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Accumulation and distribution of N in flue-cured tobacco growing in different soilsAccumulation dynamics of N in different soilsNitrogen gradually increased in loam soil, clay loam, and sandy loam soils with plant growth (Fig. 1), attaining a maximum at the mature-plant stage(2.10 g/plant, 1.43 g/plant, and 2.90 g/plant, respectively). Nitrogen accumulation was lower in plants grown in clay loam than in plants grown in loam soil and sandy loam during the entire growth period, indicating that the N supply capacity of clay loam was relatively weak, and tobacco plants grown in this soil had the lowest levels of N uptake and utilisation. The N uptake and accumulation in flue-cured tobacco grown in loam soil and sandy loam were basically the same before the ceiling stage, but at the mature stage, N accumulation was significantly higher in plants grown in sandy loam than in plants grown in loam soil and clay loam (P  More

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The crystalline nature and phase purity of the synthesized NiO, PdO and PdO–NiO nanocomposite were confirmed by XRD analysis and shown in Fig. 1a. All three XRD patterns show the highly crystalline nature, which confirms the purity of the samples. In PdO–NiO mixed metal oxide, The intense diffraction peaks at 2θ = 37.16, 43.24, 62.81, 75.32 and 79.34° indexed to (101), (012), (110), (113) and (202) planes of the cubic phase of NiO, which is highly consistent with standard JCPDS NO: 01-071-475120. On the other hand, the sharp peaks at 2θ = 34.54 and 55.79° indexed to (101) and (112) planes corresponding to the tetragonal crystalline phase of PdO with an average lattice parameter 3.043 Å, which is highly associated with standard JCPDS NO: 043-102421. Interestingly, metallic diffraction peaks and other impurity phases were not observed in the hybrid PdO–NiO nanocomposite. This confirming that metal source (Pd) completely oxidized to the metal oxide (PdO) and formed hybrid PdO–NiO nanocomposite. In addition, the single metal oxide (PdO, NiO) diffraction patterns were compared in Fig. 1a.Figure 1(a) X-ray diffraction pattern and (b) FTIR spectra of NiO, PdO and PdO–NiO (c) TGA curve of PdO–NiO nanocomposite.Full size imageThe sharp peaks of each metal oxide confirm the high crystallinity of the tetragonal phase of the PdO and cubic phase of NiO. Both diffraction pattern of the single metal oxides is well associated with standard JCPDS file: 01-071-4751 and 043-1024 respectively. The average crystallite size, micro-strains of the NiO, PdO and PdO doped NiO were calculated by using Scherrer analysis and W–H analysis and plotted in Fig. 1b. A small variation was observed in the crystallite size of the catalysts (PdO, NiO and PdO–NiO) in Fig. 1b, which is due to the difference in the distribution of the crystal in the catalysts. The average crystallite size of the PdO, NiO and PdO–NiO was found to be 10.8, 7.8 and 7.36 nm, respectively. The PdO doping reduces the crystallite size in the PdO–NiO composite, consistent with previous reports. The calculated crystallite sizes, d-spacing, micro-strains, and binding energies of the PdO, NiO, and PdO–NiO are shown in the Table 1. In addition, the experimentally calculated d-spacing value of the pure PdO and NiO was well correlated with theoretical values and shown in Table SI. 1. The synthesized catalyst showed the negative and positive slopes of ε are corresponds to the compressive and tensile stress, respectively.Table 1 Crystallite sizes, d-spacing, micro-strains, and binding energies of the PdO, NiO, and PdO–NiO.Full size tableFurthermore, the chemical bonding and functional groups were analyzed by FTIR spectrometer. Figure 1c show FTIR spectra of the synthesized NiO, PdO and PdO–NiO nanocomposite. Similar spectra were observed for the three catalyst, the absorption peaks at high-frequency region 3200–3400 cm−1 belongs to O–H stretching vibration of the water molecules, due to surface adsorption phenomenon. Furthermore, three absorption peaks appeared at 1398.2, 1237.8 and 1057.8 cm−1, which ascribed to the C–O, CH2 and C=O stretching vibrations, which is well associated with XPS analysis data. The metal oxide bonding peaks appeared in the frequency range of 480.2–702.6 cm−1. Hence the Pd–O and Ni–O stretching frequency in the PdO doped NiO sample confirmed the formation of hybrid PdO–NiO nanocomposite22. After that, the thermal stability of the PdO–NiO nanocomposite was studied by TGA analysis. Figure 1d shows the thermogram of the PdO–NiO nanocomposite. The first weight loss (7%) started in the temperature range of 65 to 180 °C. Due to the H2O molecules, evaporation and then the sustainable weight loss of around 10% was observed in the range of 187 to 574 °C. Beyond 600 °C a significant weight loss 20% was observed, which may be assigned to the unreacted CO3 combustion23. Hence, the XRD, FTIR and TGA spectral studies confirmed the formation of hybrid PdO–NiO nanocomposite.The electronic state and chemical bonding of the PdO doped NiO composite was analyzed by using XPS spectra. Figure 2 shows the XPS spectra of the PdO–NiO nanocomposite, the broad scan spectrum (Fig. SI. 1) of the PdO–NiO, which show the existence of the Pd (3d), Ni (2p), O (1s) and C(1s) elements. The deconvoluted Pd 3d XPS spectrum in Fig. 2a shows two major peaks at a binding energy of 336.4 and 342.2 eV corresponds to spin–orbit doublets of Pd 3d5/2 and Pd 3d3/2, respectively, which confirmed Pd2+ ions in the form of PdO in the PdO–NiO nanocomposite24,25. In addition, the satellite peak of Pd species appeared at a binding energy of 339.2 eV and 345.3 eV. In Ni 2p spectra (Fig. 2b), Ni 2p3/2 and Ni 2p3/2 spin–orbit doublets peaks were observed at 854.9 eV and 872.5 eV, which corresponded to Ni–O and Ni–OH, respectively and their corresponding satellite peaks are located at a binding energy of 867.2 eV and 879.1 eV26,27. Furthermore, O 1s spectra (Fig. 2c) show the two peaks at a binding energy of 529.3 eV and 534.2 eV, which ascribed M–O and M–OH species. The obtained XPS spectra of the PdO–NiO nanocomposite are well associated with the XRD and EDX analysis.Figure 2High resolution X-ray photoelectron spectroscopy (a) Pd 3d, (b) Ni 2p, (c) O 1s and (d) C 1s spectra of PdO–NiO nanocomposite.Full size imageThe morphology feature and elemental composition of the prepared NiO, PdO and PdO–NiO nanocomposite was scrutinised by FE-SEM. Figure 3 shows the SEM morphology images of NiO, PdO and PdO–NiO nanocomposite at low and high magnification. Pure metal oxides NiO and PdO samples in Fig. 3a–d shows the porous crystalline morphology with high purity of the respective elements. On the other hand, PdO doped NiO sample in Fig. 3e,f shows the uniform, monodisperse, spherical crystalline morphology. Which confirms that PdO uniformly distributed with NiO. Hence, the PdO doping enhances the surface area of the catalyst. In addition, the elemental composition and elemental mapping was analysed for PdO–NiO sample and shown in Fig. 3g,h. The EDX spectra and elemental mapping clearly confirms the presence of the Pd, Ni and O elements in the composite with high purity.Figure 3FESEM images (a,b) NiO (c,d) PdO, (e,f) PdO–NiO and (g,f) EDS spectra and elemental mapping of PdO–NiO nanocomposite.Full size imageThe detailed morphology and particle size distribution of the PdO–NiO NPs was measured by HR-TEM and the results are presented in Fig. 4. Figure 4a–c shows the typical HRTEM images of the as-synthesized PdO–NiO nanocomposite. The obtained TEM images confirmed the uniform distribution of the spherical PdO–NiO NPs, which agrees with FESEM results. In Fig. 4c (inset), the histogram reveals that formed PdO–NiO nanoparticles are uniformly distributed with an average particle size of about 9.64 ± 2.1 nm, which is well associated with XRD crystallite size. Furthermore, the SAED pattern was analyzed to understand the crystallinity and the crystal quality of the PdO–NiO nanoparticles are shown in Fig. 4d. The clear ring-like structure suggests the polycrystalline nature of PdO–NiO. The obtained diffraction rings d-spacing values are corresponding to the (101), (012), (110), (113) and (202) planes of the NiO nanoparticles. Figure 4e shows the lattice fringes of the PdO doped NiO nanoparticles. The fringes show the lattice planes for both metal oxides. The interplanar d-spacing value of 0.1997 nm to correspond to the (012) plane of the NiO phase and the d-spacing value of 0. 2145 nm to correspond to the (110) plane of the PdO in the composite. Which is well correlated with the XRD d-spacing values. Elemental mapping in Fig. 4f shows the presence of Ni, Pd and O elements with uniform distribution as similar as SEM mapping. The morphology results of the synthesized catalysts are well associated with XRD, FTIR and XPS analysis.Figure 4High resolution transmission electron microscopy HRTEM images (a–c), (d) SAED pattern, (e) interplanr spacing and (f) elemental mapping of PdO–NiO nanocomposite.Full size imageUV–Vis absorption spectra were analyzed for the as-synthesized catalysts NiO, PdO and PdO–NiO and the respective results are presented in Fig. 5a. The absorption spectra show the strongest absorption maxima at 234.8 nm for all three catalysts. In addition, the characteristic absorption band of NiO and PdO were observed at 338.2 nm and 422.1 nm respectively23, on the other hand, no characteristic absorption band was observed for PdO–NiO sample. Furthermore, the bandgap energy was calculated for three catalysts by using the Schuster-Kubelka–Munk function.$$(alpha {text{h}}nu ),{text{n}} = {text{A}}({text{h}}nu – {text{Eg}})$$
(1)
Figure 5(a) UV–Vis absorption spectra and (b) Plot of (αhν)2 Vs hν for NiO, PdO and PdO–NiO nanocomposite (Inset shows the bandgap energy of the catalyst).Full size imageThe bandgap energy (Eg) was achieved by extrapolating against the photon energy and the obtained results are shown in Fig. 5b. The calculated bandgap (Eg) of NiO, PdO and PdO–NiO are 4.05 eV, 3.84 eV and 4.24 eV, respectively25 (Fig. 5b, inset). The PdO doping with NiO increases its bandgap value. This suggests that the PdO interface and NiO interface are closely combined in the composite. The obtained band gap value of the catalysts is much higher than the reported bandgap energy. The bandgap energy is highly dependent on the particle’s size. The bandgap energy increases with decreasing particle size, which confirmed that the synthesized catalysts are in nanoscale. The bandgap energy (Eg) of the PdO–NiO catalyst is well associated with FESEM and TEM results.The photoluminescence (Pl) spectra of NiO, PdO and PdO–NiO materials were measured at 325 nm excitation wavelength and presented in Fig. 6. Figure 6a shows the Pl spectra of the pure NiO, PdO and PdO–NiO nanocomposite. The blue/violet emission was observed for all three samples at 364 nm due to the excitation of 3d8 electrons of Ni2+ ions from the conduction band to the valence band24. From Fig. 6a, it can be seen that the intensity of the PdO–NiO nanocomposite is lower than the pure NiO and PdO, which indicated the higher electron transfer between the NiO and PdO, which is well correlated with electrochemical results. The deconvoluted PL spectra of NiO, PdO and PdO–NiO materials are shown in Fig. 6b–d; four peaks have been fitted for each sample as shown in Fig. 6b–d. The UV emission at 364 nm (3.4 eV) corresponds to the near band edge (NBE) excitation of NiO25. The obtained PL spectra confirmed that the PdO–NiO nanocomposite has more conductivity than the pure metal oxides.Figure 6(a) PL spectra and (b–d) deconvoluted spectra of NiO, PdO and PdO–NiO nanocomposite respectively.Full size imageThe electrode kinetics of the NiO, PdO and PdO–NiO modified GC electrode was explored in 1 M KOH at different scan rate variation at room temperature. In addition, the resistance of the aforesaid electrodes was monitored in impedance analysis and shown in Fig. 7. Figure 7a, the NiO/GC show a pair of well-defined redox peaks at around 0.49 V and 0.44 V respectively, which corresponding to the reversible reaction between Ni2+ and Ni3+26. In addition, the redox peak currents linearly increase with increasing scan rate.Figure 7(a–c) Cyclic voltammetry and (d) EIS curves of NiO, PdO and PdO–NiO nanocomposite in 1 M KOH electrolyte solution.Full size imageFigure 7b shows CV pattern of PdO/GC electrode in 1 M KOH solution, which sows poor peaks palladium oxide and palladium reduction peak at 0.52 V and 0.53 V respectively due to the formation of oxyhydroxide on the electrode surface in basic medium. Whereas the PdO–NiO NPs modified GC electrode in Fig. 7c show well-defined Ni2+ and Ni3+ kinetics with eightfold higher peak current. Which confirms the Efficient electron transfer between NiO and PdO in the composite. Which is well associated with PL results. Furthermore, the impedance spectra were achieved for three electrodes at fixed over potential (500 mV s−1) in 1 M KOH electrolyte and presented in Fig. 7d. In Fig. 7d the Rct value of the pure metal oxides NiO/GC and PdO/GC electrode were obtained as 236 Ω and 2702 Ω respectively. Whereas the bimetal oxide PdO–NiO/GC show 425.7 Ω, which is lower than the pure PdO. Due to the superior electron transformation between each metal oxide.Catalytic reduction of Azo compoundsAzo compounds are highly toxic to the environment as well as human beings. Especially, nitrophenols are listed as the topmost hazardous chemical in the world. Hence the reduction of nitrophenols gains the most attention. Generally, the nitrophenol reduction reaction is thermodynamically favourable (E0 = − 0.76 V) at optimized conditions, whereas the NaBH4 acts as a reducing agent (E0 = − 1.33 V)27,28. However, the reduction rate is prolonged without the catalyst due to the kinetic barrier between the reducing agent and reactant. Hence, the catalytic reduction nitrophenols are a good way to convert to non-toxic aminophenol (AP) with the presence of NaBH4 as a reducing agent. The reduction reaction was easily monitored with a UV–Vis spectrometer. It is known that NaBH4 alone cannot reduce the nitrophenols into aminophenol’s. As shown in Fig. SI. 2 the fresh nitrophenols (NP, DNP and TNP) absorption peak appeared at 300–370 nm respectively. When the addition of reducing agent, the peak was shifted to 402–450 nm28. In addition, the solution color was turned light yellow to deep yellow, due to the formation of corresponding nitrophenolate ions in basic solution. However, no reduction was achieved over 2 h, indicating that nitrophenolate ions were very stable with NaBH4. Furthermore, the catalytic activity of the NiO was explored with three nitrophenols and shown in Fig. SI. 3. The pure NiO exhibits a poor catalytic reduction of nitro compounds. In contrast, the PdO–NiO catalyst show excellent activity on the nitrophenols, as shown in Fig. 8.Figure 8Catalytic activity and kinetic rate of PdO–NiO nanocomposite on reduction of nitrophenols with NaBH4 solution (a,b) NP, (c,d) DNP and (e,f) TNP.Full size imageIt can be seen that the nitrophenolate peak absorbance at 400 nm gradually decreases with reaction time, which confirmed that PdO–NiO promotes the electron and hydrogen transfer between the reactant. Due to the higher active sites of the PdO–NiO. The present PdO–NiO catalyst completes the reduction reaction of NP, DNP and TNP within 10, 13, 25 min respectively. In addition, the aminophenol absorption peak appeared around 300 nm for all three nitrophenols. On the other hand, the deep yellow solution turned colorless, indicating the formation of aminophenol. The rate constant κapp for each nitrophenol was calculated from the plot of ln (At/Ao) Vs. time. The proposed PdO–NiO catalyst exhibits excellent rate constant 0.1667, 0.0997, 0.0686 min−1 for NP, DNP and TNT, respectively, which is the higher rate constant than the previously reported catalyst (Table SI. 2). Generally, the reduction mechanism of nitrophenols to aminophenols follows many intermediate steps from nitro to nitroso and then to hydroxylamine and to final aminophenol. For these reaction required both electron transfer and active hydrogen atoms. Here, the BH4− ions produce the active hydrogen atoms on the surface of the catalyst and subsequently, the PdO–NiO catalyst enhances the electron transfer. As a result, the reduction of NP could be efficiently accelerated by the PdO–NiO catalyst. Furthermore, comparison of the catalytic reduction performance of nitrophenols with varies catalyst are shown in Table SI. 2. The reduction mechanism of the nitrophenols with PdO–NiO catalyst with NaBH4 as shown in Fig. SI. 6.Furthermore, the catalytic activity of PdO–NiO composite was explored by the reduction of organic azo dye compounds such as Methylene blue (MB), Rhodamine B (RhB) and Methyl orange (MO) with the addition of NaBH4 in the presence of PdO–NiO catalyst29. The reduction of each dye was monitored at different absorption peaks, as shown in Fig. 9. The intensity of each dye at respective wavelengths linearly reduced with time in the presence of the PdO–NiO. In addition, the reduction rate κapp for each dye was calculated from the plot of ln (At/Ao) Vs. time. PdO–NiO catalyst exhibited excellent reduction rate as 0.099, 0.0416 and 0.0896 min−1 for MB, RhB and MO, respectively, superior catalytic activity than previous reports. In addition, the azo dye solution turned into colourless, indicating the complete reduction occurs in the presence of PdO–NiO. The pure NiO catalyst exhibits poor reduction activity towards azo dyes with the presence of NaBH4 (Fig. SI. 4). Furthermore, comparison of the catalytic reduction performance azo dyes with varies catalyst are shown in Table. SI. 3. Additionally, the reduction of the mixture of nitrophenols (NP, DNP and TNP) and azo dyes (MB, RhB and MO) was tested with PdO–NiO nanocomposite and obtained results are shown in Fig. 10. Initially, the mixture of azo compounds is formed dark solution then rapidly turned into a colourless and became a transparent solution with the addition of PdO–NiO in the presence of NaBH430. The complete azo compounds reduction was achieved within 8 min. Hence, the proposed PdO–NiO is a promising catalyst for wastewater treatment. In addition, the effect of the catalyst loading on the reduction of mixture of azo compounds were studied with different loading amount of PdO–NiO catalyst (3–10 mg) and shown in the Table. SI. 4. Which show that the reduction rate increased with loading amount of the catalyst. In addition, the catalytic reduction performance of toxic azo compounds by various catalysts are shown in Table. 2. PdO–NiO catalyst exhibit superior reduction performance than the previously reported catalyst.Figure 9Catalytic activity and kinetic rate of PdO–NiO nanocomposite on reduction of azo dyes with NaBH4 solution (a,b) MB, (c,d) RhB and (e,f) MO.Full size imageFigure 10Reaction progress of an azo compounds mixture (4-NP, 2,4-DNP, 2,4,6-TNP, MB, RhB, MO) with PdO–NiO and NaBH4. Conditions: Dye: 100 ppm, 25 ml, Nitrophenol: 0.12 mM, 25 ml, NaBH4: 0.1 M, 5 ml and catalyst: 3 mg.Full size imageTable 2 Comparison of catalytic reduction performance of toxic azo compounds by various catalysts.Full size tableFurthermore, the reduction mechanism of azo dyes over PdO–NiO catalyst with reducing agent shown in Fig. SI. 7.After the complete reduction reaction, the catalyst property was analyzed to understand the stability of PdO–NiO. In Fig. 11a, FTIR spectra showed no noticeable changes before and after catalytic reduction of mixture reduction. Additionally, the SEM image (Fig. 11b) also showed no changes in the morphology of the PdO–NiO. In addition, HR-TEM image (Fig. 11c) was also analyzed to study the change in the particles size after catalytic reduction, show no considerable change in the particle size. Which proved that PdO–NiO is highly stable in the reduction conditions.Figure 11(a) FTIR spectra of PdO–NiO nanocomposite before and after reducing the mixture of azo compounds (b) FE-SEM image (c) HR-TEM image of PdO–NiO after reducing the mixture of azo compounds.Full size image More

Simulating competition for space using the “BacGo” modelTo investigate how spatial positioning of populations affects the outcome of microbial competition, we simulated two populations competing for space with a limiting size by building an individual-based model (named “BacGo”). The model was implemented in discrete grid boxes of a 20 × 20 array. As shown in Fig. 1a, our simulations were based on three basic assumptions. First, the two competing populations possess the same inherent growth rate and equal initial cell numbers, thus the only differences between them are their manners of colonizing free space. Second, the newly born daughter cell is located around its mother cell but with a random direction of spatial positioning [34], resulted in a microcolony with different spatial patterning. Lastly, if the selected box has been occupied, the newborn cell will compete for the box against the original occupants of the box and possesses a probability of 50% to survive [37].We first explored the outcome of spatial competition, which started by randomly distributing two populations on the grids with the same initial cell numbers of 10 for each. Based on our basic assumptions and the predictions of competitive exclusion theory [38], we hypothesized that only one population could win the competition and finally occupy all grids. As shown in 20,000 independent simulations with random initial distributions, we discovered that at the end of each simulation, only one population survived (Video S1 and Video S2). The Chi-square test showed no significant difference (P = 0.211) between the simulated winning times (10,177 of 20,000 simulations) and the random winning times (10,051 of 20,000 simulations) of the focus population. This result conformed with our initial assumption that cells possess a probability of 50% to survive in competing with original occupants. When we replicated simulations initiated with the same cell distribution, we found that the winning probabilities for each population changed in line with the initial distributions (Fig. S1). However, the winning probabilities never reached 100% no matter how the initial distribution changes. Together, these results suggested that unknown random factors may affect the final outcome of the competition.Next, we analyzed the dynamics of microbial colonization during our simulations. As summarized in Fig. 1b, we divided the competition process into two stages, the “occupation stage” and the “exclusion stage” (see Methods). To statistically characterize the competitive outcome at t3, we defined the winning asymmetry index, WinR, and the abundance asymmetry index, AbunR (see Methods). As shown in Fig. S2a, we found a strong positive correlation (R2 = 0.740, P  More

SamplingWater was sampled from Cep lake in Czechia, at a regular sampling site of 10 m depth (48.944 °N, 14.877 °E). The lake originates from sand mining in the 1970–80s. It is a permanent meso-oligotrophic (chlorophyll-a concentrations ranged from 1.4–16.4 µg L−1) seepage reservoir filled with groundwater penetrating from the nearby river Lužnice. The lake area is about 1.16 km2, with the maximum depth about 11–12 m. These characteristics are representative for most of temperate and boreal lakes [16].Samples were collected every four weeks from April till October in 2018, and from April till November in 2019. Ten liters of water were collected from 0.5 m depth using a Ruttner Water Sampler (model 11.003KC Denmark AS). Temperature and oxygen profiles were taken with an EXO1 multi-parameter probe (YSI Inc., Yellow Springs, USA). Water was transported to the laboratory within 2 h from the collection in a closed container made from high-density polyethylene, rinsed three times with the sampled water and stored in a cooled box.NutrientsSamples were filtered through glass fiber filters with 0.4 µm nominal porosity (GF-5, Macherey-Nagel, Düren, Germany). Concentrations of soluble reactive phosphorus (SRP) were determined spectrophotometrically [17, 18]. Concentrations of nitrate and ammonium were measured according to Procházková [19] and Kopáčkek and Procházková [20]. Dissolved organic carbon (DOC) and dissolved nitrogen (DN) were measured with a TOC 5000 A analyzer (Shimadzu, Kyoto, Japan).PigmentsSeston from 1.43 to 3.65 L of water was collected onto GF-5 glass fiber filters (diameter 47 mm, Macherey-Nagel). The filters were dried of excess water by gently pressing in a paper towel, and flush frozen in liquid nitrogen. Pigments were extracted and analyzed by HPLC as described in Piwosz et al. [21].Net primary production (NPP) and community respiration (oxygen measurements)Oxygen concentration was measured with the Winkler method [22]. It was chosen because it allows O2 concentration to be measured directly in the water without the need to consider carbonic equilibrium, which is the case when changes in CO2 concentration is measured [23]. Samples were unfiltered to avoid the removal of particle-associated bacteria and also of free-living AAP bacteria, which tend to be larger than average freshwater bacteria [12]. Glass stoppered Winkler type oxygen bottles (115 mL nominal capacity, VTR glass, Prague, Czechia) were filled with the sampled water directly from the sampler via a rubber tube. Each bottle was first rinsed three times and then filled without the formation of air bubbles. Water was allowed to overflow the neck of the bottle for about 1 min, and the bottle was closed with a glass stopper to avoid air bubbles. The closed bottles were kept in the dark in a cooled box. On the shore, three bottles were selected as T0, and 1.2 mL of manganese (II) chloride solution (concentration 3 mol L−1) was addded, followed by the addition of 1.2 mL of a mixture containing 4 mol L−1 of sodium iodide solution and 8 mol L−1 of sodium hydroxide solution. These samples were processed in the laboratory within 3 h. The remaining bottles were incubated for 24 h at in situ temperature in the IR-box prepared from the MAKROLON IR polycarbonate sheet (4 mm thickness, Professional Plastics, Inc. Fullerton, CA, USA). These panels have a maximum transmittance of 90% in the infrared region 850–2000 nm, 50% at 780 nm, and 0% 10 °C according to our measurements (180 days, Equation 3 in Supplementary File S1). Subsequently, the differences in the Cep Lake’s carbon budget for the surface layer (down to 0.5 m depth) were calculated by multiplying the integrated values by the volume of this layer (Equation 4 and 5 in Supplementary File S1).HCO3
− incorporationTriplicated water samples (32 mL) were incubated for 3.2–5.2 h in the IR light and the dark at in situ temperature, as described for respiration. Total activity added to each bottle was measured from 1 mL aliquot of the incubated sample that was transferred to a scintillation vial containing 20 μl of 5 mol L−1 NaOH (to prevent a loss of 14C-bicarbonate). Thirty mL of sample was filtered through 2.5 μm nitrate cellulose filters (Pragopor, Prague, Czechia, diameter 25 mm). Five mL of the filtered water was collected and subsequently filtered through a 0.17 μm nitrate cellulose filter. The resulting cell-free filtrate, which contained 14C-DOC was collected. The filtration was done at a low vacuum (0.02 MPa) to avoid cell breakage. The total CO2 assimilation rate was calculated as the sum of all these fractions.The filters were kept in an HCl-saturated atmosphere for 24 h at room temperature in a fume hood. They were placed in scintillation vials and dissolved in 1 mL of ethyl acetate (Penta, Prague, Czechia). Then, 5 mL of Ultima Golt LLT scintillation cocktail (PerkinElmer, Waltham, MA, USA) was added. Five mL of cell-free filtrates were acidified by adding 100 μL 5 mol L−1 HCl to volatilize non-incorporated H14CO3 and incubated 24 h at room temperature in a fume hood. Then, 10 mL of the scintillation cocktail was added. Finally, 5 mL of the scintillation cocktail was added to the total activity samples. Subsequently, the samples were gently mixed and left in the dark for 48 h. The radioactivity in the samples was measured using a Tri-Carb 2810 TR scintillation counter (PerkinElmer).To estimate carbon fluxes (μmol C L–1 h–1), a fraction of the added H14CO3 incorporated or released was corrected for the incubation time and multiplied by the concentration of total dissolved inorganic carbon (DIC). The DIC concentration was calculated based on temperature, pH, and alkalinity measurements (Inolab pH 720, WTW Xylem Inc. Rye Brook, NY, US) determined by Gran titration.Assimilation of organic monomersThe difference between microbial activity in the IR light and dark was also estimated based on assimilation rates of radiolabeled glucose, pyruvate, leucine and thymidine (American Radiolabeled Chemicals, St. Louis, MO, USA). Tritiated glucose (specific activity (SA): 2220 GBq mmol−1), leucine (SA: 4440 GBq mmol−1) and thymidine (SA: 2275.5 GBq mmol−1) were added to 5 mL samples to a final concentration of 5 nmol L−1. 14C-pyruvate (SA: 2.035 GBq mmol−1) was added to a final concentration of 10 nmol L−1. Trichloroacetic acid (TCA) was added to the killed controls to a final concentration of 1%. Samples were incubated for 1 h in the dark and IR light as described for respiration. The incubations were terminated as the killed controls and kept at 4 °C in the dark until processed within 65% except for the samples from 9th May and 29th Aug 2018 (10% each), 1st Aug 2018 (21%), 25th Nov 2018 (1%), and 14th Aug 2019 (22%), Supplementary Fig. S1A). Thus, we decided to concatenate the fastaq files and analyze both fractions together as the total community. This also facilitated statistical analysis, as the activity rates were measured for the whole community without fractionations.Reads quality was evaluated using FastQC v0.11.7 (Babraham Bioinformatics, Cambridge, UK). After primer sequences trimming using Cutadapt [29] (v1.16), the number of reads per sample ranged from 49,354 to 188,942. Subsequent analyses were done in the R/Bioconductor environment using the dada2 package (version 1.14.1) [30]. Forward and reverse reads were truncated to 225 bp and low quality sequences were filtered out with the filterAndTrim function (truncLen = c(225, 225), maxN = 0, maxEE = c(2, 2), truncQ = 2), which reduced the number of reads per sample to range from 30,190 to 143,552. After merging and chimera removal using the removeBimeraDenovo function, 4,893 amplicon sequence variants (ASV) were obtained. Rare ASVs (not seen >3 times in at least 20% of the samples) were removed, which reduced the number of ASVs to 658, and the number of reads to 14,613–69,046 per sample. Taxonomic assignment was done using SILVA 138.1 database [31, 32] released on August 27, 2020. ASVs identified as Chloroplast or Cyanobacteria were excluded from the analyses, giving the final number of 546 ASVs and from 10,819 to 54,799 reads per sample. The bacterial community composition graphs were done using phyloseq [33] and ggplot2 [34] packages.AAP community compositionThe composition of AAP community was analyzed by amplicon sequencing of pufM gene encoding the M subunit of bacterial type-2 reaction centers. This gene is routinely used for diversity studies of AAP bacteria [35].PufM gene amplicons (approx. 245 bp) were prepared using pufM_UniF (5′-GGN AAY YTN TWY TAY AAY CCN TTY CA-3′) and pufM_WAW (5′-AYN GCR AAC CAC CAN GCC CA-3′) primers [36]. PCR was performed in triplicate 20 μL reactions using Phusion™ High-Fidelity DNA Polymerase (Thermo Scientific, USA) with the following reaction conditions: 98 °C for 3 min, 27 cycles at 98 °C for 10 s, 58 °C for 30 s, 72 °C for 30 s, and a final extension at 72 °C for 5 min. The triplicate product reactions for each sample were pooled and gel purified using the kit Wizard SV Gel and PCR Clean-Up System (Promega, USA). The sequencing was performed on the Illumina MiSeq platform (2 × 150 bp) at Macrogen, South Korea.The fastq files were concatenated as described for bacteria communities. The Bray-Curtis similarity between two fractions for each sampling day was >70%, except for the samples from 1st Aug 2018 (47%) and 14th Aug 2019 (18%, Supplementary Fig. S1B).The samples were analyzed as described for bacterial communities. The number of reads per sample ranged from 192,360 to 239,418 after the cutadapt trimming. Forward and reverse reads were truncated to 130 bp, and the number of reads per sample after the quality filtering and denoising ranged from 189,432 to 235,311. Merging the forward and reverse reads with mergePairs function created 12,692 ASVs and reduced the number of reads to 183,136–221,281 per sample. The chimera removal lowered the number of ASVs to 1816, and the number of reads to 159,451–208,679. Rare ASVs (not seen >3 times in at least 20% of the samples) were removed, which resulted in the final 566 ASVs, and a number of reads ranging from 155,915 to 203,021 per sample. A manually curated taxonomic database was used for taxonomic assignment following the naïve Bayesian classifier method [37]. It contained 1580 unique pufM sequences, downloaded from the Fungene repository on May 16, 2019 (http://fungene.cme.msu.edu [38]), from metagenomes from the Římov Reservoir [39, 40] and from the Genome Taxonomy database accessed on September 16, 2020 [41].Statistical analysisLinear mixed-effects models were calculated in R (version 3.6.2) using lme function from the nlme package (version 3.1.143) on untransformed activity data and log10 transformed environmental variables [42]. Models’ parameters were estimated using maximum likelihood method and their significance was tested with ANOVA. Relationships between the activity measures, the environmental variables and the composition of AAP communities were investigated with distance-based linear models (DistLM) [43, 44] in Primer (version 7.0.13) with PERMANOVA + 1 add on (e-Primer, Plymouth, UK) [45]. The sequence reads were transformed with the varianceStabilizingTransformation function of the DESeq2 package [46] (version 1.14.1, blind = FALSE, fitType = “mean”).Data accessibilityThe sequences of 16S and pufM amplicons that support the findings of this study have been deposited in the EMBL database as the BioProject with the accession number PRJEB41596, together with most of the environmental metadata. The scripts and the remaining data supporting the results are included in the Supplementary Material. More

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