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Metabolic profiling of EP and LE root exudates and root periderm samplesHigh performance liquid chromatography (HPLC) analysis of root exudates and root periderm reported the presence of five bioactive NQs. The identified NQs included shikonin (SK), acetylshikonin (AS); isobutyrylshikonin (IBS); β, β-dimethylacrylshikonin (DMAS); and isovalerylshikonin (IVS) (Fig. 1a–d). This suggests that SK and its derivatives accumulate in the rhizosphere of both EP and LE via root exudation. Though all the five NQs were found to be exuded in the rhizosphere however they varied quantitatively among EP and LE species. LE samples had higher SK and its derivatives production compared to EP (Figs. S5–S6). Our results also displayed quantitative variations in SK and its derivatives production among two soil types (Table 1a,b). However, regardless of variation, SK, AS, DMAS, and IVS were consistently present among all the samples.Figure 1Images and chromatograms representing qualitative and quantitative variation of SK and its derivatives production in root periderm extracts. Chromatograms of root extracts of E. plantagenium (EP) and L.erythrorhizon (LE) specimens grown in Peat potting artificial soil (a) EP.PP, (c) LE.PP; and Natural campus soil (b) EP.NC, (d) LE.NC. Resulting peaks correspond to shikonin (SK), acetylshikonin (AS); isobutylshikonin (IBS); β, β-dimethylacrylshikonin (DMAS); and isovalerylshikonin (IVS). Chromatogram for each sample represents a composite sample of 3–4 individual plants. Figure represents only one replicate for each sample while the rest of the two replicates for each sample with standard chromatogram are provided in Fig. S5.Full size imageTable 1 Quantitative analysis of shikonin and its derivatives via HPLC in (a) root periderm; (b) root exudates samples of E. plantagineum (EP) and L.erythrorhizon (LE).Full size tablePacBio sequence reads statistics and taxonomic profilingAfter quality filtering, removal of chimera, chloroplast and mitochondrial sequences, approximately 165,570 high quality sequences (Tags) were obtained. Tags were clustered into 14,429 microbial operational taxonomic units (OTUs) at a 97% sequence similarity cutoff level (Table S2). All OTUs with species annotation are summarized in Table S3. Taxonomic profiling for taxonomic affiliations revealed Proteobacteria, Bacteroidetes, Planctomycetes, Cyanobacteria, Acidobacteria, and Actinobacteria to be the dominant phyla among all the samples (Fig. S7). These 6 phyla accounted for 70.97–96.61% of the total microbial OTUs. The Proteobacterial microbes mainly belonged to Classes Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria that accounted for 13.94–40.54% of the total microbes (Table S4).Host plant genetics are the drivers for distinct microbiomeTo identify the effects of host plant genetics on microbial acquisition, microbial community composition of bulk soil was compared with root and rhizospher soils of EP and LE. α-diversity estimates revealed a significantly higher observed species richness (Sobs), and shannon diversity for bulk soil (Fig. 3a,b; Table S5). This indicates that bulk soil serves as a reservoir for microbial acquisition in other rhizo-compartments. At different taxonomic levels, microbes associated with Proteobacteria, Planctomycetes, Bacteroidetes and Cyanobacteria were all present in relatively higher abundance in EP and LE rhizo-compartments compared to bulk soil in two different soil types (Fig. 2a; Table S6). Wilcox test also displayed quantitative variation in microbial acquisition at order level. For example, compared to bulk soil, Flavobacteriales, Sphingomonadales, and Verrucomicrobiales had a relatively higher abundance in EP rhizosphere, while Caulobacterales, and Sphingomonadales were significantly higher in LE rhizosphere (Fig. S8, P  More

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P. aeruginosa produces novel glycolipids in response to Pi stressTo determine changes in the membrane lipidome in response to P-stress, the model P. aeruginosa strain PAO1 was grown in minimal medium under high (1 mM) or low Pi (50 µM) conditions (Fig. 1a). The latter condition elicited strong alkaline phosphatase activity, measured through the liberation of para-nitrophenol (pNP) from pNPP (Fig. 1b), this being a strong indication that cells were P-stressed. Analysis of membrane lipid profiles using high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS) revealed the presence of several new lipids under Pi stress conditions (Fig. 1c). Thus, during Pi-replete growth (1 mM phosphate), the lipidome is dominated by two glycerophospholipids: PG (eluted at 6.8 min) and PE (eluted at 12.2 min). During Pi-stress a lipid species with mass to charge ratio (m/z) of 623 and 649 were also found, with MS fragmentation resulting in a 131 m/z peak, a diagnostic ion for the amino-acid containing ornithine lipid. This is consistent with previous reports of ornithine lipids in the P. aeruginosa membrane in response to Pi stress [29, 30].Fig. 1: Lipidomics analysis uncovers novel glycolipid formation in Pseudomonas aeruginosa strain PAO1 in response to phosphorus limitation.a Growth of strain PAO1 WT in minimal medium A containing 1 mM phosphate (+Pi, blue) or 50 µM phosphate (−Pi, black) over 12 h. Data are the average of three independent replicates. b Liberation of para-nitrophenol (pNP) from para-nitrophenol phosphate (pNPP) through alkaline phosphatase activity, under Pi-replete (1 mM, black) and Pi-deplete (50 µM, yellow) conditions. Error bars represent the standard deviation of three independent replicates. c Representative chromatograms in negative ionisation mode of the P. aeruginosa lipidome when grown under phosphorus stress (−Pi, black) compared to growth under phosphorus sufficient conditions (+Pi, orange). PG phosphatidylglycerol, PE phosphatidylethanolamine, OL ornithine lipids. Lower panel: extracted ion chromatograms of three new glycolipid species in P. aeruginosa which are only produced during Pi-limitation (black, 1 mM; orange, 50 µM). MGDG monoglucosyldiacylglycerol, GADG glucuronic acid-diacylglycerol and UGL unconfirmed glycolipid. d Mass spectrometry fragmentation spectra of three glycolipid species present under Pi stress in P. aeruginosa, at retention times of 7.7 (m/z 774.7), 8.7 (m/z 786.7) and 9.8 (m/z 788.6) minutes, respectively. Each spectrum depicts an intact lipid mass with an ammonium (NH4+) adduct exhibiting neutral loss of a head group, yielding diacylglycerol (DAG) (595 m/z). Further fragmentation yields monoacylglycerols (MAG) with C16:0 or C18:1 fatty acyl chains.Full size imageFurther to ornithine lipids, three unknown lipids eluting at 7.7, 8.7 and 9.8 min, were only present under Pi stress conditions (Fig. 1c). Using several rounds of MS fragmentation (MSn), with a quadrupole ion trap MS, fragmentation patterns characteristic of glycolipids were found for all three peaks. For each peak of interest, the most predominant lipid masses of 774.7, 786.8 and 788.6 m/z were analysed by MSn in positive ionisation mode (Fig. 1d). In each case, an initial head group was lost leaving a significant signal of 595.6 m/z, the mass of the glycolipid building block diacylglycerol (DAG). Further fragmentation leads to the loss of either fatty acyl chain from DAG, leaving monoacylglycerols of 313.2 and 339.3 m/z. Two monoacylglycerols with different masses are produced as a result of the original lipid containing 16:0 and 18:1 fatty acids (313.2 and 339.3 m/z monoacylglycerols, respectively). To further elucidate the identity of the peaks, a search for a neutral loss of a polar head group was carried out. Thus, the intact masses of 774.7 and 788.6 m/z in positive ionisation mode leads to the loss of a head group of −179 and −193 m/z, which corresponds to a hexose- and a glucuronate- group, respectively (Fig. 1d), suggesting the occurrence of novel monoglucosyldiacylglycerol (MGDG) and glucuronic acid diacylglycerol (GADG) glycolipids in P. aeruginosa. The third glycolipid peak at 8.7 min remains an unknown lipid with intact mass of 786.8 m/z (hereafter designated as a putative unknown glycolipid, UGL). Together, these data confirm the production of new glycolipids in P. aeruginosa in response to Pi stress.Comparative proteomics uncover the lipid renovation pathway in P. aeruginosa
To determine the proteomic response of P. aeruginosa to phosphorus limitation, and identify the genes involved in glycolipid formation, strain PAO1 was cultivated under high and low Pi conditions for 8 h and the cellular proteome then analysed. A total of 2844 proteins were detected, 175 of which were found to be differentially regulated by Pi availability (Fig. 2a, Table S1). In line with previous transcriptomic studies of strain PAO1 [18], major phosphorus acquisition mechanisms were highly expressed under Pi stress conditions, e.g. the Pi-specific transporter PstSCAB, the two-component regulator PhoBR (Table S1) [31].Fig. 2: Comparative multi-omic analyses for the identification of the PlcP-Agt pathway responsible for glycolipid formation in Pseudomonas aeruginosa strain PAO1.a Volcano plot depicting differentially expressed proteins when comparing Pi-replete and Pi-deplete conditions. Significantly upregulated proteins when under Pi stress are shown in red (left), and those that are significantly upregulated when Pi is sufficient are in green (right). Significance was accepted when the false discovery rate (FDR) was More

IACUC approval: all work described below was approved by the Boise State Institutional Animal Care and Use Committee: AC15-021.Site layoutWe selected 20 sites, across five drainages, within the Pioneer Mountains of Idaho—matched for elevation and riparian habitat. We split these 20 sites into 10 noise playback sites, and 10 control sites (Fig. 1A; S1). The control sites ranged from quiet, slow-moving streams to relatively loud whitewater torrents. Noise playback sites, on the other hand, were relatively quiet (not whitewater) sites, where we broadcast loud whitewater river recordings with speaker arrays hung from towers (Fig. S1; S2; S3; S4; see supplementary information for more details on noise file creation, playback equipment, and experimental setup). At five of the noise playback sites we broadcast normal river noise (hereafter referred to as ‘river noise’ sites), and at the other five noise sites we broadcast spectrally-altered river recordings (hereafter referred to as “shifted noise” sites).Our field sites were oriented along the riparian zone, with data collection occurring at three primary locations within each site (Fig. S1): (1) roughly in the middle of the speaker tower systems, (2) at a shorter distance from the middle location (mean: 198.2 ± 54.5 m SD; range: 117.6–384.5 m), and (3) and a longer distance from the middle location (in the opposite direction from the nearer location; mean: 312.7 ± 64.7 m SD; range: 249.1–479.6 m). Thus, sites were approximately 510.9 ± 98.3 m long (range: 374.7–850.6 m), along the riparian corridor. All control sites were, at minimum, 1 km apart along the riparian corridor from any noise site, to maintain acoustic independence (see Fig. 1A; S1).Data collectionBirds
We conducted three-minute avian point counts between one half hour before sunrise and 6 h after sunrise (roughly 0530–1130 h). During the project, we conducted 1330 point-counts from 28 May to 20 July 2017 and 1639 point-count events occurred from 7 May to 24 July in 2018.
Caterpillar deploymentWe deployed a total of 720 clay caterpillars throughout the 2018 breeding season. Forty caterpillars were glued to stems and branches of trees between 1 and 2.5 m high at each site (Fig. S8). Twenty caterpillars surrounded the middle point count location at each site (a set of 10 were placed upstream, and another set of 10 were placed downstream starting from the middle ARU location), while the other twenty were at upstream and downstream sampling locations (10 each at upstream and downstream locations). We placed each caterpillar along the riparian corridor, at least 1 m apart from each other30. See Supplementary information for details on caterpillar predation scoring.Bird trait analysisWe performed a trait-based analysis to understand the mechanistic patterns of bird distributions in our study paradigm. Avian vocal frequencies and body mass were collected from Hu and Cardoso 2009, Cardoso 2014, and Francis 201516,31,32. When multiple sources contained data, the values were averaged. There were a few cases where none of those sources contained a vocal frequency or mass measurement for species of interest. Thus, representative songs were downloaded from the Macaulay Library of the Cornell Lab of Ornithology based on recording quality and geographical relevance (MacGillivray’s warbler: ML42249; dusky flycatcher: ML534684; red-naped sapsuckers: ML6956), and analyzed with Avisoft SASLab Pro to obtain a peak frequency measure. Mass measurements for these ‘missing’ birds were taken from the ‘All about birds’ webpage of the Cornell Lab of Ornithology.BatsMeasuring and identifying bat callsWe measured bat activity using Song Meter 3 (hereafter “SM3”) recording units (Wildlife Acoustics Inc., Massachusetts, USA) equipped with a single SMU (Wildlife Acoustics Inc.) ultrasonic microphone. One recording unit was used at each site and we pseudo-randomly rotated the unit between the three point-count locations so that each location was monitored for at least 21 days. We mounted microphones on metal conduit at a height of ~3 m, oriented perpendicular to the ground and facing away from the stream to optimize recording conditions (Fig. S9; S10; see Supplementary information for more information).Robotic insectsWe used a modified version of Lazure and Fenton’s26 apparatus to present bats with a fluttering target (Fig. S12). This consisted of a 3 cm2 piece of masking tape affixed to a metal rod [30.48 cm length × 3.25 mm diameter], which itself was connected to a 12-volt brushed DC motor (AndyMark 9015 12 V, AndyMark Inc., Kokomo, IN, USA). The no-load revolution speed of these motors (267 Hz) falls within the range of wingbeat frequency measured in Chironomidae27,33, a group that is an important food source for many North American bat species34.We attached each motor to a tripod made of PVC piping and positioned the tripod such that the target was approximately 1.2 m above the ground. Each motor was powered by a 12 V battery (35Ah AGM; DURA12-35C, Duracell) which was controlled by a programmable 12 V timer (CN101, FAVOLCANO) to automatically start and stop the motor each night. The rotors were powered for 2 h following sunset.Prey-sound speaker playbackWe created a playlist composed of several insect acoustic cues to present gleaning bats: a beetle (Tenebrio molitor) walking on dried grass, a cricket (Acheta domesticus) walking on leaves, mealworm larvae (Tenebrio molitor) on leaves, fall field cricket (Gryllus pennsylvanicus) calls, and fork-tailed bush katydid (Scudderia furcata) calls. The cricket and katydid calls were sourced from the Macaulay Library (ML527360 and ML107505, respectively).Experimental setup for bat foraging testsMost sites received two rotors (Fig. S12) and two speakers (Fig. S13): one of each at the center of the site, and one of each at approximately 125 m from the center of the site (in opposite directions in order to have tests in a range of acoustic environments), placed roughly 10 m from the edge of the riparian zone. Rotors and speakers at the center locations were separated by at least 50 m. The exception to this setup were the four positive control (loud whitewater river) sites, which only received a single rotor and speaker separated by 50 m because of logistical difficulties of accessing those sites. We paired each rotor and speaker with an SM2BAT + bat detector equipped with an SMX-US microphone (Wildlife Acoustics Inc.)35, using tripods to elevate the microphones approximately 1 m off the ground and ~1 m from the speaker/rotor. We programmed the bat detectors with a gain of 36 dB and a trigger level of 18 dB to limit recordings to bats that were passing within the immediate vicinity. To allow for a comparison of activity between speakers and rotors, bat activity was only considered for the first two hours following sunset.Bat trait analysisWe collected bat foraging behavior and peak echolocation frequency information to use as predictors in a phylogenetically controlled trait analysis (Tables S8; S13). We based our behavioral foraging classifications on the categories of Ratcliffe et al.36 and followed the classifications of Gordon et al.37 where possible, and others38,39,40,41,42,43 where necessary. We extracted peak echolocation frequency from the 2017 and 2018 SM3 field recordings and employed two controls to decrease variability in call parameters potentially introduced via this method. First, we selected only recordings made on control sites in 2017 and 2018 (n = 740,848 calls), as echolocation call characteristics may be affected by local acoustic environments (e.g., Bunkley et al.)22. Secondly, we averaged all call parameters per species per hour at each site to decrease the possible effects of few individuals driving measurements. This resulted in 9538 species-hours of recordings, which themselves were averaged per species (Table S13).Quantifying environmental variablesWe used long-term monitoring of the acoustic environment (via Roland R05 recorders) to calculate daily sound pressure level (L50 dBA) and median frequency (kHz) values for each location (see supplementary information for details on quantification of all predictor variables).Sound pressure level (SPL)We converted 106,769 h of long-term ARU recordings into daily-averaged median sound pressure levels (L50; measured as dBA rel. 20 µPa) see refs. 13,44 using custom software ‘AUDIO2NVSPL’ and ‘Acoustic Monitoring Toolbox’ (Damon Joyce, Natural Sounds and Night Skies Division, National Park Service).Acoustic environment spectrumWe used custom software45 in the programming language R and the package ‘FFmpeg’ in command prompt to convert 106,769 h of long-term recordings into 71,282 individual 3-minute files starting each hour of the day (Fig. S5). Thus 24, 3-min files were created per acoustic recording location per day (one for every hour). We then used the packages “tuneR” and “seewave” to read in and measure the median frequency of sound files, respectively45,46,47. These hourly metrics were then averaged by date to create a daily metric.StatisticsAll models of abundance, activity, and foraging transects were generalized linear mixed effects models (glmm) in R48 using the package ‘lme4’49,50 or ‘glmmTMB’51. All distribution families were selected based on theoretical sampling processes of the data, models were checked for collinearity (VIF scores)52, and model fits were visually checked with residual plots (see supplemental R code)53.Bird abundance and bat activity
Model predictors and covariates
Both bird and bat models had the following variables in a glmm: site and bird/bat species were random effects terms and sound pressure level (dBA L50), sound spectrum (median frequency), the interaction between sound pressure level and spectrum, elevation, percent riparian vegetation, ordinal date (and a quadratic version of this), and year as fixed effects. While year is sometimes used as a random-effect term, it is suggested to be used as a fixed effect if fewer than five levels exist for that factor, as variance estimates become imprecise54,55. Additionally, moon phase was a fixed effect in the bat models56, while spectral overlap (the absolute difference between sound spectrum and bird species vocalization frequencies) and the interaction between sound pressure level and spectral overlap were fixed effects in bird models.
We attempted to fit both sound pressure level and spectrum as having random slopes for each species, yet both bat and bird models would not converge with such complex model structure. Thus, we followed group models with individual species models (see Supplementary information).

Model family distribution and link function
For both bird and bat counts, we used a negative binomial distribution with a log link, rather than a Poisson distribution, because data were over-dispersed. We plotted variance-mean relationships and residuals of multiple models to select the appropriate variance structure, and compared these with AIC to select the best-fitting distribution (see R script for further justification of these methods)54.

Individual species models
Individual species models were parameterized the same as above (except without the species term). All 12 bat species (see Tables S6; S10) and 26 of the most common birds (see Tables S2; S9) were modeled individually to be able to interpret model parameter estimates, with complex interactions, for each species.
Clay caterpillar predationWe modeled caterpillar predation with a glmm (binomial family; logit link function), using the number of individual scorers as weights in the model. Like the bird abundance model, we used site as a random effect and sound pressure level (dBA L50), spectral frequency (median), elevation, percent riparian vegetation, ordinal date, and year as fixed effects (Table S4). Additionally, the predicted number of birds at a site were modeled as fixed effects to control for varying amounts of foraging birds on the landscape.Robotic moths and prey-sound speakersRobotic moth and prey-sound speaker models were parameterized exactly the same as the overall bat activity model. That is, the model was fit with a negative binomial family (log link) with site and species as random effects and sound pressure level (dBA L50), sound spectrum (median frequency), the interaction between sound pressure level and spectrum, moon phase, elevation, percent riparian vegetation, ordinal date (and a quadratic version of this), and year as fixed effects. Additionally, the predicted number of bats at a site were modeled as fixed effects to control for varying amounts of foraging bats on the landscape.Trait analysesWe performed trait analyses with phylogenetic generalized least squares (PGLS) to control for relatedness while predicting species responses to noise12. We performed PGLS analyses with the gls function in the R package nlme57, and accounted for error in the response variable with a fixed-variance weighting function of one divided by the square root of the standard error of the response estimate58,59. We accounted for phylogenetic structure by estimating Pagel’s λ60. When λ estimates fell outside of the zero to 1 range, we fixed λ at the nearest boundary. For bird models, we used a pruned consensus tree from a recent class-wide phylogeny61. For bats, we used a pruned mammalian tree62. We used initial global models with all traits as variables that explained the responses to sound pressure level (SPL; birds and bats), spectral overlap with birdsong (birds), background frequency (bats), and the interaction between SPL and each measure of frequency (birds and bats). We then used AIC model selection63 to choose top models in explaining these patterns. Models with dAIC ≤4 are included in Table S3 (birds) and Table S8 (bats), and the top model is interpreted in the main text.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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