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Experimental design
Following five focus groups with SERNAPESCA’s head of enforcement and other personnel, we designed and implemented an online survey that targeted fisheries enforcement officers who are responsible for monitoring IUU activities in Chile. The survey was structured to capture expert knowledge on various aspects of illegal activities, as well as the relative experience of the officers. The survey defined illegal fishing as a fishing activity carried out in national jurisdiction waters by national or international boats that is in violation of the national fishing law, conducted without a legal permit, or activities that involve unreported or misreported captures to the authorities. The Director of SERNAPESCA delivered the survey via email to all SERNAPESCA enforcement officers. The list of officers was constructed by the Director (n = 86). The survey was anonymous in that the officers were not asked to report their name nor any information that could be used for identification (e.g., email). Answers to questions were not mandatory; that is, respondents could opt-out of answering particular questions and continue with the survey. The survey was available online for ten weeks, over which five reminder emails were sent to officers requesting them to complete the survey.
The survey, in Spanish, consisted of two sections. First, we asked respondents to rank the magnitude of illegal activity for twenty fisheries on a nominal scale (1–5), along with their relative experience with each fishery (nominal scale, 1–5). The twenty fisheries were selected a priori based on our focus groups and known information about illegal activity. All fisheries were single species, with the exception of four that included multiple species: skates (2 species, Zearaja chilensis and Bathyraja macloviana), kelp (4 species: Lessonia spicate, L. berteroana, L. traberculata, Macrocystis pyrifera), red algae (3 species: Sarcothalia crispate, Gigartina skottsbergii, Mazzaella laminarioides), and crabs (10 species excluding southern king crab: Cancer edwardsi, C. porter, C. setosus, C. coronatus, Homalaspis plana, Ovalipes trimaculatus, Taliepus dentatus, T. marginatus, Mursia gaudichaudi, Hemigrapsus crenulatus). In the second part of the survey, we asked respondents additional questions for four focal fisheries: South Pacific hake (Merluccius gayi gayi), southern hake (M. australis), loco or Chilean abalone (Concholepas concholepas), and kelp. For each fishery, we asked respondents to score on a nominal scale (1–5),

The frequency of six specific illegal activities in the industrial sector: size, gear, season, area, transshipment, and port.

The frequency of six specific illegal activities in the small-scale sector: size, gear, season, area, transshipment, and port.

The participation of illegal activity for six different stakeholders along the supply chain: fisher, purchaser, processor, wholesaler, exporter, and restaurateur.

The utilization of seven infrastructure types in illegal activities: fishing boats, refrigeration trucks, processing plants, markets, transshipment boats, export vehicles, and restaurants.

This study was approved by the Advanced Conservation Strategies and Pontificia Universidad Católica ethics institutional review boards and followed guidelines established by their ethics committees, which complies with national and international standards. The surveys included a written informed consent approved by all interviewees, which acknowledged research objectives and established that the survey was anonymous and that interviewees were free to choose to not answer questions. While all species have common names in Chile (which were used in the survey), we use Fishbase and Sealifebase as the taxonomic authority and for the common names reported here to facilitate comparisions34,35.
Statistical analysis
For both sections of the survey, we used a Bayesian cumulative multinomial logit model to predict illegal estimates. First, we fitted a model for illegal estimates for each of the twenty fisheries jointly. Second, we fitted models for illegal estimates for various aspects of the four focal fisheries (i.e., activities, stakeholders, and infrastructure) in a single analysis for each aspect. In both models, we included a random intercept term for respondent, along with a fixed effect for fishery. We evaluated the role of experience, as self-reported by the respondents, by comparing the difference between the illegal score by a respondent for a fishery and the model prediction for that fishery across respondents. If higher levels of expertise increased or decreased the value of a respondent’s scoring, there would be a relationship between the size of the differences and the level of experience reported for a fishery. Experience may also affect the difference in mean responses (i.e., bias), potentially due to more personal experience over a longer period of time, which would lead to a correlation between expertise and mean illegality scores. Depending on the patterns observed in the data, there are several ways to control for a respondent’s experience in illegality estimates. In our case, we used experience scores as a covariate in the model.
For the twenty fisheries, we used the following model,

$$Prleft{{S}_{ij}=kright}=phi left({tau }_{k}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}right)right)-phi left({tau }_{k-1}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{j}}}right)right)$$
(1)

in which the probability that the score for the level of illegal landings ({S}_{ij}) for the ith species by the jth respondent is equal to category k, can be represented as a latent continuous variable which is divided into K categories, by K − 1 thresholds at ({tau }_{k}). This latent continuous variable is represented by the cumulative normal distribution, (phi). For a given observation, the regression equation is composed of coefficients multiplied times predictor variables ({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}) plus a design matrix for the random effect, multiplied times the error term for the jth respondent, ({{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}) . The probability of that observation falling in category k, (Prleft{{S}_{ij}=kright}), is thus the probability of it being in a category equal to or smaller than k, (phi left({tau }_{k}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}right)right)), less the probability of the observation being in a category smaller than k, (phi left({tau }_{k-1}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{j}}}right)right)). Implemented in the R statistical language, using the brms package36, the call to fit this model looks like the following:

$${text{Score}}; , sim ;{text{Species}} + {text{Experience }} + left( {{1}|{text{Respondent}}} right),;{text{ data}} = {text{SurveyData}},;{text{family}} = {text{cumulative}}),$$

where Score is ({S}_{ij}) in (1) above, the fixed effects, ({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}) are the experience of the respondent and the species that was scored, and (1|Respondent) denotes a random intercept model, where each has a different intercept term, drawn from a shared error distribution. For more information on the application of this model to ordinal response data, see Burkner and Vuorre37.
For the estimates for the various aspects of the four focal fisheries, we used the following model,

$${text{Response}}; sim ;{text{Species}} + {text{Experience}} + left( {{1}|{text{Respondent}}} right),;{text{data}} = {text{SurveyData}},;{text{family}} = {text{cumulative}}),$$

which is structured as per (1) above, but with the responses to the various focal species questions (i.e., activities per sector, stakeholders, and infrastructure) substituted for the species scores as in (1).
We compared both models with simpler models, including a single-term null model using leave-one-out cross-validation. We did so in the R statistical language using the loo packages36,38,39. Prior distributions for all regression terms were improper flat priors over the real numbers, the default in the brms package for population parameters. The priors on the intercept and the random effects were student t3,0,10 distributions, as per the default for uninformative priors in the brms package.
We carried out a Principal Components Analysis (PCA) with the four focal fisheries as categorical variables and the illegal activity, stakeholder, and infrastructure estimates from the Bayesian cumulative multinomial logit model. For each fishery, we used 10,000 estimates from the model, along with a qualitative variable that represented the different factors (e.g., restaurateur). The latter has no influence on the principal components of the analysis but helps to interpret the dimensions of variability. Principal Components Analysis is especially powerful as an approach to visualize patterns, such as clusters, clines, and outliers in a dataset40. In our case, we sought to visualize whether there were common illegal factors with similar set of scores and whether there was any association between high or low scores of illegal factors and the focal fisheries. We used the FactoMineR package in the R statistical language41. More

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Community composition of Nitrospira in the saline-alkaline lakes
Members of the genus Nitrospira are the most diverse and widespread known NOB. However, reports of Nitrospira occurrence in alkaline habitats are scarce [23, 30], and a systematic assessment of their presence and activity in such extreme environments is missing. In this study, we discovered and investigated unusually alkalitolerant Nitrospira in saline-alkaline lakes of the national park “Neusiedler See-Seewinkel”, Burgenland, Austria using targeted amplicon profiling of the 16S rRNA gene and nxrB, of which the latter encodes the beta-subunit of nitrite oxidoreductase (the key enzyme for nitrite oxidation). In sediment samples from nine lakes, we detected phylogenetically diverse Nitrospira phylotypes which were affiliated with Nitrospira lineages I, II and IV (Fig. 2) [1].
Fig. 2: Phylogenetic maximum likelihood analysis based on the 16S rRNA gene sequences of selected representatives from the genus Nitrospira and of the Nitrospira members detected in sediments from nine saline-alkaline lakes.

Sequences obtained in this study are printed in bold. “Ca. N. alkalitolerans” is the Nitrospira species cultured and further analyzed in this study. The tree was constructed using full length sequences and a 50% conservation filter resulting in 1310 valid alignment positions. Shorter sequences from this study, generated through amplicon and Sanger sequencing were added to the tree using the Evolutionary Placement Algorithm (EPA) without changing the overall tree topology. Numbers in brackets behind these sequences firstly denote the likelihood score of the exact placement and secondly the cumulative likelihood score of the placement within the cluster. Filled, gray, and open circles denote branches with ≥90%, ≥70% and ≥50% bootstrap support, respectively. Leptospirillum ferrooxidans (AJ237903), Ca. Magnetobacterium bavaricum (FP929063), Thermodesulfovibrio yellowstonii DSM 11347 (CP001147), and Ca. Methylomirabilis oxyfera (FP565575) were used as outgroup. The scale bar indicates 6% estimated sequence divergence.

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The genomes of sequenced Nitrospira possess one to six paralogous copies of nxrB, and the nxrB copy numbers per genome remain unknown for the majority of uncultured Nitrospira [42]. This large variability likely affects relative abundance estimations of Nitrospira OTUs based on nxrB amplicon data. In contrast, all sequenced Nitrospira genomes contain only one ribosomal RNA (rrn) operon. Therefore, our further assessment of the Nitrospira community structures relies on the 16S rRNA gene amplicon datasets.
The estimated alpha-diversity of Nitrospira 16S rRNA gene phylotypes was compared across the nine examined lakes (Fig. S1). The inverse Simpson’s index of the Nitrospira communities was negatively correlated with pH and the nitrite concentration (p = 0.00004, Tau-b = −0.53 for pH and p = 0.03, Tau-b = −0.36 for nitrite). The decrease of Nitrospira diversity with increasing pH may indicate that only specific Nitrospira phylotypes tolerate highly alkaline conditions.
The Nitrospira communities clustered into two distinct major groups (Fig. 3). Group 1 mainly comprised the communities from those lakes, which are located closely to the shore of the much larger Lake Neusiedl, whereas group 2 contained the communities from the remaining lakes that are farther away from Lake Neusiedl (Fig. 1). The average pH and salinity in the water of lakes from the group 1 cluster were 9.97 ± 0.24. and 6.1 ± 4.1 g/l, respectively. These values were significantly higher (Welch’s t-test; p = 0.00001 for pH and p = 0.017 for salinity) than the mean pH of 9.37 ± 0.26 and salinity of 2.74 ± 0.88 g/l in the group 2 lakes (Table 1). None of the other determined lake properties at time of sampling differed significantly between the two groups. The Nitrospira phylotypes with the highest relative abundance in the sediments from group 1 were OTU1 and OTU20, both affiliated with Nitrospira lineage IV, whereas these OTUs were nearly absent from the sediments of the lakes in group 2 (Fig. 3). In contrast, the predominant phylotypes in the group 2 lake sediments were affiliated with Nitrospira lineage II (Fig. 3). Consistent with these results, a principal coordinate analysis showed a clear separation of the Nitrospira communities with the same two groups separated on the first axis of the ordination (Fig. S2). These results indicate a strong influence of pH and salinity on the composition of the Nitrospira communities. Members of Nitrospira lineage IV are adapted to saline conditions and are commonly found in marine ecosystems [15, 43,44,45,46,47]. However, to date no Nitrospira species have been described to tolerate elevated pH conditions. Our results show that a substantial diversity of Nitrospira is able to colonize alkaline environments. The data also indicate a niche differentiation between lineages IV and II in saline-alkaline lakes, which likely includes a higher tolerance of the detected lineage IV organisms toward an elevated pH and salinity.
Fig. 3: Normalized abundances of Nitrospira 16S rRNA gene phylotypes detected in triplicate sediment samples from nine saline-alkaline lakes.

Nitrospira communities are grouped by hierarchical clustering on the y-axis, and OTUs are grouped by phylogenetic affiliation on the x-axis. Lake names are abbreviated as in  Fig. 1. Lin. IV, Nitrospira lineage IV ; Lin. II, Nitrospira lineage II; I, Nitrospira lineage I; Freq normalized frequency counts; Grp.1, group 1 lakes; Grp.2, group 2 lakes (see also Fig. 1).

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Metagenome sequencing and physiology of alkalitolerant Nitrospira enrichments
Following the inoculation of mineral nitrite medium flasks with sediment and/or water samples from four saline-alkaline lakes (LL, WW, KS and OEW; abbreviations see Table 1), we initially obtained 17 enrichment cultures that oxidized nitrite to nitrate. Based on FISH analyses with Nitrospira-specific 16S rRNA gene-targeted probes and Sanger sequencing of cloned 16S rRNA genes, several of these preliminary enrichment cultures contained co-existing phylotypes from Nitrospira lineages I, II, and IV as well as from the genus Nitrobacter (data not shown). Members of the genera Nitrotoga and Nitrospina were screened for by FISH or PCR, but were not detected.
We used three of the enrichments which contained only Nitrospira NOB and originated from different lakes (referred to as EN_A from lake OEW, EN_B from lake LL, and EN_C from lake WW comprising ~35% Nitrospira in relation to the total microbial community based on FISH analysis) to determine the pH range for activity of the enriched Nitrospira members. Enrichment cultures EN_A and EN_C contained phylotypes from Nitrospira lineages I and II, while EN_B contained phylotypes from lineages I, II, and IV as determined by 16 rRNA gene amplicon cloning and Sanger sequencing (Fig. 2). The continued presence of these Nitrospira phylotypes for more than 2 years, despite several serial dilution transfers, demonstrates their tolerance to the alkaline incubation conditions and suggests that they were native to the saline-alkaline environment which they were sampled from. Hence, we conclude that at least the highly similar uncultured Nitrospira OTUs detected by amplicon sequencing (Fig. 2) were most likely also native inhabitants of the saline-alkaline lakes. Aliquots of each enrichment culture were incubated with nitrite as the sole added energy source for six weeks at pH 7.61–7.86 and 9–9.04, respectively. During this period, pH had no significant effect on nitrite utilization (Pearson correlation coefficient ≥0.96 with, p ≤ 0.01 for all three enrichments) and nitrate production (Pearson correlation coefficient ≥0.98 with, p ≤ 0.01 for all three enrichments) over time for any of the three enrichments (Fig. S3). Subsequently, the enrichment culture aliquots that had been incubated at pH 9–9.04 were sequentially incubated at pH 9.97–10, 10.24–10.52, and 10.72–11.02 for eight to nine days at each pH (Table S1). For all three enrichments, the observed nitrate production tended to be slower at pH 9.97–10 and 10.24–10.52 than at pH 9–9.04 (Fig. S3 and S4). At pH 10.72–11.02, no nitrite consumption was detected (Fig. S4). The trends observed at pH 10.24–10.52 and above were in stark contrast to the persistently high nitrite-oxidizing activity of the enrichments when routinely cultured at pH 9–10 for several weeks. While it was not possible to determine based on our data whether all Nitrospira phylotypes present in the three enrichments responded equally to the tested pH conditions, we can conclude that the activity of at least some Nitrospira remained unaffected up to pH 9 and had an upper limit between pH 10.5 and 10.7. This is remarkable, because previously enriched or isolated Nitrospira strains were not cultivated above pH 8.0 except for two Nitrospira cultures from geothermal springs, which showed activity up to pH 8.8 [4] or pH 9.0 [7]. To our knowledge, this is the first report of nitrite oxidation by Nitrospira at pH values above 9 and as high as 10.5.
Further analyses focused on one additional enrichment, which had been inoculated with sediment from lake Krautingsee, belonging to the group 2 of the analyzed lakes (KS, Table 1). In contrast to the other enrichment cultures, this enrichment contained only lineage IV Nitrospira based on FISH analysis (Fig. 4a). Nitrospira-specific, 16S rRNA gene and nxrB-targeted PCR and phylogeny detected one phylotype from Nitrospira lineage IV that was related to other phylotypes detected from the lakes, specifically OTU 5 and EN_B_1 (16S rRNA gene, 100% and 98% nucleotide sequence identity, respectively; Fig. 2) and OTU 2 (nxrB, 98.5% nucleotide sequence identity; Fig. S5). Both these OTU phylotypes occurred in most of the analyzed lakes (Fig. 3). Thus, the closely related enrichment from lake KS may represent Nitrospira that could adapt to a relatively broad range of conditions, while some of the other OTUs were more abundant in specific lakes only (Fig. 3). The enriched Nitrospira reached a high relative abundance in the enrichment culture of ~60% of all bacteria based on metagenomic read abundance (see below) and observation by FISH.
Fig. 4: Visualization and metagenomic analysis of the “Ca. N. alkalitolerans” enrichment.

a FISH image showing dense cell clusters of “Ca. N. alkalitolerans” in the enrichment culture. The “Ca. N. alkalitolerans” cells appear in red (labeled by probe Ntspa1151 which has 1 mismatch at the 3’ end to the 16S rRNA gene sequence of “Ca. N. alkalitolerans”; the absence of lineage II Nitrospira in the enrichment culture was confirmed by the application of the competitor oligonucleotides c1Ntspa1151 and c2Ntspa1151 as indicated in the Supplementary text). Other organisms were stained by DAPI and are shown in light gray. Scale bar, 25 µm. b Phylogenetic affiliation of the metagenome scaffolds from the “Ca. N. alkalitolerans” enrichment, clustered based on sequence coverage and the GC content of DNA. Closed circles represent scaffolds, scaled by the square root of their length. Clusters of similarly colored circles represent potential genome bins.

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High-throughput metagenome sequencing, scaffold assembly, and binning revealed that the enrichment contained three Nitrospira strains that could be separated into three genome bins based on sequence coverage data (Table S2, Fig. S6). No other NOB were identified in the metagenome, and the three Nitrospira bins represented the most abundant organisms in the enrichment culture (Fig. 4b). Since the genome-wide average nucleotide identity (gANI) values were above the current species threshold of 95% [48] (Table S2), the three bins likely represented very closely related strains of the same Nitrospira lineage IV species with unique genetic components. From the predominant (based on coverage data) Nitrospira sequence bin, an almost complete metagenome-assembled genome (MAG) was reconstructed, which met the criteria for a “high-quality draft” genome [49] (Table S2), and used for comparative genomic analysis. Genome-wide, pairwise comparison of the gANI and average amino acid (gAAI) identity between this MAG and Nitrospira marina as the only other genome-sequenced and cultured Nitrospira lineage IV representative resulted in values of 80.1 and 77.3, respectively. The 16S rRNA gene, which had been retrieved from the MAG, was 97.90% identical to the 16S rRNA gene of N. marina, 97.87% identical to “N. strain Ecomares 2.1”, 94.92% to “Ca. N. salsa”, and 94.51% to “Nitrospira strain Aa01”, which are the other cultured members of Nitrospira lineage IV [15, 43, 46, 47]. These values are below the current species threshold of 98.7–99% for 16S rRNA genes [50]. Based on the low gANI and 16S rRNA gene sequence identities to described Nitrospira species, and additionally considering the distinct haloalkalitolerant phenotype (see also below), we conclude that the enriched Nitrospira represent a new species and propose “Ca. Nitrospira alkalitolerans” as the tentative name.
The enrichment culture was maintained at a pH of 9–10 and a salt concentration of 2 g/l, resembling the natural conditions in the saline-alkaline lakes based on available data from 5 years. “Ca. N. alkalitolerans” grew in dense flocks (Fig. 4a), thereby possibly relieving the pH stress [51]. Its nitrite-oxidizing activity was not affected when the pH in the cultivation medium decreased below 8. However, no nitrite oxidation was observed when the enrichment culture was transferred into medium with 4× to 8× higher salt concentrations, the latter resembling marine conditions. Thus, “Ca. N. alkalitolerans” is best described as a facultatively haloalkalitolerant organism that oxidizes nitrite as an energy source over a wide range of pH and under hyposaline conditions. This phenotype is certainly advantageous in the investigated saline-alkaline lakes, as these lakes are prone to evaporation in summer, which causes a temporarily elevated salinity and alkalinity in the remaining water body and the sediment [35].
The enrichment culture of “Ca. N. alkalitolerans” oxidized nitrite over a broad range of initial nitrite concentrations tested, although an extended lag phase of 10–15 days occurred at the higher concentrations of 0.7 and 1 mM nitrite (Fig. S7). Similarly, a lag phase at elevated nitrite concentrations was also observed for the Nitrospira lineage II member Nitrospira lenta [52]. A preference for low nitrite levels is consistent with the presumed ecological role of nitrite-oxidizing Nitrospira as slow-growing K-strategists, which are adapted to low nitrite concentrations [50, 52, 53].
Genomic adaptations to the saline-alkaline environment
As described below, comparative genomic analysis of “Ca. N. alkalitolerans” revealed several features that distinguish this organism from other known NOB and likely form the basis of its tolerance toward elevated alkalinity and salinity (Fig. 5).
Fig. 5: Cell metabolic cartoon constructed from the genome annotation of “Ca. N. alkalitolerans”.

Features putatively involved in the adaptation to high alkalinity and salinity, and selected core metabolic pathways of chemolithoautotrophic nitrite-oxidizing Nitrospira, are shown. Note that the transport stoichiometry of the ion transporters in “Ca. N. alkalitolerans” remains unknown. Colors of text labels indicate whether adaptive features are present (i.e., have homologs) in the genomes of other NOB (red, feature is not present in any other characterized NOB; blue, feature is present only in the marine Nitrospina gracilis; purple, feature is present in several other characterized NOB).

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Cytoplasmic pH and ion homeostasis
At high pH, alkaliphilic and alkalitolerant microbes maintain a higher transmembrane electrical potential (ΔΨ) component of the proton motive force (PMF) than usually found in neutrophiles. The high ΔΨ is required to maintain PMF, because the ΔpH component of the PMF is reversed when the extracellular pH is higher than the intracellular pH [54]. Like in neutrophiles, the ΔΨ of alkaliphiles is negative inside the cell relative to the outside [54]. Furthermore, the intracellular pH must be kept below the (extremely) alkaline extracellular pH. At elevated salinity, resistance against high salt concentrations is an additional, fundamental necessity for survival. All this requires a tightly regulated pH and ion homeostasis, in which cation transmembrane transporters play key roles [54,55,56]. The “Ca. N. alkalitolerans” genome codes for various Na+-dependent transporters (Fig. 5, Table S3) including secondary Na+/H+ antiporters that are involved in pH homeostasis in other organisms: two copies of a group 3 Mrp-type Na+/H+ antiporter [57, 58] encoded by the seven genes mrpA-G, and monovalent cation-proton antiporters of the types NhaA and NhaB, each of which is encoded by a single gene [59]. The Mrp antiporter is crucial for growth at high pH and elevated salinity in alkaliphilic Halomonas spp. and Bacillus spp., where it exports Na+ and imports H+, thus contributing to the maintenance of a lower intracellular pH compared to the environment (e.g., cytoplasmic pH 8.3 at external pH ~ 10.5) [[60] and references cited therein, [55]]. The Mrp proteins may form a large surface at the outside of the cytoplasmic membrane that could support proton capture under alkaline conditions [54, 57]. Nha-type antiporters are widely distributed among non-extremophilic and extremophilic organisms [55]. Being involved in the homeostasis of Na+ and H+, they are important for survival under saline and/or alkaline conditions [56]. In E. coli, NhaA is regulated by the cytoplasmic pH and it catalyzes the import of 2H+ with the concurrent export of one Na+. This electrogenic activity is driven by ΔΨ and maintains pH homeostasis at elevated external pH [[52] and references cited therein]. The simultaneous presence of the two antiporters NhaA and NhaB has been associated with halophilic or haloalkaliphilic phenotypes in other organisms [55, 59]. Although the regulation and cation transport stoichiometry of the homologs in “Ca. N. alkalitolerans” remain unknown, the Mrp- and Nha-family antiporters most likely exhibit important physiological roles in this organism and support its survival under haloalkaline conditions. Possibly, “Ca. N. alkalitolerans” can even combine its growth in dense flocks with the extrusion of protons by its numerous proton transporters thereby lowering the pH inside the flock [51].
One of the two nhaB genes present in the “Ca. N. alkalitolerans” genome is located in an interesting genomic region that also contains all genes encoding the group 3 Mrp-type Na+/H+ antiporter (Fig. S8). The two genes downstream from mrpD display sequence similarity to the NADH dehydrogenase (complex I) subunits NuoM and NuoL. However, based on the genomic context they are more likely additional mrpA- and/or mrpD-like genes, as these Na+/H+ antiporter subunits are evolutionary related to NuoM and NuoL [61]. Multiple copies of subunits NuoM and NuoL of the NADH dehydrogenase are encoded elsewhere in the genome, partially in larger nuo operons (see Table S3). Moreover, the locus contains one gene coding for the low-affinity, high flux Na+/HCO3− uptake symporter BicA [62] and gene motB encoding a H+-translocating flagellar motor component (Fig. S8). In the haloalkalitolerant cyanobacterium Aphanothece halophytica, a similar clustering of bicA with genes coding for Na+/H+ antiporters has been described. The authors proposed a model of cooperation between these transporters, where Na+ extruded by the Na+/H+ antiporters could drive the uptake of HCO3− by BicA under alkaline conditions when CO2 becomes limiting [63]. Sodium-driven import of HCO3− could be an essential feature for “Ca. N. alkalitolerans”, because bicarbonate is the main source of inorganic carbon for autotrophic organisms, but becomes less accessible at high pH >10 [55]. A carbonic anhydrase, which is also present in the genome (Fig. 5, Table S3), can convert the imported HCO3− to CO2 for carbon fixation via the reductive tricarboxylic acid cycle (Fig. 5).
Since cytoplasmic K+ accumulation may compensate for Na+ toxicity at elevated intracellular pH [64], many alkaliphiles retain an inward directed K+ gradient [55]. The potassium uptake transporters of the Trk family contribute to pH and K+ homeostasis of halo- and/or alkaliphiles [55]. TrkAH catalyzes the NAD+-regulated uptake of K+ possibly coupled with H+ import [65]. Moreover, kinetic experiments revealed that TrkAH of the gammaproteobacterium Alkalimonas amylolytica is salt-tolerant and functions optimally at pH > 8.5 [66]. “Ca. N. alkalitolerans” encodes a TrkAH complex (Fig. 5, Table S3), which may be a specific adaptation to its haloalkaline environment as no homologous K+ transporter has been identified yet in any other NOB genome. Under more neutral pH conditions, Kef-type K+ efflux pumps, which are present in two copies in the “Ca. N. alkalitolerans” genome, could excrete excess K+ (Fig. 5, Table S3).
Adaptations of the energy metabolism
Aside from the different cation transporters (see above), “Ca. N. alkalitolerans” also encodes several mechanisms for cation homeostasis that are linked to membrane-bound electron transport and energy conservation. Like in other aerobic alkaliphiles [56], ATP synthesis is likely catalyzed by a canonical, H+-translocating F1FO-ATPase (Fig. 5, Table S3). In addition, the genome contains all genes of a predicted Na+-translocating N-ATPase [67] (Fig. 5, Fig. S9, Table S3). N-ATPases form a separate subfamily of F-type ATPases and have been suggested to be ATP-driven ion pumps that extrude Na+ cations [67] or H+ [68]. The c subunit of the N-ATPase in the genome of “Ca. N. alkalitolerans” contains the typical amino acid motifs for Na+ binding and transport [67] (Fig. S10). Subunits a and c of the N-ATPase, which are involved in ion transport, are most similar to homologs from the halotolerant, sulfate-reducing Desulfomicrobium baculatum (81.5% AA identity) and the haloalkalitolerant, sulfur-oxidizing Sulfuricella denitrificans (88.2% AA identity), respectively. Hence, in “Ca. N. alkalitolerans”, the N-ATPase may contribute to the maintenance of ΔΨ, the generation of a sodium motive force (SMF), and salt resistance (Fig. 5).
The genome of “Ca. N. alkalitolerans” encodes two different types of NADH:quinone oxidoreductase (complex I of the electron transport chain) (Fig. 5, Table S3). Firstly, the organism possesses all 14 genes of type I NADH dehydrogenase (nuoA to nuoN). They are present in one to three copies each. The nuo genes are mostly clustered at several genomic loci (Table S3) and are most similar to either of the two nuo operons present in Nitrospira defluvii [39], with AA identities between 41% and 90%. As mentioned above, nuoL/M-like genes at loci without other nuo genes might represent subunits of cation antiporters.
The genome furthermore contains a locus encoding all six subunits of a Na+-dependent NADH:quinone oxidoreductase (Nqr or type III NAD dehydrogenase) (Fig. 5, Table S3). The locus is situated on a single contig in the vicinity of transposase genes, indicating that “Ca. N. alkalitolerans” might have received this type of complex I by lateral gene transfer. The gene of subunit E, which takes part in Na+ translocation [69], is most similar to a homolog in the ammonia-oxidizing bacterium Nitrosomonas nitrosa (86% AA identity).
The metabolic model for N. defluvii [39] assumes that two different versions of the H+-dependent complex I (Nuo) are used for forward or reverse electron transport, respectively. Nitrospira possess a canonical Nuo that is likely used for PMF generation during the forward flow of low-potential electrons from the degradation of intracellular glycogen or from hydrogen as an alternative substrate (see also below). In addition, reverse electron transport is essential in NOB to generate reducing power for CO2 fixation. In Nitrospira, a second (modified) form of Nuo with duplicated proton-translocating NuoM subunits might use PMF to lift electrons from quinol to ferredoxin [70]. The reduced ferredoxin is required for CO2 fixation via the rTCA cycle. As expected, “Ca. N. alkalitolerans” possesses these two Nuo forms that are conserved in other characterized Nitrospira members. In addition, the Na+-dependent Nqr complex might function in two directions in “Ca. N. alkalitolerans” as well. During forward electron flow, Nqr would contribute to SMF generation (Fig. 5). Reverse operation of the Nqr could generate NADH while importing Na+, thus utilizing SMF for the reduction of NAD+ with electrons derived from quinol (Fig. 5). Hence, the two types of complex I are likely involved in essential electron transport and the fine-tuning of PMF and SMF. They probably cooperate with the Na+- and the H+-translocating ATPases and the various cation transporters (see above) to adjust the cytoplasmic ion concentrations and the membrane potential in response to the environmental salinity and pH.
In addition to a novel “bd-like” cytochrome c oxidase, which is commonly found in Nitrospira genomes [16, 39], the genome of “Ca. N. alkalitolerans” contains a locus with fused genes for a cbb3-type cytochrome c oxidase (Fig. 5, Table S3) similar to the one present in the marine nitrite oxidizer Nitrospina gracilis [41]. The cbb3-type terminal oxidases usually exhibit high affinities for O2 [71] and may allow “Ca. N. alkalitolerans” to sustain respiration at low oxygen levels.
Interestingly, “Ca. N. alkalitolerans” encodes two different hydrogenases and the accessory proteins for hydrogenase maturation (Fig. 5, Table S3). First, it possesses a group 2a uptake hydrogenase that is also found in N. moscoviensis, which can grow autotrophically on H2 as the sole energy source [16]. Second, “Ca. N. alkalitolerans” codes for a putative bidirectional group 3b (sulf)hydrogenase that also occurs in other NOB and in comammox Nitrospira [18, 41] but has not been functionally characterized in these organisms. Experimental confirmation of H2 utilization as an alternative energy source and electron donor by “Ca. N. alkalitolerans” is pending. However, we assume that this capability would confer ecophysiological flexibility, especially if nitrite concentrations fluctuate and H2 is available at oxic-anoxic boundaries in biofilms or upper sediment layers. While electrons from the group 2a hydrogenase are probably transferred to quinone [16], the group 3b hydrogenase might reduce NAD+ [41] and fuel forward electron transport through the Nuo and Nqr complexes (see above).
Osmoadaptation
The intracellular accumulation of compatible solutes is an important mechanism allowing microorganisms to withstand the high osmotic pressure in saline habitats [55]. “Ca. N. alkalitolerans” has the genetic capacity to synthesize or import the compatible solutes trehalose, glycine betaine, and glutamate (Fig. 5). For trehalose synthesis the gene treS of trehalose synthase (Table S3), which enables trehalose synthesis from maltose, is present. The genes opuD and opuCB for glycine betaine import (Table S3) have been identified in the marine Nitrospina gracilis [41], but not yet in any Nitrospira species. For glutamate synthesis, the genes gltB and gltD were identified (Table S3). They code for the alpha and beta subunits of glutamate synthase, which catalyzes L-glutamate synthesis from L-glutamine and 2-oxoglutarate with NADPH as cofactor. In addition, we identified adaptations of “Ca. N. alkalitolerans” to the low availability of iron and the presence of toxic arsenite in saline-alkaline systems (Supplementary text). More

Site and sampling
We selected twelve monoculture tree stands of the most common tree species in Britain, Scots pine (Pinus sylvestris L.), Sitka spruce (Picea sitchensis Bong. Carr.), pedunculate oak (Quercus robur L.) and common beech (Fagus sylvatica L.). The majority of the stands were experimental sites within the Level II- ICP intensive forest monitoring network (http://icp-forests.net/), with the exception of Covert Wood, Shobdon and Goyt. The Goyt site was added as a high Ndep site as a contrast to the low Ndep Sitka spruce site in Scotland (Fig. 1, Table 1, Supplementary Table 1). For each species, forests were selected with similar soil type and age, but with contrasting Ndep, Sdep and climate, particularly rainfall and temperature, as described in Fig. 1, Table 1 and Supplementary Table 1. Stand information (mean tree height, mean diameter at the breast height and basal area) as measured for target years and for some of the forest stands are shown in Fig. S4.
At each ICP forest site, a plot of 0.25 ha was established in 1995 to carry out monitoring30 and a similar protocol was followed at the Goyt and Shobdon sites. Within each plot, 10 trees were selected for the collection of 3 wood cores per tree by using a 5 mm diameter increment borer, which were placed in paper straws for transport. Sampling was carried out between November 2010 and March 2011. Once in the laboratory, samples were dried at 70 °C for 48 h. Of the three wood cores sampled, one was kept for dendrochronology, with the other two used for stable isotope analyses.
Climate and atmospheric deposition data
Climate data (Temperature, T, Vapour Pressure Deficit, VPD, Precipitation, P) were obtained from automated weather stations at the sites and/or the nearest local meteorological stations (data were provided by the British Atmospheric Data Centre). Annual mean (Ta) and mean maximum (Tamax) values for temperature were calculated from monthly mean and maximum air temperature, T, respectively, and annual precipitation (Pa) was calculated as the sum of total monthly precipitations. Annual VPD was calculated from averaging monthly values obtained from mean monthly maximum temperature and minimum monthly relative humidity. For all the parameters, mean values were also calculated over the growing season, i.e., from May to September. We also considered the standardised precipitation-evapo-transpiration index, SPEI, relative to August, with 1 (SPEI8_1), 2 (SPEI8_2) and 3 (SPEI8_3) months time-scale and SPEI relative to December, with 1 and 12 months time-scale, the latter providing year-cumulated soil moisture conditions. SPEI values were obtained from the global database with 0.5 degrees spatial resolution available online at: https://sac.csic.es/spei/.
Yearly wet nitrogen (Ndep) and sulphur deposition (Sdep) were obtained from measured bulk precipitation and throughfall water volumes at the sites and measured elemental concentrations (NO3−, NH4+ and SO2–4) as previously described30. For the spatial analyses, we considered mean of annual deposition (sNdep and sSdep), obtained as the sum of total (NH4-N + NO3-N for Ndep) wet and dry deposition. The latter were estimated as difference between throughfall and bulk precipitation N fluxes30. For Rogate only 1 year (2010) of monitoring was available. For Goyt site, atmospheric deposition data collected at Ladybower were considered, as the two sites are 30 km apart. For two sites (i.e., Shobdon and Covert Wood), which were not part of the regular ICP forest sites, the wet deposition obtained from the UK 5 × 5 km grid Ndep and Sdep dataset was used4. The estimate included wet and dry NHx-N (NH4, NH3), NOy-N (NO2, NO3, HNO3) and S (SOx = SO2 and SO4) deposition, modelled using FRAME with 2005 emissions data4. However, only the total wet deposition was included in the analyses here, as we previously reported a good agreement between modelled and measured wet Ndep50.
For the temporal analyses, only wet deposition (as calculated from NO3−, NH4+ and SO2–4 concentrations in bulk precipitation) was considered (indicated as aNdep and aSdep), given the uncertainties associated with the quantification of the dry deposition. For instance, when differences between throughfall and bulk precipitation are  More

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