Ecology

Study site, field survey, and in situ filtration
The field survey was performed in Tateyama Bay (34° 60′ N, 139° 48′ E), central Japan, in the proximity of the Kuroshio warm current facing the Pacific Ocean (Fig. 1). This area has many artificial reefs (ARs) created to improve fishing efficiency for fishers. Among the ARs, we focused on one high-rise steel AR (AR1), with a height of 30 m, where fish tended to aggregate (Fig. 1 and S1). Sampling stations were set up at the AR1 and at six linear distant points extending northeast and southwest. These stations were named E150, E500, E750, W150, W500, and W750, where “W” or “E” and the number of each station name represented northeast or southwest and distance in meters from the AR1, respectively (Table S1 and Fig. 1). Another station was set up at a second AR (AR2: 25 m height) 220 m from AR1 because we found AR2 by chance during the survey (Table S1 and Fig. 1), and it might affect the eDNA concentration at other stations.Figure 1(a) Location of sampling stations, cruise track, and a set net in Tateyama Bay. Gray areas indicate landmasses, a gray bold line indicates cruise track, and gray thin lines indicate depth contours with an interval of 20 m. The maps were created using ArcGIS Software 10.6.0.8321 by ESRI (https://www.esri.com/) based on the municipal boundary data of Japan (Esri Japan) and Global Map Japan (Geospatial information Authority of Japan) as well as the M7000-series isobath data set (Japan Hydrographic Association). A picture of the artificial reef (AR1) (b) taken one year after this survey (June 2019) and pictures of the dominant species, (c) splendid alfonsino (Beryx splendens), (d) chicken grunt (Parapristipoma trilineatum), (e) chub mackerel (Scomber japonicus), (f) red seabream (Pagrus major), and (g) jack mackerel (Trachurus japonicus). Photograph credits: (b) Nariaki Inoue, (c) Fumie Yamaguchi, (d, e, g) Yutaro Kawano, and (f) Masaaki Sato.Full size imageWe conducted water sampling at eight stations for eDNA analysis and performed an acoustic survey for estimating relative fish density using research vessel Takamaru (Japan Fisheries Research and Education Agency: FRA) on May 23, 2018. We started the echo sounder survey at the eastern part of the bay and continued it during the water sampling (Fig. 1). Although the echo sounder survey could not differentiate between fish species, we collected this data to assess the association between the estimated concentration of fish eDNA and the echo intensity measured by the echo sounder. Water sampling began at E750, then continued along the transect line to E150, AR1, W150, W500, W750, before going back to AR2. At each sampling station, we collected 10 L of seawater from both the middle and bottom layers by one cast of two Niskin water samplers (5L × 2 samples) and measured vertical profiles of water temperature and salinity with a conductivity-temperature-depth sensor (RINKO profiler, JFE Advantech Co., Ltd.). We subsampled 2L seawater from the 5 L seawater of Niskin sampler using measuring bottle and remaining 3 L seawater was used for pre-wash of measuring bottle and filtration devices. Two 2L samples were collected from two Niskin water samples, and then immediately filtered using a combination of Sterivex filter cartridges (nominal pore size = 0.45 μm; Merck Millipore) through an aspirator (i.e., the two filters were subsets of a single water collection) in a laboratory on the research vessel. After filtration (average time of 15 min), an outlet port of the filter cartridge was sealed with an outlet luer cap, 1.5 ml RNAlater (Thermo Fisher Scientific Inc., Waltham, MA) was injected into the cartridge using a filtered pipette tip to prevent eDNA degradation, and an inlet port was also sealed with an inlet luer cap14. The Niskin water samplers were bleached before each water collection using a commercial bleach solution while filtering devices (i.e., filter funnels and measuring cups used for filtration) were bleached after every filtration. We filtered 2L MilliQ water with a filter funnel and measuring cup as a field negative control to test for possible contamination. The filter cartridges were placed in a freezer immediately after filtration until eDNA extraction. In total we collected and filtered 32 eDNA samples (eight stations × two depth layers × two replicates). Disposable latex or nitrile gloves were worn during sampling and replaced between each sampling station.DNA extraction and purificationWorkspaces were sterilized prior to DNA extraction using 10% commercial bleach, and filter tip pipettes were used to safeguard against cross-contamination. Following the method developed by Miya et al.15, the eDNA was extracted and purified. Briefly, after removing RNAlater inside the cartridge using a centrifuge, proteinase-K solution was injected into the cartridge from the inlet port, and the port was re-capped with the inlet lure cap. The eDNA captured on the filter membrane was extracted by constant stirring of the cartridge at a speed of 20 rpm using a roller shaker placed in an incubator heated at 56 °C for 20 min. The eDNA extracts were transferred to a 2-ml tube from the inlet of the filter cartridges by centrifugation. The collected DNA was purified using a DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer’s protocol. After the purification, DNA was eluted using 100 μl of the elution buffer (buffer AE). All DNA extracts were frozen at − 20 °C until paired-end library preparation.Preparation of internal standard DNAsFive artificially designed and synthetic internal standard DNAs, which were similar but not identical to the region of any existing fish mitochondrial 12S rRNA, were included in the library preparation process to estimate the number of fish DNA copies [i.e., quantitative MiSeq sequencing (qMiseq)]7,16. They were designed to have the MiFish primer‐binding regions as those of known existing fishes and to have the conserved regions in the insert region. Variable regions in the insert region were replaced with random bases so that no known existing fish sequences had the same sequences as the standard sequences. The standard DNA size distribution of the library was estimated using an Agilent 2100 BioAnalyzer (Agilent, Santa Clara, CA, USA), and the concentration of double-stranded DNA of the library was quantified using a Qubit dsDNA HS assay kit and a Qubit fluorometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). Based on the quantification values obtained using the Qubit fluorometer, the copy number of the standard DNAs was adjusted as follows: Std. A (100 copies/µl), Std. B (50 copies/µl), Std. C (25 copies/µl), Std. D (12.5 copies/µl) and Std. E (2.5 copies/µl). Then, these standard DNAs were mixed.Paired-end library preparationTwo PCR‐level negative controls (i.e., each with and without internal standard DNAs) were employed for MiSeq run to monitor contamination during the experiments. The first-round PCR (1st PCR) was carried out with a 12-µl reaction volume containing 6.0 µl of 2 × KAPA HiFi HotStart ReadyMix (Roche, Basel, Switzerland), 0.7 µl of each primer (5 µM), 2.6 µl of sterilized distilled H2O, 1.0 µl of standard DNA mix and 1.0 µl of template. Note that the standard DNA mix was included for each sample. The final concentration of each primer was 0.3 µM. We used a mixture of the following four PCR primers modified from original MiFish primers16: MiFish-U-forward (5′-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT NNN NNG TCG GTA AAA CTC GTG CCA GC-3′) and MiFish-U-reverse (5′-GTG ACT GGA GTT CAG ACG TGT GCT CTT CCG ATC TNN NNN CAT AGT GGG GTA TCT AAT CCC AGT TTG-3′), MiFish-E-forward (5′-ACA CTC TTT CCC TAC ACG ACG CTC TTC CGA TCT NNN NNG TTG GTA AAT CTC GTG CCA GC-3′), and MiFish-E-reverse (5′-GTG ACT GGA GTT CAG ACG TGT GCT CTT CCG ATC TNN NNN CAT AGT GGG GTA TCT AAT CCT AGT TTG-3′). These primer pairs co-amplify a hypervariable region of the fish mitochondrial 12S rRNA gene (around 172 bp) and append primer-binding sites (5′ ends of the sequences before five Ns) for sequencing at both ends of the amplicon. The five random bases were used to enhance cluster separation on the flow cells during initial base call calibrations on the MiSeq platform. The thermal cycle profile after an initial 3 min denaturation at 95 (^circ)C was as follows (35 cycles): denaturation at 98 (^circ)C for 20 s; annealing at 65 (^circ)C for 15 s; and extension at 72 (^circ)C for 15 s, with a final extension at the same temperature for 5 min. Eight replications were performed for the 1st PCR, and the replicates were pooled to minimize the PCR dropouts. The 1st PCR products from the eight tubes were pooled in a single 1.5-ml tube. Then, we sent the 1st PCR products to IDEA consultants, Inc. to outsource the following MiSeq sequencing processes. The pooled products were purified and size-selected for 200–400 bp using a SPRIselect (Beckman Coulter, Inc.) to remove dimers and monomers following the manufacturer’s protocol.The second-round PCR (2nd PCR) was carried out with a 24 µl reaction volume containing 12 µl of 2 × KAPA HiFi HotStart ReadyMix, 2.8 µl of each primer (5 µM), 4.4 µl of sterilized distilled H2O, and 2.0 µl of template. We used the following two primers to append the dual-index sequences (8 nucleotides indicated by Xs) and flowcell-binding sites for the MiSeq platform (5′ ends of the sequences before eight Xs): 2nd-PCR-forward (5′-AAT GAT ACG GCG ACC ACC GAG ATC TAC ACX XXX XXX XAC ACT CTT TCC CTA CAC GAC GCT CTT CCG ATC T-3′); and 2nd- PCR-reverse (5′-CAA GCA GAA GAC GGC ATA CGA GAT XXX XXX XXG TGA CTG GAG TTC AGA CGT GTG CTC TTC CGA TCT-3′). The thermal cycle profile after an initial 3 min denaturation at 95 (^circ)C was as follows (12 cycles): denaturation at 98 (^circ)C for 20 s; combined annealing and extension at 72 (^circ)C for 15 s, with a final extension at 72 (^circ)C for 5 min. The concentration of each second PCR product was measured by quantitative PCR using TB Green Fast qPCR Mix (Takara inc.). Each sample was diluted to a fixed concentration and combined (i.e., one pooled 2nd PCR product that included all samples). The pooled 2nd PCR product was size-selected to approximately 370 bp using BluePippin (Sage Science). The size-selected library was purified using the Agencourt AMPure XP beads, adjusted to 4 nM by quantitative PCR using TB Green Fast qPCR Mix (Takara Bio Inc.), and sequenced on the MiSeq platform using a MiSeq v2 Reagent Kit (2 × 150 bp) (Illumina, Inc.).Data preprocessing and taxonomic assignmentThe raw MiSeq data were converted into FASTQ files using the bcl2fastq program provided by Illumina (bcl2fastq v2.18). The FASTQ files were then demultiplexed using the command implemented in Claident17. We adopted this process rather than using FASTQ files demultiplexed by the Illumina MiSeq default program in order to remove sequences with low-quality scores and PCR artifacts (chimeras).The processed reads were subjected to a BLASTN search against the full NCBI database. We excluded unique sequences of the following settings: the sequence belonged to organisms other than bony fishes, sharks, and rays; the sequence similarity between queries and the top BLASTN hit was  More

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From the phytosociological relevés performed in each sampling area it is evident that hives were positioned in grasslands rich in Alpine herbaceous species (Table S1). In fact, among the 169 identified species, 85% were herbaceous species common in meadows (of Arrhenatherion elatioris and Triseto flavescentis-Polygonion bistortae phytosociological alliance) and acidophilus pastures (Siversio-Nardetum). 15% of the species were trees and shrubs (not abundant in the floristic relevés of the apiary areas considered), including some of beekeeping interest such as: Rhododendron ferrugineum, Castanea sativa and Rubus idaeus. From the MDS biplot (Fig. 2) elevation is the main ecological variable that differentiates sampling areas. In particular, the relevés of stations B and F are characterized by a floristic composition which is different from the areas at higher elevation (characterized by a higher presence of microthermal alpine species). This is due to the separation between the sub-montane belt and the high mountain belt vegetation on the 1.300 m a.s.l. line in the study area25.Figure 2MDS of the phytosociological relevés. Capital letters indicate the six sampling areas, the 1.300 m a.s.l. contour line that separates sub-montane belt and high mountain belt vegetation is highlighted in red.Full size imageAlthough the beehives were positioned in mountain grasslands, melissopalynological analysis presented a different picture. The pollen of numerous species detected through the floristic relevés were found in the honey samples via melissopalynological analysis, although the latter did not totally overlap with the floristic characterization of the area, in particular from a “quantitative” point of view. In fact, the floristic relevés showed a relative richness of herbaceous species (Table S1) peculiar of mountain grasslands that would seem promising for the production of wildflower honeys. Conversely, in the melissopalynological analysis the species considered interesting but not predominant in the botanical description were relevant (Fig. 3 and Table S2).Figure 3MDS of the melissopalynological analysis of the six samples (dots) of mountain wildflower honeys produced in the stations considered. The crosses are the pollens found in the honey samples, the most important are indicated.Full size imageThe premises to produce wildflower honey is that the botanical species contributing must be different and sometimes very numerous, without any of them assuming a dominant character. However, this was not fully evident in our research: although it was possible to identify more than seventy species through melissopalynological analysis and even more through the floristic characterization of the areas, most of them were defined as minor or sporadic pollen (Table S2). Even though apiaries were in mountain grasslands, the most relevant role was played by some woody species/shrubs: Rubus (presumably Rubus idaeous L., identified in the floristic relevés) and rhododendron (Rhododendron ferrugineum L.) for the mountain/subalpine belt and Castanea and Ericaceae (heather) in the submountain belt. Following the rules to define ‘‘unifloral honey’’, three of the wildflower honeys could be defined unifloral or bifloral:

Rhododendron unifloral: honey A (Rhododendron 47.18%), and honey C (Rhododendron 62.93%);

Raspberry unifloral: honey B (Rubus 67.12%)

Raspberry and Rhododendron bifloral: honey D (Rhododendron 34.27% and Rubus 34.74%) as well as honey E (Rubus 44.25%, Rhododendron 34.14%).

Honey F, due to the contribution of pollen from Tilia genus (that was detected only in this sample as an important sporadic pollen, 3.5%) Castanea (96.4% in honey F, but it should be noted that chestnut pollen is an overrepresented pollen) and in the second count Ericaceae (32.45%, that was considered a secondary pollen together with Rubus, with a percentage of 38.59% in honey F) differed from the other honeys (Fig. 3).Rubus pollen was anyway present in good amounts in all the samples considered, and was a dominant pollen in honey B, a secondary pollen in honeys C, D, E and F and a minor pollen in honey A. Sorbus and Tilia pollens were detected only in honey F, while no rhododendron was detected in honey F. Honey D was characterized by a percentage higher than the “rare pollen” category of some important alpine essences, such as Liliaceae, Centaurea, Campanulaceae, Anthyllis f., Polygonum bistorta, Lotus alpinus and Potentilla/fragaria (Table S2).Although wildflower honeys are intrinsically characterized by a high variability compared with unifloral honey, this shows the importance of the formal characterization of honey to obtain a product which satisfies consumer expectations, and it was demonstrated that the botanical origin of honey cannot be based on the claims of local beekeepers by considering the predominant flowers surrounding the hive.Although honeybees are considered supergeneralists in their foraging choices, there are certain key species or plant groups that are particularly important in honeybee foraging2, and many were identified in the botanical characterization of the area, including Rubus idaeus L., Calluna vulgaris L., rhododendron and some present in the broad-leaved woods mentioned such as chestnut (Castanea sativa Mill.) or plants of Tilia genus. In the research work by Hawkins et al.2, Rubus fruticosus L. was among the frequently found species and tree pollen belonging to Castanea sativa L. as well as, for example, species of Malus, Salix and Quercus spp, was frequently seen. These kinds of preferences could relate to the ease of availability and abundance of the plant, the quality and abundance of the nectar and pollen and/or specific nutrients or trace elements provided by these species or neurological aspects (as will be discussed further). As referred by beekeepers, over the last decades the production of mountain wildflower honey, that often does not meet the characteristics expected and presents flavours that are reminiscent of other kinds of honey such as rhododendron or linden or chestnut, is becoming more and more critical and this was absolutely confirmed by this study.This could be linked to the fragmentation of an important habitat of the Alps—mountain grasslands (meaning pastures and meadows) for anthropic and climatic reasons8,9. Honeybees from the same colony forage across areas spanning up to several hundred square kilometres, and at linear distances as far as 9 km from the hive41. Onlooker bees are those in charge of finding nectar sources and of giving instructions to the employed bees, the other foraging bees, that communicate the necessity to look for new resources of food to the onlookers through continuous dance communication42. Among the onlookers, there is a difference between the bees that scout for different nectar sources or recruit to well known floral resources43 and there is an optimal ratio of scouts to recruits, for the most effective collective foraging41. However, this balance may change based on the structure of the landscape in which the bees forage for food44,45,46. Theoretical models47,48 and empirical tests49 suggest that when resources are concentrated into a small number of highly rewarding patches, colonies perform best with few scouts and many recruits, while when resource patches are small, evenly distributed, and easy to locate, successful colonies invest more in scouting than in recruitment. This is strictly linked to climate and social changes in the mountains: mountain grasslands are no longer evenly distributed and easily localizable, as they are scattered among expanding areas of shrublands and forests9 and, for the above-mentioned reasons, it is more efficient for the colony to invest in more recruiters than scouters, as recruiters will identify a small number of highly rewarding patches, such as raspberry or rhododendron shrublands or linden and chestnut woods, that are highly rewarding and very different in quality.This overlaps with individual and collective honeybee behaviour driven by proximate physiological mechanisms that involve the tryptophan metabolism via kynurenine pathway that is one of main neuroprotective mechanisms. In this research, many of the differences/similarities among the samples might be attributed to metabolic alterations within this pathway, represented by relative amounts of kynurenic acid. However, different quinoline structures have also been identified (Fig. 4). Neurotransmitters play a central role in several of the biological processes that honeybees require to perform activities such as foraging behaviour50. A considerable amount of literature highlights the involvement of the neuroprotective kynurenine pathway (KP) final product kynurenic acid (KinA) in the regulation of the stress-related hormone dopamine in the honeybee as well as in other animal species51,52. The major known source of dietary KynA are pollen and nectar produced by sweet chestnuts53,54 and it has been verified that this compound is found in high concentrations in chestnut flowers55. This is coherent with the results of this study: chestnut pollen was found in honey F, produced in the lower station where chestnuts also appear in the floristic relevés, and KynA was found to be a dominant compound in honey F. Interestingly, chestnut pollen was found as sporadic pollen in all the other samples, even those produced in the highest apiary stations (Table S2).Figure 4Kinurenic acid and 3-hydroxyquinaldic acid structure and content in the six honey samples, performed in triplicate. The box diagram representing the median with distribution interval between 25 and 75%.Full size imageFurther, KinA may possess positive properties in a number of pathologies of the gastrointestinal tract, especially colitis, colon obstruction or ulceration56,57. It has been proposed that KinA may also possess antioxidative properties56,57,58,59. This was confirmed by this study, since the wildflower honey with a high component of chestnut pollen was the one with the highest antioxidant properties at the FRSA test (66.61 ± 4.77%), even if lower than manuka honey (84.21 ± 1.04%), a dark honey that is a well-known nutraceutical product and has recently attracted attention for its biological properties, especially for its antioxidant and anti-microbial capacities60. Honey A showed the lowest power (22.40 ± 0.28%) while the other honeys ranked around 40% (Fig. 5). Interestingly, metabolomic analysis revealed the presence of 3-hydroxyquinaldic acid (Fig. 4), which is a kynurenic acid isomer and, although its function has not been elucidated in detail, a few literature data indicate its role as a precursor of naturally occurring peptide antibiotics from the quinomycin family61.Figure 5Results of the FRSA test. Capital letters represent the six honey samples considered. Manuka honey was used as a control.Full size imageIn order to evaluate the ability of honey to induce wound closure, a scratch wound assay was performed (Fig. 6)62. Scratch assay creates a gap in confluent keratinocyte monolayer to mimic a wound. It has already been demonstrated that honeys are able to induce wound closure63 to different extents depending on honey origins and properties.Figure 6The scratch wound test in keratinocytes, HaCaT cells, exposed to honeys. (a) The digitalized pictures of scratched cells after 24 h exposure to 0.5% (w/v) of honeys. (b) The closing percentage wound values after 24 h exposure. Statistics on bars indicate differences compared to the control (CTRL) condition determined by a One-Way ANOVA followed by Dunnett’s test (****p  More

Soil and litter samplingMineral soil (0–15 cm) was collected at the Elizabeth Woods site, a 120-year-old deciduous forest in West Virginia, US (39° 32′ 50.6″ N, − 80° 00′ 00.4″ W). Soils were collected from four 20 × 20 m plots dominated by either AM-associated trees (i.e. Liriodendron tulipifera and Acer saccharum), or ECM-associated trees (i.e. Quercus rubra, Quercus velutina and Carya ovata). These sites have been characterized previously as Culleoka-Westmoreland silt loam soils at the AM sites and Dormont and Guernsey silt loams at the ECM sites40. Soils were also characterized by C:N ratios 11.7 and 14.1 for the AM and ECM soils respectively, with a pH of 6.8 for both soils. Soils with the same mycorrhizal status were pooled and homogenized, air-dried at room temperature for ~ 24 h and sieved through 2.0 mm mesh before the initiation of the experiment. Uniformly 13C labeled litter ( > 97 atom % 13C) from Quercus robur (i.e., ECM substrate) and Liriodendron tulipifera (i.e. AM substrate) leaves (Isolife BV, Wageningen, NL) were incubated in soil mesocosms in a factorial design with five replicates for each treatment combination (2 soil types × 2 substrate types), along with five replicate controls (no 13C substrate addition) for each soil type. The 13C enriched substrates were dried and ground to a powder and added in a suspension of 0.5 ml sterile water to 20 g of soil at a concentration of 400 ug 13C g−1 soil. The control soils received 0.5 ml sterile water additions. These incubations were well mixed and kept at 60% water-holding capacity for the 21-day period at room-temperature18. Chemical characteristics of soils and plant substrates are provided in Table S1.DNA processing and qSIPFor quantitative stable isotope probing, DNA was extracted, quantified, ultracentrifuged, fractionated and sequenced as described in18,26. DNA was extracted using a MoBio PowerSoil HTP Kit following the manufacturer’s instructions. For stable isotope probing, 5 ug of DNA was loaded into a 5-ml ultracentrifuge tube with ~ 3.5 ml of a saturated cesium chloride (CsCl) solution and ~ 900 ml gradient buffer (200 mM Tris, 200 mM KCl, 2 mM EDTA). DNA was separated via ultracentrifugation at 127,000g for 72 h using a TLN-100 rotor in an Optima Max bench top ultracentrifuge (Beckman Coulter, Fullerton, CA, USA). Tubes were fractionated into ~ 25 fractions of 150 µl each, and the density of each fraction was measured with a Raichart AR200 digital refractometer. DNA was purified using an isopropanol precipitation method. The 16S rRNA gene was subsequently quantified and sequenced in samples containing DNA, within the density range 1.660–1.735 gml−1 (~ 10 fractions per sample). To quantify the 16S rRNA gene, quantitative PCR was performed in triplicate using a QuantStudio 5 applied biosystems (Thermo Fisher Scientific) and primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACVSGGGTATCTAAT-3′)41. The PCR program used was as follows: 95 °C for 2 min followed by 45 cycles of 95 °C for 30 s, 64.5 °C for 30 s and 72 °C for 1 min. Libraries were sequenced on an Illumina MiSeq instrument (Illumina, Inc., San Diego, CA, USA) using a 300-cycle v2 reagent kit. Fungal 18S rRNA gene copies in each fraction were also quantified using primers 1380F (5′-CCCTGCCHTTTGTACACAC-3′) and 1510R (5′-CCTTCYGCAGGTTCACCTAC-3′). The PCR program used was as follows: 98 °C for 3 min followed by 40 cycles of 98 °C for 45 s, 60 °C for 45 s and 72 °C for 30 s. DNA fractions were amplified for fungal ITS rRNA genes using primers ITS4F (5′-AGCCTCCGCTTATTGATATGCTTAART-3′) and 5.8SF (5′-AACTTTYRRCAAYGGATCWCT-3′)42 and 300-bp paired-end read chemistry on an IlluminaMiSeq (Illumina, Inc., San Diego, CA, USA). The PCR program used was as follows: 95 °C for 6 min followed by 35 cycles of 95 °C for 15 s, 55 °C for 30 s, and 72 °C for 1 min. DNA fractions were then sequenced using a 500 cycle v2 reagent kit.Files came pre-split and joined multiple paired ends that we combined to pick operational taxonomic units (OTU). Open reference OTUs were picked at 97% identity using SILVA 128 release database for Bacteria and RDP database for Fungi. Taxa were analyzed at the ‘OTU’ level from the QIIME L7 table. Calculation of 13C excess atom fraction (EAF) was performed for each taxon as described previously18,19. Briefly, using the CsCl density gradient data, a weighted average density (WAD) was computed for each taxon’s DNA extracted from control soils that did not receive an isotopically enriched substrate. This natural abundance WAD was then compared to the taxon’s WAD following incubation with the 13C enriched material. The change in WAD can be used to quantify the amount of isotope incorporated into the DNA17,18. Preliminary data analysis revealed an effect of ultracentrifuge tube on estimation of phylotype weighted average density, probably a consequence of slight differences in CsCl density gradients between tubes. This technical error was corrected as previously described18,19. In addition to the samples subjected to qSIP analysis we also extracted and analyzed fungal and bacterial OTU’s from control soils where the DNA was extracted prior to incubation.FTICR-MS and lipidomic analysesSoil from substrate-incubated and controls mesocosms were processed and analyzed with Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), using a 12 T Bruker SolariX FTICR mass spectrometer at the Environmental Molecular Sciences Laboratory in Richland, WA, as described in Fudyma et al.43. Briefly, 100 mg of dried soil or litter substrate was extracted using an adjusted Folch extraction44. Extraction was performed on each sample by sequentially adding 2 ml MeOH, followed by a 5 s vortex; 4 ml CHCl3, followed by a 5 s vortex; sonication at 25 °C for 1 h (CPX3800 Ultrasonic Bath, Fisherbrand); addition of 1.25 ml of H2O, followed by a slight mix to achieve bi‐layer separation; and incubated at 4 °C overnight. The top, aqueous layer (metabolite—polar) was pipetted off into 1 ml glass vials and stored at − 80 °C until FTICR‐MS. The bottom, chloroform layer was dried down and stored in 50:50 methanol:chloroform until lipidomics analysis.A standard Bruker electrospray ionization (ESI) source was used to generate negatively charged molecular ions in the metabolite fraction. Samples were then introduced directly to the ESI source. The instrument settings were optimized by tuning on a Suwannee River fulvic acid (SRFA) standard, purchased from International Humic Substances Society (IHCC). Blanks (HPLC grade methanol) were analyzed at the beginning and end of the day to monitor potential carry over from one sample to another. The instrument was flushed between samples using a mixture of water and methanol. One hundred and forty‐four individual scans were averaged for each sample and internally calibrated using an organic matter homologous series separated by 14 Da (CH2 groups). The mass measurement accuracy was less than 1 ppm for singly charged ions across a broad m/z range (m/z 300– 800). Data analysis software (Bruker Daltonik version 4.2) was used to convert raw spectra to a list of m/z values, applying the FTMS peak picker module with a signal-to noise ratio (S/N) threshold set to 7 and absolute intensity threshold set to the default value of 100. Chemical formulae were then assigned using in-house software following the compound identification algorithm that was described in Tolić et al.45. Peaks below 200 and above 800 were dropped to select only for calibrated and assigned peaks. Chemical formulae were assigned based on the following criteria: S/N  > 7 and mass measurement error  800 were not detected in our samples. The m/z values represent the molecular mass (in Dalton) of the detected ions since all detected ions were singly charged ions. While our results do not represent a quantitative characterization of OM, the values presented are relative differences and should be representative of the samples. Finally, we would like to acknowledge that we were not able to see any clear evidence of 13C label in our FTICR-MS analysis of the soil samples. The lack of 13C label in our FTICR-MS analysis of the soil samples even though they received labeled substrate could be either due to the fact that most of the labeled substrates produced by microbial activities were of low molecular weight, which cannot be detected by FTICR-MS and/or the leftover labeled substrate was of low abundance compared to the organic compounds previously present in the soil matrix. As such, we used the FTCIR-MS data to identify shifts in the overall composition of the chemical compounds in each soil.Lipids in the chloroform fraction were analyzed by LC‐MS/MS in both positive and negative ESI modes using a linear trap quadropole (LTQ) Orbitrap Velos mass spectrometer (Thermo Fisher Scientific), as described in detail previously46. Lipid species were identified using the LIQUID tool46 followed by manual data inspection. Confidently identified lipid species were quantified using MZmine47 and the peak intensities were normalized by linear regression and central tendency (i.e., identifying a central or typical value for a probability distribution) using InfernoRDN.Statistical analysisAll data analyses were performed using R 3.2.048. To examine the effects of soil type, substrate type and their interaction in the bacterial, fungal and chemical composition of DOM and the lipid pool; Bray–Curtis distance matrices were compared with permutational multivariate analysis of variance (PerMANOVA) and visualized with Principle Coordinate Analysis (PCoA) using vegan package49. PerMANOVA analysis were run on the relative abundance and on the 13C EAF of individual microbial taxa, separately for both bacterial and fungal communities.The analyses for FTICR-MS were performed separately for control and incubated soils using all assigned molecular formulae remaining after quality filtering31. In all cases, we applied a Z-score standardization before calculating Bray–Curtis distance matrices49. We analyzed the results from FTICR-MS as resulting from the decomposition of the added substrates for two reasons. First, this is a fully factorial design where individual soil samples were split to either receive AM poplar or ECM oak litter substrate. Thus, each soil sample starts with the same characteristics and the changes at the end of the incubation period should reflect the processing of litter. Second, we excluded molecular formulae present in the litters and thus, the differences we report in each soil type are derived from this processing (or the lack of it).We calculated aggregated indices that characterize both the composition and the physicochemical properties of the microbial (both bacteria and fungi) and the SOM and lipid pool34,36. For bacterial and fungal communities, we quantified Shannon–Weaver diversity index for each sample H′ = (-{sum }_{i=1}^{S} pi ln(pi)) (where pi is the proportion of species I) using the relative abundance of individual microbial taxa50. To find the percent of substrate assimilation by individual taxa, we calculated the proportion of C assimilated by each group as previously described18,51 as a percent. For SOM and lipid molecular formulae, we separately calculated weighted means of formula-based characteristics (i.e. m/z, Aromaticity Index—AI; H/C, O/C, and Nominal Oxidation State of Carbon-NOSC) as the sum of the product of the single-formula information (i.e. m/zi, AIi, H/Ci and NOSCi) and the relative intensity (Ii) divided by the sum of all intensities (e.g., m/z sample1 = ({sum }_{i=1}^{S})(m/zi ·Ii)/Σ(Ii)). With these metrics we obtained sample-level information related to the molecular size (i.e. m/z), the molecular bioavailability (i.e. higher H/C ratio), the molecular reactiveness (i.e. lower AI) and the energetic rewards from molecular oxidative degradation (i.e. higher NOSC) of the SOM, which allows to infer the potential of decomposition products to form stable SOM12,31,35. Detailed information of the calculated indices can be found in the literature31,35,36.We further tested the effects of soil type, substrate type and their interaction on each index using the “lm” function from the “stats” package. In these analyses, P values were approximated by an F test using Type II ANOVA tests with Kenward-Roger Degrees of Freedom52. When interactions between soil and substrate type were found at P  More

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