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Study siteOur study was undertaken in the Yellingbo Nature Conservation Reserve located around 45 km east of Melbourne, Victoria, Australia (Fig. 2). The reserve is embedded in an agricultural landscape and is around 640 ha comprising narrow riparian zones bordering local creeks. Low-lying floodplains along the Cockatoo and Macclesfield Creeks, which were focus of our surveys, are dominated by ‘Sedge-rich Eucalyptus camphora Swamp’ community26. These forests naturally experience seasonal to near-permanent inundation and vary in structure from open forest to woodland. The highly flood-tolerant mountain swamp gum Eucalyptus camphora is the sole overstorey species. The midstorey is dominated by thickets of woolly tea tree Leptospermum lanigerum and scented paperbark Melaleuca squarrosa, both of which are flood tolerant small trees or shrubs27. The largest remnants of this forest type are found within the Yellingbo Nature Conservation Reserve where they suffer dieback as a result of past human alterations of local watercourses28. The long-term survival of this threatened riparian forest likely depends on management interventions. Despite thorough documentation of declining tree and shrub condition, the ecology of the three major woody species is not well understood29,30.Figure 2Map of all surveyed individuals of the three studied species within the Yellingbo Nature Conservation Reserve (green polygon). Shading represents flooding gradient categories used for sample point stratification (with grey indicating non-flood-prone areas and blues indicating flood-prone areas with darker blues representing higher flood-proneness). The map was generated in ArcMap version 10.5.1 (https://desktop.arcgis.com/).Full size imageSurvey designWe confined the survey area to elevations lower than 120 m above sea level as Eucalyptus camphora swamp does not occur above this elevation within the reserve27. Only areas mapped as vegetation communities containing the studied species were included. The survey area was further limited to match the extent of a hydrological model (see below) and was in total 1.69 km2.In order to ensure that survey points were distributed across the hydrological gradient, we simulated different sized flooding events using a hydrologic model (described below). The spatial extents of these events were then used to classify the study area into four broad flooding categories. Flooding categories one, two and three comprised areas which were flooded by low, medium and high flow events, respectively. Flooding category one represents the wettest parts of the floodplain whereas categories two and three are less frequently flooded. Flooding category four contained the rarely inundated parts of the survey area that remained unflooded in the modelled flow events.To equally represent all flooding categories, we used a stratified random sampling approach. We generated 40 random coordinates within the area represented by each category and four additional points per point were generated as spares in case some positions were unsuitable for sampling.During field surveys, we visited locations by navigating with a handheld GPS device (Garmin etrx30) to the predefined points. From there, we surveyed the nearest individual of each of the species E. camphora, M. squarrosa and L. lanigerum and mapped their actual geographic position. If no tree was found within a radius of 10 m of a given sample point, we visited the closest point from the spare dataset instead. If no individual was present near any of the four closest spare points no tree was recorded at the location.After visiting all of the original points (including the four extra points) additional points were generated randomly in the areas where the two shrub species were found during the course of previous sampling. We thereby increased sample size for each of these less widespread species to approximately 20 individuals per flooding category. We conducted all surveying and tree and shrub measurements from March to April 2018 to take advantage of low water levels and therefore best accessibility. In total, we sampled 292 trees comprising 133 E. camphora, 78 L. lanigerum and 84 M. squarrosa.Tree surveysElongated stems are a major feature of woody plants defining their overall architecture. To characterise and compare growth habits, we measured diameter at breast height (DBH) and height (to highest live foliage) of each tree and shrub. In some cases (for 21/133 E. camphora and 1/84 L. lanigerum) visibility impairment precluded height measurement via clinometer. For multi-stemmed individuals, we counted all stems, measured their DBH, and determined height of the tallest one. To yield crown width we measured maximum crown diameter and perpendicular crown diameter for every sampled plant and calculated the mean.Flooding and associated unstable, boggy substrates might deter trees from the usual vertical growth and force them into leaning positions. Thus, we recorded inclination angle of the main stem (at DBH level) relative to vertical using a protractor.The emergence of epicormic sprouts, be it a symptom of stress or sign of recovery31, is a common reaction to disturbance and reflects a tree’s ability to regenerate vegetatively. We estimated epicormic growth using a scale from 0 to 3 indicating absent, scarce, common or abundant expression of epicormic growth32.Using the same scale, we assessed sexual reproduction by estimating the combined relative abundance of reproductive structures, namely buds, flowers and capsules. Flowers indicate only current reproductive activity and not all species were flowering during the fieldwork campaign. Owing to serotiny and the long timespan for bud crop development, different developmental stages of reproductive structures (current and past reproduction) can appear simultaneously on a single tree.Growth and reproduction may both be affected by plant condition, for which crown vigour has been proven as a suitable and rapid measure22,31,33. For each sampled plant, we assessed crown vigour by visually estimating the proportion of the potential crown supporting live foliage to the nearest 5%.Moreover, growth rates and tree shapes can be significantly influenced by competition. For each sampled tree or shrub, we therefore measured the distances to its nearest four neighbours, one in each compass quadrant and calculated the average nearest-neighbour distance. For E. camphora, only neighbouring trees were included, whereas for L. leptospermum and M. squarrosa, both trees and shrubs were considered neighbours.Hydrologic modellingThe surveyed floodplain area has a very low elevational gradient such that floods are low energy and geomorphology does not vary greatly across the system. As such, we did not explicitly examine geomorphology in this study and focused on hydrology. After completing tree surveys, we determined local flood regime history for each study tree using the output of a grid-based, 2-dimensional hydrological model built in TUFLOW classic (www.tuflow.com), which was calibrated with recent water-level data from four sites within the study area. The model generated historic-flow series (1998–present) of water levels across the study area with a 5-m grid-cell spatial resolution and a daily temporal resolution. See Greet et al.34 for more details.We extracted water-level time series for each surveyed tree and shrub from the model output. Using the recorded coordinates, individual water-level data were extracted for the grid cell in which the respective tree or shrub was located. Some individuals that were located next to the stream were allocated to a grid-cell that the model designated as the stream channel, resulting in them being erroneously characterised as permanently inundated. In these cases, water level data was extracted for the eight surrounding cells. We then excluded those that were also permanently inundated, and the average of the remaining cells was used to create a water level time series for that individual.To characterise the flood regime history for each tree, we considered water levels of zero as dry and values greater than zero as inundated. Therefore, the first day with a water level greater zero marked the start of a flooding event and the reduction to zero the end of the respective event. Consequently, the number of consecutive days of flooding defined the length of a flooding event.We calculated the following flood regime metrics for the modelled 20-year period (1998–2018): mean and maximum length of flooding periods, mean and maximum length of dry spells (not inundated periods), the mean length of flooding periods during the growing season (November–June), the average number of flooding events per year and mean flooding depth. All variables were skewed and thus log-transformed before we tested for correlation (Online Resource 1, Fig. SM1). Although flood magnitude has been found to affect herbaceous riparian vegetation in other systems35, we assumed the observed flooding magnitude, i.e. mean water levels (mean = 0.06 cm, median = 0.01 cm, max = 0.92 cm), to be less important for the relatively tall trees and shrubs studied here (Fig. 3b). We further assumed maximum values to be less influential for tree and shrub growth over long periods. Hence, we selected two contrasting aspects characterizing long-term flood regime as predictors for further statistical analysis. These were the mean length of flooding events and the average number of flooding events per year representing duration and frequency of flooding. They were not strongly correlated with each other (Pearson correlation coefficient = 0.18). Both flooding duration and frequency have been shown to impact tree development in riparian ecosystems36,37.14 out of 292 sampled trees from across the study area were excluded from statistical analyses due to model outputs suggesting unrealistic high flood duration (i.e. mean inundation duration  > 500 days) or frequency (i.e.  > 300 events), likely owing to errors of local topography representation based on our field observations.Statistical analysisWe performed multiple regression separately for each of the three species to:

1.

Assess the strength of relationships between flood regime (flood frequency, flood duration) and tree and shrub morphology (DBH of main stem, height, crown width, stem number, leaning and crown extent); and flood regime and reproductive strategy (the extent of sexual reproduction and epicormic growth), thereby testing hypothesis 1 and 2 (H1 and H2); and

2.

assess the relationships between morphology and both reproduction types, testing hypothesis 3 (H3).

For each analysis we used hierarchical partitioning to identify those variables which independently explained the most variance in morphology and reproduction, respectively.First, we tested how much variation in morphology and reproductive strategy variables was explained by each of the two flood regime variables (H1 and H2). We fit 8 generalised linear models (response variables: main stream DBH, height and crown width, stem number, leaning, crown extent, sexual reproduction, and epicormic growth; predictor variables: flood frequency, flood duration). We chose the appropriate distribution used in the linear model for each variable (Table 1). Beta regression was undertaken using the betareg package38 and ordinal regression using the MASS package39.Table 1 Measured morphology and reproduction variables and distribution for model fitting.Full size tableWe initially included the average nearest neighbour distance (a surrogate for competition) in models predicting morphology variables (H1). However, we later omitted this additional predictor as it generally did not increase the proportion of explained variance (Online Resource 2, Table SM1).To assess how much variation in reproductive strategy variables was explained by morphology variables (H3), for each species, we calculated two additional linear models for the response variables of sexual reproduction and epicormic growth with each six predictor variables (main stem DBH, height, crown width, stem number, leaning and crown extent). Both of these models used a binomial distribution adapted for ordered factors.For each model, we used hierarchical partitioning of log-likelihood values using the hier.part package40 to determine the proportion of explained variance explained independently by each predictor variable41. This method allows identification of variables that have a strong independent correlation with the dependent variable, in contrast to variables that have little independent effect but have a high correlation with the dependent variable resulting from joint correlation with other predictor variables. Variables that independently explained a larger proportion of variance than could be explained by chance were identified by comparison of the observed value of independent contribution to explained variance (I) to a population of Is from 1000 randomizations of the data matrix. Significance was accepted at the upper 95% confidence limit (Z score  > 1.65: Mac Nally42, Mac Nally and Walsh40).To assess the goodness of fit for each model, we present R2 or pseudo-R2 values (according to Nagelkerke using the DescTools package: Signorell et al.43) for ordinal regression and Ferrari and Cribari-Neto62 for beta regression, respectively. We considered variables with a total contribution to explained variance (i.e. proportion explained × R2)  > 0.05 to be influential variables and the direction of their effect important.Lastly, we performed a PCA analyses and ordination to assess associations between different morphology attributes and reproduction variables across all species (H3).All statistical analysis was performed in R version 3.5.044. More

Cyanate analysisTo test soil extractants for cyanate analysis, three soils (0–15 cm depth) differing in soil pH were collected in Austria, sieved to 2 mm and stored at 4 °C. An alkaline grassland soil was collected in the National Park Seewinkel (47° 46′ 32′′ N, 16° 46′ 20′′ E; 116 m a.s.l.), a neutral mixed forest soil in Lower Austria (N 48° 20′ 29′′ N, 16° 12′ 48′′ E; 171 m a.s.l.) and an acidic grassland soil at the Agricultural Research and Education Centre Raumberg-Gumpenstein (47° 29′ 45′′ N, 14° 5′ 53′′ E; 700 m a.s.l.). The recovery of cyanate was assessed by using cyanate-spiked (15 nM potassium cyanate added) and unspiked extraction solutions. We used water (Milli-Q, >18.2 MOhm, Millipore), 10 mM CaSO4 and 1 M KCl as extractants. The three soils (n = 4) were extracted using a soil:extractant ratio of 1:10 (w:v), shaken for 10 min, and centrifuged (5 min at 14,000 × g). The supernatant was stored at −80 °C until analysis, as it has been shown that cyanate is stable at −80 °C over a period of 270 days27. In our study, the storage time of samples ranged from a few days to a few months.To explore soil cyanate concentrations across different soil and land management types, and along a climatic gradient, we collected 42 soils from Europe. Sites ranged from Southern France to Northern Scandinavia and included forests (F), pastures (P), and arable fields (A) (Fig. 2a). At each site five soil cores (5 cm diameter, 15 cm depth) were collected, after removal of litter and organic horizons. Soil samples were shipped to Vienna and aliquots of the five mineral soil samples of each site were mixed to one composite sample per site and the fresh soil was sieved to 2 mm. In addition to those 42 samples, we collected a rice paddy soil in Southern France (sample code A1; four replicates) and three grassland soils (G) in close vicinity of Vienna, Austria (G1 and G2 from saline grassland, three replicates; G3, one soil sample). Soil samples were stored at 4 °C and extracted within a few days. All sampling sites with their location, soil pH, and cyanate, ammonium, and nitrate concentrations are listed in Supplementary Data 1. For cyanate and ammonium analysis, soils (2 g fresh soil) were extracted with 15 mL 1 M KCl, shaken for 30 min and centrifuged (2 min at 10,000 × g). The supernatants were transferred to disposable 30 mL syringes and filtered through an attached filter holder (Swinnex, Millipore) containing a disc of glass microfiber filter (GF/C, Whatman). To reduce abiotic decay of cyanate to ammonium during extraction, the extraction was performed at 4 °C with the extracting solution (1 M KCl) cooled to 4 °C prior to extraction. Soil extracts were stored at −80 °C until analysis.To compare cyanate availability across different environments, we analyzed cyanate in salt marsh sediments and activated sludge from municipal wastewater treatment plants, and, additionally, we collected published data on cyanate concentrations in the ocean. We collected sediment samples (0-10 cm, n = 4) from a high and low salt marsh dominated by Spartina alterniflora Loisel in New Hampshire, USA (43° 2′ 26′′ N, 70° 55′ 36′′ W), and from a S. alterniflora and a S. patens (Aiton) Muhl salt marsh in Maine, USA (43° 6′ 31′′ N, 70° 39′ 56′′ W). We chose these types of salt marsh because they have been shown to accumulate cyanide44, which potentially could be oxidized to cyanate. Sediment samples were stored at 4 °C and extracted within a few days after collection using 2 M KCl at a sediment:extractant ratio of 1:10 (w:v) for 30 min at room temperature. The supernatants were filtered through glass microfibre filters as described above for soil samples. Pore water was extracted with Rhizon samplers (Rhizon CSS, 3 cm long, 2.5 mm diameter, Rhizosphere Research Products, Netherlands) with a filter pore size of 0.15 µm. Triplicate samples of activated sludge were collected from four municipal Austrian wastewater treatment plants (WWTPs), i.e., from Alland (48° 2′ 30′′ N, 16° 6′ 1′′ E), Bruck an der Leitha (48° 2’ 4” N, 16° 49′ 7′′ E), Wolkersdorf (48° 21′ 31′′ N, 16° 33′ 31′′ E) and Klosterneuburg (48° 17′ 39′′ N, 16° 20′ 30′′ E). Samples from the discharge were also collected from the first three listed WWTPs. Samples were cooled on gel ice packs during the transport to Vienna. Upon arrival in Vienna, samples were transferred to disposable 30 mL syringes and filtered through an attached filter holder (Swinnex, Millipore) containing a disc of glass microfiber filter (GF/C, Whatman). All samples were immediately stored at −80 °C until analysis.Cyanate concentrations were determined using high performance liquid chromatography (HPLC) with fluorescence detection, after conversion to 2,4(1H,3H)-quinazolinedione27. Briefly, a 230 µL aliquot of the sample was transferred to a 1.5 mL amber glass vial, 95 µL of 30 mM 2-aminobenzoic acid (prepared in 50 mM sodium acetate buffer, pH = 4.8) were added, and samples were incubated at 37 °C for 30 min. The reaction was stopped by the addition of 325 µL of 12 M HCl. Standards (KOCN) were prepared fresh daily and derivatized with samples in the same matrix. Derivatized samples were frozen at −20 °C until analysis. Just before analysis samples were neutralized with 10 M NaOH. The average detection limit was 1.2 nM (±0.2 SE). Ammonium concentrations were quantified by the Berthelot colorimetric reaction. As direct comparison of cyanate concentrations was not possible across the different environments and matrices, we normalized cyanate concentrations relative to ammonium concentrations, by calculating ammonium-to-cyanate ratios. Data on marine cyanate and ammonium concentrations were taken from Widner et al.22. For marine samples where cyanate was detectable but ammonium was below detection limit, we used the reported limit of detection of 40 nM for ammonium. The presented soil and sediment data are biased toward higher cyanate availabilities (i.e., low NH4+/NCO− ratios), due to the exclusion of samples where cyanate was possibly present but was below detection limit. Soil pH was measured in 1:5 (w:v) suspensions of fresh soil in 0.01 M CaCl2 and water.Dynamics of cyanate consumption in soil using stable isotope tracerFor the determination of half-life of cyanate, we used two soils: a grassland soil (G3) and a rice paddy soil (A1). Both soils had a pH of 7.4 (determined in 0.01 M CaCl2). The grassland soil had a soil organic C concentration of 37 mg g−1, soil N concentration of 1.92 mg g−1, molar C:N ratio of 22.4, ammonium concentration of 5.60 nmol g−1 d.w., nitrate concentration of 1.03 µmol g−1 d.w., and an electrical conductivity of 82.0 mS m−1. The rice paddy soil had a soil organic C concentration of 10 mg g−1, soil N concentration of 0.98 mg g−1, molar C:N ratio of 11.9, ammonium concentration of 2.47 nmol g−1 d.w., nitrate concentration of 0.91 µmol g−1 d.w., and an electrical conductivity of 21.7 mS m−1. To equilibrate soil samples after storage at 4 °C, soil water content was adjusted to 55% water holding capacity (WHC; gravimetric water content of water saturated soil) and soils incubated at 20 °C for one week prior to the start of the experiment. To correct for abiotic reactions of cyanate, a duplicate set of soil samples was prepared and one set of them was sterilized by autoclaving prior to label addition while the other set was left under ambient conditions. Soil samples were autoclaved three times at 121 °C for 30 min with 48 h-incubations at 20 °C between autoclaving cycles to allow spores to germinate prior to the next autoclaving cycle and to inactivate enzymes45.Preliminary experiments indicated rapid consumption of added cyanate. Thus, to avoid fast depletion of the added cyanate pool, we added 5 nmol 13C15N-KOCN g−1 f.w. (13C: 99 atom%; 15N: 98 atom%), which equals to approximately 250-fold the in-situ cyanate concentration. With the tracer addition the soil water content was adjusted to 70% WHC. After tracer addition, non-sterile and sterile soil samples were incubated at 20 °C for a period of 0, 10, 20, 30, 45, 60 and 90 min (n = 3) before stopping the incubation by extraction. Soil extractions were performed with 1 M KCl as described above for the 46 soil samples. Soil extracts were stored at −80 °C until analysis.As no method for compound-specific isotope analysis of cyanate existed, we developed a method to measure isotopically labeled and unlabeled forms of cyanate in soil extracts using hydrophilic interaction chromatography coupled to high-resolution electrospray ionization mass spectrometry (HILIC-LC-MS). For this analysis, cyanate was converted to 2,4(1H,3H)-quinazolinedione as described above for the RP-HPLC method but with some modifications. Aliquots of 280 µL of each sample were transferred to 2 mL plastic reaction vials, and 20 µL of internal standard solution (4 µM 13C-KOCN, 98 atom%) were added. To start the reaction, 120 µL of 30 mM 2-aminobenzoic acid (prepared in ultrapure water) were added, and samples were incubated at 37 °C for 30 min. The reaction was stopped by the addition of 420 µL 12 M HCl. To remove HCl and bring the target compound into an organic solvent that can be easily evaporated, we performed liquid-liquid extractions using a mixture of ethyl acetate/toluene (85/15 (v/v)). Each sample was extracted 3 times with 1 mL organic solvent mixture. For extraction, samples were thoroughly mixed by vortexing and the tubes were briefly spun down to separate the two phases. The organic phases of each extraction were combined in a 10 mL amber glass vial and dried under a stream of N2. Before analysis, samples were redissolved in 200 µL mobile phase. Samples were analyzed on a UPLC Ultimate 3000 system (Thermo Fisher Scientific, Bremen, Germany) coupled to an Orbitrap Exactive MS (Thermo Fisher Scientific). 2,4(1H,3H)-quinazolinedione was separated using an Accucore HILIC column (150 mm × 2.1 mm, 2.6 µm particle size) with a preparative guard column (10 mm × 2.1 mm, 3 µm particle size; Thermo Fisher Scientific). We used isocratic elution with 90/5/5 (v/v/v) acetonitrile/methanol/ammonium acetate, with a final concentration of ammonium acetate of 2 mM (pH = 8). The sample injection volume was 7 µL, and the flow rate 0.2 mL min−1. The Orbitrap system was used in negative ion mode and in full scan mode at a resolution of 50,000. The source conditions were: spray voltage 4 kV, capillary temperature 275 °C, sheath gas 45 units, and AUX gas 18 units. The instrument was calibrated in negative ion mode before sample acquisition using Pierce LTQ ESI Negative Ion Calibration Solution (Thermo Fisher Scientific). To improve the accuracy of absolute quantification, external calibration (concentration standards and 13C15N-KOCN standards) was paired with an internal calibrant (13C-potassium cyanate) to correct for deviations in liquid-liquid extraction efficiency, ionization efficiency and ion suppression. 13C-KOCN (98 atom%) and 13C15N-KOCN (13C: 99 atom%; 15N: 98 atom%) were purchased from ICON Isotopes. The mass-to-charge (m/z) ratio of unlabeled, 13C- and 13C15N-labeled cyanate was 161.0357, 162.0391, and 163.0361, respectively, and the retention time was 2.2 min. The limit of detection was 9.7 nM.To obtain biotic cyanate consumption rates, the non-sterile samples were corrected for abiotic decomposition of cyanate derived from the sterile (autoclaved) samples. Dynamics of cyanate consumption over time for the corrected non-sterile soils were then described by fitting a first order exponential decay curve:$$C(t)={C}_{0}{e}^{(-kt)},$$
(1)
Where C(t) is the remaining 13C15N-cyanate concentration at time t, C0 is the initial concentration of 13C15N-cyanate and k is the exponential coefficient for 13C15N-cyanate consumption. The half-life (t1/2) of the 13C15N-cyanate pool was calculated as:$${t}_{1/2}=frac{{{{{mathrm{ln}}}}}(2)}{k}.$$
(2)
Abiotic reactions of cyanate and isocyanic acidUrea (CO(NH2)2) exists in chemical equilibrium with ammonium cyanate (NH4CNO) in aqueous solution:$${{{{{rm{CO}}}}}}{({{{{{{rm{NH}}}}}}}_{2})}_{2}rightleftarrows {{{{{{rm{NH}}}}}}}_{4}{{{{{rm{CNO}}}}}}rightleftarrows {{{{{{rm{NH}}}}}}}_{4}^{+}+{{{{{{rm{NCO}}}}}}}^{-}$$
(3)
The rate constant for the decomposition of urea (k1a) and for the conversion of ammonium cyanate into urea (k1b) were taken from Hagel et al.46, and temperature dependence was calculated by using the Arrhenius equation:$${k}_{1a}=1.02times {10}^{16}{e}^{-1600+/T}({min }^{-1})$$
(4)
$${k}_{1b}=4.56times {10}^{13}{e}^{-11330/T},({{{{{{rm{M}}}}}}}^{-1},{min }^{-1})$$
(5)
where T is temperature in Kelvin.Cyanate is the anionic form of isocyanic acid. The latter exists as two isomers in aqueous solution, where isocyanic acid is the dominant species. Thus, the acid will be referred to as isocyanic acid. The decomposition of isocyanic acid and cyanate in aqueous solution was found to take place according to three simultaneous reactions:$${{{{{{rm{HNCO}}}}}}+{{{{{rm{H}}}}}}}_{3}{{{{{{rm{O}}}}}}}^{+}to {{{{{{rm{NH}}}}}}}_{4}^{+}+{{{{{{rm{CO}}}}}}}_{2},$$
(6)
$${{{{{rm{HNCO}}}}}}+{{{{{{rm{H}}}}}}}_{2}{{{{{rm{O}}}}}}to {{{{{{rm{NH}}}}}}}_{3}+{{{{{{rm{CO}}}}}}}_{2},$$
(7)
$${{{{{{rm{NCO}}}}}}}^{-}+2{{{{{{rm{H}}}}}}}_{2}{{{{{rm{O}}}}}}to {{{{{{rm{NH}}}}}}}_{3}+{{{{{{rm{HCO}}}}}}}_{3}^{-},$$
(8)
Eq. (6) is for the hydronium ion catalyzed hydrolysis of isocyanic acid (rate constant k2a; dominant reaction at low pH), Eq. (7) is for the direct hydrolysis of isocyanic acid (k2b), and Eq. (8) is for the direct hydrolysis of cyanate (k2c; dominant reaction at high pH). The rate constants are as follows46:$${k}_{2a}=3.75times {10}^{11}{e}^{-7382/T},({{{{{{rm{M}}}}}}}^{-1}{min }^{-1}),$$
(9)
$${k}_{2b}=1.54times {10}^{10}{e}^{-7637/T}({min }^{-1}),$$
(10)
$${k}_{2c}=2.56times {10}^{11}{e}^{-119333/T}({min }^{-1}).$$
(11)
Isocyanic acid reacts with amino groups of proteins, in a process called carbamoylation19:$${{{{{{rm{R}}}}}}-{{{{{rm{NH}}}}}}}_{2}+{{{{{rm{HNCO}}}}}}to {{{{{rm{R}}}}}}-{{{{{rm{NHC}}}}}}({{{{{rm{O}}}}}}){{{{{{rm{NH}}}}}}}_{2}.$$
(12)
We used glycine as an example for an amino acid, with the following rate constant47:$${k}_{3}=8.68times {10}^{15}{e}^{-80008/T}({{{{{{rm{M}}}}}}}^{-1}{min }^{-1}).$$
(13)
Urea-derived cyanate formation in a fertilized agricultural soilFor studying the formation and consumption of cyanate after urea addition, we used a rice paddy soil (A1; the same soil as used in the stable isotope tracer experiment), which was cultivated with rice once every second year with a urea application rate of 180 kg N ha−1 y−1. Treatment of the soil samples was the same as for the stable isotope tracer experiment. Briefly, soil water content was adjusted to 55% water holding capacity (WHC) and soil samples (4 g of fresh soil in a 5 mL centrifugation tube) were incubated at 20 °C for one week prior to the start of the experiment. With the addition of the urea solution, the soil water content was adjusted to 70% WHC. We added 140 µg urea g−1 soil d.w., which corresponds to ~180 kg N ha−1. Soil samples were incubated at 20 °C for a period of 0, 6, 12, 24, and 30 h (n = 4). At each sampling, we collected the soil solution. For this a hole was pierced in the bottom of the 5 mL centrifugation tube containing the soil sample. This tube was then placed into another, intact, 15 mL centrifugation tube and this assembly was then centrifuged at 12,000 × g for 20 min at 4 °C to collect the soil solution. Soil solution samples were stored at −80 °C until analysis. For comparative analysis, we converted rates based on nmol L−1 soil solution to rates based on a dry soil mass basis. For the conversion, we recorded the volume of the soil solution collected and determined the water content of the soil samples after centrifugation.Cyanate concentrations in soil solution were determined as described above using HPLC. Urea was quantified by the diacetyl monoxime colorimetric method, ammonium by the Berthelot colorimetric reaction and ammonium, and nitrite and nitrate by the Griess colorimetric procedure. For cyanate analysis, aliquots of two replicates were pooled because of insufficient sample volume.We used the well-established rate constants for the equilibrium reaction of urea in aqueous solution and decomposition of cyanate to ammonia/ammonium and carbon dioxide/bicarbonate, to model gross cyanate production and consumption after urea amendment from observed changes in urea, ammonium and cyanate concentrations over time. Cyanate accumulation was calculated as cyanate formation from urea (rate constant k1a, Eq. (4)) minus the conversion of ammonium cyanate into urea (rate constant k1b, Eq. (5)), and minus abiotic cyanate hydrolysis to ammonium and carbon dioxide (rate constants k2a, k2b, k2c, Eqs. (9)–(11)). It has been found that only the ionic species (i.e., NCO− and NH4+) are involved in the reaction of ammonium cyanate to urea. The difference between cyanate accumulation and the net change in cyanate concentration over time gives then cyanate consumption, as follows:$$frac{d[{{{{{rm{consumed}}}}}},{{{{{rm{NCO}}}}}}^{-}]}{dt}= {k}_{1a}[{{{{{rm{CO}}}}}}({{{{{rm{NH}}}}}}_{2})_{2}]-{k}_{b}left(frac{{K}_{HNCO}[{{{{{rm{NCO}}}}}}^{-}]}{{K}_{HNCO}[{{{{{rm{H}}}}}}_{3}{{{{{rm{O}}}}}}^{+}]}right)left(frac{[{{{{{rm{H}}}}}}_{3}{{{{{rm{O}}}}}}^{+}][{{{{{rm{NH}}}}}}_{4}^{+}]}{{K}_{N{H}_{3}}+[{{{{{rm{H}}}}}}_{3}{{{{{rm{O}}}}}}^{+}]}right)\ -({k}_{2a}[{{{{{rm{H}}}}}}_{3}{{{{{rm{O}}}}}}^{+}])left(frac{[{{{{{rm{H}}}}}}_{3}{{{{{rm{O}}}}}}^{+}][{{{{{rm{NCO}}}}}}^{-}]}{{K}_{HNCO}+[{{{{{rm{H}}}}}}_{3}{{{{{rm{O}}}}}}^{+}]}right)+{k}_{2b}left(frac{[{{{{{rm{H}}}}}}_{3}{{{{{rm{O}}}}}}^{+}][{{{{{rm{NCO}}}}}}^{-}]}{{K}_{HNCO}+[{{{{{rm{H}}}}}}_{3}{{{{{rm{O}}}}}}^{+}]}right)\ +left(frac{{K}_{HNCO}[{{{{{rm{NCO}}}}}}^{-}]}{{K}_{HNCO}+[{{{{{rm{H}}}}}}_{3}{{{{{rm{O}}}}}}^{+}]}right)-[{{{{{rm{NCO}}}}}}^{-}],$$
(14)
where [NCO-] represents the concentration of cyanate and isocyanic acid, [NH4+] is the sum of ammonium and ammonia, KHNCO and KNH3 is the acid dissociation constant of isocyanic acid and ammonia, respectively, and [H3O+] is the hydronium ion concentration. Urea concentration over time was described by a first order reaction (Eq. (15); unit of rate constant is min−1), and ammonium and cyanate concentrations were fitted with a third and fourth degree polynomial function, respectively (Eqs. (16) and (17), respectively), as follows:$$frac{d[{{{{{rm{CO}}}}}}({{{{{rm{NH}}}}}}_{2})_{2}]}{dt}=8.64times {10}^{-4}[{{{{{rm{CO}}}}}}({{{{{rm{NH}}}}}}_{2})_{2}],$$
(15)
$$frac{d[{{{{{rm{NH}}}}}}_{4}^{+}]}{dt}=2.74times {10}^{-13}{t}^{2}-3.52times {10}^{-10}t+8.04times {10}^{-8},$$
(16)
$$frac{d[{{{{{rm{NCO}}}}}}^{-}]}{dt}=3.47times {10}^{-19}{t}^{3}-1.20times {10}^{-15}{t}^{2}times {10}^{-12}t-4.41times {10}^{-10},$$
(17)
where t is time in min and concentrations are mol/L soil solution.The input parameters were 7.4 for pH (pH of solution: 7.4 ± 0.1 SD) and 20 °C for temperature. As rate constant k1b is dependent on the ionic strength, we corrected the rate constant (given at I = 0.2546) using the Extended Debye–Hückel expression:$$-,log ,f=frac{A{z}^{2}sqrt{I}}{I+aBsqrt{I}},$$
(18)
Where f is the activity coefficient, A and B are constants that vary with temperature (at 20 °C, A = 0.5044 and B = 3.28 × 108), z is the integer charge of the ion, and a is the effective diameter of the ion (a = 5 Å46). We used an ionic strength I = 0.01, which is within the range observed for soils.Statistical analysisStatistical significance of the difference between extractants within each soil type was analyzed by one-way ANOVA followed by Tukey HSD post-hoc test. Levene’s Test was used to test equality of variances and QQ plot and Kolmogorov Smirnov Test were used to assess normal distribution of residuals. For each extractant, statistical significance of the difference between added and recovered cyanate was tested using t test on raw data, where F-test was used for testing equality of variances. To analyze the effect of type of environment on relative cyanate availability (i.e., NH4+/NCO−), we used the Kruskal-Wallis test (assumption for parametric procedure were not met) followed by a non-parametric multiple comparison test (Dunn’s test). For solving differential equations in the model, we used the “deSolve” package in R48. More

The Ner River has been for decades the major route of disposing sewage and storm water from the Łódź City, a million people municipality located on the upper course of the river24. The improvement in water quality, and resulting fish recovery in the Ner, which are described in this study, was a consequence of two major processes that began in the early 1990s. Both these processes were management measures undertaken as part of the preparation for Poland’s accession to the European Community (now European Union), which took place in 2004. One of the processes was the liquidation of textile industry in the Łódź City, once one of the greatest textile production centers in the world24. The other of the processes was the modernization of agriculture and construction of numerous sewage purification stations in the Ner catchment, which took place over the 1990s and 2000s. The most important of the stations was the huge Łódź City Sewage Treatment Plant (STP), whose first part was launched in 1994. By 1995 all sewage disposed to the Ner (which was 3–4 m3/s) had been mechanically treated, by 1998 half of it had also been biologically treated, and since 2001 all of it has been biologically treated24, although the STP was further modernized in the whole 2000s. As a result of the above processes, oxygen content or transparency of the Ner River water much increased, while the load of nutrients or heavy metals much decreased in the study period.There were three things that were essential for obtaining the significant fish analysis results that are presented above. One of them was frequent fish monitoring, which consisted of seven surveys. If the number of surveys over the period of 2000–2012 had been lower, say two or three, the intimate relation between Prussian carp and ide, for example, would not have been noticed, because no useful regression model could either be constructed or be significant. Such frequent monitoring as ours was exceptional in the early 2000s in Poland, and this is probably why the relation between the two fish species had not been detected before our study.The frequent sampling was also little biased. Electrofishing, which was used in the surveys, might be reliably applied owing to several factors. Firstly, the recovered course was of slow water current, which resulted from a 17 m difference in elevation (and thus a 0.43‰ slope) between the upstream and downstream ends of the course. Such slow current made drifting of stunned fish too fast to be captured impossible. Secondly, turbidity which obstructs discernment of stunned fish, was low. Thirdly, conductivity was very stable, only once slightly exceeding 1000 μS/cm, and being 700–960 μS/cm on other sampling occasions (Table 1); such range of conductivity does not create technical or assessment problems of sampling efficiency or sampling selectivity36.Finally, fish biomass data were standardized in a way that enabled constructing significant regression models. This occurred owing to the Hellinger transformation of data. Transformation of the data was necessary because of high variation in raw fish biomass between some of the sampling occasions.Prussian carp invasion, reversal of the invasion, recovery of the native fish species, and their drivers in the NerResults of the above analysis, in particular that of the RDA, indicate that the trait that enabled Prussian carp invasion of the recovered course in the phase of the initial environmental stress decrease was most probably the species’ ability to exist in worse oxygen conditions than other species. This is congruent with Prussian carp’s capacity for anaerobic metabolism, which is absent or weaker in other fish species15,37. Owing to this metabolism, Prussian carp can survive weeks of hypoxia, and even several hours of anoxia. Perhaps, other traits additionally enabling the invasion were Prussian carp’s tolerance of high phosphorus and nitrogen levels16, which were also noticed in the Ner in the late 1990s and early 2000s, and phenotypical plasticity of reproduction12,38.The RDA results also indicate that additional factors favouring Prussian carp might have been high calcium and total phosphorus contents. In contrast, weatherfish were able to thrive and avoid competition with Prussian carp in the recovered course till 2000 owing to their ability to breath atmospheric air, detritus-oriented feeding tactics, and preference for vegetated zones of extremely shallow water depths39,40.Yet, when the next phase of environmental stress decrease (over the course of the fish sampling period) made the recovered course of the Ner good enough to become colonisable by other fish species, the situation of Prussian carp changed dramatically. As the amount of dissolved oxygen further considerably increased in that period, the ability of anaerobic metabolism was no longer an asset, while the new colonizers became its competitors. Of these competitors ide may have been the most important species for Prussian carp decline (the causes of which are explained in the next subchapter). This is indicated by results of regression analysis presented in Tables 6 and 7 and Fig. 6 (see “Results”).An open question is whether slower decrease in environmental stress than that presently observed in the recovered course would enable Prussian carp to develop defence mechanisms that would reduce their replacement by ide. This might be possible owing to Prussian carp’s phenotypical plasticity. This plasticity might produce modifications of the niche occupied by Prussian carp, and in this way lessened the interference competition between the two species. Unfortunately, there is no MA (or any other) model II regression that may be used with multiple predictors (and hence no such multispecies models are presented here), by analogy to multiple regression31. Multispecies model II regression might be useful because a probable long term interaction of Prussian carp with roach, for example, was observed by Paulovits et al.41, although it occurred in a shallow reservoir instead of a river.Why was ide the replacer of Prussian carp rather than other fishes?The explanation why ide acted as the replacer of Prussian carp is difficult, but at least to some extent possible. Schiemer and Wieser42 defined food and feeding, ecomorphology, and energy assimilation and conversion as four groups of traits that decide about the success of given cyprinids, and used the traits to substantiate increasing roach dominance in Central European rivers. Although much less is known about these groups as regards ide (but see Rothla et al.43), yet ecomorphology seems to be most important also in its case. Large body depth of ide makes it similar to Prussian carp and thus its tough competitor. As the shape of ide is much less streamlined than that of most other large-bodied obligatorily riverine cyprinids, ide, like Prussian carp, avoids water current zone44 in order to reduce energy loss resulting from water resistance during movement. This increases the risk of occupying similar ecological niches by these two species. However, ide grow to bigger body sizes than Prussian carp, which gives the former a big advantage over the latter while searching for food (interference competition) and while avoiding predation.Moreover, while Prussian carp is one of the most resistant fish species in general, ide belongs to the most resistant obligatory riverine (i.e. fluvial specialist) cyprinids, although its occurrence may sometimes even resemble that of limnophilic fishes45,46. The capacity of ide to be successful in more than averagely polluted river courses is manifest in the Warta, the parent river of the Ner. Przybylski47 and Kruk46, who distinguished contrasting reaches in the Warta, noticed a significantly higher biomass of ide in the middle, most polluted reach (to which the Ner empties), as early as in 1986–87 and 1996–1998, respectively. Ide usually dominated poor, several-species rich assemblages there. The situation was much similar in the Warta much later, in 2011–2012, when ide was significantly associated with the middle course, in which fish assemblages were in the poorest condition as compared to the upper and lower courses48.Kruk46 attributes the high abundance of ide in the most polluted middle Warta River to weak competition from other rheophils, which were absent there because river degradation was too severe for them. In contrast, in the other sections of the Warta, ide were much less abundant owing to improved water quality and thus higher abundance of other rheophils, competitors of ide. If this presumption is correct, i.e. if the consequences of a spatial degradation gradient may become reflected in a temporal degradation gradient, then further decrease in environmental stress in forthcoming years may result in the replacement of ide by other rheophilic species in the Ner, too. This prognosis is supported by Eklöv et al.’s45 observation of ide decline coinciding with trout increase after a long-term improvement in water quality in streams of southern Sweden.All fish species that colonized the recovered course of the Ner were species recorded for several dozen years in the Warta catchment46,49,50,51,52,53,54, and the fish species list of the catchment is about 20–40% longer than the list of species determined in the Ner. The list of the Warta is also similar to those of other nearby catchments of central Poland55,56. This indicates that all species that colonized the Ner in recent decades may have originated from the regional species pool57,58 rather than from stocking, aquaculture or unintentional introductions. Nevertheless, ide are frequently used in stocking, which increases their chance to become an instrument of controlling non-native fish species, while the present study contributes to the purposefull exploitation of the fish species. A quite different perspective of an invasion was presented by Bøhn et al.59. While monitoring the invasion of vendace (Coregonus albula L.) into upstream and downstream lakes 50 km apart located on the Norwegian sub-arctic Pasvik watercourse they observed great life history variability of the non-native fish entering a new environment. This consisted in decrease in the mean length in all age-classes, in fecundity, in the mean weight and size of individuals at first maturation, and increase in growth rate. Unfortunately, in the Ner we could only check the mean weight of individuals (results not shown): it varied in both Prussian carp and ide, but no clear decreasing or increasing trends were observed over the study period.Ide as the suppressor of Prussian carp, and other methods of extirpating the latter speciesIf the presumption that ide contributes as a biotic extirpator to Prussian carp decline is true then a comparison of ide with other suppression drivers is worth considering. One thing that may limit ide importance in other environments, for example, may be the above mentioned Prussian carp’s phenotypical plasticity: consequently, further research in this respect is necessary. Although the herpesviral hematopoietic necrosis virus (Cyprinid herpesvirus 2, CyHV-2) operates much faster than ide it cannot practically be used because it is uncontrollable in natural environments. This is the case because the virus, which is believed to have global occurrence, causes epizootics only when triggered by a specific range of water temperatures60, which of course can hardly be manipulated.Besides, the virus suffers from the problem of selectivity. In the Czech Republic, the virus caused an epizootic that killed probably most individuals of numerous Prussian carp populations within weeks, but the fish were all triploid females18. It is not known why other ploidy forms38 were not affected, which is important because there is a natural tendency of invasive triploid female populations (with a few percent of males) to quickly transform themselves into diploid bisexual populations12. Moreover, first information about the virus indicated mass mortality of cultured goldfish [Carassius auratus (auratus)] in many countries, and it is not certain that it will not affect other fish species in the future20. Finally, the virus-assisted extirpation would be a very drastic form of animal control.Reduction in frequency of desiccation events is an environmental measure of Prussian carp suppression that was discovered in Hungary21. It was observed there that in reservoirs, lakes and canals in which few or no desiccation events occurred, the relative abundance of Prussian carp constituted between one fifth and half of that recorded in fish ponds, for example, where desiccation was frequent. Moreover, the method is probably selective, affecting no other, native species. However, it cannot be applied to all freshwater bodies, for technical or financial reasons, and the elimination of Prussian carp is far from total. Interestingly, desiccation, and its relation to small water body sizes, was determined as one of factors favouring Prussian carp occurrence by Górski et al.61 in the Volga floodplain areas, where large water body size was also assessed as a factor favouring ide occurrence.Theoretical perspectiveGenerally, both the invasion by Prussian carp and its reversal comply with major theoretical predictions: the invasion with community ecology as a framework for biological invasions62,63 and the reversal with both the framework and the concept of biotic (ecological) resistance27,28,64,65. In the case of the invasion, because mostly the amount of resource (in this case: increase in dissolved oxygen, accompanied by decrease in BOD5, decrease in total phosphorus, etc.; in short—water quality) increased to a level that allowed the invader to exploit the environment, but was too low for other, native fishes, and thus Prussian carp (and weatherfish) colonized the river instead of the others. This also agrees with scenario 2 of the theoretical framework for invasions defined by Facon et al.66, in which environmental change is the main factor of invasion.In the case of the reversal of the invasion, compliance with the theories occurs because the resource (mainly water quality) increased/improved high enough to be exploited by other, native species, and also because the native species became then competitors of the invader and thus biotic resistance drivers23,28. These drivers are defined in the biotic resistance hypothesis64, which describes the chances of an invasive species to be successful in a new environment. According to the hypothesis native-species-diverse environments are more resistant to invasive species than native-species-poor environments through a combination of predation, competition, parasitism, disease, and aggression. In this context, ide may resist Prussian carp, for example, owing to occupying similar spawning grounds as both species are open substratum spawners [ide being a phyto-lithophil (A.1.4), and Prussian carp a phytophil (A.1.5)]67. In the case of these two species, the resistance may be extended to ide predation on Prussian carp’ eggs, larvae or juveniles. Besides, ide grows to bigger body sizes than Prussian carp, which may result in aggressive behaviour in the form of scaring Prussian carp away from feeding grounds or hiding places.In contrast, both the invasion and its reversal do not support the concept of invasional meltdown68, according to which in the initial phase an invasive species causes rapid changes in an ecosystem (by altering the trophic chain, for example), in this way paving the way for the invasion of subsequent non-native species66. In a next phase, when two or more alien species have invaded the ecosystem, synergistic interactions among them accelerate the invasion process68.Yet, it is possible that the occurrence of biotic resistance rather than invasional meltdown has been an effect of insufficient biomass or abundance of other invasive species in the regional species pool57,58, of other aspects of the biotic context or small spatial and/or temporal scales of the processes26, or of environmental filters that might have prevented the invasion of other non-native species in the Ner69. Consequently, a number of quite different possible scenarios for the Ner are imaginable, for example no reversal of Prussian carp invasion if ide had not been abundant in the parent Warta River, or if species composition there had been quite different in other respects. This problem requires further research to reach reliable conclusions. More