Ecology

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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Biostimulation of ureolytic communitiesEnrichmentThe native communities of the soils were successfully grown in the enrichment media (NB5U). The cultivated communities after two subcultures were serially diluted (10–2 to 10–6) and were spread with a sterile loop over Nutrient agar plates supplemented with 2% urea. Later, 36 morphologically distinct single colonies were obtained on the urea agar base plates based on visual observation.Isolation, identification, and characterization of the ureolytic isolatesOut of 36 isolated bacteria, six isolates (BS1, BS2, BS3, BS4, LS1, and LS2) were selected after checking for the urease activity test on the urea agar base (UAB) plate. These selected isolates turned the color of the UAB plate from orange to pink within 12 h. The 16 s rRNA sequence revealed the isolates as relatives of Sporosarcina pasteurii (SP). The details of the identified isolates are provided by NCMR (details in Supplementary Table 1). The biochemical characterization (details in Supplementary Table 2) of the isolates revealed that all the isolates are Gram-positive. All the isolates were rod-shaped, endospore-forming, urease, and oxidase positive. All the isolates were not able to utilize the Lysine and ONPG, contrary to SP.Further investigation of the isolated sequence was done via the NCBI database. The sequences were submitted to the GenBank database of the NCBI (National Center for Biotechnology Information) under the accession number MW024144 to MW024149. The BLAST analysis suggested that these strains are close relatives and indicate the possibility of being novel strains of the Sporosarcina family. We found that the isolate BS1 and BS2 had 96.62% (coverage 100%) and 96.22% (coverage 99%) identity with Sporosarcina siberinisis (NCBI accession number NR 134188). BS3 had 98.8% identity (coverage 97%) with Sporosarcina pasteurii (NCBI accession number NR 104923). BS4 and LS1 had 97.4% (coverage 99%) and 97.37% identity (coverage 100%) with Sporosarcina soli (NCBI accession number NR 043527). Contrarily, LS2 was found to be related to the Pseudogracilibacillus family. LS2 was observed to be closely related to Pseudogracilibacillus auburnensis P-207 with 97.06% identity (96% coverage). Based on these findings, the Phylogenetic tree was constructed with bootstrap (1000 replicates) considering the reference sequences obtained from the BLAST analysis, as shown in Fig. 3. The threshold criteria to differentiate two species is defined as 98.65% similarity score with the reference culture from databank40, while another study has suggested that in case of similarity index is  > 99%, the unknown isolate should be assigned to a species, and if the unknown isolates have similarity score between 95 to 99% to a reference sequence, the isolate should be assigned to the genus41. However, further investigation is suggested to conclude if the reported strains are novel or merely mutants of the reference strains of the databank. Similar observations were made at Graddy et al.22, where the majority of the isolated strains (47 out of 57) from bio-stimulation soil tanks were found to be strains of the Sporosarcina genus. It is worth noting that the soil enrichment media for stimulation was rich in urea, similar to Gomez et al.42 and Graddy et al.22, which is conditional stress for selective stimulation of ureolytic microorganisms. Moreover, the isolated strains were screened based on morphology and qualitative urease activity.Figure 3Neighbor-joining phylogenetic tree based on the 16S rRNA sequence of the isolates and reference sequence from the GenBank database (NCBI).Full size imageEvaluation of biocementation potential of the isolated strainsGrowth and pHThe various parameters of the biocementation potential of the isolated strains in comparison with Sporosarcina pasteurii (SP) have been plotted in Fig. 4. The growth characteristics of the isolates in NBU media and pH during growth have been represented in Fig. 4a and b. The initial pH of the growth media is kept at 7.5. It was observed that the pH of the growth media rises to 9.5 within 24 h of growth, indicating that these strains favor an alkaline environment to grow similar to SP43. All the isolates start growing when the pH of the media rises to 8.5 or above. Isolate LS2 was observed to have slower growth when compared with other isolates. This can be explained as LS2 belongs to different genera (Pseudogracilibacillus).Figure 4(a) Growth characteristics, (b) pH, (c) specific urease activity, (d) calcium utilization rate, and (e) carbonate precipitation rate of the isolates and consortia.Full size imageSpecific urease activityThe specific urease activities of the isolates were found to be comparable with SP (shown in Fig. 4c). Based on the provided NBU media and growth condition, the specific urease activity of SP is found to be 173.44 mM urea hydrolyzed h−1 (OD600)−1, which is around 2.9 mM urea hydrolyzed min−1 (OD600)−1. The specific urease activity of the isolate BS3 was observed to be maximum as 186.6 mM urea hydrolyzed h−1 (OD600)−1 during a growth period of 24 h and pH  > 9. Consortia also demonstrated significant urease activity as 160 mM urea hydrolyzed h−1 (OD600)−1 at a growth period of 48 h. The maximum ureolytic activity in BS1 was observed after 72 h of growth with a value of 106.67 mM urea hydrolyzed h−1 (OD600)−1. Maximum specific urease activity of the isolate BS2, BS4, and LS1 was observed to be 160.2, 120, and 173.4 mM urea hydrolyzed h−1 (OD600)−1 respectively after a growth duration of 48 h. LS2 demonstrated the maximum specific urease activity of 146.4 mM urea hydrolyzed h−1 (OD600)−1. The observed order of specific urease activity at 24 h of growth period is BS3  > SP  > Consortia  > LS1  > BS2  > BS4  > LS2  > BS1. As the urease activity of the strains depends on the growth media, urea content, and environmental conditions such as pH and Temperature44, we considered the conditions at the riverbank at the time of isolation, and the pH and temperature of the growth media were set at 7.5 and 37 degrees Celsius. The specific urease measured by the electrical conductivity method is reported to be between 3 to 9.7 mM urea hydrolyzed min−1 (OD600)−1 in yeast-extract urea media at pH 7 and temperature 30 degrees Celsius43. It is reported around 5 mM urea hydrolyzed min−1 (OD600)−1 in the nutrient broth urea (2%) media at a temperature of 25 degrees Celsius44. The comparative analysis of the urease activity (measured by electrical conductivity method) was done considering SP as positive control in this study. The maximum specific urease activities of all isolates were found to be in a range of 106.67 to 186.67 mM urea hydrolyzed h−1 (OD600)−1 (1.78 to 3.11 mM urea hydrolyzed min−1 OD600–1), which indicates that all of the isolated strains are capable of biocementation43,45.Calcium utilization and carbonate precipitation potentialIt was experimentally observed that the depletion of the supplemented soluble calcium in the precipitation media (PM) was corresponding to the ureolytic activities of the isolated strains. Within 48 h of introducing 1% bacteria (OD600 = 1) in the precipitation media, the soluble calcium chloride (50 mM) was utilized to precipitate carbonate crystals, as illustrated in Fig. 4d. Within 12 h of the inoculation period, BS3 was able to utilize 75% of the supplied calcium, while SP was able to utilize only 62.5% of the soluble calcium. The order of the calcium utilization potential in the isolates was observed as BS3 ≥ LS2  > L.S.1  > Consortia  > SP  > BS4  > BS2  > BS1 during the inoculation period. Contrarily, LS2, despite being a comparatively slow urease-producing bacteria, was able to utilize calcium ions at par with other isolates. Negligible changes were observed in the soluble calcium concentration of the control group eliminating the possibility of abiotic precipitation.The carbonate precipitation rate for each isolate (1% at OD600 = 1) for the 50 mM cementation media is plotted in Fig. 4e. The isolate BS3 with maximum ureolytic activity (specific urease activity 186.6 mM urea hydrolyzed min−1 OD600–1) precipitated the highest carbonate crystals after 96 h of the incubation period. BS3 precipitated 438 mg/100 ml of carbonate crystals, which is around 87.66% precipitation from the total supplied CaCl2, while precipitation with SP was quantified as 389 mg/100 ml (78%). The precipitation in consortia was observed to be 407 mg/100 ml (81%), which is slightly higher than SP. Precipitation in other isolates was found to be significantly lower than isolate BS3. Isolate BS1and BS2 precipitated 334 mg/100 ml (67%) and 343 mg/100 ml (69%) of carbonate crystals respectively, whereas isolate LS1 and LS2 precipitated around 357 mg/100 (71%) ml of carbonate crystals each. Isolate BS4 precipitated minimum carbonate crystals 292 mg/100 ml (58%). No precipitation was observed in the negative control set. Low concentrations of bacterial cells (1%) were considered in this experiment to slow down the urea hydrolysis in order to differentiate the calcium utilization potential of the isolated strains. This approach was modified from Dhami et al.46, and our results show agreement with their finding where 1% of SP cells depletes the 25 mM of CaCl2 within 24 h. It was observed that all the isolates took approximately 48 h to deplete the 50 mM CaCl2. The depletion of soluble calcium concentration was rapid in the initial 24 h in all the isolates. After 48 h, the residual soluble calcium was observed to be in the range of 2.5–5 mM in all the isolates (except BS1 and BS2), which might be due to loss of super-saturation caused by the unavailability of nutrient for bacterial cells to continue urea hydrolysis in the precipitation media13,43,47. The mineralogy of precipitated carbonate polymorphs (calcite, aragonite, vaterite) and the residual calcium are also influenced by pH, temperature, saturation index, dissolved organic carbon concentration, and the Ca2+ /CO32−ratio along with the presence of metabolites in the precipitation media13,47,48. As maximum precipitation was recovered with the isolate BS3, the isolate BS3 was selected for further investigation on soil improvement.Microstructure analysis of the precipitatesThe FESEM images of the carbonate crystal precipitated from BS3 were investigated further. The shape of the precipitated crystals was observed to be rhombohedral and trigonal (Fig. 5a). The average size of the crystals was observed in a range of 25 to 50 microns. The entrapped bacteria and rod-shaped bacterial imprints were identified (Fig. 5b), indicating that the bacteria acted as a nucleation site14. The smaller crystals were observed to coagulate in layers to develop larger calcite crystals. The entrapped bacteria were noticed on the grown and coagulated calcite crystal in Fig. 5c. After taking the FESEM image (Fig. 5a) of the precipitate, EDX analysis was conducted, and the elemental composition suggested an abundance of calcium, carbon, and oxygen, which indicates the presence of calcium carbonate crystals (Supplementary Fig. 1). XRD analysis was conducted to confirm the mineralogy of the precipitates, and the majority of the observed peaks of the XRD plot belonged to calcite, which is consistent with the observation of rhombohedral crystal shapes in the FESEM image. The XRD analysis also suggested an insignificant presence of aragonite in the precipitates.Figure 5FESEM images of the calcite precipitated from BS3 (a) Coagulated crystals (b) Bacterial imprints, (c) Entrapped bacteria on the precipitates.Full size imageApplication of native communities on riverbank soil and its influence on soil strengthNeedle penetration resistance of treated soilThe average NPI (N/mm) for different cases has been shown in Fig. 6a. No notable resistance was observed in the loose untreated sand (control) against the needle penetration. With one bio cementation cycle treatment, the consortia-treated soil sample (Consortia-BC1) demonstrated a higher value of NPI (5.15 N/mm) than SP-BC1 (4.19 N/mm) and BS3-BC1 (4.64 N/mm). The increase in the biocementation cycle treatment significantly improved the needle penetration resistance. Sample BS3-BC2 showed 116% improvement with the NPI value of 10.03 N/mm when compared to one cycle treated sample BS3-BC1. A similar trend was observed in the sample BS3-BC3 (NPI = 16.12 N/mm), which showed around 347% improvement in NPI when compared to sample BS3-BC1. From the needle penetration test, it was evident that the penetration resistance of treated soil improves significantly with the increased level of biocementation cycles, indirectly indicating an improvement in the soil erodibility resilience. Since non-uniformity is one of the undesired traits of MICP, a contour was plotted corresponding to the 25 points NPI, as shown in Fig. 6b. The contrasting color difference in the contours of the samples BS3-BC1, BS3-BC2, and BS-BC3 clearly demonstrates the stark difference in the strength of treated samples. The non-uniformity in the strength of treated soil crust of sample BS3-BC2 and BS3-BC3 can also be realized with the contrasting color gradient of the NPI contours.Figure 6Comparison of the Needle penetration resistance (N/mm) of the treated soil specimen (a) average values and (b) the contours.Full size imageSince the rate of penetration has an insignificant influence on the test results, the needle penetration test is recommended by the International Society of Rock Mechanics (ISRM) for quick, non-destructive testing of the strength of the stabilized soils and soft rocks49. As a large number of tests can be conducted due to the small diameter of the needle without destroying the sample, the needle penetration test is a better alternative to evaluate the local grain bonding in the biocemented soil than bulk strength properties like unconfined compressive strength and calcite content. Another rationale for choosing needle penetration test over conventional soil strength evaluation tests was that a pocket type penetrometer could be developed with the configuration in the present study for non-destructive monitoring of the soil strength improvement with biocementation application in the field. The response of the needle penetration resistance in terms of nominal strain (ratio of penetration to rod diameter) also indicated that the measured responses are independent of needle diameter for a small range, i.e., 1 to 3 mm49,50. A portable penetrometer of Maruto. Co. ltd. (needle maximum diameter 0.84 mm at 12 mm from the tip) have been correlated with high confidence value to conventional physicochemical parameters such as unconfined compressive strength (UCS), elasticity modulus, and elastic wave velocity in several studies50. In our setup, we have utilized a similar configuration chenille 22 needle with (maximum diameter 0.86 mm at 9 mm from the tip) and a penetration rate of 15 mm/minute for measuring the strength properties of cemented soil. Adopting the UCS and NPI correlation suggested by Ulusay et al.51, the UCS of samples BS3-BC1, BS3-BC2, and BS3-BC3 are around 1.67 MPa, 3.4 Mpa, and 5.3 Mpa.It is worth noting that in the needle penetration resistance tests, the boundary of the Petri dish can influence the test results. Therefore, trials were conducted and based on the findings, all penetrations were conducted at points at least five times the diameter of the needle away from the boundary to negate the influence of boundary conditions. The maximum penetration was conducted only up to 50% of the depth of prepared biocemented soil samples in the Petri dishes to avoid inference from the bottom of the Petri dish.Erodibility test in the hydraulic flumeTo investigate the influence of hydraulic current on different levels of biocementation, all the treated samples were exposed to hydraulic current gradually varying from gentle flow (0.06 m/s) to five times the critical velocity (0.75 m/s) in a 45-min duration test and soil mass loss percentage by the initial dry mass of the treated sample is presented as a measure of soil erodibility in Fig. 7. As expected, with an increase of biocementation cycles, i.e., calcite content, the soil erodibility reduced substantially. The initial dry weight of the samples control, BC1, BC2, and BC3, were measured as 398, 403, 406, and 410 g, respectively. Approximately 7.3% of calcite content resulted in a drastic reduction in erodibility (12% mass loss), while 56% soil mass loss was recorded for control (untreated sand). One biocementation cycle treatment (sample BS3-BC1) produced an average of 2.5% of calcite, reducing the soil loss to 31%. Sample BS3-BC2 with 4.93% calcite content resulted in 22% soil mass loss during the hydraulic flume test. It is worth noting that higher precipitation in the soil pores may hinder the flow of water in the soil matrix and increase the pore water pressure resulting in catastrophic failures. However, the MICP technique is reported to be a great tool to improve soil strength, maintaining an adequate hydraulic conductivity to prevent the build-up of the excess pore water pressure11. Theoretically, the percentage pore volume filled with precipitates for samples BS3-BC1, BS3-BC2, and BS3-BC3 considering the observed calcite contents and pore volume (100 ml) is around 3.7, 7.14, and 11.08%. The influence of pore water pressure on erodibility has not been established in the present study, and it certainly is one of the exciting parameters to consider for future studies.Figure 7Weight of the eroded soil (%) after the hydraulic flume test.Full size imageFrom the visual observation of the soil specimen after the flume test, it was evident that the soil particles start bonding with an increased level of bio cementation. A tough crust was formed on the top of BS3-BC2 and BS3-BC3 the samples, which got eroded with the fluvial current. Insignificant aggregation was observed in the sample BS3-BC1. However, with two and three cycles of biocementation treatment (BS3-BC2 and BS3-BC3), the biocemented soil particles (BCS) were evidently noticed (photos are shown in Supplementary Fig. 4).Clarà Saracho et al.27 addressed the erosion due to tangential flow (similar to river current) by treating the soil specimen with ten pore volume of low concentration of cementation media (0.02 M to 0.1 M) by injection strategy and tested the specimen in the flow velocity ranging from 0.035 to 0.185 m/sec for 120 min in a modified erosion function apparatus (EFA). The study concluded that the treatment with 0.08 M cementation media (calcite content varying from 1.2 to 4%) resulted in negligible erosion in the stated test conditions, and with the increase in MICP treatment, a shift in the erosion mode from particulate mode to block failure was observed indicating that with the increase in calcite content and needle penetration resistance, there might be a threshold for biocemented soil, where the soil erosion might be catastrophic due to block failure. However, in this study, a consistent decrease in soil erodibility is observed with the increase in needle penetration resistance. We found that 7.3% of calcite content was required to control the soil erodibility substantially in the test flow range (0.06 m/s to 0.75 m/s). A similar trend was observed by Kou et al.52 and Chung et al.53, where consistent reduction in wave-induced erosion and rainfall-induced erosion was observed with an increase in needle penetration resistance for biocemented fine sand treated with the exogenous bacteria.Another aspect to note in the context of the applied treatment is the produced ammonia which can be toxic for riparian flora and fauna15. From stochiometric calculations, for each biocementation cycle, the produced ammonia is evaluated as 8.5 kg per metric ton of soil treated, and for the best performing treatment approach, i.e., BS3-BC3, the ammonia generated is evaluated as 2.63% by weight of the retained soil. The acceptable limit of ammonia in the surface water was recommended as 17 mg per liter for acute exposure and 1.9 mg/l for chronic exposure for protecting the aquatic life in the freshwater as per the environmental protection agency54. With the MICP technique, the threat of produced ammonia crossing the maximum acceptable quantity is highly plausible; however, for the field application, the ammonia generated can be reduced by reducing the quantity of reagents and increasing the period of applications. It is to be noted that the produced ammonia will also be subjected to dilution in the river stream. The average discharge of the Brahmaputra river is around 19.8 megaliters per second in the Assam valley33. Therefore, the area of the riverbank to be biocemented must be decided judiciously with the context of the produced ammonia quantity and its possible dilution to non-toxic levels.Microstructure and mineralogical analysis of the biocemented samplesTo investigate the influence of different biocementation levels on the erodibility of the treated sand grains, FESEM imaging was conducted for light biocemented samples (BS3-BC1) and heavy biocemented samples (BS3-BC3). While precipitated crystals were observed to be growing on the grooves of sand grains in the light biocemented sample (BS3-BC1), bridging of sand grains with rhombohedral crystals was observed in the heavy biocemented sample (BS3-BC3), as shown in Fig. 8. The effective calcium carbonate bridging between sand grains increases the frictional and cohesive property of sand grains55,56, leading to a substantial reduction in the erodibility of the soil. Bacterial imprints were observed in both cases, suggesting the precipitation to be biodgenic14. Further EDX analysis on a bridged sand grain (Supplementary Fig. 5) suggested an abundance of silicon and oxygen on the sand grains with a trace amount of chlorine and calcium. This indicates the presence of residual calcium chloride on silica grains. The EDX analysis on the grain bridge indicated the presence of calcium, carbon, and oxygen, suggesting CaCO3 precipitation. XRD analysis on treated and untreated sand confirmed the precipitation of calcite. Most of the peaks correspond to quartz (silica). In the biocemented sand sample, a visible peak of calcite was observed at around 29 degrees of 2Ɵ (Details in Supplementary Fig. 6). Therefore, the incorporation of microbial calcite as a binding agent for loose grain silica soil was found to reduce the soil erodibility.Figure 8FESEM images of the treated sand grains (a). Calcite crystal growing on the sand grains on BS3- BC1 sample (b). Calcite bridging in BS3-BC3 samples.Full size image More

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Sampling and dataThe data consist of gillnet catches of brown trout (N = 5733, caught during 2008–2009) from 21 lakes situated along an altitudinal gradient (30 m above sea level, m.a.s.l.-800 m.a.s.l.) in mid-Norway and Sweden (Fig. 5). The lakes were sampled within three main types of vegetation zonation in the catchment area that ranged from the southern boreal to the alpine zone. The lowland lakes were situated in the southern boreal zone dominated by coniferous woodland and forest, but there were also large areas of alder (Alnus sp.) as well as some broad-leaved deciduous woodland. Average annual and July air temperature are 4–6 and 12–16 °C, respectively46. Middle boreal catchment area is dominated by coniferous woodland, forest and mires. Average annual and July air temperature are, respectively, 2–4 and 8–12°C46. Vegetation around the high altitude lakes were dominated by bilberry (Vaccinium myrtillus), grass heaths and dwarf birch (Betula nana) scrub, with annual and July air temperature of − 2 to 0 and 6–12°C46. The clustering of lakes within vegetations zones can be seen in Fig. 5. The epilimnetic water temperature across a sample of the lakes in the altitudinal gradient in this study seems to be within the general trends in the air temperature47.Figure 5Study lake positions (filled dots) and names. Unfilled large circles connects the different lakes with the most representative weather stations (stars) in the area (in terms of altitude, vegetation zones and landscape). The dashed line constitute the national border between Norway and Sweden. The figure was produced using Adobe illustrator.Full size imageAll lakes were sampled using standardised gillnet series consisting of single mesh nets (25 × 1.5 m) with mesh sizes 12.5, 16, 19.5, 24, 29 and 35 mm47. Three nets were linked together making chains with alternating mesh sizes in order to represent all mesh sizes at different depths in each lake at each sampling. This gillnet series catches brown trout with a slight bias in favour of larger individuals48 that was assumed similar in all lakes. The nets were distributed along the shoreline, and the lakes were fished during summer, with different effort (i.e., number of gillnet series) depending on lake size. Weight per unit catch effort (CPUE) based on total weight of the brown trout catch per 100 m2 gillnet area per night was used as a proxy for biomass density. Since, differences in environmental conditions across lakes cause large variations in body size and hence per capita resource demands, biomass was considered a better measure of population density than number of individuals for among-lake comparisons. Length (total length, mm) and weight (g) at catch were measured for every individual in the full data set. Age, sex, maturation status and back-calculation of length-at-age was undertaken for a randomly selected representative subset (N = 889) of the data. Growth and spawning probability ogives49 were modeled based on this subset. Scale samples and otoliths were taken and used for age determination, of which scales were used primarily, and scales were used for back-calculation of growth50. Distance between the annuli was measured, and a direct proportional relationship between the length of the fish and the scale radius was assumed51. If the scales were difficult to read, which was the case for more slow-growing individuals from the low altitude lakes were the annuli were less distinct, otoliths were used for determining the age. As we did not have complete records of water temperature, area and time specific summer air temperature and precipitation measurements were obtained from an online database (www.eklima.no, Norwegian Metrological Institute). The database contained historical weather data from the closest representative (i.e., corresponding in distance, altitude and operational period) weather stations to the respective lakes (Fig. 5). This resulted in overlapping temperature and precipitation regimes for some of the lakes as there were in total five different weather stations that were most representative within the area containing the 21 lakes. Further, as there was some variation in how complete the different measurements were within years, we also had to calculate the sum of summer precipitation for a shorter period of the summer compared to the average mean air temperature. Both measurements still being good proxies for experienced summer conditions in the bulk of the growth season. The effect of temperature and precipitation was thus derived from the spatio-temporal variation in observations between these five weather stations, where the historic temporal variation corresponds to recorded climate components relevant to years for the back calculated age of the individual fish in the specific lakes (resulting in a total of 29 distinct measurements, see variation in Table 2) Epilimnetic water of lakes usually reflects warming trends in air temperature well, however hypolimnetic temperature variation might not be very correlated to the air temperature. Yet, changes in air temperature might indeed influence the thermal stratification of a lake and thus the environment and conditions for a fish52. There are good reasons to believe that most of the lakes in our study obtained some sort of thermal stratification during the summer season. Nonetheless, we chose not to model air to water temperature for the few measurements of water temperatures we had, and extrapolate this relationship to the full spatio-temporal resolution of the data. The rationale for this was threefold: (1) We were interested in exploring potential effects and relationships of easily available climate components, such as air temperature, simplifying the model concept; (2) we did not have access to detailed data on lake bathymetry so that hypothetical modeled air-to-water relationships would be rather uncertain; (3) we had no detailed information on how the brown trout was distributed in the water column during the summer period in study lakes. However, compared to similar lakes, there are reasons to believe that brown trout mainly feed and stay in the upper six meters of the water column, as well as epibenthic areas with high invertebrate abundances53,54, where both areas often are overlapping and highly influenced by the air temperature.Table 2 Description of candidate variables used in the model selection process determining the most supported model for individual growth of brown trout.Full size tableData analysis and model descriptionsOverall processWe used linear mixed model approaches to parameterize environmental effects on key life history traits for brown trout. Specifically: Length at age was parameterized as function of the environment (e.g., summer temperature, population density, winter NAO and summer precipitation). Spawning probability were modeled as functions of individual length and age. We also allowed either the age effect or length effect on spawning probability to vary with temperature or summer precipitation. Individual fecundity (number of eggs produced) was predicted as a function of length and spawning probability. Annual survival estimates from age 1 and up was accessed using catch curve analysis, while first year survival was estimated based on a stock-recruitment function. The estimated parameters were utilized to feed an age structured matrix projection model23, enabling long-term population viability projections in an changing environment (see overview in Fig. 6). Although there are several choices of population models that might be utilized for inferring the population dynamics, such as IBMs55 and IPMs56, the age structured matrix model was deemed especially suited to model our systems as they are highly seasonal (with very reduced growth during winter) and thus producing a clear age structure in the data. Further description of the various modeling approaches are described below. All statistical analyses was done in R57.Figure 6A schematic overview of the processes involved in our model-setup. Red lines indicate drivers and connections acting on individual life history traits, blue lines indicates traits driving the population model and green lines indicates links to climate variables. In short, existing area and time specific climate data on summer precipitation (Prec) and mean summer air temperature (Temp), as well as time specific data on winter NAO-index (recorded NAO values during December, January, February and March, NAO.DJFM), were used to parameterize models for length at age 1 and length at age  > 1, as well as spawning probability at age. Length at age 1 was allowed to affect length at age  > 1, and in the simulations achieved length at age  > 1 was also influenced by the achieved length the previous year (L*). Length at age and spawning probability, both defined by climate variables, interacted in defining how many eggs a female was likely to produce (i.e. fecundity). Survival from eggs to small juvenile fish was based on a stock-recruitment relationship, where the stock was defined by the results from the population model (expected number of fish). Expected number of fish across all ages was also allowed to affect length at age  > 1. The model parameters was used to simulate long term population dynamics, where we also varied expected temperature change scenarios (steadily increasing mean temperatures and temperature variation, respectively, as well as a combination of the two latter scenarios). The populations long term rate of increase (λ) was inferred using the age structured population matrix model.Full size imageSize at ageData inspections prior to model development showed length at age to be surprisingly linear within the size and age distribution in our data (i.e. no obvious signs of asymptotic growth for fish in any of the sampled lakes). Length (L) was thus explored using a linear mixed effects model approach with the lme4-package58. Denoted, length for individual j in population i (Lij) could thus be expressed as:$${L}_{ij}={sumlimits_{k=1}^{p}}{chi }_{ijk}{beta }_{k}$$Here, β = (β1, …, βp)T is px1 vector (one column matrix) of unknown regression parameters, χiT = (χi1, …, χip) ∈ ℝp is the explanatory variables of interest (k + p  1, age was always included as a variable, and we also tested models including an effect of CPUE and first year growth on subsequent growth trajectories. Multiple candidate models where the different environmental effects were allowed to vary with age were constructed (Supplementary information S1). Population ID and individual ID were included as nested random effects in all candidate models exploring size at age  > 1, and population ID was included as a random effect for the models exploring size at age 1. The most supported models were selected based on AIC-values59. During the population simulation the variation in the predictions attributed to the random effect(s) was treated as random noise, and not explicitly included in the simulations.Spawning probabilityBrown trout is an iteroparous species, however under normal food conditions and harsh winters in Norway it might not spawn every year following maturity. Accordingly, we modelled likelihood of spawning at age, derived from the number of female individuals that was going to spawn the following autumn, rather than probability of maturation at age. Aging effects on spawning probability was included in the modelling as skipped-spawning individuals (i.e., mature females that skip spawning episodes, sensu Rideout and Tomkiewicz 60) were coded as non-spawners in the analysis. Probability of spawning (P) was calculated based on a maturation-ogives approach61, utilizing generalized linear mixed effects models in the lme4-package58. Binomial models as two-dimensional ogives, o(A, L) were considered in the model selection. Here, A and L represent age and length, respectively. In addition, we also explored how these ogives might change due to either a temperature effect, summer precipitation effect, or a measure of fish abundance (CPUE) including either as an additive effect in some candidate models (see Supplementary information S2). Population ID was always included as a random effect. In general, the probability of spawning could thus be described as:$${mathrm{Pr}left(spawningright)}_{ij}={beta }_{0i}+{beta }_{1i}{A}_{ij}+{beta }_{2i}{L}_{ij}+{beta }_{3i}{A}_{ij}{L}_{ij}+{beta }_{4i}{x}_{1i}+{a}_{i}+{varepsilon }_{ij}$$
where βs represent coefficients under estimation, Aij = age of individual j in population i, L = individual length, x1 represent a lake-specific environmental variable (if present in the candidate model, either summer temperature, CPUE or precipitation), ai is the estimated random lake-specific intercept and εij is the random residual variation assumed normally distributed on logit scale. The most supported model was selected based on AICc-values59.FecundityFemale fecundity (i.e., number of eggs per female) was predicted as a function of female length (mm) and two constants based upon published values for brown trout from Norway (F = e log(l)*2.21–6.15)62 multiplied by the probability of spawning (P) at size and age.SurvivalAnnual survival rates (s) for fish age ≥ 1 were based on estimations from catch-curve slopes utilizing the Chapman-Robson function in the FSA-package63. The survival was estimated based on descending catch curves, i.e., where numbers of caught individuals decreased as a function of age in the catch. Based on this slope we can derive an instantaneous mortality rate (Z), and from this the annual survival rate could be estimated from S = e-Z. Due to a restricted number of populations available for survival rates, the survival was estimated across all population. As it is unlikely that S would be constant across all age classes we choose to make age specific survival rates, Sa, where the S1 (survival from age one to age two) was reduced, and S3-5 was slightly increased whereas all other Sa = S. The respective reduction and increase are described more in detail below. Survival rates for age 0–1, S0, was based on a stock-recruitment function (see further description under “Climate scenarios, calibration and population projections”).The projection matrixPopulation projections were derived utilizing an age-structured matrix population model23 in the popbio-package in R64. Changes in the age structure and abundance of brown trout was modelled from Nt+1 = K(E,N,t)Nt or rather:$${left[begin{array}{c}{N}_{1}\ {N}_{2}\ vdots \ vdots \ {N}_{{a}_{max}}end{array}right]}_{t+1}=left[begin{array}{ccccc}{f}_{1}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)& {f}_{2}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)& cdots & cdots & {f}_{{a}_{max}}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)\ {s}_{a}& 0& cdots & cdots & 0\ 0& {s}_{a}& cdots & cdots & 0\ vdots & vdots & vdots & vdots & vdots \ 0& 0& 0& {s}_{a}& 0end{array}right]times {left[begin{array}{c}{N}_{1}\ {N}_{2}\ vdots \ vdots \ {N}_{{a}_{max}}end{array}right]}_{t}$$
where Nt is the abundance of brown trout across all age classes a = 1,…, amax at year t. Census time is chosen so that reproduction occurs at the beginning of each annual season. fa is the fecundity at age a (i.e., the number of offspring produced per individual of age a during a year). More specifically, f varies according to f(L,P,N), where variations in L (length) and P (probability to spawn) in turn is defined by climate variables and the number of individuals N. s is a constant and represent the survival probability of individuals from age a to age a + 1, and amax is the maximum age considered in the model. amax was set to 10 years in the simulations, as was also was the age of the oldest fish in the aged subset of the data (see frequency table in Supplementary information S2). Although varying between systems, the maximum age observed and simulated also corresponds to expected maximum age found in other systems in Norway65. S0 is a function of E, the numbers of eggs laid, where the relationship is determined by a stock-recruitment function.Consequently, K(E,N,t), the Leslie matrix, is a function of N and E. In each time step, the survival of individuals in age class amax is 0, whereas individuals at all other ages spawn and experience mortality as defined above. From the Leslie matrix K, we can infer the population’s long-term rate of increase, λ, from the dominant eigenvector of the matrix23.Climate scenarios, calibration and population projectionsTo explore the population effects of changes in summer air temperature or winter conditions we simulated different 100-years climate-change scenarios for a single lake, which included variations the climate variables in focus. The first scenario represented a status quo setting. Here, annual average summer air temperatures were randomly drawn from a normal distribution with mean and standard deviation from observed summer air temperatures from 1998–2009 in the study area. The second climate scenario randomly assigned temperatures as in scenario one, as well as allowing for more and more fluctuating annual summer temperatures as time progressed. This was done by adding a random variable t (~ N(0,0.03) times the number of the specific year (i.e., 1–100) in the 100-years climate change scenario. The third climate scenario, drew annual summer temperatures as in the first scenario, but included an increase in the average air summer temperature by 0.04 °C each year (i.e., 4 °C in total for the 100-year-scenario which is close to the expected mean increase in regional temperature following the regionally down-scaled RCP8.5 IPCC scenario66). The fourth climate scenario included an average summer temperature increase of 0.02 °C each year (close to the expected average temperature increase following the regionally down-scaled RCP4.5 IPCC scenario66), as well as allowing for more and more fluctuating annual summer temperatures as time progressed (as in scenario two). For all climate scenarios above, annual winter NAO-values was randomly drawn from a uniform distribution between − 1.5 and 1.5.We also simulated a second set of climate change scenarios, where summer temperatures were as described in the four scenarios above, however in all these scenarios we also included a trend of higher winter NAO values (meaning a general trend of warmer winters with more precipitation/snow in the study area, as predicted by the down scaled climate scenarios66). This was done by letting annual NAO-values be drawn from a random normal distribution with mean = 0.5, and standard deviation of 0.5.During the calibration process for the simulations, we altered the age specific survival estimates S1 and S3-5 so that average lambdas for the status quo climate scenario was relatively stable and close to 1 (i.e. no large changes in population size) based on 100 iterations of a 100 year-climate scenario. Specifically, S1 = S*0.6 and S3-5 = S*1.2, which is also assumed to be within the realistic range of survival rates for the specific age classes in the focal populations. S0 was derived from a stock recruitment function, and was thus allowed to vary as a function of density in the population. Specifically, from the total egg number (Et) at year t and the number of one-year olds at year t + 1 (N1,t+1) the stock-recruitment function could be estimated by fitting a Shepherd function67:$${N}_{1,t+1}=frac{a{E}_{t}}{{left(1+b{E}_{t}right)}^{c}}$$
where a = 0.04, b = 0.0000003 and c = 3.5. E is number of eggs deposited during t-1 spawning season, estimated as the total fecundity. The estimated N1,t+1 was used to estimate first-year survival (s0) from:$${s}_{0,t}=mathrm{ln}left({E}_{t-1}right)-mathrm{ln}left({N}_{1,t}right)$$All 100-years scenarios were simulated with 100 iterations to extract the variation in the expected population projections. CPUE in the simulations was included as a dynamic variable in the growth model, recalculated through the matrix projection model for each time step, i.e. year. Length at age, spawning probability and fecundity was predicted for each time step (i.e. pr year) as described above. The spawning probability did however not vary annually according to changes in the environment but was predicted according to the mean values of the environmental variables across all years the climate scenario. However, for climate scenarios with increasing mean temperature over time, the expected spawning probability was a function of the gradual mean temperature increase. Thus, by allowing the spawning probability reaction norm gradually to follow changes in the temperature, as predicted from the spawning model, we allowed the populations to gradually adapt the reaction norm to the respective changes. More