In the fall, species whose distributions are most influenced by bottom salinity have shifted mean centroids of biomass significantly more than species whose distributions are most influenced by depth or substrate (nonparametric pairwise Wilcoxon test p = 0.00007 and p = 0.0013, Fig. 1a). Species whose distributions are most influenced by bottom temperature have shifted mean centroids of biomass significantly more than species whose distributions are most influenced by depth (nonparametric pairwise Wilcoxon test p = 0.0083, Fig. 1a) and shifted the northern and southern extent of their biomass weighted ranges significantly more than species whose distributions are most influenced by depth and substrate (nonparametric pairwise Wilcoxon test p = 0.00018 and p = 0.014, Fig. 1c and nonparametric pairwise Wilcoxon test p = 0.0038 and p = 0.024, Fig. 1d). Percentage change in biomass weighted range size did not depend on the strongest predictor variable for a species distribution (Fig. 1b). These patterns were not as strong in the spring (see Supplemental Fig. 1).
Fig. 1: Historic distribution shifts versus the most important predictor variable for a species distribution in the fall.

Shifts in mean centroid of biomass from first 5 years to last 5 years in kilometers (a), percentage change in biomass range size from first 5 years to last 5 years in meters squared (b). Change in northern (c) and southern (d) extent of the biomass weighted range from first 5 years to last 5 years (n = 93). Brackets and numbers represent p-value. Whiskers represent 1.5* interquartile range. Box represents interquartile range as distance between first and third quartiles. Line represents median, red point represents mean, and black points represent outliers (outside of 1.5*IQR).

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Our results show that in the spring and fall, pelagic species’ distributions are primarily influenced by ocean temperature and depth, while demersal species’ distributions are predominately influenced by ocean temperature and substrate, and benthic species’ distributions are influenced by substrate (Fig. 2).
Fig. 2: Strongest predictor variable for each species group in the fall.

Percentage of each species group that had substrate, depth, bottom temperature, and salinity as the strongest predictor variable in terms of deviance explained for the entire time series (n = 93). Results were similar across seasons (see Supplemental Fig. 2).

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Pelagic species have shifted mean center of biomass significantly more over the historic time period compared with benthic species in the fall and significantly more than demersal species in the spring (nonparametric pairwise Wilcoxon test p = 0.039 and p = 0.045, Figs. 3a and 4a). Similarly, pelagic species have expanded their range size significantly more than demersal species and have shifted the northern extent of their range boundary significantly more than demersal species in the spring (nonparametric pairwise Wilcoxon test p = 0.024 and p = 0.028, Fig. 4c, d).
Fig. 3: Historic distribution shifts for three species types in the fall.

Fall shifts in southern mean centroid (a), percentage change in range size (b), shifts in northern range boundary (c), and southern range boundary (d) from first 5 years and last 5 years in latitudinal degrees for each species group (n = 93). Brackets and numbers represent p-value. Whiskers represent 1.5* interquartile range. Box represents interquartile range as distance between first and third quartiles. Line represents median, red point represents mean, and black points represent outliers (outside of 1.5*IQR).

Full size image

Fig. 4: Historic distribution shifts for three species types in the spring.

Spring shifts in southern mean centroid (a), percentage change in range size (b), shifts in northern range boundary (c), and southern range boundary (d) from first 5 years and last 5 years in latitudinal degrees for each species group (n = 91). Brackets and numbers represent p-value. Whiskers represent 1.5* interquartile range. Box represents interquartile range as distance between first and third quartiles. Line represents median, red point represents mean, and black points represent outliers (outside of 1.5*IQR).

Full size image

These results indicate that benthic species, more influenced by substrate than pelagic species, have retained their historical distributions in response to climate change, while pelagic species have shifted drastically. Exemplars of benthic fish that have retained their distributions include American plaice (Hippoglossoides platessoides), witch flounder (Glyptocephalus cynoglossus), and winter flounder (Pseudopleuronectes americanus) (Fig. 5a). These species distributions are strongly influenced by benthic substrate (Supplemental Data 1 and 2). Exemplars of pelagic fish that have shifted their distributions include rough scad (Trachurus lathami), Atlantic menhaden (Brevoortia tyrannus), and round herring (Etrumeus teres) (Fig. 5b). These species distributions are strongly influenced by bottom temperature and salinity (Supplemental Data 1 and 2).
Fig. 5: Changes in the biomass weighted mean centroid of species distributions in the Fall.

Biomass weighted mean centroids were calculated for two time periods: time period 1 (1986–1990) and time period 2 (2014–2018). Benthic species associated with bottom substrate retain their historical distributions (a) whereas pelagic species have shifted distributions (b). Species were selected to show extreme shifts and extreme retentions of distributions, and all shifts can be found in Supplemental Data 1 and 2.

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By linking the historic evidence of species distribution shifts with their relationship with bottom temperature, bottom salinity, and benthic substrate, we have identified a broad generalization on how species with specific life history traits may be influenced by future climate change. Recent work suggests that pelagic species may shift farther under climate change compared with benthic invertebrates14, and recent studies have included sediment type as a constraining variable in projections of future species distributions15. In conjunction with this work, our research highlights that these strong shifts have already occurred historically, and they are influenced by the strength of a pelagic fish species’ relationship with bottom temperature or salinity, compared with benthic species, which are more influenced by substrate. Given the importance of bottom substrate on benthic species distributions, we may see shifts in population dynamics, such as abundance and productivity as temperatures warm instead of geographic shifts in distributions. For, increased ocean warming that may go beyond their preferred thermal envelope is expected in the Northeast LME and Mid Atlantic9. If affinity for substrate type keeps benthic species in regions that become too warm, these species may find themselves in suboptimal conditions and will not be able to relocate as easily as pelagic species.
Moreover, we examine demersal species that are influenced by both bottom temperature and substrate and have shifted moderately compared to benthic or pelagic species. Research in the North Sea suggests that demersal species are shifting to deeper waters, which may suggest an interaction between bottom temperature and depth that requires further research16. Research in the Northeast LME has linked the shrinking spatial distribution of cusk, a demersal species, to a combination of ocean warming and the ensuing fragmentation of suitable bottom habitat17, suggesting another interaction requiring future examination. As an intermediate case, these species may be the most unpredictable under climate change, and thus fisheries management will have to consider the varying nature of these species’ distribution shifts.
These results provide historical evidence of pelagic species shifting distributions while benthic species remain more associated with their preferred substrate, which is most likely a result of the functional differences between these types of species. For example, the recruitment success of Atlantic menhaden (Brevoortia tyrannus), a pelagic schooling species we identified to shift historical distributions, is strongly related to ocean temperatures and larger climate dynamics such as the Atlantic Multidecadal Oscillation which influences temperatures and salinity18. The suitable habitat and migration timing of mackerel (Scomber scombrus), a pelagic schooling species, has been linked to changes in ocean temperatures and multidecadal variability19, relying on temperature cues for their seasonal migrations. Research in the North Sea and Baltic Sea suggest that the northward shift of anchovies (Engraulis encrasicolus) and sardines (Sardina pilchardus), two pelagic species, is strongly linked to temperature20. Benthic species, on the other hand, rely on structured biotic habitats, such as marshes, coral reefs, and submerged aquatic vegetation as well as abiotic sediment for their survival21. The importance of abiotic sediment stems from the productivity of these habitats, as they usually contain high levels of detritus, microbes, and microinvertebrates22.
It is important to identify the limitations of the approaches used in this study. The original species-CPUE data were collected from North Carolina to the Gulf of Maine, meaning we sampled a realized niche of the studied species. In comparison, the fundamental niche of the species may extend beyond our study area, in both the southern and northern directions. This study examined the role of bottom temperature, salinity, depth and substrate on species distributions, but was unable to examine the potential effects of fishing pressure, interspecific interactions, demographic changes, population sizes, or larval dispersal on species distributions and abundance23. While research has demonstrated that simple area-weighted center of distributions can be biased, we attempted to account for this by using several metrics of distribution (area occupied, northern, and southern extents of ranges)24.
Despite these limitations, we expect our results will be valuable for fisheries managers as they anticipate the likelihood of species distribution shifts in their management areas. While current work has examined the role of temperature in determining species shifts, our work highlights the importance of examining the differential role of other static ecological variables on determining a species likelihood of shifting distributions under climate change. By uncovering the differential effects of certain habitat constraints on pelagic versus demersal and benthic species, we can begin to understand why certain species have shifted dramatically over the last thirty years, while others have retained their historical distributions. These results highlight the need for stock assessments and future species distribution models that include the functional differences among species as well as the environmental variables that constrain their distributions in order to more appropriately understand the potential impacts of climate change on their future distributions. Only then can we understand how future fisheries will be impacted by the ecological effects of climate change. More

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Cultivation of novel Nitrospinae representatives
For the initial enrichment of marine NOB, nitrite-containing mineral media were inoculated with coastal surface sediment samples taken in Vancouver, Canada, and Elba, Italy. Within 4 weeks of incubation, nitrite oxidation to nitrate was detected in the cultures and this activity continued to be observed after subsequent replenishment of nitrite and transfers of culture aliquots into fresh medium. Usually, the further purification of NOB from accompanying organisms in the enrichment cultures is hindered by the very slow growth and inability of most NOB (including all cultured Nitrospinae) to grow on solid media. To expedite the purification of Nitrospinae strains from our initial enrichments, which was previously a laborious and lengthy process [24], we developed a method for the physical separation, activity-based identification of NOB, and subcultivation in 96-well microtiter plates. This method uses random, non-fluorescent, single-cell sorting using a fluorescence activated single cell sorting (FACS) instrument paired with a nitrite consumption activity screen (Fig. S1a). Thus, it differs from a previously reported FACS isolation approach for Nitrospira NOB from activated sludge, where the NOB were targeted based on their known cell cluster size and shape, which had been determined by Nitrospira-specific rRNA-targeted FISH analysis before FACS was performed [43]. Our method does not rely on prior knowledge of the identity and morphology of the NOB [43] but is solely based on the detection of nitrite oxidation after sorting. Still, it might allow for a flexible selection of the sorted cell morphologies by adjusting the gating parameters. While the previous method facilitated the isolation of already known NOB, the approach used in our study was designed for the discovery of novel nitrite oxidizers that grow under the given conditions. It may also be suitable for the isolation of other microorganisms that can be efficiently sorted (i.e., grow in suspension or that can be suspended by sonication or other methods) and that perform a specific metabolism of interest, which is detectable by a colorimetric, fluorimetric, or other high-throughput assay. Examples include previously performed, high-throughput enzyme discoveries and the isolation of microalgae [44,45,46]. After sorting cells from the initial Vancouver and Elba enrichment cultures into one 96-well plate each, several wells showed nitrite-oxidizing activity within 3–4 weeks. Cells from three active wells from the Elba enrichment and 4 for the Vancouver enrichment ( >4 were active) were progressively transferred into larger culture volumes. This procedure led to the separate enrichment of two different Nitrospinae, one from each of the initial enrichments, that were identified by 16 S rRNA gene sequencing (Fig. S1b): strain VA (Vancouver) and strain EB (Elba). Interestingly, despite attempted single-cell sorting and various dilution to extinction attempts, an axenic culture could not be established for either of the two obtained Nitrospinae strains. Rather, these cultures represent binary co-cultures each containing one Nitrospinae strain, one alphaproteobacterial strain, and no other detectable microorganisms. Overall, using this single-cell sorting and screening method, we were able to obtain the two binary co-cultures from the environmental samples in ~10 months.
Co-enrichment with Alphaproteobacteria
Pure cultures of the two co-enriched heterotrophic strains were obtained on Marine Broth Agar. Subsequent 16S rRNA gene analysis of these isolates showed that the two Nitrospinae strains had been co-cultured with members of two distinct genera within the Alphaproteobacteria. Strain VA was co-cultured with a bacterium most closely related to a Stappia stellulata strain (NR_113809.1, 99.58% 16 S rRNA gene sequence identity), whereas the EB strain culture contained a Maritimibacter alkaliphilus strain (NR_044015.1, 100% 16 S rRNA gene sequence identity). Both these species have previously been isolated from marine environments and have been described as alkaliphilic chemoorganoheterotrophs that can use a wide variety of simple and complex organic substrates [47, 48]. In our cultures, which were only provided with nitrite for growth, the alphaproteobacterial strains may have lived off simple organic compounds that were excreted by the autotrophic Nitrospinae. Since the NOB could not be grown separately from the heterotrophs, it is tempting to speculate that the Nitrospinae strains also benefitted, for example, from reactive oxygen species (ROS) protection by the heterotrophs. Superoxide dismutase and catalase genes are present in the genomes of both co-cultured alphaproteobacteria, and other isolates of both species are catalase positive [47, 48]. Similar interactions have already been observed in marine autotroph-heterotroph co-cultures, including other nitrifiers [49,50,51].
Phylogeny of the novel Nitrospinae
Closed genomes were reconstructed for both Nitrospinae strains by co-assembling Illumina and Nanopore sequencing data. Since the genome of N. gracilis was nearly completely sequenced but not closed [25] (Table S3), the obtained genomes represent the first closed genomes from Nitrospinae. With a length of >3.9 Mbp, Nitrospinae strain EB has a larger genome than the other cultured Nitrospinae strains sequenced to date (Table S3).
Phylogenetic analysis of the 16S rRNA genes (Fig. S1b) showed that the Nitrospinae strains VA and EB are only distantly related to N. gracilis (93% sequence identity with EB and 91% with VA), N. watsonii (92% sequence identity with EB and 90% with VA), and to each other (90% sequence identity). Since public databases contained only few 16 S rRNA gene sequences closely related to strains VA and EB (Fig. S1b), these strains seem to represent a yet underexplored diversity of Nitrospinae. A phylogenetic tree using conserved concatenated marker proteins from all available Nitrospinae genomes that are >80% complete and 70%, and that these genera are distinct from the genus containing strain VA (Fig. 2). In order to make taxonomic inferences beyond the genus level, the GTDB-TK tool [38] was applied to the Nitrospinae genome dataset. GTDB-TK confirmed that strains VA and EB belonged to the Nitrospinaceae family but, in agreement with the AAI analysis, did not assign them to a genus (Tab. S2). While we showed that nitrite oxidation is spread among phylogenetically distant members within the Nitrospinaceae, no nitrite oxidation phenotype has been observed yet for the Nitrospinae members that do not belong to the Nitrospinaceae family.
Fig. 2: Average amino acid identity (AAI) analysis of the Nitrospinae.

Pairwise AAI values were calculated for the same set of Nitrospinae genomes that was used to reconstruct the phylogenetic tree in Fig. 1. The same tree is used here to annotate the heatmap. The two newly cultured strains are highlighted in red. Clades of the Nitrospinae are indicated as proposed elsewhere [6]. The boxes indicate the genus boundary at 60% identity.

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Taken together, the phylogenetic, ANI, AAI, and GTDB-TK analyses revealed a high genus-level diversity within the Nitrospinae, the vast majority remaining uncultured and poorly characterized. Among all cultured Nitrospinae members, strain VA is most closely related with the uncultured but environmentally abundant clades 1 and 2 [6, 7, 20, 21].
Cell morphology
The morphologies of the new Nitrospinae strains were visualized using SEM, and by 16S rRNA-targeted CARD-FISH using a Nitrospinae-specific oligonucleotide probe (Ntspn759) [21]. Strain VA cells were helically shaped rods (Fig. 3a, d, g), whereas strain EB appeared to be short, slightly curved rods (Fig. 3b, e, h). Interestingly, neither of the two strains displayed the long, slender, rod-shaped morphology that was mostly observed for the previously isolated Nitrospina species, N. gracilis (Fig. 3c, f, i) [23]. The shape of strain VA (Fig. 3a, d, g) rather resembled Nitrospira species [53]. The short, slightly curved rod morphology of strain EB (Fig. 3b, e, h) resembled the compact cell shape reported for aging cultures of N. watsonii [24] and environmental Nitrospinae [5]. While the helical shape of strain VA could be clearly distinguished from the co-cultured Stappia sp. using Nitrospinae-specific FISH (Fig. 3g) and SEM of the Stappia-like isolate (Fig. S3a), assigning a morphology to strain EB was slightly more difficult due a more similar morphotype of the co-cultured Maritimibacter-like bacterium (Figs. 3e and S3b). According to SEM, the isolated Maritimibacter had a coccoid morphology (Fig. S3b) similar to the slightly smaller coccoid cells that were observed in the active co-culture with strain EB (Fig. 3e). Therefore, we assume that the slightly larger, curved rods in the SEM pictures (Fig. 3b, e) were Nitrospinae strain EB cells. However, the previously described morphological variability suggests that the cell shape of Nitrospinae is influenced by the growth stage and environment [23, 24]. Thus, morphology would be of limited use as the sole criterion to differentiate Nitrospinae strains from each other and from other organisms. We propose the name “Candidatus Nitrohelix vancouverensis” VA for strain VA based on its observed morphology and isolation source, and “Candidatus Nitronauta litoralis” EB for strain EB based on its isolation source.
Fig. 3: Scanning electron microscopy (SEM) and CARD-FISH images of the two newly cultured Nitrospinae strains and N. gracilis.

a, d, g “Ca. Nitrohelix vancouverensis” strain VA. b, e, h “Ca. Nitronauta litoralis” strain EB. c, f, i N. gracilis. The short rod in (d) is a cell of the accompanying heterotroph Stappia sp. in the enrichment. e Shows slightly longer and slightly curved rods (strain EB, arrows) and shorter rods, which are cells of the accompanying heterotroph M. alkaliphilus in the enrichment. The scale bars in (a–c) depict 1µm; all other scale bars depict 2µm. g–i The 16S rRNA-targeted probe Ntspn759 was used to detect Nitrospinae cells (magenta), and total nucleic acids were stained with SYBR Green (cyan).

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Nitrite oxidation: activity and kinetics
Both Nitrospinae strains oxidized nitrite stoichiometrically to nitrate (Fig. S4). Exponential growth of “Ca. N. vancouverensis” correlated with the consumption of nitrite (Fig. S4a), whereas “Ca. N. litoralis” did not enter the exponential growth phase during the incubation period (Fig. S4b). During the experiment, the relative abundance of “Ca. N. vancouverensis” compared to the co-cultured Stappia sp. increased pronouncedly from 6 to 75% (Fig. S4a). The relative abundance of “Ca. N. litoralis” compared to the Maritimibacter sp. could not be reliably determined during the incubation experiment (Fig. S4b), but measurements taken after the MR experiments (see below) showed that the relative abundance of “Ca. N. litoralis” ranged from ~56 to 99%. Thus, the quantitative composition of each co-culture appeared to fluctuate and likely depended strongly on the availability of nitrite as the substrate for the NOB strains.
The kinetics of nitrite oxidation of N. gracilis and the two novel Nitrospinae strains were assessed by measuring the nitrite-dependent oxygen consumption in MR experiments. For all three NOB, nitrite oxidation followed Michaelis–Menten kinetics (Fig. S5). The stoichiometry of NO2− and O2 consumption was always close to 1:0.5 (N. gracilis: mean = 1:0.49; s.d. = 0.02; n = 8; “Ca. N. vancouverensis”: mean = 1:0.51; s.d. = 0.01; n = 4; “Ca. N. litoralis”: mean = 1:0.51; s.d. = 0.02; n = 5). This ratio was expected for NOB [54] and indicates that O2 consumption by the co-enriched heterotrophs was only minor during the relatively short MR experiments (maximum 1 h) and did not affect the kinetic analysis of “Ca. N. vancouverensis” and “Ca. N. litoralis”. The apparent half-saturation constant, Km(app), of N. gracilis was determined to be 20.1 µM NO2− (s.d. = 2.1, n = 8) and the maximum reaction rate, Vmax, to be 41.4 µmol NO2− mg protein−1 h−1 (s.d. = 9.4, n = 6), which is highly similar to the previously reported Km(app) and Vmax of the closely related N. watsonii (18.7 ± 2.1 µM NO2− and 36.8 µmol NO2− mg protein−1 h−1) [55]. The Km(app) measured for “Ca. N. litoralis” was 16.2 µM NO2− (s.d. = 1.6, n = 7) and thus resembled the values of N. gracilis and N. watsonii. In contrast, with a Km(app) of 8.7 µM NO2− (s.d. = 2.5, n = 3), “Ca. N. vancouverensis” showed a higher affinity for nitrite that was comparable with non-marine Nitrospira members (Km(app) = 6–9 µM NO2−), which have been the cultured NOB with the highest nitrite affinity known so far [54, 56]. Indeed, strain VA turned out to have the lowest Km(app) of all hitherto analyzed marine NOB in culture (Fig. 4). Interestingly, among all cultured marine NOB, “Ca. N. vancouverensis” is also most closely related to the Nitrospinae clades 1 and 2 that are abundant in oligotrophic waters [20] (see above). However, its Km(app) is still 1–2 orders of magnitude higher than the Km(app) values of nitrite oxidation reported for environmental samples from an OMZ (0.254 ± 0.161 μM NO2−) and South China Sea waters (0.03–0.5 µM) [22, 57]. The very high nitrite affinity observed with these samples might be explained by the presence of uncharacterized nitrite oxidizers, whose nitrite affinity exceeds that of all cultured NOB. However, it remains to be tested whether known NOB can persist under extremely low in situ nitrite concentrations. For example, the half-saturation constant for ammonia oxidizing bacteria spans several orders of magnitude under different growth temperatures [58]. Strongly different substrate affinities have also been observed for Nitrobacter winogradskyi and Escherichia coli under oligotrophic versus copiotrophic growth conditions [59, 60]. Systematic assessments of the kinetic plasticity of NOB under different conditions are still pending, mainly because the production of sufficient biomass of NOB isolates has been a major obstacle for such studies.
Fig. 4: Comparison of the whole-cell apparent half-saturation constants (Km(app)) for nitrite between marine and non-marine NOB.

The Km(app) values measured in this study (highlighted in red) are the mean from all biological replicates (n = 3 for “Ca. Nitrohelix vancouverensis” VA; n = 5 for “Ca. Nitronauta litoralis” EB; n = 8 for N. gracilis). The other Km(app) values were retrieved from previous studies [54, 55, 81].

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General genomic features of cultured Nitrospinae
A pan-genomic analysis of the two novel cultured Nitrospinae strains and N. gracilis showed that these three organisms share a core genome of 1347 proteins, which have at least 50% amino acid sequence identity over 80% of the alignment (Fig. S6). The core genome included universally highly conserved bacterial genes, such as those coding for ribosomal proteins, translational elongation factors and the DNA replication machinery, as well as the genes for the core metabolism of chemolithoautotrophic NOB. Interestingly, among the shared conserved genes we also found highly conserved glutaredoxins, thioredoxin reductases, and peroxidases ( >80% amino acid identity among the respective homologs). Like N. gracilis [25], “Ca. N. vancouverensis” and “Ca. N. litoralis” lack the canonical defense systems of aerobic organisms against oxidative stress, catalase and superoxide dismutase. While the aforementioned core genes could thus be essential for the detoxification of peroxides in all three organisms [61, 62], it remains a mystery how Nitrospinae deal with superoxide radicals [25]. Each of the strains encode a number of unique proteins (Fig. S6), many of which are phage related, corroborating a recently proposed hypothesis predicting extensive phage predation on Nitrospinae [21] (Supplemental Results and Discussion, Fig. S7). Yet, the majority of the variable genome content is still functionally uncharacterized. However, a few genes of the variable genome have known functions and might be important for niche adaptations. In the following sections, we address these genes as well as the shared core metabolism of the three analyzed Nitrospinae.
Nitrite oxidation and respiration
Among the highly conserved proteins are the three known subunits of a periplasmic NXR, NxrABC. Details of the predicted subunit composition and cofactors of the NXR of Nitrospinae, which is closely related to the NXR of Nitrospira, have been described elsewhere [3, 25, 63]. Briefly, all three Nitrospinae strains possess two genomic copies of the substrate-binding subunit NxrA, two or three (only “Ca. N. vancouverensis”) copies of the electron-channeling subunit NxrB (Fig. S8), and several copies of putative NxrC subunits, which may transfer electrons from NXR to a downstream acceptor in the electron transport chain (Table S4). Homologs to all of the different putative NxrC subunits of N. gracilis [25] were also found in “Ca. N. vancouverensis” and “Ca. N. litoralis” (Table S4), but it remains to be determined whether all of these proteins are functionally involved in nitrite oxidation.
The respiratory electron transport chain of NOB is short, as electrons derived from nitrite are directly transferred, via a- or c-type cytochromes, to the terminal oxidase (complex IV) [25, 63, 64]. N. gracilis carries a cbb3-type high affinity heme-copper cyt. c oxidase (HCO) [25], whereas both “Ca. N. vancouverensis” and “Ca. N. litoralis” lack any canonical HCO. However, all three organisms encode highly conserved, putative “bd-like oxidases” [25]. These proteins, which also occur in all Nitrospira genomes, are phylogenetically related with but clearly distinct from the canonical cyt. bd-type quinol oxidases [63]. Interestingly, one variant of the bd-like oxidases from Nitrospira contains all conserved amino acid residues for heme and Cu binding in HCOs [65], indicating that this enzyme could be a novel cyt. c-oxidizing HCO [63]. The bd-like oxidases of N. gracilis, “Ca. N. vancouverensis”, and “Ca. N. litoralis” have most of these conserved residues; however, one of the three histidine ligands of the CuB is replaced with a glutamine, and the histidine ligand of the high-spin heme is replaced with a phenylalanine. Thus, without the cbb3-type oxidase found only in N. gracilis, it remains unclear how the final electron transport step from cyt. c to O2 occurs in “Ca. N. vancouverensis” and “Ca. N. litoralis”. Future biochemical and protein structural research may reveal whether the cyt. bd-like oxidases can catalyze this reaction despite their divergence from bona fide HCOs at two of the predicted cofactor-binding residues, and whether these proteins are capable of proton translocation for proton motive force generation. Corroborating evidence for a function of this enzyme in the context of electron transport stems from its highly conserved genetic synteny within Nitrospinae, Nitrospirae and anammox bacterial genomes. A conserved cluster within these organisms contains a cyt. bd-like oxidase with the aforementioned glutamine and phenylalanine residues, a diheme- and a triheme- cyt. c, and a membrane integral, alternative NxrC subunit (Tab. S4). This putative NxrC might be involved in the electron transfer from NO2− to the terminal oxidase [25, 63, 66].
In addition to the putative cyt. bd-like oxidase discussed above, “Ca. N. litoralis” possesses a canonical cyt. bd-type (quinol) oxidase that is lacking in “Ca. N. vancouverensis” and N. gracilis. Since quinol oxidases cannot accept electrons from the high-potential donor nitrite, we assume that this oxidase receives electrons from quinol during the degradation of intracellular glycogen or during hydrogen oxidation (see below). The cyt. bd-type oxidase may also be involved in oxidative stress defense, as homologous oxidases in other organisms can degrade H2O2 [67] and protect from dioxygen [68]. Taken together, the diverse repertoire of terminal oxidases may be a key feature of Nitrospinae that contributes to the ecological success of this lineage over a broad range of redox conditions in marine ecosystems.
Carbon metabolism and alternative energy metabolisms
Like N. gracilis and other Nitrospinae [6, 25], the novel strains encode all key genes of the reductive tricarboxylic acid (rTCA) cycle for CO2 fixation, including the hallmark enzymes ATP-citrate lyase (ACL), pyruvate:ferredoxin oxidoreductase (POR), and 2-oxogluterate:ferrodoxin oxidoreductase (OGOR) (Table S5). As in N. gracilis, all genes required for the oxidative (oTCA) cycle are also present. All three strains can form glycogen as storage compound, which is degraded via glycolysis and the oTCA cycle. Since N. gracilis lacks pyruvate kinase, the final step of glycolysis may be catalyzed by pyruvate phosphate dikinase (PPDK) in this organism. In contrast, strains VA and EB possess both pyruvate kinase and PPDK, indicating a strict regulatory separation between glycolysis and gluconeogenesis.
Alternative energy metabolisms such as the oxidation of hydrogen, sulfide or organic carbon compounds have been demonstrated in NOB with representatives from the genera Nitrospira, Nitrococcus, Nitrolancea, and Nitrobacter [3, 26,27,28, 69]. Among the three Nitrospinae strains analyzed here, N. gracilis has the largest potential to exploit energy sources other than nitrite: its genome harbors a bidirectional type 3b [NiFe] hydrogenase, which could enable aerobic hydrogen utilization, and a sulfite:cyt. c oxidoreductase [25]. In addition, N. gracilis contains the genes prpBCD for 2-methylisocitrate lyase, 2-methylcitrate synthase, and 2-methylcitrate dehydratase, and might thus be able to catabolically degrade propionate via the 2-methylcitrate pathway (Fig. 5). Of these potential alternative energy metabolisms, “Ca. N. litoralis” shares only the type 3b hydrogenase, whereas “Ca. N. vancouverensis” seems to be an obligate nitrite oxidizer. No genes for the uptake and utilization of urea and cyanate as organic N sources were found in the genomes of the new strains. These genes show a patchy distribution among Nitrospinae [6, 7, 20, 21, 25], suggesting further niche differentiation of these organisms based on the capacity to use organic compounds as sources of reduced N for assimilation.
Fig. 5: Cell cartoon based on the annotation of “Ca. Nitrohelix vancouverensis” VA, “Ca. Nitronauta litoralis” EB and N. gracilis 3/211.

The colored squares indicate the presence or absence of the respective genes, “Ca. Nitrohelix vancouverensis” VA is shown in green, “Ca. Nitronauta litoralis” EB in blue, and N. gracilis 3/211 in purple. The gene annotations are detailed in Table S5. Fd ferredoxin, RE restriction enzymes, TO terminal oxidase.

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Adaptations to saline environments
The intracellular accumulation of ions or the production of organic osmolytes are two main strategies of microorganisms to cope with the osmotic stress in highly saline, marine environments. Interestingly, the three Nitrospinae strains seem to utilize different osmotic stress defense mechanisms. N. gracilis has the genetic potential to produce glycine betaine, an organic osmolyte that is ubiquitously found in bacteria (Fig. 5) [70]. It also encodes for OpuD, a betaine/carnitine/choline transporter, whereas the genes for glycine betaine synthesis and import are missing in “Ca. N. vancouverensis” and “Ca. N. litoralis”. These two strains harbor the canonical genes ectABC for ectoine biosynthesis (Fig. 5), another widely distributed osmolyte in bacteria [70]. “Ca. N. litoralis” additionally encodes the ectoine hydroxylase EctD and is thus able to form hydroxyectoine. Directly downstream of the (hydroxy-)ectoine synthesis cassette, the two strains further encode an ABC transporter that has similarity to ectoine or amino acid transporters from other organisms and may be utilized for (hydroxy-)ectoine import across the cytoplasmic membrane. Since genes for the synthesis and transport of (hydroxy-)ectoine were also found in “Ca. Nitromaritima” (Nitrospinae clade 1) [6], we assume that usage of (hydroxy-)ectoine is wide-spread among the Nitrospinae. N. gracilis and “Ca. N. litoralis” may also be able to synthesize sucrose as an additional compatible solute (Fig. 5).
Moreover, “Ca. N. vancouverensis” and “Ca. N. litoralis” genomes harbor the gene glsA coding for a glutaminase (Fig. 5), which allows them to deaminate glutamine to glutamate while releasing ammonia. The strains further possess the gltP gene coding for a glutamate/H+ symporter. Both glsA and gltP seem to be lacking in the N. gracilis genome. Since glutamate can play a role in osmoregulation [70], the ability to regulate the intracellular glutamate level via transport or the degradation of glutamine may be one of various adaptations by “Ca. N. vancouverensis” and “Ca. N. litoralis” to rapidly respond to stress caused by fluctuating salinities.
All three Nitrospinae strains have the genomic capacity to synthesize the hopanoids hopan-(22)-ol and hop-22(29)-ene (Fig. 5). Hopanoids are pentacyclic, bacterial lipids that integrate into bacterial membranes, much like cholesterol in eukaryotes [71]. They help regulate membrane fluidity and may be important in highly saline environments [70, 72]. Knock-out studies have shown that cells lacking the ability to make hopanoids are more sensitive to various stresses, such as temperature, pH and osmolarity [72]. Interestingly, a metagenomic study of hopanoid-producing bacteria in the Red Sea revealed Nitrospinae to be among the main organisms harboring squalene hopene cyclase, the key gene for hopanoid production [73]. Other marine NOB (members of Nitrospira, Nitrobacter and Nitrococcus mobilis), and even non-marine Nitrospira and Nitrobacter, also have the squalene hopene cyclase and therefore the genomic potential to produce hopanoids. Insight into the chemical structure of the hopanoids produced by NOB could help to gain insight into early NOB evolution as hopanoids are important lipid biomarkers and commonly used to deduce ancient microbial activity from sediment fossil records [73].
Vitamin B12 auxotrophy
The three Nitrospinae strains lack multiple genes involved in vitamin B12 (cobalamin) synthesis and seem to be auxotrophic for this vitamin, as previously suggested for N. gracilis [74]. To acquire vitamin B12 from the environment, all strains encode the vitamin B12 ABC transporter btuCDF and the outer membrane permease btuB [75, 74]. Alternatively, this transporter may also import cobinamide that may then be converted to vitamin B12 via a salvage pathway that uses the genes yvqK, bluB, and cobS, each encoded in all three Nitrospinae genomes [76]. Hence, the availability of externally supplied vitamin B12 or cobinamide is likely of crucial importance for Nitrospinae in situ and also in lab cultures. The incomplete cobalamin pathway in the already available N. gracilis genome led us to amend the cultivation medium with vitamin B12. Indeed, the addition of vitamin B12, either alone or together with other vitamins, has allowed us to cultivate N. gracilis in a more defined medium that is based on Red Sea salt. Previously, the standard medium for this organism had to be prepared from natural seawater [25]. Furthermore, the addition of vitamin B12 was likely an essential prerequisite for our successful enrichment of novel Nitrospinae after cell sorting. The co-cultured alphaproteobacteria may also provide additional vitamin B12 as they both have the genomic repertoire for its synthesis. In the environment, vitamin B12 could be supplied by different heterotrophic or autotrophic microorganisms, including ammonia-oxidizing thaumarchaeota, which have been shown to produce vitamin B12 [77,78,79] and often co-occur with Nitrospinae [10, 21, 80]. More