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Study areaThe study was carried out in Bunnefjorden, Norway. The fjord froze over from January to April and we here analyze data from ice-free conditions in early winter (12 Nov to 2 Dec 2009). Bunnefjorden is a 150 m deep inner branch of the Oslofjord, and a 57 m deep sill at the entrance restricts water exchange with the outer part of the fjord. Klevjer and Kaartvedt24 provide a map of the study area. The fjord branch normally becomes hypoxic in the lower part of the water column. During the current study, oxygen contents were 2–3 ml l−1 between 15 and 60 m, while waters below 70–80 m were severely hypoxic and devoid of fishes9.Studies of overwintering sprat have been undertaken in Bunnefjorden during several winters, and the biology of sprat as well as the identity of the main acoustic targets in the fjord are well established9,18,24. In the winter of the current study, catches from 33 trawl samples were dominated by sprat; with ~ 40 times higher catches than the next most abundant species, herring (Clupea harengus)9.Study designSolberg and Kaartvedt9 and Solberg et al.18 provide details on methods, and we here only give a summary of the acoustic setup. In short, upward-looking Simrad EK 60 echosounders kept in pressure-proof casings were deployed at the bottom (150 m) and in buoys (80 and 30 m) for enhanced resolution in shallower part of the water column. Cables for electricity and transfer of data to a PC on shore enabled continuous operation of the systems. We here use the data from the shallowest echosounder (200 kHz) that provided superior resolution in near-surface water, though did not cover the full depth range of the population distribution. Echograms from the deeper located echosounders covering the whole (inhabited) water column and showing the full diel population behavior are given in Solberg and Kaartvedt9 and Solberg et al.18.Records of gas releaseReleased gas appeared as ascending lines in the echogram (Fig. 4). We quantified the release as explained by Solberg and Kaartvedt9. We only included ascending traces connected to the acoustic record of a fish, but without enumerating the release per individual fish. Since the same fish may release several bursts of bubbles within a short time interval, we here pooled any sequences of gas release within a 10-s period as one event. This procedure will also exclude cases with several different individuals releasing bubbles in the course of this short time interval, yet we chose this conservative approach not to generate an artificial high connection of gas releases between the fishes.Figure 4Echogram showing sprat releasing gas, with every oblique line representing one release event and lines with a different angle to the release events representing swimming sprat. Colors represent the volume-backscatter coefficient (Sv).Full size imageAnalyses of dataThe frequency of gas releases varied with time, both within a day and between the weeks. Such patterns compare to service systems like call centres and hospital emergency rooms25 that can be modelled as a Poisson process26,27. We therefore started our analysis with the statistical procedure suggested by Brown et al.28 in their influential analysis of the call dynamics in a banking call centre. The first step is to subdivide the day into time intervals, which are short enough to consider event rates as approximately constant. Here we chose to investigate alternative periods of respectively 1, 5, and 30 min, as well as 1, 2, 4, and 6 h. At the longest interval, the peaks in the gas release intensity are expected to be the result of a non-constant Poisson parameter, and therefore more likely to induce rejection of the null-hypothesis of a homogenous random process. In contrast, we expect to find higher concordance with a random process for the short intervals of 5 min. In assessing connectivity among gas bubble releases, we formulate a new model allowing for a formal test of non-randomness (summarized in Fig. 5). We name this approach the simulated connectivity test (S-CON test), which we implemented in R29, with the code being available in the Supplementary appendix.Figure 5Illustration of the steps related to the simulated connectivity test (S-CON test). Bubbles occurring within 10 s are pooled into single release events. We then determine the connectivity of each release event—aka the number of release events within the following 30 s time window. From these, we calculate the average connectivity for a specified period (1, 5, 30 min, 1-, 2-, 4- and 6-h intervals). In a final step, we compare the observed average connectivity to the critical value which is defined as the 95th percentile of 1000 simulations of random placements of the same number of releases. If the observed connectivity is larger than the critical value, we reject the null hypothesis of random gas releases for the specified time interval.Full size imageIf there is a common physiological reason or some form of communication among sprat, a burst of gas release is likely followed by subsequent releases. Thus, it is reasonable to assume that the total number of releases within a short time interval like 30 s would be effective in detecting dependencies between the releases. We therefore define the concept of connectivity as follows:Let the gas be released at times T1, T2, …, Tn and define the connectivity at each event as the numbers of records within the following 30 s. The average connectivity in any considered window of the investigated time-period (for example a window of 1 h) is defined as the average connectivity of all cases of connectivity within the considered window (see also Fig. 5).In order to test the null hypothesis of no dependency between gas bubble releases, we compare the measured average connectivity in the data set with the simulation of 1000 random placements of the total number of observations in a given time window. For example, if we consider a window of 30 min with 15 release events having an average connectivity of 2.1, we performed 1000 random placements of 15 points between 1 and 1800. In this way, we get 1000 simulated values of the average connectivity, from which we pick out the critical 95th percentile, following the common significance level of 0.05 in biology. If the observed average connectivity is larger than this critical value, we reject the null-hypothesis and conclude that the releases of gas bubbles are dependent random variables. Thus, if our example obtains a critical value of 1.7, the null-hypothesis of random arrival times of bubbles is rejected (because the observed value of 2.1 is larger than the critical value of 1.7).Since a dependency between the fish will induce a higher concentration of release events than produced by random releases, we expect the average connectivity to be quite sensitive to the alternative hypothesis of dependent arrival times. Also, note that the concept of connectivity has a combinatory nature, so we need only require that the considered window contains at least two releases of gas bubbles. In contrast, alternative approaches using Kolmogorov–Smirnov tests28 are based on the cumulative distribution function and therefore require at least five observed bubble releases.To test the dependency of the results on the chosen time interval, we also ran the analysis using connectivity intervals of 25 and 35 s, which revealed some variability to the estimates of non-random bubble release (Fig. 3) but did not influence the general pattern. We also tested whether the interval within which we consider subsequent bubbles to be part of one single release event influences our results. The more we consider sequential bubbles to be independent of each other, i.e. their own release event, the higher the proportion of non-random gas release and vice versa.Fish abundanceTo exclude the possibility that apparent connectivity would be a mere result of fluctuating fish abundance, we tested whether the number of released bubbles is a function of fish biomass. For this, we first calculated the total number of gas release events within 30-min periods. We then compared these values to the summed surface integrated acoustic scattering coefficient (SA) for the same periods and for the same depth interval (upper 30 m), assuming that the integrated scattering coefficient (SA) serves as a proxy for the total fish biomass9. We filtered the scattering data to remove noise from non-biological sources prior to use. Both variables were log-transformed prior to analysis. We then fitted a linear model of the two variables using generalized least squares. To account for temporal autocorrelation in the data, we also included a correlation structure of type 1 (corAR1). The analysis was done in R29 using the nlme package30.Ethics declarationsLive animals (fish) were not used in this study. More

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Bacterial strain and growth conditionsWe used Pseudomonas protegens (formerly Pseudomonas fluorescens)55 CHA0 as a model strain, which was initially isolated from tobacco roots56. The strain was chromosomally tagged with GFP and a kanamycin resistance cassette to enable specific tracking of the strain and detection of contaminations19. This bacterium has the genetic potential to produce various bioactive metabolites, including the plant hormone indole-3-acetic acid (IAA), antimicrobial compounds and lytic enzymes25. Prior to the experiment, bacteria were grown for 48 h on a King′s medium B57 (KB) agar plate supplemented with 50 µg ml−1 kanamycin, a single colony was randomly picked and grown for 12 h in KB at 28 °C with agitation. The cell culture was then washed for three times in 10 mM MgSO4 and adjusted to 107 cells ml−1 and used as inoculant for all plants. This inoculant was also stored at −80 °C as frozen ancestral stock, from which ʻAncestorʼ isolates were picked in later experiments.Host plant and growth conditionsWe used Arabidopsis thaliana ecotype Col-0 as a model host plant. Surface-sterilized seeds were first sown in Petri dishes with agar-solidified (1.5% agar (w/v)) modified Hoagland′s medium: (KNO3 (3 mM), MgSO4 (0.5 mM), CaCl2 (1.5 mM), K2SO4 (1.5 mM), NaH2PO4 (1.5 mM), H3BO3 (25 μM), MnSO4 (1 μM), ZnSO4 (0.5 μM), (NH4)6Mo7O24 (0.05 μM), CuSO4 (0.3 μM), MES (2.5 mM) and 50 μM Fe(III)EDTA, pH = 5.8) and stratified for 2 days at 4 °C after Petri dishes were positioned vertically and transferred to a growth chamber (20 °C, 10 h light/14 h dark, light intensity 100 μmol m−2 s−1). After 2 weeks of incubation, two seedlings were transferred to closed and sterile ECO2 boxes (http://www.eco2box.com/ov80xxl_nl.htm) for selection experiment. The ECO2 boxes were filled with 260 g of dry, carbon-free silver sand that was previously washed with MilliQ water to remove dissolvable chemical elements and heated to 550 °C for 24 h to remove remaining organic material. Prior to transplantation the sand was amended with 13 ml of modified Hoagland medium.Design of the selection experimentThe selection experiment was conducted in a gnotobiotic system to remove confounding effects that may emerge as a result of competitive interactions with other microorganisms, and to place the focus on plant-mediated selective pressures. Moreover, we allowed only the bacteria to evolve during the experiment and used new clonal plants at every bacterial transfer. We set up five independent plant–bacterium replicate lines, which were grown for six independent plant growth cycles (see Fig. S1 for an overview of the experimental design). The experiment was started by inoculating 106 cells of the stock P. protegens CHA0 culture (from here on abbreviated as ʻancestorʼ) into the rhizosphere of 2-week-old A. thaliana seedlings growing in sterile silver sand within ECO2 boxes (two plants per replicate selection line). Inoculated plants were then grown for 4 weeks (20 °C, 10 h light/14 h dark, light intensity 100 μmol m−2 s−1) after which the plant growth cycle was terminated and root-associated bacteria were harvested by placing the roots of both plants into a 1.5 ml Eppendorf tubes filled with 1 ml 10 mM MgSO4 and two glass beads. Rhizosphere bacteria were suspended into the liquid using a TissueLyser II at a frequency of 20 s−1 for 1 min after which bacterial cell densities were determined using flow cytometry (BD Accuri™ C6 Plus, thresholds for FSC: 2000, SSC: 8000). After this, 106 cells were inoculated to the rhizosphere of new A. thaliana plants to initiate the next plant growth cycle. Possible contaminations were checked by plating the suspension on 3 g l−1 tryptic soy agar (TSA) plates and it was verified that all colonies carried the GFP marker gene, as observed under UV light.Bacterial life-history traits measurementsIndividual bacterial colonies were isolated from all replicate plant selection lines for life-history measurements at the end of the second, fourth and sixth plant growth cycle by dilution plating the rhizosphere suspension on 3 g l−1 TSA plates. After incubation at 28 °C for 24 h, 16 colonies were randomly picked from each replicate selection lines resulting in a total of 240 evolved and 16 ancestral colonies. All these colonies were characterized for a set of key bacterial life-history traits representative of growth, stress resistance and traits linked with plant–microbe interactions.

a.

Bacterial growth yield in KB medium
All the bacterial isolates were grown in 96-well plates with 160 µl 1/3 strength liquid KB, at 20 °C without shaking. Bacterial yield was determined as the maximum optical density at 600 nm after 3 days of growth using a spectrophotometer (SPECTROstar Nano).

b.

Bacterial stress resistance
We measured bacterial resistance to a range of different stresses using various 96-well microplate assays. Abiotic stress resistance was determined by growing bacteria in 160 µl of 1 g l−1 TSB containing 0.0025% H2O2 (oxidative stress), 15% polyethylene glycol (PEG)−6000 (water potential stress) or 2% NaCl (salt stress). We used resistance to antibiotics commonly produced by rhizosphere microorganisms as indicator of biotic stress resistance. Antibiotic resistance was tested in 160 µl of 1 g l−1 TSB supplemented with 1 µg ml−1 streptomycin, 1 µg ml−1 tetracycline, or 5 µg ml−1 penicillin, respectively. Bacterial growth were determined after 3 days of growth at 20 °C without shaking as optical density at 600 nm.

c.

Traits linked with plant–microbe interactions
P. protegens CHA0 harbours several traits that are linked to plant growth including production of antibiotics and plant hormones. To assess these traits, we grew each bacterial colony in 96-well plates containing 160 µl of 1/3 strength liquid KB per well at 20 °C without shaking for 72 h. Cell-free supernatants were obtained by filter sterilization (0.22 µm) using Multiscreen HTS 96-well filtration plates (1000 × g, 30 min), which were used to measure the production of the plant hormone auxin (Indole-3-acetic acid (IAA)), iron-chelating siderophores and proteolytic activity. Furthermore, we also measured antifungal and antibacterial activity of all colonies.

IAA detectionThe production of the plant hormone auxin was determined with a colorimetric test58. Briefly, 30 µl P. protegens CHA0 cell-free filtrate was incubated with 30 µl R1 reagent (12 g l−1 FeCl3, 7.9 M H2SO4) for 12 h in the dark and optical density read at 530 nm of the colorimetric complex was used as a measurement of IAA concentration.Siderophore activityIron-chelating ability was measured as a proxy for siderophore production59. To this end, 100 µl of P. protegens CHA0 cell-free filtrate was mixed with 100 µl of modified CAS solution (with 0.15 mM FeCl3) and optical density read at 630 nm after 3 h of incubation was used as a proxy of siderophore production. The iron-chelating ability was calculated based on the standard curve based on modified CAS assay solution with a range of iron concentration (0, 0.0015, 0.003, 0.006, 0.009, 0.012, 0.015 mM FeCl3).Proteolytic activityThe proteolytic activity assay we used was adapted from Smeltzer et al.60. Briefly, 15 µl of P. protegens CHA0 cell-free filtrate was incubated with 25 µl of azocasein (2% w/v in 50 mM Tris-HCl pH 8.0) at 40 °C for 24 h. One hundred and twenty-five of 10% w/v cold trichloroacetic acid (TCA) was added to precipitate superfluous azocasein, and then 100 µl supernatant was neutralized with 100 µl of 1 M NaOH after centrifugation at 5000g for 30 min. Optical density read at 440 nm was used as a proxy of exoprotease activity.Tryptophan side chain oxidase (TSO) activityTSO activity, an indicator of quorum sensing activity in P. protegens CHA0, was measured based on an modified established colorimetric assay61: Three-day-old bacterial cultures grown in 1/3 strength liquid KB were mixed at a 1:1 ratio with a reagent solution (5 g l−1 SDS, 37.6 g l−1 glycine 2.04 l−1 g tryptophan, pH 3.0) and TSO activity was measured as optical density at 600 nm after overnight incubation.Biofilm formationWe quantified bacterial biofilm formation using a standard protocol62. Briefly, bacteria were grown at 20 °C for 72 h in 160 µl 1 g l−1 TSB in a 96-well microtiter plate with TSP lid (TSP, NUNC, Roskilde, Denmark). Planktonic cells were removed by immersing the lid with pegs three times in phosphate-buffered saline solution (PBS). Subsequently, the biofilm on the pegs was stained for 20 min in 160 µl 1% crystal violet solution. Pegs were washed five times in PBS after which the crystal violet was extracted for 20 min from the biofilm in a new 96-well microtiter plate containing 200 µl 96% ethanol per well. Biofilm formation was defined as the optical density at 590 nm of the ethanol extracted crystal violet63.Inhibition of other microorganismsAntimicrobial activity was defined as the relative growth of the target organism in P. protegens supernatant compared to the control treatment. Antifungal activity of the cell-free supernatant was assessed against the ascomycete Verticillium dahliae. The fungus was grown on potato dextrose agar at 28 °C for 4 days, after which plugs of fungal mycelium were incubated in potato dextrose broth medium at 28 °C and gentle shaking for 5 days. Fungal spores were collected by filtering out the mycelium from this culture over glass wool. Subsequently, spores were washed and resuspended in water and the OD595 of the suspension was adjusted to 1. Five microlitres of this spore suspension was then inoculated with 15 µl P. protegens CHA0 cell-free filtrate and incubated in 160 µl of 1 g l−1 PDB medium for 2 days at 20 °C in 96-well plates. Fungal growth was measured as optical density at 595 nm after 2 days of growth and contrasted with the growth in the control treatment (PDB medium without P. protegens supernatant). Antibacterial activity was determined using the plant pathogen Ralstonia solanacearum as a target organism. R. solanacearum was grown in 160 µl of 1 g l−1 TSB medium supplemented with 15 μl of P. protegens CHA0 cell-free filtrate or 15 µl of 1/3 strength liquid KB as a control for 2 days at 20 °C. R. solanacearum growth was measured as optical density at 600 nm.Determining changes in P. protegens CHA0 interactions with A. thaliana after the selection experimentBased on the life-history trait measurements, five distinct bacterial phenotypes were identified using K-means clustering analysis (Fig. S2). In order to assess whether phenotypic changes reflected shifts in the strength and type of plant–bacterium interaction, we chose five isolates from each bacterial phenotype group representing each replicate selection line and five ancestral isolates for further measurements (a total of 30 isolates, Table S2).Effects of ancestor and evolved bacteria on plant performanceFor each isolate we measured root colonizing ability and impact on plant performance. All 30 bacterial isolates were incubated overnight in 1/3 KB strength liquid at 20 °C. The culture was centrifuged twice for 5 min at 5000 × g and the pellet was washed and finally resuspended in 10 mM MgSO4. The resulting suspension was adjusted to an OD600 of 0.01 for each strain64. Ten microlitres of the bacterial suspension (or 10 mM MgSO4 as a control) was applied to the roots of three 10-day old sterile Arabidopsis thaliana Col-0 seedlings (excluding 2 days of stratification at 4 °C) grown on vertically positioned Petri dishes with agar-solidified (1.5% agar (w/v)) modified Hoagland′s medium (n = 3 biological plant replicates, each containing 3 seedlings). Plants were grown for 14 days before harvesting. Plants were photographed before and 14 days after bacterial inoculation.Bacterial effects on plant health were quantified as leaf ʻgreennessʼ as the presence of ancestral strain was observed to lead to bleaching and loss of chlorophyll in A. thaliana leaves. The ʻgreennessʼ was quantified from photographs by measuring the number of green pixels. To this end, photographs were first transformed in batch using Adobe Photoshop 2021 by sequentially selecting only green areas followed by thresholding balancing green tissue over background noise (Level 80). This resulted in black-and-white images for further analysis, and the mean number of white pixels per fixed-sized region-of-interest of the aboveground tissue was subsequently determined as ʻgreennessʼ using ImageJ (version 1.50i). The numbers of lateral roots and the primary root length were also measured using ImageJ. The root morphology data measured at the end of the experiment was normalized with the data collected at the time of inoculation for each individual seedling.To determine shoot biomass, the rosette of each plant was separated from the root system with a razor blade and weighted. The roots were placed into a pre-weighted 1.5 ml Eppendorf tubes to quantify the root biomass. To determine the bacterial abundance per plant, these tubes were subsequently filled with 1 ml 10 mM MgSO4 buffer solution and two glass beads. The rhizosphere bacteria were suspended into the buffer solution using a TissueLyser II at a frequency of 20 s−1 for 1 min after which bacterial densities were determined using flow cytometry (BD Accuri™ C6 Plus, thresholds for FSC: 2000, SSC: 8000). Shoot biomass, root biomass, root length and number of lateral roots were used in a principal component analysis (PCA) to calculate an overall impact of the bacteria on plant performance (Fig. 2e). The first principal component (PC1) explained 79.9% of the variation and was normalized against the control treatment to be used as a proxy of ʻPlant performanceʼ in which positive values reflect plant growth promotion and negative values plant growth inhibition.Root derived carbon source utilizationTo measure changes in bacterial growth on potential root derived carbon sources, we measured the growth of all 256 isolates using modified Ornston and Stanier (OS) minimal medium65 supplemented with single carbon sources at a final concentration of 0.5 g l−1 in 96-well plates containing 160 µl carbon supplemented OS medium per well. The following carbon sources were selected based on their relatively high abundance in Arabidopsis root exudates21: alanine, arabinose, butyrolactam, fructose, galactose, glucose, glycerol, glycine, lactic acid, putrescine, serine, succinic acid, threonine and valine. Bacterial growth was determined by measuring optical density at 600 nm after 3 days incubation at 20 °C.GUS histochemical staining assay and bacterial growth under scopoletin stressTo investigate effects of the ancestor and evolved strains of P. protegens CHA0 on expression of MYB72, we applied a GUS histochemical staining assay to the 30 selected isolates (Table S2). MYB72 is a transcription factor involved in production of the coumarin scopoletin in Arabidopsis roots and specific rhizobacteria can upregulate expression of MYB72 in the roots66. Scopoletin is an iron-mobilizing phenolic compound with selective antimicrobial activity22. Seedlings of the A. thaliana MYB72pro:GFP-GUS24 reporter line were prepared as described above. Seven-day-old seedlings were inoculated directly below the hypocotyls with 10 μl of a bacterial suspension (OD660 = 0.1)24. At 2 days after inoculation, the roots were separated from the shoots and washed in MilliQ water (Milliport Corp., Bedford, MA) to remove all the adhered bacteria. GUS staining of the roots was performed in 12-well microtiter plates where each well contained roots of 5–6 seedlings and 1 ml of freshly prepared GUS substrate solution (50 mM sodium phosphate with a pH at 7, 10 mM EDTA, 0.5 mM K4[Fe(CN)6], 0.5 mM K3[Fe(CN)6], 0.5 mM X-Gluc, and 0.01% Silwet L-77)67. Plates were incubated in the dark at room temperature for 16 h. The roots were fixed overnight in 1 ml ethanol:acetic acid (3:1 v/v) solution at 4 °C and transferred to 75% ethanol. Then the pictures of each microtiter plates were taken, and GUS activity was quantified by counting the number of blue pixels in each well of the microtiter plates using image analysis in ImageJ (version 1.52t). To assess the effects of scopoletin on ancestral and evolved P. protegens CHA0 isolates, we applied a sensitivity assay to the 30 selected isolates (Table S2). In brief, growth of bacterial isolates was measured in 1 g l−1 TSB medium (160 µl) supplemented with scopoletin at final concentrations of 0 µM (control), 500 µM, 1000 µM, and 2 mM using optical density at 600 nm after 72 h incubation at 20 °C without shaking in 96-well microtiter plates. Maximal effect (Emax) of scopoletin was calculated via R package ʻGRmetricsʼ68 as an indication of scopoletin tolerance.Whole-genome sequencingAll 30 isolated phenotypes were whole genome sequenced to identify possible mutations and affected genes. To this end, isolates were cultured overnight at 28 °C in 1/3 strength liquid KB. Chromosomal DNA was isolated from each culture using the GenElute™ Bacterial Genomic DNA Kit Protocol (NA2100). DNA samples were sheared on a Covaris E-220 Focused-ultrasonicator and sheared DNA was then used to prepare Illumina sequencing libraries with the NEBNext® Ultra™ DNA Library Prep Kit (New England Biolabs. France) and the NEBNext® Multiplex Oligos for Illumina® (96 Index Primers). The final libraries were sequenced in multiplex on the NextSeq 500 platform (2 × 75 bp paired-end) by the Utrecht Sequencing Facility (http://www.useq.nl) yielding between 1.0 and 6.4 million reads per sample equivalent to ~10–70-fold coverage (based on comparison with the original 6.8 Mbp reference genome NCBI GenBank: CP003190.1).Variant calling analysisWe first constructed an updated reference genome of P. protegens CHA0, carrying the GFP marker gene on its chromosome, from the ancestral strain using the A5 pipeline with default parameters69. The input dataset for this sample consisted of 3,1M reads and totals an approximate 34-fold coverage. The size of the updated reference genome is 6.8 Mbp, with a G + C content of 63.4%, and it comprises 80 scaffolds, with a N50 value of 343 kbp. We subsequently used PROKKA70 (version 1.12; https://github.com/tseemann/prokka) for full annotation of the updated reference genome, and this resulted in the identification of 6147 genes. The updated genome is deposited in NCBI GenBank with following reference: RCSR00000000.1.Having established the ancestral genome sequence, we subsequently used Snippy (version 3.2-dev; https://github.com/tseemann/snippy) to identify and functionally annotate single-nucleotide polymorphisms and small insertions and deletions (indels) for each individual strain. In addition, we investigated the breadth of coverage for each gene per sample with BedTools71 to identify genes with large insertions or deletions. An overview of the polymorphisms is shown in Supplementary Table S3. Raw sequencing data for this study are deposited at the NCBI database under BioProject PRJNA473919.Relative competitive fitness of gac mutants measured in vivo and in vitroThe relative competitive fitness of selected gac mutants was measured in direct competition with their direct ancestors both in vivo in the rhizosphere of A. thaliana and in vitro in different standard culture media. Relative fitness was measured as deviation from initial 1:1 ratio of bacterial clone pairs based on PCR-based high-resolution melting profile (RQ-HRM) analysis. Three pairs of isolates were selected: (A) evolved gacA ID 242 (genotype oafAY335X ∙ RS17350A77A.fsX14 ∙ gacAD49Y) and its direct ancestral genotype 133 (genotype oafAY335X ∙ RS17350A77A.fsX14) from evolutionary line 1; (B) evolved gacA ID 220 (genotype galEV32M ∙ accCE413K ∙ gacAD54Y) and its direct ancestral genotype 28 (genotype galEV32M ∙ accCE413K) from line 2; (c) evolved gacS ID 222 (genotype oafAK338S.fsX18 ∙ gacSG27D) and its direct ancestral genotype 66 (genotype oafAK338S.fsX18) from line 3. Bacterial isolates were first grown overnight in KB medium at 28 °C, centrifuged at 5000g for 10 min and the pellet resuspended in 10 mM MgSO4. This washing procedure was repeated twice. The resulting bacterial suspensions were diluted to OD600 = 0.05. The initial inoculum for the competition assays was then generated by mixing equal volumes of evolved and ancestral competitors in a ratio of 1:1.Measuring competitive fitness in A. thaliana rhizosphereThis assay was performed on the roots of 10-day old A. thaliana seedlings grown on full strength Hoagland agar plates, which were prepared as described earlier. Twenty microlitres of the initial inoculum, containing a total of 106 bacterial cells, was inoculated on to the root–shoot junction of each seedling. After 14 days of growth, bacterial populations were isolated from the roots and stored at −80 °C in 42.5% glycerol for relative abundance measurements.Measuring competitive fitness in culture mediaCompetition assays were also performed in three commonly used nutrient-rich growth media: KB, LB and TSB. KB contained 20 g proteose peptone, 1.5 g MgSO4.7H2O, 1.2 g KH2PO4 and 10 g glycerol per litre and the pH was adjusted to 7.3 ± 0.2. TSB contained 30 g tryptic soy broth per litre and pH was adjusted to 7.3 ± 0.2. LB contained 10 g peptone, 5 g yeast extract and 5 g NaCl per litre. Twenty microlitres inoculum of competing strains, containing about 106 bacterial cells, were added into wells containing 140 μl fresh medium in a 96-well plate. The microplates were incubated at 28 °C without shaking for 48 after 80 μl sample was harvested and stored at −80 °C in 42.5% glycerol from each well for relative abundance measurements.RQ-HRM assay for quantifying changes in genotype frequencies after competitionWe used a high-resolution melting (HRM) curve profile assay with integrated LunaProbes to quantify the ratio of mutant to wild-type genotypes72,73,74. The probes and primers used in this study are listed in Table S4. Primers were designed using Primer3. Probes were designed with the single-nucleotide polymorphism (SNP) located in the middle of the sequence, and the 3′ end was blocked by carbon spacer C3. The primer asymmetry was set to 2:1 (excess primer: limiting primer) in all cases. Pre-PCR was performed in a 10-μl reaction system, with 0.25 μM excess primer, 0.125 μM limiting primer, 0.25 μM probe, 0.5 μl bacterial sample culture (100-fold diluted saved sample, OD600 is about 0.01), 1× LightScanner Master Mix (BioFire Defense). DMSO with the final concentration 5% was supplemented in all reactions to ensure the targeted melting domains are within the detection limit of the LightScanner (Idaho Technology Inc.). Finally, MQ water was used to supplement up to 10 μl. A 96-well black microtiter plate with white wells was used to minimize background fluorescence. Before amplification, 25 μl mineral oil was loaded in each well to prevent evaporation, and the plate was covered with a foil seal to prevent the degradation of fluorescent molecules. Amplification was initiated by a holding at 95 °C for 3 min, followed by 55 cycles of denaturation at 95 °C for 30 s, annealing at 60 °C for 30 s and extension at 72 °C for 30 s and then kept at 72 °C for 10 min. After amplification, samples were heated in a ThermalCycler (Bio-Rad) shortly to 95 °C for 30 s to denature all double-stranded structures followed by a rapid cooling to 25 °C for 30 s to facilitate successful hybridization between probes and the target strands. The plate was then transferred to a LightScanner (Idaho Technology Inc.). Melting profiles of each well were collected by monitoring the continuous loss of fluorescence with a steady increase of the temperature from 35 to 97 °C with a ramp rate of 0.1 °C/s. The relative quantification was based on the negative first derivative plots using software MATLAB. The areas of probe-target duplexes melting peaks were auto-calculated by ʻAutoFit Peaks I Residualsʼ function in software PeakFit (SeaSolve Software Inc.). The mutant frequency X was calculated using the formula shown below:$$X=frac{{rm{Area}}_{{mathrm{mutant}}}}{{{{mathrm{{Area}}}}}_{{mathrm{{mutant}}}}+{{rm{Area}}}_{{mathrm{{WT}}}}}$$
(1)
To validate the RQ-HRM method, standard curves were generated by measuring mixed samples with known proportions of mutant templates: 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100%. Measurements for each sample were done in triplicate. Linear regression formula of each mutant between actual frequencies and measured frequencies are shown in Fig. S7. The high R2 values, and nearly equal to 1 slope values of these equations, confirmed that the RQ-HRM method can accurately detect mutantsʼ frequency in a mixed population.The relative fitness of the evolved strains was calculated according to previous studies using the following equation75,76:$${mathrm{{relative}}; {mathrm{{fitness}}}}(r)=frac{{{{X}}}_{1}(1-{{{X}}}_{0})}{{{{X}}}_{0}(1-{{{X}}}_{1})}$$
(2)
where X0 is the initial mutant frequency; (1−X0) the initial ancestor frequency; X1 the final mutant frequency; and (1−X1) is the final ancestor frequency.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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CORRESPONDENCE
22 June 2021

Ancient oaks of Europe are archives — protect them

Christian Sonne

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Changlei Xia

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Su Shiung Lam

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Christian Sonne

Aarhus University, Roskilde, Denmark.

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Changlei Xia

Nanjing Forestry University, Nanjing, China.

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Su Shiung Lam

University Malaysia Terengganu, Terengganu, Malaysia.

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Kongeegen, the King Oak, in Denmark could be up to 2,000 years old.Credit: Andreas Altenburger/Alamy

Some of the oldest trees in Europe are in danger because they are not being given the necessary level of protection. Oak trees (Quercus robur) that are more than 1,000 years old are found in the United Kingdom and in Fennoscandia, which includes Denmark, Sweden and Norway.For example, Denmark’s King Oak (pictured) is one of the world’s oldest living trees, dating to around 1,900 years of age. The United Kingdom has the largest collection of ancient oaks, reflecting 1,500 years of ship-building.The trees contain rings that represent archives of historical climate fluctuations and levels of atmospheric gases, so they can help to answer pressing questions about climate change and ecosystem dynamics (P. M. Kelly et al. Nature 340, 57–60; 1989).Fennoscandia and the United Kingdom could better safeguard their oaks using mechanisms such as those offered by the European Union’s Natura 2000 network of protected areas, or the protections conferred by UNESCO World Heritage sites in the United Kingdom. Otherwise, unsustainable management practices, deforestation, air pollution and climate change could leave these ancient species vulnerable to disease and extinction, with the loss of irreplaceable scientific information and cultural heritage.

Nature 594, 495 (2021)
doi: https://doi.org/10.1038/d41586-021-01699-0

Competing Interests
The authors declare no competing interests.

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Field location and soil samplingThe soil used in this experiment was collected from an agricultural field cultivated with maize at the “Instituto Tecnológico Superior del Oriente del Estado de Hidalgo” (ITESA) located in Apan, State of Hidalgo, Mexico (19° 73′ N, 98° 46′ W). The 0–20 cm top soil layer of three 400 m2 plots was sampled 20 times. The soil from each plot was pooled separately so that three soil samples (n = 3) were obtained. This field based replication was maintained in the greenhouse experiment so as to avoid pseudo-replication. The soil samples were passed separately through a 5 mm sieve and characterized.The soil is classified as a Phaeozem according to “World Reference Soil (WRS) system”, with pH 6.6, electrolytic conductivity (EC) 0.22 dS m−1 and water holding capacity (WHC) 515 g kg−1. The sandy clay loam soil with clay content 240 g kg−1, sand content 530 g kg−1 and silt content 230 g kg−1, had an ammonium content 8.16 mg kg−1 dry soil, nitrate 1.91 mg kg−1 dry soil and nitrite 0.01 mg kg−1 dry soil. The maize seeds were the hybrid variety 215 W obtained from Eagle® Sinaloa (Mexico).Characteristics of the biofertilizerAlthough a biofertilizer can be described in different ways we use the definition as given by38. Vessey defined (2003) a biofertilizer as “a substance which contains living micro-organisms which, when applied to seeds, plant surfaces, or soil, colonize the rhizosphere or the interior of the plant and promotes growth by increasing the supply or availability of primary nutrients to the host plant”. As the consortium used in this study fits the definition of a biofertilizer as given by Vessey38 we will refer to the consortium as the biofertilizer or when sterilized to the sterilized biofertilizer throughout the manuscript.The “biofertilizer” used in this study was a mixture of bacteria and leachate from compost of cow manure and was obtained from a local farmer in Hidalgo (Mexico) and characterized chemically and microbiologically. The cow manure was composted on a cement floor with a small inclination so that leachate could be collected easily. The farmer adds the leachate to the mixture of the bacteria to guarantee their survival and as an additional plant nutrient source. The farmer applies this solution regularly to fertilize his fields cultivated with maize. A same application protocol and procedure was used in this study to mimic the field experiment. Half of the biofertilizer obtained from the local farmer was sterilized by autoclaving at 121 °C for 20 min on three consecutive days so as to determine the effect of the microorganisms in the biofertilizer on the maize plants and the bacterial community structure, and the effect of the nutrients added with the biofertilizer.Experimental design and a greenhouse experimentThe research was done in a greenhouse at Cinvestav-Zacatenco situated to the north of Mexico City (Mexico). The experiment used a completely randomized block design with six treatments. The treatments combined as a first factor soil cultivated with maize or left uncultivated. A second factor included soil amended with the biofertilizer, sterilized biofertilizer or not fertilized. The daily temperature in the greenhouse ranged from 15 °C as minimum and reached a maximum 35 °C from April to August of 2017.As the experimental protocol was complex, a diagram of the different treatments and sampling is given in Supplementary Fig. S11 online. A total of 162 PVC columns with diameter 17 cm and height 60 cm were used in the experiment. Each pot was filled at the bottom with 0.5 kg tezontle, a highly porous volcanic rock, and 10 kg soil was added on top. The 162 columns included 6 treatments (uncultivated unamended soil, uncultivated soil amended with biofertilizer, uncultivated soil amended with sterile biofertilizer, maize cultivated unamended soil, maize cultivated soil amended with biofertilizer, maize cultivated soil amended with sterile biofertilizer; n = 6), 3 sampling times (day 44, day 89 and day 130; n = 3), three different soil samples (n = 3), with three columns planted with a maize plant per soil sample (n = 3). Three columns of each soil sample were planted with a maize plant to account for plants that might die so that at least one mature plant was obtained per treatment, sampling time and soil sample. The soil in the 162 PVC columns was adjusted to 40% WHC with distilled water and conditioned in the greenhouse for a week. Additionally, three PVC columns were filled with soil from each soil sample (n = 3), adjusted to 40% WHC with distilled water and conditioned for a week. These three soil samples were used to extract DNA as described below and defined the bacterial community at the onset of the experiment, i.e. time 0.Maize seeds variety 215 W Eagle hybrid seeds® were obtained from the farmer that provided us with the biofertilizer. Three washed maize seeds were planted at 3 cm depth in 81 columns, while the remaining columns were left uncultivated. Seven days after emergence, the most vigorous plantlet was kept and the other two discarded. After 44 days, the biofertilizer or the sterilized biofertilizer was diluted with water and applied with an atomizer (10 ml m−2 or similar to 100 l applied ha−1 by the farmer) so that it was added as fine spray evenly on soil of each pot when the seeds were planted. A similar volume of water was applied in the same way to the unfertilized treatment. Five more applications of the biofertilizer, sterilized biofertilizer or water by aspersion were done during the cultivation of the maize plants. As such, the uncultivated or maize plant cultivated soil was applied with the biofertilizer, sterile biofertilizer or water on 13th April, 28th May, 5th June, 13th July, 2nd August and 12th August 2017.Soil and plant samplingAfter 44 (27th May 2017), 89 (11th July 2017) and 130 days (21st August 2017), three columns from each treatment (n = 6) and soil sample (n = 3) were selected at random. Soil was removed from each column. The cultivated and uncultivated soil was sampled, characterized, and extracted for DNA as described below. The non-rhizosphere soil was separated from the rhizosphere soil by shaken the plants gently. The soil adhered to the roots was considered the rhizosphere soil. A 20 g sub-sample of the uncultivated, non-rhizosphere and rhizosphere soil was stored at − 20 °C pending extraction of DNA, while the pH and mineral N was determined in the remaining soil. Roots and shoots were separated, weighted and their length measured. The roots and shoots were dried in an oven at 60 °C for 24 h and weighed.Soil physicochemical characterizationThe moisture content of the soil was determined by weight loss after samples were dried at 60 °C in an oven for 24 h. The WHC was determined by saturating 50 g dry soil with distilled water, left to drain overnight and measuring the amount of water retained. The EC was measured in a soil paste (200 g soil/110 ml distilled H2O) with an HI 2300 microprocessor (HANNA Instruments, Woonsocket, RI, USA), while the particle size distribution was determined with the hydrometer method as described by Gee and Bauder39. The pH was determined in a 10 g soil–25 ml distilled water mixture with a calibrated pH meter (Denver Instrument, Bohemia, NY, USA) fitted with a glass electrode (3007281 pH/ATC Termofisher Scientific, Waltham, MA, USA).Mineral nitrogen (NO3−, NO2− and NH4+) was measured in the soil and biofertilizer. A 20 g soil sub-sample was extracted with 100 ml 0.5 M K2SO4 and filtered through Whatman filter paper® while mineral N was measured with a SKALAR automatic analyser system (Breda, the Netherlands)40. A 20 g biofertilizer sub-sample was mixed with 80 ml 0.5 M K2SO4, filtered through Whatman filter paper® and mineral N measured as described previously.DNA extraction and PCR amplificationA 5 ml sub-sample of the sterilized and unsterilized biofertilizer was centrifuged at 3500 rpm for 15 min and the supernatant removed. A 0.5 g sub-sample of soil was washed with 10 ml 0.15 mol l−1 sodium pyrophosphate to eliminate the humic and fulvic acids, centrifuged at 3500 rpm for 15 min and this process was repeated until the supernatant was clear41. The excess pyrophosphate was eliminated with 10 ml 0.15 mol l−1 phosphate buffer pH 8. Three different methods were used to extract DNA from the soil and the sterilized and unsterilized biofertilizer samples. The first technique was based on the method described by Green and Sambrook42. In the second method, cells were lysed with two lysis solutions and a thermal shock as described by Valenzuela-Encinas et al.43. The third method consisted of a mechanical disruption and detergent solution for cell lysis44. Each method was used to extract three times 0.5 g soil or 5 ml sterilized and unsterilized biofertilizer (a total of 1.5 g soil or 15 ml sterilized and unsterilized biofertilizer). The extracts from the soil and sterile or unsterilized biofertilizer were pooled separately.The 16S rRNA gene (V3–V4 region of bacteria) was amplified using the primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-ACHVGGGTATCTAATCC-3′45. The PCR conditions were 94 °C for 5 min, followed by 25 cycles of 60 s at 94 °C, 45 s at 53 °C, and 60 s at 72 °C, with a final extension of 10 min at 72 °C. The PCR was repeated three times for each sample. After PCR amplification, the obtained products were cleaned using the FastGen Gel/PCR extraction Kit (Nippon Genetics Duren, Germany) and quantified using a Nanodrop 3300 fluorospectrometer (TermoFisher, Wilmington, DE, USA) with PicoGreen dsDNA. The samples were mixed in equimolar amounts and sequenced using MiSeq 300-pb paired-end runs (Illumina, CA, USA) at Macrogen Inc. (Seoul, Korea).16S rDNA sequences analysisThe raw sequences were analysed with “Quantitative insights into microbial ecology pipeline” (QIIME) software (version 1.9.1)46. The barcode reads were demultiplexing removed from the sequences using the script extract_barcodes.py. The chimeric sequences were identified using “identify_chimeric_seqs.py” with the usearch61 method and removed47. The taxonomic assignment was done using the Ribosomal Data Project (rdp)48, against the Greengenes 16S rRNA database with a 0.8 confidence49. The sequences were clustered as operational taxonomic units (OTU) at 97% similarity level with the UCLUST algorithm47. Sequences were aligned against the Greengenes reference database using PyNAST version 1.2.250. The obtained 16S dataset was filtered, all OTUs assigned to Archaea were discarded and the dataset normalized. Alpha diversity indices (Chao1, Shannon and Simpson) were calculated from 478000 rarefied sequences with QIIME.Statistical analysisAll statistical analyses were done in R (R 4.0.2 GUI 1.72 Catalina build51). The characteristics of the maize plants (n = 3) obtained per plot (n = 3) were averaged and the sequences obtained from the replicate rhizosphere or non-rhizosphere soil were summed (n = 3) per plot before the statistical analysis. A non-parametric test was used to determine the effect of biofertilizer application and time on the plant and soil characteristics with the non-parametric t1way test of the WRS2 package (A collection of robust statistical methods)52. A non-parametric test was used to determine the effect of biofertilizer application or cultivation of maize on the bacterial alpha diversity with the non-parametric t1way test of the WRS2 package52. Heatmaps of the relative abundances of the bacterial groups were constructed with the pheatmap package53. Ordination [principal component analysis (PCA)], multivariate comparison (perMANOVA) and differential abundance (ALDEx2) was done with converted sequence data using the centred log-ratio transform test returned by the aldex.clr argument (ALDEx2 package54). The PCA was done with the vegan package55. Effect of biofertilizer application and cultivation of maize on the bacterial groups was determined using a compositional approach, i.e. analysis of differential abundance taking sample variation into account (aldex.kw argument, ALDEx2 package). A permutational multivariate analysis of variance (perMANOVA) analysis was also done with sequence counts converted using the centred log-ratio transform, i.e. aldex.clr argument (ALDEx2 package (aldex.clr(counts, mc.samples = 128, denom = ”all”, verbose = FALSE, useMC = FALSE)). The adonis2 argument (Vegan package) was used for the perMANOVA analysis to test the effect of cultivation of maize, time and its interaction, biofertilizer application, time and their interaction, and cultivation of maize, biofertilizer application and their interaction on the bacterial community structure (#adonis2(clrcounts ~ maize*biofertilizer, data = code, permutations = 999, method = ”euclidean”). Raw counts were used as input and Monte Carlo Dirichlet instances of the clr transformation values were generated with the function ‘aldex.clr’ of ALDEx2 (v.1.23.2) R package54. Distance pairwise matrices were calculated using the Aitchison distance and the principal coordinate analysis (PCoA) was calculated on the distance matrices with vegan R package55.Informed consentPermission was obtained from the farmer to use the maize seeds he provided.Ethical approvalThe experiment in the greenhouse complied with and was conducted as stipulated by national regulations. More

The Mara Hyena ProjectThis study uses data and samples from the Mara Hyena Project (approved by MSU IACUC and KWS), a long-term field study of individually known spotted hyenas that have been observed since May 1979. Study hyenas are monitored daily and behavioral, demographic, and ecological data are systematically collected and entered into a database. Here, we used data from four different hyena groups, called clans, as well as historic information about ecological conditions in the Masai Mara National Reserve. We maintained detailed records on the demographics of our study population, including sex, age, and the dates of key life-history milestones such as birth, weaning, dispersal and death. In the ensuing sections, we describe data collection and data processing procedures for assessment of T. gondii infection diagnosis, quantification of demographic and ecological determinants of infection status, and assessment of behavioral (boldness) and fitness (cause of mortality) characteristics hypothesized to be a consequence of positive T. gondii infection. The present analysis includes 168 hyenas, but specific subsamples vary depending on the particular hypothesis being tested.Biospecimen collection and assessment of Toxoplasma gondii exposureAs part of our long-term data collection, we routinely darted study animals in order to collect biological samples and morphological measurements. Of special relevance to this study is our blood collection procedure. We immobilized hyenas using 6.5 mg/kg of tiletamine-zolazepam (Telazol ®) in a pressurized dart fired from a CO2 powered rifle. We then drew blood from the jugular vein into sodium heparin-coated vacuum tubes. After the hyena was secured in a safe place to recover from the anesthesia, we took the samples back to camp where a portion of the collected blood was spun in a centrifuge at 1000 × g for 10 min to separate red and white blood cells from plasma. Plasma was aliquoted into multiple cryogenic vials. Immediately, the blood derivatives, including plasma, were flash frozen in liquid nitrogen where they remained until they were transported on dry ice to a −80 °C freezer in the U.S. All samples remained frozen until time of laboratory analysis for the T. gondii assays.Using archived plasma, we diagnosed individual hyenas using the multi-species ID Screen® Toxoplasmosis Indirect kit (IDVET, Montpellier). This ELISA-based assay tests for serological (IgG) reactivity to T. gondii’s P-30 antigen and has been used in many prior studies of T. gondii in diverse mammals22. The output of the assay is an SP ratio, which is calculated as colorimetric signal of immunoreactivity for a tested blood sample (S) divided by that of a positive control (P), after subtracting the background signal for the ELISA plate (i.e., a negative control) from both S and P. We tested 168 plasma samples from 168 individual spotted hyenas and determined infection status based on the kit manufacturer’s criteria for interpreting S/P: ≤ 40% = negative result, 40%  More