Ecology

Degree of hydrolysisOrganic silages prepared from fat fish (FFS) and lean fish (LFS) had a characteristic tawny brown colour which was accompanied with a strong characteristic salty-fishy odour. At the end of 5 DoF, both FFS and LFS exhibited sluggish liquefaction which increased progressively concomitant with the DoF (Table S1). Liquefaction is an indicator of tissue hydrolysis due to the action of acid. During 35 DoF, the degree of hydrolysis (measured in terms of liquefaction volume) increased progressively with the DoF in both types of ensilages and was relatively higher in LFS compared to FFS on all sampled DoF (Table S1). In general, lipolysis supersedes the proteolysis in all major biochemical processes23. A relatively higher degree of hydrolysis recorded in LFS may be attributed to the presence of a greater proportion of light muscles compared to dark muscles. Relatively greater susceptibility of light muscles to hydrolysis compared to dark muscles might be due to lower lipid content in the former23.Irrespective of fish type, the measured pH values in both types of ensilages (FFS and LFS) were similar (data not shown) and the values showed a progressive increase from 1.0 ± 0.03 (0 DoF) to 6.0 ± 0.03 (35 DoF). Such an increasing trend in pH with the advancement in DoF could be attributable to gradual solubilisation of boney material with the advancement fermentation time24,25,26.Changes in principal biochemical constituentsDuring the 35 DoF, the concentrations of total protein (TP) in both FFS and LFS progressively increased with the DoF and showed significant differences with the advancement of DoF (p  phenylalanine (2.6 ± 0.033)  > serine (2.4 ± 0.033)  > aspartic acid (2.3 ± 0.033)  > alanine (2.1 ± 0.033)  > histidine (1.8 ± 0.033)  > valine (1.6 ± 0.033)  > methionine (1.5 ± 0.033)  > isoleucine (1.5 ± 0.033)  > threonine (1.4 ± 0.033)  > cysteine (0.946 ± 0.033).Figure 1Composition of total amino acids (mg/g) in two types of fish ensilages (FFS—fat fish silage; LFS—lean fish silage) during 35 days of fermentation (DoF). Data are mean ± SD. * p  glutamic acid (4.97 ± 0.033)  > arginine (4.5 ± 0.033)  > phenylalanine (3.38 ± 0.033)  > aspartic acid (2.92 ± 0.033)  > alanine (2.23 ± 0.033)  > methionine (2.19 ± 0.033)  > lysine (1.882 ± 0.033)  > serine (1.881 ± 0.033)  > tyrosine (1.410 ± 0.033)  > glycine (1.219 ± 0.033)  > threonine (0.953 ± 0.033)  > valine (0.945 ± 0.033)  > isoleucine (0.864 ± 0.033)  > histidine (0.417 ± 0.033).A comparative assessment of profiles of TAA in both FSS and LFS during all DoF revealed a similar pattern, albeit with obvious differences in the concentration of few amino acids (Fig. 1). It has been hypothesised that the occurrence of decarboxylation that follows transamination of amino acids as a consequence of increase in pH during fermentation is known to cause a decrement in the concentration of few amino acids, especially valine and isoleucine34. During the present study, the concentrations of histidine, valine, isoleucine, glycine and lysine were significantly higher (p  leucine (3.09 ± 0.003)  > glutamic acid (2.61 ± 0.003)  > alanine (1.83 ± 0.003)  > phenylalanine (1.79 ± 0.003)  > cysteine (1.67 ± 0.003)  > histidine (1.56 ± 0.003)  > aspartic acid (1.54 ± 0.003)  > serine (1.32 ± 0.003)  > lysine (1.16 ± 0.003)  > threonine (1.09 ± 0.003)  > valine (1.07 ± 0.003)  > isoleucine (1.06 ± 0.003) followed by methionine (0.93 ± 0.003)  > tyrosine (0.92 ± 0.003)  > tryptophan (0.72 ± 0.003)  > asparagine (0.57 ± 0.003)  > glutamine (0.15 ± 0.003).Figure 2Composition of free amino acids (mg/g) in two types of fish ensilages (FFS—fat fish silage; LFS—lean fish silage) during 35 days of fermentation (DoF). Data are mean ± SD. * p  More

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Our study uniquely points to differences among scientific disciplines in data availability as published along with the article and upon request from the authors. We demonstrate that in several disciplines such as forestry, materials for energy and catalysis and psychology, critical data are still unavailable for re-analysis or meta-analysis for more than half of the papers published in Nature and Science in the last decade. These overall figures roughly match those reported for other journals in various research fields8,11,13,22, but exceed the lowest reported values of around 10% available data13,23,24. Fortunately, data availability tends to improve, albeit slowly, in nearly all disciplines (Figs. 3, 7), which confirms recent implications from psychological and ecological journals13,31. Furthermore, the reverse trend we observed in microbiology corroborates the declining metagenomics sequence data availability22. Typically, such large DNA sequence data sets are used to publish tens of articles over many years by the teams producing these data; hence releasing both raw data and datasets may jeopardise their expectations of priority publishing. The weak discipline-specific differences among Nature and Science (Fig. 2) may be related to how certain subject editors implemented and enforced stringent data sharing policies.After rigorous attempts to contact the authors, data availability increased by one third on average across disciplines, with full and at least partial availability reaching 70% and 83%, respectively. These figures are in the top end of studies conducted thus far8,22 and indicate the relatively superior overall data availability in Science and Nature compared with other journals. However, the relative rates of data retrieval upon request, decline sharing data and ignoring the requests were on par with studies covering other journals and specific research fields10,12,25,26,28. Across 20 years, we identified the overall loss of data at an estimated rate of 3.5% and 5.9% for initially available data and data effectively available upon request, respectively. This rate of data decay is much less than 17% year−1 previously reported in plant and animal sciences based on a comparable approach24.While the majority of data are eventually available, it is alarming that less than a half of the data clearly stated to be available upon request could be effectively obtained from the authors. Although there may be objective reasons such as force majeure, these results suggest that many authors declaring data availability upon contacting may have abused the publishers’ or funders’ policy that allows statements of data availability upon request as the only means of data sharing. We find that this infringes research ethics and disables fair competition among research groups. Researchers hiding their own data may be in a power position compared with fair players in situations of big data analysis, when they can access all data (including their own), while others have more limited opportunities. Data sharing is also important for securing a possibility to re-analyse and re-interpret unexpected results9,32 and detect scientific misconduct25,33. More rigorous control of data release would prevent manuscripts with serious issues in sampling design or analytical procedures from being prepared, reviewed and eventually accepted for publication.Our study uniquely recorded the authors’ concerns and specific requests when negotiating data sharing. Concerns and hesitations about data sharing are understandable because of potential drawbacks and misunderstandings related to data interpretation and priority of publishing17,34 that may outweigh the benefits of recognition and passive participation in broader meta-studies. Nearly one quarter of researchers expressed various concerns or had specific requests depending on the discipline, especially about the specific objectives of our study. Previous studies with questionnaires about hypothetical data sharing unrelated to actual data sharing reveal that financial interests, priority of additional publishing and fear of challenging the interpretations after data re-analysis constitute the authors’ major concerns12,35,36. Another study indicated that two thirds of researchers sharing biomedical data expected to be invited as co-authors upon use of their data37 although this does not fulfil the authorship criteria6,38. At least partly related to these issues, the reasons for declining data sharing differed among disciplines: while social scientists usually referred to the loss of data, psychologists most commonly pointed out ethical/legal issues. Recently published data were, however, more commonly declined due to ethical/legal issues, which indicates rising concerns about data protection and potential misuse. Although we offered a possibility to share anonymised data sets, such trimmed data sets were never obtained from the authors, suggesting that ethical issues were not the only reason for data decline. Because research fields strongly differed in the frequency of no response to data requests, most unanswered requests can be considered declines that avoid official replies, which may harm the authors’ reputation.Because we did not sample randomly across journals, our interpretations are limited to the journals Nature and Science. Our study across disciplines did not account for the particular academic editor, which may have partly contributed to the differences among research fields and journals. Not all combinations of disciplines, journals and time periods received the intended 25 replicate articles because of the poor representation of certain research fields in the 2000–2009 period. This may have reduced our ability to detect statistically significant differences among the disciplines. We also obtained estimates for the final data availability for seven out of nine disciplines. Although we excluded the remaining two disciplines from comparisons of initial and final data availability, it may have slightly altered the overall estimates. The process of screening the potentially relevant articles chronologically backwards resulted in overrepresentation of more recent articles in certain relatively popular disciplines, which may have biased comparisons across disciplines. However, the paucity of residual year effect and year x discipline interaction in overall models and residual time effect in separate analyses within research fields indicate a minimal bias (Figure S1).We recorded the concerns and requests of authors that had issues with initial data sharing. Therefore, these responses may be relatively more sceptic than the opinions of the majority of the scientific community publishing in these journals. It is likely that the authors who did not respond may have concerns and reasons for declining similar to those who refused data sharing.Our experience shows that receiving data typically required long email exchanges with the authors, contacting other referred authors or sending a reminder. Obtaining data took on average 15 days, representing a substantial effort to both parties39. This could have been easily avoided by releasing data upon article acceptance. On the other hand, we received tips for analysis, caution against potential pitfalls and the authors’ informed consent upon contacting. According to our experience, more than two thirds of the authors need to be contacted for retrieving important metadata, variance estimates or specifying methods for meta-analyses40. Thus, contacting the authors may be commonly required to fill gaps in the data, but such extra specifications are easier to provide compared with searching and converting old datasets into a universally understandable format.Due to various concerns and tedious data re-formatting and uploading, the authors should be better motivated for data sharing41. Data formatting and releasing certainly benefits from clear instructions and support from funders, institutions and publishers. In certain cases, public recognition such as badges of open data for articles following the best data sharing practices and increasing numbers of citations may promote data release by an order of magnitude42. Citable data papers are certainly another way forward43,44, because these provide access to a well-organised dataset and add to the authors’ publication record. Encouraging enlisting published data sets with download and citation metrics in grant and job applications alongside with other bibliometric indicators should promote data sharing. Relating released data in publicly available research accounts such as ORCID, ResearcherID and Google Scholar would benefit both authors, other researchers and evaluators. To account for many authors’ fear of data theft17 and to prioritise the publishing options of data owners, setting a reasonable embargo period for third-party publishing may be needed in specific cases such as immediate data release following data generation45 and dissertations.All funders, research institutions, researchers, editors and publishers should collectively contribute to turn data sharing into a win-win situation for all parties and the scientific endeavour in general. Funding agencies may have a key role here due to the lack of conflicting interests and a possibility of exclusive allocation to depositing and publishing huge data files46. Funders have efficient enforcing mechanisms during reports periods, with an option to refuse extensions or approving forthcoming grant applications. We advocate that funders should include published data sets, if relevant, as an evaluation criterion besides other bibliometric information. Research institutions may follow the same principles when issuing institutional grants and employing research staff. Institutions should also insist their employees on following open data policies45.Academic publishers also have a major role in shaping data sharing policies. Although deposition and maintenance of data incur extra costs to commercial publishers, they should promote data deposition in their servers or public repositories. An option is to hire specific data editors for evaluating data availability in supplementary materials or online repositories and refusing final publishing before the data are fully available in a relevant format47. For efficient handling, clear instructions and a machine-readable data availability statement option (with a QR code or link to the data) should be provided. In non-open access journals, the data should be accessible free of charge or at reduced price to unsubscribed users. Creating specific data journals or ‘data paper’ formats may promote publishing and sharing data that would otherwise pile up in the drawer because of disappointing results or the lack of time for preparing a regular article. The leading scientometrics platforms Clarivate Analytics, Google Scholar and Scopus should index data journals equally with regular journals to motivate researchers publishing their data. There should be a possibility of article withdrawal by the publisher, if the data availability statements are incorrect or the data have been removed post-acceptance30. Much of the workload should stay on the editors who are paid by the supporting association, institution or publisher in most cases. The editors should grant the referees access to these data during the reviewing process48, requesting them a second opinion about data availability and reasons for declining to do so. Similar stringent data sharing policies are increasingly implemented by various journals26,30,47.In conclusion, data availability in top scientific journals differs strongly by discipline, but it is improving in most research fields. As our study exemplifies, the ‘data availability upon request’ model is insufficient to ensure access to datasets and other critical materials. Considering the overall data availability patterns, authors’ concerns and reasons for declining data sharing, we advocate that (a) data releasing costs ought to be covered by funders; (b) shared data and the associated bibliometric records should be included in the evaluation of job and grant applications; and (c) data sharing enforcement should be led by both funding agencies and academic publishers. More

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Data collectionWe systematically searched all peer-reviewed publications that were published prior to May 2021, which investigated the effects of plant diversity on terrestrial C:N:P ratios (i.e., plants, soils, soil microbial biomass, and extracellular enzymes) using the Web of Science (Core Collection; http://www.webofknowledge.com), Google Scholar (http://scholar.google.com), and the China National Knowledge Infrastructure (CNKI; https://www.cnki.net) using the search term: “C:N or C:P or N:P or C:N:P AND plant OR soil OR microbial biomass OR extracellular enzyme OR exoenzyme AND plant diversity OR richness OR mixture OR pure OR polyculture OR monoculture OR overyielding”, and also searched for references within these papers. Our survey also included studies summarized in previously published diversity-ecosystem functioning meta-analyses15,17,20,33. The literature search was performed following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) (Moher, Liberati44; Supplementary Fig. 5).We employed the following criteria to select the studies: (i) they were purposely designed to test the effects of plant diversity on C:N:P ratios, (ii) they had at least one species mixture treatment and corresponding monocultures, (iii) they had the same initial climatic and soil properties in the monoculture and mixture treatment plots. In thirteen publications, several experiments, each with independent controls, were conducted at different locations and were considered to be distinct studies. In total, 169 studies met these criteria (Supplementary Fig. 5 and Supplementary Table 3). When different publications included the same data, we recorded the data only once. When a study included plant species mixtures of different numbers of species, we considered them as distinct observations.For each site, we extracted the means, the number of replications, and standard deviations of the C:N, N:P, and C:P ratios of plants (including leaves, shoots, fine roots, total roots), soils, soil enzymes as well as soil microbial biomass C:N ratios, if reported. Similar to Zhou and Staver45, we collected nine types of soil enzymes and integrated individual soil enzymes into combined enzymes to represent proxies targeting specific resource acquisitions: C-acquisition (average of Invertase, α-Glucosidase, β-1,4-Glucosidase, Cellobiohydrolase, β-1,4-Xylosidase), N-acquisition (average of β-1,4-N-acetylglucosaminidase, Leucine-aminopeptidase, Urease), and P-acquisition (phosphatase). The ratios of each type of enzyme were subsequently calculated, referred to as soil enzyme C:N, C:P, and N:P. When an original study reported the results graphically, we used Plot Digitizer version 2.0 (Department of Physics at the University of South Alabama, Mobile, AL, USA) to extract data from the figures. This resulted in 52 studies for plant C:N ratios, 35 studies for plant N:P ratios, 17 studies for plant C:P ratios, 83 studies for soil C:N ratios, 42 studies for soil N:P ratios, 19 studies for soil C:P ratios, 33 studies for soil microbial biomass C:N ratios, 41 studies for soil enzyme C:N ratios, 40 studies for soil enzyme N:P ratios and 34 studies for soil enzyme C:P ratios (Supplementary Table 3).We also extracted species compositions in mixtures, latitude, longitude, stand age, ecosystem type (i.e, forest, grassland, cropland, pot), mean annual temperature (MAT, °C), management practice (fertilization or not), soil type (FAO classification) and sampled soil depth from original or cited papers, or cited data sources. The mean annual aridity index and solar radiation data were retrieved from the CGIAR-CSI Global Aridity Index data set46 and WorldClim Version 247 using location information. The annual aridity index was calculated as the ratio of the mean annual precipitation to mean annual potential evapotranspiration48. Stand age (SA) was recorded as the number of years since stand establishment following stand-replacing disturbances in forests, and the number of years between the initiation and measurements of the experiments in grasslands, croplands, and pots. Observations were averaged if multiple measurements were conducted during different seasons within a year. The species proportions in plant mixtures were based on the stem density in forests and pots, coverage in croplands, and sown seeds in grasslands. Soil depth was recorded as the midpoint of each soil depth interval49. We employed the weighted averages of soil C:N, C:P, and N:P ratios of monocultures in each study as proxies for the status of background nutrients. For studies that did not report soil C:N, C:P, and N:P ratios of monocultures, we used the initial soil C:N, C:P, and N:P ratios (before experiment establishment, if reported) as proxies for the status of background nutrients. When a study reported the soils, soil microbial biomass or soil enzyme C:N:P data from multiple soil depths, we used the soil C:N, C:P, and N:P ratios of the corresponding depths as background nutrient proxies. For plant C:N:P data, we used the uppermost soil layer C:N, C:P, and N:P ratios as background nutrient proxies, since it contains the majority of the available nutrients essential for plant growth50. We compared the estimates for the data sets with and without pot studies and found that both data sets yielded qualitatively similar results (Supplementary Tables 2 and 4). Thus, we reported results based on the whole data set.We employed two key functional traits to describe the functional composition: ‘leaf nitrogen content per leaf dry mass’ (Nmass, mg g−1), and “specific leaf area” (SLA, mm2 mg−1; i.e., leaf area per leaf dry mass), as they are expected to be related to plant growth rate, resource uptake and use efficiency27, and are available for large numbers of species. We obtained the mean trait values of Nmass and SLA data by using all available measurements for each plant species from the TRY Plant Trait Database51 except for two studies that included the data in their original publication52, or related publications in the same sites53. Functional diversity (FDis) was calculated as functional dispersion, which is the mean distance of each species to the centroid of all species in the functional trait space, based on the two traits together54. The calculation of FDis was conducted using the FD package54.Data analysisThe natural log-transformed response ratio (lnRR) was employed to quantify the effects of plant mixture following Hedges, Gurevitch55:$${{{{{{mathrm{ln}}}}}}}{RR}={{{{{{mathrm{ln}}}}}}}({bar{X}}_{{{{{{mathrm{t}}}}}}}/{bar{X}}_{{{{{{mathrm{c}}}}}}})={{{{{{{mathrm{ln}}}}}}}bar{X}}_{{{{{{mathrm{t}}}}}}}-{{{{{{{mathrm{ln}}}}}}}bar{X}}_{{{{{{mathrm{c}}}}}}}$$
(1)
where ({overline{X}}_{{{{{{rm{t}}}}}}}) and ({overline{X}}_{{{{{{rm{c}}}}}}}) are the observed values of a selected variable in the mixture and the expected value of the mixture in each study, respectively. If a study has multiple richness levels in mixtures (for example, 1, 4, 8, and 16), lnRR was calculated for the species richness levels 4, 8, and 16, respectively. We calculated ({overline{X}}_{{{{{{rm{c}}}}}}}) based on weighted values of the component species in monocultures following Loreau and Hector39:$$overline{{X}_{{{{{{mathrm{c}}}}}}}}=sum ({p}_{i}times {m}_{i})$$
(2)
where mi is the observed value of the selected variable of the monoculture of species i and pi is the proportion of species i density in the corresponding mixture. When a study reported multiple types of mixtures (species richness levels) and experimental years, ({overline{X}}_{{{{{{rm{t}}}}}}}) and ({overline{X}}_{{{{{{rm{c}}}}}}}) were calculated separately for each mixture type and experimental year.In our data set, sampling variances were not reported in 37 of the 169 studies, and no single control group mean estimate is present with standard deviation or the standard error reported. Like the previous studies6,56, we employed the number of replications for weighting:$${W}_{{{{{{mathrm{r}}}}}}}=({N}_{{{{{{mathrm{c}}}}}}}times {N}_{{{{{{mathrm{t}}}}}}})/({N}_{{{{{{mathrm{c}}}}}}}+{N}_{{{{{{mathrm{t}}}}}}})$$
(3)
where Wr is the weight associated with each lnRR observation, and Nc and Nt are the number of replications in monocultures and corresponding mixtures, respectively.The C:N, N:P, and C:P ratios of plants, soils, and soil enzymes, as well as soil microbial biomass C:N ratios were considered as response variables and analyzed separately. To validate the linearity assumption for the continuous predictors, we initially graphically plotted the lnRR vs. individual predictors, including FDis, SA, and background nutrient status (N, i.e., C:N, C:P, and N:P ratios of soil) and identified logarithmic functions as an alternative to linear functions. We also statistically compared the linear and logarithmic functions with the predictor of interest as the fixed effect, and “study” and measured plant parts (i.e., leaves, shoots, fine roots, total roots) or soil depth as the random effects, using Akaike information criterion (AIC). The random factors were used to account for the autocorrelation among observations within each “Study”, and potential influences of variation in measured plant parts and soil depth. We found that the linear FDis, SA, and N resulted in lower, or similar AIC values (∆AIC  More

AOA kinetic propertiesIn this study we investigated the kinetic properties of 12 AOA strains, including representatives from all four described AOA phylogenetic lineages: Nitrosopumilales (Group I.1a), ‘Ca. Nitrosotaleales’ (Group I.1a-associated), Nitrososphaerales (Group I.1b), and ‘Ca. Nitrosocaldales’ (thermophilic AOA clade) [58, 59] (Fig. 1). These AOA isolates and enrichments were obtained from a variety of habitats (marine, soil, sediment, hot spring) and have optimal growth pH and temperatures ranging from 5.3–7.8 to 25–72 °C, respectively (Table S2). The substrate-dependent oxygen consumption rates for all AOA tested followed Michaelis–Menten kinetics. Below, the kinetic properties of these AOA are put into a broader context with comparisons to previously characterized AOM. It is important to note that the whole cell kinetic properties, such as substrate competitiveness, detailed here were generated from instantaneous activity measurements in the absence of growth. It is unknown how the substrate competitiveness of nitrifiers may or may not differ from their competitiveness when cellular processes such as growth, division, stress, and repair are involved.Fig. 1: Phylogenetic reconstruction of ammonia oxidizing archaea (AOA) rooted on closely related non-AOA members of the “Thaumarchaeota”.Black taxon labels correspond to AOA from cultures or enrichments. Gray taxon labels correspond to representative metagenome assembled genomes from release 05-RS95 of the genome taxonomy database [41]. AOA that were kinetically characterized in the current study are highlighted in gray and AOA that were previously characterized are indicated with an asterisk (*). The phylogeny was calculated with IQ-TREE under model LG + F + R6 using an alignment of 34 universal genes (43 markers) produced by CheckM [42]. Support values (UFboot) greater than 95% for bipartitions are shown with a black circle and support values between 80% and 95% are shown with a gray circle. Order designations reflect lineages proposed by Alves et al. [59]. The scale bar indicates amino acids changes per site.Full size imageNitrosopumilales (Group I.1a)From this lineage, three mesophilic marine (N. piranensis D3C, N. adriaticus NF5, and N. maritimus SCM1) [3, 60], two agricultural soil (N. koreense MY1 and ‘Ca. N. chungbukensis’ MY2) [61, 62] and one thermal spring isolate (‘Ca. N. uzonensis’ N4) [40] were kinetically characterized (Fig. S1). These AOA all displayed a high substrate affinity for NH3, ranging from ~2.2 to 24.8 nM. Thus, all characterized Nitrosopumilales, and not just marine isolates, are adapted to oligotrophic conditions. All possess substrate affinities several orders of magnitude higher (lower Km(app)) than any characterized AOB, with the exception of the recently characterized acidophilic gammaproteobacterial AOB ‘Ca. Nitrosacidococcus tergens’ [55] (Fig. 2a). This finding appears to support the widely reported hypothesis that regardless of the environment, AOA in general are adapted to lower substrate concentrations than AOB [22, 29, 30]. However, as described later, this trend does not apply to all AOA.Fig. 2: Substrate-dependent oxidation kinetics of ammonia-oxidizing microorganisms.The (a) apparent substrate affinity (Km(app)) for NH3, (b) specific substrate affinity (a°) for NH3, (c) Km(app) for total ammonium, (d) a° for total ammonium, and (e) maximum oxidation rate (Vmax), of AOA (red), comammox (blue), and AOB (black) are provided. Symbols filled with light gray represent previously published values from reference studies (references provided in Materials and Methods). The four different gradations of red differentiate the four AOA phylogenetic lineages: (I) Nitrosopumilales, (II) ‘Ca. Nitrosotaleales’, (III) Nitrososphaerales, and (IV) ‘Ca. Nitrosocaldales’. Measurements were performed with either pure (circles) or enrichment (diamonds) cultures. Multiple symbols per strain represent independent measurements performed in this study and/or in the literature. The individual Michaelis–Menten plots for each AOM determined in this study are presented in Figs. S1, S3–5, and S8. Note the different scales.Full size imageAs the substrate oxidation kinetics of the marine AOA strain, N. maritimus SCM1, originally characterized by Martens-Habbena et al. [29] have recently been disputed [63], they were revisited in this study (Fig. S2). With the same strain of N. maritimus used in Hink et al. [63] (directly obtained by the authors), we were able to reproduce (Figs. S1 and S2) the original kinetic properties of N. maritimus SCM1 reported in Martens-Habbena et al. [29] ruling out strain domestication during lab propagation as cause for the observed discrepancy. Therefore, the reported differences in the literature possibly reflect the measurements of two distinct cellular properties, Km(app) [29] and Ks [63], representing the half saturation of activity and growth, respectively. In addition, differences in pre-measurement cultivation and growth conditions could also contribute to these unexpected differences [63, 64]. More details are provided in the Supplementary Results and Discussion.‘Ca. Nitrosotaleales’ (Group I.1a-associated)The only isolated AOA strains in this lineage ‘Ca. Nitrosotalea devanaterra’ Nd1 and ‘Ca. Nitrosotalea sinensis’ Nd2, are highly adapted for survival in acidic environments and grow optimally at pH 5.3 [25, 65]. Both display a relatively low affinity for total ammonium (Km(app) = 3.41–11.23 μM), but their affinity for NH3 is among the highest calculated of any AOA characterized (Km(app) = ~0.6–2.8 nM) (Fig. 2a,c, and Fig. S3). This seemingly drastic difference in substrate affinity for total ammonium versus NH3 is due to the combination of the high acid dissociation constant of ammonium (pKa = 9.25) and the kinetic properties of these strains being carried out at pH 5.3. The very limited availability of NH3 under acidic conditions has led to the hypothesis that these acidophilic AOA should be highly adapted to very low NH3 concentrations and possess a high substrate affinity (low Km(app)) for NH3 [66, 67]. Our data corroborate this hypothesis.Nitrososphaerales (Group I.1b)The AOA strains ‘Ca. N. nevadensis’ GerE (culture information provided in Supplementary Results and Discussion), ‘Ca. N. oleophilus’ MY3 [68] and ‘Ca. N. franklandus’ C13 [69] were kinetically characterized, and contextualized with the previously published kinetic characterization of Nitrososphaera viennensis EN76 and ‘Ca. Nitrososphaera gargensis’ [5]. Together, the Nitrososphaerales AOA possess a wide range of affinities for NH3 (Km(app) = ~0.14–31.5 µM) (Fig. 2a and Fig. S4). Although this range of NH3 affinities spans more than two orders of magnitude, none of the Nitrososphaerales AOA possess an affinity for NH3 as high as any Nitrosopumilales or ‘Ca. Nitrosotaleales’ AOA (Fig. 2a).The moderately thermophilic enrichment culture ‘Ca. N. nevadensis’ GerE displayed a higher substrate affinity (lower Km(app)) for NH3 (0.17 ± 0.03 µM) than the other characterized AOA strains within the genus Nitrososphaera (Fig. 2a). In contrast, ‘Ca. N. oleophilus’ MY3 and ‘Ca. N. franklandus’ C13, which belong to the genus Nitrosocosmicus, had the lowest affinity (highest Km(app)) for NH3 (12.37 ± 6.78 μM and 16.32 ± 14.11 μM, respectively) of any AOA characterized to date. In fact, their substrate affinity is comparable to several characterized AOB (Fig. 2a). In this context it is interesting to note that several Nitrosocosmicus species have been shown to tolerate very high ammonium concentrations [68,69,70], a trait usually associated with AOB [24, 54]. The low substrate affinity observed in Nitrosocosmicus AOA correlates with the absence of a putative Amt-type high affinity ammonium transporter in the genome of any sequenced Nitrosocosmicus species to date [68, 69, 71].
‘Ca. Nitrosocaldales’ (Thermophilic AOA lineage)The thermophilic AOA enrichment cultures, ‘Ca. Nitrosocaldus yellowstonensis’ HL72 [72] and ‘Ca. N. tenchongensis’ DRC1 (culture information provided in Supplementary Results and Discussion), possess affinities for NH3 (Km(app) = ~1.36 ± 0.53 μM and ~0.83 ± 0.01 μM; respectively comparable to AOA within the genus Nitrososphaera (Fig. 2a). Notably, the substrate oxidation rate of these two AOA quickly dropped with increasing substrate concentrations after Vmax was reached (Fig. S5). This trend was not observed with any other AOA tested here and may reflect an increased susceptibly to NH3 stress at high temperatures, as the free NH3 concentration increases with increasing temperatures [33]. It should be noted that both of these AOA cultures are enrichment cultures, as no member of the ‘Ca. Nitrosocaldales’ has been isolated to date.Together, these results highlight that the substrate affinity for NH3 among AOA species is much more variable than previously hypothesized, spanning several orders of magnitude and in some cases overlapping with the substrate affinity values of characterized non-oligotrophic AOB. In addition, the substrate affinity of AOA is related, to a certain degree, to their phylogenetic placement within each of the four AOA phylogenetic lineages mentioned above (Fig. 2). Although the substrate affinity ranges of these AOA lineages overlap, the link between AOA phylogeny and kinetic properties provides deeper insights into the physiological and evolutionary differences among AOA species. As a limited number of AOA have been isolated and characterized to date, the continued isolation and characterization of AOA from underrepresented phylogenetic lineages and new habitats is needed. While substrate affinity is certainly one of multiple factors that contribute to niche differentiation between AOM in general, it may also present a previously under acknowledged factor in AOA niche differentiation.Maximum substrate oxidation rates (V
max)The normalized maximum substrate oxidation rate of all the AOA characterized to date only span about one order of magnitude from 4.27 to 54.68 μmol N mg protein−1 h−1. These normalized AOA Vmax values are in the same range as the recorded Vmax for the comammox N. inopinata (~12 μmol N mg protein−1 h−1) and the marine AOB strain Nitrosococcus oceani ATCC 19707 (~38 μmol N mg protein−1 h−1) but are lower than the normalized Vmax of the AOB Nitrosomonas europaea ATCC 19718 (average of 84.2 μmol N mg protein−1 h−1; Fig. 2e). The high Vmax value for N. europaea is the only real outlier among the AOM characterized to date and it remains to be determined whether other AOB related to N. europaea also possess such a high Vmax or if members of the Nitrosomonadales possess a broad range of Vmax values. Similarly, as additional comammox strains become available as pure cultures their kinetic characterization will be vital in understanding the variability of these ecologically important parameters within this guild.Specific substrate affinity (a°)Although the Km(app) and Vmax of AOM can be compared by themselves and provide useful information on cellular properties, the ability of an AOM to scavenge (and compete for) substrate from a dilute solution is most appropriately represented by the a°, which takes into account both the cellular Km(app) and Vmax [28]. In previous studies, the a° of AOM has been calculated using the Km(app) value for total ammonium (NH3 + NH4+) and not the Km(app) value for NH3 [5, 29]. Calculating the a° based on the Km(app) value for total ammonium allows for the a° of AOM to be compared with the a° of microorganisms that do not use NH3 as a sole energy generating substrate, such as ammonium assimilating heterotrophic bacteria or diatoms [29]. While this is useful when evaluating competition for total ammonium in mixed communities or environmental settings, an a° calculated using the Km(app) value for NH3 may be more useful when directly comparing the interspecies competitiveness of AOM for the following reasons: (i) our data support the hypothesis that the substrate for all AOM is NH3 and not NH4+ (see below) and (ii) the Km(app) value for total ammonium is more dependent on the environmental factors it was measured at (e.g., pH, temperature, salinity) than the Km(app) for NH3.All characterized AOA (with the exception of representatives of the genus Nitrosocosmicus) and the comammox bacterium N. inopinata possess much higher a° for total ammonium or NH3 (~10–3000×) than the AOB, N. oceani or N. europaea (Fig. 2b–d), indicating that they are highly competitive in environments limited in either total ammonium or only NH3. However, due to the low number of published normalized Vmax values for AOB, a° could only be calculated for these two AOB representatives. Thus, extrapolations to the a° of all AOB species, based solely on these observations should be approached with caution.The low variation in experimentally measured Vmax values (Fig. 2e) across all measured AOM in combination with the high variation in Km(app) values leads to a strong relationship between cellular a° and the reciprocal of Km(app) (Fig. 3) according to Eq. 2 (see Materials and Methods). AOM adapted to oligotrophic (low substrate) conditions should possess both a high substrate affinity (low Km(app)) and a high ao [28]. Therefore, the AOM best suited for environments limited in total ammonium are the AOA belonging to the Nitrosopumilales and the comammox isolate N. inopinata, (top right corner of Fig. 3a). Overall, when looking at solely NH3 or total ammonium, the separation of species in these plots remains similar, with the exception that the acidophilic AOA belonging to the ‘Ca. Nitrosotaleales’ are predicted to be best suited for life in environments limited in NH3 (Fig. 3b). The adaptation correlates well with the fact the AOA ‘Ca. Nitrosotalea devanaterra’ Nd1 and ‘Ca. Nitrosotalea sinensis’ Nd2 were isolated from acidic soils with a pH of 4.5 and 4.7, respectively [25, 65], where the NH3:NH4+ equilibrium is heavily shifted toward NH4+.Fig. 3: The reciprocal relationship between the substrate affinity (Km(app)) and specific substrate affinity (a°) of ammonia-oxidizing microorganisms (AOM).Reciprocal plots for both (a) total ammonium and (b) NH3 are depicted. The Km(app) and a° values correspond to the values presented for pure AOM isolates in Fig. 2. Data for AOA (red), comammox (blue), and AOB (black) are shown. The correlation (R2) indicates the linear relationship between the logarithmically transformed data points.Full size imageIn either case, when looking at NH3 or total ammonium, the AOA belonging to the genus Nitrosocosmicus (‘Ca. N. oleophilus’ MY3 and ‘Ca. N. franklandus’ C13) and AOB populate the lower left section of these plots, indicating that they are not strong substrate competitors in NH3 or total ammonium limited environments (Fig. 3). Here, the Vmax of all the AOM reported spans ~10×, whereas the difference in Km(app) spans about five orders of magnitude. If the cellular kinetic property of Vmax really is so similar across all AOB, AOA, and comammox species (Fig. 2e) compared to the large differences in Km(app) values, then substrate competitiveness can be predicted from an AOMs Km(app) for either NH3 or total ammonium (Fig. 2a–c). This may prove especially helpful when characterizing enrichment cultures, where normalizing ammonia-oxidizing activity to cellular protein in order to obtain a comparable Vmax value is not possible. However, there is also a need for more kinetically characterized AOB and comammox species to confirm this hypothesis. In addition, when comparing AOM, differences in the Vmax cellular property will play a larger role, the closer the Km(app) values of the AOM strains are. This is important to consider when comparing AOM from similar habitats and likely adapted to similar substrate concentrations.The effect of environmental and cellular factors on AOA kinetic propertiesThe concentration of NH3 present in a particular growth medium or environment can vary by orders of magnitude, based solely on the pH, temperature, or salinity of the system [73]. This is notable because at a given total ammonium concentration, the concentration of NH3 is ~10 times higher at 70 °C versus 30 °C and ~1000 times lower at pH 5.3 versus pH 8.4 (representative of maximum ranges tested). While it should be recognized that in our dataset no AOM were included that have a pH optimum between 5.3 and 7.0, the effect of pH and temperature on the ammonia oxidation kinetics of AOM must be considered in order to understand their ecophysiological niches. However, there was no correlation between the kinetic properties of AOM (Km(app), Vmax, and a°) measured in this study and their optimal growth temperature or pH. This lack of correlation between AOM species kinetic properties and growth conditions does not imply that the cellular kinetic properties of an individual AOM species will remain the same over a range of pH and temperature conditions. Therefore, we investigated the effect of pH and temperature variation on the substrate-dependent kinetic properties of the AOA strain ‘Ca. N. oleophilus’ MY3, and the effect of pH on the comammox strain N. inopinata. Here, the AOA ‘Ca. N. oleophilus’ MY3 was selected based on the fact that it is a non-marine, mesophilic, pure culture, that does not require external hydrogen peroxide scavengers for growth. These traits are shared with the previously characterized AOB, N. europaea [35], and the comammox organism, N. inopinata (this study) and thus facilitate comparison.The effect of temperatureThe effects of short-term temperature changes on the substrate-dependent kinetic properties of ‘Ca. N. oleophilus’ MY3 were determined. Temperature shifts of 5 °C above and below the optimal growth temperature (30 °C) had no effect on the Km(app) for total ammonium. However, the Km(app) for NH3, Vmax, and a° of ‘Ca. N. oleophilus’ MY3 all increased with increasing temperatures (Fig. S6). Therefore, as temperature increased, ‘Ca. N. oleophilus’ MY3 displayed a lower substrate affinity (higher Km(app) for NH3) but would be able to turnover substrate with a higher Vmax and better compete for substrate with a higher a°. Increasing AOA Km(app) values for NH3 with increasing temperatures have also been observed across studies with N. viennensis EN76 (Fig. S2), and this is discussed in more detail in the Supplementary Results and Discussion. In addition, similar observations have previously been made for AOB strains belonging to the genus Nitrosomonas [33, 34]. The increase in Vmax and a° can be explained in terms of the Van’t Hoff rule (reaction velocity increases with temperature) [74], or in terms of a temperature sensitivity coefficient (Q10; change in reaction velocity over 10 °C) [75]. Here, the maximal reaction velocity of ‘Ca. N. oleophilus’ MY3, displays a relative Q10 of 2.17 between 25 and 35 °C, which is in line with more general microbial respiration measurements [75, 76].The increase in Km(app) for NH3 (lower NH3 affinity) with increasing temperature is less straightforward to interpret. As this is a whole cell measurement, the observed differences may result from either broad cellular changes or from changes in individual enzymes involved in the ammonia oxidation pathway specifically. At the cellular level, changes in the proteinaceous surface layer (S-layer) or lipid cell membrane could affect substrate movement/transport and enzyme complex stability. It has been suggested that the negatively charged AOA S-layer proteins act as a substrate reservoir, trapping NH4+ and consequently increasing the NH3 concentration in the AOA pseudo-periplasmic space [77]. It is interesting to note that sequenced representatives from the genus ‘Ca. Nitrosocosmicus’ lack the main S-layer protein (slp1) found in all Nitrosopumilales, Nitrososphaerales, and ‘Ca. Nitrosotaleales’ sequenced isolates [71], although it remains to be demonstrated whether ‘Ca. Nitrosocosmicus’ members actually lack a S-layer or form S-layers composed of other proteins. In addition, it has been demonstrated that elevated temperatures significantly alter the lipid composition in the AOA cell membrane [78, 79]. However, it is unclear how differences in the cell membrane or S-layer composition between AOA species may affect the observed kinetic properties. In this context it is important to note that on the single enzyme level, previous studies have shown the same trend of decreasing substrate affinity and increasing maximal reaction velocity with increasing temperatures, due to altered protein structures and an increased enzyme-substrate dissociation constant [80, 81].Notably, differing optimum growth and activity conditions were previously determined for the marine AOB strain Nitrosomonas cryotolerans [34]. These observations raise interesting, albeit unanswered, questions about why the growth and activity temperature optima are or can be uncoupled in AOM, and what this means for AOM niche differentiation and their competitiveness in-situ. Moving forward, investigations into the growth and cellular kinetic properties of AOM across a range of environmental factor gradients will be essential in understanding competition between AOM in engineered and environmental systems.The effect of pHThe effects of short-term pH changes on the substrate-dependent kinetics of ‘Ca. N. oleophilus’ MY3 and N. inopinata were determined. The Vmax of both ‘Ca. N. oleophilus MY3’ and N. inopinata were stable at 37.3 ± 6.6 μmol N mg protein−1 h−1 and 11.2 ± 2.5 μmol N mg protein−1 h−1, respectively, in medium with a pH between ~6.5 and ~8.5 (Table S3). The Km(app) for total ammonium of ‘Ca. N. oleophilus MY3’ and N. inopinata decreased by more than an order of magnitude (~11×) across this pH range, while the Km(app) for NH3 remained more stable, increasing only 3–4 times (Fig. 4). This stability of the Km(app) for NH3 compared with the larger change in the Km(app) for total ammonium across this pH range suggests that the actual substrate used by AOA and comammox is indeed the undissociated form (NH3) rather than the ammonium ion (NH4+), as previously demonstrated for AOB [34, 35, 54, 82]. As these kinetic measurements were performed with whole cells, the change in Km(app) for NH3 across this pH range may be due to cellular effects of the differing pH values unrelated to the direct ammonia oxidation pathway. The changes in Km(app) for NH3 and Km(app) for total ammonium demonstrated here for ‘Ca. N. oleophilus’ MY3 and N. inopinata are similar to what has been observed for AOB. That AOA and AOB utilize the NH3 as a substrate, aligns with the fact that both are competitively inhibited by the non-polar acetylene compound [83, 84].Fig. 4: The effect of medium pH on the substrate affinity of ‘Ca. N. oleophilus MY3’ and N. inopinata.The substrate affinities for both (a,b) NH3 and (c,d) total ammonium (NH3 + NH4+) are provided. Individual substrate affinity values determined at each pH are shown as single points (circles). The boxes represent the first and third quartiles (25–75%) of the substrate affinity range under each condition. The median (line within the boxes) and mean substrate affinity (black diamonds) values are also indicated. The whiskers represent the most extreme values within 1.58× of quartile range. The variation of the substrate affinity values across the entire tested pH range are indicated in each panel. In all four instances there was a significant difference between the affinity at the lowest pH and the highest pH, as determined by a Student’s t test (p  More

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